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International Workshop on Advanced Computational Intelligence (IWACI)

作者： M. Norouzi M. Mansouri M. Teshnehlab M. Aliyari Shoorehdeli Faculty of Engineering Department of Computer Engineering Science and Research Branch Islamic Azad University Iran Intelligent System Laboratory (ISLAB) Faculty of Electrical Engineering Control Department K.N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Department of Mechatronics Engineering Advanced Process Automation &Control Laboratory (APAC) K.N. Toosi University of Technology Tehran Iran

In this study, a new type of trigonometric neural network is presented by adding frequency and phase to trigonometric activation functions. The proposed trigonometric neural network has more flexibility in comparison with conventional trigonometric neural networks and even other types of neural networks. Due to the low convergence rate and high posibility of trapping in a local minimum of backpropagation algorithm, Extended Kalman Filter algorithm is used to train the neural network's parameters which they appear in a nonlinear form. The Simulation of the suggested neural network based on the prediction of Mackey-Glass time series and identification of a nonlinear dynamic ystem reveals the efficiency of the proposed network. To show the efficiency of this method, the results are compared with the results of the others.

关键词： Biological neural networks Kalman filters Equations Neurons Vectors Covariance matrix Mathematical model

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Composition Harmony Assessment Method Based on Rule of Thirds

Composition Harmony Assessment Method Based on Rule of Third...

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Culture-oriented science & technology (ICCST), International Conference on

作者： Aixia Tang Wei Jiang Jingjing Zhang Shuang Wang Yanting Gao School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of China Ministry of Culture and Tourism Beijing Key Laboratory of Modern Entertainment Technology Communication University of China School of Computer Science and Cybersecurity Communication University of China Beijing China

ISBN: (数字)9781728181387

ISBN: (纸本)9781728181394

Composition harmony mainly refers to the various components of the image that are sensed together as a harmonious and unified organism, it plays a key role in the assessment of image aesthetic quality. In this paper, a composition harmony assessment method based on the rule of thirds was proposed. The eye-tracking dataset was used to extract the saliency regions of the image and the composition harmony assessment method was designed by computing how close the centroid of the largest saliency region and the rule of thirds. Moreover, the effectiveness of the assessment method was analyzed by the subjective evaluation experiment results of the composition harmony. The experimental results showed that the composition harmony assessment method proposed in this paper is effective.

关键词： 6G mobile communication Artificial intelligence

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Backlash neural network model for hysteresis nonlinearity

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of technology 2007年第SUPPL. 1期27卷 226-230页

作者： Liao, Xiao-Zhong Lu, Guo-Hui Liu, Xiang-Dong Department of Automatic Control School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing 100081 China

A Backlash neural network model is proposed for the systems with hysteresis nonlinearity. In order to approximate the hysteresis nonlinearity, Backlash-type operators are used as one layer of the neural network. Then, the number of Backlash operators and neurons of hidden layer are gotten through simulation. Compared with the normal BP network, the results show the improvement validity of the proposed Backlash neural network model for describing the hysteresis nonlinearity.

关键词： Nonlinear network analysis

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Enhancing thermoelectric performances of indium oxide through silicon incorporation

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Results in Physics 2024年 65卷

作者： Yang, Sheng Xiong, Tongqiang Wang, Zhibin Zheng, ZhiPeng Feng, Bo Institute of Engineering and Technology Hubei University of Science and Technology Xianning 437100 China School of Automation Hubei University of Science and Technology Xianning 437100 China The Key Laboratory of Optoelectronic Sensing and Intelligent Control Hubei University of Science and Technology Xianning 437100 China School of Electronic and Electrical Engineering Wuhan Textile University Wuhan 430200 China

Herein, the modification of indium oxide thermoelectric materials was carried out to investigate the effect of Si doping on the thermoelectric properties of In2O3. By precisely controlling the doping concentration of ... 详细信息

Herein, the modification of indium oxide thermoelectric materials was carried out to investigate the effect of Si doping on the thermoelectric properties of In₂O₃. By precisely controlling the doping concentration of Si, a series of Si-doped In₂O₃ have been successfully prepared, and the thermoelectric properties have been systematically studied. The experimental results indicate that appropriate Si doping content can optimize the band structure and carrier concentration, significantly increase the electrical conductivity and power factor of In₂O₃, and effectively reduce the thermal conductivity, increase the thermoelectric value (ZT value) of the material with the highest value of ∼0.316 (973 K). In addition, Si doping could enhance the Vickers hardness of the material and improve the mechanical properties of the material. This study not only provides a new idea for optimizing the performances of indium oxide thermoelectric materials, but also provides a valuable reference for the development and application of thermoelectric materials. © 2024 The Author(s)

关键词： Indium oxide Modification method Preparation technology Thermoelectric value

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Tensor and Minimum Connected Dominating Set based Confident Information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： Data reliability

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Channel Reflection: Knowledge-Driven Data Augmentation for Eeg-Based Bcis

SSRN

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SSRN 2024年

作者： Wang, Ziwei Li, Siyang Luo, Jingwei Liu, Jiajing Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China China Electronic System Technology Co. Ltd. Beijing100089 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: 1) CR is effective, i.e., it can noticeably improve the classification accuracy;2) CR is robust, i.e., it consistently outperforms other data augmentation approaches in the literature;and, 3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further increase the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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TAO-Based Data Governance in intelligent Transportation Systems

TAO-Based Data Governance in Intelligent Transportation Syst...

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International Conference on intelligent Transportation

作者： Xiaolong Liang Juanjuan Li Rui Qin Wenwen Ding Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems and State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences and Collaborative Laboratory for Intelligent Science and Systems Beijing China

intelligent Transportation Systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

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The Tianwen-1 Guidance,Navigation,and control for Mars Entry,Descent,and Landing

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空间科学与技术（英文） 2023年第4期3卷 367-379页

作者： Xiangyu Huang Maodeng Li Xiaolei Wang Jinchang Hu Yu Zhao Minwen Guo Chao Xu Wangwang Liu Yunpeng Wang Ce Hao Lijia Xu Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100091China Beijing Institute of Control Engineering Beijing 100091China

Tianwen-1,the first mission of China's planetary exploration program,accomplished its goals of orbiting,landing,and roving on the *** entry,descent,and landing(EDL)phase directly determines the success of the entire mission,of which the guidance,navigation,and control(GNC)system is *** paper outlines the Tianwen-1 EDL GNC system design by introducing the GNC requirements followed by presenting the GNC system architecture and algorithms to meet such *** actual flight results for the whole EDL phase are also provided in this paper.

关键词： control mars descent entry guidance landing navigation tianwen-1

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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A Large Capacity Expansion and Robust Recognition Method of Dot-Dispersing Coded Targets with Binary Step-Response Serial Encoding

SSRN

引用

SSRN 2023年

作者： Song, Limei Zheng, Tenglong Li, Yunpeng Deng, Sanpeng Yang, Yangang Zhu, Xinjun Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin300387 China School of Control Science and Engineering Tiangong University Tianjin300387 China School of Mechanical Engineering Tiangong University Tianjin300387 China School of Artificial Intelligence Tiangong University Tianjin300387 China School of Mechanical Engineering Tianjin University of Technology and Education Tianjin300222 China Institute of Robotics and Intelligent Equipment Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Intelligent Robot Technology and Application Tianjin300350 China Tianjin Bonus Robotics Technology Co. Ltd Tianjin300350 China

In close-range photogrammetry, it is difficult to meet the measurement requirements of large scenes in actual engineering due to the limited capacity of coded targets. To expand the capacity of the coded target, we propose a binary step-response serial-coded target (BSSCT). The BSSCT introduces periodic binary wave information as an additional feature in the dot-dispersing coded target. Also, a robust recognition algorithm for the BSSCT is developed, the P2-Invariant, and the step period is used for decoding. The capacity of the coded target reaches 7 magnitudes without increasing the auxiliary points. Under different lighting conditions and viewing angles, the measurement experiments show that the BSSCT outperforms other state-of-the-art coded targets. Our BSSCT is a promising standard method for large field system calibration and object measurement. © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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