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arXiv 2023年

作者： Lei, Shanglin Wang, Xiaoping Dong, Guanting Li, Jiang Liu, Yingjian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China Wuhan430074 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Emotion Recognition in Conversation (ERC) has attracted widespread attention in the natural language processing field due to its enormous potential for practical applications. Existing ERC methods face challenges in achieving generalization to diverse scenarios due to insufficient modeling of context, ambiguous capture of dialogue relationships and overfitting in speaker modeling. In this work, we present a Hybrid Continuous Attributive Network (HCAN) to address these issues in the perspective of emotional continuation and emotional attribution. Specifically, HCAN adopts a hybrid recurrent and attention-based module to model global emotion continuity. Then a novel Emotional Attribution Encoding (EAE) is proposed to model intra- and inter-emotional attribution for each utterance. Moreover, aiming to enhance the robustness of the model in speaker modeling and improve its performance in different scenarios, A comprehensive loss function emotional cognitive loss LEC is proposed to alleviate emotional drift and overcome the overfitting of the model to speaker modeling. Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work. Another extensive comparative experiments and ablation studies on three benchmarks are conducted to provided evidence to support the efficacy of each module. Further exploration of generalization ability experiments shows the plug- and-play nature of the EAE module in our method. © 2023, CC BY-SA.

关键词： Emotion Recognition

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Data-Driven for Affine Nonlinear System via Adaptive control and LDI technology 10

Data-Driven for Affine Nonlinear System via Adaptive Control...

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10th IEEE Data Driven control and Learning Systems Conference, DDCLS 2021

作者： Tu, Yidong Chen, Huan He, Shuping Key Laboratory of Intelligent Computing and Signal Processing School of Electrical Engineering and Automation Anhui University Ministry of Education Hefei230601 China

ISBN: (纸本)9781665424233

This paper investigates a novel optimal control scheme for affine nonlinear systems. With the complexity of Hamilton-Jacobi-Bellman function and the linear differential inclusion based on neural network model is used to linearize the affine system, Meanwhile, a data-driven policy iterative algorithm is proposed to solve the relevant optimal solution. Finally, a simulation example is given to demonstrates the feasibility and applicability of the designed algorithm. © 2021 IEEE.

关键词： Neural network models

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Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection 39

Provable Discriminative Hyperspherical Embedding for Out-of-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zou, Zhipeng Wan, Sheng Li, Guangyu Han, Bo Liu, Tongliang Zhao, Lin Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Hong Kong Baptist University China Sydney AI Centre The University of Sydney Sydney Australia Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Out-of-distribution (OOD) detection aims to identify the test examples that do not belong to the distribution of training data. The distance-based methods, which identify OOD examples based on their distances from the centroids of in-distribution (ID) examples, have demonstrated promising OOD detection performance. However, the objectives utilized in prior approaches are typically designed for classification and thus might not yield sufficient discriminative power to distinguish between ID and OOD examples. Therefore, this paper proposes a prototype-based contrastive learning framework for OOD detection, which is termed provable Discriminative Hyperspherical Embedding (DHE). The proposed framework provides a theoretical analysis of inter-class dispersion, which is proved to be fundamental in reducing the false positive rate (FPR) on OOD examples. Based on this, we devise an angular spread loss to achieve the maximal dispersion of the prototypes of different classes prior to training. Subsequently, a prototype-enhanced contrastive loss is introduced to align embeddings of ID examples closely with their corresponding prototypes. In our proposed DHE, the maximal prototype dispersion is theoretically proved, thereby avoiding the pitfalls of local optima commonly encountered by most existing methods. Experimental results demonstrate the effectiveness of our proposed DHE, which showcases a remarkable reduction in FPR95 (i.e., 5.37% on CIFAR-100) and more than doubling the computational efficiency when compared with the state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Emission Evaluation for Diesel Vehicles under Typical Operating Conditions 8

Emission Evaluation for Diesel Vehicles under Typical Operat...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiaren Xu, Zhenyi Cao, Yang Xia, Xiushan Kang, Yu Anhui University AHU-IAI AI Joint Laboratory Hefei230601 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China University of Science and Technology of China Department of Automation Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China

ISBN: (纸本)9798350300178

china's ambient air quality has improved significantly as a result of the formulation and implementation of the Action Plan for the Prevention and control of Air Pollution and the Three-Year Action Plan for Winning the Blue Sky Defense War, but the air pollution problem in key regions remains severe, diesel truck pollution has not been effectively addressed, and mobile sources are an important source of nitrogen oxide emissions. The existing diesel vehicle emission model is primarily divided based on average vehicle speed and VSP binning methods, and it lacks an instantaneous vehicle speed model. This paper collected data from 10 diesel vehicles in order to accurately evaluate driving conditions and promote energy saving and emission reduction of heavy-duty diesel vehicles. Then we extracted 51,925 kinematic fragment data after cleaning abnormal data and built a kinematic feature evaluation system of diesel vehicles closely related to NOx emission. The sample data of kinematic segments were clustered into two groups of low-speed conditions and medium-speed conditions using the standardized mutual information to extract the characteristics with strong correlation with NOx and the vehicle driving conditions assessment model of cluster analysis. From the clustering results, the high NOx emission is completely classified into the low-speed working condition, which matches the conclusion that the high NOx emission region appears in the low-speed acceleration region reached by previous researchers through experimental studies, indicating that the kinematic feature evaluation model of diesel vehicles established in this paper is reasonable and effective. © 2023 IEEE.

关键词： Cluster analysis

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Efficient Load Identification Method Based on Time-image Features and Dsdcn Model

SSRN

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SSRN 2023年

作者： Jiang, Jiahao Wang, Zhelong Qiu, Sen Zhao, Hongyu Liu, Ruichen Shi, Xin Huang, Zhaowu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

HighlightsEfficient Load Identification Method based on Time-image Features and DSDCN ModelJiahao Jiang,Zhelong Wang,Sen Qiu,Hongyu Zhao,Ruichen Liu,Xin Shi,Zhaowu Huang• A new image feature named color AP was proposed, which captures the overall patterns of the load characteristics.• A new time series feature called DCT-AF was introduced to capture the temporal dependencies and periodicities of the signal in an efficient representation.• A lightweight model called DSDCN was proposed, which can simultaneously process both image features and time series features.• The proposed model was evaluated on three dataset and was deployed on a low-power device. © 2023, The Authors. All rights reserved.

关键词： Discrete cosine transforms

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A Gender Recognition Method for Chinese Mitten Crab in Low Light Environment Based on Machine Vision

A Gender Recognition Method for Chinese Mitten Crab in Low L...

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2024 china Automation Congress, CAC 2024

作者： Tian, Junlong Guo, Ya Liu, Yingfei Zhang, Jianguo Jiang, Yongnian International Joint Research Center for Intelligent Optical Sensing and Applications Jiangnan University Wuxi China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Aquatic and Livestock Station of Yixing Agriculture and Rural Bureau Yixing China Wuxi Agricultural Equipment Technology Extension Center Wuxi China Jiangsu Zhongnong IoT Technology Co. LTD Yixing China

ISBN: (纸本)9798350368604

The acquisition of gender information of Chinese mitten crabs is crucial for survival rate assessment and biomass estimation. Machine vision is commonly used for the automatic gender recognition of Chinese mitten crabs, but the low light conditions caused by deeper containers reduce the accuracy of existing algorithms. To improve the accuracy of gender recognition of Chinese mitten crabs in low light environments, this study incorporates the GAM into the backbone of YOLOv7, improving the capability to distinguish gender-specific features. A lightweight upsampling operator, CARAFE, replaces the original upsampling module, expanding the model’s receptive field and better aggregating global information. Using Focal-CIoU to mitigate the impact of low-quality prediction boxes, enabling the model to better optimize high-quality ones. By constructing an image dataset of Chinese mitten crabs in low-light environments to validate the model’s performance, experimental results demonstrate that the average precision of the model established in this study is 0.870, 10.6% higher than the most update algorithm GMNet-YOLOv4, effectively performing the classification task of Chinese mitten crabs in low-light environments. © 2024 IEEE.

关键词： Machine vision

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Optimizing Binary Sequence Excitation Signals for Rapid Impedance Spectrum Measurement in Proton Exchange Membrane Fuel Cells

Optimizing Binary Sequence Excitation Signals for Rapid Impe...

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第43届中国控制会议

作者： Jingzhi Lei Chuang Sheng Jingjing Wang Liansong Yu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology State Grid Electric Power Research Institute Wuhan Nanrui Limited Liability Company Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

With the increasing demand for efficient and reliable renewable energy sources,Proton Exchange Membrane Fuel Cells(PEMFCs) have emerged as a promising *** experiments demonstrate that Electrochemical Impedance Spectroscopy(EIS) effectively differentiates various states of *** broadband signals as testing excitations significantly reduces EIS measurement time,facilitating real-time monitoring of PEMFCs' health under dynamic *** signal design requires maintaining adequate amplitude across frequency components for noise resistance while minimizing signal peak value to reduce operational *** paper introduces an EIS simulation environment,leveraging PEMFCs' impedance and electrochemical noise *** focuses on optimizing the excitation signal's amplitude-frequency distribution using discrete interval binary *** results indicate that our method achieves minimal perturbation amplitude while preserving measurement accuracy in amplitude-frequency signal design.

关键词： Proton exchange membrane fuel cell Electrochemical impedance spectroscopy Electrochemical noise Excitation signal optimization

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Cross-Modality Guided Multi-Scale Feature Representation Based image Compression for Industrial Scene Understanding

Cross-Modality Guided Multi-Scale Feature Representation Bas...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Jingzheng Tu Yan Yan Ying Lu Cailian Chen Shiru Zhou China Academy of Aerospace Science and Innovation Beijing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Industrial surveillance videos capture subsequent video frames and deliver them to processing servers for video analytics. In such a case, image compression is a promising technology in industrial scene understanding due to its reduced transmission burden. Existing works focus on establishing macroscopic visual feature maps that contain compact image information. In this article, a cross-modality guided multi-scale feature representation based image compression method is proposed for understanding industrial scenes. Firstly, an image compressing framework with multi-grained representation (MGRIC) is proposed for industrial scene understanding, which builds image patch, structure, semantic, and signal levels feature maps. Secondly, an encoder-decoder architecture is designed for image compression with diverse granularities. Thirdly, an interaction policy between different levels is developed to ensure the subsequent level's decoding. Experiments on the CUB-200-2011 dataset demonstrate that the proposed MGRIC method achieves competitive performance, which verifies its effectiveness.

关键词： image coding Visual analytics Surveillance Semantics Video compression Decoding Servers Informatics image reconstruction Videos

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Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging

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PhotoniX 2024年第1期5卷 654-675页

作者： Xingye Chen Chang Qiao Tao Jiang Jiahao Liu Quan Meng Yunmin Zeng Haoyu Chen Hui Qiao Dong Li Jiamin Wu Department of Automation Tsinghua UniversityBeijing 100084China Research Institute for Frontier Science Beihang UniversityBeijing 100083China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China National Laboratory of Biomacromolecules New Cornerstone Science LaboratoryCAS Center for Excellence in BiomacromoleculesInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Detection noise significantly degrades the quality of structured illumination microscopy(SIM)images,especially under low-light *** supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts,the requirement of a large amount of high-quality training data severely limits their *** we developed a pixel-realignmentbased self-supervised denoising framework for SIM(PRS-SIM)that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction *** demonstrated that PRS-SIM generates artifact-free images with 20-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth(GT).Moreover,we developed an easy-to-use plugin that enables both training and implementation of PRS-SIM for multimodal SIM platforms including 2D/3D and linear/nonlinear *** PRS-SIM,we achieved longterm super-resolution live-cell imaging of various vulnerable bioprocesses,revealing the clustered distribution of Clathrin-coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

关键词： Super-resolution microscopy Structured illumination microscopy Deep learning Self-supervised learning Live-cell imaging

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Multi-target tracking control system of phased array antenna with mechanical scanning and beam scanning method

Multi-target tracking control system of phased array antenna...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Jijun Ma Dewei Li Lianghai Li Shibei Xue Yuning Wang Jun Jia Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Beijing Research Institute of Telemetry Beijing China

In the field of telemetry, track, and command system, this study presents a phased array antenna control system for achieving multi-target tracking tasks through a set of systems. Firstly, electric beam scanning of a planar array phased array antenna is used in this paper to implement multi-target tracking. In addition, the phased array antenna is mounted on the antenna pedestal, which can operate on the azimuth and elevation mechanical axes. Thirdly, the mechanical scanning method of the azimuth-elevation antenna pedestal and the beam scanning method of the phased array antenna are combined to realize multi-target tracking tasks. Finally, the experiments of flight tracking are completed by employing the technique of UAV tagging, and the results validate the viability of the system.

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