In practical applications, input saturation is an unavoidable phenomenon that can degrade system performance and even lead to instability. This work, therefore, addresses the practical finite-time formation control problem of two-wheeled mobile robots under input saturation, a consequence of the physical limitations of their drive motors. To mitigate the adverse effects of input saturation while achieving finite-time convergence, a saturated controller is developed to realize leader-follower formation control. Furthermore, event-triggered strategies—both with and without continuous monitoring—are incorporated into the proposed controller to accommodate the limited computational resources of two-wheeled mobile robots. The Zeno phenomenon is explicitly excluded by ensuring that all inter-event intervals are bounded below by a strictly positive constant. The experimental results demonstrate the effectiveness of the developed controller in achieving finite-time formation for two-wheeled mobile robots in real-world applications.

The serverless nature of Decentralized Federated Learning (DFL) requires allocating the aggregation role to specific participants in each federated round. Current DFL architectures ensure the trustworthiness of the aggregator node upon selection. However, most of these studies overlook the possibility that the aggregating node may turn rogue and act maliciously after being nominated. To address this problem, this paper proposes a DFL structure, calledTrustChain, that scores the aggregators before selection based on their past behavior and additionally audits them after the aggregation. To do this, the statistical independence between the client updates and the aggregated model is continuously monitored using the Hilbert-Schmidt Independence Criterion (HSIC). The proposed method relies on several principles, including blockchain, anomaly detection, and concept drift analysis. The designed structure is evaluated on several federated datasets and attack scenarios with different numbers of Byzantine nodes.

Formation control of differential mobile robots (DMRs) has attracted considerable interest owing to its broad potential applications in industrial transportation, service robotics, and autonomous cooperative tasks. Accordingly, this work investigates the leader–follower finite-time formation control for DMRs under a directed communication graph. To address the kinematic underactuation problem, the heading angle is excluded from the control design, and a coordinate transformation is introduced to derive a generalized system representation for DMRs. A chattering-free fixed-time observer is developed to estimate the input to the leader, and a nonlinear distributed protocol is proposed to achieve practical finite-time formation control. Finally, two experiments are conducted under three scenarios to validate the effectiveness of the proposed protocol for real-world applications.

This article proposes a sampled-data event-triggered adaptive neural network (NN) control strategy to cope with the digital communication and attack compensation problems of networked systems with actuator attacks and exogenous disturbance. By combining event triggering state, parameter estimation signals, and disturbance observer, a novel digital state feedback controller is designed to reduce its updating frequency and compensate for the deliberate impact of unknown actuator attacks. Moreover, considering that the state is partially measurable, a novel observer-based digital controller is designed via a double-ended event-triggering mechanism (ETM). Then, two new Lyapunov functionals are created to analyze the system stability, and two design methods are given to solve the control gain. Finally, the feasibility and validity of the derived results are verified by a visual servo control system and an offshore structure system.

This study investigates the vulnerabilities of autonomous navigation and landing systems in Urban Air Mobility (UAM) vehicles. Specifically, it focuses on Trojan attacks that target deep learning models, such as Convolutional Neural Networks (CNNs). Trojan attacks work by embedding covert triggers within a model’s training data. These triggers cause specific failures under certain conditions, while the model continues to perform normally in other situations.We assessed the vulnerability of Urban Autonomous Aerial Vehicles (UAAVs) using the DroNet framework. Our experiments showed a significant drop in accuracy, from 96.4% on clean data to 73.3% on data triggered by Trojan attacks. To conduct this study, we collected a custom dataset and trained models to simulate real-world conditions. We also developed an evaluation framework designed to identify Trojan-infected models. This work demonstrates the potential security risks posed by Trojan attacks and lays the groundwork for future research on enhancing the resilience of UAM systems.

In this paper, we propose a conflict-free multi-agent flight scheduling that ensures robust separation in constrained airspace for Urban Air Mobility (UAM) operations application. First, we introduce Pairwise Conflict Avoidance (PCA) based on delayed departures, leveraging kinematic principles to maintain safe distances. Next, we expand PCA to multi-agent scenarios, formulating an optimization approach that systematically determines departure times under increasing traffic densities. Performance metrics, such as average delay, assess the effectiveness of our solution. Through numerical simulations across diverse multi-agent environments and real-world UAM use cases, our method demonstrates a significant reduction in total delay while ensuring collision-free operations. This approach provides a scalable framework for emerging urban air mobility systems.

As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, designed to adjust the detection threshold (ε) in real-time. The threshold is updated based on the recursive mean and standard deviation of displacement errors between GPS and in-vehicle sensors data, but only at instances classified as non-anomalous. Furthermore, an initial threshold, determined from 120,000 clean data samples, ensures the capability to identify even subtle and gradual GPS spoofing attempts from the beginning. To assess the performance of the proposed method, five different subsets from the real-world Honda Research Institute Driving Dataset (HDD) are selected to simulate both large and small magnitude GPS spoofing attacks. The modified algorithm effectively identifies turn-by-turn, stop, overshoot, and multiple small biased spoofing attacks, achieving detection accuracies of 98.62±1%, 99.96±0.1%, 99.88±0.1%, and 98.38±0.1%, respectively. This work provides a substantial advancement in enhancing the security and safety of AVs against GPS spoofing threats.

A novel non-singular terminal sliding mode controller (NTSMC) has been developed for the purpose of tracking and stabilizing tasks in uncertain electro-hydraulic robot manipulators. It is supposed that the controller communicates with the robot through a network that is vulnerable to cyber-attacks. To reduce the communication burden on the network layer and achieve resiliency against cyber-attacks, an event-based strategy is employed. For this purpose, the event-triggering rule is derived so that the Zeno-free behavior is guaranteed. Then, based on the cyber-attack characteristics, i.e., frequency and duration of the attacks, the resilient behavior of the proposed scheme in the presence of denial of service attacks, unmodelled dynamics, and external disturbance are analyzed. Moreover, to capture the nonlinear nature of the robot an experimentally validated analytical model of an uncertain 7-DoF manipulator with a hydraulic model of the joints and actuators, namely, Brokk-Hydrolek, is employed. Finally, the merits of the proposed methodology in terms of resiliency, robustness, and preservation of the communication resources are validated, and the results are compared to the state-of-the-art approaches based on the$\rho$index criterionNote to Practitioners—The aim of this study is to address the problem of network-based control of robotic manipulators. These systems, relying on the network layer for data collection and control commands, are highly vulnerable to catastrophic cyber-attacks. Furthermore, they should comply with network restrictions, such as limited bandwidth, to achieve the desired performance. Therefore, due to the collaborative behavior of robot manipulators in industries, it is vital for engineers and practitioners to be assured of achieving desired performance in the presence of these threats and limitations. As a first step to deal with these issues, a 7-DoF robotic manipulator model is mathematically formulated and experimentally validated. Then, a controller design procedure that guarantees the desired performance in spite of model uncertainties, denial-of-service cyber-attacks, and network restrictions is derived. Additionally, a clear relation between cyber-attack characteristics and designed parameters is defined while resilient behavior is maintained. Note that the proposed approach can be applied to a wide range of network-based nonlinear dynamic systems.

This study examines the output tracking control of discrete-time networked semi-Markov jump systems (SMJSs) under cyber-attacks in the framework of finite-time control methodology. Different from most networked systems that employ single-channel communication, this work considers the case of multi-channel communication in the controller-to-actuator networks. Aimed at better reflecting the practical situation, a type of hybrid attacks is taken into consideration, which is a mixture of denial-of-service attacks and false data injection attacks. Subsequently, the dynamic characteristics of hybrid attacks among multiple channels are modeled by two stochastic processes. The goal is to design a state feedback controller such that the resulting closed-loop system is not only finite-time boundedness with dissipative performance but also has robustness against hybrid attacks. Finally, the effectiveness of the proposed novel controller design method is verified by an illustrative example.

This paper proposes a non-isolated high-gain DC-DC converter capable of delivering a 10x voltage gain with a 50 percent duty cycle. The topology represents an enhanced version of the Zeta converter and aims to address issues such as discontinuous input current and low voltage gain of the conventional Zeta converters, making it suitable for renewable applications. Moreover, it offers a common ground between input source and load. Semiconductor stress is also kept below unity in terms of normalized voltage/current. The proposed converter is analyzed in both ideal and non-ideal modes, with a particular focus on sensitivity analysis regarding voltage gain and efficiency in the latter. Simulation results are provided to validate the theoretical relations expressed.

Intrusion detection systems are primarily designed to flag security breaches upon their occurrence. These systems operate under the assumption of single-label data, where each instance is assigned to a single category. However, when dealing with complex data, such as malware triage, the information provided by the IDS is limited. Consequently, additional analysis becomes necessary, leading to delays and incurring additional computational costs. Existing solutions to this problem typically merge these steps by considering a unified, but large, label set encompassing both intrusion and analytical labels, which adversely affects efficiency and performance. To address these challenges, this paper presents a novel framework for multi-label classification by employing an ensemble of sequential models that preserve the original label sets during training. Each model focuses on learning the distribution specifically related to its assigned set of labels, independent of the other label sets. To capture the relationship between different sets of labels, the parameters of each trained model initialize the subsequent model, ensuring that information from unrelated label sets does not interfere with the learning objective. Consequently, the proposed method enhances prediction performance without increasing computational complexity. To evaluate the effectiveness of our approach, we conduct experiments on a real-world dataset related to intrusion detection. The results clearly demonstrate the effectiveness of our proposed method in handling multi-label classification tasks.

In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions.Note to Practitioners—In petrochemical industries, several reports have been issued about the load reduction of fired-heater furnaces imposed by combustion system faults and emergency shutdowns to carry out un-planned repairs due to fouling and wax-formation in tubes. Different activities such as detecting abnormal conditions, identifying faults, and enforcing corrective action can be performed by operators through manual actions. This paper is focused on designing a new supervisory control system (SCS) to be able to recover the fired heater furnace from abnormal conditions and keep running the plant. The SCS evaluates the condition of the unit by acquiring information from main variables, sensors, actuators, operating status of components and utilities, and operator commands. By identifying the root cause of faults, SCS makes decision on recognizing hazard degree, raising alarms, and applying automatic corrective actions.

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possible variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study.

This study proposes a resilient asynchronous controller for Markovian jump neural networks, which can be impervious to the dual cyber attacks that act on actuators and sensors. The complicated occasions of uncertain system modes, actuator and sensor attacks, and unknown attack information are all considered. It is known that sensor attacks can generate corrupted signals to destroy the controller, and actuator attacks can maliciously tamper with the control signals. Mindful of such circumstances, a resilient controller is developed to defend against actuator and sensor attacks as well as to guarantee good synchronization performances. To overcome the unknowns of the occurred attacks, some new adaptive laws for adjusting attack parameters are introduced into the controller to assist with offsetting attack-induced influences. Under the designed controller, the synchronization error dynamic system is proven to be ultimately bounded within a known region, and then the obtained results are extended to address some other cases. Furthermore, a practical example of an analog resistance-capacitance network circuit and some comparative studies are demonstrated to verify the feasibility and superiority of the proposed controller.

This article studies the resilient finite-time consensus tracking problem for high-order nonholonomic chained-form systems against denial-of-service (DoS) attacks. The first step is to develop a novel secure distributed observer for each follower in which the tangent hyperbolic function is used to accelerate the convergence speed of the observer by inducing a high-gain effect. The paralyzed-connectivity graphs resulting from DoS attacks are repaired to the initially connected graphs by integrating both acknowledgment-based attack detection techniques and the communication recovery process. In addition, it is demonstrated that the duration of DoS attacks directly affects the convergence time of the proposed scheme. Then, a fast finite-time backstepping control (FFTBC) algorithm is established for each follower to track the estimated leader's information, ensuring fast convergence performance regardless of whether the follower states are near or far from the equilibrium point. An approximation-based approach is also presented for reducing the conservatism of the upper estimate of the settling time. An evaluation of the proposed control algorithm under DoS attacks is conducted using a group of wheeled mobile robots.

Trust-building is of paramount importance for managing and improving consensus in group decision-making (GDM). This mechanism usually involves a trust propagation process for estimating the level of trust among decision-makers (DMs). However, this process is computationally expensive and hinders the speed of consensus reaching. To address this issue, this work proposes a novel trust-building mechanism that does not rely on the trust propagation process to quantify DMs' level of trust. Instead, it makes use of Blockchain technology to facilitate communication between the moderator and the group of DMs. This novel trust-building mechanism does not rely on trust propagation, which makes it computationally efficient for building trust among DMs while also providing a secure and efficient communication protocol to accelerate the consensus-reaching process. The proposed GDM model is illustrated through an example, and the sensitivity of the model to various assumptions is analyzed, demonstrating the practical applicability of this approach.

Conventional classification approaches for EEG- based emotion recognition cannot often adapt to different domains, such as cross-subject or cross-dataset scenarios, leading to poor performance. To handle this challenge, we introduce a novel fusion method using a combination of multiple domain adaptation techniques to improve the emotional states in EEG datasets via classification accuracy. For this aim, Our proposed approach exploits domain adaptation approaches such as Transfer Component Analysis (TCA), Correlation Alignment (CORAL), Transfer Joint Matching (TJM), Geodesic Flow Kernel (GFK), and Joint Distribution Adaptation (JDA), to enhance the overall classification performance. Later, a new fusion approach called Multiple Domain Adaptation based on a Neuro-Fuzzy Inference System (MDA-NF) is applied to combine the classifiers using proper fuzzy membership functions and deliver maximum separation between classes. The main contribution is by applying the fusion approach using MDA- NF technique, adaptability is sufficiently enhanced. Another advantage is to employ multiple adaptation techniques that improve separation between classes. In experimental test results conducted with cross-subject and cross-dataset scenarios, the MDA-NF approach demonstrates superior performance in terms of accuracy for both the valence and arousal aspects, as observed in two public DEAP and DREAMER datasets.

To manage consensus in opinion dynamics models (ODMs), removing bias from agents' interactions and considering their willingness are critical. It can be accomplished by providing a secure mechanism that does not disclose agents' identities and opinions in their interactions, eliminating the impact of opinion similarity on trust building. To build trust and consensus opinion, we propose a linguistic ODM based on the Blockchain technology. This model allows agents' opinions to be expressed usingZ-numbers, as opposed to regular ODMs with numerical opinions. Agents are encouraged to modify their initial opinions in response to a minimum cost consensus model. Willingness of agents to accept or refuse the suggested modifications is realized through a Blockchain regime to avoid bias. The regime, however, must be supported by a trust-building mechanism to persuade agents to alter their opinions. To this end, we propose a Blockchain-enabled trust-building mechanism to improve agents' trust and guide them toward a consensus opinion. Following a sensitivity analysis of the underlying assumptions in the developed model, the proposed ODM is tested for its efficiency and validity.

Emerging data-driven technologies and big data analytics generate and deal with high-dimensional data. Transformation of such data into a low-dimensional feature space brings about numerous benefits, such as a more discriminant feature space, performance enhancement, less computational burden, and facilitating data visualization. This paper proposes a novel dimensionality reduction algorithm based on generative adversarial networks to tackle the issues related to high-dimensional data and common challenges in dimensionality reduction. To this aim, two constraints are defined to preserve the characteristics of the original data while rectifying the data distribution upon transformation. Formulating the transformation as sequential projections, the proposed Constrained Adversarial Dimensionality Reduction (CADR) method finds a set of sequential projection vectors that lead to a feature space in which between-class separability and within-class integrity are satisfied. This is while the transformed data perfectly comply with the pairwise affinity correlation in the original feature space. To evaluate the proposed method, nine advanced dimensionality reduction techniques are employed to enable a comparative study. The experiments are performed on several real-world benchmark datasets in terms of classification accuracy, F-measure, and G-mean. The obtained results show that the CADR could yield classification performance at a satisfactory level and outperforms the other competitors.

The number of discussion rounds and harmony degree of decision makers are two crucial efficiency measures to be considered in the design of the consensus-reaching process for the group decision-making problems. Adjusting the feedback parameter and importance weights of the decision makers in the recommendation mechanism has a great impact on these efficiency measures. This work aims to propose novel and efficient reinforcement learning-based adjustment mechanisms to address the tradeoff between the aforementioned measures. To employ these adjustment mechanisms, we propose to extract the dynamics of state transition from consensus models based on the distributed trust functions and$Z$-Numbers in order to convert the decision environment into a Markov decision process. Two independent reinforcement learning agents are then trained via a deep deterministic policy gradient algorithm to adjust the feedback parameter and importance weights of decision makers. The first agent is trained toward reducing the number of discussion rounds while ensuring the highest possible level of harmony degree among the decision makers. The second agent merely speeds up the consensus reaching process by adjusting the importance weights of the decision makers. Various experiments are designed to verify the applicability and scalability of the proposed feedback and weight-adjustment mechanisms in different decision environments.

This article investigates the finite-time leader–follower tracking problem for consensus control of nonholonomic MASs with chained-form dynamics under unknown time-varying disturbances. First, a finite-time distributed observer, consisting of the tangent hyperbolic function with an induced high gain effect, is constructed to estimate the leader’s information both quickly and accurately for each follower leading to communication loop avoidance. Besides, to cope with the adverse impact resulting from external disturbances, a finite-time disturbance observer is developed to provide accurate estimations of unknown terms within a finite time. A finite-time backstepping control scheme with a fast convergence rate is then proposed for each follower based on estimated disturbances to track the estimated states of the leader. Regardless of how close or how far away from the equilibrium point the follower states are, this method accelerates the convergence rate. An approximation technique using piecewise functions is also employed to bring the upper estimate of the convergence time closer to its real value. Finally, the efficiency of the presented control protocol is verified by some simulations on a number of connected wheeled mobile robots under external disturbances.

In this paper, a cooperative adaptive cruise control (CACC) system is presented with integrated lidar and vehicle-to-vehicle (V2V) communication. Firstly, an adaptive cruise control system (ACC) is designed for the Q-Car electrical vehicle, an autonomous car. Secondly, a CACC system and V2V communication are designed based on a new algorithm to improve the ACC system performance. Lastly, the CACC agent was trained by Deep Q learning (DQN) and tested. The proposed CACC system improved the stability of the vehicle. Experimental results demonstrate that the CACC system can decrease the average inter-vehicular distance of ACC by 44.74%, with an additional 40.19% when DQN was utilized. The vehicles communicate with each other through a WiFi module to transmit information with 1ms latency.

Deep brain stimulation (DBS) is an effective and promising therapy to control Parkinson’s tremor movement in patients with advanced Parkinson’s disease (PD). This paper proposes a new alternative medication that has several advantages, including compatibility with individual needs and low side effects. There has been a rapid improvement in the literature on the development of the dynamic computational model of neuroscience, alongside the development of DBS. A combination of DBS and model-based control strategies opens up a new vision for Parkinson’s disease treatment. Despite the numerous studies on basal ganglia (BG) modeling, researchers are required to employ adaptive and robust strategies to eliminate Parkinson’s patients’ tremors. This paper proposes a new adaptive optimal fast terminal sliding mode control (AOFTSMC) method to mitigate tremors by tuning GABA thorough DBS. This approach represents finite-time convergence law, a new method to stimulate the inner nuclei of BG in a robust and optimum manner that leads to removing tremors of PD fluctuation signal in the presence of uncertainties. Finally, simulation results of the basal ganglia model under the addressed approach are adopted to demonstrate the effectiveness of the proposed method.

Misbehavior detection is a critical task in vehicularad hocnetworks (VANETs). However, state-of-the-art data-driven techniques for misbehavior detection are usually conducted through complete V2X communications data collected from simulated experiments. This article evaluates main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communications data. This article proposes two novel methods for imputing and tolerating missing data. The former is a novel imputation method that is based on the collaborative clustering and the latter is a missing-tolerant method that is an ensemble learning based on the random subspace selection and Dempster–Shafer fusion. The effectiveness of the proposed techniques is evaluated by the ground-truth vehicular reference misbehavior (VeReMi) data. Moreover, a multifactor amputation framework has been developed to induce missingness over V2X communications data with different missing ratios, mechanisms, and distributions. This provides a comprehensive benchmark resembling missingness over V2X communications data. The proposed methods are compared with five missing-tolerant and nine imputation methods. The attained results over the benchmark data indicate that the proposed missing-tolerant method is significantly better than other treatment methods in terms of accuracy and F-measure.

Many efforts have been dedicated to addressing data loss in various domains. While task-specific solutions may eliminate the respective issue in certain applications, finding a generic method for missing data estimation is rather complex. In this regard, this article proposes a novel missing data imputation algorithm, which has supreme generalization ability for a vast variety of applications. Making use of both complete and incomplete parts of data, the proposed algorithm reduces the effect of missing ratio, which makes it suitable for situations with very high missing ratios. In addition, this feature enables model construction on incomplete training sets, which is rarely addressed in the literature. Moreover, the nonparametric nature of this new algorithm brings about supreme flexibility against all variations of missing values and data distribution. We incorporate the advantages of denoising autoencoders and ladder architecture into a novel formulation based on deep neural networks. To evaluate the proposed algorithm, a comparative study is performed using a number of reputable imputation techniques. In this process, real-world benchmark datasets from different domains are selected. On top of that, a real cyber-physical system is also evaluated to study the generalization ability of the proposed algorithm for distinct applications. To do so, we conduct studies based on three missing data mechanisms, namely: 1) missing completely at random; 2) missing at random; and 3) missing not at random. The attained results indicate the superiority of the proposed method in these experiments.

There has been an increasing interest in the use of data-driven techniques for classifying cyber-attacks and physical faults in cyber-physical systems. In real-world applications, the number of cyber-attack and faulty samples is usually far less than normal samples. This causes the skewed class distribution in data collected from cyber-physical systems. Training an accurate predictive model under skewed class conditions is not an easy task. In this work, we introduce a new generative adversarial framework for learning from skewed class distributions. This novel Adversarial Class-Imbalance Learning (ACIL) scheme has a novel loss function that is used during the adversarial training session. ACIL tries to iteratively adjust weights of an auxiliary multilayer perceptron to learn the minority class (i.e., cyber-attacks and physical faults) distributions along with the majority class (i.e., normal) distribution. Moreover, we devise an inclusive data-driven scheme for classifying cyber-attacks and faults, which includes four experiments of a baseline, nine state-of-the-art class-imbalance learning methods, two different generative-adversarial network-based approaches, and ACIL. These techniques are verified and compared through several experimental cyber-physical power scenarios. The obtained results show the effectiveness of ACIL for classifying samples of cyber-attacks and faults with skewed class distributions.

This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework dynamically detects and classifies false data injection attacks. Then, it retrieves the control signal using the acquired information. This process is accomplished in three main modules, with novel designs, for detection, classification, and control signal retrieval. The detection module monitors historical changes of phasor measurements and captures any deviation pattern caused by an attack on a complex plane. This approach can help to reveal characteristics of the attacks including the direction, magnitude, and ratio of the injected false data. Using this information, the signal retrieval module can easily recover the original control signal and remove the injected false data. Further information regarding the attack type can be obtained through the classifier module. The proposed ensemble learner is compatible with harsh learning conditions including the lack of labeled data, concept drift, concept evolution, recurring classes, and independence to external updates. The proposed novel classifier can dynamically learn from data and classify attacks under all these harsh learning conditions. The introduced framework is evaluated w.r.t. real-world data captured from the Central New York Power System. The obtained results indicate the efficacy and stability of the proposed framework.

In this study, first, a comprehensive model is introduced to model actuator faults, and then a novel fault-tolerant control (FTC) strategy is proposed to compensate the loss of actuator’s effectiveness in networked control systems (NCSs). A Markov chain is exploited to represent networked-induced random delays, and data packet dropouts as well as disorders to address the stochastic characteristic of the network issues. Accordingly, the resulting closed-loop system lies in the framework of Markovian jump systems (MJSs). Moreover, partly unknown transition probabilities are considered in the current study since the identification of the exact value of transition probabilities of the Markov chain is difficult or even impractical due to the complex structure of the network. Sufficient conditions for the stochastic stability are derived by means of the solutions of a finite set of linear matrix inequalities (LMIs) to design a novel robust FTC through the output feedback technique, which requires only the outputs. A numerical example and an engineering benchmark system are presented to verify the capability of the proposed method in practical applications.

In this study, a constrained nonlinear model predictive control (NMPC) is presented for the purpose of voltage and current stabilization of a DC microgrid that supplies constant power loads. The proposed technique can handle all constraints of the stand-alone DC microgrid in different configurations. Furthermore, in order to reduce the complexity of the NMPC, a novel approach, which is called fast NMPC is proposed, and it is shown that it has superior performance than NMPC.

The performance of intrusion classification systems is often hampered by the presence of missing values in data collected from cyber-physical systems. Therefore, it is of paramount importance to robustly handle such missing scores, which in turn enhances the efficiency of intrusion classification task, and, consequently, the cybersecurity of cyber-physical systems. To this aim, this paper studies the efficacy of missing data imputation techniques for safeguarding intrusion classification systems against missing scores. To do this, a hybrid intrusion classification system is designed that comprises several advanced imputation techniques. To evaluate this intrusion classification framework, various incomplete scenarios have been simulated from data collected from a cyber-physical water system. In total, forty-four incomplete scenarios are considered throughout the experiments. The evaluation is conducted based on the classification accuracy and F-measure, as well as the root mean square error of the imputed data. The experimental results indicate the efficiency of the proposed intrusion classification system and find the best match missing data imputation technique for the sake of intrusion classification.

This paper deals with the problem of robust H∞fault-tolerant controller design for fuzzy networked control systems using static state feedback. The stability of networked control systems is affected by delay, sensor failure and data drift. Therefore, a Lyapunov–Krasovskii functional are exploited to establish asymptotic stability conditions for the underlying system considering these imperfections. It is assumed that sensor failure and data drift, which occur during data transmission over the network, are modeled by a stochastic variable with Bernoulli distribution. The design conditions are presented in terms of linear matrix inequalities, and the efficiency of the proposed approach is shown through a numerical example.

Condition Monitoring (CM) has become an essential tool in complex engineering systems like wind turbines. They can prevent unexpected failures and contribute to a more reliable system. Information attained from monitoring can be employed for maintenance scheduling, hence, minimizing maintenance costs. Remaining Useful Life (RUL) is a critical aspect of CM. This paper introduces a new RUL prediction method for wind turbine blades using a novel fuzzy-based failure dynamic modeling via a Supervisory Control and Data Acquisition (SCADA) system. For this goal, a recursive Principal Component Analysis (PCA) is employed to compress the SCADA data and extract real-time Principal Components (PCs). Next, a wavelet-based Probability Density Function (PDF) is applied to obtain the probability of staying healthy from the extracted PCs. It is anticipated that blade degradation will lead to a subsequent decline in the PDF curve. A failure trajectory is then captured by transforming the PDF into the PC’s surface. Subsequently, the T–S fuzzy system is utilized to form the mathematical model of degradation from this failure trajectory. Next, a Bayesian algorithm is adaptively administered to predict the RUL. Experimental test results on Canadian wind farms explain a high performance of the proposed failure prognosis method in comparison with a Bayesian algorithm.

The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

Correct classification of rare samples is a vital data mining task and of paramount importance in many research domains. This article mainly focuses on the development of the novel class-imbalance learning techniques, which make use of oversampling methods integrated with bagging and boosting ensembles. Two novel oversampling strategies based on the single and the multiple imputation methods are proposed. The proposed techniques aim to create useful synthetic minority class samples, similar to the original minority class samples, by estimation of missing values that are already induced in the minority class samples. The re-balanced datasets are then used to train base-learners of the ensemble algorithms. In addition, the proposed techniques are compared with the commonly used class imbalance learning methods in terms of three performance metrics including AUC, F-measure, and G-mean over several synthetic binary class datasets. The empirical results show that the proposed multiple imputation-based oversampling combined with bagging significantly outperforms other competitors.

Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.

This paper considers the design of an integrated observer structure termed as Proportional Integral Fading Unknown Input Observer (PIFUIO). The advantages of PIO and UIO observers are used in robust fault detection. The UIO decouples the unknown input disturbance while PIO allows to estimates the faults. It is shown that the fading term of this observer plays a distinct role in reliable estimation of faults decoupled from the unknown disturbance or vice versa. The robust detection of sensor fault is also considered with the presence of unknown inputs. Indirect and direct design procedures for sensor fault detection are provided. Numerical examples are included to illustrate the advantage of PIFUIO.

Security maintenance of Supervisory Control and Data Acquisition (SCADA) systems has been a point of interest during recent years. Numerous research works have been dedicated to the design of intrusion detection systems for securing SCADA communications. Nevertheless, these data-driven techniques are usually dependant on the quality of the monitored data. In this work, we propose a novel feature selection approach, called MSFS, to tackle undesirable quality of data caused by feature redundancy. In contrast to most feature selection techniques, the proposed method models each class in a different subspace, where it is optimally discriminated. This has been accomplished by resorting to ensemble learning, which enables the usage of multiple feature sets in the same feature space. The proposed method is then utilized to perform intrusion detection in smaller subspaces, which brings about efficiency and accuracy. Moreover, a comparative study is performed on a number of advanced feature selection algorithms. Furthermore, a dataset obtained from the SCADA system of a gas pipeline is employed to enable a realistic simulation. The results indicate the proposed approach extensively improves the detection performance in terms of classification accuracy and standard deviation.

Data-driven diagnostic frameworks for large-scale power grid networks usually deal with a large number of features collected by means of sparse measuring devices. As a pre-processing task, dimensionality reduction methods can improve the efficiency of data-driven diagnostic methods by extracting sets of informative and relevant features from the raw data through appropriate transformations. This work is devoted to studying the applicability of various well-known dimensionality reduction techniques in combination with four classification models in diagnosing open circuit faults in smart grids. By providing a comparative study, this work aims at finding the best combination of dimensionality reduction techniques and classification models for diagnosing faults under normal, high signal-to-noise-ratio, low sampling rate, and high fault-resistance conditions. Various fault scenarios have been simulated on the IEEE 39-bus system and a rigorous analysis of the attained results is fulfilled so as to determine the best combinations under different conditions.

This paper is devoted to studying the effect of false data injection attacks on the state estimation of discrete linear time-invariant systems in the presence of unknown disturbance. The proposed scheme firstly decouples the disturbance signal from the estimation error by exploiting the concepts of unknown input observers. Then, the observer gain has been designed based on the Kalman filter algorithm while a saturation term has been assigned to the output error in the update rule of the estimated states. Thanks to the saturation-limit dynamics introduced into the error dynamics of the Kalman filter-based estimation, the proposed method is applicable for the real-time applications. The effectiveness of the proposed scheme has been validated through a numerical example by taking two different scenarios into considerations. First, the comparative results show the superiority of the proposed scheme in state estimation under the presence of high-frequency measurement noise. Next, further to the high-frequency measurement noise, it is assumed that the sensed measurements are also manipulated by an adversary, leading to outliers in the measurements. As for this scenario, the attained results show how successfully the proposed scheme can mitigate the effect of the outliers in the presence of unknown disturbances.

In this study, an efficient strategy for fault detection and isolation (FDI) of an Industrial Gas Turbine is introduced based on ensemble learning methods. Four independent Wiener models are identified by employing plant input/output data to determine system behavior. Following that, an ensemble-based method is established, which utilizes all the Wiener models and relevant residuals to detect the faults. A fault isolation structure is then developed based on ensemble bagged tree procedure such that it is capable of isolating faults in a steady-state runtime. As a crucial goal, increasing accuracy and robustness simultaneously are mainly centered. The proposed FDI method is tested on nonlinear gas turbine simulation using real data from a combined cycle power plant. The obtained results illustrate the correctness and accuracy of the presented FDI scheme.

Missing data imputation is a critical part of data cleaning tasks and vital for learning from incomplete data. This paper proposes a novel cooperative clustering imputation (CCI) method to estimate missing values. The proposed method aims to find a better clustering model and donor for imputation, comparing with individual clustering algorithms. It makes use of agreements among different clustering algorithms to generate a set of sub-clusters, and, then, merges these sub-clusters based on the matrix of the performance measures of sub-clusters. The proposed method is evaluated using ten public datasets from UCI data repository and V2X communication data with induced missing samples, and compared with three standard clustering based imputation methods, k-means imputation, fuzzy c-means imputation, and partition around medoids imputation. Missing values are induced through each dataset by different missing mechanisms, missing rates, and missing distribution, and, thus, various incomplete datasets are generated. The performance of these methods are checked using normalized root mean square error (NRMSE). The attained experimental results indicate the effectiveness of the proposed missing values imputation method.

This paper addresses the problem of sensor selection in a sensor network for tracking a moving target. By considering network uncertainties and unpredictable movements of the target, reliable sensor selection approaches such as sigma points probability and target trajectory are proposed. An updated unscented Kalman filter is proposed to achieve effective tracking of the target through the sensor selection. A multialgorithm genetically adaptive multiobjective is utilized to have a selection strategy without knowing the number of sensors to be selected. Extensive experiments are conducted to evaluate the effectiveness of the proposed approach both in simulation and practical experimentation. The proposed algorithm is also tested in the industrial setting where providing safety is of great importance for a human worker who walks in a potentially dangerous workplace. The results confirm the effectiveness and utility of the proposed scheme.

This article proposes a clustering-based hierarchical framework that includes a consensus decision support system for locating faults in smart grids. Frequency measurements are initially collected by distributed frequency disturbance recorders, and then, decomposed in the time-frequency domain. Extracted time-frequency variational modes are further analyzed through statistical analysis. The resulted features are then used by the affinity propagation (AP) clustering technique to partition the power grid. The faulty partition is determined by evaluating a heuristic index, and, is then fed to a zNumber-based multicriteria group decision support system to decide on the fault location. The effect of various preferences on AP clustering has been handled by resorting to an aggregation scheme, which considers multiple criteria into account. The feasibility and effectiveness of the proposed framework have been validated through a comprehensive study on the IEEE 39-bus system.

There has been a growing interest in using smart grids due to their capability in delivering automated and distributed energy level to the consumption units. However, in order to guarantee the safe and reliable delivery of the high-quality power from the generation units to the consumers, smart grids need to be equipped with diagnostic systems. This paper presents an efficient data-driven scheme for diagnosing faults in smart grids. In order to reduce the computational burden and monitor the state of the system with a lower number of smart meters, a method based on the affinity propagation clustering algorithm is suggested for the placement of meters, that makes use of the graph-based representation of the system. The collected voltage data measurements from the installed meters are then decomposed by matching pursuit decomposition in order to generate informative features. Extracted features are then used to train a convolutional neural network, and the constructed deep learning model is then tested using unseen samples of normal and faulty conditions. Simulation results based on the IEEE 39-Bus System demonstrate the effectiveness of the proposed data-driven fault diagnostic system.

Human-robot collaboration in industrial settings calls for implementing safety measures to ensure there is no risk to humans working in such an environment. In human-robot physical collaboration, an object or a load is handled by both human and the robot. Developing a safety framework for the robot is a requirement for preventing collisions during performing a task. In this paper, force myography (FMG) data are used to develop a control scheme for the robot such that it can work with the human worker while avoiding collisions. Force myography quantifies the activities of human muscles when applying forces to handle an object. A neural network-based approach is then used to select the most informative features of the FMG signal. The developed control scheme incorporates the FMG data and the robot dynamics to obtain a prediction about the next step of the cooperation task and to plan the robot motion accordingly. The proposed approach is evaluated experimentally in real time in a moving objects task which requires appropriate complementary actions from the robot and the human user. The results of this study show that the proposed scheme can successfully plan the robot motion based on the actions of the human user.

By applying robots while collaborating with a human in an industrial setting to provide more flexible and productive industries, safe interaction and collision detection have become an indispensable element of the collaborative robots. In such a dynamic environment, safe collaboration scenarios are needed to be designed using reliable methods to monitor collision-related signals and avoid a dangerous collision. Since human's hand is the most exposed limb to collision during cooperation with a robot, new flexible methods should be conducted to use in industries by considering hand safety. In this study, collision monitoring is developed using force myography of a worker forearm and robot dynamic parameters. A method based on deep neural network is proposed to distinguish any occurrence of a collision between a worker's hand and robot's arm during the collaboration. The proposed approach can be applied to provide a reliable interaction with no unnecessary robot stop during working by classifying unintended collision. Various experiments have been conducted to evaluate the proposed method. The results show that the proposed scheme can successfully detect a collision and classify human intention to provide safe and reliable cooperation with a robot in an industrial environment.

Smart grids aim to move the energy industry into a new era of availability, quality, reliability, and efficiency of power at generation, transmission and distribution levels. Transmission lines, like the arteries of smart grids, play an important role in delivering high-quality power from the generation units to the consumers. However, because of their vast geographical spread, they are always exposed to different threats. This paper proposes a computational intelligence method for increasing the protection of the transmission lines in smart grids. Three-phase current measurements of only one side of the faulty transmission line have been collected and passed through a signal processing module to extract novel informative features from the transient current signals generated due to the fault occurrence. Obtained features are then fed to the fault location algorithms to construct predictive models including adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and artificial neural networks (ANN) to estimate the exact location of the fault. Multiple scenarios have been simulated on the IEEE 14-bus system, and the attained results validate the superiority of the ANFIS over the other methods.

This paper introduces a cyber-secure strategy for radar tracking systems. Two common cyber attacks including denial-of-service (DoS) and false data injection (deception) attacks are investigated. The proposed secure control strategy consists of two subsystems: 1) an attack detection and isolation (ADI) subsystem, and 2) a resilient observer (RO) subsystem. The ADI subsystem is used to observe the state of the system using a bank of Kalman Filters and multi-sensor measurements. Then, residuals generated by local Kalman filters are used to isolate the cyber attacks. Afterward, ordered weighted averaging (OWA) operator is utilized to drive a resilient observer to estimate the real correct value of variables such as position under cyber attacks. Weighting factors of the OWA operator are derived using the covariance matrix, and proof of convergence is provided. Simulation studies on a radar tracking system show that the proposed secure control strategy using multi-sensor fusion enhances the performance of the system and results in a more resilient control system against cyber attacks.

Design of the data-driven diagnostic systems usually requires to have labeled data during the training session. This paper aims to design a hybrid data-driven framework for diagnosing faults, where the data labels are not available to a large extent. This hybrid framework has five steps for transforming raw vibration signals to informative sets of samples for decision making. It uses several state-of-the-art approaches for feature extraction and semi-supervised feature reduction. The decision-making step uses a number of state-of-the-art semi-supervised learners. This step also comprises a novel surface estimation approach that is developed for SSL. The proposed hybrid framework is applied for diagnosing bearing defects in induction motors and validated based on four scenarios, each of which is experimented with different amounts of labeled samples. The attained diagnostic accuracies show the efficiency of the proposed hybrid framework, including the novel semi-supervised learner in classifying bearing defects, regardless of the number of labeled samples.

Fault diagnosis and prognosis (FDP) are important capabilities that can enable autonomous detection and prediction of failures' progress in complex engineering systems. This paper introduces an innovative modular FDP method for a hydro-control valve system. The hydro-control valve is a critical part of the space launch vehicle propulsion system, and health monitoring of this hydro-valve is essential to ensure safety and reliability of the spacecraft. In this study, three main failures, i.e., piston leakage, drain blockage, and filter malfunction, in the hydro-control valve system are considered for monitoring and prognosis. The proposed FDP system has three main components including fault detection and diagnosis (FDD) unit, failure parameter estimation unit, and remaining useful life (RUL) estimation unit. A feature selection strategy and a support vector machine technique are together utilized to capture redundancy in multisensor data information and to isolate failures in the FDD unit. Then, a decentralized network of three adaptive neuro-fuzzy inference systems (ANFIS) is developed to estimate the failure parameters. Afterward, the RUL unit is constructed using an adaptive Bayesian algorithm. Finally, a performance measure, called the relative accuracy index, is introduced and applied to evaluate the performance of the proposed health monitoring system. Simulation studies confirm the effective performance of the proposed design methodology.

The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well-logging estimation problem by using a fusion methodology. The natural gamma-ray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (Th), and potassium (K). The main goal of this paper is to have precise estimation of the concentrations of U, Th, and K. Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies.

Any action dataset may contain similar classes such as running, walking and jogging. Therefore, equivalent probabilities may be provided for different classes upon action classification. In this case, the classifier cannot indubitably assign a class to a given sample. To address this problem, we propose a new hybrid classifier to automatically compress the features and classify them using SVM with polynomial or sigmoid kernels. Furthermore, we hypothesize that motion saliency detection can strength the power of motion feature extraction in the bag of visual words framework (BoVW). To this end, we evaluate the effect of 3D-discrete wavelet transform (3D-DWT), as the preprocessing step, on motion feature extraction. The experimental results show that the proposed framework achieves promising results on KTH, Weizmann, and URADL datasets, and outperforms recent state-of-the-art approaches.

Machine learning techniques are widely used for diagnosing faults to guarantee the safe and reliable operation of the systems. Among various techniques, semi-supervised learning can help in diagnosing faulty states and decision making in partially labeled data, where only a few number of labeled observations along with a large number of unlabeled observations are collected from the process. Thus, it is crucial to conduct a critical study on the use of semi-supervised techniques for both dimensionality reduction and fault classification. In this work, three state-of-the- art semi-supervised dimensionality reduction techniques are used to produce informative features for semi-supervised fault classifiers. This study aims to achieve the best pair of the semisupervised dimensionality reduction and classification techniques that can be integrated into the diagnostic scheme for decision making under partially labeled sets of observations.

This paper presents a new approach for broken rotor bars detection in induction motors. When broken rotor bar occurs, the rotor resistance will increase. Furthermore, thermal effects of rotor resistance are considered to address practical aspects. Therefore, a suitable method to detect broken rotor bar is rotor resistance estimation. An applicable method in state estimation is Cubature Kalman Filter (CKF). To display the advantages of the CKF, simulation results are compared with Unscented Kalman Filter (UKF). In addition to not needing setting parameters, this filter is more accurate in rotor resistance estimation compared to the UKF.

This work deals with the problem of filter design for disturbance decoupling in discrete-time linear fractional order systems (FOS) under noisy environments. To this end, Fractional Unknown Input Filter (FUIF) is developed based on Fractional Kalman Filter (FKF) framework. Accordingly, the proposed structure can result in robustness against unknown inputs (UIs) in noisy environments. This algorithm can be used for robust fault detection since the disturbance is decoupled from state estimation error. The designed filter is applied to a fractional order (FO) model of an ultra-capacitor (UC), under noisy condition, for the state estimation and fault detection purposes. Simulation results illustrate the benefits of the proposed approach.

This paper proposes an adaptive incremental ensemble of extreme learning machines for fault diagnosis. The diagnostic system contains a data processing unit which aims to progressively generate discriminant features from the vibration signals for decision making. The decision making unit receives a few sets of labeled discriminant features in a chunk by chunk manner, incrementally learns the features-faults relations, dynamically diagnoses multiple bearing defects, and adaptively adjusts itself to learn new concept classes. This adaptive ensemble system is based on incremental learning of multiple extreme learning machines that are able to consult together and adjust themselves based on their confidence in the decision making. Extreme learning machines are used to construct the hybrid ensemble due to their good controllability and fast learning rate. Experimental results show the efficiency of the hybrid diagnostic system. The proposed diagnostic system is applied to diagnosing bearing defects in an induction motor.

This paper presents a fine-coarse time interval measurement scheme which is resilient to the variations of process, voltage, and temperature (PVT). Two Delay Locked Loops (DLLs) have been utilized to minimize the effects of PVT on the measured time intervals. A two-step time-to-digital converter is designed to ensure a high-resolution measurement over a wide dynamic range. The proposed scheme has been implemented using CMOS 65nm technology. Simulation results using ADS tools indicate that the measurement resolution varies by less than 0.12ps with ±15% variations of power supply voltage. The proposed method also presents a robust performance against process and temperature variations. The measurement resolution changes by few femto-seconds from slow to fast corners for process variations and it varies by a maximum of 0.1ps with changes from −40 °C to +100 °C in temperature.

This paper investigates the problem of network-based fault-tolerant controller design for networked control systems (NCSs) in the presence of random delays and data packet dropouts. A novel actuator fault model which is more general and practical than the conventional actuator fault models is developed. Considering this new fault model, the NCSs are firstly modeled as a Markovian jump system (MJS) with partly unknown transition probabilities (TPs), upon which sufficient conditions based on linear matrix inequalities (LMIs) are then developed to design the output feedback fault-tolerant controller to ensure the stochastic stability of the NCS. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method compared to the existing approaches in the literatures.

Efficient diagnosis of bearing defects in induction motors usually requires extracting informative features from the vibration signal and efficiently reducing the dimensionality of the features. In this paper, the vibration signal is primarily analyzed by the empirical mode decomposition technique to extract informative intrinsic mode functions as a set of features. The dimensionality of the extracted feature set is reduced by means of maximally collapsing metric learning (MCML) to create an informative set of small-sized features for fault classification. MCML is an efficient supervised dimensionality reduction technique which aims to collapse patterns of the similar class to a point in the feature space while separates patterns of other classes to the maximum extent possible. To compare the performance of MCML, other state-of-the-art unsupervised and supervised techniques are used for the dimension reduction of the features. The fault diagnosis unit includes various classifiers which aim to diagnose multiple bearing defects that are ball, inner race and outer race defects of different diameters.

Feature ranking from video-wide temporal evolution brings reliable information for complex action recognition. However, a video may contain similar features in the sequence of frames which deliver unnecessary information to the ranking function. This paper proposes a method to improve the rank-pooling strategy which captures the optimized latent structure of the video sequence data. The optimization is followed by removing the redundant features from the sequence data. The cosine and correlation distance metrics are employed to detect the identical features and extract the most efficient information from the video frames. Then, the ranked features are generated from the optimized and clean sequence data. The proposed improvement is easy to implement, fast to compute and effective in recognizing complex actions. As a result, the proposed approach reaches remarkable action recognition performance on benchmark datasets, namely Hollywood2, URADL, and HMDB51. The results are further compared with state-of-the-art techniques in the experiment section to confirm the effectiveness of the improved rank pooling framework.

This paper focuses on the development of an integrated scheme for diagnosing bearing defects in induction motors, under the class-imbalanced condition. This scheme comprises of four main modules: segmentation, feature extraction, feature reduction, and fault classification. Various state-of-the-art techniques have been devised in the feature extraction and reduction modules to extract informative sets of features from a raw vibration signal, filter redundant features, and produce the most distinct features for the following module. The fault classification module adapts various state-of-the-art class-imbalanced learning techniques for diagnosing bearing defects. This module contains a novel imputation-based oversampling technique for class-imbalanced learning. This integrated diagnostic scheme is evaluated on three experimental scenarios with different imbalance ratios. The reasonable diagnostic performances confirm the ability of the proposed novel class-imbalanced learning technique in diagnosing bearing defects, independently from the imbalance ratios.

This paper presents a novel supervised clustering technique including different clustering algorithms which cooperate together to span the decision space in a supervised manner. It uses a variety of clustering methods for an efficient partitioning. An evolutionary algorithm is used to tune the key parameters of the cooperative scheme which minimizes an error-based objective function on the training dataset. The proposed supervised scheme is developed for diagnosing faults in the Tennessee Eastman process, which is a standard benchmark for fault detection and diagnosis. Experimental results show that the proposed technique can efficiently diagnose the process faults.

Imputation of missing data is of paramount importance in machine learning and data mining tasks with incomplete data. In this paper, a fuzzy-neighborhood density-based clustering technique is developed for imputation of missing data. The proposed technique makes use of the density measure, in order to group the similar patterns and find the best donors for each incomplete target pattern to impute its missing values. The fuzzy neighborhood membership degrees are adjusted using an invasive weed optimization algorithm. The performance of the proposed imputation technique is evaluated using eight synthetic publicly available datasets with induced missing values and compared with the performance of other existing competitors, k-means imputation, fuzzy c-means imputation and fuzzy c-means with genetic algorithm imputation. Various types of missingness have been induced to each dataset. The attained results show the effectiveness of the proposed missing data imputation technique.

This paper presents a Genetic Algorithm (GA) based evolution framework in which Spiking Neural Network (SNN) of single or a colony of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed of randomly connected Izhikevich spiking reservoir neural networks. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulation results prove that the evolutionary algorithm has the capability to find or synthesis artificial creatures which can survive in the environment successfully and also simulations verify that colony approach has a better performance in comparison with a single complex creature.

This paper presents a model to extract and select a proper set of features for diagnosing bearing defects in induction motors. An efficient pre-processing of the vibration signals is of paramount importance to provide informative features for the fault classification module. The vibration signals are firstly analyzed by the wavelet packet transform to extract informative frequency domain features. The dimension of the set of extracted features is reduced by resorting to linear discriminant analysis to provide a small-size set of informative features for decision making. The fault classification module contains different classifiers that can learn the features-faults relations and classify multiple bearing defects including ball, inner race and outer race defects of different diameters. Experimental results verify the effectiveness of the proposed technique for diagnosing multiple bearing defects in induction motors.

Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapse-astrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows that the designed astrocyte circuit is able to imitate its biological model and regulate the synapse transmission, successfully. In addition, synthesis results confirms that the proposed design uses less than 1% of available resources of a VIRTEX II FPGA which saves up to 4.4% of FPGA resources in comparison to other designs.

In this paper we propose a hardware implementation of a new deep brain stimulator that is able to desynchronize an abnormally synchronized neural population model. The proposed stimulator is based on a delay feedback technique and is applied on a population of neurons described with Izhikevich model. The whole structure of the stimulator and the neural population model are first simulated in software and then implemented on hardware FPGA platform. The results of software/hardware simulation/implementation show that the proposed stimulator has a good performance and is able to desynchronize a hyper-synchronized neuronal population model.

It is expensive to simulate large-scale neural networks on hardware while ensuring a high resemblance to the original neurons' behavior. This paper introduces a novel technique to facilitate digital implementation and computer simulation of neuron models that contain an exponential term. This technique is applied to a biologically realistic neuron model called Adaptive Exponential integrated and fire (AdEx). Hardware synthesis and physical implementations show that the resulting model can reproduce precise neural behavior with high performance and considerably lower implementation costs compared with the original AdEx model.

This paper focuses on the use of different multi-step prediction techniques for long-term prognosis of the Lithium-ion batteries condition. Various inductive algorithms including adaptive neuro-fuzzy inference systems, random forests, and group method of data handling are used along with three strategies for multi-step prediction and prognosis. These prediction strategies including iterative, direct, and DirRec schemes make use of the historical and current data in different manners to forecast the future values of the capacity over a long horizon for estimation of the remaining useful life (RUL) of the Li-ion batteries. These multi-step predictors are trained by means of constant current Li-ion battery datasets. The attained results present the effectiveness of these techniques for the long-term prognosis of the RUL of the batteries. Besides, a statistical analysis of the attained results indicates that the RF predictor outperforms other techniques.

In this paper, a supervised fuzzy-neighborhood density-based clustering approach is proposed for the fault diagnosis of induction motors' bearings. The proposed approach makes use of the labeled data regarding the actual classes of faulty and fault-free cases, in order to train the fuzzy-neighborhood density-based clustering algorithm in a supervised manner, by resorting to an invasive weed optimization algorithm that aims to minimize an error-based objective function. The proposed classifier can properly classify multi-class data with complex and variously shaped decision boundaries among the different classes of faults and the fault-free state, and is robust against noise. This is due mainly to the fact that the classifier is constructed using the fuzzy-neighborhood density based clustering method, which is not sensitive to the geometrical shape of clusters in the feature space.

Handling the missing data is considered as a crucial requirement for the performance of diagnostic systems. In the proposed diagnostic system, the preprocessing module receives sets of residuals generated by a combined set of observers, and feeds the proceeded residuals to a fault classification module. It is necessary for the fault classification module to receive complete feature sets. Multiple missing data imputation techniques have been devised in the preprocessing module to guarantee feeding complete sets of features to the fault classification module. The proposed diagnostic scheme is validated using incomplete batch of residuals for sensor fault diagnosis in a doubly fed induction generator (DFIG) of a wind turbine.

Location positioning in indoor environments is a major challenge. Various algorithms have been developed over years to address the problem of indoor positioning. One of the most cost effective choice for indoor positioning is based on received signal strength indicator (RSSI) using existing Wi-Fi networks in commercial and/or public areas. This solution is infrastructure-free and offers meter-range accuracy. In this paper, machine learning approaches including k-nearest neighbor (k-NN), a rules-based classifier (JRip), and random forest have been investigated to estimate the indoor location of a user or an object using RSSI based fingerprinting method. Experimental measurements were carried out using 1500 reference points with received RSSIs of 86 installed APs in the second floor of Centre for Engineering Innovation (CEI) building at the University of Windsor. The results indicate that the random forest classifier presents the best performance as compared to k-NN and JRip classifiers with positioning accuracy higher than 91%.

One of the crucial requirements for the practical implementation of empirical diagnostic systems is the capability of handling missing data. This is done by resorting to missing data imputation techniques in a pre-processing module. The pre-processing module is a part of a previously developed diagnostic system which receives batches of residuals generated by a combined set of observers and progressively feeds the processed residuals to a fault classification module that incrementally learns the residuals-faults relations and dynamically classifies the faults including multiple new classes. The proposed method is tested with respect to sensor fault diagnosis of the incomplete scenarios in a doubly fed induction generator (DFIG) of a wind turbine.

In this paper an adaptive super twisting sliding mode cascaded control strategy to control superheat temperature of an evaporator of Heating Ventilation Air Conditioning Systems(HVAC) is presented. Two internal loop and external loop of the cascaded controller are designed using sliding mode by utilizing feedback linearization method. By controlling superheat temperature, Tsh, in the external loop, and evaporating temperature of refrigerant, Te, in the internal loop, a better performance with robustness against parameter uncertainty is achieved. The value of superheat temperature is determined by using the estimated value of length of two phase flow of the refrigerant inside the evaporator. The performance of the proposed control strategy against disturbance and parameter uncertainties is illustrated through simulation in MATLAB/Simulink environment. It is shown that in comparison with super-twisting method, the proposed adaptive super twisting method improves the performance of system by reduction of undesirable chattering in the response of system.

Twisting, second order sliding mode control is presented to accommodate the thruster faults in the nonlinear MIMO picosatellite formation flying system with unknown time-varying disturbances. Robustness and stability of the proposed scheme is proved using twisting algorithm and its capability for formation keeping demonstrated against periodic thruster faults and external disturbances through simulations.

In this paper, our recent work on robust fault diagnosis (FD) for satellite control systems using sliding mode and learning approaches are summarized. Firstly, a variety of nonlinear mathematical models for satellites are described and analyzed for the purpose of fault diagnosis. Then, fault diagnostic sliding mode observer with time-varying gains is presented and analyzed. Two classes of learning estimators are integrated with the sliding mode observer to construct robust fault diagnosis schemes, which are investigated as well. Finally, conclusions and future work on the health monitoring and fault diagnosis for satellite systems are proposed.

This paper considers observer based actuator fault isolation schemes for a class of uncertain nonlinear systems. To deal with a broader class of uncertain non-linearities, we propose novel diagnostic observers, which combine Thau's observer with sliding mode observers and are primarily designed for actuator fault diagnostic purposes. The uncertain nonlinearities that can be attacked may include both Lipschitz uncertain nonlinearities and those uncertain nonlinearities that are not Lipschitz but satisfy certain matching conditions. The design of observer boils down to the solving of LMIs, which can easily be done using the Matlab LMI toolbox. Using the proposed observers, two actuator fault isolation schemes are designed. Unlike the existing techniques, using only m observers in the first approach and only one observer in the second approach, our proposed schemes can isolate any number of actuator faults occurring at the same time. In addition, both proposed schemes are capable of estimating the faults.

This paper deals with the design of a Luenberger-like observers in a class of nonlinear differential-algebraic systems (DAS) described by the so-called semi-explicit forms with the differential variables being coupled with algebraic variables. The key point of designing the observer for the DAS is the reconstruction of the algebraic variables because its distribution matrix is singular. The reconstruction consists of a serial elementary matrices followed by differentiation such that the algebraic variables can be directly estimated in the observer. The stability of the proposed observer is proved and an illustrative example and simulations are given to describe the design of the observer.

In this paper, a robust fault detection and diagnosis scheme using neural state space models has been developed for a class of nonlinear systems. The neural state space models are adopted to estimate the modeling uncertainties in the states and outputs of the system. Subsequently, a residual is generated to identify the characteristics of the fault. Moreover, the robustness, sensitivity and stability properties of the proposed fault detection and diagnosis scheme are rigorously derived. Finally, the neural state space model based fault detection and diagnosis scheme is applied to a satellite attitude control system and the simulation results demonstrated its good performance.

A new fuzzy system containing a dynamic rule base is proposed in this paper. The novelty of the proposed system is in the dynamic nature of its rule base which has a fixed number of rules and allows the fuzzy sets to dynamically change or move with the inputs. The number of the rules in the proposed system can be small, and chosen by the designer. The focus of this article is mainly on the approximation capability of this fuzzy system. The proposed system is capable of approximating any continuous function on an arbitrarily large compact domain. Moreover, it can even approximate any uniformly continuous function on infinite domains. This paper addresses existence conditions, and as well provides constructive sufficient conditions so that the new fuzzy system can approximate any continuous function with bounded partial derivatives. Finally, an example is given to show how the proposed fuzzy system can be effectively used for system modeling and control