The Metaverse refers to the integration of physical and virtual realities, offering new possibilities for enhancing operations and services across various industries. However, its application in the energy sector is still in its nascent stage. The energy industry, crucial for the global economy and society, faces significant challenges due to its complex and risky nature, such as health, safety, and environmental (HSE) concerns, and the remote locations of extraction sites. Although some studies have explored the use of the Metaverse in this industry for data visualization, energy process modeling, and training, a comprehensive review of existing technologies and applications is lacking. This article addresses this gap by examining the potential of the Metaverse for the energy industry, using the Oil&Gas sector as a case study. We identify the essential technologies needed to create a realistic and immersive Metaverse experience and review the current literature on its industrial applications in the energy sector. Distinguishing our work from others, we present four practical case studies developed from our own experience in the Oil&Gas sector. These cases demonstrate the tangible benefits of Metaverse solutions in improving operational efficiency, reducing costs, and enhancing worker safety and productivity, highlighting the unique value of the Metaverse in addressing industry-specific challenges.

Energy storage systems (ESSs)-based demand response (DR) is an appealing way to save electricity bills for consumers under demand charge and time-of-use (TOU) price. In order to counteract the high investment cost of ESS, a novel operator-enabled ESS sharing scheme, namely, the "operator-as-a-consumer (OaaC)," is proposed and investigated in this article. In this scheme, the users and the operator form a Stackelberg game. The users send ESS orders to the operator and apply their own ESS dispatching strategies for their own purposes. Meanwhile, the operator maximizes its profit through optimal ESS sizing and scheduling, as well as pricing for the users' ESS orders. The feasibility and economic performance of OaaC are further analyzed by solving a bilevel joint optimization problem of ESS pricing, sizing, and scheduling. To make the analysis tractable, the bilevel model is first transformed into its single-level mathematical program with equilibrium constraints (MPEC) formulation and is then linearized into a mixed-integer linear programming (MILP) problem using multiple linearization methods. Case studies with actual data are utilized to demonstrate the profitability for the operator and simultaneously the ability of bill saving for the users under the proposed OaaC scheme.

This article studies the adaptive fuzzy fault tolerant control (FTC) problem for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with unknown control directions in the presence of time-varying asymmetric output constraints. Our contribution includes a step forward beyond usual FTC results to exhibit that the system output of nonsquare and square MIMO systems is uniformly bounded against actuator faults by a novel FTC methodology without the fault detection unit, as well as stay in the preselected constraints. To obtain new results, an equivalent unconstrained system is established by employing an error transformation technique. Furthermore, a learning-based switching function scheme is proposed to automatically activate different groups of actuators without human intervention for attenuating the influence of faulty actuators. By this means, no explicit fault detection and isolation units are needed to result in reducing the risk of false alarm or missed detections and expediting the responsiveness of the controller. Moreover, the obstacle caused by unknown control directions is circumvented by a novel technique combining the matrix decomposition technique and Nussbaum-type function. It is proved that the desired tracking performance with prescribed output bounds and the boundedness of all the signals in the closed-loop system can be guaranteed via an improved average dwell time approach. Finally, simulation results demonstrate the merits of the proposed controller.

In complex industrial processes, it is difficult to measure the key quality variables online. It takes a long time to obtain quality variables through offline testing, which makes it difficult to get timely information to guide the production process. Therefore, this article proposes a novel dynamic time feature expanding and extracting framework for the sinter quality prediction. First, the original data are differentiated, compensated for time delay, expanded, and serialized by using time characteristics, and the input time series is reconstructed. Second, the integrated time features extractor is used to obtain the process information. Then, the recurrent neural network regression is applied to obtain the prediction of key quality variables. Finally, the effectiveness of the proposed method is verified by a numerical example, the actual data of sintering process and various comparative experiments, and the prediction effect of FeO content in sintering process is improved.

Soft sensor techniques have been applied to predict the hard-to-measure quality variables based on the easy-to-measure process variables in industry scenarios. Since the products are usually produced with prearranged processing orders, the sequential dependence among different variables can be important for the process modeling. To use this property, a dual attention-based encoder-decoder is developed in this article, which presents a customized sequence-to-sequence learning for soft sensor. We reveal that different quality variables in the same process are sequentially dependent on each other and the process variables are natural time sequences. Hence, the encoder-decoder is constructed to explicitly exploit the sequential information of both the input, that is, the process variables, and the output, that is, the quality variables. The encoder and decoder modules are specified as the long short-term memory network. In addition, since different process variables and time points impose different effects on the quality variables, a dual attention mechanism is embedded into the encoder-decoder to concurrently search the quality-related process variables and time points for a fine-grained quality prediction. Comprehensive experiments are performed based on a real cigarette production process and a benchmark multiphase flow process, which illustrate the effectiveness of the proposed encoder-decoder and its sequence to sequence learning for soft sensor.

This article is concerned with the challenge of guaranteeing output constraints for fault-tolerant control (FTC) of a class of unknown multi-input single-output (MISO) nonlinear systems in the presence of actuator faults. Most industrial systems are equipped with redundant actuators and a fault detection-isolation mechanism for accommodating unexpected actuator faults. To simplify the system design and reduce the risk of false alarm or missed detection brought by the detection unit, a learning-based switching function scheme is proposed to automatically activate different sets of actuators in a rotational manner without human intervention. By this means, no explicit fault detection mechanism is needed. An additional step has been made to guarantee that the system output remains in user-defined time-varying asymmetric output constraints all the time during the occurrence of failures by utilizing error transformation techniques. The stability of the transformed system can equivalently deliver the result that the original system output stays in the required bounds. Hence, system crash or further catastrophic outcomes can be avoided. A neural network is integrated to embody the adaptive FTC design for dealing with unknown system dynamics. The dynamic surface control (DSC) technique is also invoked to decrease complexity. Furthermore, the stability analysis is carried out by the standard Lyapunov approach to guarantee that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results are provided to verify the effectiveness of the proposed scheme.

Reliable and effective fault diagnosis methods are necessary for complex industrial processes that consists of various units. After a process fault is detected, it remains a challenging task to locate the root cause unit and determine the propagation path of the fault. In this paper, a novel method, termed Sparse Causal Residual Neural Network (SCRNN), is proposed and applied for modern industrial root cause diagnosis. The advantage of SCRNN lies in that it can not only recognize linear and nonlinear causal relationships in parallel, but also automatically determine the causality lags and deduce the time delay of causal transmission. Besides, due to the specially designed sparse constraint and optimization algorithm, the SCRNN model can realize the function of key dependent variable selection, avoiding the high computational complexity and complicated procedure brought by pairwise comparison. The feasibility of the proposed method is illustrated through the benchmark TE process.

Process monitoring is a challenging task for modern industrial processes which are commonly nonstationary in nature, revealing typical non-Gaussian characteristics. Nowadays, data-driven based fault detection methods have drawn increasing attention, most of which work under an assumption that the process is subject to Gaussian distribution. But in practice, the underlying non-Gaussian characteristics may be typical in the complex process, which cannot be properly enclosed by a statistical model with a close confidence region and thus may be insensitive to fault detection. Hence, it is necessary to explore and separate the underlying Gaussian and non-Gaussian distributions in fine-grain. In this work, a Gaussian and non-Gaussian subspace decomposition method is proposed by designing a variant of stationary subspace analysis (VSSA) for nonstationary process monitoring. First, the whole time-wise nonstationary process can be neatly converted to condition-wise slices. Then, a Monte Carlo sampling based VSSA technique is designed to separate Gaussian and non-Gaussian subspaces from each other, which focuses on analyzing sample distribution rather than time series properties. Here the Gaussian subspace, which is readily characterized by a statistical model, is used for revealing similar condition slices and affiliate them into the same condition mode. And two monitoring statistics are developed to explore the Gaussian and non-Gaussian distribution structures, thus providing fine-grained distribution analytics and promoting monitoring performance. The feasibility and performance of the proposed method are demonstrated on a real thermal power plant process.

The papermaking industry supplies the carrier for literature and packaging via chemi-mechanical pulping, which is the most commonly used approach for converting wood chips into cellulose fibers. In 2016, Chinese papermaking enterprises consumed 41.05 million tons of standard coal. Thus, it is important to reduce the electrical energy consumed in mechanical pulp production by optimizing the operating conditions of primary stage refiners. This article reports a two-stage optimization algorithm to achieve this objective. First, mixed data sampling regression combined with a new weighting scheme derived from mixed flow conditions is used to encode the characteristics of high consistency refiner (HCR) in the manufacturing system. Then, the genetic algorithm with self-adaptive population searches for the optimal operating parameters that can minimize the power cost of the papermaking process. The proposed mixed framework and its separate components are validated through numerical functions and practical datasets, all of which have competitive performances compared to other algorithms. After being applied to a papermaking plant for two months, the intelligent optimization algorithm is found to have significant economic achievements in reducing the HCR energy consumption by 1.67 kWh/adt on an average scale.

This paper deals with the synchronization control problem in the leader-follower format of a class of high-order nonaffine nonlinear multiagent systems under a directed communication protocol. A novel adaptive neural distributed synchronization scheme with guaranteed performance is proposed. The main contribution lies in the fact that both nonaffine agent dynamics, which basically makes most existing agent dynamics as special cases, and guaranteed synchronization performance are taken into account. The difficulty lies mainly in the nonaffine terms and coupling terms due to the interactions of agents. To overcome this challenge, an augmented quadratic Lyapunov function by incorporating the lower bounds of control gains is proposed. The problems resulting from the nonaffine dynamics and the coupling terms among agents are solved by incorporating the special property of radial basis function neural network into the derivative of the augmented quadratic Lyapunov function. The unknown nonaffine terms are addressed by using an indirected neural network approach. A nonlinear mapping is built to relate the local consensus error to a new one, which is subsequently stabilized via Lyapunov synthesis. As a result, the proposed approach can ensure the outputs of all follower agents to track the outputs of the leader, while the synchronization performance bounds can be quantified on both transient and steady-state stages. All other signals in the closed loop are ensured to be semiglobally, uniformly, and ultimately bounded. Finally, the effectiveness of the proposed controller is verified through a heterogeneous four-agent example.

This paper presents novel cooperative tracking control for a class of input-constrained multiagent systems with a dynamic leader. Each follower agent is described by a high-order nonlinear dynamics in strict feedback form with input constraints. Our main contribution lies in presenting a system transformation method that can convert the input-constrained state feedback cooperative tracking control of agents into an unconstrained output feedback control of agents with dynamics in Brunovsky normal form. As a result, the original problem is simplified to be a simple stabilization of the transformed system for the agents. Thus, the use of the backstepping scheme is obviated, and the synthesis and computation are extremely simplified. It is strictly proved that all follower agents can synchronize to the leader with bounded synchronization errors, and all other signals in the closed-loop system are semi-global uniformly ultimately bounded. Finally, numerical analysis is carried out to validate the theoretical results and demonstrate the effectiveness of the proposed approach.

The coaxial-rotor micro-aerial vehicles (CRMAVs) have been proven to be a powerful tool in forming small and agile manned-unmanned hybrid applications. However, the operation of them is usually subject to unpredictable time-varying aerodynamic disturbances and model uncertainties. In this paper, an adaptive robust controller based on a neural network (NN) approach is proposed to reject such perturbations and track both the desired position and orientation trajectories. A complete dynamic model of a CRMAV is first constructed. When all system states are assumed to be available, an NN-based state-feedback controller is proposed through feedback linearization and Lyapunov analysis. Furthermore, to overcome the practical challenge that certain states are not measurable, a high-gain observer is introduced to estimate the unavailable states, and then, an output-feedback controller is developed. Rigorous theoretical analysis verifies the stability of the entire closed-loop system. In addition, extensive simulation studies are conducted to validate the feasibility of the proposed scheme.

In this paper, a novel adaptive control strategy is presented for the tracking control of a class of multi-input-multioutput uncertain nonlinear systems with external disturbances to place user-defined time-varying constraints on the system state. Our contribution includes a step forward beyond the usual stabilization result to show that the states of the plant converge asymptotically, as well as remain within user-defined time-varying bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system from the original one, whose asymptotic stability guarantees both the satisfaction of the time-varying restrictions and the asymptotic tracking performance of the original system. The uncertainties of the transformed system are overcome by an online neural network (NN) approximator, while the external disturbances and NN reconstruction error are compensated by the robust integral of the sign of the error signal. Via standard Lyapunov method, asymptotic tracking performance is theoretically guaranteed, and all the closed-loop signals are bounded. The requirement for a prior knowledge of bounds of uncertain terms is relaxed. Finally, simulation results demonstrate the merits of the proposed controller.

Wireless charging technology has drawn great attention of both academia and industry in recent years, due to its potential of significantly improving the system performance of sensor networks. The emergence of an open-source experimental platform for wireless rechargeable sensor networks, Powercast, has made the theoretical research closer to reality. This pioneering platform is able to recharge sensor nodes much more efficiently and allows different communication protocols to be implemented upon users’ demands. Different from the RFID-based model widely used in the existing works, Powercast designs the charger and sink station separately. This leads to a new design challenge of cooperatively deploying minimum number of chargers and sink stations in wireless rechargeable sensor networks. Such a co-deployment issue is extremely challenging, since the deployments of chargers and sink stations are coupled, and each subproblem is known to be NP-hard. The key to the design is to understand the intrinsic relationship between data flow and energy flow, which is interdependent. In this article, we tackle this challenge by dividing it into two subproblems and optimizing charger and sink station deployment iteratively. Specifically, we first transform each subproblem to a max-flow problem. With this, we are able to select chargers or sink stations according to their contributions to the total flow rate. We design greedy-based algorithms with a guaranteed worst-case bound ln R/ξ for the subproblems of charger deployment and sink station deployment, respectively. Further, we address the original problem by designing an iterative algorithm that solves two subproblems alternatively to achieve a near optimal performance. We corroborate our analysis by extensive simulations under practical coefficient settings and demonstrate the advantage of the proposed algorithm.

In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

In this paper, we present a novel adaptive consensus algorithm for a class of nonlinear multiagent systems with time-varying asymmetric state constraints. As such, our contribution is a step forward beyond the usual consensus stabilization result to show that the states of the agents remain within a user defined, time-varying bound. To prove our new results, the original multiagent system is transformed into a new one. Stabilization and consensus of transformed states are sufficient to ensure the consensus of the original networked agents without violating of the predefined asymmetric time-varying state constraints. A single neural network (NN), whose weights are tuned online, is used in our design to approximate the unknown functions in the agent's dynamics. To account for the NN approximation residual, reconstruction error, and external disturbances, a robust term is introduced into the approximating system equation. Additionally in our design, each agent only exchanges the information with its neighbor agents, and thus the proposed consensus algorithm is decentralized. The theoretical results are proved via Lyapunov synthesis. Finally, simulations are performed on a nonlinear multiagent system to illustrate the performance of our consensus design scheme.

Wireless charging is a promising way to solve the energy constraint problem in sensor networks. While extensive efforts have been made to improve the performance of charging and communication in wireless rechargeable sensor networks (WRSNs), little has been done to address the operation scheduling problem. To fill this void, we propose a joint energy replenishment and scheduling mechanism so as to maximize the network lifetime while making strict sensing guarantees in the WRSN. We first formulate the problem in a general 2-D space and prove its NP-completeness. We then devise an f-approximate scheduling mechanism by transforming the classical minimum set cover problem and develop an optimal energy-replenish strategy based on the energy consumption of nodes returned by the scheduling mechanism. Large-scale simulation results validate our design and show a 39.2% improvement of network lifetime over a baseline method.

An adaptive consensus algorithm is proposed for a class of nonlinear multiagent systems with completely unknown agent dynamics. Due to uncertainties in the agent's dynamics, previous consensus approaches usually yield uniformly ultimately bounded consensus error. Our main contribution includes a novel robust consensus algorithm which can guarantee that the consensus error converges to zero asymptotically. In order to address the unknown dynamics, a two-layer neural network (NN) is utilized to learn the unknown dynamics in an online manner, and a robust continuous term is introduced to alleviate effects of the NN residual reconstruction error and external disturbances. The continuousness of the control signal is guaranteed to remove the actuator bandwidth requirement and avoid the caused chattering phenomenon. The proposed consensus algorithm is distributed in the sense that each agent only exchanges information with its neighbor agents. The asymptotic consensus result is achieved via Lyapunov synthesis. Furthermore, the proposed algorithm can also be extended to the case where the agents are required to form a prescribed formation. Finally, simulation studies on a nonlinear multiagent system are provided to demonstrate the performance of the scheme.

In this paper, we present a novel tracking controller for a class of uncertain nonaffine systems with time-varying asymmetric output constraints. Firstly, the original nonaffine constrained (in the sense of the output signal) control system is transformed into a output-feedback control problem of an unconstrained affine system in normal form. As a result, stabilization of the transformed system is sufficient to ensure constraint satisfaction. It is subsequently shown that the output tracking is achieved without violation of the predefined asymmetric time-varying output constraints. Therefore, we are capable of quantifying the system performance bounds as functions of time on both transient and steady-state stages. Furthermore, the transformed system is linear with respect to a new input signal and the traditional backstepping scheme is avoided, which makes the synthesis extremely simplified. All the signals in the closed-loop system are proved to be semi-globally, uniformly, and ultimately bounded via Lyapunov synthesis. Finally, the simulation results are presented to illustrate the performance of the proposed controller.

In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is sufficient to solve the output constraint problem. It is shown that output tracking is achieved without violation of the output constraint. More specifically, we can shape the system performance arbitrarily on transient and steady-state stages with the output evolving in predefined time-varying boundaries all the time. A single neural network, whose weights are tuned online, is used in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control coefficient matrix is avoided without assumption on the prior knowledge of control input's bound. All the signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded via Lyapunov synthesis. Finally, the merits of the proposed controller are verified in the simulation environment.

This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced. The NN in the control law learns the system dynamics in an online manner, while the NN residual reconstruction errors and the bounded disturbances are overcome by the error sign signal. Since both of the NN output and the error sign signal are included in the integral, the continuity of the control input is ensured. The controller structure and the NN weight update law are novel in contrast with the previous effort, and the semiglobal asymptotic tracking performance is still guaranteed by using the Lyapunov analysis. In addition, the NN weights and all other signals are proved to be bounded simultaneously. The proposed approach also relaxes the need for the upper bounds of certain terms, which are usually required in the previous designs. Finally, the theoretical results are substantiated with simulations.

This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the original nonaffine nonlinear system into an equivalent affine one. Then, to deal with the unknown dynamics and unknown control coefficient contained in the affine system, online approximator and Nussbaum gain techniques are utilized in the controller design. It is proven rigorously that asymptotic convergence of the tracking error and ultimate uniform boundedness of all the other signals can be guaranteed by the proposed control method. The control feasibility is further verified by numerical simulations.

Multiple channels in Wireless Sensor Networks (WSNs) are often exploited to support parallel transmission and to reduce interference. However, the extra overhead posed by the multi-channel usage coordination dramatically challenges the energy-constrained WSNs. In this paper, we propose a Regret Matching based Channel Assignment algorithm (RMCA) to address this challenge, in which each sensor node updates its choice of channels according to the historical record of these channels’ performance to reduce interference. The advantage of RMCA is that it is highly distributed and requires very limited information exchange among sensor nodes. It is proved that RMCA converges almost surely to the set of correlated equilibrium. Moreover, RMCA can adapt the channel assignment among sensor nodes to the time-variant flows and network topology. Simulations show that RMCA achieves better network performance in terms of both delivery ratio and packet latency than CONTROL, MMSNand randomized CSMA. In addition, real hardware experiments are conducted to demonstrate that RMCA is easy to be implemented and performs better.

Nonlinear degradation trajectories are encountered frequently, and not all of them evolve homogeneously in practical systems. To take nonlinearity, heterogeneity, and the entire historical degradation data into account, we propose a nonlinear heterogeneous Wiener process model with an adaptive drift to characterize degradation trajectories. A state-space based method is employed to delineate our model. Due to the introduction of the adaptive drift, it is difficult to directly apply Kalman filter methods to update the distribution of the estimated degradation drift. To address this issue, we develop an online filtering algorithm based on Bayes' theorem. The expectation-maximization (EM) algorithm, as well as a novel Bayes'-theorem-based smoother, are adopted to estimate the unknown parameters in our model. Moreover, the distribution of the predicted remaining useful life (RUL) incorporating the complete distribution of the estimated degradation drift is achieved analytically. Finally, a simulation, and a case study are provided to validate the proposed approach.

The IEEE 802.15.4 standard, which supports low-cost communications, has been applied in a variety of wireless networks. Developing accurate analytical models for the IEEE 802.15.4 medium access control (MAC) protocol is critical for the design and performance evaluation of such networks. Periodic traffic is a common traffic pattern generated in many practical application scenarios, for which most existing analytical models assuming either saturated or random network traffic patterns become inapplicable. In this paper, we develop an accurate and scalable analytical model to analyze the IEEE 802.15.4 MAC protocol with the periodic traffic. Our model can accurately capture the protocol stochastic behavior in each period in scenarios such as with or without retransmissions and with single clear channel assessment (CCA) or double CCAs. Extensive simulations are conducted to validate the proposed model by both transient and aggregate performance evaluations, and the results show that the model captures MAC behavior with periodic traffic accurately. We also discuss about extending the proposed model to account for heterogeneous scenarios and the hidden node problem.

We investigate the state estimation problem in cyber-physical systems (CPS) where the dynamical physical process is measured by a wireless sensor and the measurements are transmitted to a remote state estimator. It has been shown that the estimation performance strongly depends on the wireless communication quality. To enhance the estimation performance, we apply the cognitive radio technique to the system and propose a CHAnnel seNsing and switChing mEchanism (CHANCE) to explore opportunistic accessibility of multiple channels. We consider two types of wireless channels, i.e., one unlicensed channel which can be accessed freely and several licensed channels which have been pre-assigned to primary users. For the single-licensed-channel case, we develop a necessary condition for the estimation stability based on the physical process dynamics, channel quality and the channel sensing accuracy. This condition becomes also sufficient under certain conditions. We also derive the conditions under which the estimation performance is guaranteed to be improved by CHANCE. The above results are then extended to multi-licensed-channel cases. Simulations based on a particular linear system show that, the long-run mean estimation error covariance with CHANCE is at least 63% less than that without CHANCE. It is also shown that CHANCE outperforms the existing RANDOM mechanism in terms of estimation performance.

The barrier coverage problem in emerging mobile sensor networks has been an interesting research issue due to many related real-life applications. Existing solutions are mainly concerned with deciding one-time movement for individual sensors to construct as many barriers as possible, which may not be suitable when there are no sufficient sensors to form a single barrier. In this paper, we aim to achieve barrier coverage in the sensor scarcity scenario by dynamic sensor patrolling. Specifically, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensor's current movement strategy is derived from the information of intruder arrivals in the past. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. We study the scenario where sensors are moving on two barriers and propose two heuristic algorithms to guide the movement of sensors. Finally, we generalize our results to work for different intruder arrival models. Through extensive simulations, we demonstrate that the proposed algorithms have desired barrier coverage performances.

We consider the problem of event capture by a rechargeable sensor network. We assume that the events of interest follow a renewal process whose event inter-arrival times are drawn from a general probability distribution, and that a stochastic recharge process is used to provide energy for the sensors' operation. Dynamics of the event and recharge processes make the optimal sensor activation problem highly challenging. In this paper we first consider the single-sensor problem. Using dynamic control theory, we consider a full-information model in which, independent of its activation schedule, the sensor will know whether an event has occurred in the last time slot or not. In this case, a simple and optimal greedy policy for the solution is developed. We then further consider a partial-information model where the sensor knows about the occurrence of an event only when it is active. This problem falls into the class of partially observable Markov decision processes (POMDP). Since the POMDP's optimal policy has exponential computational complexity and is intrinsically hard to solve, we propose an efficient heuristic clustering policy and evaluate its performance. Finally, our solutions are extended to handle a network setting in which multiple sensors collaborate to capture the events. We also provide extensive simulation results to evaluate the performance of our solutions.

This paper studies deterministic sensor deployment for barrier coverage in wireless sensor networks. Most of existing works focused on line-based deployment, ignoring a wide spectrum of potential curve-based solutions. We, for the first time, extensively study the sensor deployment under a general setting. We first present a condition under which the line-based deployment is suboptimal, revealing the advantage of curve-based deployment. By constructing a contracting mapping, we identify the characteristics for a deployment curve to be optimal. Based on the optimal deployment curve, we design sensor deployment algorithms by introducing a new notion of distance-continuous. Our findings show that i) when the deployment curve is distance-continuous, the proposed algorithm is optimal in terms of the vulnerability corresponding to the deployment, and ii) when the deployment curve is not distance-continuous, the approximation ratio of the vulnerability corresponding to the deployment by the proposed algorithm to the optimal one is upper bounded by min (π, ||ÃB̃||/||ÃG̃B̃|| 2n+√2-1/2n ), where ||ÃB̃|| and ||ÃG̃B̃|| are some constants, and n is the number of sensors. We generalize the study to the heterogeneous sensing model, and show that the proposed algorithm can provide close-to-optimal performance. Extensive numerical results corroborate our analysis.

This paper studies deterministic sensor deployment to ensure barrier coverage in wireless sensor networks. Most of existing work focused on line-based deployment, ignoring a wide spectrum of potential curve-based solutions. We, for the first time, extensively study the sensor deployment under general settings. We first present a condition under which line-based deployment is suboptimal, pointing to the advantage of curve-based deployment. By constructing a contracting mapping, we identify the characteristics for a deployment curve to be optimal. We then design sensor deployment algorithms for the optimal deployment curve by introducing a new notion of distance-continuous. Our findings show that i) when the deployment curve is distance-continuous, the proposed algorithm is optimal in terms of the vulnerability corresponding to the deployment, and ii) when the deployment curve is not distance-continuous, the approximation ratio of the vulnerability corresponding to the deployment by the proposed algorithm to the optimal one is upper bounded by min (π, ||AB||/||AGB|| 2n+√(2-1)/2n), where ||AB||, ||AGB|| and n are constants. Extensive numerical results corroborate our analysis.

In this paper, we address the problem of peer-to-peer networking for data dissemination among actors in wireless sensor and actor networks (WSANs), which consist of static sensors, responsible for environment monitoring, and mobile actors, in charge of data collection and task performing. This problem has not been received much attention although peer-to-peer networking has achieved great successes in other networks such as the Internet and mobile ad hoc networks (MANETs). Unlike the Internet and MANETs, WSANs contain static sensors that are energy-constrained and actors that cannot communicate with each other directly. These unique characteristics make the data dissemination problem in WSANs extremely challenging. We present an Energy-Efficient Message Dissemination protocol (EMD) to solve this problem in delay-tolerant WSANs. EMD is grounded on a novel principle of "Carry-Disseminate-Store-and-Forward" proposed for the first time here. While traveling, a source actor disseminates messages (data) to sensors upon contact, which will store the messages and forward them to other actors when they come into communication range. The actors receiving the messages from sensors work as source actors and help to distribute the messages. We theoretically analyze the data dissemination strategy under which the original source actor can distribute its messages to all other actors at minimum communication cost within a given delay bound. Through extensive simulations we demonstrate the performance of EMD.

Networked cyber-physical systems (NCPS), where control and communication are closely integrated, have been envisioned to have a large number of high-impact applications. In this paper, a joint optimization framework is presented, which combines the objective of control as well as other relevant system objectives and constraints such as communication errors, delays and the limited capabilities (e.g., energy capacities) of devices. The problem is solved by an online optimization approach, which consists of a communication protocol and a simulated annealing based control algorithm. Meanwhile, by taking into account the communication cost, we optimize the control intervals by integrating two kinds of acceptances, i.e., cyber and physical acceptances, into the control algorithm. Numerical results show the effectiveness of the proposed approach.

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

In wireless sensor networks (WSNs), trap coverage has recently been proposed to trade off between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large-scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, practical issues, such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment, are still left untouched. In this article, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. We design Localized Trap Coverage Protocol as the localized implementation of TCO. The performance of Minimum Weight Trap Coverage we find is proved to be at most O (ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution.

In this paper, we consider the issue of data broadcasting in mobile social networks (MSNets). The objective is to broadcast data from a superuser to other users in the network. There are two main challenges under this paradigm, namely 1) how to represent and characterize user mobility in realistic MSNets; 2) given the knowledge of regular users' movements, how to design an efficient superuser route to broadcast data actively. We first explore several realistic data sets to reveal both geographic and social regularities of human mobility, and further propose the concepts of geocommunity and geocentrality into MSNet analysis. Then, we employ a semi-Markov process to model user mobility based on the geocommunity structure of the network. Correspondingly, the geocentrality indicating the “dynamic user density” of each geocommunity can be derived from the semi-Markov model. Finally, considering the geocentrality information, we provide different route algorithms to cater to the superuser that wants to either minimize total duration or maximize dissemination ratio. To the best of our knowledge, this work is the first to study data broadcasting in a realistic MSNet setting. Extensive trace-driven simulations show that our approach consistently outperforms other existing superuser route design algorithms in terms of dissemination ratio and energy efficiency.

Energy harvesting is a promising technology for extending the lifetime of battery-powered sensor networks. Due to time variations of harvested energy, one of the main challenging issues is to maximize the uninterrupted sampling rates of all sensor nodes, which represents the network performance. Most of existing works do not consider the limited capacity of rechargeable battery. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize the overall network performance. To solve this problem, we firstly propose an adaptive Energy Allocation sCHeme (EACH) for each sensor node to manage its energy use in an efficient way. Then we develop a Distributed Sampling Rate Control (DSRC) algorithm to obtain the optimal sampling rate. Furthermore, an Improved adaptive Energy Allocation sCHeme (IEACH) is proposed to reduce the impact due to imprecise estimation of harvested energy. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are conducted to demonstrate the efficiency of the proposed algorithms.

It is a common class of applications with wireless sensor network to provide full coverage to the region of interest (ROI), such as environment monitoring, military detection and agricultural observation. Existing literatures on full coverage are mostly based on the binary sensing model to simplify the problem. However, the results are far from the reality since binary sensing model as a coarse approximation is too conservative. The probabilistic sensing model has been proposed as a more realistic model to characterize the sensing region. In this paper, we introduce the concept of ε-full coverage based on probabilistic model, i.e., every point in ROI has at least a probability ε of being covered by sensors. We explore the mathematic relationship between the probabilities of two adjacent points being covered and transform ε-full coverage problem into point coverage problem. Then, we design ε-full coverage optimization (FCO) to select a subset of sensors to provide ε-full coverage dynamically so that the lifetime of network is prolonged. This algorithm outperforms the state-of-the-art solution significantly, which we have validated by simulations.

We consider the problem of event capture by a rechargeable sensor network. We assume that the events of interest follow a renewal process whose event inter-arrival times are drawn from a general probability distribution, and that a stochastic recharge process is used to provide energy for the sensors' operation. Dynamics of the event and recharge processes make the optimal sensor activation problem highly challenging. In this paper we first consider the single-sensor problem. Using dynamic control theory, we consider a full-information model in which, independent of its activation schedule, the sensor will know whether an event has occurred in the last time slot or not. In this case, the problem is framed as a Markov decision process (MDP), and we develop a simple and optimal policy for the solution. We then further consider a partial-information model where the sensor knows about the occurrence of an event only when it is active. This problem falls into the class of partially observable Markov decision processes (POMDP). Since the POMDP's optimal policy has exponential computational complexity and is intrinsically hard to solve, we propose an efficient heuristic clustering policy and evaluate its performance. Finally, our solutions are extended to handle a network setting in which multiple sensors collaborate to capture the events. We provide extensive simulation results to evaluate the performance of our solutions.

Barrier coverage problem in emerging mobile sensor networks has been an interesting research issue. Existing solutions to this problem aim to decide one-time movement for individual sensors to construct as many barriers as possible, which may not work well when there are no sufficient sensors to form a single barrier. In this paper, we try to achieve barrier coverage in sensor scarcity case by dynamic sensor patrolling. In specific, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensor's current movement strategy is decided based on the past intruder arrival information. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that the sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. Through extensive simulations, we demonstrate that CSP has a desired barrier coverage performance and S-DCSP and G-DCSP have similar performance as that of CSP.

Energy harvesting is a promising technology for extending the lifetime of sensor networks with the restrictions of limited battery energy. One of the main challenging issues is to maximize the sampling rates of all sensor nodes. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize overall network utility. To solve the problem, we firstly propose an adaptive energy allocation scheme for each node to manage its energy use in an efficient way. Then we develop a distributed sampling rate control (DSRC) algorithm to obtain the optimal sampling rate. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are performed to demonstrate the efficiency of our algorithm.

Nowadays, with the dramatically increased penetration of wireless access, the conflict between spectrum scarcity and under-utilisation is becoming more and more aggravating. A promising technology to tackle such challenge is cognitive radio, of which spectrum sensing is one of the most important functionalities. In this study, the authors consider an essential problem of energy-efficient spectrum sensing in cognitive radio networks. Although most existing works of spectrum sensing mainly focus on determining an optimal sensing time to maximise the detection probability and/or to minimise the false alarm probability, our problem of how to schedule the power-constrained sensor is much more challenging, because of the trade-off among interests of the primary user, secondary user and sensor. The authors formulate it as a non-linear optimisation problem to maximise the sensor lifetime, with necessary constraints of quality and delay of spectrum sensing, and throughput for performance guarantee of primary and secondary users. Moreover, the authors incorporate the distribution information of channel occupancy/vacancy durations into the problem to yield a desirable solution. They propose a novel framework to obtain the optimal energy-efficient periodic scheduling by adopting both non-linear programming and linear programming. Extensive simulation results are provided to validate our theoretical analysis.

Photoelectric speed sensors (PSSs), which are used for velocity measuring and positioning, are key components of train control systems. In real applications, the performance of PSSs may degrade, such as the decrease in the number of the output pulses, which is caused by the existence of jammed code tracks on the shading plates of PSSs. Considering this kind of performance degradation, this paper proposes an online performance-degradation-monitoring approach that can detect the existence of the jammed code tracks and estimate the number of them. Based on the results from the performance-degradation-monitoring approach, this paper also provides a compensation algorithm for the distorted speed readings resulting from the existence of the performance degradation. The results from the mathematical analysis and numerical examples verify the effectiveness of the proposed performance-degradation-monitoring approach for PSSs.

We consider the problem of gathering correlated sensor data by a single sink node in a wireless sensor network. We assume that the sensor nodes are energy constrained and design efficient distributed protocols to maximize the network lifetime. Many existing approaches focus on optimizing the routing layer only, but in fact the routing strategy is often coupled with power control in the physical layer and link access in the MAC layer. This paper represents a first effort on network lifetime maximization that jointly considers the three layers. We first assume that link access probabilities are known and consider the joint optimal design of power control and routing. We show that the formulated optimization problem is convex and propose a distributed algorithm, JRPA, for the solution. We also discuss the convergence of JRPA. When the optimal link access probabilities are unknown, as in many practical networks, we generalize the problem formulation to encompass all the three layers of routing, power control, and link-layer random access. In this case, the problem cannot be converted into a convex optimization problem, but there exists a duality gap when the Lagrangian dual method is employed. We propose an efficient heuristic algorithm, JRPRA, to solve the general problem, and show through numerical experiments that it can significantly narrow the gap between the computed and optimal solutions. Moreover, even without a priori knowledge of the best link access probabilities predetermined for JRPA, JRPRA achieves extremely competitive performance with JRPA. Beyond the metric of network lifetime, we also discuss how to solve the problem of correlated data gathering under general utility functions. Numerical results are provided to show the convergence of the algorithms and their advantages over existing solutions.

In this brief, a continuous tracking control law is proposed for a class of high-order multi-input-multi-output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix. The proposed controller consists of high-gain feedback, Nussbaum gain matrix selector, online approximator (OLA) model and a robust term. The OLA model is represented by a two-layer neural network. The continuousness of the control signal is guaranteed to relax the requirement for the actuator bandwidth and avoid the incurred chattering effect. Asymptotic tracking performance is achieved theoretically by standard Lyapunov analysis. The control feasibility is also verified in simulation environment.

In this paper, we consider using actors to maintain the quality of sensing in the wireless sensor networks. Due to factors such as battery drainage or physical malfunctions, the number of available sensors normally decreases over time after initial deployment, resulting in performance degradation. To maintain the quality of sensing in the network, actors can be used to allocate spare sensors to sensor-deficient regions (sensor allocation) or to relocate sensors from sensor-abundant regions to sensor-deficient regions (sensor relocation). We first focus on the sensor allocation problem. We introduce a baseline centralized greedy algorithm (GA) for sensor allocation, where global sensor information is communicated to obtain the optimal solution. As GA is only efficient for small networks, we proceed to design a distributed patrolling algorithm for achieving global optimization (DPAG) by using only local information. We then extend our work to the application scenario of sensor relocation by proposing a modified GA and DPAG (M-GA and M-DPAG), respectively. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

As sensors are energy constrained devices, one challenge in wireless sensor networks (WSNs) is to guarantee coverage and meanwhile maximize network lifetime. In this paper, we leverage prediction to solve this challenging problem, by exploiting temporal-spatial correlations among sensory data. The basic idea lies in that a sensor node can be turned off safely when its sensory information can be inferred through some prediction methods, like Bayesian inference. We adopt the concept of entropy in information theory to evaluate the information uncertainty about the region of interest (RoI). We formulate the problem as a minimum weight submodular set cover problem, which is known to be NP hard. To address this problem, an efficient centralized truncated greedy algorithm (TGA) is proposed. We prove the performance guarantee of TGA in terms of the ratio of aggregate weight obtained by TGA to that by the optimal algorithm. Considering the decentralization nature of WSNs, we further present a distributed version of TGA, denoted as DTGA, which can obtain the same solution as TGA. The implementation issues such as network connectivity and communication cost are extensively discussed. We perform real data experiments as well as simulations to demonstrate the advantage of DTGA over the only existing competing algorithm [1] and the impacts of different parameters associated with data correlations on the network lifetime.

In duty-cycled wireless sensor networks (WSNs) for stochastic event monitoring, existing efforts are mainly concentrated on energy-efficient scheduling of sensor nodes to guarantee the coverage performance, ignoring another crucial issue of connectivity. The connectivity problem is extremely challenging in the duty-cycled WSNs due to the fact that the link connections between nodes are transient thus unstable. In this paper, we propose a new kind of network, partitioned synchronous network, to jointly address the coverage and connectivity problem. We analyze the coverage and connectivity performances of partitioned synchronous network and compare them with those of existing asynchronous network. We perform extensive simulations to demonstrate that the proposed partitioned synchronous network has a better connectivity performance than that of asynchronous network, while coverage performances of two types of networks are close.

We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset of the sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for the cases of an synchronous and a asynchronous network. To reduce the overhead of global synchronization, we further consider a spectrum of regionally synchronous networks where the size of the synchronization region is specifiable. Under the periodic scheduling, coordinated sleep by the sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider the interactions between the periodic scheduling and coordinated sleep. We show that the asynchronous network exceeds any regionally synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though the opportunities for coordinated sleep decreases as the synchronization region gets smaller. When the sensor density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy efficient.

In target tracking applications, active ultrasonic sensors can provide satisfactory distance estimations, but also suffer from inter-sensor-interference when they are not well scheduled. In this paper, we propose a distributed saturation degree based algorithm (DSDA), which assigns the TDMA slot in wireless sensor networks distributively in order to avoid the interference. By adopting a graph coloring technique, Saturation Degree Heuristic, this new algorithm can provide near-optimal slot number in a totally distributed way. Simulation results demonstrate the efficiency of DSDA in terms of slot number, system scalability, tracking accuracy and energy consumption.

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition, and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

In wireless sensor networks (WSNs), trap coverage has recently been proposed to tradeoff between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, the practical issues such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment is still left untouched. In this paper, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. The performance of Minimum Weight Trap Coverage we find is proved to be at most O(ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution.

We investigate the issue of synchronizing a blinking coupling mobile agent network through a blinking adaptation strategy, where each agent with blinking wave emission behavior not only adjusts its blinking period according to the local property of its neighbors, but also coordinates its blinking phase with those of neighboring agents. In leading the agents to blink orderly with a blinking period commensurate with the characteristic time of the dynamical oscillator, the presented blinking adaptation strategy works effectively in guaranteeing the synchronous motion of the considered network when the power density is large. In addition, the influence of the controlling parameter and moving velocity on network evolution is studied by assessing the convergence time.

Number estimation of controllers is a fundamental question in pinning synchronization of complex networks. This paper studies the problem of controller number in synchronizing a complex network of coupled dynamical systems by means of pinning. For a complex network with a symmetric coupling matrix and full coupling between the nodes, we formulate network synchronization via pinning as a linear matrix inequality criterion, and provide a lower bound and an upper bound of the controller number for a given complex network with fixed architecture. Several numerical examples with Barabási-Albert network topologies are provided to verify our theoretical results.

The multidimensional blast furnace system is one of the most complex industrial systems and, as such, there are still many unsolved theoretical and experimental difficulties, such as silicon prediction and blast furnace automation. For this reason, this paper is concerned with developing data-driven models based on the Volterra series for this complex system. Three kinds of different low-order Volterra filters are designed to predict the hot metal silicon content collected from a pint-sized blast furnace, in which a sliding window technique is used to update the filter kernels timely. The predictive results indicate that the linear Volterra predictor can describe the evolvement of the studied silicon sequence effectively with the high percentage of hitting the target, very low root mean square error and satisfactory confidence level about the reliability of the future prediction. These advantages and the low computational complexity reveal that the sliding-window linear Volterra filter is full of potential for multidimensional blast furnace system. Also, the lack of the constructed Volterra models is analyzed and the possible direction of future investigation is pointed out.

Abstract Vehicular Ad hoc Network (VANET) emerges as a promising technology and has chances of very likely to be deployed in the coming years. The security of vehicular networks will be an important way to facilitate road safety. In this paper, we are concerned with the problem of effective and efficient distribution of the certificate revocation units (RSUs) in vehicular networks. We propose a novel distributed approach by introducing mobile nodes that have public safety. An optimal route for mobile nodes is designed to cover blind areas under both delay and cost constraints. The performances of the movement‐assisted approach are measured and evaluated by extensive experiments in large scale networks for Certificate Revocation List (CRL) distribution in VANET. The results show that the proposed movement‐assisted approach obviously improves the performance. Copyright © 2009 John Wiley & Sons, Ltd.

The rapid development of wireless technology plays extremely important roles in monitoring and control related applications nowadays.This special issue aims at bringing together state-of-the-art contributions of wireless monitoring, control, actuator coordination as well as their co-designs.It is shown in this proposal that there are a number of potential contributors, as well as reviewers, with original research results to this special issue, and the guest editors are capable of managing the special issue.In recent years, the demand for wireless communications in many monitoring and control applications has grown tremendously, such as military, aerospace, industrial, commercial, environmental, and health monitoring, etc.Some new technologies like Zigbee, Wi-Fi, Mobile Robots, and Bluetooth have already made significant contribution to data acquisition.At the same time, there arise some new challenges to guarantee a highly reliable, accurate and fault-tolerant process.It is a critical issue to develop innovative approaches to deal with multi-variable, multi-space problem domains (detection, identification, tracking, data fusion, energy-efficiency, and fault-tolerant framework) as well as practical implementation in wireless monitoring and control application.The purpose of the special issue is to focus on the novel ways by which monitoring, detection, identification, coordination, and control schemes are applied in wireless monitoring and control applications.Specific areas of interest include (but are not limited to):

In this paper, we study the maximum throughput of IEEE 802.15.4 enabled wireless sensor networks (WSNs). In general, deploying more sensor nodes would increase the throughput, but if excessive sensor nodes were involved to report data, the throughput might decrease due to packet collisions. In this paper, we analyze the CSMA/CA performance of the IEEE 802.15.4 enabled WSNs, and develop an analytical throughput model between the maximum throughput and the optimal number of deployed sensor nodes. Extensive simulations are conducted to verify our analytical throughput model.

Multiple channels in Wireless Sensor Networks (WSNs) are often exploited to support parallel transmission and reduce interference. However, there are many challenges, such as extra communication overhead, posed to the energy constraint of WSNs by the multi-channel usage coordination. In this paper, we propose a Regret Matching based Channel Assignment algorithm (RMCA) to address those challenges. The advantage of RMCA is that it is highly distributed and requires very limited information exchanges among sensor nodes. It converges almost surely to the set of correlated equilibrium. Moreover, RMCA can adapt the channel assignment among sensor nodes to the time-variant flows and network topology. Simulations show that RMCA achieves good network performance in terms of both delivery ratio and packet latency.

In this paper, we formulate multi-channel assignment in Wireless Sensor Networks (WSNs) as an optimization problem and show it is NP-hard. We then propose a distributed Game Based Channel Assignment algorithm (GBCA) to solve the problem. GBCA takes into account both the network topology information and transmission routing information. We prove that there exists at least one Nash Equilibrium in the channel assignment game. Furthermore, we analyze the sub-optimality of Nash Equilibrium and the convergence of the Best Response in the game. Simulation results are given to demonstrate that GBCA can reduce interference significantly and achieve satisfactory network performance in terms of delivery ratio, throughput, channel access delay and energy consumption.

In mobile social networks (MSNets), data dissemination is an important topic, which has not been widely investigated yet. Active data dissemination is a networking paradigm where a superuser intentionally facilitates the connectivity in the network. One of the key challenges under this paradigm is how to design the most efficient superuser route to achieve certain properties of end-to-end connectivity. Most existing solutions only focus on the network with stationary users or strongly constrained node mobility, and assume the superuser always moves with a fixed route. In this paper, we propose a flexible approach to design the superuser routes, considering the realistic user movements in MSNets. To the best of our knowledge, this work is the first to study active data dissemination from the social network perspective. We explore the geographic regularity of human mobility in the network, employ a semi-Markov analytical model to describe such mobility pattern, and hence formulate the superuser route design as a combinational optimization problem of Convex Optimization and Traveling Salesman Problem by exploiting social network concepts including communities and centrality. Extensive trace-driven simulations show that our approach consistently outperforms other existing superuser route design algorithms in terms of delivery ratio and energy efficiency.

We consider the problem of gathering correlated sensor data by a sink node in a wireless sensor network. We design efficient distributed protocols to maximize the network lifetime subject to nodal energy constraints. Many existing approaches address the routing layer only, but the routing often interacts with physical-layer power control and MAC-layer link access. We present a first effort to maximize the network lifetime by jointly considering the three layers. We first solve the joint power control and routing problem, by assuming that the link access probabilities are known. We show that the problem is convex and propose a distributed algorithm, JRPA, as solution. When the link access probabilities are unknown, we then generalize the problem to encompass all three layers of routing, power control, and link random access. The general problem is non-convex; a duality gap exists when the Lagrangian dual method is employed. We propose an efficient heuristic algorithm, JRPRA, to solve the general problem. Numerical results show that JRPRA is highly effective; particularly, even without the best link access probabilities pre-determined for JRPA, JRPRA achieves extremely competitive performance. Our results also show the convergence of the algorithms and their advantages over existing solutions.

The linear frequency-modulated continuous-wave (LFMCW) millimeter-wave radar has been widely used in automotive industry applications. The motivation of this paper is to propose a segmented range detection (SRD) method on the foundation of plentiful mature research on the target detection of LFMCW radar. According to the SRD method, the detection range (about 150m in the scene of vehicular collision warning) is divided into several segments and triangular modulation waveform which consists of several different frequencies is designed. Compared with typical digital signal processing method, the SRD method can availably decrease the bandwidth of the IF signal, therefore the in-band noise will be significantly reduced and the signal to noise ratio (SNR) improved. Additionally, different range resolutions can be achieved and average range resolution improved. The SRD method is also validated through multiple outdoor experiments with the use of 60 GHz LFMCW millimeter-wave radar system designed by our research group. The efficiency of the SRD method is demonstrated in terms of ranging accuracy and range resolution after the analysis of experiment results.

Technology advancements in robotics and communication make it possible to combine mobility and static wireless sensor networks. Being able to locomote enables sensors to (1) adjust their positions for better sensing quality, (2) ensure field coverage by spreading out and (3) fill up communication gap to guarantee information sharing in wireless environment. Concerning the above three criteria, we propose a autonomous management scheme for mobile sensors, aims to achieve distributed sensor position control. Accordingly, we developed a testbed for mobile sensor network verification, with good interfaces to obtain ranging measurements and control sensor motion. Implementation details of building static and mobile sensors are given. Finally we present experiment results to verify the proposed sensor control scheme.

In this paper, we formulate a flow control optimization problem for wireless sensor networks with lifetime constraint and link interference in an asynchronous setting. Our formulation is based on the network utility maximization framework, in which a general utility function is used to characterize the network performance such as throughput. To solve the problem, we propose a fully asynchronous distributed algorithm based on dual decomposition, and theoretically prove its convergence. The proposed algorithm can achieve the maximum utility. Extensive simulations are conducted to demonstrate the efficiency of our algorithm and validate the analytical results.

Abstract With the development of robotics and embedded system, utilizing mobile sensors to capture stochastic events is emerging as a promising method to monitor a region of interest (RoI). In previous work, the quality of monitoring (QoM) is evaluated based on the stochastic events capture without taking the energy of motion into consideration. Since sensor nodes are normally constrained by limited energy capability, it is desirable to guide the mobile sensor in an energy‐efficient motion to capture events information. In this paper, by analyzing the different kinds of surveillance that may result in different required QoM, we obtain the expected Information captured Per unit of Energy consumption (IPE), which is a function with multiple parameters including the event type, the event dynamics, and the velocity of the mobile sensor. Our analysis is based on a realistic energy model of motion, and can achieve suboptimal motion plans by adopting existing typical approximation algorithm, and thus enable the sensor velocity to be optimized for capturing stochastic events information. We propose approximation algorithms to enable the tradeoff between the computation and efficiency, which make motion plans more practical in some realistic scenarios. The efficiency and effectiveness of proposed algorithm are validated by the extensive simulations. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract This paper addresses the localization problem in wireless sensor networks using signal strength. We use a kernel function to measure the similarities between sensor nodes. The kernel matrix can be naturally defined in terms of the signal strength matrix. We show that the relative locations of sensor nodes can be obtained by solving a dimension reduction problem. To capture the structure of the whole network, we use the kernel spectral regression (KSR) method to estimate the relative locations of the sensor nodes. Given sufficient anchor nodes, the relative locations can be aligned to global locations. The key benefits of adopting KSR are that it allows us to define a graph to optimally preserve the topological structure of the sensor network, and a kernel function can capture the nonlinear relationship in the signal space. Simulation results show that we can achieve small average location error with a small number of anchors. We also compare our method with several related methods, and the results show that KSR is more efficient than the others in our simulated sensor networks. Copyright © 2009 John Wiley & Sons, Ltd.

In the paper, we study the utility-lifetime tradeoff in wireless sensor networks (WSNs) by formulating it as a constrained multi-objective optimization problem. Because of the coupling in the objective function, auxiliary variables are introduced to decouple it. We adopt Lagrange duality method to decompose the problem and regulate the rates through the link congestion price. We introduce the concept of inconsistent coordination price to balance the energy consumption of the sensor nodes. Based on the congestion and inconsistent coordination price, a distributed algorithm using gradient projection is proposed. Numerical results show the convergence of our algorithm, the tradeoff of utility-lifetime as well as the necessity of congestion control in WSNs.

As an emerging application, tracking mobile targets using distributed and networked sensors is gaining noticeable attentions in recent years. In this paper, we utilize ultrasonic sensors, which are traditionally viewed as basic ranging components, to build a target detection and tracking system. An integrated sensor node, with one communication/computation module, one Passive InfraRed (PIR) module and three ultrasonic modules, is designed to support local detection/ranging, data filtering and result reporting. Comprised of a set of the designed sensor nodes, our system is able to track the position of a dynamic target, using Extended Kalman Filter. Simulations and experimental results are given to evaluate performance bound and demonstrate the efficiency of our system.

Wireless sensor location estimation is an important area and attracts considerable research interests. In this paper, we present a novel graph embedding method for the localization problem by using signal strengths. We view the wireless sensor nodes as a group of distributed devices, and employ an appropriate kernel function to measure the similarity between sensors. The kernel function can be naturally defined according to the signal strength matrix. Then we formulate the localization problem as a graph embedding problem. Finally, we use the kernel locality preserving projection (KLPP) technique to estimate the relative locations of all sensor nodes. Given sufficient number of anchors, the relative locations can be transformed into physical locations. The main advantage of formulating the localization problem as graph embedding problem is that it allows us to construct a graph to preserve the topological structure of the sensor networks. We evaluate our method based on various network topologies, and analyze its performance. We also compare our method with several existing methods, and demonstrate the high efficiency of our proposed method.

This paper presents an adaptive sensor activation for target tracking in wireless sensor networks by dynamically adjusting the range of sensor selective activation instead of fixed one. A closed-loop control algorithm for the range of adaptive sensor activation is designed according to the online feedback of the tracking quality. The failed tracking case can also be handled by the proposed algorithm. Extensive simulation results show that the adaptive sensor activation achieves higher performance in terms of tracking effect and energy efficiency.

This paper studies the problem of determining the sensors' locations in wireless sensor networks. To alleviate the influence of the noise and the inaccurate measurement in the complicated environment, rather than estimating the pair-wise Euclidean distance between sensors, we use the geodesic distance to measure the dissimilarity between sensors, and employ the isomap algorithm to determine the relative locations of sensors. Given sufficient anchors, the relative locations can be aligned to absolute locations by using coordinate transformation. The coordinate transformation matrix can be obtained by minimizing the sum of squares of the errors between the true locations of the anchors and their transformed locations. Since Isomap is parameter-sensitive, we also present an adaptive parameter selection procedure based on the locations of anchors. Simulation results show that the Isomap algorithm achieves smaller average location error with little quantity of anchors.

Protocol testing has been one of the most active fields in computer networks. In Wireless Sensor Networks (WSNs), before directly employing protocols on hardware testbed, we expect an effective test framework to verify the logical correctness of protocols on computers, which could facilitate the whole test process. Towards this goal, this paper introduces an upper test framework in WSNs, namely Protocol Testing Framework for WSNs (PTFW).

A mobile sensor is used to cover a number of points of interest (PoIs) where dynamic events appear and disappear according to given random processes. It has been shown in [1] that for Step and Exponential utility functions, the quality of monitoring (QoM), i.e., the fraction of information captured about all events, increases as the speed of the sensor increases. This work, however, does not consider the energy of motion, which is an important constraint for mobile sensor coverage. In this paper, we analyze the expected information captured per unit of energy consumption (IPE) as a function of the event type, the event dynamics, and the speed of the mobile sensor. Our analysis uses a realistic energy model of motion, and it allows the sensor speed to be optimized for information capture. We present simulation results to verify and illustrate the analytical results.

We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset of the sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for both cases of a synchronous and an asynchronous network. Under the periodic scheduling, coordinated sleep by the sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider four points in the design space: synchronous periodic scheduling with and without coordinated sleep, and asynchronous periodic scheduling with and without coordinated sleep. We show that the asynchronous network exceeds the synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though it may also reduce the opportunities for coordinated sleep. When the sensor density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy-efficient.

A two-hop neighborhood information-based routing protocol is proposed for real-time wireless sensor networks. The approach of mapping packet deadline to a velocity is adopted as that in SPEED; however, our routing decision is made based on the novel two-hop velocity integrated with energy balancing mechanism. Initiative drop control is embedded to enhance energy utilization efficiency, while reducing packet deadline miss ratio. Simulation and comparison show that the new protocol has led to lower packet deadline miss ratio and higher energy efficiency than two existing popular schemes. The result has also indicated a promising direction in supporting real-time quality-of-service for wireless sensor networks.

Abstract Energy constraint is the most conspicuous characteristic in wireless sensor networks (WSN). Node deployment, dynamic topology control, and data transmission in WSN all consume a large amount of energy. Therefore, proper adjustment of transmission power (TP) contributes much energy saving. In this paper, a new TP adjustment method based on Fuzzy Control Theory, called FCTP, is proposed for the dynamic topology control. The simulation results show that this method is more robust to tolerate accidental interfere, more rapidly convergent, and more energy efficient than other TP adjustment approaches, which lead to longer network lifetime. Copyright © 2008 John Wiley & Sons, Ltd.

Channel capacity and node energy represent resources and constraints in designing efficient routing schemes for Wireless Sensor Networks(WSNs). To delivery more data, a higher rate is desirable, which however consumes more energy and may demand more bandwidth. Hence, data transmission in WSNs should take into account both limited capacity and constrained energy. In this paper, we propose an utility-based nonlinear convex optimization formulation to maximize utility subject to the capacity and energy constraints. To achieve this, we introduce link interference set to represent all flows contention over a link, and it offers the capacity constraint over the specific link. For each node, we express the energy constraint with network lifetime requirement. A distributed solution with dual decomposition approach is proposed to address the optimization formulation. In addition, an Optimal Rate Routing (ORR) is developed by incorporating the optimization result to select the optimal rate route. Comparing with the previous schemes, ORR is able to achieve the highest utility, optimal rate selection during routing, and well-balanced performance.

This paper presents a theoretical model of control and communication over wireless sensor and actuator networks (WSANs). We propose two control schemes, a centralized control scheme (CC) in which decisions are made based on global information, and a distributed control scheme (DC) that enables distributed actuators to make decisions locally. Because of global information, CC can obtain optimal control at each step. However, when that information is delivered over lossy wireless channels, it could become unstable. It is demonstrated by simulations that DC could also stabilize the control system analogously with the CC, though with more steps.

Wireless sensor and actuator networks (WSAN) are increasingly popular due to their abilities to detect and react to interesting events in the physical world. In this paper, a new indoor WSAN system in the context of pursuit evasion games (PEGs) is designed and implemented. Our goal is to take advantage of the WSAN capabilities to perform target tracking and capture. We design the system control model, and present the detailed implementation of the WSAN, in particular the distributed evader tracking, pursuer control components and the system/user interfaces. We also present algorithms used to achieve low cost, low energy consumption, and robustness of performance in the WSAN. Experiments demonstrate that the pursuer captures the evader successfully in all the runs. Our work provides practical solutions for designing and implementing a working WSAN for PEG tasks.

To preserve network connectivity is an important issue especially in wireless sensor network, where wireless links are easy to be disturbed and tiny sensors are even easy to fail accidently. Therefore, it is necessary to design a fault-tolerant network. A feasible method is to construct a k-vertex connected topology. In this paper, we consider k-connectivity of wireless network and propose a simple global algorithm (GAFTk) which preserves the network k-connectivity and reduces the maximal transmission power (TP). The average degree expectation of the topology generated by GAFTkis O(k2). Based on GAFTk, we propose a simple local algorithm (LAFTk) which preserves k-vertex connectivity while maintaining bi-directionality of the network. Simulation results show that GAFT/LAFT have better performance than other current fault-tolerant protocols.

BACKGROUND: The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. RESULTS: Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%-90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. CONCLUSION: We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.

It is difficult to directly coregister the 3-D fluorescence molecular tomography (FMT) image of a small tumor in a mouse whose maximal diameter is only a few millimeters with a larger CT image of the entire animal that spans about 10 cm. This paper proposes a new method to register 2-D flat and 3-D CT image first to facilitate the registration between small 3-D FMT images and large 3-D CT images. A novel algorithm combining differential evolution and improved simplex method for the registration between the 2-D flat and 3-D CT images is introduced and validated with simulated images and real images of mice. The visualization of the alignment of the 3-D FMT and CT image through 2-D registration shows promising results.

In this paper, the recurrent neural networks (RNNs) with a generalized activation function class is proposed. In this proposed model, every component of the neuron's activation function belongs to a convex hull which is bounded by two odd symmetric piecewise linear functions that are convex or concave over the real space. All of the convex hulls are composed of generalized activation function classes. The novel activation function class is not only with a more flexible and more specific description of the activation functions than other function classes but it also generalizes some traditional activation function classes. The absolute exponential stability (AEST) of the RNN with a generalized activation function class is studied through three steps. The first step is to demonstrate the global exponential stability (GES) of the equilibrium point of original RNN with a generalized activation function being equivalent to that of RNN under all vertex functions of convex hull. The second step transforms the RNN under every vertex activation function into neural networks under an array of saturated linear activation functions. Because the GES of the equilibrium point of three systems are equivalent, the next stability analysis focuses on the GES of the equilibrium point of RNN system under an array of saturated linear activation functions. The last step is to study both the existence of equilibrium point and the GES of the RNN under saturated linear activation functions using the theory of M-matrix. In the end, a two-neuron RNN with a generalized activation function is constructed to show the effectiveness of our results.

This paper describes a basic coverage issue, and proposes a scheme named Grid Scan which is applied to calculate the basic coverage rate with arbitrary sensing radius of each node. Based on Grid Scan, a re-deployment approach is suggested to meet any k-covered rate in some region according to application requirements. The objective of our re-deployment scheme is to get equivalent coverage rate using less number of sensor nodes or to achieve higher coverage rate with the same number of sensor nodes. The results of simulation experiments support that Grid Scan based re-deployment is more effective to cover monitored area than random spread.

A CPN based IEC FB model and its application are introduced in this paper. The model not only can analyze the internal procedure of IEC FB, especially the parameter's status propagation and the mode's switch, but also can be integrated into modeling IEC FB application. The latter is explained with an FB application, FB based boiler water level control system.

This paper presents a classifier modeling technique called RSFAC, by combining on rough set theory and factor analysis technology. Factor analysis technology is introduced to classify the attributes in the dataset at first. Then attribute selection is performed by entropy measure. Thirdly, classification rules are deduced based on rough set analysis, and a classifier is built based on these rules. At the end, new examples can be predicted by a heuristic way. Experimental results show that the classifier established by above approach gets a better prediction than that by some well-known algorithms on some standard datasets.

The theory of support vector machine (SVM), as a tool of pattern classification and regression estimation, draws much attention on this field. In this method, it maps the input data into a high dimensional characteristic space in which it constructs an optimal separating hyperplane. In many applications it has provided high generalization ability. In this paper we provide the fuzzy SVM for regression estimation problem. This method combines the fuzzy logic with the generalize SVM to construct multi-layer SVM. The first layer is the fuzzification process. We apply a fuzzy membership to each data point of SVM and reformulate the SVM such that different input points can make different contributions to the learning of regression function. The second layer is the generalize SVM which make the kernel function and may not satisfy the Mercer's condition. The proposed method enhances the SVM in reducing the effect of outliers and noises in the application of data fitting.

Modern manufacturing companies need flexible manufacturing control systems (FMCSs) to compete in today's unpredictable and ever-changing global markets. Traditional methodologies, however, can't meet the requirements of FMCS design. The function block (FB) concept in IEC61499 may provide a promising approach. In this present work, a FB based design pattern has been introduced to systematize the task of FMCS design in the IEC61499 context, which is enhanced by adopting formal reference models in the design cycle. The FB based framework and engineering methodology have been proposed, together with requirements specification of CASE tools to facilitate the development process. An illustrative example using the proposed design pattern is presented. When properly applied, it can substantially reduce the time and cost of systems engineering, while enhancing the flexibility, modularity, extensibility, reusability and interoperability of FMCSs.

This paper proposed a hybrid modeling method based on mechanism analysis, identification and RBF neural networks. First, Get a industrial object's low-order model by the mechanism analysis and identification method. Second, adopt RBF neural networks modeling method to compensate unmodeled high-order model. The sum of the low-order model and high-order model is the hybrid model. This kind of hybrid model has more accuracy than a model which is gotten by mechanism analysis and identification method and has more generalization capability than a model which is gotten by neural networks modeling method.

By investigating the transmission of information and uncertainty in multivariable LTI systems disturbed by stationary random processes, the present paper discussed the connections within information rates, Bode integral and H/sub /spl infin// entropy. For the regulation problem, performance limits of disturbance rejection are formulated in the framework of information theory, by connecting entropy rate to Bode integral formula. The "conservation law of variety" is revised. For the tracking problem, an upper bound of disturbance rejection performance is derived based on a proved relation between information rates and H/sub /spl infin// entropy. The present results demonstrate that information rates are useful in control system analysis.

In this paper, we introduce a SVM based soft sensor method for resultant concentration of antibiotic fermentation. Non-linear black-box models for resultant concentration are established used SVM with poly and Gaussian kernels respectively. Some treatments such as the outliers' elimination and data smoothness are done before modeling. Simulation results with Matlab toolbox show that the SVM with poly and Gaussian kernels both can do the soft sensor work. Compare to BP neural network, the SVM with Gaussian kernel based soft sensor has better performance in generalization ability and training speed. According to the analysis, the method of SVM based soft sensor for resultant concentration is feasible.

This article addresses parameter convergence problem in identification of nonlinear dynamic systems using fuzzy models. We first establish persistent excitation conditions and then propose several detailed algorithms to generate input signals that guarantee the convergence of the parameter estimates in the fuzzy system models to the true values in identifications of second-order nonlinear moving-average and auto-regressive-moving-average systems. Numerical example is given to illustrate the ideas and results.

Fuzzy systems have been proved to be universal approximators, yet a large number of fuzzy rules may be needed for high approximation accuracy. In this paper, we consider how to determine the minimum number of fuzzy rules required in a fuzzy system for approximation to achieve a given accuracy and how to construct this fuzzy system when there are only a limited number of input-output data pairs of the unknown system. The key point is to partition the input space nonuniformly. In particular, a tunnel algorithm is utilized for the single-input case. Numerical examples are given to demonstrate the proposed idea and algorithm.

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform same task repetitively. In this paper, a current cycle error assisted ILC scheme for a class of discrete nonlinear time-varying systems is presented. Under a few reasonable assumptions, sufficient conditions for guaranteeing the convergence of the learning scheme are given and then proved by an inductive method. It is shown that the conditions are independent of the specific form of the state space model. Finally, some simulation results are given to illustrate the new control strategy.