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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Fang, Wenxing Su, Yanxu Institutes of Physical Science and Information Technology Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei230601 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

With the rapid development of Transformers in the field of computer vision, models based on Transformers have become highly competitive architectures in the area. Although variants of Transformer models have achieved increasing accuracy on image classification tasks, the size of the training set and the number of parameters required by the models have increased dramatically. When dealing with small datasets, such models face problems such as overfitting and undergeneralization, leading to poor accuracy on the test set. We propose a new lightweight vision transformer (LVT) to address these issues. We reconstructed the backbone network, which learns the relationship between pixels through local window self-attention and global self-attention computation. We also use the attention pooling approach to fuse the token sequences generated by the backbone network more meticulously. We trained on the CIFAR-10 and CIFAR-100 datasets from scratch and compared them with a modern convolutional neural network. The experimental results show that LVT outperforms the modern convolutional neural network in terms of accuracy and efficiency. On the test set of CIFAR-10, we have obtained an accuracy of 96.83%, which indicates that our model can effectively solve the problems facing the training of small datasets and has a wide range of application prospects. © 2023 IEEE.

关键词： lightweight pooling self-attention windows

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A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination systems 17th

A Reinforcement Learning Mission Supervisor with Memory for ...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Mo, Zhibin Zhang, Zhenyi Chen, Yutao Huang, Jie School of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China

ISBN: (纸本)9789811663192

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Modeling and Parameter Identification of Constant-Speed Centrifugal Chiller Based on Experiments

Modeling and Parameter Identification of Constant-Speed Cent...

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2023 International Conference on Power system Technology, PowerCon 2023

作者： Han, Xinchan Zhang, Wen Cheng, Dingyi Zhang, Tingting Liu, Meng Xie, Yuxin Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China State Grid Shandong Electric Power Research Institute Jinan China JinXianDai Information Industry Co. Ltd. Jinan China

ISBN: (纸本)9798350300222

The expanding penetration of intermittent renewable energy resources is motivating the development of demand response (DR) services for balancing generation and load. Central air conditionings (CACs) are potential candidates due to their thermal energy storage capability. A suitable load model is the foundation for DR services. However, CACs contain multiple control subsystems whose parameters are difficult to obtain because of trade secrets. This paper focuses on the modeling and parameter identification of the constant-speed centrifugal chiller, which accounts for 60-70% of the power of CAC. A thermal-electrical model of a constant-speed centrifugal chiller is established to describe the dynamic operation process. In addition, the critical control parameters and the delay time of control systems are identified based on experimental data for a practical building chiller. The experimental results verified the effectiveness of the proposed model. © 2023 IEEE.

关键词： Air conditioning

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Hybrid signal fusion for target detection in distributed PA-MIMO radar systems on moving platforms

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhou, Dingsen Yang, Minglei Lian, Hao Greco, Maria Sabrina Gini, Fulvio Xidian University National Key Laboratory of Radar Signal Processing Shaanxi Xi'an710071 China Science and Technology on Underwater Test and Control Laboratory Dalian116013 China University of Pisa Department of Information Engineering Pisa56122 Italy

Distributed phased multiple-input multiple-output (PA-MIMO) radar systems on moving platforms combine the spatial diversity of MIMO radars with the coherent gain of phased-array radars, making them ideal for dynamic environments. However, challenges such as motion-induced channel variability, Doppler shifts, and complex parameter estimation often hinder target detection. Traditional methods like non-coherent fusion (NCF) and coherent fusion (CF) struggle to adapt to such variability, leading to suboptimal performance. In this paper, we propose a hybrid signal fusion (HSF) scheme that integrates joint space-time-phase compensation (JSTPC) with a compressed fusion network. The JSTPC module mitigates motion-induced phase errors and ensures precise alignment of received signals, while the compressed fusion network combines CF and NCF stages, leveraging their respective strengths to adaptively manage channel correlation variability. We also develop a joint parameter estimation algorithm to accurately extract direction of departure, direction of arrival, and Doppler frequency, thereby ensuring precise signal reconstruction and enhancing the detection process by estimating key parameters in real time. Theoretical analyses of detection thresholds and probabilities, derived using moment-matching techniques, further validate the reliability of the proposed scheme. Monte Carlo simulations and theoretical analyses for diverse radar configurations and channel correlation scenarios show that the proposed HSF scheme achieves 2 dB detection gain improvement over NCF. © 2025 IEEE.

关键词： Frequency estimation

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3D bioprinting of in vitro porous hepatoma models:establishment,evaluation,and anticancer drug testing

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Bio-Design and Manufacturing 2024年第2期7卷 137-152页

作者： Xiaoyuan Wang Zixian Liu Qianqian Duan Boye Zhang Yanyan Cao Zhizhong Shen Meng Li Yanfeng Xi Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors and Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Department of Pathology Shanxi Provincial Cancer HospitalTaiyuan 030024China General Hospital of Taiyuan Iron and Steel Company Taiyuan 030809China

Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted *** address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of *** GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 *** analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the *** with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and ***,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three *** addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude *** evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was *** research therefore acts as a foundation for the application of 3DP-7721 models in drug devel

关键词： 3D bioprinting Hepatoma tumor models Drug screening Antitumor drug development

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User-assisted Caching Strategy Based on Reverse Auction and Recommendation in Railway Station 5

User-assisted Caching Strategy Based on Reverse Auction and ...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Sun, Chenghao Wu, Hao Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety School of Electronics and Information Engineering Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Frontiers Science Center for Smart High-speed Railway System Beijing China

ISBN: (纸本)9781665485081

In the field of high-speed railway communication, solving the growing demand for multimedia services of passengers has always been a problem. To provide users with stable video services, caching technology has received more and more attention. In this paper, we refine the details when users assist the on-board relay to cache videos in railway stations. Based on this scenario, we improve the existing asynchronous allocation strategy and propose a user-assisted caching strategy named sequential allocation algorithm with recommendation for caching videos. The experimental results show that the algorithm is superior to synchronous and asynchronous allocation algorithm in social revenue and user participation ratio. © 2022 IEEE.

关键词： Multimedia services

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Depth-Aware Fusion Method Based on Image and 4D Radar Spectrum for 3D Object Detection

Depth-Aware Fusion Method Based on Image and 4D Radar Spectr...

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IEEE International Conference on Robotics and Biomimetics

作者： Yue Sun Yeqiang Qian Chunxiang Wang Ming Yang The Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China The Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Safety and reliability are crucial for the public acceptance of autonomous driving. To ensure accurate and reliable environmental perception, intelligent vehicles must exhibit accuracy and robustness in various environments. Millimeter-wave radar, known for its high penetration capability, can operate effectively in adverse weather conditions such as rain, snow, and fog. Traditional 3D millimeter-wave radars can only provide range, Doppler, and azimuth information for objects. Although the recent emergence of 4D millimeter-wave radars has added elevation resolution, the radar point clouds remain sparse due to Constant False Alarm Rate (CFAR) operations. In contrast, cameras offer rich semantic details but are sensitive to lighting and weather conditions. Hence, this paper leverages these two highly complementary and cost-effective sensors, 4D millimeter-wave radar and camera. By integrating 4D radar spectra with depth-aware camera images and employing attention mech-anisms, we fuse texture-rich images with depth-rich radar data in the Bird's Eye View (BEV) perspective, enhancing 3D object detection. Additionally, we propose using GAN-based networks to generate depth images from radar spectra in the absence of depth sensors, further improving detection accuracy.

关键词： Three-dimensional displays Accuracy Snow Radar detection Object detection Radar imaging Millimeter wave radar Cameras Sensors Meteorology

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TL-KDNET: A Malicious Traffic Detection Model Integrating Transfer Learning and Knowledge Distillation 21

TL-KDNET: A Malicious Traffic Detection Model Integrating Tr...

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21st International Computer Conference on Wavelet Active Media Technology and information processing, ICCWAMTIP 2024

作者： Yu, Kun Huang, YuanYuan Zhang, Linshan Lv, Jun Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China Co. Ltd. China

ISBN: (纸本)9798331519254

With the evolution of cyberattacks, traditional methods for malicious traffic detection face increasingly complex threats. To address this challenge, we propose a novel model, TL-KDNet, which integrates transfer learning, domain adaptation, and knowledge distillation techniques to enhance the accuracy and generalization capabilities of malicious traffic detection. In our model, KullbackLeibler (KL) divergence serves as a core module to quantify the differences in probability distributions between the source and target domains, thereby effectively facilitating knowledge transfer from known source domains to unknown target domains. Additionally, by leveraging Maximum Mean Discrepancy (MMD) and a single-sample distillation loss function, we achieve alignment of domain features and transfer of knowledge. Experimental results demonstrate that TL-KDNet improves detection accuracy by 4% on public datasets compared to existing methods. © 2024 IEEE.

关键词： Domain Knowledge

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Self-Supervised Learning with Min-Max Distance Approach for Few-Shot Steel Surface Defects Recognition 43

Self-Supervised Learning with Min-Max Distance Approach for ...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Tianlei Chen, Xinyao Li, Zeliang Zhou, Zhencai Zhou, Zhiheng Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Province Kejie Machinery Automation Company Technical Department Jiangmen China

ISBN: (纸本)9789887581581

Surface defects in steel are crucial for quality inspection in the manufacturing of industrial metal products. While deep learning methods have been extensively studied in this field, the lack of large-scale annotated defect samples imposes significant labeling pressure, making training fully supervised deep models challenging. In response to this issue, we employ self-supervised learning for few-shot recognition, effectively learning representations from limited unlabeled data and achieving defect recognition with a small amount of labeled samples. In particular, we construct Other encoder with multi-view information and Base encoder with single view information based on the distillation self-supervised learning framework through the multi-crops mechanism, and the Other encoders and Base encoders perform information share. Our main contribution is that, based on this framework, we propose a new regularization method to solve the problem of instances deviating from the center of self-supervised clustering by minimizing the maximum instance distance (MMD). In testing, fine-tuning the MMD pre-trained model with a small number of labeled data achieves superior recognition performance on the steel surface defect dataset NEU, significantly outperforming other testing methods. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Self-supervised learning

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LOGNET: A Knowledge Distillation-Based Model for Log Anomaly Detection 21

LOGNET: A Knowledge Distillation-Based Model for Log Anomaly...

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21st International Computer Conference on Wavelet Active Media Technology and information processing, ICCWAMTIP 2024

作者： Yu, Kun Huang, Yuanyuan Zhang, Linshan Lv, Jun Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China Co. Ltd. China

ISBN: (纸本)9798331519254

Due to the diverse nature of attack forms, supervised log anomaly detection models require a large volume of labeled data, making the annotation process time-consuming and labor-intensive. Conversely, unsupervised log anomaly detection models often struggle to effectively capture log features and accurately identify anomalous logs. To address these challenges, this study constructs a collaborative learning framework and proposes the LogNet model. This framework enables mutual learning between two distinct models, enhancing their performance synergistically. In the LogNet model, log data is first converted into directed graphs, leveraging the strengths of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Through collaborative learning, the two models guide each other, boosting their detection capabilities. The effectiveness of LogNet is validated on five public datasets, with experimental results demonstrating that LogNet significantly outperforms traditional models in malicious log detection, achieving up to a 4 % improvement in accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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