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Chinese control Conference (CCC)

作者： Sheng-Min Li Yuan-Yuan He Yue-Yue Che Xu-Xia Sun Academy of Automation & Information Engineering Xi’an University of Technology Xi’an Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Xi’an

In order to solve the problem that it is difficult to detect the weak sinusoidal response signal when the frequency response method is used to detect the transformer winding deformation fault online, this paper proposes a method to detect the amplitude of the response signal by using Duffing oscillator. Secondly, in order to solve the influence of the target signal phase shift on the amplitude detection in the actual detection process, a method of first detecting the signal phase using a vibrator array composed of Duffing vibrators and then detecting the amplitude is proposed. Finally, using this method to process the two sets of measured response signals of online detection, the detection results show that the maximum error between the detected signal amplitude and the standard signal is 5. 69%, which meets the requirements of the power industry standard, which proves that the method used in this article is It is feasible in winding deformation detection.

关键词： Phased arrays Simulation Windings Frequency response Power industry Oscillators Standards

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An Adaptive Framework of Multimodal Emotion Recognition Based on Collaborative Discriminative Learning

An Adaptive Framework of Multimodal Emotion Recognition Base...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yadi Wang Xiaoding Guo Yibo Zhang Yiyuan Ren Wendi Huang Zunyan Liu Yuming Feng Xiangguang Dai Wei Zhang Hangjun Che School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

Multimodal emotion recognition has great application scenarios in the field of human-computer interaction, and the main task lies in how to capture and handle the consistency and complementarity of multimodal signals. However, in numerous scenarios, multimodal fusion is significantly challenged by variances in the strengths and weaknesses of different modality signals in carrying emotional information. This paper aims to learn the essential information of strong modalities and inter-modality correlations thus achieving better emotion recognition. We propose an adaptive multimodal emotion recognition framework based on collaborative discriminative learning (AMCDL), a framework for emotion recognition that engages in collaborative learning and adjusts to the information-processing capabilities of various emotion recognition models. AMCDL calculates the inter-modality correlation using Canonical Correlation Analysis (CCA) and uses it as an influencing factor to boost the multiplicative combination method to create a class loss function. When trained on several modalities, the class loss function can integrate complementary information to enable strong modal expression, enhancing emotion perception. On the benchmark dataset CMU-MOSI, AMCDL significantly improves classification accuracy when compared to the state-of-the-art models. The experiments showed the flexibility and generality of AMCDL by applying several emotion recognition models to it and producing better classification results than the original model.

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Performance Analysis of Full-Duplex NOMA Assisted Satellite-Terrestrial Systems with Hardware Impairments and Imperfect CSI

Performance Analysis of Full-Duplex NOMA Assisted Satellite-...

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International Conference on information, Communication and Networks (ICICN)

作者： Jinlong Zhao Mingming Huo Wanwei Tang Xinwei Yue School of Artificial Intelligence Gansu University of Political Science and Law Lanzhou China School of Electronic and Information Engineering Beihang university Beijing China Archives Center Lanzhou University of Technology Lanzhou China Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665490832

In this paper, we investigate the satellite-terrestrial system employing non-orthogonal multiple access (NOMA) scheme, where a full-duplex (FD) decode-and-forward (DF) relaying assists the user in weak channel condition. The effects of hardware impairments, imperfect channel state information (CSI) and imperfect interference cancellation on system performance are considered. For this setting, we derive the closed-form expressions of the exact and asymptotic outage probabilities as well as delay-limited sum throughput for NOMA users. Numerical results are provided to demonstrate the correctness of theoretical analysis and the superiority of adopting FD NOMA scheme in the proposed system.

关键词： NOMA Interference cancellation Satellites System performance Full-duplex system Probability Throughput

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuquan Zhang Yu Wu Xinxin Xu Chuansheng Wang Minjia Ma Antoni Grau Jiayan Huang School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices.

关键词： Accuracy Computational modeling Image edge detection Frequency-domain analysis Fatigue Brain modeling Feature extraction Electroencephalography Real-time systems Time-domain analysis

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An improved biogeography-based optimization with hybrid migration and feedback differential evolution and its performance analysis

Research Square

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Research Square 2021年

作者： Zhang, Ziyu Gao, Yuelin Li, Jiahang Zuo, Wenlu School of Mathematics and Information Science North Minzu University Yinchuan750021 China Ningxia Province Key Laboratory of Intelligent Information and Data Processing Yinchuan750021 China

Biogeography-based optimization (BBO) is not suitable for solving high-dimensional or multi-modal problems. To improve the optimization efficiency of BBO, this study proposes a novel BBO variant, which is named ZGBBO. For the selection operator, an example learning method is designed to ensure inferior solution will not destroy the superior solution. For the migration opeartor, a convex migration is proposed to increase the convergence speed, and the probability of finding the optimal solution is increased by using opposition-based learning to generate opposite individuals. The mutation operator of BBO is deleted to eliminate the generation of poor solutions. A differential evolution with feedback mechanism is merged to improve the convergence accuracy of the algorithm for multi-modal and irregular problems. Meanwhile, the greedy selection is used to make the population always moves in the direction of a better area. Then, the global convergence of ZGBBO is proved with Markov model and sequence convergence model. Quantitative evaluations, compared with three self-variants, seven improved BBO variants and six state-of-the-art evolutionary algorithms, experimental results on 24 benchmark functions show that every improved strategy is indispensable, and the overall performance of ZGBBO is better. Besides, the complexity of ZGBBO is analyzed by comparing with BBO, and ZGBBO has less computation and lower complexity. © 2021, CC BY.

关键词： Evolutionary algorithms

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Context-Aware Residual Module for Image Classification

Context-Aware Residual Module for Image Classification

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International Conference on Pattern Recognition

作者： Jing Bai Ran Chen Ningxia Province Key Laboratory of Intelligent Information and Data Processing Yinchuan China School of Computer Science and Engineering North Minzu University Yinchuan China

ISBN: (纸本)9781728188089;9781728188096

Attention module has achieved great success in numerous vision tasks. However, existing visual attention modules generally consider the features of a single-scale, and cannot make full use of their multi-scale contextual information. Meanwhile, the multi-scale spatial feature representation has demonstrated its outstanding performance in a wide range of applications. However, the multi-scale features are always represented in a layer-wise manner, i.e. it is impossible to know their contextual information at a granular level. Focusing on the above issue, a context-aware residual module for image classification is proposed in this paper. It consists of a novel multi-scale channel attention module MSCAM to learn refined channel weights by considering the visual features of its own scale and its surrounding fields, and a multi-scale spatial aware module MSSAM to further capture more spatial information. Either or both of the two modules can be plugged into any CNN-based backbone image classification architecture with a short residual connection to obtain the context-aware enhanced features. The experiments on public image recognition datasets including CIFAR10, CIFAR100, Tiny-ImageNet and ImageNet consistently demonstrate that our proposed modules significantly outperforms a wide-used state-of-the-art methods, e.g., ResNet and the lightweight networks of MobileNet and SqueezeeNet.

关键词： Visualization Image recognition Semantics Focusing data mining Task analysis Image classification

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Multi-scale feature fusion distilled attention network for efficient image super-resolution

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Applied Soft Computing 2025年 180卷

作者： Yinggan Tang Mengjie Su Xiuli Zhang School of Electrical Engineering Yanshan University Qinhuangdao Hebei 066004 China Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Yanshan University Qinhuangdao Hebei 066004 China Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Qinhuangdao Hebei 066004 China School of Hebei Vocational and Technical College of Building Materials Qinhuangdao 066004 China

Efficient single image super-resolution (SISR) seeks to enhance reconstruction quality while minimizing computational complexity and model parameters for practical deployment on resource-constrained edge devices. A critical limitation of existing approaches lies in the restricted receptive fields of conventional convolutions, which hinder attention mechanisms and backbone networks from effectively capturing non-local features, ultimately degrading super-resolution performance. To solve this challenge, we propose a novel Multi-scale Feature Fusion Distilled Attention Network (MFFDAN) that synergizes multi-scale feature extraction with lightweight attention mechanisms. Our framework introduces three key innovations. First, we proposed a Multi-scale Convolution (MConv) module that dynamically extracts hierarchical features with multi-scale receptive fields while maintaining parameter efficiency, subsequently extended into a Multi-scale Feature Fusion Distillation Block (MFFDB). Second, we proposed a Distilled Spatial Attention (DSA) mechanism that enhances spatial feature interactions through multi-scale downsampling and distillation-based information refinement. Third, a lightweight Depth-wise Channel Attention (DCA) module optimized via depth-wise convolutions for efficient channel feature selection is proposed. These components are systematically integrated through residual connections to form the Multi-scale Feature Fusion Distilled Attention Block (MFFDAB), the core building block of our architecture. Extensive experiments demonstrate that MFFDAN achieves state-of-the-art performance while maintaining superior computational efficiency, achieving an optimal balance between reconstruction quality (PSNR/SSIM), model complexity (parameters/Multi-Adds), and inference speed across benchmark datasets. The proposed innovations provide a practical solution for deploying high-quality SISR models on edge computing platforms.

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Robust adaptive neural network consensus tracking control of multi-robot systems

Robust adaptive neural network consensus tracking control of...

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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper studies the problem of distributed consensus tracking control for multi-robot systems with different friction coefficients and external disturbances based on the multi-agent *** the case where the communication topology is a weighted directed graph and leader node is the neighbor of a small portion of the follower *** follower nodes are modeled as two second-order nonlinear dynamical system with coupling parts.A distributed robust adaptive control law based on the neural networks(NN) is designed for each follower node,where the controller requires only relative state information from its adjacent *** such control scheme,the states of all follower nodes ultimately synchronize to the leader node with bounded residual *** results are provided to validate the effectiveness of the algorithm.

关键词： Consensus control Multi-robot systems Agent systems Neural networks Sliding mode control

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Adaptive Back-stepping Based control for Active Suspension Systems with Prescribed Performance

Adaptive Back-stepping Based Control for Active Suspension S...

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第40届中国控制会议

This paper proposes an adaptive back-stepping tracking control with prescribed performance function for vehicle suspension systems with uncertain parameters. The prescribed performance function can lead the tracking error from a large set to an arbitrarily small residual set. Firstly, the constrained tracking error system incorporating the prescribed performance functions is transformed into unconstrained form. Then an adaptive updating scheme based on the projection algorithm is designed to estimate the uncertain parameter. Following that an adaptive nonlinear controller is designed using the back-stepping technique, and the stability of whole controlled systems is proved by the Lyapunov theory. Meanwhile, the state convergence of the internal dynamical system is also guaranteed. Finally, simulation results are provided to verify the proposed approach.

关键词： Active Suspension System Adaptive control Back-stepping technique prescribed performance

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