The open radio access network (RAN) technology is crucial to the realization of autonomous driving, however, it also leads to the emergence of privacy protection and network security issues. This article explores the privacy-preserving consensus tracking problem of connected vehicular group systems over open RANs subject to stochastic false data injection (FDI) attacks. First, based on the polytope linearization technique and graph theory, a distributed connected vehicle group system model with multiple uncertain parameters is established under the two-layer cyber-physical framework. Then, by resorting to the homomorphic encryption method, a novel privacy-preserving consensus control strategy is designed via a vanishing affine output mask function, which not only prevents the leakage of initial state information but also improves the resilience of vehicle state to FDI attacks. Furthermore, an intelligent event-triggered network transmission scheme is proposed by introducing the Q-learning algorithm to dynamically adjust the triggering threshold during each iteration, which greatly lightens the communication load of open RANs. Finally, an illustrative example is presented to verify the validity of the derived theoretical results.

Output tracking control has been extensively applied in the cooperative control of multiagent systems (MASs), including mobile robot obstacle avoidance and autonomous aerial vehicle formation. This article investigates the $H_{\infty }$ output consensus tracking problem of interval type-2 (IT2) fuzzy heterogeneous MASs subject to multiple-channel denial-of-service (DoS) attacks. First, in the presence of DoS attacks on both leader-follower and follower-follower communication channels, a fully distributed adaptive compensator is designed to approximate the convex hull of the leader's state. Then, to address DoS attacks occurring in the compensator-controller and observer-controller channels, an observer-based fuzzy switched controller is developed for each follower agent to accomplish the tracking objective. Furthermore, a two-layer hierarchical hybrid event-triggered mechanism (ETM) is established to significantly reduce the communication burden of the MAS network. In the proposed ETM, asynchronous communication and Zeno behavior are rigorously excluded, while the triggering frequency is effectively decreased. Moreover, a sufficient condition is derived to guarantee the exponential stability of the tracking error with a prescribed $H_{\infty }$ performance. Finally, simulation results are provided to demonstrate the feasibility and superiority of the proposed approach.

This study examines denial-of-service (DoS) attacks on networked control systems (NCSs) caused by TCP/IP protocol vulnerabilities. It conducts a comprehensive analysis of hacker tactics to uncover vulnerability exploitation techniques. The research reformulates the performance error estimation (PEE) problem, framing it as a search for ellipsoid constraints within a P-dependent set. It introduces the Higher-Order Weight Method (HOWM) to optimize sampling intervals, leveraging the unique properties of zero-order-hold sampling. The study enhances Lyapunov-Krasovskii functions (LKFs) using HOWM by partitioning integral terms and applying quadratic scaling to optimize control algorithms, thereby reducing conservatism and tightening upper-bound criteria. Additionally, it proposes an innovative Integral Event-Triggered Control (IETC) strategy for estimating system performance errors. The performance of the proposed control algorithm is rigorously evaluated through simulations on a complex 2-degree of freedom (DoF) helicopter system (HS). Note to Practitioners—This study delves into DoS attacks targeting NCSs due to TCP/IP protocol vulnerabilities. It conducts an exhaustive analysis of hacker tactics to uncover techniques used to exploit these vulnerabilities. The research reframes the PEE problem, casting it as a search for ellipsoid constraints within a P-dependent ellipsoidal set. It introduces the HOWM to optimize sampling intervals, leveraging the unique characteristics of zero-order-hold sampling. Additionally, the study enhances LKFs using HOWM by partitioning integral terms and applying quadratic scaling to optimize the control algorithm, thereby reducing conservatism and tightening upper-bound criteria. Moreover, it introduces an innovative IETC strategy for estimating system performance errors. The performance of the proposed control algorithm is rigorously evaluated through simulations on a complex 2-DoF HS.

This article presents a novel event-triggered sliding-mode control (ET-SMC) strategy for impulsive nonlinear systems (INS) in the presence of matched disturbances. Most of the existing sliding mode control (SMC) strategies work well when the system continually converges toward a predefined sliding surface, but have been proven to be inapplicable for discontinuous systems subjected to impulsive disturbances. Consequently, it becomes crucial and imperative to develop SMC strategies tailored for discontinuous dynamics affected by impulsive phenomena. Leveraging the event-triggering mechanism with a time-varying threshold and incorporating piecewise Lyapunov function techniques, we propose a novel ET-SMC strategy. It is proved that the proposed control strategy can effectively avoid Zeno behavior and ensure the finite-time stability (FTS) of the considered system via finite-time control theory and innovative impulsive estimation schemes. Furthermore, our results quantitatively measure changes in convergence rate under varying impulsive conditions, which provides valuable insights for performance analysis and the design of SMC strategies in such scenarios. As a specific application, we apply the proposed ET-SMC strategy to coupled reaction-diffusion neural networks (RDNNs) in the presence of both cyber-attacks and matched disturbances. Finally, we present several simulation examples to illustrate the effectiveness and practical applicability of the analytical methods presented in this article.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

This paper focuses on distributed sliding-mode consensus tracking control of fuzzy multi-agent systems (MASs) under time-varying transmission delays and hybrid cyber-attacks, including deception and denial-of-service (DoS) attacks. Impulsive and switching signals are adopted to describe the dynamic process of the fuzzy MAS subject to hybrid cyber-attacks. In order to address the hybrid cyber-attacks, a novel integral-type sliding surface function grounded on lumped consensus tracking errors is presented, and a resilient control scheme based on this surface is designed accordingly. By adopting the analysis method of hybrid systems and finite-time control theory, sufficient conditions are deduced to guarantee that the tracking error dynamics can achieve the intended sliding surface in a finite time and remain on the surface thereafter. Meanwhile, the quantitative relationships between the influence of hybrid cyber attacks on the convergence rate of the control strategy are clearly revealed. Moreover, the stabilities of the corresponding sliding mode dynamics are investigated. Finally, the example of cooperative fuzzy robot manipulators is used to illustrate the effectiveness and validity of the proposed theoretical results.

This study proposes a specific view of profiling a smart building's energy consumption and built environment performance among multi-periods of Covid-19 pandemic. This case study leverages air-handling unit (AHU) as our specific observation among energy consumption and built environment, such as power meter (kW), carbon dioxide (CO2ppm), temperature (°C) and humidity (%) under a scope of typical one working week without any public holiday. This study proposes an approach to quantify the change significance and sign by performing relative normalization and statistical analysis on multi-periods' datasets. The results show that energy consumption has a largest change significance at negative 13.23% (during Covid-19 Resume Activity: Phase 3 period), compared with those in the period of pre Covid-19 as baseline. On the other hand, built environment has carbon dioxide, temperature and humidity as the largest change significance at positive 58.57% (post Covid-19 period), at positive 54.71% (during Covid-19 Resume Activity: Phase 1 period) and at negative 50.75% (during Covid-19 Resume Activity: Phase 1 period) respectively, compared with baseline. Moreover, the results also may be potentially useful and applicable to further use-cases, such as for modelling and optimization problems for occupancy and built environment monitoring with more explicit features like carbon dioxide, temperature and humidity. With the help of the proposed approach, feature selection and engineering techniques for AI/ML could be also adopted to enhance performance.

This article analyzes the mechanism of denial-of-service (DoS) attacks initiated by hackers from the perspective of computer networks. In order to effectively estimate the performance error generated by the T–S fuzzy networked control systems under DoS attacks, we transform the performance error estimation problem into the one of finding ellipsoid constraints$\mathfrak {J}(P_{i})$. First, improved Lyapunov–Krasovskii functions (LKFs) based on fuzzy membership functions are constructed, which combine the characteristics of nonlinear problems in the system to reduce the initial constraints. Then, a second-order weight method (SOWM) is introduced to divide the time interval of sampling meticulously. Besides, the information stored in the LKFs is enhanced. Furthermore, we construct the novel looped functions by relying on the SOWM. Next, a suitable integral elastic event trigger mechanism is established to ensure that the performance errors caused by the attacks are estimated. Finally, the feasibility of the proposed method is verified by a two-degree-of-freedom helicopter system.

This article investigates the stability problem of Takagi–Sugeno fuzzy networked control systems (TSNCSs) under deception cyberattacks via a reliable sampling mechanism, which has important research value for applications in network security. First, a fuzzy weight functional method is introduced, and a new Lyapunov–Krasovskii functional is developed, which better incorporates nonlinear problems in the model. Then, in order to reduce the initial constraints, improved time delay closed-loop functions are constructed that consider the delay information and the characteristics of the sampling time points. Furthermore, considering the reliability issues of controllers in real industry, we establish some sufficient conditions and implement a novel reliable sampling controller with deception attacks (DAs) to control the asymptotic stability of TSNCS. Finally, the correctness and feasibility of the proposed method are verified experimentally using an inverted pendulum system.

Human activity recognition (HAR) based on inertial sensors has been investigated for many industrial informatics applications, such as healthcare and ubiquitous computing. Existing methods mainly rely on supervised learning schemes, which require large labeled training data. However, labeled data are sometimes difficult to acquire, while unlabeled data are readily available. Thus, we intend to make use of both labeled and unlabeled data with semisupervised learning for accurate HAR. In this paper, we propose a semisupervised deep learning approach, using temporal ensembling of deep long short-term memory, to recognize human activities with smartphone inertial sensors. With the deep neural network processing, features are extracted for local dependencies in the recurrent framework. Besides, with an ensemble approach based on both labeled and unlabeled data, we can combine together the supervised and unsupervised losses, so as to make good use of unlabeled data that the supervised learning method cannot leverage. Experimental results indicate the effectiveness of our proposed semisupervised learning scheme, when compared to several state-of-the-art semisupervised learning approaches.

To alleviate traffic congestion in urban areas, electronic toll collection (ETC) systems are deployed all over the world. Despite the merits, tolls are usually pre-determined and fixed from day to day, which fail to consider traffic dynamics and thus have limited regulation effect when traffic conditions are abnormal. In this paper, we propose a novel dynamic ETC (DyETC) scheme which adjusts tolls to traffic conditions in realtime. The DyETC problem is formulated as a Markov decision process (MDP), the solution of which is very challenging due to its 1) multi-dimensional state space, 2) multi-dimensional, continuous and bounded action space, and 3) time-dependent state and action values. Due to the complexity of the formulated MDP, existing methods cannot be applied to our problem. Therefore, we develop a novel algorithm, PG-beta, which makes three improvements to traditional policy gradient method by proposing 1) time-dependent value and policy functions, 2) Beta distribution policy function and 3) state abstraction. Experimental results show that, compared with existing ETC schemes, DyETC increases traffic volume by around 8%, and reduces travel time by around 14:6% during rush hour. Considering the total traffic volume in a traffic network, this contributes to a substantial increase to social welfare.

Occupant's thermal comfort detection is a significant contributor to building energy efficiency. However, the predictions from the traditional PMV (Predicted Mean Vote) method often deviate from the actual thermal sensation of occupants. This paper proposes two new approaches for TS (thermal state: Discomfort/Comfort) detection, based on personal physiological features extracted using wearable technology. The first approach, CNN-(Tsk)TP, is based on a deep convolutional neural network (CNN) that associates TS with images of hand skin temperature temporal profile (TP). CNN has shown great success in the classification of captured images, however, its application to 2-D sensor data is rather less explored. In this study, the hand skin temperature was observed to show distinct temporal patterns under different TS. Leveraging this high responsiveness of skin temperature, the two-dimensional sensor data was transferred to image domain. A 4-step domain transfer process was adopted to obtain 5-minute TP for the CNN. The second approach, SVMphyis based on a Support Vector Machine (SVM) model with 6 distinct physiological input features. SVM-RBF performed better among four kernel types evaluated (linear, polynomial, radial, sigmoid). Our proposed approaches CNN-(Tsk)TPand SVMphyachieved 93.33% and 90.6% accuracy, outperforming the existing methods (PMV, ePMV, aPMV and PTS models). Additionally, practical advantages of our approaches are discussed.

This study proposes a novel method of energy efficiency improvement in Air-Conditioning and Mechanical Ventilation (ACMV) systems on the basis of occupant thermal states (ComforyDiscomfort) evaluated by Predictive Thermal State (PTS) models. An ACMV Operating State (OS) algorithm is proposed and integrated with PTS models under assumptions of Markov Decision Process (MDP). The ACMV OS algorithm and the developed PTS models are applied in our thermal laboratory. The results show that NN based PTS models perform better than ELM based ones in terms of energy saving in our case studies. The optimal sampling time of the applied ACMV OS algorithm is 10 mins without the issues of system lagging and losing sharpness of tracking thermal states of occupants. The experimental results show that the proposed ACMV OS algorithm can significantly reduce around 10 kWh out of 74 kWh (about 13.5% energy saving) daily in laboratory conditions without compromising the thermal comfort level of occupants.

This paper is concerned with solving a large category of convex optimization problems using a group of agents, each only being accessible to its individual convex cost function. The optimization problems are modeled as minimizing the sum of all the agents' cost functions. The communication process between agents is described by a sequence of time-varying yet balanced directed graphs which are assumed to be uniformly strongly connected. Taking into account the fact that the communication channel bandwidth is limited, for each agent we introduce a vector-valued quantizer with finite quantization levels to preprocess the information to be exchanged. We exploit an event-triggered broadcasting technique to guide information exchange, further reducing the communication cost of the network. By jointly designing the dynamic event-triggered encoding-decoding schemes and the event-triggered sampling rules (to analytically determine the sampling time instant sequence for each agent), a distributed subgradient descent algorithm with constrained information exchange is proposed. By selecting the appropriate quantization levels, all the agents' states asymptotically converge to a consensus value which is also the optimal solution to the optimization problem, without committing saturation of all the quantizers. We find that one bit of information exchange across each connected channel can guarantee that the optimiztion problem can be exactly solved. Theoretical analysis shows that the event-triggered subgradient descent algorithm with constrained data rate of networks converges at the rate of O(lnt√t). We supply a numerical simulation experiment to demonstrate the effectiveness of the proposed algorithm and to validate the correctness of theoretical results.

The issue of diminishing global energy resources has been of significant concern in recent years. One of the most energy hungry components is the Air-Conditioning and Mechanical Ventilation (ACMV) Systems for buildings, which consumes more than 40% of the total building energy consumed on average. Since people are spending more time staying indoor, the indoor environmental conditions have to be taken into special considerations. One of the most important aspects for indoor environmental conditions is occupants' thermal comfort sensations. Occupants will be more productive and healthy with good indoor thermal comfort sensations. In this research, we will introduce the Extreme Learning Machine (ELM) networked models of energy consumption with indoor air temperature and velocity as two main subjects of investigation. Then, a swarm-type sparse Firefly Algorithm (FA-MSE regression) is developed for ACMV optimizations of balancing both thermal comfort sensations and energy consumption. The numerical results show that the recommended optimizations could potentially save up to 16% of energy consumption of an experimental ACMV system in the thermal laboratory of the Nanyang Technological University, Singapore.

In cloud computing, a private (secondary) cloud can: 1) outsource workload to public (primary) clouds via vertical federation or 2) share resources with other secondary clouds through horizontal federation to enhance its service quality. While there have been attempts to establish a joint vertical and horizontal cloud federation (VHCF), little is known regarding the economic aspects (e.g., what stable cooperation pattern will form, will it improve efficiency) of such a complex cloud network, where secondary clouds are self-interested. To fill the gap, we analyze the interrelated workload factoring and federation formation among secondary clouds, while providing scalable algorithms to assist them to optimally select partners and outsource workload. We use a game theoretic approach to model the federation formation of clouds as a coalition game with externalities. We adopt a pessimistic core to characterize the cooperation stability and formulate its computation as a bilevel optimization problem. The properties of the problem are explored and efficient algorithms are developed to solve it. Experimental results show that the two common practices (no-cooperation and all-in-one federation) are not always stable. The results also show that compared with the two common practices, secondary clouds can decrease service delay penalty by around 11% with the proposed VHCF network.

Occupancy estimation in buildings can benefit various applications such as heating, ventilation, and air-conditioning control, space monitoring, and emergency evacuation. Due to the consideration of temporal dependency in occupancy data, hidden Markov model (HMM) has been shown to be effective in occupancy estimation. However, the conventional HMM that assumes invariant temporal dependency of occupancy dynamics for different time instances is unrealistic. Moreover, the performance of the conventional HMM that utilizes mixture of Gaussian for emission probability in terms of continuous observations can be easily affected by the noise in sensory data. To address these problems, in this paper, we propose a new architecture, i.e., inhomogeneous hidden Markov model with multinomial logistic regression (IHMM-MLR), for building occupancy estimation using nonintrusive environmental sensors. Instead of using the time-invariant transition probability matrix, we apply a time-dependent (inhomogeneous) transition probability matrix which can capture the temporal dependency for different time instances. Meanwhile, we employ an efficient probabilistic model, i.e., MLR, for emission probability. Online and offline occupancy estimation schemes are presented for real-time and accurate long-term applications respectively. Real experiments have indicated the effectiveness of our proposed approach.

Human activity recognition using either wearable devices or smartphones can benefit various applications including healthcare, fitness, smart home, etc. Instead of using wearable devices which are intrusive and require extra cost, we shall leverage on modern smartphones embedded with a variety of sensors. Due to the flexibility of using smartphones, the recognition accuracy will degrade with orientation, placement, and subject variations. In this paper, we propose a robust human activity recognition system in terms of orientation, placement, and subject variations based on coordinate transformation and principal component analysis (CT-PCA) and online support vector machine (OSVM). The proposed CT-PCA scheme is utilized to eliminate the effect of orientation variations. Experiments show that the proposed scheme significantly improves the activity recognition accuracy and outperforms the state-of-the-art methods on leave one orientation out experiments, which demonstrates the generalization ability of the proposed scheme on the data from unseen orientations. We also show the effectiveness of this scheme on placement and subject variations. However, the inherent difference of signal properties for different placement and subject dramatically reduces the recognition accuracy, especially for different placement. Thus, we present an efficient OSVM algorithm, that is, online-independent support vector machine (OISVM), which utilizes a small portion of data from the unseen placement or subject to online update the parameters of the SVM algorithm. The experimental results demonstrate the effectiveness of this OISVM algorithm on placement and subject variations.

This paper is concerned with cooperative output regulation of heterogeneous multi-agent systems. Agents are allowed to be heterogeneous general linear time-invariant systems and the communication graph is not restricted to the acyclic type. New distributed state-feedback controllers with extra scalar parameters are constructed. A global sufficient solvability condition is first derived and simplified stability conditions for closed-loop poles in a specified region are then obtained in terms of an H∞-type performance of local sub-systems coupled through a matrix associated with the graph. Linear matrix inequality conditions are further presented for allocating the H∞-type performance levels and designing controllers for each sub-system. A numerical example is presented for illustrating the advantages of the proposed design method. Both continuous- and discrete-time multi-agent systems are investigated in a unified framework.

A key problem in event-triggered control of multi-agent systems is to design triggering conditions. We first give an overview of existing triggering conditions used in the literature. However, not all existing triggering conditions can both relax continuous communication between neighboring agents and admit a positive lower bound of inter-event times. Then, we propose two new triggering conditions based on edge information rather than neighbor information, and show that Zeno behavior is ruled out by using a time-dependent threshold and a periodic event detector, respectively. Moreover, we list some open problems which are worth the effort to launch future investigations.

A series of online multi-task learning (OMTL) algorithms have been proposed to avoid the expensive training cost and poor adaptability of traditional batch multi-task learning (MTL) algorithms in recent years. However, these OMTL algorithms usually assume that all tasks are closely related, which may not hold in practical scenarios. More importantly, their theoretical reliability is weakened due to the lack of proof on the cumulative regrets. To overcome these limitations, we present a robust online multi-task classification framework (ROM) and its two optimization algorithms (ROM-PGD, ROM-RDA). The proposed algorithms can not only automatically capture the common features among all tasks and individual features for each task, but also identify the potential existence of outlier task. Theoretically, we prove that the regret bounds of these two algorithms are sub-linear compared with the best separating algorithm in hindsight. Empirical studies on both synthetic and real-world datasets also demonstrate the effectiveness of our proposed algorithms when compared with the state-of-the-art OMTL algorithms.

Mean Radiant Temperature (MRT) is an important factor of Fanger's PMV model, which is the most popular method to study human thermal comfort. However, it has often been a practice to assume MRT equal to air temperature (Ta) during indoor thermal studies. In this paper, we have studied the consequences of this simplistic assumption on the thermal comfort of occupants in air-conditioned buildings. A worldwide database of about 10000 occupants covering 9 climatic zones and 4 seasons has been studied. The effect on comfort indices-Predicted Mean Vote (PMV), Actual Mean Vote (AMV), Thermal Acceptability Vote (TSA) and Thermal Preference Vote (MCI) are presented. It is observed that even a small difference in Taand MRT can lead to significant error in determination of thermal comfort. A correlation study between AMV and the six Macpherson factors reveals that MRT has the highest positive correlation with the thermal sensation reported by the occupants. Study of TSA and MCI show that the assumption is more likely to affect comfort level determination in the uncomfortable range.

The Global Positioning System (GPS) can be readily used for outdoor localization, but GPS signals are degraded in indoor environments. How to develop a robust and accurate indoor localization system is an emergent task. In this paper, we propose a smartphone inertial sensor-based indoor localization and tracking system with occasional iBeacon corrections. Some important issues in a smartphone-based pedestrian dead reckoning (PDR) approach, i.e., step detection, walking direction estimation, and initial point estimation, are studied. One problem of the PDR approach is the drift with walking distance. We apply a recent technology, iBeacon, to occasionally calibrate the drift of the PDR approach. By analyzing iBeacon measurements, we define an efficient calibration range where an extended Kalman filter is utilized. The proposed localization and tracking system can be implemented in resource-limited smartphones. To evaluate the performance of the proposed approach, real experiments under two different environments have been conducted. The experimental results demonstrated the effectiveness of the proposed approach. We also tested the localization accuracy with respect to the number of iBeacons.

In this paper, we consider the distributed parameter estimation problem over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage algorithm aiming at achieving the centralized sample mean estimate in a distributed manner. The running average technique is utilized in the proposed algorithm to smear out the randomness caused by the probabilistic quantization scheme. It is shown that the centralized estimate can be achieved in the mean square sense, which is not observed in the conventional consensus algorithms. Simulation results are presented to illustrate the effectiveness of the proposed algorithm and highlight the improvements by using running average technique.

An integral part of visualizing an air-conditioned space is to know its occupancy in real-time, in order to make intelligent control decisions about the operation of its Air Conditioning and Mechanical Ventilation (ACMV) system. The sensing mechanisms used in occupancy estimation such as cameras and wearable sensors are generally intrusive and expensive. Alternatively, the effect that occupants have on environmental parameters such as CO2, temperature, humidity and pressure can be utilized to extract information about the occupancy levels. Environmental sensors are relatively inexpensive and are non-intrusive. From these sensor data, we need to extract and select relevant features that may yield occupancy information. The filter model feature selection approach used in previous works compromises on the classification accuracy in order to limit the computational burden. An alternative is the wrapper model of feature selection, which uses the inference algorithm itself to search for the best features. It guarantees better classification accuracy but is computationally expensive, especially with slow iterative machine learning techniques such as the Artificial Neural Network (ANN) used in previous works. To address this problem, this work capitalizes on the fast learning speed of Extreme Learning Machines (ELM) to implement a wrapper model of feature selection. To the best of our knowledge, the use of the wrapper model in an occupancy estimation problem has not been documented. A comparison between the filter and wrapper model feature selection is made. The tracking accuracy was seen to have notably improved with the wrapper model. Also, it was demonstrated that the pressure data, which has not been used for occupancy estimation in previous works, is useful.

This paper provides a new approach to reconstruct a fluid field from sparse sensor observations. Using the extreme learning machine (ELM) autoencoder, we can extract a dominant basis of the fluid field of interest from a database consisting of a series of fluid field snapshots obtained from offline computational fluid dynamics (CFD) simulations. The output weights of ELM autoencoder can be viewed as the compressed feature representations of the fluid field and represent the dominant behaviors of the database. With such a compressed representation, the fluid field of interest can be easily reconstructed from sparse sensor observations. The simulation results show that the new compressed representation approach can achieve better reconstruction accuracy as compared with the traditional principal component analysis (PCA) method.

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. It provides a unified solution that can be used directly to solve regression, binary, and multiclass classification problems. In this paper, we propose a stacked ELMs (S-ELMs) that is specially designed for solving large and complex data problems. The S-ELMs divides a single large ELM network into multiple stacked small ELMs which are serially connected. The S-ELMs can approximate a very large ELM network with small memory requirement. To further improve the testing accuracy on big data problems, the ELM autoencoder can be implemented during each iteration of the S-ELMs algorithm. The simulation results show that the S-ELMs even with random hidden nodes can achieve similar testing accuracy to support vector machine (SVM) while having low memory requirements. With the help of ELM autoencoder, the S-ELMs can achieve much better testing accuracy than SVM and slightly better accuracy than deep belief network (DBN) with much faster training speed.

A dynamic control policy with optimized dynamics is explored for its use in a model predictive control (MPC) algorithm for a nonlinear system modeled with a feedforward neural network. The nonlinear system is expressed as a polytopic quasi-linear-parameter-varying (quasi-LPV) system over a region of the state-input space and the dynamics of the policy are allowed to depend on the time-varying parameter of the quasi-LPV model. The policy dynamics are optimized off-line to obtain an enlarged domain of attraction which matches with the state-input region over which the polytopic approximation of the system holds good. A complete MPC algorithm using the dynamic policy as the terminal policy ensures stabilization and improved performance over a larger domain without a larger horizon length.

This paper studies the blind identification of multi-channel FIR systems in the context of sensor networks. Distributed identification algorithms are developed for both noise-free and noise-contaminated networked systems. The proposed algorithms distribute the data storage and computational load among multiple agents connected by a specified topology, and are fulfilled via information exchanges among neighboring agents without the need of fusion centers. In the presence of measurement noises, a stabilized distributed algorithm is provided which can avoid trivial estimations of the multiple channels. In addition, convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.

Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron alike and perform well in both regression and classification applications. The problem of determining the suitable network architectures is recognized to be crucial in the successful application of ELMs. This paper first proposes a dynamic ELM (D-ELM) where the hidden nodes can be recruited or deleted dynamically according to their significance to network performance, so that not only the parameters can be adjusted but also the architecture can be self-adapted simultaneously. Then, this paper proves in theory that such D-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results obtained over various test problems demonstrate and verify that the proposed D-ELM does a good job reducing the network size while preserving good generalization performance.

Credit risk evaluation has become an increasingly important field in financial risk management for financial institutions, especially for banks and credit card companies. Many data mining and statistical methods have been applied to this field. Extreme learning machine (ELM) classifier as a type of generalized single hidden layer feed-forward networks has been used in many applications and achieve good classification accuracy. Thus, we use ELM (kernel based) as a classification tool to perform the credit risk evaluation in this paper. The simulations are done on two credit risk evaluation datasets with three different kernel functions. Simulation results show that the kernel based ELM is more suitable for credit risk evaluation than the popular used Support Vector Machines (SVMs) with consideration of overall, good and bad accuracies.

In this paper, we shall examine the design of sliding mode observers for Lipschitz nonlinear systems for state and faults/unknown input estimations. The robust terms or the switching terms are designed such that the faults are tracked by their robust terms and so can be reconstructed from the sliding mode. The stability condition for the reduced order system is analyzed and the feedback gain is designed such that the reduced order system is stable. An application example to robotic manipulator is examined to demonstrate the effectiveness of the proposed method in reconstruction of unknown inputs/faults.

This brief presents a curve clustering technique based on a new multivariate model. Instead of the usual Gaussian random effect model, our method uses the multivariate t-distribution model which has better robustness to outliers and noise. In our method, we use the B-spline curve to model curve data and apply the mixed-effects model to capture the randomness and covariance of all curves within the same cluster. After fitting the B-spline-based mixed-effects model to the proposed multivariate t -distribution, we derive an expectation-maximization algorithm for estimating the parameters of the model, and apply the proposed approach to the simulated data and the real dataset. The experimental results show that our model yields better clustering results when compared to the conventional Gaussian random effect model.

Online Sequential Extreme Learning Machine (OS-ELM) proposed by Liang et al [1] is a faster and more accurate online sequential learning algorithm as compared to other current sequential algorithms. It can learn data one-by-one or chunk-by-chunk with fixed or varying chunk size. However, there is one of the remaining challenges for OS-ELM that it could not determine the optimal network structure automatically. In this paper, we propose a Constructive Enhancement for OS-ELM (CEOS-ELM), which can add random hidden nodes one-by-one or group-by-group with fixed or varying group size. CEOS-ELM is searching for the optimal network architecture during the sequential learning process, and it can handle both additive and radial basis function (RBF) hidden nodes. The optimal number of hidden nodes can be obtained automatically after training. The simulation results show that with CEOS-ELM, the network can achieve comparable generalization performance with OS-ELM and more compact network structure.

Elman networks (ENs) can be viewed as a feedforward (FF) neural network with an additional set of inputs from the context layer input (feedback from the hidden layer). Therefore, a standard on-line (real time) backpropagation (BP) algorithm, instead of the off-line backpropagation through time (BPTT) algorithm, can be applied for the training of ENs, which is usually called Elman backpropagation (EBP) for discrete time sequence prediction applications. However, the standard BP training algorithm is not the most suitable one for ENs. Using a small learning rate may help improve the training of ENs, but it can result in very slow convergence speed and poor generalization performance, while a large learning rate may lead to unstable training in terms of weight divergence. Therefore, an optimal trade-off between ENs training speed and weight convergence with good generalization capability is desired. In this paper, a robust extended Elman backpropagation (eEBP) training algorithm of ENs with a nonlinear adaptive dead zone scheme is developed based on a novel training concept. The optimized adaptive learning rate with the adaptive dead zone maximizes the training speed of the ENs for each weight updating step while generalization performance of the eEBP training is improved. Computer simulations are carried out to show the improved performance of eEBP for discrete-time sequence prediction.

This paper studies the optimal bit error rate (BER) performance of chip-level detection for optical code division multiple-access (OCDMA) systems in the presence of shot noise. To attain the optimal BER of chip-level detection, a chip-level detector that uses a limiter on the photon counts at each weighted chip is first proposed. Then, the monotonicity of the likelihood ratio used in the maximum a posteriori (MAP) probability criterion is established. With this monotonicity, a sufficient condition characterized by the cap levels of the limiters and the average photon counts per pulse is obtained, under which the detector is simply an AND detector to attain the optimal BER of chip-level detection. The detector' performance and its comparison to those of other detectors are also studied by numerical studies.

Formation keeping control design for formations in eccentric Earth orbits is considered. The overall control design follows a non-hierarchical control architecture based on virtual structure approach. For the local level control, the linear parameter varying model describing the relative motion of member satellites is approximated by an uncertain model with polytopic uncertainty and an infinite horizon robust MPC is used that efficiently handles model uncertainty as well as state and control constraints while minimizing the fuel requirement. Also, a scheme for switching on/off of the individual satellite controllers for minimizing overall fuel consumption is explored. The performance of this control strategy is studied with an extensive nonlinear satellite formation simulation. Simulation results show the effective performance of the overall control strategy.

In this paper, we present a novel image clustering algorithm that has a new dissimilarity measure which incorporates the adaptive spatial information. The spatial connectivity of an image is controlled by a weighting factor so that it enhances the smoothness towards piecewise-homogeneous region and reduces the edge-blurring effect. Our method also utilizes the capacity maximization to evaluate the quality of the clustering result via mutual information maximization. The unreliable data points will be further processed to improve the clustering results. Experimental results with synthetic and real images demonstrate the effectiveness of our algorithm.

In this paper, Extreme Learning Machine (ELM) is introduced to predict the subcellular localization of proteins based on the frequent subsequences. It is proved that ELM is extremely fast and can provide good generalization performance. We evaluated the performance of ELM on four localization sites with frequent subsequences as the feature space. A new parameter called Comparesup was introduced to help the feature selection. The performance of ELM was tested on data with different number of frequent subsequences, which were determined by different range of Comparesup. The results demonstrated that ELM performed better than previously reported results, for all of the four localization sites.

This paper presents a rate control scheme for H.264/AVC scalable extension. Based on our previous work on H.264/AVC rate control, a switched model is proposed to predict the mean absolute difference (MAD) of the residual texture from the available MAD information of the previous frame in the same layer and the same frame in its ldquobase layer.rdquo Thus, abrupt MAD fluctuations could be predicted properly in the enhancement layer. Moreover, a bit allocation scheme is proposed for the hierarchical B-frames structure by taking into consideration the relative importance of each frame. With our algorithm, the rate control for all the coarse-grain-scalability, spatial, temporal and combined enhancement layer could be realized, and the target bit rate for each layer can be achieved. Our method encodes the sequence only once and the buffer is well controlled to prevent it from overflowing and under flowing.

Due to the complexity of H.264/AVC, it is very challenging to apply this standard to design a conversational video communication system. This problem is addressed in this paper by using region-of-interest (ROI) based bit allocation and computational power allocation schemes. In our system, the ROI is first detected by using the direct frame difference and skin-tone information. Several coding parameters including quantization parameter, candidates for mode decision, the number of referencing frames, accuracy of motion vectors and the search range of motion estimation are adaptively adjusted at the macroblock (MB) level according to the relative importance of each MB. Subsequently, the encoder could allocate more resources such as bits and computational power to the ROI, and the decoding complexity is also optimized at the encoder side by utilizing an ROI based rate-distortion-complexity (R-D-C) cost function. The encoder is thus simplified and decoding-friendly, and the overall subjective visual quality can also be improved.

This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed neural control system guarantees closed-loop stability of the estimation system, and a good tracking performance. The performance improvement of the proposed system over existing systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against system disturbance.

For a recurrent neural network (RNN), its transient response is a critical issue, especially for real-time signal processing applications. The conventional RNN training algorithms, such as backpropagation through time (BPTT) and real-time recurrent learning (RTRL), have not adequately addressed this problem because they suffer from low convergence speed. While increasing the learning rate may help to improve the performance of the RNN, it can result in unstable training in terms of weight divergence. Therefore, an optimal tradeoff between RNN training speed and weight convergence is desired. In this paper, a robust adaptive gradient-descent (RAGD) training algorithm of RNN is developed based on a novel RNN hybrid training concept. It switches the training patterns between standard real-time online backpropagation (BP) and RTRL according to the derived convergence and stability conditions. The weight convergence and L(2)-stability of the algorithm are derived via the conic sector theorem. The optimized adaptive learning maximizes the training speed of the RNN for each weight update without violating the stability and convergence criteria. Computer simulations are carried out to demonstrate the applicability of the theoretical results.

With radio-over-fiber (RoF) enabled remote antenna, gigabit Ethernet service operating at 60-GHz frequencies can be accessed cordlessly. Due to limited linear operation region of 60-GHz optoelectronic modulators commercially available so far, optical weak modulation is required leading to limited link performance. This paper demonstrates in RoF transmission of 1.25-Gigabit Ethernet signal on 60-GHz band subcarrier, the fiber downlink performance is improved by suppressing the optical carrier-to-sideband ratio based on the usage of a simple and low-cost fabricated 2 times 2 all-fiber optical interleaver. Simultaneously by using the interleaver, the downlink optical carrier is recovered and is reused as optical source for fiber uplink hence laser-free access point is achieved.

This paper presents a rate control scheme for H.264/AVC spatial/ coarse-gain-SNR (CGS) scalable extension. A switched model is proposed to predict mean absolute difference (MAD) either from the previous frame of the current layer or from the current frame of the previous layer. This way, abrupt MAD fluctuations could be predicted properly in the enhancement layer. Moreover, a sum bits R-Q model is formulated to describe the relationship between the total amount of bits for texture and non-texture information and quantization parameter (QP) so as to reduce the negative effect caused by inaccurate non-texture bits estimation of the previous rate control schemes. With the relation between PSNR and QP value of H.264/AVC, our proposed sum bits R-Q model could further optimize the QP calculation at the MB level. With our algorithm, the rate control for all the coarse-grain-scalability (CGS) and spatial enhancement layer could be realized, and the encoder could achieve fixed bitrate encoding for each scalable layer. Our method encodes the sequence only once and the buffer is well controlled to prevent it from overflowing and underflowing.

In this paper, the monotonicity of the likelihood ratio used in the optimal correlation detection is established for optical code division multiple-access (OCDMA). The monotonicity property not only guarantees the optimality of the detection, it also enables us to calculate the bit-error-rate bounds for OCDMA in the presence of uncertain a priori probabilities.

This paper presents a novel rate control scheme for low delay video communication of H.264/AVC standard. A switched mean-absolute-difference (MAD) prediction scheme is introduced to enhance the traditional temporal MAD prediction model, which is not suitable for predicting abrupt MAD fluctuations. Our new model could reduce the MAD prediction error by up to 69%. Furthermore, an accurate linear rate-quantization (R-Q) model is also formulated to describe the relationship between the total amount of bits for both texture and nontexture information and the quantization parameter (QP), so that the negative effect caused by the inaccurate estimation of nontexture bits is removed. By exploring the relationship between peak signal-to-noise ratio and QP value, the proposed linear R-Q model could further optimize QP calculation at the macroblock level. When compared with the rate control scheme JVT-G012 which is adopted by the latest JVT H.264/AVC reference model JM9.8, the proposed rate control algorithm could reduce the mismatch between actual bits and target ones by up to 75%. To meet the low delay requirement, the buffer is better controlled to prevent overflowing and underflowing. The average luminance PSNR of reconstructed video is increased by up to 1.13 dB at low bit rates, and the subjective video quality is also improved

This paper presents an improved rate control scheme for the H.264/AVC video coding scheme. By analyzing the relationship between direct mean absolute difference (MAD) and actual MAD, a new MAD prediction scheme is introduced to enhance traditional linear MAD prediction model, which is unable to predict abrupt MAD fluctuations. Our proposed adaptive model could reduce MAD prediction error by up to 34%. One simple sum bit quadratic R-Q model is also presented to solve the problem caused by inaccurate texture bits estimation of H.264/AVC. With the new MAD prediction model and R-Q model, our proposed scheme could reduce the mismatch between actual frame bits and target frame bits by up to 32%, and the buffer occupancy is much closer to the ideal status. Meanwhile, reconstructed video quality is also improved by up to 0.21 dB at low bitrate

Although region-of-interest (ROI) based video coding has been well studied for some other video coding standards, its application for H.264 is still of significant interest because there exists a dilemma in the detection of the ROI due to the rate distortion optimization. In this paper, the ROI is first detected by using the direct MAD that is determined without motion information. In this way, the ROI-motion dilemma of H.264/AVC is solved. The rate control scheme with ROI concern can then adjust the quantization parameter (QP) to allocate more bits to the ROI, so the overall subjective visual quality is improved.

This paper presents a novel image segmentation algorithm that has a new dissimilarity measure which incorporates the spatial information. Our method uses a fully automatic technique to obtain the segmentation result and cluster number, and the new clustering objective function incorporates the spatial information and can compensate for the misclassification errors due to noise shifting. The capacity maximization and structure risk minimization are utilized to evaluate the quality of the clustering result via a trade-off between the number of unreliable data points and model complexity (i.e. cluster number). The weighting factor for neighborhood effect is adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous region and reduces the edge-blurring effect. The experimental results with synthetic and real images demonstrate that the proposed method is effective in determining the optimal cluster number and eliminating the noise artifact.

Cross SNR layer motion estimation/motion compensation (ME/MC) scheme is proposed in this paper for the SNR scalability of scalable video coding. Both the motion information and residual information are refined at the enhancement layers by our cross SNR layer ME/MC scheme. A good trade-off between motion information and residual information can be obtained at all bit rates. The coding efficiency is improved by up to more than 1 dB at high bit rate.

In this paper, we design a discrete-time sliding mode (DSM) nonlinear observer for a class of nonlinear uncertain systems. Taylor series expansion together with a nonlinear state transformation is used to discretize the system. A strategy to avoid switching across the sliding manifold is employed, and the sliding trajectory is confined to a boundary layer once it converges to the sliding manifold. We call this phenomenon DSM. The conditions for existence of DSM are derived. The condition for the asymptotical stability of the estimation error is analyzed.

A novel motion compensated temporal filtering (MCTF) scheme is proposed in this paper by properly using forward and backward motion compensations. The mean, the second moment and the maximum value for the temporal distance of all motion compensation pairs (MCPs) in a group of frames (GOF) are minimized such that the number of "unconnected" pixels is minimized. The overall coding efficiency is improved by up to 1.5 dB when compared to the scheme provided in [?] while the total number of motion estimation remains the same.

The paper proposes a digital chaotic secure communication by introducing a magnifying glass concept, which is used to enlarge and observe minor parameter mismatch so as to increase the sensitivity of the system. The encryption method is based on a one-time pad encryption scheme, where the random key sequence is replaced by a chaotic sequence generated via a Chua's circuit. We make use of an impulsive control strategy to synchronize two identical chaotic systems embedded in the encryptor and the decryptor, respectively. The lengths of impulsive intervals are piecewise constant and, as a result, the security of the system is further improved. Moreover, with the given parameters of the chaotic system and the impulsive control law, an estimate of the synchronization time is derived. The proposed cryptosystem is shown to be very sensitive to parameter mismatch and hence the security of the chaotic secure communication system is greatly enhanced.

Integral quadratic constraints (IQC) arise in many optimal and/or robust control problems. The IQC approach can be viewed as a generalization of the classical multiplier approach in the absolute stability theory. In this paper, we study the relationship between the two approaches for robust stability analysis. The key result shows that for many applications, the existence of an IQC is equivalent to the existence of a multiplier. Because the multiplier approach is typically simpler and more intuitive, this result suggests that the multiplier approach may be more useful than the IQC approach in many applications.

This paper is concerned with the H/sub 2/ estimation and control problems for uncertain discrete-time systems. We first present an analysis result of H/sub 2/ norm bound for a stable uncertain system in terms of linear matrix inequalities (LMIs). A solution to the robust H/sub 2/ estimation problem is then derived in term of two LMIs. As compared to the existing results, our result on robust H/sub 2/ estimation is more general. In addition, explicit search of appropriate scaling parameters is not needed as the optimization is convex in the scaling parameters. The LMI approach is also extended to solve the robust H/sub 2/ control problem which has been difficult for the tradition Riccati equation approach since no separation principle has been known for uncertain systems. The design approach is demonstrated through a simple example of target tracking.

In image deconvolution or restoration using a Kalman filter, the image and blur models are required to be known for the restoration process. Generally, the accuracy of the restoration depends on the accuracy of the given models. Unfortunately, the image and blur models are normally unknown in practice. To solve the problem, an identification stage is employed to estimate the image and blur models. However, the estimated models are seldom accurate, especially with the presence of noise in the image. This paper presents a robust Kalman filter design for image deconvolution that can accommodate the inaccuracy in the estimated image and blur models. If the inaccuracy can be modelled as additive white Gaussian noise with a known variance, it can be stochastically accounted for in the robust filter design. In the simulation tests performed, the robust design achieved improved accuracy in the image restoration, even though inaccurate image and blur models were used.

In many applications, one needs to approximate a filter of very high order with that of lower order. To reduce the order of the filter, some techniques such as the balanced model reduction approach are often applied. In this paper, we introduce a new technique which is based on minimizing the H/sub 2/-norm between the filter of very high order and the reduced one. This technique shows much better performance than other existing model reduction methods and is applied to estimating the vocal tract filter for speech processing systems. A speech processing example is presented to demonstrate the design procedure and the performance of the proposed algorithm.

A robust backpropagation training algorithm with a dead zone scheme is used for the online tuning of the neuralnetwork (NN) tracking control system. This assures the convergence of the multilayered NN in the presence of disturbance. It is proved in this paper that the selection of a smaller range of the dead zone leads to a smaller estimate error of the NN, and hence a smaller tracking error of the NN tracking controller. The proposed algorithm is applied to a three-layered network with adjustable weights and a complete convergence proof is provided. The results can also be extended to the network with more hidden layers.

This paper tackles a FMS jobshop scheduling problem with alternative routings. The objective function is to minimize the weighted flow time of jobs. We use Lagrangian relaxation method to relax the precedence constraints and propose a two-stage approach to solve the decomposed one-machine multi-operation problems. Three examples are tested and the computational results are compared to the Lagrangian relaxation method proposed by Hoitomt (1993). The comparison studies show that with the minimum weighted flow time of jobs as the objective of the scheduling problem the method proposed in this paper yields better solution with less computational time than the existing Lagrangian method.