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5th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Han, Liang Yang, Ting Pu, Xiujuan Sun, Longjuan Yu, Bei Xi, Jiandong Chongqing University School of Microelectronics and Communication Engineering Chongqing China Chongqing Key Laboratory of Bio-perception and Intelligent Information Processing Chongqing China

Alzheimer's Disease (AD) classification is very helpful for timely treatment and intervention in its early stage so as to reduce the incidence rate of AD and delay AD progression in elderly population. In this paper, a feature extraction method utilizing Euclidean distance map was proposed. Then the features for AD classification were selected in accordance with the feature importance score calculated by LightGBM (LGB). At last, the LGB classifier was applied to classifying AD patient utilizing selected features. The proposed AD classification method was evaluated on real dataset. The experimental results show that the proposed feature extraction and selection methods are effective, and the LGB classifier using selected features is superior to other conventional classifiers. © 2021 IEEE.

关键词： Classification (of information)

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Recurrent Neural Network for Nonconvex Economic Emission Dispatch

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 46-55页

作者： Jiayu Wang Xing He Junjian Huang Guo Chen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China Key Laboratory of Machine Perception and Children's Intelligence Development Chongqing University of EducationChongqing 400067China School of Electrical Engineering and Computer Science University of NewcastleNewcastleNSW 2308Australia

In this paper, an economic emission dispatch(EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network(RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization(PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.

关键词： Recurrent neural network(RNN) nonconvex economic emission dispatch optimization problem

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Alzheimer's Disease Classification using LightGBM and Euclidean Distance Map

Alzheimer's Disease Classification using LightGBM and Euclid...

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IEEE Advanced information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Liang Han Ting Yang Xiujuan Pu Longjuan Sun Bei Yu Jiandong Xi Chongqing University Chongqing China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing Chongqing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

关键词： Sociology Euclidean distance Feature extraction Alzheimer's disease Statistics Hippocampus Diseases

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing 48

MSP-Former: Multi-Scale Projection Transformer for Single Im...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Chen, Sixiang Ye, Tian Liu, Yun Liao, Taodong Jiang, Jingxia Chen, Erkang Chen, Peng Southwest University College of Artificial Intelligence Chongqing China Jimei University School of Ocean Information Engineering Xiamen China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China

ISBN: (纸本)9781728163277

Snow removal causes challenges due to its characteristic of complex degradations. To this end, targeted treatment of multi-scale snow degradations is critical for the network to learn effective snow removal. In order to handle the diverse scenes, we propose a multi-scale projection transformer (MSP-Former), which understands and covers a variety of snow degradation features in a multi-path manner, and integrates comprehensive scene context information for clean reconstruction via self-attention operation. For the local details of various snow degradations, the local capture module is introduced in parallel to assist in the rebuilding of a clean image. Such design achieves the SOTA performance on three desnowing benchmark datasets while costing the low parameters and computational complexity, providing a guarantee of practicality. © 2023 IEEE.

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Robust Design for signal mismatch with Steering Vector and Covariance Matrix Constraints

Robust Design for signal mismatch with Steering Vector and C...

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IEEE International Conference on Communications in China (ICCC)

作者： Junhui Qian Mengchen Lu Shuya Zhang Fengchun Tian School of Microelectronic and Communication Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Bio-perception and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

This paper is concerned with a new robust design algorithm for signal mismatch, the modified steering vector and covariance matrix are investigated. Under the correlation coefficient and norm constraints, the mismatched desired signal steering vector is estimated via output power maximization. The original design is nonconvex, and usually Np-hard. Then the relaxed semidefinite programming (SDP) is employed to solve the problem, and the relaxation is tight via analytically proven. Subsequently, the sample covariance matrix is further restructured with the shrinkage method and the subspace theorem based on the corrected steering vector in order to remove the desired signal. Finally, we provide some numerical results to show that the proposed method gives satisfactory results under different scenarios.

关键词： Correlation coefficient Array signal processing Simulation Convex functions Covariance matrices Power generation

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A Survey of Multimodal Composite Editing and Retrieval

arXiv

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arXiv 2024年

作者： Li, Suyan Huang, Fuxiang Zhang, Lei The Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing Chongqing University Chongqing400044 China The Hong Kong University of Science and Technology Hong Kong The Chongqing Key Laboratory of Bio-perception and Multimodal Intelligent Information Processing the School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

In the real world, where information is abundant and diverse across different modalities, understanding and utilizing various data types to improve retrieval systems is a key focus of research. Multimodal composite retrieval integrates diverse modalities such as text, image and audio, etc. to provide more accurate, personalized, and contextually relevant results. To facilitate a deeper understanding of this promising direction, this survey explores multimodal composite editing and retrieval in depth, covering image-text composite editing, image-text composite retrieval, and other multimodal composite retrieval. In this survey, we systematically organize the application scenarios, methods, benchmarks, experiments, and future directions. Multimodal learning is a hot topic in large model era, and have also witnessed some surveys in multimodal learning and vision-language models with transformers published in the PAMI journal. To the best of our knowledge, this survey is the first comprehensive review of the literature on multimodal composite retrieval, which is a timely complement of multimodal fusion to existing reviews. To help readers’ quickly track this field, we build the project page for this survey, which can be found at https://***/fuxianghuang1/Multimodal-Composite-Editing-and-Retrieval. © 2024, CC BY-NC-ND.

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Stability analysis of hybrid neural networks with impulsive time window

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International Journal of biomathematics 2017年第1期10卷 195-208页

作者： Xin Wang Hui Wang Chuandong Li Tingwen Huang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronics and Information EngineeringSouthwest University Chongqing 400715 P. R. China Key Laboratory of Machine Perception and Children's Intelligence DevelopmentChongqing University of Education P. R. China College of Mathematics Science Chongqing Normal University Chongqing 401331 P. R. China Texas A&M University at QatarDoha P. O. Box 23874 Qatar

The urgent problem with impulsive moments cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, in this paper, a novel class of system with impulsive time window is proposed. Different from the conventional impulsive control strategies, the main characteristic of the impulsive time window is that impulse occurs in a random manner. Moreover, for the importance of the hybrid neural networks, using switching Lyapunov functions and a generalized Hanlanay inequality, some general criteria for asymptotic and exponential stability of the hybrid neural networks with impulsive time window are established. Finally, some simulations are provided to further illustrate the effectiveness of the results.

关键词： Hybrid neural networks impulsive time window stability.

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Joint Optimization of the Spectral Coexistence of Radar and Communications System

Joint Optimization of the Spectral Coexistence of Radar and ...

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IEEE National Conference on Radar

作者： Junhui Qian Shuya Zhang Mengchen Lu Fengchun Tian School of Microelectronic and Communication Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Bio-perception and Intelligent Information Processing Chongqing University Chongqing China

ISBN: (数字)9781728189420

ISBN: (纸本)9781728189437

In the present paper, we proposed and analyzed a novel algorithmic framework for the joint design of the spectral coexistence of radar and communications system. The objects of interest in system design are the radar transmit code and the communication system code-book, whereby we state the design problem as a constrained maximization of the communication mutual information (MI) subject to a number of constraints guaranteeing both the radar performance, through a similarity constraint with some standard waveform and the effective power of radar interference. An iterative procedure, whose convergence is analytically proved, based on iterative alternating optimization of the suitably designed subproblems, is proposed and analyzed. Finally, we assess the effectiveness of the proposed approaches by numerical results.

关键词： Communication systems Simulation Radar Optimization System analysis and design Standards Convergence

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DEHRFormer: Real-Time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes 48

DEHRFormer: Real-Time Transformer for Depth Estimation and H...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Chen, Sixiang Ye, Tian Shi, Jun Liu, Yun Jiang, Jingxia Chen, Erkang Chen, Peng Xinjiang University School of Information Science and Engineering Uruqmi China Southwest University College of Artificial Intelligence Chongqing China Jimei University School of Ocean Information Engineering Xiamen China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China

ISBN: (纸本)9781728163277

Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene depth are lost. In this paper, we propose a real-Time transformer for simultaneous single image Depth Estimation and Haze Removal (DEHRFormer). DEHRFormer consists of a single encoder and two task-specific decoders. The transformer decoders with learnable queries are designed to decode coupling features from the task-Agnostic encoder and project them into clean image and depth map, respectively. In addition, we introduce a novel learning paradigm that utilizes contrastive learning and domain consistency learning to tackle weak-generalization problem for real-world dehazing, while predicting the same depth map from the same scene with varicolored haze. Experiments demonstrate that DEHRFormer achieves significant performance improvement across diverse varicolored haze scenes over previous depth estimation networks and dehazing approaches. © 2023 IEEE.

关键词： Signal encoding

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