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Showing papers in "IEEE/CAA Journal of Automatica Sinica in 2020"


Journal ArticleDOI
TL;DR: A survey of trends and techniques in networked control systems from the perspective of ‘ control over networks ’ is presented, providing a snapshot of five control issues: sampled-data control, quantization control, networking control, event-triggered control, and security control.
Abstract: Networked control systems are spatially distributed systems in which the communication between sensors, actuators, and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices, increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems, process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The ‘ control over networks ’ is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of ‘ control over networks ’ , providing a snapshot of five control issues: sampled-data control, quantization control, networked control, event-triggered control, and security control. Some challenging issues are suggested to direct the future research.

423 citations


Journal ArticleDOI
TL;DR: Insight is provided into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1) high definition maps, big data, and high performance computing; 2) augmented reality / virtual reality enhanced simulation platform; and 3) 5G communication for connected AVs.
Abstract: The advancement of artificial intelligence ( AI ) has truly stimulated the development and deployment of autonomous vehicles ( AVs ) in the transportation industry. Fueled by big data from various sensing devices and advanced computing resources, AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion. To achieve goal of full automation ( i.e., self-driving ( , it is important to know how AI works in AV systems. Existing research have made great efforts in investigating different aspects of applying AI in AV development. However, few studies have offered the research community a thorough examination of current practices in implementing AI in AVs. Thus, this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue. Specifically, it intends to analyze their use of AIs in supporting the primary applications in AVs: 1) perception; 2) localization and mapping; and 3) decision making. It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation. Based on the exploration of current practices and technology advances, this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1) high definition maps, big data, and high performance computing; 2) augmented reality( AR ) / virtual reality ( VR ) enhanced simulation platform; and 3) 5G communication for connected AVs. This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

223 citations


Journal ArticleDOI
TL;DR: The objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
Abstract: Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things ( IIOT ) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

153 citations


Journal ArticleDOI
TL;DR: This paper proposes enhancements to Beetle Antennae search algorithm to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function by adaptively adjusting the step-size in each iteration using the adaptive moment estimation ( ADAM ) update rule.
Abstract: In this paper, we propose enhancements to Beetle Antennae search ( BAS ) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation ( ADAM ) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer ( PSO ) and the original BAS algorithm.

133 citations


Journal ArticleDOI
TL;DR: This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance, and proposes a new ensemble approach to calculate and update weights for the models regarding their mean squared error values.
Abstract: Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed, equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties, performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance. Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.

124 citations


Journal ArticleDOI
TL;DR: A data-based fault tolerant control ( FTC) scheme is investigated for unknown continuous-time affine nonlinear systems with actuator faults, which consists of the NN identifier and the fault compensator to achieve actuator fault tolerance.
Abstract: In this paper, a data-based fault tolerant control ( FTC ) scheme is investigated for unknown continuous-time ( CT ) affine nonlinear systems with actuator faults. First, a neural network ( NN ) identifier based on particle swarm optimization ( PSO ) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network ( PSOCNN ) is employed to solve the Hamilton-Jacobi-Bellman equation ( HJBE ) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

96 citations


Journal ArticleDOI
TL;DR: This work leverages the blockchain technology to achieve decentralized PVFC, a system where network activities in computation offloading become transparent, verifiable and traceable to eliminate security risks.
Abstract: Vehicular fog computing ( VFC ) has been envisioned as an important application of fog computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VFC. They cooperate with fog servers to process offloading requests at the vehicular network edge, leading to a new paradigm called parked vehicle assisted fog computing ( PVFC ) . However, each coin has two sides. There is a follow-up challenging issue in the distributed and trustless computing environment. The centralized computation offloading without tamper-proof audit causes security threats. It could not guard against false-reporting, free-riding behaviors, spoofing attacks and repudiation attacks. Thus, we leverage the blockchain technology to achieve decentralized PVFC. Request posting, workload undertaking, task evaluation and reward assignment are organized and validated automatically through smart contract executions. Network activities in computation offloading become transparent, verifiable and traceable to eliminate security risks. To this end, we introduce network entities and design interactive smart contract operations across them. The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users. Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.

94 citations


Journal ArticleDOI
TL;DR: A new method based on deep Q-learning with experience replay and heuristic knowledge to alleviate path planning and obstacle avoidance problems and can converge to an optimal action strategy with less time and explore a path in an unknown environment with fewer steps and larger average reward.
Abstract: Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the “ curse of dimensionality ” issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network ; such a process is called experience replay. Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.

83 citations


Journal ArticleDOI
TL;DR: A novel fixed-time sliding control scheme is developed, by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time.
Abstract: In this paper, we investigate formation tracking control of autonomous underwater vehicles ( AUVs ) with model parameter uncertainties and external disturbances. The external disturbances due to the wind, waves, and ocean currents are combined with the model parameter uncertainties as a compound disturbance. Then a disturbance observer ( DO ) is introduced to estimate the compound disturbance, which can be achieved within a finite time independent of the initial estimation error. Based on a DO, a novel fixed-time sliding control scheme is developed, by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time. The effectiveness and performance of the method are demonstrated by numerical simulations.

83 citations


Journal ArticleDOI
TL;DR: It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state Feedback Control with the traditional performance indexfunction.
Abstract: This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems. Unlike existing optimal state feedback control, the control input of the optimal parallel control is introduced into the feedback system. However, due to the introduction of control input into the feedback system, the optimal state feedback control methods can not be applied directly. To address this problem, an augmented system and an augmented performance index function are proposed firstly. Thus, the general nonlinear system is transformed into an affine nonlinear system. The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically. It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function. Moreover, an adaptive dynamic programming ( ADP ) technique is utilized to implement the optimal parallel tracking control using a critic neural network ( NN ) to approximate the value function online. The stability analysis of the closed-loop system is performed using the Lyapunov theory, and the tracking error and NN weights errors are uniformly ultimately bounded ( UUB ) . Also, the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals. Finally, the effectiveness of the developed optimal parallel control method is verified in two cases.

82 citations


Journal ArticleDOI
TL;DR: A new parallel controller is developed for continuous-time linear systems, where both state and control are considered as the input, and the structure and relationship between the parallel control and traditional feedback controls are provided.
Abstract: In this paper, a new parallel controller is developed for continuous-time linear systems The main contribution of the method is to establish a new parallel control law, where both state and control are considered as the input The structure of the parallel control is provided, and the relationship between the parallel control and traditional feedback controls is presented Considering the situations that the systems are controllable and incompletely controllable, the properties of the parallel control law are analyzed The parallel controller design algorithms are given under the conditions that the systems are controllable and incompletely controllable Finally, numerical simulations are carried out to demonstrate the effectiveness and applicability of the present method

Journal ArticleDOI
TL;DR: This paper exploits an ensemble of CNNs, trained over Gramian angular fields images, generated from time series related to the Standard & Poor ʼ s 500 index future; the aim is the prediction of the future trend of the U.S. market.
Abstract: In the last decade, market financial forecasting has attracted high interests amongst the researchers in pattern recognition. Usually, the data used for analysing the market, and then gamble on its future trend, are provided as time series; this aspect, along with the high fluctuation of this kind of data, cuts out the use of very efficient classification tools, very popular in the state of the art, like the well known convolutional neural networks ( CNNs ) models such as Inception, ResNet, AlexNet, and so on. This forces the researchers to train new tools from scratch. Such operations could be very time consuming. This paper exploits an ensemble of CNNs, trained over Gramian angular fields ( GAF ) images, generated from time series related to the Standard & Poor ʼ s 500 index future; the aim is the prediction of the future trend of the U.S. market. A multi-resolution imaging approach is used to feed each CNN, enabling the analysis of different time intervals for a single observation. A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach. Our method outperforms the buy-and-hold ( B & H ) strategy in a time frame where the latter provides excellent returns. Both quantitative and qualitative results are provided.

Journal ArticleDOI
TL;DR: A proposed simulated-annealing-based bees algorithm ( SBA) can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications.
Abstract: An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud ( DGC ) systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G / G / 1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm ( SBA ) to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

Journal ArticleDOI
TL;DR: An adaptive learning law is proposed to deal with unknown nonlinear dynamics of fractional-order multi-agent systems and it is proved that all signals in the closed-loop systems are guaranteed to be uniformly ultimately bounded.
Abstract: In this paper, the leader-following tracking problem of fractional-order multi-agent systems is addressed. The dynamics of each agent may be heterogeneous and has unknown nonlinearities. By assumptions that the interaction topology is undirected and connected and the unknown nonlinear uncertain dynamics can be parameterized by a neural network, an adaptive learning law is proposed to deal with unknown nonlinear dynamics, based on which a kind of cooperative tracking protocols are constructed. The feedback gain matrix is obtained to solve an algebraic Riccati equation. To construct the fully distributed cooperative tracking protocols, the adaptive law is also adopted to adjust the coupling weight. With the developed control laws, we can prove that all signals in the closed-loop systems are guaranteed to be uniformly ultimately bounded. Finally, a simple simulation example is provided to illustrate the established result.

Journal ArticleDOI
TL;DR: The proposed network attack detection method combining a flow calculation and deep learning algorithm is suitable for the real-time detection of high-speed network intrusions.
Abstract: In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine ( DBN-SVM ) . Sliding window ( SW ) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented. Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method ʼ s real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.

Journal ArticleDOI
TL;DR: A distributed impulsive controller using a pinning strategy is redesigned, which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks, and two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.
Abstract: Cyber attacks pose severe threats on synchronization of multi-agent systems. Deception attack, as a typical type of cyber attack, can bypass the surveillance of the attack detection mechanism silently, resulting in a heavy loss. Therefore, the problem of mean-square bounded synchronization in multi-agent systems subject to deception attacks is investigated in this paper. The control signals can be replaced with false data from controller-to-actuator channels or the controller. The success of the attack is measured through a stochastic variable. A distributed impulsive controller using a pinning strategy is redesigned, which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks. Some sufficient conditions are derived, in which upper bounds of the synchronization error are given. Finally, two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.

Journal ArticleDOI
TL;DR: Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms, and the nature of objective functions and the properties of the problem are analyzed.
Abstract: The multitrip pickup and delivery problem with time windows and manpower planning ( MTPDPTW-MP ) determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP ( MO-MTPDPTWMP ) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection ( MOILS-ANS ) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

Journal ArticleDOI
TL;DR: Experimental results validate that the proposed PRL approach to construct EMS for a hybrid tracked vehicle can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
Abstract: As a complex and critical cyber-physical system ( CPS ) , the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy. Energy management strategy ( EMS ) is playing a key role to improve the energy efficiency of this CPS. This paper presents a novel bidirectional long short-term memory ( LSTM ) network based parallel reinforcement learning ( PRL ) approach to construct EMS for a hybrid tracked vehicle ( HTV ) . This method contains two levels. The high-level establishes a parallel system first, which includes a real powertrain system and an artificial system. Then, the synthesized data from this parallel system is trained by a bidirectional LSTM network. The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning ( RL ) framework. PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules. Finally, real vehicle testing is implemented and relevant experiment data is collected and calibrated. Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.

Journal ArticleDOI
TL;DR: This paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound.
Abstract: This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles ( UAVs ) to perform coordinated circular tracking around a set of circles on a target sphere. Different from the previous results limited in bidirectional networks and disturbance-free motions, this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound. Distinguishing from the design of a common Lyapunov function for bidirectional cases, we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input. Then the whole control system is regarded as a cascade connection of these two subsystems, which is proved to be stable by input-to-state stability ( ISS ) theory. For the purpose of encountering the external disturbance, the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere ( say, the circular tracking control algorithm ) with the help of the switching function. Meanwhile, the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere ( say, the formation keeping control law ) for formation keeping. The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.

Journal ArticleDOI
TL;DR: A survey on the decision making between human drivers and highly automated vehicles, to understand their architectures, human driver modeling, and interaction strategies under the driver-vehicle shared schemes.
Abstract: Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driverʼ s abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-making processes. The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans. Many open-ended questions arise, such as what proper role of human drivers should act in a shared control scheme? How to make an intelligent decision capable of balancing the benefits of agents in shared control systems? Due to the advent of these attentions and questions, it is desirable to present a survey on the decision making between human drivers and highly automated vehicles, to understand their architectures, human driver modeling, and interaction strategies under the driver-vehicle shared schemes. Finally, we give a further discussion on the key future challenges and opportunities. They are likely to shape new potential research directions.

Journal ArticleDOI
TL;DR: This review is expected to serve as a tutorial and source of references for fault prognosis researchers and reveal the current research trends and look forward to the future challenges in this field.
Abstract: Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.

Journal ArticleDOI
TL;DR: From the obtained results, it is confirmed that the proposed NSPID controller exhibits improved performance over the DAC both in terms of accurate position tracking and quick damping of link deflection when subjected to variable payloads.
Abstract: In this paper, a new nonlinear self-tuning PID controller ( NSPIDC ) is proposed to control the joint position and link deflection of a flexible-link manipulator ( FLM ) while it is subjected to carry different payloads. Since, payload is a critical parameter of the FLM whose variation greatly influences the controller performance. The proposed controller guarantees stability under change in payload by attenuating the non-modeled higher order dynamics using a new nonlinear autoregressive moving average with exogenous-input ( NARMAX ) model of the FLM. The parameters of the FLM are identified on-line using recursive least square ( RLS ) algorithm and using minimum variance control ( MVC ) laws the control parameters are updated in real-time. This proposed NSPID controller has been implemented in real-time on an experimental set-up. The joint tracking and link deflection performances of the proposed adaptive controller are compared with that of a popular direct adaptive controller ( DAC ) . From the obtained results, it is confirmed that the proposed controller exhibits improved performance over the DAC both in terms of accurate position tracking and quick damping of link deflections when subjected to variable payloads.

Journal ArticleDOI
TL;DR: This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data, classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.
Abstract: Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in cellular data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, analyzing people ʼ s activities, e.g., counting them at fixed locations and tracking them by generating origin-destination matrices is crucial. The former can be used to determine the utilization of assets like roads and city attractions. The latter is valuable when planning transport infrastructure. Such insights allow a government to predict the adoption of new roads, new public transport routes, modification of existing infrastructure, and detection of congestion zones, resulting in more efficient designs and improvement. Smartphone data exploration can help research in various fields, e.g., urban planning, transportation, health care, and business marketing. It can also help organizations in decision making, policy implementation, monitoring, and evaluation at all levels. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data. We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

Journal ArticleDOI
TL;DR: This paper investigates the sliding mode control problem for a class of discrete-time nonlinear networked Markovian jump systems ( MJSs) in the presence of probabilistic denial-of-service ( DoS) attacks, using Lyapunov theory and stochastic analysis methods to solve the problem.
Abstract: This paper investigates the sliding mode control ( SMC ) problem for a class of discrete-time nonlinear networked Markovian jump systems ( MJSs ) in the presence of probabilistic denial-of-service ( DoS ) attacks. The communication network via which the data is propagated is unsafe and the malicious adversary can attack the system during state feedback. By considering random Denial-of-Service attacks, a new sliding mode variable is designed, which takes into account the distribution information of the probabilistic attacks. Then, by resorting to Lyapunov theory and stochastic analysis methods, sufficient conditions are established for the existence of the desired sliding mode controller, guaranteeing both reachability of the designed sliding surface and stability of the resulting sliding motion. Finally, a simulation example is given to demonstrate the effectiveness of the proposed sliding mode control algorithm.

Journal ArticleDOI
TL;DR: The proposed framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster is implemented and the adopted communication protocol performs more efficiently than other conventional communication protocols.
Abstract: Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical, but also very challenging and risky. Despite first responders putting their lives at risk in saving others, human-physical limits cause delays in response time, resulting in fatality and property damage. In this paper, we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster. The framework consists of unmanned aerial vehicles ( UAVs ) , unmanned ground vehicles ( UGVs ) , a cloud-based remote control station ( RCS ) . A light-weight message queuing telemetry transport ( MQTT ) based communication is adopted for facilitating collaboration between autonomous systems. To effectively work under unfavorable disaster conditions, antenna tracker is developed as a tool to extend network coverage to distant areas, and mobile charging points for the UAVs are also implemented. The proposed frameworkʼ s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language ( AADL ) . Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols, and the implemented UAV control mechanisms are functioning properly. Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.

Journal ArticleDOI
TL;DR: A novel spatial-temporal attention ( ST-Attention) model is proposed, which studies spatial and temporal affinities jointly and introduces an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants.
Abstract: Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory ( LSTM ) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention ( ST-Attention ) model, which studies spatial and temporal affinities jointly. Specifically, we introduce an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

Journal ArticleDOI
TL;DR: This paper proposes to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain.
Abstract: This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain. The navigation scheme uses the benefits of the original real-time technical vision system ( TVS ) based on a dynamic triangulation principle. The method uses TVS output data with fuzzy logic rules processing for resolution stabilization. Based on previous researches, the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group. According to the comparative analysis of approximation methods, in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory. The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.

Journal ArticleDOI
TL;DR: An event-triggered sliding mode control approach for trajectory tracking problem of nonlinear input affine system with disturbance has been proposed and shows better performance in terms of reduced control updates, ensures system stability which further guarantees optimization of resource usage and cost.
Abstract: In this paper, an event-triggered sliding mode control approach for trajectory tracking problem of nonlinear input affine system with disturbance has been proposed. A second order robotic manipulator system has been modeled into a general nonlinear input affine system. Initially, the global asymptotic stability is ensured with conventional periodic sampling approach for reference trajectory tracking. Then the proposed approach of event-triggered sliding mode control is discussed which guarantees semi-global uniform ultimate boundedness. The proposed control approach guarantees non-accumulation of control updates ensuring lower bounds on inter-event triggering instants avoiding Zeno behavior in presence of the disturbance. The system shows better performance in terms of reduced control updates, ensures system stability which further guarantees optimization of resource usage and cost. The simulation results are provided for validation of proposed methodology for tracking problem by a robotic manipulator. The number of aperiodic control updates is found to be approximately 44% and 61% in the presence of constant and time-varying disturbances respectively.

Journal ArticleDOI
TL;DR: This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network ( CMA-MemNet), an improved model based on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects.
Abstract: This paper presents a method for aspect based sentiment classification tasks, named convolutional multi-head self-attention memory network ( CMA-MemNet ) This is an improved model based on memory networks, and makes it possible to extract more rich and complex semantic information from sequences and aspects In order to fix the memory networkʼ s inability to capture context-related information on a word-level, we propose utilizing convolution to capture n-gram grammatical information We use multi-head self-attention to make up for the problem where the memory network ignores the semantic information of the sequence itself Meanwhile, unlike most recurrent neural network ( RNN ) long short term memory ( LSTM ) , gated recurrent unit ( GRU ) models, we retain the parallelism of the network We experiment on the open datasets SemEval-2014 Task 4 and SemEval-2016 Task 6 Compared with some popular baseline methods, our model performs excellently

Journal ArticleDOI
TL;DR: A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations and MVCA, a distributed real-time multiple vehicle collision avoidance algorithm, is proposed by extending the reciprocal n-body collision avoidance method.
Abstract: A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance ( MVCA ) algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently, without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore, MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay ( less than 100 ms ) and low packet loss ( less than 5% ) can bring little influence to those trajectory planning algorithms that only depend on V2V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.