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arXiv 2024年

作者： Zhan, Gen Yang, Li Tang, Yunlong Liao, Yiting Lin, Jiongzhi Huang, Baitao Moradi, Morteza Moradi, Mohammad Rundo, Francesco Spampinato, Concetto Borji, Ali Palazzo, Simone Zhu, Yuxin Sun, Yinan Duan, Huiyu Cao, Yuqin Jia, Ziheng Hu, Qiang Min, Xiongkuo Zhai, Guangtao Fang, Hao Cong, Runmin Lu, Xiankai Zhou, Xiaofei Zhang, Wei Zhao, Chunyu Mu, Wentao Deng, Tao Tavakoli, Hamed R. Moskalenko, Andrey Bryncev, Alexey Vatolin, Dmitry Timofte, Radu ByteDance China ByteDance United States University of Rochester United States College of Electronic and Information Engineering Shenzhen University China Shenzhen Key Laboratory of Digital Creative Technology China Guangdong Key Laboratory of Intelligent Information Processing China University of Catania Italy STMicrolectronics ADG Central R&D Italy Quintic AI San Francisco United States Shanghai Jiao Tong University China School of Control Science and Engineering Shandong University China School of Software Shandong University China School of Automation Hangzhou Dianzi University China School of Information Science and Technology Southwest Jiaotong University China Nokia Technologies Finland Lomonosov Moscow State University Russia AIRI Moscow Russia MSU Institute for Artificial Intelligence Russia University of Würzburg Germany

This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://***/challenges/***. © 2024, CC BY.

关键词： Video analysis

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Deep learning models for EEG-based rapid serial visual presentation event classification

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Journal of Information Hiding and Multimedia Signal processing 2018年第1期9卷 177-187页

作者： Zhang, Fu-Quan Mao, Zi-Jing Huang, Yu-Fei Xu, Lin Ding, Gang Yi Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Software Beijing Institute of Technology Room A9-301 Juyuan Jinshan Jiyuan Fuzhou350008 China Department of Electronic Engineering University of Texas at San Antonio One UTSA Circle San Antonio Arrow Oaks 126 San AntonioTX78249 United States Innovative Information Industry Research Institute Fujian Normal University Fuqing Brunch of Fujian Normal University Campus County 1 Longjiang Road FuqingFuzhou350300 China School of Software Beijing Institute of Technology Room 611 Software Building 5 South Zhongguancun Street Haidian Beijing100081 China

We consider deep learning (DL) for event classification using electroencephalogram (EEG) measurements of brain activities. We proposed HDNN or hierarchical deep neural network, and CNN4EEG, a new convolution neural network (CNN). Both DL models are designed to improve the representation of spatial and local temporal correlations inherent in EEG data. These models were tested for image target prediction in a time-locked rapid serial visual presentation (RSVP) experiment. The performances were compared with the state-of-the-art RSVP classification algorithms including HDCA and XDAWN and with other popular machine learning algorithms. The results show that global spatial local temporal CNN (CNN4EEG) achieved a 13% improvement over the best competing non-DL algorithm and a 9% improvement over canonical CNN for image processing, and a 6% over deep neural network (DNN). Our results suggest that the unique design of temporal and spatial filters in CNN4EEG can improve the representation of EEG data, hence the prediction performance. © 2018, Ubiquitous International. All rights reserved.

关键词： Deep neural networks

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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Design and Implementation of Carrier Detection System Based on FPGA

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Journal of Physics: Conference Series 2020年第4期1624卷

作者： Xian Zhang Taiguo Qu Yiwen Liu Dong Yin Shaowei Deng School of Computer Science and Engineering Huaihua University Huaihua 418000 Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua 418000 Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua 418000 Hunan P. R. China

In order to better monitor and manage the signal transmission in the communication system and base station, and carrier detection is more effective. This paper designs a carrier(Carrier frequency) detection system based on FPGA, including system design requirements, FPGA selection, system framework design, carrier detection method design and so on. The performance indexes of the system are tested, such as carrier frequency range, carrier search time, carrier detection number and CPU consumption rate. The system has the advantages of high flexibility and good portability, and has a good application prospect in communication system, base station and other equipment.

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Self adaptation of chimera states

arXiv

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arXiv 2018年

作者： Yao, Nan Huang, Zi-Gang Ren, Hai-Peng Grebogi, Celso Lai, Ying-Cheng Department of Applied Physics Xi’an University of Technology Xi’an710048 China School of Life Science and Technology Xi’an Jiao Tong University Xi’an710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Institute for Complex Systems and Mathematical Biology King’s College University of Aberdeen AberdeenAB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of "intelligence" in achieving robustness through self adaptation. The behavior can be exploited for controlled generation of chimera states with their coherent component placed in any desired spatial region of the system. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

引用

arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Structural Performance Analysis of Wall Climbing Robot

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IOP Conference Series: Materials Science and Engineering 2019年第4期569卷

作者： Li Huang Shangchun Liao College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan 430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan 430081 China

With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot.

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Future Development Trend of Multimedia Network

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IOP Conference Series: Materials Science and Engineering 2018年第1期466卷

作者： Li Huang Kai Zhang Wei Hu Chengcheng Li College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan 430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan 430081 China

With the rapid development of computer network, multimedia technology has gradually spread throughout all aspects of people's life. This article, based on the concept and characteristics of multimedia, expounds the application of multimedia technology and the trend of future development. It makes a heavy analysis of multimedia teaching and analyses the meaning and advantage of multimedia teaching.

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Bayesian network for motivation classification in creative computation

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Journal of Information Hiding and Multimedia Signal processing 2017年第4期8卷 888-902页

作者： Zhang, Fuquan Ding, Gangyi Xu, Lin Zheng, Xiaoyan School of Software Beijing Institute of Technology Room A9#301 Jiyuan Juyuan Jinshan Fuzhou350008 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Room A9#301 Jiyuan Juyuan Jinshan Fuzhou350008 China School of Software Beijing Institute of Technology Room A9#301 Jiyuan Juyuan Jinshan Fuzhou350008 China Innovative Information Industry Research Institute Fujian Normal University Fuqing Brunch of Fujian Normal University Campus County 1# Longjiang Road FuqingFuzhou350300 China Foreign Language Institute Minjiang University Foreign Language Institute of Minjiang University Xiyuan Gong Road 200# Shangjie Minhou Fuzhou350121 China

This paper used clustered embedding features and language ontology to simulate the process of creative idea generation from human brain. Conventional creative idea generation used predefined motivation words. This study proposed a new motivation classification algorithm based on Bayesian network. We first applied the crawler algorithm to obtain motivation vocabulary corpus information. Bayesian network is applied to filter motivation words and project to a vector space for building information clustering model. Then we applied ontology theory to generating the phrase and sentence structure. By creative computing we try to discover how the idea comes from step by step and display the emergence, self-organization, self- coordination of idea creation process of human brain. From this paper, you will see the process in five steps from blurry motivations to clear creative ideas. © 2017, Ubiquitous International. All rights reserved.

关键词： Bayesian networks

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Correction to: Parallel optimization of the ray-tracing algorithm based on the HPM model

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The Journal of Supercomputing 2021年第9期77卷 10333-10333页

作者： Jun-Feng, Wang Gang-Yi, Ding Yi-Ou, Wang Yu-Gang, Li Fu-Quan, Zhang School of Computer Science and Technology Beijing Institute of Technology Beijing People’s Republic of China Beijing Institute of Science and Technology Information Beijing People’s Republic of China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou People’s Republic of China

A correction to this paper has been published: https://***/10.1007/s11227-021-03680-0

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