Showing papers in "IEEE/CAA Journal of Automatica Sinica in 2018"
TL;DR: The relationship between IoV and big data in vehicular environment is investigated, mainly on how IoV supports the transmission, storage, computing and computing of the big data, and in returnHow IoV benefits frombig data in terms of IoV characterization, performance evaluation andbig data assisted communication protocol design is investigated.
Abstract: As the rapid development of automotive telematics, modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real time and long term information processing, the traditional Vehicular Ad- Hoc Networks U+0028 VANETs U+0029 are evolving to the Internet of Vehicles U+0028 IoV U+0029, which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which are referred to as Big Data. In this article, we first investigate the relationship between IoV and big data in vehicular environment, mainly on how IoV supports the transmission, storage, computing of the big data, and in return how IoV benefits from big data in terms of IoV characterization, performance evaluation and big data assisted communication protocol design. We then investigate the application of IoV big data for autonomous vehicles. Finally the emerging issues of the big data enabled IoV are discussed.
463 citations
TL;DR: Simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process, and the comparison with the particle swarm optimization algorithm proves that the present method has a promising effect on energy management to save cost.
Abstract: The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming U+0028 ADHDP U+0029 method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First, the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions. Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.
191 citations
TL;DR: A modified cuckoo search algorithm is proposed to solve economic dispatch problems that have non-convex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits.
Abstract: A modified cuckoo search ( CS ) algorithm is proposed to solve economic dispatch ( ED ) problems that have non-convex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance. Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.
172 citations
TL;DR: An adaptive neural network control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints and all closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem.
Abstract: In this paper, an adaptive neural network ( NN ) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions ( BLFs ) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.
165 citations
TL;DR: In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods, and a Computational experiment-based parallel lane detection framework is proposed.
Abstract: Lane detection is a fundamental aspect of most current advanced driver assistance systems U+0028 ADASs U+0029. A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.
144 citations
TL;DR: This work proposes an online detection model based on asystematic parameter-search method called SVM U+002D Grid, whose construction is based on a support vector machine U+0028 SVMU+0029, and can achieve more efficient and accurate fault detection for cloud systems.
Abstract: Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM U+002D Grid, whose construction is based on a support vector machine U+0028 SVM U+0029. SVM U+002D Grid is used to optimize parameters in SVM. Proper attributes of a cloud system U+02BC s running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.
143 citations
TL;DR: A systematic introduction to the Bayesian state estimation framework is offered and various Kalman filtering U+0028 KF U-0029 techniques are reviewed, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KFFor nonlinear systems.
Abstract: This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective, which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics U+0028 e.g., mean and covariance U+0029 conditioned on a system U+02BC s measurement data. This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering U+0028 KF U+0029 techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter U+002F input estimation.
115 citations
TL;DR: The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method.
Abstract: This paper focuses on designing an adaptive radial basis function neural network U+0028 RBFNN U+0029 control method for a class of nonlinear systems with unknown parameters and bounded disturbances The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method The novel adaptive control method is designed to reduce the amount of computations effectively The uniform ultimate boundedness of the closed U+002D loop system is guaranteed by the proposed controller A coupled motor drives U+0028 CMD U+0029 system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system
115 citations
TL;DR: The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented, and the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented.
Abstract: Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
110 citations
TL;DR: This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in describing relationships present among the data, and advocates that the level of abstraction can be flexibly adjusted through Granular computing.
Abstract: In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling, humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data. We advocate that the level of abstraction, which can be flexibly adjusted, is conveniently realized through Granular Computing. Granular Computing is concerned with the development and processing information granules – formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there. This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in describing relationships present among the data.
108 citations
TL;DR: A survey on iterative learning control with incomplete information and associated control system design is conducted, expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory.
Abstract: This paper conducts a survey on iterative learning control ( ILC ) with incomplete information and associated control system design, which is a frontier of the ILC field. The incomplete information, including passive and active types, can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection, storage, transmission, and processing, such as data dropouts, delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects: the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory.
TL;DR: In this paper, the authors presented an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream, by transforming the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms.
Abstract: Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly, the features extracted from a subsequent fully connected layer are fed into U+0028 bidirectional U+0029 gated recurrent neural networks, which are followed by a single highway layer and a softmax layer; finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events U+0028 DCASE U+0029. On the evaluation set, an accuracy of 64.0 U+0025 from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0 U+0025 baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy, when fusing with a spectrogram-based system.
TL;DR: This work analyses the data from these disengagement reports with the aim of gainingetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers Level 2 and Level 3 automation technologies.
Abstract: In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle U+02BC s safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports: Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers U+0028 SAE U+0029 Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed.
TL;DR: The experimental results demonstrate the feasibility and efficiency of the proposed collaborative and adaptive intrusion detection method and are shown to be more predominant than the methods that use a set of single type support vector machine U+0028 SVM U-0029 in terms of detection precision rate and recall rate.
Abstract: As a primary defense technique, intrusion detection becomes more and more significant since the security of the networks is one of the most critical issues in the world. We present an adaptive collaboration intrusion detection method to improve the safety of a network. A self-adaptive and collaborative intrusion detection model is built by applying the Environmentsclasses, agents, roles, groups, and objects U+0028 E-CARGO U+0029 model. The objects, roles, agents, and groups are designed by using decision trees U+0028 DTs U+0029 and support vector machines U+0028 SVMs U+0029, and adaptive scheduling mechanisms are set up. The KDD CUP 1999 data set is used to verify the effectiveness of the method. The experimental results demonstrate the feasibility and efficiency of the proposed collaborative and adaptive intrusion detection method. Also, the proposed method is shown to be more predominant than the methods that use a set of single type support vector machine U+0028 SVM U+0029 in terms of detection precision rate and recall rate.
TL;DR: A novel robust fault tolerant controller is developed for the problem of attitude control of a quadrotor aircraft in the presence of actuator faults and wind gusts and can successfully accomplish the tracking of the desired output values.
Abstract: A novel robust fault tolerant controller is developed for the problem of attitude control of a quadrotor aircraft in the presence of actuator faults and wind gusts in this paper. Firstly, a dynamical system of the quadrotor taking into account aerodynamical effects induced by lateral wind and actuator faults is considered using the Newton-Euler approach. Then, based on active disturbance rejection control U+0028 ADRC U+0029, the fault tolerant controller is proposed to recover faulty system and reject perturbations. The developed controller takes wind gusts, actuator faults and measurement noises as total perturbations which are estimated by improved extended state observer U+0028 ESO U+0029 and compensated by nonlinear feedback control law. So, the developed robust fault tolerant controller can successfully accomplish the tracking of the desired output values. Finally, some simulation studies are given to illustrate the effectiveness of fault recovery of the proposed scheme and also its ability to attenuate external disturbances that are introduced from environmental causes such as wind gusts and measurement noises.
TL;DR: This work proves the existence of its onewafer cyclic schedule that features with the ease of industrial implementation and the use of the found schedules enables industrial multi-cluster tools to operate with their highest productivity.
Abstract: A treelike hybrid multi-cluster tool is composed of both single-arm and dual-arm cluster tools with a treelike topology. Scheduling such a tool is challenging. For a hybrid treelike multi-cluster tool whose bottleneck individual tool is process-bound, this work aims at finding its optimal one-wafer cyclic schedule. It is modeled with Petri nets such that a onewafer cyclic schedule is parameterized as its robots’ waiting time. Based on the model, this work proves the existence of its onewafer cyclic schedule that features with the ease of industrial implementation. Then, computationally efficient algorithms are proposed to find the minimal cycle time and optimal onewafer cyclic schedule. Multi-cluster tool examples are given to illustrate the proposed approach. The use of the found schedules enables industrial multi-cluster tools to operate with their highest productivity.
TL;DR: In this paper, modeling of the different types of imperfections that affect NCS is discussed and a presentation of several theories that have been applied for controlling networked systems are presented.
Abstract: This paper provides a survey on modeling and theories of networked control systems ( NCS ) In the first part, modeling of the different types of imperfections that affect NCS is discussed These imperfections are quantization errors, packet dropouts, variable sampling / transmission intervals, variable transmission delays, and communication constraints Then follows in the second part a presentation of several theories that have been applied for controlling networked systems These theories include: input delay system approach, Markovian system approach, switched system approach, stochastic system approach, impulsive system approach, and predictive control approach In the last part, some advanced issues in NCS including decentralized and distributed NCS, cloud control system, and co-design of NCS are reviewed
TL;DR: Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.
Abstract: Recently, the smart grid has been considered as a next-generation power system to modernize the traditional grid to improve its security, connectivity, efficiency and sustainability. Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economical, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional U+0028 RSC U+0029 code and Kalman filter U+0028 KF U+0029 based method in the context of smart grids. Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information, which is affected by random noises and cyber attacks. Once the estimated states are obtained by KF algorithm, a semidefinite programming based optimal feedback controller is proposed to regulate the system states, so that the power system can operate properly. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.
TL;DR: A novel concept called social manufacturing U+0028 SM U-0029 and service are proposed as an innovative manufacturing solution for the coming personalized customization era and an SM platform prototype is developed.
Abstract: After reviewing the development of industrial manufacturing, a novel concept called social manufacturing U+0028 SM U+0029 and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer U+02BC s requirements of U+201C from mind to products U+201D, and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend, and is expected to become popular in more and more industries. First, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies, like Internet of Things U+201C IoT U+201C, social networks, cloud computing, 3D printing, and intelligent systems, are introduced and analyzed, and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3D printing are suggested for different user cases.
TL;DR: It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.
Abstract: The aim of this paper is to employ fractional order proportional integral derivative ( FO-PID ) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system ( MLS ), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization ( dPSO ) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.
TL;DR: Experiments have shown that the improved approach significantly reduces the energy consumption of nodes and the delay time, thus improving the reliability of WSN, and proposes a framework called the improved software defined wireless sensor network U+0028 improved SD-WSN U-0029.
Abstract: As communication technology and smart manufacturing have developed, the industrial internet of things U+0028 IIoT U+0029 has gained considerable attention from academia and industry. Wireless sensor networks U+0028 WSNs U+0029 have many advantages with broad applications in many areas including environmental monitoring, which makes it a very important part of IIoT. However, energy depletion and hardware malfunctions can lead to node failures in WSNs. The industrial environment can also impact the wireless channel transmission, leading to network reliability problems, even with tightly coupled control and data planes in traditional networks, which obviously also enhances network management cost and complexity. In this paper, we introduce a new software defined network U+0028 SDN U+0029, and modify this network to propose a framework called the improved software defined wireless sensor network U+0028 improved SD-WSN U+0029. This proposed framework can address the following issues. 1 U+0029 For a large scale heterogeneous network, it solves the problem of network management and smooth merging of a WSN into IIoT. 2 U+0029 The network coverage problem is solved which improves the network reliability. 3 U+0029 The framework addresses node failure due to various problems, particularly related to energy consumption. Therefore, it is necessary to improve the reliability of wireless sensor networks, by developing certain schemes to reduce energy consumption and the delay time of network nodes under IIoT conditions. Experiments have shown that the improved approach significantly reduces the energy consumption of nodes and the delay time, thus improving the reliability of WSN.
TL;DR: An eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution and a near big data approach is used to excavate hidden information and knowledge from the historical production data.
Abstract: Under industry 4.0, internet of things U+0028 IoT U+0029, especially radio frequency identification U+0028 RFID U+0029 technology, has been widely applied in manufacturing environment. This technology can bring convenience to production control and production transparency. Meanwhile, it generates increasing production data that are sometimes discrete, uncorrelated, and hard-to-use. Thus, an efficient analysis method is needed to utilize the invaluable data. This work provides an RFID-based production data analysis method for production control in IoT-enabled smart job-shops. The physical configuration and operation logic of IoT-enabled smart job-shop production are firstly described. Based on that, an RFID-based production data model is built to formalize and correlate the heterogeneous production data. Then, an eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution. Furthermore, a near big data approach is used to excavate hidden information and knowledge from the historical production data. A demonstrative case is studied to verify the feasibility of the proposed model and methods. It is expected that our work will provide a different insight into the RFIDbased production data analysis.
TL;DR: A new machine learning framework is developed for complex system control, called parallel reinforcement learning, which combines the transfer learning, predictive learning, deep learning and reinforcement learning to tackle the data and action processes and to express the knowledge.
Abstract: In this paper, a new machine learning framework is developed for complex system control, called parallel reinforcement learning. To overcome data deficiency of current data-driven algorithms, a parallel system is built to improve complex learning system by self-guidance. Based on the Markov chain ( MC ) theory, we combine the transfer learning, predictive learning, deep learning and reinforcement learning to tackle the data and action processes and to express the knowledge. Parallel reinforcement learning framework is formulated and several case studies for real-world problems are finally introduced.
TL;DR: The sufficient robust frequency stabilization result for multi-area power system with time delay is presented in terms of linear matrix inequalities U+0028 LMIs U-0029 and a two- area power system is provided to illustrate the usefulness and effectiveness of the obtained results.
Abstract: This paper is devoted to investigate the robust H U+221E sliding mode load frequency control U+0028 SMLFC U+0029 of multi-area power system with time delay. By taking into account stochastic disturbances induced by the integration of renewable energies, a new sliding surface function is constructed to guarantee the fast response and robust performance, then the sliding mode control law is designed to guarantee the reach ability of the sliding surface in a finite-time interval. The sufficient robust frequency stabilization result for multi-area power system with time delay is presented in terms of linear matrix inequalities U+0028 LMIs U+0029. Finally, a two-area power system is provided to illustrate the usefulness and effectiveness of the obtained results.
TL;DR: A dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work, and time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.
Abstract: Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled. Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.
TL;DR: A comprehensive review of the encoding-decodingbased control and filtering problems for different types of NSs for digital communications, data compression, information encryption, etc.
Abstract: In order to make the information transmission more efficient and reliable in a digital communication channel with limited capacity, various encoding-decoding techniques have been proposed and widely applied in many branches of the signal processing including digital communications, data compression, information encryption, etc. Recently, due to its promising application potentials in the networked systems U+0028 NSs U+0029, the analysis and synthesis issues of the NSs under various encoding-decoding schemes have stirred some research attention. However, because of the network-enhanced complexity caused by the limited network resources, it poses new challenges to the design of suitable encoding-decoding procedures to meet certain control or filtering performance for the NSs. In this survey paper, our aim is to present a comprehensive review of the encoding-decodingbased control and filtering problems for different types of NSs. First, some basic introduction with respect to the coding-decoding mechanism is presented in terms of its engineering insights, specific properties and theoretical formulations. Then, the recent representative research progress in the design of the encodingdecoding protocols for various control and filtering problems is discussed. Some possible further research topics are finally outlined for the encoding-decoding-based NSs.
TL;DR: This strategy aims to keep a good balance between traffic safety and efficiency while also taking into consideration the unavoidable uncertainty of position / speed perception / measurement of vehicles and other drivers.
Abstract: In this paper, we discuss how to develop an appropriate collision avoidance strategy for car-following. This strategy aims to keep a good balance between traffic safety and efficiency while also taking into consideration the unavoidable uncertainty of position / speed perception / measurement of vehicles and other drivers. Both theoretical analysis and numerical testing results are provided to show the effectiveness of the proposed strategy.
TL;DR: A purposeful way to design artificial scenes and automatically generate virtual images with precise annotations and the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets is investigated, in order to discover the flaws of trained models.
Abstract: In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM U+0028 Deformable parts model U+0029 and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.
TL;DR: The coupling behavior between the trolley movement and the payload swing is enhanced and, therefore, the transient performance of the proposed controller is improved, and the Lyapunov techniques and the LaSalle U+02BC s invariance theorem are employed in to support the theoretical derivations.
Abstract: In this paper, an adaptive proportional-derivative sliding mode control U+0028 APD-SMC U+0029 law, is proposed for 2D underactuated overhead crane systems. The proposed controller has the advantages of simple structure, easy to implement of PD control, strong robustness of SMC with respect to external disturbances and uncertain system parameters, and adaptation for unknown system dynamics associated with the feedforward parts. In the proposed APD-SMC law, the PD control part is used to stabilize the controlled system, the SMC part is used to compensate the external disturbances and system uncertainties, and the adaptive control part is utilized to estimate the unknown system parameters. The coupling behavior between the trolley movement and the payload swing is enhanced and, therefore, the transient performance of the proposed controller is improved. The Lyapunov techniques and the LaSalle U+02BC s invariance theorem are employed in to support the theoretical derivations. Experimental results are provided to validate the superior performance of the proposed control law.
TL;DR: This paper defines Petri nets with data operations U+0028 PN-DO U-0029 that can model the operations on data such as read, write and delete and proposes a method to reduce the reachability graph, which can be detected rapidly.
Abstract: In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations U+0028 PN-DO U+0029 that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.