This paper addresses the stability analysis of DC distribution networks with voltage source rectifiers (VSRs) and constant power loads (CPLs). Existing stability criteria for DC networks often rely on simplified models neglecting source converter controller dynamics, limiting their applicability in practical controller design. To overcome this, a singular perturbation-based framework is proposed to derive analytical stability conditions for closed-loop DC distribution network systems. By decomposing the high-dimensional Jacobian matrix into two structured low-dimensional matrices, tractable stability criteria are established using properties of Karush-Kuhn-Tucker (KKT) matrices. Sufficient stability conditions for local stability of a VSR-based DC distribution network are derived without requiring global VSR information, while a robust stability criterion dependent solely on maximum load data is developed to handle uncertainties. These results provide explicit design guidelines to enhance robustness and reduce computational complexity in stability analysis. Simulations validate the effectiveness of the proposed approach.

This paper investigates the strategic placement of inverter-based generators (IBGs) in power electronics penetrated (PE-penetrated) power systems, aiming to mitigate power blackout risk. Two metrics, namely distance of load to IBGs (DL2IBG) and Gini coefficient (GI), are employed to guide this placement strategy. DL2IBG assesses how concentrated the distribution of IBGs is in the network, while GI evaluates the uniformity of power generation among different IBGs. By setting the network configuration based on these metrics, the relationship between the metrics and the blackout risk evaluated through cascading failure simulation is examined. The simulation results on the IEEE 118-bus system emphasize the crucial role of determining GI across various DL2IBG in mitigating blackout risks, particularly as the penetration levels of IBGs vary. This research offers valuable and practical insights into optimizing the placement of IBGs to enhance the resilience of PE-penetrated power systems.

Negative reactance does not exist in the real world, which creates a unique challenge for electrical systems design. Due to the absence of the negative reactance, the only way to counteract any undesired reactance is to exploit resonance to null the positive reactance for a limited range of frequency, i.e., capacitance counteracting inductance. In this paper, we present a practical realization of negative reactance for power processing applications that will change the way electrical systems will be designed. We show that by using two DC-DC power converters and the appropriate control, we can obtain a standalone negative inductance that can directly cancel an existing positive inductance or any portion of it, opening up an entirely new direction for electrical systems design.

In this paper, we find that multiple attractors may coexist with the stable equilibrium point in an islanded microgrid containing identical grid-forming converters and a ring power network, which was once believed to be impossible in previous study. The existence of such an attractor separates the microgrid into several clusters, each of which is internally synchronized, but desynchronized with each other. We define a cluster coherence metric to measure the internal coherence of each cluster. Finally, full-circuit cycle-to-cycle simulation is provided for verification.

In conventional power systems dominated by synchronous generators, transient stability was found to be less influenced by the secondary control (e.g., automatic generation control). However, due to the flexibility of control and communication of grid-connected converters, more advanced control schemes such as the virtual-impedance-based distributed secondary control can be applied to multi-converter islanded microgrids, which may have a significant impact on the microgrid’s transient stability. In this paper, a simplified model considering the essential synchronization dynamics is established, and the metric named microgrid stable basin is proposed to measure the impact of critical secondary control parameters on the transient stability. Finally, laboratory measurement is provided to verify the theoretical findings.

In this paper, we demonstrate the interdependence among buses in power systems that experience voltage instability events by deriving interaction graphs through successive transition probability matrices and the Markov transition matrix. A quasi-stationary convergence has been observed in the probability transition matrices, suggesting a pattern in voltage instability failure. We propose algorithms to identify the pattern, referred to as fault chains, by tracing the interaction graphs. The interaction graphs and fault chains can be used to formulate strategies for enhancing network robustness, including the assignment of new photovoltaic buses, load rescheduling, and equipping droop controllers. Numerical experiments performed on the IEEE 118- and 300-bus systems demonstrate that equipping droop controllers at selected buses is an effective approach for improving network robustness.

The increasing integration of inverter-based resou- rces is a prominent trend in power systems, which may increase the risk of large-scale blackouts. This paper proposes a complex network-based cascading failure model that incorporates the failure mechanism of power electronics-based (PE-based) nodes. This model facilitates the evaluation of cascading failure vulnerability in power electronics-penetrated (PE-penetrated) power systems, accounting for the impact of an increasing penetration level of inverter-based resources. Focusing on generation nodes with inverter-based resources (PE-based nodes) that may deviate from their stable operating limits during cascading failure and disconnect from the grid, we introduce a novel mechanism to characterize their failure behavior. Through a case study conducted on the IEEE 39-bus system, the model demonstrates its ability to accurately replicate the salient features observed in two real blackout events. Furthermore, simulation outcomes from the IEEE 118-bus system indicate the substantial impact of PE-based node failure on the cascading failure process. These results also underscore the effectiveness of utilizing the proposed model to prevent underestimating power outage risks within PE-penetrated power grids. This work emphasizes the critical importance of considering the penetration of inverter-based resources across different scenarios to safeguard the resilience of evolving power grids.

In recent years, graph neural networks (GNNs) have gained significant attention due to their outstanding performance on graph-related tasks by utilizing neighborhood aggregation. However, traditional GNNs are primarily designed based on the homophily assumption, which means that they show poor performance on heterophilic networks where dissimilar nodes prefer to connect. To address this issue, several heterophilic GNNs have been proposed that employ techniques, such as extending local neighbors and improving GNN architectures. From another perspective, recent studies have shown that, unlike homophilic GNNs, heterophilic GNNs exhibit higher robustness against graph adversarial attacks. In these attacks, the attackers try to inject small perturbations into the graph. Therefore, in this study, we delve into the core designs of heterophilic GNNs, including high-order neighbor, potential neighbor, ego-neighbor separation, and interlayer combination designs. We further analyze the influence of these key designs on the robustness of GNNs. We conducted comprehensive experiments to compare the impact of different designs on baseline and real-world GNN models. The findings of this work can serve as a reference for future studies aiming to design more robust and universal GNNs.

This paper investigates the wireless in-wheel motor drive system based on the series/capacitor-parallel (S/CP) compensation topology. Despite the structural similarity with the conventional series/series-parallel (S/SP) circuit, the S/CP compensation embraces a different design concept to enhance the voltage gain flexibility. The study examines the input impedance and voltage gain characteristics of the system across all load and misalignment conditions. Subsequently, a novel design method is proposed to facilitate soft switching and ensure compliance with the bus voltage requirement. Consequently, the proposed design method could avoid wireless communication and additional DC/DC converters. Finally, a 600 W prototype is constructed to validate the above analysis, yielding a remarkable maximum efficiency of 96.81%.

Strengthening critical components is considered one of the most essential means to enhance the robustness of power networks against cascading failure. This paper proposes an iterative method to strengthen the critical power network branches identified from a tailor-made failure propagation graph. To construct the failure propagation graph, we generate numerous cascading failure trees, capturing both temporal and spatial features of failure propagation processes from cascading failure simulations. The constructed graph is a weighted and directed graph that is able to characterize failure propagation patterns in a power network. By employing weighted eigenvector centrality to assess node criticality, we iterate through the graph to identify the most significant nodes and subsequently determine the critical power network branches to be strengthened. Simulation results in the IEEE 118 bus system demonstrate the effectiveness and efficiency of our strategy in mitigating cascading failure compared to existing methods.

Frequency damping control is a crucial aspect of islanded microgrids utilizing multiple virtual synchronous generators (VSGs). This paper studies the effects of line impedance mismatches and transient frequency out-of-sync leading to frequency interactive oscillation in VSGs. To address this issue, we propose a distributed interactive damping and zero-error control method that utilizes sparse communications. The objective of this method is to effectively suppress frequency oscillation by minimizing the differences in frequency dynamics among all VSGs. By implementing this approach, a low rate of change of frequency (RoCoF), accurate active power sharing, and zero frequency deviation are ensured. Through dynamic performance analysis, frequency characteristic analysis, and stability analysis based on LaSalle’s invariance principle, we demonstrate significant improvements in system stability, as well as dynamic and steady state performances. Finally, simulation and experimental results obtained under load changes, short-circuits, and communication faults validate the effectiveness of the proposed control method.

In this paper, we investigate the influence of the locations of inverter-based resources (IBRs) on the synchronization performance of power networks. We propose two indexes to measure the distribution of inertia in power networks, considering the distribution of control parameters and the topological factors jointly. The first index is the inertia clustering coefficient, which measures how densely the neighbors of each node are connected in a power network. The second index is the inertia centrality coefficient, which captures whether the distribution of inertia in a power network is centralized or peripheral. We characterize synchronous generators (SGs), grid-following inverters (GFLs), and grid-forming inverters (GFMs) by their damping and inertial properties. We evaluate the synchronization performance of the system after disturbances by adopting the settling time and hertz-sec metric. Both the frequency response and the trajectories of eigenvalues show that the location of IBRs has a significant impact on the synchronization performance of the system. Monte Carlo simulations are conducted on two test networks, the results of the IEEE 30-, 57-, and 118-bus systems demonstrate a strong correlation between synchronization performance and the two inertia distribution indexes.

This paper studies cascading failure propagation in power systems and presents methods for the quantification of important properties of failure propagation. First, the topological properties of cascading failure propagation are examined. This includes an analysis of the electrical distance between consecutive failures, shedding light on the spatial spread of failures. Additionally, the formation of islands in cascading failure processes is explored to understand their topological characteristics. Second, the paper measures the evolution of a power system during cascading failure processes, considering both the topological changes of the overall system and the propagation rates of system loss. This analysis provides a measurable comprehension of how the system evolves and adapts as failures propagate. Third, this study investigates system loss and analyzes the contributions of various failure mechanisms to the overall system loss. Numerical experiments yield valuable insights into the propagation of cascading failures, leading to several significant conclusions. The findings from this research can inform the development of effective strategies for resilience enhancement and risk mitigation.

Power systems around the world exhibit diverse topological characteristics depending on historical and geographical factors. In this paper, we aim to acquire insights into the relationship between the topological characteristics of power systems and their robustness through controlled experiments. To achieve this, we generate synthetic networks that incorporate the generation and load settings of practical power systems. We test various aspects, such as topological properties of regional power grids, interconnections among regional power grids, network sizes, and power balance of regional power grids. We derive inspiring results by performing cascading failure trials in test networks. Specifically, a wide and compact power grid can be more robust than a narrow and elongated power grid. Moreover, increasing the capacity of interconnection links among regional power grids may result in a less robust power system concerning cascading failures.

This study proposes a cascading failure model for power systems that addresses event-triggered power flow divergence. After a cascading failure event, voltage instability is a major cause of event-triggered power flow divergence. To detect voltage instability events, we track the steady-state voltage profiles across successive cascade generations by adopting continuation power flow (CPF). Various cascading failure events are incorporated into the CPF computation. Hence, the proposed model can identify voltage instability by monitoring the emergence of buses that hit a saddle-node bifurcation (SNB) point. In this way, the proposed cascading failure model is able to derive equilibrium points, if they exist, under all conditions. Furthermore, the model incorporates primary frequency control instead of slack buses to account for power losses, thereby improving the accuracy of the results. In addition, we propose a set of metrics to measure the cascading failure propagation rate and the influence of voltage instability events on cascading failure outcomes. Experimental results indicate that power system configurations exert a significant influence on voltage instability events.

Graph neural networks (GNNs) have found successful applications in various graph-related tasks. However, recent studies have shown that many GNNs are vulnerable to adversarial attacks. In a vast majority of existing studies, adversarial attacks on GNNs are launched via direct modification of the original graph such as adding/removing links, which may not be applicable in practice. In this article, we focus on a realistic attack operation via injecting fake nodes. The proposed global attack strategy via node injection (GANI) is designed under the comprehensive consideration of an unnoticeable perturbation setting from both structure and feature domains. Specifically, to make the node injections as imperceptible and effective as possible, we propose a sampling operation to determine the degree of the newly injected nodes, and then generate features and select neighbors for these injected nodes based on the statistical information of features and evolutionary perturbations obtained from a genetic algorithm, respectively. In particular, the proposed feature generation mechanism is suitable for both binary and continuous node features. Extensive experimental results on benchmark datasets against both general and defended GNNs show strong attack performance of GANI. Moreover, the imperceptibility analyses also demonstrate that GANI achieves a relatively unnoticeable injection on benchmark datasets.

DC-DC converters are major components of a DC cascaded converter system. The converters interface with external power inputs, internal DC buses and loadings of subsystems. Stability of these constituent converters under all operating conditions is vital to the operation of the whole system. Current research results show that the system's stability can be guaran-teed when the system's parameters are within specific ranges, which are similar to the results obtained from standalone DC-DC converters. However, DC cascaded systems are high order systems and the dynamic behaviors are more complex than their standalone counterparts. In this paper, we identify and analyze the bidirectional bifurcation phenomena in the current-mode controlled DC cascaded system. Based on the derived averaged model, the bidirectional bifurcation phenomena and the associated mechanism will be studied in depth. Finally, the validity of the analysis is verified by laboratory experiments.

Graph neural networks (GNNs) have been widely applied to network related tasks in recent years, including node classification, link prediction, community detection, etc. The core idea of GNNs is neighborhood aggregation where nodes in a network can learn adequate representations by aggregating the information from their neighbors. Despite the great success of GNNs, few studies investigate how different types of structure of the network based data, such as random networks, small-world networks, and scale-free networks, affect the performance of GNNs in completing network related tasks. Moreover, recent studies have pointed out that the homophily of labels is one of the key properties that influence the performance of GNNs. In this work, we study the performance of GNNs for different types of network structure at different homophily levels. Comprehensive simulations on synthetic networks show the considerable impact of network structure and homophily on the performance of GNNs in terms of prediction effectiveness in node classification tasks. The findings of this work emphasize the necessary consideration of network structure of datasets in designing reliable GNNs so that the performance of these GNNs will not deviate due to structural change of the underlined data.

Some components in a power system are more vulnerable than others. Limited by the capability of the existing quasi-steady-state (QSS) models of cascading failures, previous analysis on vulnerable components typically omits the influence of voltage collapse. In this paper, we identify the nodes that are most susceptible to voltage collapse in cascading failures with a QSS model that applies practical measures in response to voltage collapse, i.e., undervoltage load shedding (UVLS) and protection relays. The proposed model is based on the continuation power flow (CPF). We apply the model to the IEEE 118-bus system, the IEEE 300-bus system, and the Polish 2383 bus system. We count the times and plot the locations of buses that exhibit a voltage collapse event in the network to assess structural vulnerability. Numerical results show that some parts of the network are more vulnerable to voltage collapse events than others.

The widely deployed grid-following converters (GFLCs) may lose synchronization under grid disturbances, presenting a critical challenge to the power electronics penetrated power grid. While some methods have been proposed for analyzing the transient synchronization stability, their models tend to assume an ideal current loop. Using a full-order system model, this paper demonstrates that the nonideal current loop signif-icantly weakens the stability of GFLCs. Specifically, the basin of attraction gets decreased and more sensitive to parameter changes. The complex bifurcation behavior behind these changes is revealed via the numerical continuation. Accordingly, a design principle is derived for enhancing the transient synchronization stability.

This paper first presents a systematic extension of basic second-order compensated capacitive power transfer (CPT) converters, designed for achieving load-independent current (LIC) or load-independent voltage (LIV) output, to higher order compensated CPT converters through adding an inductor or capacitor at the input or output side. Conditions on the parameters to achieve the required output are given. The analysis provides a convenient connecting pathway from second-order circuits to higher order circuits, mapping each type of topological extension with the specific performance outcome.

Cascading failure modeling and analysis provide convenient tools for assessing and enhancing the robustness of power systems against severe power outages. In this paper, we apply a neural network-based classifier to predict the onset time of cascading failure. Onset time, which has been reported as the time when the number of component failure begins to rapidly increase in the failure propagation, serves as a crucial metric to evaluate the vulnerability of power systems to cascading failure. We formulate the prediction task as a multi-class classification problem and adopt a neural network-based classifier where topological and electrical information of a power system network can be exploited for learning. Experimental results on the UIUC 150-Bus power system demonstrate a high classification accuracy by only leveraging the initial states of power networks and the initial failure sets containing the power components to be tripped at the beginning of cascading failure.

In this paper, we report for the first time that in an islanded microgrid comprising a number of grid-forming converters, several hidden attractors may exist, including periodic orbits, quasi-periodic orbits, and chaotic attractors. The system operating at a stable equilibrium point may be brought to one of these hidden attractors under some transient disturbance. Then, the system will exhibit sustained oscillation, and the grid-forming converters will cease to synchronize. Full-circuit cycle-by-cycle simulations are provided to verify these findings.

Cascading failure is a ubiquitous phenomenon that can paralyze networked systems in a short time. Many traditional studies of cascading failures have been conducted from the perspective of either an attacker or a defender. In reality, however, malicious attacks on networks are rarely a one-sided process. Instead, both the attacker and defender are actively involved. We use game theory to study the strategies of both sides in terms of an attacker–defender game on complex networks. Based on the concept of the Stackelberg competition, we propose a multiround attacker–defender game model on complex networks, allowing high flexibility in the available actions for both sides in the game. The model we propose allows the two sides to specify certain parameters of the network to attack/defend and further allocate a certain amount of resources for the attack/defense. Such flexibility allows the model to capture the actions of the attackers and defenders more precisely and simulate the attack process in a more realistic manner. We propose an iterative search algorithm to search for desirable strategies with systematic experiments on various types of networks and associated parameters and in terms of different relative resource owned by the attacker and defender.

The dynamic estimation of epidemic spreading on networks is essential for controlling morbidity. Nonlinear Kalman filters, which are capable of estimating the hidden state of a nonlinear system, can be utilized for dynamic state estimation of epidemic spreading. Traditional nonlinear Kalman filters perform optimization using the minimum mean square error (MMSE) criterion. Since observable measurements are generally corrupted by non-Gaussian noise, maximum correntropy criterion (MCC)-based nonlinear Kalman filters have been used for improving robustness against non-Gaussian noise. In comparison with the MCC, generalized correntropy is more robust and flexible in the presence of non-Gaussian noise. In this article, epidemic spreading is described by a compartmental model, i.e., susceptible-infected-recovered-susceptible model. Based on the compartmental model, the generalized correntropy-based sparse Gauss–Hermite quadrature filter (GCSGHQF) is proposed for epidemic tracking on homogeneous networks. The GCSGHQF approximates the Gaussian-weighted integrals by utilizing the sparse Gauss–Hermite quadrature rule. In addition, the generalized correntropy is applied to enhancing robustness in the presence of non-Gaussian noise. Simulation results show that the GCSGHQF exhibits a higher filtering accuracy and improves robustness compared to traditional nonlinear Kalman filters.

Spectrum sharing in a cognitive radio system involves a secondary user updating transmit power for sharing spectrum with a primary user. The deep$Q$-network in the framework of deep reinforcement learning achieves transmit power control by a deep neural network for learning a nonlinear mapping from states to$Q$-values. Since a deep neural network is confronted with noise susceptibility, the deep$Q$-network produces deteriorative network parameters and volatile$Q$-values in the presence of contaminated states. In view of the positive effect of kernel least mean square (KLMS) for signal smoothing, we combine KLMS with the deep$Q$-network for smoothing network-generated outputs. Since an inappropriate step size of KLMS causes under-smoothing or over-smoothing issues, a weighting procedure using past$Q$-values is proposed for cooperating with KLMS. We assess the incremental ratio of the success rate of the smooth deep$Q$-network to that of the deep$Q$-network (RSR) in cognitive radios. Simulations show that RSR has averaged almost over 30% at the early stage of power control. In particular, the maximum RSR reaches almost over 80% or 180% at different scenarios of power control for the primary user. In addition, the smooth deep$Q$-network achieves an improved success rate in comparison with other algorithms.

Under some transient disturbance, the grid-forming voltage source converter may lose its synchronization with the grid, inducing low-frequency oscillation in the instantaneous power, current and angle. The physical origin of such oscillations is found to be a homoclinic bifurcation in this paper. Before the system runs into a homoclinic bifurcation, a stable equilibrium point and a stable periodic orbit co-exist. When a large disturbance is applied, the system exhibits a periodic orbit, which manifests itself as low-frequency oscillation. Moreover, no periodic orbit exists after the homoclinic bifurcation. On the basis of bifurcation analysis, boundaries of stable operation are derived in the parameter space which serve as practical design guidelines to avoid oscillation.

Modern power systems utilize information, communication, and computation technologies for control and coordination of the various subsystems and enhancement of the system's operating performance. In this paper, we develop a model to simulate cascading failure in a cyber-coupled power system considering the system-level control provided by the cyber network. In the events of failure of components or subsystems, the states of the power network will be monitored, collected, and transmitted in real time to the control center, where control algorithms are performed to generate commands for controlling the power at each node. We develop an optimization algorithm for the control center that maximizes power generation and avoids power imbalance between generators and loads and overloading on each component. The vulnerability of the cyber-coupled power system to initial component failures is assessed based on the model and cyber faults that damage the state-monitoring and controlling function of the cyber layer are considered. Simulation results demonstrate that a power system coupled with a cyber control system can effectively reduce the cascading failure risk. However, the control dysfunction of the cyber layer leads to catastrophic cascading failure and intensifies the extent of power outage, making the system more vulnerable to cascading failure.

Not identified as being exposed or infected, the group of asymptomatic and presymptomatic patients has become the key source of infectious hosts for the COVID-19 pandemic, triggering the re-emergence of outbreaks. Acknowledging the impacts of movement of unidentified patients and the limited testing capacity on understanding the spread of the virus, an augmented Susceptible-Exposed-Infectious-Confirmed-Recovered (SEICR) model integrating intercity migration data and testing capacity is developed to probe into the number of unidentified COVID-19 infected patients. This model allows evaluation of the effectiveness of active interventions, and more accurate prediction of the pandemic progression in a country, region or city. A pseudo-coevolutionary algorithm is adopted in the model fitting to provide an effective estimation of high-dimensional unknown parameter sets using a limited amount of historical data. The model is applied to 175 regions in Australia, Canada, Italy, Japan, Spain, the UK and USA to estimate the number of unconfirmed cases using limited historical data. Results showed that the actual number of infected cases could be 4.309 times as many as the official confirmed number. By implementing mass COVID-19 testing, the number of infected cases could be reduced by about 50%.

In metro systems, the train passenger load is an important parameter that reflects both the utilization level of the trains provided by the operator and the comfort level of passengers in terms of crowdedness. In addition to minimizing the energy consumption and passengers' time cost, the train passenger load should also be optimized in order to maintain a high utilization rate of trains and an adequate level of comfort for passengers. In this paper, a multi-objective train timetable optimization procedure is proposed to minimize the total energy consumption, the average waiting time, and the average maximum load deviation. Case studies on the Beijing Yizhuang line show that the proposed approach could effectively reduce the total energy consumption, the average waiting time, and the average maximum load deviation, thus ensuring a high service quality and a low operational cost for the metro system.

Cascading failure on complex networks has been extensively studied over the past decade. However, restoration of networks from cascading failure is still relatively unexplored. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing the failed nodes in a sequential fashion. Depending on the availability of resources, we tackle the sequential recovery problem from two distinct approaches, namely, result-oriented and resource-oriented restoration approaches. In the result-oriented approach, we aim to restore the network to the largest extent and within the shortest time. Heuristic network restoration strategies based on node load or degree are proposed. For resource-oriented restoration, we aim to maximize the increase of network size with a given number of nodes to be repaired, and we propose a novel iterative strategy to improve performance. Simulation results on the Barábasi-Albert scale-free network, Internet autonomous system-level network, and IEEE 300 bus power system have demonstrated the effectiveness of the proposed sequential recovery strategies.

The hyperbolic cosine function with high-order errors can be utilized to improve the accuracy of adaptive filters. However, when initial weight errors are large, the hyperbolic cosine-based adaptive filter (HCAF) may be unstable. In this paper, a novel normalization based on the logarithmic hyperbolic cosine function is proposed to achieve the stabilization for the case of large initial weight errors, which generates a logarithmic HCAF (LHCAF). Actually, the cost function of LHCAF is the logarithmic hyperbolic cosine function that is robust to large errors and smooth to small errors. The transient and steady-state analyses of LHCAF in terms of the mean-square deviation (MSD) are performed for a stationary white input with an even probability density function in a stationary zero-mean white noise. The convergence and stability of LHCAF can be therefore guaranteed as long as the filtering parameters satisfy certain conditions. The theoretical results based on the MSD are supported by the simulations. In addition, a variable scaling factor and step-size LHCAF (VSS-LHCAF) is proposed to improve the filtering accuracy of LHCAF further. The proposed LHCAF and VSS-LHCAF are superior to HCAF and other robust adaptive filters in terms of filtering accuracy and stability.

This paper studies the dynamic performance of subway transportation systems. A three-layer model, consisting of a rail layer, a train layer, and a passenger layer, is proposed to describe the structure and operation of a metro system. Two parameters, namely, time efficiency and maximum load ratio, are proposed to assess the transport efficiency and the train utilization rate, respectively. Case studies of the metro networks in Beijing, Tokyo, and Hong Kong show that the time efficiency decreases nonlinearly with the increase of vehicle resource and passenger demand, whereas maximum load ratio varies in an opposite manner. For the three metro networks under study, the Tokyo metro system has the highest time efficiency when passengers adopt a shortest-path (SP) routing strategy, while the Hong Kong system's highest time efficiency exceeds the others' when passengers adopt a minimum-transfer-path (MTP) strategy. In general, the maximum time efficiency is higher when passengers adopt SP routing rather than MTP routing. Moreover, the Beijing metro system has the highest maximum load ratio, regardless of the passengers' routing behavior. This paper can be applied to a metro network to optimize the train departure interval under a certain passenger entrance rate, with the aim to maximize the time efficiency and maximum load ratio. Our model permits assessment of the operational effectiveness of metro systems, which helps the metro operators to improve the performance and reduce the cost.

The coupling pattern of a cyber-coupled power system is defined by the way power nodes and cyber nodes are connected. In this paper, we study the effect of coupling patterns on robustness and functionality of cyber-coupled power systems. In a cyber-coupled power system, the cyber nodes to be coupled with power nodes are regarded as decision-making cyber nodes that are selected using a measurement called average propagation latency. Basically, a coupling pattern is formed by ranking the node criticality of the coupled cyber and power nodes, and then connecting them by a specific sequence. We introduce a parameter, called relative coupling correlation coefficient, to quantify the coupling pattern. A lower relative coupling correlation coefficient generally indicates a lower assortativity of coupling, which leads to a degradation of the functionality of coupled systems. Preliminary results show that a coupled system of a lower relative coupling correlation coefficient has better robustness. The finding indicates that increasing coupling assortativity and improving the robustness of a coupled system, are two conflicting objectives. Thus, a multi-objective problem is formulated and the Pareto-optimal solution is derived to balance the two objectives in the optimization of coupling patterns.

An implicit first-order non-dimensional model of open loop dc-dc boost converter operating in Discontinuous Conduction Mode (DCM) with Constant Power Load (CPL) is derived. Analysis of this model shows that successive period doubling bifurcations and subharmonic oscillation take place when certain parameters such as the switching period, the operating duty cycle and the load power are varied. The resulting typical period-doubling route to chaos is confirmed by numerical simulations under the mentioned operating conditions.

This paper proposes a new single-inductor multi-input multi-output (MIMO) DC-DC converter. The proposed converter allows input sources to be added or removed without affecting the input side and the output side. Meanwhile, output channels are independently controlled. The corresponding control method is the constant current control. When the constant current control is applied to all input cells, the power provided by each source is proportional to the corresponding voltage value. When the constant current control is applied to some of the input cells, the other sources can provide specific power through direct duty-cycle control of input cells. Moreover, the consideration of switching time of switches is unnecessary. Therefore, it is easy to realize the high extension capability for arbitrary inputs/outputs. A dual-input dual-output prototype is constructed to illustrate the performance of the proposed converter.

This paper presents a detailed analysis of sensitivity of various second-order and third-order compensated inductive power transfer (IPT) converters, designed for achieving load-independent current (LIC) or load-independent voltage (LIV) output, against input voltage variation. Comparisons of sensitivity for varying input-side inductances are systematically investigated through theoretical circuit analysis and extensive simulation studies. Relationship between second-order and third-order compensation is also explored through the transformation between voltage sources and current sources. The sensitivity analysis provides a convenient guidance for parameter design of IPT systems for achieving the required LIV and LIC operation under wide input range. Besides, the analysis effectively reveals the roles of extra input-side inductors in making the equivalent current source less sensitive, and hence provides a direct and handy interpretation of the choice of higher-order compensation circuits for applications addressing wide ranges of input variations.

The penetration of power electronics into power generation and distribution systems has deepened in recent years, as prompted by the increasing use of renewable sources, quest for higher performance in the control of power conversion, as well as the increasing influence of economical plans that necessitate power trading among different regions or clusters of power distribution. As a result of the increased use of power electronics for controlling power flows in power systems, interactions of power electronics systems and conventional synchronous machines' dynamics would inevitably cause stability and robustness concerns, which can be readily understood by the coupling effects among interacting dynamical systems of varying stability margins (or transient performances). In this overview lecture, we present the various problems of power electronics penetration into power grids and the implications on the stability and robustness of power networks.

Additive manufacturing technologies have been widely applied in both household and industrial applications. The fabrication time of a three-dimensional (3-D) printed object can be shortened by optimizing its segments printing order. The computational times required by existing nozzle path planning algorithms can increase rapidly with the number of printing segments. In this article, the nozzle path planning problem is formulated as an undirected rural postman problem and a computationally efficient heuristic search algorithm is proposed to find fast routes and mitigate overheads in printing processes. Both simulation and experimental results concur that the proposed algorithm can significantly speed up printing processes and outperform its counterparts in 3-D printing applications.

This paper presents a topological study using power flow diagrams to derive all possible basic and non-isolated double-input single-output (DISO) converters. Unlike most reported DISO converters with one bidirectional port, this paper considers up to two bidirectional ports. The paper focuses on providing a general guideline of all power flow combinations and corresponding converter configurations. After eliminating the impractical configurations due to their indirect connection to some ports and their multiple conversion stages, three converter configurations have been identified and corresponding circuit realizations are demonstrated.

There exist many types and configurations of switched-capacitor converters. However, they are mainly designed for specific applications such as DC-DC or DC-AC (multilevel converters) power conversions. Consequently, they are inapplicable in situations that require multi-applications. A better alternative would be to develop SC converters that are of multi-functions. Different types of power conversions can be achieved by a generalized SC converter, and is possible to fabricate the proposed converter into an IC package. The manufacturing cost can be further reduced through a mass production process. Nevertheless, fundamental studies and clarifications on the feasibility of the idea are still required as relevant literature are not reported. In this work, a preliminary study on a universal SC converter is presented. By programming the control sequence of power switches, the proposed converter is capable of achieving DC-DC, AC-DC, and DC-AC power conversions, while having the properties of bi-directional power flow and variable conversion ratios. The detailed explanations of its circuit operations, control methodology, and basic design procedures, are discussed. Experimental results are included to verify the feasibility of the proposed converter for low voltage applications.

Tool-path optimization has been applied in many industrial applications, including subtractive manufacturing likes drilling and additive manufacturing likes 3D printing. The optimization process involves finding a time-efficient route for tools to visit all the required sites, which is often computationally intensive. In practice, heuristics and meta-heuristics are used to generate sub-optimal results within reasonable durations. The aim of this work is to use artificial neural networks to yield better tool-paths.

This paper derives a complete family of interconnected dc-dc converters for single-input multi-output applications. In addition to conventional voltage-source-mode (VSM) converters, the less known current-source-mode (CSM) converters are used to complete the set of configurations that cover all possible input types, load types and connection styles. A total of sixteen configurations are derived and compared against their power sharing capability, extended flexibility, mutual effects among interconnected modules, basic control requirements and load characteristics. A two-stage configuration consisting of a VSM converter and two CSM converters is presented as an example to drive light-emitting-diodes (LEDs) under high input voltage conditions.

Precipitation or rainfall prediction is an important practical problem in meteorology research. However, a reliable forecast is difficult to achieve due to the complexity of the climate system and the huge set of available climatic data at nearby sites. Thus, in most existing precipitation prediction systems or methods, inevitable partial (selective) consideration of the types of climatic data has often led to less satisfactory prediction results. Thanks to the advent of modern sensor technology, various types of climatic data in time-series forms can be collected at observation sites or satellites. In this work, we reconstruct a climate dataset including more than 30 climatic variables measured by more than 103,473 observation sites covering the world surface from 1800 to 2017. Then, we apply state-of-the-art machine learning methods, including deep learning (CNN, RNN, and LSTM networks) and ensemble learning (Adaboost, GBDT, and XGBoost), to develop a short-term precipitation system. Experiments on real-world data show that incorporating multiple climate variables into a prediction system improves the prediction results. The best performance of the proposed method reaches an accuracy of more than 80%.

Summary Efficient energy and temperature management techniques are essential elements for operators of cloud data centers. Dynamic virtual machine (VM) consolidation using live migration techniques presents a great opportunity for cloud service providers to adaptively reduce energy consumption and optimize their resource utilization. In recent studies, power consumption readings of individual physical hosts were chosen as the main monitoring parameters in their allocation policies, whereas very few have considered host temperature, which has shown to have a negative impact on server reliability, as a migration criterion. In this work, a thermal‐aware VM consolidation mechanism is proposed for resource allocation optimization and server reliability assurance. We consider the variability in host temperature as a migration criterion to avoid outage incidents via having better VM consolidations. Extensive simulation results obtained from CloudSim show the promising performance of the proposed mechanism in energy saving while reducing the number of server outage incidents due to fluctuations in host temperature.

Layered additive manufacturing, also known as three-dimensional (3-D) printing, has revolutionized transitional manufacturing processes. Fabrication of 3-D models with complex structures is now feasible with 3-D printing technologies. By performing careful tool-path optimization, the printing process can be speeded up, while the visual quality of printed objects can be improved simultaneously. The optimization process can be perceived as an undirected rural postman problem (URPP) with multiple constraints. In this paper, a tool-path optimizer is proposed, which further optimizes solutions generated from a slicer software to alleviate visual artifacts in 3-D printing and shortens print time. The proposed optimizer is based on a modified ant colony optimization (ACO), which exploits unique properties in 3-D printing. Experiment results verify that the proposed optimizer can deliver significant improvements in computational time, print time, and visual quality of printed objects over optimizers based on conventional URPP and ACO solvers.

Ant colony optimization (ACO) algorithms have been widely adopted in solving combinatorial problems, like the traveling salesman problem (TSP). Nevertheless, with a proper transformation to TSP, ACO is capable of solving undirected rural postman problems (URPP) as well. In fact, nozzle path planning problems in 3D printing can be represented as URPP. Therefore, in this work, ACO is utilized as a URPP solver to accelerate the printing process in fused deposition modeling applications. Furthermore, mechanisms which exploit unique properties in 3D models are proposed to further extend the ACO in the above optimization process. These mechanisms are capable of accelerating ACO by adaptively adjusting its number of iterations on-the-fly. Simulation results using real-life 3D models show that the proposed extensions can accelerate ACO without affecting the quality of its solutions significantly.

Modeling and analysis of cascading failures draws wide attention recently due to frequent occurrences of large blackouts all over the world. Models based on complex network theory contribute significantly on analyzing the robustness of power systems and assessing the risk probability, while they fall short of producing the propagation of the cascading failure in exact time points. This paper presents an improved topological model taking timescale into consideration, as well as the relay setting which reveals its operation more accurately according to industrial standards. The paper then validates the model using UIUC 150-bus and IEEE 39-bus test system. In order to assess the vulnerability of a power network, the multi-step attack strategy has been provided. The results demonstrate that timescales and relay settings have a critical impact during the cascading failure and the proposed attack strategy may lead to larger blackouts than normal strategies.

In this paper, we consider the failure propagation in a power system which is coupled with a cyber network. We identify the ratio of the infection rate of malware to the tripping rate of elements in the power network, defined as cyber-physical propagation ratio (CPPR), as a crucial parameter, and show that CPPR affects the propagation pattern. When CPPR is very small, the overloading effect in the power system dominates, and the failure propagation profile shows a typical "jump" or step pattern. Moreover, as CPPR increases, the step magnitude rapidly reduces and becomes less noticeable. Furthermore, we develop a simple approach to distinguish various propagation patterns for different values of CPPR. Results show that CPPR effectively characterizes the significance of cyber coupling and hence the relative impact of malicious attack from the cyber network, and its value determines how cascading failure propagates in a cyber-coupled power network.

In this paper, we propose a novel method called supernode graph structure representation to model the public transport network structure of the London city. Supernode is a set of geographically closely associated nodes. Using the supernode graph structure, the bus transport and the metro transport network structures are analyzed by treating them as independent mono-layer or multi-layer network structures. A method of spatial amalgamation is proposed to integrate the two transport layers. A set of most influential nodes in the network is identified by assigning node weight to each node with respect to both mono-layer and multi-layer analysis. The behavior of these influential nodes is better characterized by categorizing them as either emitter, absorber or neutral zones.

The bounded rationality of agents plays an important role in the performance of complex systems. In this work, we explore the influence of bounded rationality on the evolution of cooperation, defection and extortion strategies on a regular lattice by applying the Fermi dynamics in the Prisoner's Dilemma game. It is found that the rationality level plays a non-trivial role in the emergence of cooperation with extortion in structured systems. On the one hand, for the perfect rationality case, it is difficult for cooperators to resist the invasion of defection. On the other hand, being too irrational breaks the stable structure between cooperators and extortioners, and results in the extinction of cooperation. Hence, there is a proper level of bounded rationality which can slow down the expansion of defective and extortionate behaviors, and promote the emergence of cooperation-extortion alliances, which can yield a better outcome to the whole system. This study may provide some clues to optimize the performance of multi-agent systems with bounded rationality.

Finding a shortest feasible path between two given locations is a common problem in many real-world applications. Previous studies have shown that mobile platforms would consume excessive energy when moving along shortest paths on uneven terrains, which often consist of rapid elevation changes. Mobile platforms powered by portable energy sources may fail to follow such paths due to the limited energy available. This paper proposes a new heuristic search algorithm called constraints satisfying A* (CSA*) to find solutions to such resource constrained shortest path problems. When CSA* is guided by admissible heuristics, it guarantees to find a globally optimal solution to a given constrained search problem if such a solution exists. When CSA* is guided by consistent heuristics, it is optimally efficient over a class of equally informed admissible constrained search algorithms with respect to the set of paths expanded. Test results obtained using real terrain data verify the applicability of the proposed algorithm in shortest path planning for energy-constrained mobile platforms on uneven terrains.

After a blackout, it is essential to restore the blackout area rapidly to minimize possible losses. In parallel restoration, the blackout area is first partitioned into several subsystems, which will then be restored in parallel to accelerate the restoration process. In order to ensure restoration reliability, each subsystem should have enough generation power and satisfy a set of constraints before triggering the parallel restoration process. This paper models this as a constrained optimization problem and proposes a partitioning strategy to solve it in three steps. In the first step, some existing methods and expert knowledge are used for initialization of the partitioning process. The second step ensures the satisfaction of modeled constraints. The third step operates greedily to find suitable partitions for parallel restoration. The proposed strategy is implemented and evaluated on IEEE 39- and 118-bus power systems. Evaluation results show that it provides adequate subsystems for parallel restoration. Unlike some existing partitioning strategies, the proposed strategy can be used to partition a power system into multiple subsystems in a single execution.

A refinement process for nozzle path planning in 3D printing is presented in this paper. The nozzle path planning problem is formulated as an undirected rural postman problem (URPP). Based on the unique characteristics of URPP in 3D printing applications, a new refinement process is proposed to shorten the processing time of a conventional URPP solver. Performances of the proposed refinement process is evaluated using computer simulations. Simulation results show that when comparing with other URPP solvers, solvers with the proposed process have shorter processing time and can provide solutions with shorter transition length.

With the tremendous increase of the Internet traffic, achieving the best performance with limited resources is becoming an extremely urgent problem. In order to address this concern, in this paper, we build an optimization problem which aims to maximize the total utility of traffic flows with the capacity constraint of nodes and links in the network. Based on Duality Theory, we propose an iterative algorithm which adjusts the rates of traffic flows and capacity of nodes and links simultaneously to maximize the total utility. Simulation results show that our algorithm performs better than the NUP algorithm on BA and ER network models, which has shown to get the best performance so far. Since our research combines the topology information with capacity constraint, it may give some insights for resource allocation in real communication networks.

In this paper, we develop a stochastic model from the perspective of complex networks to investigate the effects of cyber coupling on cascading failures in coupled power systems. The failure spreading in the coupled system is described by state transition and modeled as a Markov process. We simulate the dynamic profile of the cascading failures caused by the attack of cyber malwares, considering the effects of power overloading, contagion and interdependence between power grids and cyber networks. We study the coupled system created by coupling the UIUC 150 Bus System with synthesized scale-free cyber network. Simulation results present that the dynamic profile of the cascading failures in a coupled system displays a “staircase-like” pattern which can be interpreted as a combined feature of the typical step propagation profile triggered repeatedly by cyber attacks due to cyber network coupling. Results also show that cyber coupling can intensify both the extent and rapidity of power blackouts. Moreover, adopting assortative coupling patten accelerates the failures propagation, in particular under high-degree cyber node targeted attack.

Live migration, which allows virtual machines (VMs) to move across distinct physical hosts, is widely adopted for realizing energy saving and load balancing in modern Cloud data centers. However, putting VMs with high correlations on their CPU utilization patterns onto the same host is very likely to trigger overloading incidents and commit Service Level Agreements (SLA) violations, even when the host has not yet reached its critical limits. To avoid such SLA violations and to maintain a low energy consumption, this work proposes a heuristics-based VM allocation mechanism. Under the proposed mechanism, VMs showing high CPU utilization correlations to other co-located peers and hosts with extreme utilization values are both assigned with high heuristic values. VMs with high heuristic values are therefore less preferred to be co-located onto a host that has a high heuristic value. Performance of the proposed mechanism was evaluated using CloudSim with real-world workload data. Simulation results show that when comparing with other existing mechanisms, the proposed idea can further reduce both energy consumption and SLA violations.

In this paper, we investigate the dynamic profiles of the cascading failure propagations in power systems. We use a circuit-based power flow model and combine it with a stochastic model to describe the uncertain failure time instants. The sequence of failures is determined by power stresses of individual elements which are governed by deterministic circuit equations, while the time durations between failures are described by stochastic processes. The use of stochastic processes here addresses the uncertainties in individual components' physical failure mechanisms which may depend on manufacturing quality and environmental factors. In this model, the element failure rate is related to the extent of overloading. A network-based stochastic model is developed to study the failure propagation dynamics of the entire power network. Simulation results show that our model generates dynamic profiles of cascading failures that contains all salient features displayed in historical blackout data. The proposed model thus offers predictive information about occurrences of large-scale blackouts.

Existing path planning algorithms are capable of finding physically feasible, shortest, and energy-efficient paths for mobile robots navigating on uneven terrains. However, shortest paths on uneven terrains are often energy inefficient while energy-optimal paths usually take long time to be traversed. Therefore, due to time and energy constraints imposed on mobile robots, these shortest and energy-optimal paths might not be applicable. We propose a multiobjective path planner that can find pareto-optimal solutions in terms of path length and energy consumption. It is based on NAMOA* search algorithm that utilizes a proposed monotone heuristic cost function. The simulation results show that nondominated path options found by the proposed path planner can be very useful in many real-world applications.

Extortion strategies, which can let an individual's surplus exceed her opponent's by a fixed percentage, have played an important role in the understanding of the evolution of cooperation in the Prisoner's Dilemma game. In this paper, we combine individuals' extortion ability with their degrees, and study the influence of extortion diversity in the heterogeneous scale-free network. Our investigation shows that, when individuals' extortion factors are negative correlation with their degrees, these extortionate hubs play as catalysts to enhance the emergence of cooperative behavior. However, when the extortion factors are positively correlated with degrees, the effect of hub's catalyst is weaken.

As renewable energy plants are becoming more widely accessible, one trend of power grids' evolution is decentralization which poses many challenges for grid management. In this paper, we propose a generator allocation method, based on community structure detection, for placing decentralized generators. We take node-generator distance (DG) as an indicator of optimal generators' locations and the underlying community structure is detected with a series of iterating steps. Simulation results show that our method can effectively achieve satisfying allocation solutions as well as enhance the robustness of power systems.

Virtualization is the enabling technology that makes resource provisioning in Cloud computing feasible. With virtualization, virtual machines (VMs) can be migrated across physical hosts to achieve better utilization of resource with a minimum impact on service quality. The VM allocation problem can be formulated as a stable matching problem. In this paper, we propose a VM allocation mechanism based on stable matching. A deferred acceptance procedure is adopted to handle conflicts among preferences of VMs and physical hosts. Unlike ordinary stable matching problems, both involving party groups in our matching process are having a mutual objective, that is to reduce the overall energy consumption of a Cloud data center while maintaining a high level of Quality of Service. The proposed mechanism is evaluated using CloudSim with real-world workload data. Simulation results show that Cloud data centers with the proposed mechanism can reduce energy consumption and avoid violations of Service-Level Agreement.

Mobile sensor networks (MSNs) are often used for monitoring large areas of interest (AoI) in remote and hostile environments, which can be highly dynamic in nature. Due to the infrastructure cost, MSNs usually consist of a limited number of sensor nodes. In order to cover large AoI, the mobile nodes have to move in an environment while monitoring the area dynamically. MSNs that are controlled by most of the previously proposed dynamic coverage algorithms either lack adaptability to dynamic environments or display poor coverage performances due to considerable overlapping of sensing coverage. As a new class of emergent motion control algorithms for MSNs, antiflocking control algorithms enable MSNs to self-organize in an environment and provide impressive dynamic coverage performances. The antiflocking algorithms are inspired by the solitary behavior of some animals who try to separate from their species in most of daily activities in order to maximize their own gains. In this paper, we propose two distributed antiflocking algorithms for dynamic coverage of MSNs, one for obstacle-free environments and the other for obstacle-dense environments. Both are based on the sensing history and local interactions among sensor nodes.

Mobile robots are often utilized in remote and hostile outdoor environments with uncertainties and unknown dangerous. The energy-efficient paths generated based on prior information can be impracticable due to the changes in the environment. Recently proposed Z* search algorithm is capable of finding physically feasible energy-efficient paths on uneven terrains. It can achieve the same accuracy as any brute force algorithm, but with a low computational complexity. However, neither Z* nor any other energy-efficient path planners can effectively handle path replanning triggered by environment changes such as emergence of obstacles. In order to fill this void, we propose a novel algorithm which can recompute optimal paths efficiently. Simulation results show that the proposed algorithm can find equally energy-efficient paths as Z* does, but at a considerably lower computational cost. Therefore, the proposed algorithm can be very useful in mobile robot navigation on uneven terrains with unknown obstacles.

Mobile sensor networks (MSNs) are commonly used for monitoring an area of interest (AoI) in security and surveillance systems. Maximizing the area coverage is one of the primary objectives of such systems. With the added mobility over traditional stationary sensor nodes, mobile nodes can adjust their positions inside the AoI to increase the overall coverage. In this paper, we propose an emergent motion control algorithm for MSNs utilized in surveillance applications. The proposed algorithm is inspired by the anti-flocking behaviour of solitary animals. It facilitates robust distributed control for the MSNs to maximize the network coverage. Computer simulations were performed to analyze performances of the proposed algorithm. Simulation results show that under certain conditions, a MSN with the proposed algorithm can achieve similar network coverage as one with centralized anti-flocking control. Furthermore, the proposed distributed control algorithm provides improved scalability and adaptivity over the centralized anti-flocking control and coordinated motion control models.

Recent literature on flocking in multi-agent systems show that a minority of informed agents in a group of dynamic agents can influence a majority to follow a virtual leader. However, it is not reported how to select these informed agents from the group in order to increase the number of agents which will eventually follow the virtual leader. In this paper, we propose a cluster-based informed agents selection method to achieve this objective. The proposed method enables us to select informed agents such that they are spatially evenly distributed within the group of agents. We carried out extensive simulations to analyze performances of the proposed method against the traditional random-based informed agents selection method. Simulation results show that the proposed method can increase the number of agents which eventually follow the virtual leader for a given number of informed agents. Therefore, the proposed cluster-based informed agents selection method is useful for leading the majority of a group with less number of informed agents.

Cooperation phenomena are ubiquitous in social systems. In this paper, based on the Prisoner's dilemma game model, we study the evolution of cooperation in two social networks, namely, the scientific collaboration network (SCN) and the “Pretty-Good-Privacy” (PGP) trust network. Our investigation shows that due to the existence of community structures in real social networks, small-degree individuals tend to follow their local hubs' behavior and would adopt their strategies in the long run. Hence, the community structure promotes the emergence of persistence behavior. This provides a clue to understanding the evolution of cooperation in social systems.

In this paper, we study the robustness assessment of power systems. Since the power grid is an electrical network by nature, it can be fully and accurately described by circuit laws. Based on Kirchhoff's laws and the properties of network elements, we propose a complex network model that generates power flow information given the electricity consumption and generation information. It has been widely known that large scale blackouts are results of a series of cascading failures triggered by the malfunctioning of specific critical components. Power systems can be more robust if there are fewer such critical components or the network configuration is suitably designed. The percentage of unserved nodes (PUN) caused by a failed component and the percentage of non-critical links (PNL) that will not cause severe damage are used to provide quantitative indication of a power system's robustness. We study the IEEE 118 Bus, Northern European Grid and other practical power systems. Simulation results show that small-world connection of the grid can significantly degrade a power system's robustness.

In this letter, a novel kernel adaptive filtering algorithm, namely the kernel least mean square with single feedback (SF-KLMS) algorithm, is proposed. In SF-KLMS, only a single delayed output is used to update the weights in a recurrent fashion. The use of past information accelerates the convergence rate significantly. Compared with the kernel adaptive filter using multiple feedback, SF-KLMS has a more compact and efficient structure. Simulations in the context of time-series prediction and nonlinear regression show that SF-KLMS outperforms not only the kernel adaptive filter with multiple feedback but also the kernel adaptive filter without feedback in terms of convergence rate and mean square error.

Motions of mobile robots need to be optimized to minimize their energy consumption to ensure long periods of continuous operations. Shortest paths do not always guarantee the minimum energy consumption of mobile robots. Moreover, they are not always feasible due to climbing constraints of mobile robots, especially on steep terrains. We utilize a heuristic search algorithm to find energy-optimal paths on hilly terrains using an established energy-cost model for mobile robots. The terrains are represented using grid-based elevation maps. Similar to A*-like heuristic search algorithms, the energy-cost of traversing through a given location of the map depends on a heuristic energy-cost estimation from that particular location to the goal. Using zigzag-like path patterns, the proposed heuristic function can estimate heuristic energy-costs on steep terrains that cannot be estimated using traditional methods. We proved that the proposed heuristic energy-cost function is both admissible and consistent. Therefore, the proposed path planner can always find feasible energy-optimal paths on any given terrain without node revisits, provided that such paths exist. Results of tests on real-world terrain models presented in this paper demonstrate the promising computational performance of the proposed path planner in finding energy-efficient paths.

The path planning is an important issue as it allows a robot to get from a point to another. Such a path between two points has to be optimized based on user defined requirements and environmental conditions. Most of the existing solutions to path planning problem assume robots to be holonomic. However, ordinary mobile robots are kinematically constrained in practice. A novel solution has been proposed for the path planning problem based on existing ant colony optimization methods which can be realized with practical mobile robots with the aforementioned constraints. Simulation results show the applicability of the proposed path planner to the nonholonomic mobile robots. Performance of the proposed algorithm has been compared with its preceding version. The performance of the proposed path planner may be further improved by fine-tuning its parameters.

The power grid in a typical micro distribution system is non-ideal, presenting itself as a voltage source with significant impedance. Thus, grid-connected converters interact with each other via the the non-ideal grid. In this study, we consider the practical scenario of voltage-source converters connected to a three-phase voltage source with significant impedance. We show that stability can be compromised in the interacting converters. Specifically, the stable operating regions in selected parameter space may be reduced when grid-connected converters interact under certain conditions. In this paper, we develop bifurcation boundaries in the parameter space with respect to Hopf-type instability. A small-signal model in the dq-frame is adopted to analyze the system using an impedance-based approach. Moreover, results are presented in design-oriented forms so as to facilitate the identification of variation trends of the parameter ranges that guarantee stable operation.

In this paper, we study the traffic performance in communication networks from a complex network perspective. The node usage probability is an effective metric for characterizing the traffic load distribution and how frequently a node is chosen to relay packets in a network. Based on the concept of node usage probability, we propose an effective routing strategy to maintain balanced traffic loads in the network nodes by avoiding overuse of certain nodes. Simulation results show that routing algorithms based on the proposed strategy can effectively balance traffic loads and improve the overall traffic performance.

The series/series-parallel (S/SP) compensated contactless resonant converter has a desirable feature of being insensitive to variation of parameters including the transformer's coupling coefficient. However, the error resulting from the use of fundamental harmonic approximation is significant, thus complicating the design procedure and affecting the effectiveness of the control. In this paper a steady-state analysis of the S/SP converter is presented. A generalized equivalent circuit of the S/SP resonant tank is derived, and the cause of the waveform distortion is revealed. Then, more accurate expressions for the key variables of the resonant tank and the output voltage gain are given. Finally, a 1.5 kW contactless resonant converter with S/SP compensation is constructed. Experimental results are in good agreement with the analytical results.

In this letter, a new class of cubature Kalman filters (CKF) based on a spherical simplex-radial rule is proposed to further improve accuracy and efficiency of the traditional CKF. The transformation group of the regular simplex and the moment matching method are utilized to compute the spherical and radial integrals, respectively. In addition, with the increase of the state dimension, the new CKF of the fifth degree uses fewer quadrature points than the traditional CKF with the same degree. Simulations on moments calculation and nonlinear state estimation show that the proposed CKFs can achieve higher accuracy and better efficiency than the traditional CKFs.

Three-phase voltage source converters are commonly used to convert ac power from a three-phase grid to a regulated dc voltage with controlled unity input power factor. However, the non-ideal power grid may drive the converter to enter a low-frequency instability region. As a result of the emergence of low-frequency oscillation, the stability boundary leading to a catastrophic bifurcation shrinks dramatically. Simulation results show the interaction of the low-frequency oscillation and the catastrophic bifurcation. Analytical calculations based on harmonic balance are adopted to show how the stability margin is affected.

This paper shows how an energy efficient LED driving system can be implemented on a full-color large-scale Video display panel through a low-cost modification of conventional hardware configuration. The proposed system employs multiple LED driver modules connected in parallel that are combined to deliver multi-level PWM current to individual LED pixels. The implementation of the proposed driver is illustrated with a three-level driving scheme. The criteria for selecting current levels in order to maximize the luminous efficacy gain is derived mathematically. The algorithm for video signal conversion that requires for deriving sub-video signals from the original HDMI or DVI signal to activate multiple LED driver modules is also discussed. Experimental results show an average gain of 15% on luminous efficacy over the ON-OFF PWM driving scheme by the proposed method.

The current imbalance in driving LED strings will cause fast degradation or even failure in some LEDs that should be avoided in LED applications. This paper presents a current balancing method based on the pulse-width modulation of a common bus voltage to each LED string to achieve the intended averaged current. For the balance of a common averaged current, a small duty cycle will necessitate a large LED turn-on current amplitude, which may cause temporal overheat and low luminous efficacy of LEDs. An optimal feedback control scheme is proposed to maximize the duty cycles and minimize the bus voltage. As a result, at lease one of the LED string is operating at a unity duty cycle. The analysis, implementation and verification are detailed in this paper.

This paper reviews the recent advances in bridging time series analysis and complex network theory. We first introduce the main representative approaches of mapping time series into complex networks with their potential and advantages in applications in a variety of fields, and also report the latest methods to feature complex networks with typical time series after multi-scaling transformation, and to estimate and identify the uncertain connectivity of a complex network. As an example, we show an amplitude-temporal method to fast detect arrhythmia with the indicator of the average degree of associated networks.

The d-q transformation is widely used in the three-phase system analysis, targeting for symmetrical and balanced systems where the time-varying line-frequency sinusoidal terms of the system state variables can be fully canceled out. With the introduction of the distributed generations, more and more unbalanced infrastructures can be connected to the grid, creating unbalanced systems. It will be desirable to have effective analytical methods and modeling techniques for unbalanced systems. This paper proposes a new approach to analyzing unbalanced systems by demonstrating a decoupled analytical technique for resolving the unbalanced system into the balanced positive-, negative- and zero-sequence system components in the circuit level. The proposed method decomposes the unbalanced admittance of the system into independent admittance bases (IAB) each of which belongs to one of the positive-, negative-or zero-sequence system component to ease subsequent analyses.

In this paper, a generalized droop-control scheme for low-voltage microgrid is proposed. The control scheme includes three mutually interacting droop control loops for regulating the power flows between the primary energy sources and the common ac bus. The first droop control is adopted directly from the conventional P/f and Q/V droop control for enabling an automatic power sharing between parallel-connected inverters in the microgrid. The second droop control monitors the dc bus voltage's condition and uses it to modify the settings for the first droop control, and hence, adjust the power sharing capacity of the inverter based on the power generating capacity of the primary energy sources. The third droop control resembles the first droop control, but acts on the dc-dc-converter interfaced primary energy sources for enabling an automatic power sharing between them. The proposed control scheme is useful for realizing a fully decentralized control of a microgrid with improved reliability since no critical communication is needed between the main components of the system. The design methodology of the control scheme is presented, and its effectiveness is verified by both simulation results.

In this paper, we study the packet routing process in communication networks. For efficient and reliable data transmission, the traffic load should be as uniformly distributed as possible in the network and the average distance travelled by the data should be short. The Internet has been demonstrated to have small-world and scale-free properties in its topology. Under the shortest path routing strategy, the traffic intensity of high degree nodes is much higher, thus causing congestion of the whole network. We propose an adaptive routing algorithm, which takes into consideration both the network structure property and the dynamic traffic information. Simulation results show that the proposed algorithm can effectively balance the traffic in the network and improve the overall traffic performance.

Multiple-input dc/dc converters are widely used in hybrid power systems which normally derive power simultaneously from several renewable energy sources and deliver power continuously to the load. Due to intermittent features of the available energy from the renewable sources, such multiple-input conversion systems are often designed with multiple structures and multiple operating modes, and their dynamic behavior is thus quite complex. We consider a hybrid power system with the dual-input buck converter as its constituent system. We study the dynamic behavior of this system and reveal the smooth and non-smooth bifurcation phenomena with low-frequency oscillation. Moreover, a detailed analysis based on an averaged model is performed to identify the two types of bifurcation and evaluate the stability boundaries of the system.

The characteristic of Kalman gain in a cubature Kalman filter for filtering 1-D chaotic signals is investigated. It is shown theoretically that the Kalman gain converges to zero for the case of periodic nonlinear systems, and it either approaches the Cramér-Rao lower bound or oscillates aperiodically for the case of chaotic nonlinear systems. Results from analysis of the Kalman gain are verified by simulations of some representative nonlinear systems.

A decoupled structure for multi-input bi-directional converter based on the concept of paralleled current sources is proposed in this paper. In order to minimize the interaction between the multiple control loops associated with different energy sources or storages, a decoupled control strategy is implemented by dividing the load power demand into several frequency ranges according to the frequency characteristics of the energy sources or storages. The operating principles of the proposed topology and its control system design are analyzed in detail and verified by simulation results. It is shown that the proposed topology and control method can be adopted for systems containing multiple energy sources or storages, such as micro-grid and hybrid electric vehicles.

This paper presents a new LCC resonant inverter that is compatible with capacitor current balancing in multi-string AC-LED array. A thorough theoretical analysis of the voltage transfer function and input impedance characteristics of the LCC network is presented. Based on the analysis, a guided selection of the desirable operating frequency for achieving zero-voltage switching (ZVS) is developed and a systematic design procedure for choosing the reactive component values is formulated. A 100-W prototype is constructed to verify the proposed design.

In this paper, a power management control scheme based on triple-droop control strategy is proposed for low-voltage microgrid system. The proposed control method is based on traditional P/f and Q/V droop control, but adds two other levels of droop control in response to the DC bus voltage conditions. One of them regulates the output power of micro-source (MS), while the other adjusts the output parameters of Droop Control 1. With this control scheme, the output power of MS can be adjusted automatically without the need for communication among the MS systems. The proposed control scheme is proven as capable of operating in both autonomous mode and grid-connected mode. In addition, the droop-controlled VSI systems ensure a smooth transition from grid-connected mode to autonomous mode without the need for mode-switching or communication. The design methodology of the triple-droop control scheme is presented in this paper. The effectiveness of the proposed scheme and design methodology are verified by simulation results on PSIM.

This paper presents a synthesis method for two-state ladder-structured DC-DC power converters by duality transformation. The proposed method can uncover all known converters and also some configurations that are never mentioned in literature. The voltage-to-current configurations are most suitable to be used in PV and LED applications which require constant current sources.

This paper presents a method of controlling the white color point in red/green/blue (RGB) LED driver system. In contrast to conventional systems where the average driving currents of the primary-color LEDs can become saturated when the LEDs have sufficiently aged, and causes the resulting white color point to go out of regulation, the proposed method avoids this problem by adjusting the color set points when a predefined threshold current is reached by one or more of the primary-color LEDs. It is shown that the method can effectively maintain the white color point of the RGB LED at the desired value when the LEDs are subjected to an accelerated ageing through repetitive current stress cycles.

Three-phase voltage source converters (VSC) are commonly used to convert ac power from a three-phase grid to a regulated dc voltage with unity input power factor. The output voltage regulation is normally achieved by an outer voltage feedback loop and a sinusoidal pulse-width-modulated (SPWM) inner current loop. However, the non-ideal power grid may drive the VSC to enter line-frequency instability region. In this paper, a line-frequency instability phenomenon in three-phase voltage-source converters is reported. The converter can also be regarded as exhibiting a Hopf-type bifurcation in which the dc output voltage oscillates at about 130 Hz, and large amount of harmonics appear on the line current. We develop an averaged model of the VSC and non-ideal power grid which can predict the Hopf-type bifurcation. The effects of circuit and grid parameters can be studied with this model, and are verified though cycle-by-cycle simulations. The simulations also provide design-oriented boundaries of operation.

Radio coverage is limited by two parameters: (i) energy per bit radiated by the transmitter and (ii) BER performance of the receiver. In Ultra-Wideband Impulse Radio (UWB IR) the former is limited by the FCC Regulations while the latter depends on the detection algorithm. The fact that prevents the use of UWB IR devices in real applications is the unacceptable short radio coverage. The pulse compression technique, that simultaneously increases the maximum attainable energy per bit and introduces a processing gain at the receiver is proposed here to overcome this problem. As a result of pulse compression, the coverage of the new UWB chirp IR devices is 6-times longer than that of the conventional UWB IR devices, i.e., the radio coverage is extended from less than 10 meters up to 40 meters.

This paper proposes a new anti-windup dual-loop control of doubly-fed induction generator (DFIG) under unbalanced grid voltage conditions. System behavior and controller operation of windup under unbalanced conditions are illustrated. To avoid the windup problem, a new extraction method based on Discrete Fourier Series (DFS) is proposed to decouple the sequence components indifferent to sub- or super- synchronous operation of the DFIG system. A dual loop control is developed for the decoupled positive and negative sequences. The positive sequence control loop is responsible for the conventional torque control and the additional negative sequence control loop is responsible for the minimization of 2ndharmonic in torque pulsation and dc link voltage fluctuation. Simulation results using Matlab/SimPowerSystems library are presented for the built-in single loop and modified dual-loop 9 MW DFIG wind generation systems under unbalanced grid voltage condition to demonstrate the system operation and validate the proposed dual-loop control scheme.

In this paper we study the performance of communication networks from a network science perspective. We consider in particular the effects of the choice of routing algorithms and the kind of network topology on selected areas of performance, and we also study the vulnerability of the networks when subject to selected attack strategies. Contrary to intuition, our results reveal that the removal of a certain number of high-degree nodes in a scale-free network with shortest path routing does not necessarily worsen the overall network performance. Moreover, the scale-free network can perform better when high-degree nodes are coordinated to provide exclusive service to a specific group of nodes.

Assortativity is an important structural characteristic of complex networks. In this paper, we propose a simple method to generate a network with the desired assortative coefficient but still keep the degree distribution unchanged. We simulate cases with different assortative coefficients and calculate the packet drop probability when the buffer size is limited. The simulation results indicate that assortativity has a significant effect on the traffic performance. Our study helps to understand how the network structure influences the traffic dynamics in complex networks.

Standalone photovoltaic-battery hybrid power systems are attractive renewable power generation systems, a particular form of which is based on a photovoltaic system and a battery connected to an output dc bus via a buck converter and a bidirectional buck-boost converter, respectively. To operate this system, a specific control strategy is required. In this paper we consider one such system and control strategy, and study the effects of variations of the load current and sunlight intensity on the stability of the system. A Neimark-Sacker bifurcation is identified in this system. Moreover, a detailed analysis based on discrete-time mapping is performed to evaluate the effect of variations in sunlight intensity and load current on the stability boundary of the system.

Inductive power transfer (IPT) is an emerging technology that may open up new possibilities for power charging applications. However, the rather complex control method and low efficiency remain the key obstructing factors for general deployment. In a regularly compensated IPT circuit, high efficiency and controllability of the voltage transfer function are always conflicting requirements under varying load conditions. In this paper, the relationships among compensation parameters, circuit efficiency and voltage transfer function are studied. A new compensation method is proposed to achieve a better overall efficiency and good output voltage controllability. The analysis is supported by experimental results.

The safety and commercial benefits of Intelligent Transportation System (ITS) raised interests towards inter-vehicle networking technologies such as Vehicular Ad-hoc Network (VANET). Being an approved standard for wireless access in vehicular environments, IEEE 802.11p attracts a lot of research attentions, especially on its broadcasting performance. However, most of the previous network performance models paid little attention to vehicle distribution, or simply assumed homogeneous car distribution. It is obvious that vehicles are distributed non-homogeneously along a road segment due to traffic controls and speed limits at different portions of the road. In light of the inadequacy, we present in this paper an original methodology to study the performance of 802.11p VANETs with practical vehicle distribution in urban environment. An empirically verified stochastic traffic model is adopted, which incorporates the effect of urban settings (such as traffic lights) on car distribution and generates practical car density profiles. Based on the knowledge of car density at each location from the traffic model, the 802.11p broadcasting model is developed and a new metric, Broadcasting Performance Index (BPI), is introduced to better characterize the broadcasting performance and packet collision probability in VANETs. Furthermore, the analytical closed form for BPI is derived and its accuracy is confirmed with extensive simulation. In general, our results demonstrate the applicability of the proposed methodology on modeling protocol performance, and shed insights into the performance analysis of other communication protocols and network configurations in urban vehicular networks.

Path planning can be viewed as an optimization process in which an optimum path between two points is to be found under some predefined constraints. Some typical constraints are path length, fuel consumption, and path safety factor. Exact algorithms such as linear programming (LP) and dynamic programming (DP) are widely adopted in vehicle maneuvering systems. However, as the problem domain scales up, exact algorithms suffer from high computational complexity. In contrast, metaheuristic algorithms such as evolutionary algorithms (EA) and genetic algorithms (GA) can provide suboptimum solutions without the full understanding of the problem domain. Metaheuristic algorithms are capable of providing decent solutions within a finite period of time, even for large-scaled problems. In this paper, a GA-inspired unmanned underwater vehicle (UUV) path planner based on DP is proposed. Simulation results show that the proposed algorithm can outperform a GA-based UUV path planner in terms of speed and solution quality.

Wind energy generation systems are major components of distributed generation units or micro-grids. Application of the doubly fed induction generator (DFIG) in wind energy generation systems allows variable speed operation by using partially rated back-to-back quadruple active and reactive power PWM converters. The control of the system is very complex. Many new control schemes reported in the literature are designed to solve some specific control problems and have not thoroughly been tested for general stability with local loading which is common in micro-grids. In this paper, the oscillation boundaries of the system in the presence of an unbalanced three-phase reactive load are studied. Specifically, we report the system stability in terms of the amplitude modulated oscillatory magnitude of the three-phase voltage and current of the generator under a local reactive three phase load. The oscillation can also be observed as a linear modulated oscillation of the voltage link capacitor inside the wind energy generator. Oscillation boundaries of the system are collected using various damping component values of the three phase load, illustrating the potential instability problems that may be caused by imbalanced reactive loads.

This paper extends the definition of an exact consensus complex social network to that of a near consensus complex social network. A near consensus complex social network is a social network with nontrivial topological features and steady state values of the decision certitudes of the majority of the nodes being either higher or lower than a threshold value. By using eigen theories, the relationships among the vectors representing the steady state values of the decision certitudes of the nodes, the influence weight matrix and the set of vectors representing the initial state values of the decision certitudes of the nodes that satisfies a given near consensus specification are characterized.

A low-density parity-check (LDPC) code can be described by a Tanner graph composed of symbol nodes and check nodes. Short cycles in the Tanner graph should be avoided because such cycles will degrade the performance of the decoder and hence increase the error rate. In most of the circumstances, cycles of length 4 can be totally eliminated in the Tanner graph when the LDPC code has been designed properly. Locally, for each variable node in the Tanner graph, the girth may take the value of 6 or 8, or even higher. In this study, the authors attempt to increase the local girths of a Tanner graph with an aim to improving the error performance of the code. The authors propose and analyse the ‘degree spectrum’ of the symbol nodes connecting to each of the check nodes. Based on the analysis, the authors can determine if the local girths can be further increased. The authors also propose simple ways to construct codes with higher local girths. Finally, the authors show the characteristics and the error performance of the codes constructed with the proposed methods.

Elementary trapping sets (ETSs) have been found to be the main cause of error floor in the decoding of low-density parity-check (LDPC) codes. Moreover, irregular LDPC codes that avoid harmful [w; u; e] ETSs have been constructed and have shown to possess superior error performance compared with other LDPC codes. In this paper, we attempt to evaluate irregular LDPC code performance under an additive white Gaussian noise channel within the high SNR region using the importance sampling (IS) approach in conjunction with the identification of [w; u; e] ETSs. For any given irregular LDPC code, we will first apply a three-step method that aims to search as many ETSs within the code as possible. Then, we will classify these ETSs into different groups based on their labels, i.e., [w; u; e]'s. Further, by dividing the error region into various sub-regions centered by ETSs, we apply the IS simulator to evaluate the error rate of each of the sub-regions. Based on the error rates of all the sub-regions, we can estimate the overall error rate of the LDPC code. Results have indicated that our proposed IS scheme can produce speed-up gains up to 3.9×109 times compared to Monte Carlo simulations.

Path planning is an optimization process in which a path between two points is to be found that results in a user-defined optimum satisfaction of a given set of requirements. For small scale path planning, exact algorithms such as linear programming and dynamic programming are usually adopted which are able to give optimum solutions in short time. However, due to their memory intensive nature and computational complexity, exact algorithms are not applicable for medium to large scale path planning. Meta-heuristic algorithms such as evolutionary algorithms can provide sub-optimum solution without the full understanding of the search space and are widely used in large-scaled path planning. However, extra precautions are needed to avoid meta-heuristic algorithms from being trapped in local optimum points. In this paper, a path planner combining genetic algorithms (GA) with dynamic programming (DP) is proposed to solve an autonomous under-water vehicles (AUVs) path planning problem. The proposed path planner inherits the speed of exact algorithms and the scalable nature of meta-heuristic algorithms. Simulation results show that when comparing with conventional GA-based path planners, the proposed path planner can greatly improve the convergence rate and solution quality.

The energy-efficiency issue of the switched-capacitor converters is still a highly controversial topic that requires a more in-depth exploration. This paper will address the issue by dissecting the analysis of the entire efficiency problem into two parts. In the first part, the efficiency analysis of charging the capacitor of an RC circuit under different aspects (partial charging, full charging, at zero capacitor voltage, at non-zero capacitor voltage, etc.) will be conducted. In the second part, the efficiency analysis of discharging the capacitor of an RC circuit with resistive and capacitive loads will be covered. A complete evaluation of the overall efficiency is then performed in terms of both the charging and discharging efficiencies of the capacitor. Additionally, it is shown in this paper that the claim that quasi-switched-capacitor converters are more lossy than switched-capacitor converters is a common misconception.

To satisfy the huge demand for unlicensed handheld wireless networking devices, the Federal Communications Commission (FCC, USA) made the application of ultra-wideband (UWB) technology possible in 2002. Since then an IEEE standard has already been approved for the physical layer of the UWB impulse radio networking devices. Because of the ultra-wideband property the design and implementation of UWB circuits and systems set up an enormous challenge for the circuit designers. Unfortunately, an exact specification has not yet been derived for the circuit designers. Starting from the FCC Regulations and the IEEE UWB standard this contribution derives the required specifications for the blocks of UWB transceivers. It shows that if CMOS technology is used then the coverage of UWB impulse radio is limited by the low supply voltage. To overcome this limit more than one UWB carrier pulse is used to keep transmitted energy per bit high enough but to reduce the required voltage swing below the supply voltage. The optimum value of delay to be set between the successive UWB pulses is also determined.

Since the coherent receivers are not feasible to implement low-cost ultra-wideband (UWB) impulse radio (IR) devices featuring extremely low power consumption, energy detector-based (ED-based) noncoherent receivers are more and more frequently chosen. The Pulse Polarity Modulation (PPM) scheme with a threshold compensated receiver is a promising candidate for the implementation of cheap and robust UWB IR devices. Unfortunately, the field tests and computer simulations have shown the well known fact that the noise performance of noncoherent demodulators is much behind that of their coherent counterparts. The poor noise performance results in a such a bad receiver sensitivity that prevents the use of noncoherent receivers in the majority of real applications. This contribution shows how the noise performance of noncoherent threshold compensated PPM receiver can be improved considerably by disabling the receiver outside of the UWB pulse where only channel noise and interference is observed. The novel receiver configuration, referred to as gated threshold compensated noncoherent PPM receiver offers an 8-dB improvement in receiver sensitivity.

Transmitted reference (TR) modulation scheme and the autocorrelation receiver offer a simple and robust transceiver configuration for ultra-wideband (UWB) communications. Unfortunately, the TR UWB system has a poor noise performance. The paper shows that the noise performance of a TR autocorrelation system can be improved considerably by placing a noise filter before the autocorrelation detector. The optimum frequency response of required noise filter is derived.

Low-density parity-check (LDPC) codes with scale-free (SF) symbol-node degree distribution have been shown to provide very good error performance. When the code rate becomes high, however, there will be a lot of degree-2 symbol nodes in the “pure” SF-LDPC codes. As a consequence, when the codes are constructed by connecting the symbol nodes with the check nodes, many small-size cycles will be formed. Such small-cycles will degrade the error performance of the codes. In this paper, we address the issue by imposing a new constraint on the design of high-rate SF-LDPC codes. We will compare the error rates of the constrained SF-LDPC codes and other optimized LDPC codes.

This paper studies the problem of energy consumption minimization in wireless sensor networks (WSNs) under uncertainty in sensor node generated traffic. It is important to consider traffic uncertainty because WSNs designed for surveillance and monitoring applications typically operate in event-driven and query-driven modes for which the node generated traffic changes from instant to instant. The problem is formulated as a two-stage stochastic linear program with recourse where the objective is to minimize the sum of the energy cost expended on the carried traffic and the average energy cost of blocked traffic, under the constraint of a fixed energy budget at each node. The stochastic programming formulation lends to the consideration of two approaches for handling the randomly generated traffic: a deterministic approach in which all future generated random traffic are constrained by the link flows determined for the nominal traffic, and a stochastic approach where the link flows are adapted as the node generated traffic changes. Numerical results show that, compared to the deterministic approach, the stochastic approach provides up to 10% savings in network-level energy consumption and this is achieved without a loss in received data quality. The network-level energy savings translate to a prolonged network lifetime.

Trapping sets (TSs) are known to cause error floors in regular and irregular low-density parity-check (LDPC) codes. By avoiding major error-contributing TSs during the code construction process, codes with low error floors can effectively be built. In it has been shown that TSs labeled as [w;u] are considered as being equivalent under the automorphism of the graph and are therefore contributing equally to the error floor. However, TSs with the same label [w;u] are not identical in general, particularly for the case of irregular LDPC codes. In this paper, we introduce a parameter e that can identify the number of "distinguishable" cycles in the connected subgraph induced by an elementary trapping set. Further, we propose a code construction algorithm, namely the Progressive-Edge-Growth Approximate-minimum-Cycle-Set-Extrinsic-message-degree (PEG-ACSE) method, that aims to avoid small elementary trapping sets (ETSs), particularly detrimental ETSs. We also develop theorems evaluating the minimum possible ETSs formed by PEG construction algorithms in general. We compare the characteristics of the codes built using the proposed method and those built using PEG-only or PEG-Approximate-minimum-Cycle-Extrinsic-message-degree (PEG-ACE) methods. Results from simulations show that the codes constructed using the proposed PEG-ACSE method produce lower error rates, particularly at the high signal-to-noise (SNR) region, compared with codes constructed using other PEG-based algorithms.

In this paper, we attempt to evaluate irregular LDPC code performance at the high SNR region using the importance sampling (IS) approach in conjunction with primary- trapping-set identification. Results have indicated that our proposed IS scheme can produce speed-up gains up to 3.9 x 109times compared with Monte Carlo simulations.

We consider the scenario where each sensor in a sensor network observes an instantaneous linear mixture of multiple sources in a sensing field. A portion of sensors transmit their observations to a fusion center simultaneously, through channels with different gains. Due to the bandwidth constraint of links, the sensors' observations are quantized before transmission. A multiple-access interference (MAI) model is therefore adopted to measure the quality of the links under two cases. The fusion center uses a fast independent component analysis (Fast ICA) algorithm with pre-whitening to extract the sources, based on the received data from the transmitting sensors. The performance of source extraction is evaluated by numerical simulations. The results show that the second case is better regarding the impact of MAI on the performance.

Unmanned aerial vehicles (UAVs) are remote controlled or autonomous air vehicles. An UAV can be equipped with various types of sensors to perform life rescue missions or it can be armed with weapons to carry out stealthy attack missions. With the unmanned nature of UAVs, a mission can be taken in any hostile environment without risking the life of pilots. Among life rescue missions, the common objective is often defined as maximizing the total coverage area of the UAVs with the limited resources. When the number of UAVs increases, coordination among these UAVs becomes very complicated even for experienced pilots. In this paper, a cooperative path planner for UAVs is proposed. The path of each UAV is represented by a B-spline curve with a number of control points. The positions of these control points are optimized using an ant colony optimization algorithm (ACO) such that the total coverage of the UAVs is maximized.

A large organization is composed of groups, which may represent departments, centers, administrative units, etc. In this paper, we analyze the internal call interactions among telephone numbers belonging to the same or different groups in a large organization. We observe that the node degree probabilities are almost the same for degrees less than 30 and they are found to obey a power-law when the degrees are large. Further, we find two typical cumulative probability distributions of the intergroup link weight for the group-nodes in the internal telephone group network and we also find that there is a limit to the group size. Based on the results, we propose a growth model using a combination of preferential and non-preferential attachments as well as group splitting. We show that the constructed network exhibits similar properties as the real-life internal telephone call/group network.

Light-emitting-diode (LED) light sources, which are more compact, capable to change color in real time, less dissipative and more durable, are finding more applications in domestic, commercial and industrial environments. However, requirements such as high power factor, long lifetime, accurate current control and high efficiency pose challenges to the design of LED ballast circuits. This paper proposes an LED ballast with a first-stage isolated current-fed power factor correction (PFC) pre-regulator that puts the short-lifetime high-voltage storage capacitor to the secondary, thus extending the overall system lifetime. Furthermore, the high voltage stress on the main switch, which is typical in current-fed converters, is reduced substantially by appropriately exploiting the transformer leakage inductance. The design uses two secondary transformer windings and an LED current driver to form a non-cascading structure to improve efficiency. Multiple non-cascading structures can be used for LED lamps for instant independent brightness control. Analysis, design example and prototype verification are given for the LED ballast.

The switched-capacitor converters are ideal switching-mode power supplies for portable electronic consumers due to their light weight, small size, and high power density. However, they suffer from a discontinuous input current waveform with large di/dt, what leads to significant electromagnetic interference (EMI) emission. This paper proposes a configuration of switched-capacitor converters connected in parallel with their inputs and outputs interleaved. The interleaving times are calculated by taking into account the fast capacitor-charging characteristic, and the need to have the nominal operating point on its linear part for increasing the regulation range at changes in the input voltage and load. To improve control performance and avoid the use of small-signal linearization in the control design, a large-signal approach is adopted for modeling the converter, allowing for a design of a sliding mode control.

Sensor networks employ a large amount of wireless sensor nodes to provide sensing power with high redundancy. Such redundancy makes sensor networks robust under changing environments. However, without proper scheduling, the surplus sensing power will cost tremendous energy consumption to the wireless sensor nodes. A scheduling scheme based on social insect colonies is proposed here. The proposed scheme is a kind of adaptive ‘periodic on-off’ scheduling scheme that uses only local information for making scheduling decisions. The scheme is evaluated in terms of averaged detection delay, target 3-coverage hit-rate and energy consumption per successful target detection. Simulation results show that, when comparing with other generic scheduling schemes, the proposed scheme can reduce energy consumption from a minimum of 7.49% to a maximum of 90.81% and improve the target hit-rate from a minimum of 15.7% to a maximum of 58.9%. Optimisation of the network lifetime and other performances is possible by adjusting some parameters.

Study of complex networks has been conducted across many fields of science, including computer networks, biological networks and social networks. Characteristics of different types of complex networks such as random networks, regular-coupled networks, small-world networks and scale-free networks have been discovered by researchers. Application of such network properties to solve engineering problems, however, is still at the infancy stage. In this study, we make one of the first attempts in applying complex network theories to communications engineering. In particular, inspired by the shortest-average-path-length property of scale-free networks, we design short-length low-density-parity-check (LDPC) codes with an aim to shortening the average distance between any two variable nodes. We will also compare the error performance, both theoretically and by simulations, of the proposed codes with those of other well-known LDPC codes.

In this paper, the problem of source extraction in bandwidth-constrained wireless sensor networks is considered. The sensor observations are assumed to be instantaneous linear mixtures of sources in a sensing field, possibly corrupted by noise. Because of the bandwidth constraint, the observations are quantized before being transmitted. Three kinds of sensor network topologies (cluster-based sensor network, sensor network with a fusion center, and concatenated sensor network) are considered in order to show the impact of topology on the performance and energy efficiency. The mixtures are reconstructed based on the received quantized data, and source extraction is performed by using a fast fixed-point algorithm with prewhitening. This algorithm was proposed by Hyvarinen and Oja, published in Neural Computation, and has the advantage of simplicity and fast convergence. The case of source extraction where the sensed data are undistorted is used as the performance benchmark. The results show that in all these sensor networks, the performance can be close to that of the benchmarking case. The energy efficiency and lifetime of these sensor networks are compared when the same source extraction task is executed. The effects of sensor failure and link failure on the performance are also discussed.

This paper studies a multi-cluster sensor network which is applied for source extraction in a sensing field. Both the performance of source extraction and the total energy consumption in the sensor network are functions of the number of clusters. In this paper, we aim at finding the optimal number of clusters by minimizing the effective energy consumption which is defined as the ratio of the performance of source extraction to the total energy consumption in the sensor network. A particle swarm optimization (PSO) algorithm is employed to form the clusters which enables every cluster to perform source extraction. The existence and the uniqueness of the optimum number of clusters is proven theoretically and shown by numerical simulations. The relationship between the optimum number of clusters and the various system parameters are investigated. A tradeoff between the performance and the total energy consumption is illustrated. The results show that the performance is greatly improved by adopting the multi-cluster structure of the sensor network.

It has been observed that the TCP-RED system may exhibit instability and oscillatory behavior. Control methods proposed in the past have been based on the analytical models that rely on statistical measurements of network parameters. In this paper, we apply the detrended fluctuation analysis (DFA) method to analyze stability of the TCP-RED system. The DFA has been used for detecting long-range correlations in seemingly non-stationary noisy signals. The key indicator emanating from DFA is known as the scaling exponent. By examining the variations of the DFA scaling exponent when varying system parameters, we quantify the stability of the TCP-RED system in terms of system’s characteristics.

Sensor networks with a large amount of sensor nodes usually have high redundancy in sensing coverage. The network lifetime can be further extended by proper scheduling and putting unnecessary sensor nodes into sleep mode. In this paper a bio-inspired scheduling scheme is proposed. The proposed scheme is a kind of adaptive "selective on-off" scheduling scheme which uses only local information for making scheduling decisions. The scheme is evaluated in terms of target 3-coverage hit-rate, averaged detection delay, and energy consumption per successful target detection. Simulation results show that our proposed scheme can reduce energy consumption by as much as 2/3 when comparing with other generic scheduling schemes while maintaining the detection delay and target hit-rate at a comparable level. Optimization of the network lifetime and other performances is possible by adjusting some parameters.

A general noncoherent chaos-shift-keying (CSK) communication system with adjustable weights is proposed in this paper. The performance of the system under additive white Gaussian noise (AWGN) and multipath channel conditions are evaluated. Analytical expressions of the bit error rates are derived. The performance of the system is compared with that of the DCSK system and existing noncoherent CSK systems. The results show that the general CSK system can achieve the same performance as that of the DCSK system. A detailed analysis is presented to show that the DCSK system is the optimal form of such noncoherent systems.

It has been observed that a bottleneck random early detection (RED) gateway becomes oscillatory when regulating a flow in multiple TCP connections. The stability boundary of the TCP-RED system depends on various network parameters, making the adjustment of the RED gateway a difficult task. Based on a fluid-flow model, analytical conditions were formulated that describe the stable boundary of the RED gateway depending on the number of TCP Reno connections. The proposed model accurately generates a stability boundary surface in a four dimensional space, which facilitates the adjustment of parameters for stable operation of the RED gateway. The accuracy of the analytical results has been verified using the ns-2 network simulations.

This paper reports intermediate-scale instability in a single-stage power-factor-correction (PFC) power supply that employs a cascade configuration of a boost stage operating in discontinuous conduction mode (DCM) and a forward stage operating in continuous conduction mode (CCM). The two stages combine into a single stage by sharing one main switch and one control loop to achieve input PFC and tight output regulation. Main results are given by "exact" cycle-by-cycle circuit simulations. The intermediate-scale instability usually manifests itself as local oscillations within a line cycle. Based on the stability analysis of a buck converter operating in CCM, the underlying mechanism of such instability can be attributed to the Hopf bifurcation occurred in CCM forward stage. Finally, experimental results are presented for verification purposes

The unscented Kalman filter (UKF) has recently been proposed for filtering noisy chaotic signals. Though computationally advantageous, the UKF has not been thoroughly analyzed in terms of its convergence property. In this paper, non-periodic oscillatory behavior of the UKF when used to filter chaotic signals is reported. We show both theoretically and experimentally that the gain of the UKF may oscillate aperiodically. More precisely, when applied to periodic signals generated from nonlinear systems, the Kalman gain and error covariance of the UKF converge to zero. However, when the system being considered is chaotic, the Kalman gain either converges to a fixed point with a magnitude larger than zero or oscillates aperiodically.

This paper studies a system of parallel-connected dc/dc converters under master-slave current sharing and proportional-integral (PI) PWMcontrol. Two distinct types of bifurcations can be identified. Depending on the value of the integral time constant, the system exhibits either a slow-scale bifurcation (Neimark-Sacker bifurcation) or a fast-scale bifurcation (period-doubling). Extensive simulations are used to capture the behaviour. Trajectories before and after these bifurcations are shown. The boundaries between these two types of bifurcations are located. Parameter spaces of the feedback controller for stable and unstable operation are presented.

It is natural to envisage that in a telephone network, some telephone numbers originate or receive more calls than others. Indeed, real-life data have verified the conjecture that the number of calls originated from or received by a telephone number in a network has a power-law property. Further, the number of calls made or received by the same telephone number can be very different. In this paper, we construct a self-growing complex network for modeling the aforementioned telephone call network. The complex network obtained is a directed and weighted network. Moreover, the nodes (telephone numbers) of the network exhibit power-law properties in the following aspects: total-degree, out-degree, in-degree, total-strength, out-strength and in-strength.

This paper describes the coexisting attractors of parallel connected buck switching converters under a master-slave current sharing scheme. We present the basins of attraction of desired and undesired attractors, which provide design information on the conditions for hot-swap operations. The system employs a typical proportional-integral (PI) controller for regulation. It is shown that the system will converge to different attractors for different initial conditions with the same control parameters. Simulation results are given to illustrate the phenomenon. This study is relevant to practical design. Specifically we show that the stability regions obtained from linear methods (i.e., considering only local stability) can be over-optimistic as the global stability regions are found to be more restrictive in the parameter space.

This paper describes a general analytical procedure for steady-state waveform analysis of power electronics circuits using orthogonal polynomials. The proposed method exploits the time-domain piecewise property of power electronics circuits as well as the orthogonality of certain polynomials in order to maximize the accuracy and computational efficiency. This new analytical approach can provide a fast means to obtain improved and more robust solutions of steady-state waveforms of power electronics circuits

This paper discusses a family of fixed-frequency pulse-width-modulation based sliding mode voltage controllers for basic DC-DC converters operating in continuous conduction mode. An experimental comparison is offered between the proposed controller and the PWM peak current mode controller for the case of the boost converter. The performances and properties of the proposed controller, the conventional PWM voltage mode controller, and the PWM current mode controllers are also compared.

The problem of traffic congestion in complex networks is studied. Two kinds of complex network structures, namely random graphs and scale-free networks, are considered. In terms of the structure of connection, random graphs are homogeneous networks whereas the scale-free networks are heterogeneous networks. For both types of networks, we introduce an additional scale-free feature in the load generation process such that a small number of nodes are more heavily loaded than others. A traffic model similar to the routing algorithm in computer networks is used in our simulation study. We show how the network structures and parameters influence the traffic congestion status.

In the study of low-density-parity-check (LDPC) codes, most researchers are interested in their bit error rate performance. However, block error rate (BLER) is another important measure of the system performance because it provides the rate at which the blocks/packets need to be re-sent again - the smaller the better. In this paper, we apply a simple feedback technique to the decoding of LDPC codes. Extensive simulations have been performed. Results show that the proposed method can effectively improve the BLER of the codes at the waterfall region while not degrading the BER performance at the high SNR region

Intra-day measurements of three time series (DJIA, gold fixings and USD-JPY exchange rates) are examined for evidence of deterministic nonlinear dynamics. Standard linear surrogate techniques and estimation of dynamic invariants demonstrate that linear noise models are insufficient to explain dynamic variability in intra-day returns. Therefore, the data may not be modeled as a monotonic nonlinear transformation of linearly filtered noise. Furthermore, a new nonlinear surrogate technique is employed to demonstrate that conditional heteroskedastic models are also insufficient to model this data. We conclude that the most likely model of the data is a nonlinear dynamical system driven by high dimensional dynamics (noise).

Power electronics circuits are rich in nonlinear dynamics. Their operation is characterized by cyclic switching of circuit topologies, which gives rise to a variety of nonlinear behavior. This paper provides an overview of the chaotic dynamics and bifurcation scenarios observed in power converter circuits, emphasizing the salient features of the circuit operation and the modeling strategies. In particular this paper surveys the key publications in this field, reviews the main modeling approaches, and discusses the salient bifurcation behaviors of power converters with particular emphasis on the disruption of standard bifurcation patterns by border collisions.

This paper describes the bifurcation phenomena of a system of parallel-connected boost dc/dc converters. The results provide useful information for the design of stable current sharing in a master-slave configuration. Computer simulations are performed to capture the effects of variation of some chosen parameters on the qualitative behaviour of the system. It is found that variation of some parameters leads to Neimark-Sacker bifurcation. Analysis is presented to establish the possibility of the bifurcation phenomena.

This paper reexamines the conventional current-mode control strategy as applied to DC/DC converters in the light of "avoiding bifurcation". This alternative viewpoint permits convenient selection of parameter values to guarantee stable operation. Slope compensation is viewed as a means to keep the system sufficiently remote from the first bifurcation point. It is shown that excessive bifurcation clearance is accompanied by undesirably slow dynamical response. A variable ramp compensation is proposed to dynamically adjust the slope magnitude such that the system is kept clear of bifurcation yet responds sufficiently fast during transients.

A novel algorithm which is based on the linear programming technique is proposed to test the stability property of a circuit. This algorithm is efficient and non-Lyapunov equation based. Parasitic capacitances which may de-stabilize a circuit can be located.

An approach to the formulation of averaged models for a class of periodically switched networks is presented. The proposed approach is applicable for any switched network with switching frequency much higher than the eigenvalues of the free-oscillating circuits corresponding to all involved switch state combinations. In particular the approach finds applications in the modelling of DC/DC converter circuits that operate in PWM mode. Unlike other modelling techniques currently available for DC/DC converters which make no use of circuit graphs and hence require a priori knowledge of the type of circuits to be modelled, the proposed approach requires no such knowledge, and thus is suitable for generating reliable and general computer algorithms for modelling such circuits.>