IEEE/CAA Journal of Automatica Sinica
Institute of Electrical and Electronics Engineers
About: IEEE/CAA Journal of Automatica Sinica is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): Computer science & Control theory (sociology). It has an ISSN identifier of 2329-9266. Over the lifetime, 1203 publications have been published receiving 31240 citations. The journal is also known as: Automatica sinica, IEEE/CAA journal of & Journal of automatica sinica.
Topics: Computer science, Control theory (sociology), Nonlinear system, Control theory, Artificial intelligence
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.
TL;DR: A set of algorithms to design signal timing plans via deep reinforcement learning to set up a deep neural network to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output.
Abstract: In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.
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.
TL;DR: It is concluded that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration, and can provide substantial algorithmic support for parallel intelligence.
Abstract: Recently, generative adversarial networks U+0028 GANs U+0029 have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs U+02BC proposal background, theoretic and implementation models, and application fields. Then, we discuss GANs U+02BC advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence, with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.
TL;DR: The UCR time series archive as discussed by the authors has become an important resource in the time series data mining community, with at least one thousand published papers making use of one data set from the archive.
Abstract: The UCR time series archive–introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline ( 1-nearest neighbor classification ), a fraction might be mis-attributing the reasons for their improvement. Moreover, the improvements claimed by these papers might have been achievable with a much simpler modification, requiring just a few lines of code.