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5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Bai, Yiwen Yang, Lu Zhang, Lei Song, Yajing School of Transportation Science and Engineering Beihang University Beijing102206 China School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Tianjin300384 China Traffic Control Technology Co. Ltd. Beijing China

ISBN: (纸本)9798331530334

With the rapid development of railway traffic system, real-time semantic segmentation plays a crucial role in railway track scene monitoring. However, most of the existing methods use lightweight convolution to reduce the computational complexity, which results in reduced accuracy. To solve this problem, this paper proposes a real-time semantic segmentation network called TICNet, which is specifically used for accurate segmentation of railway track scenes. TICNet uses a novel three-branch structure designed for efficient feature extraction and accurate segmentation. Results on the RailSem19 dataset show that TICNet has a segmentation accuracy of 62.36% mIoU with an inference speed of 81.2 FPS, which is significantly better than many existing methods. © 2024 IEEE.

关键词： Semantic Segmentation

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Neural Network-Based Terminal Sliding Mode Controller design for Manipulator Systems 1

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Qu, Lufeng Fan, Qiming Kang, Yang Yang, Jun Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China School of Intelligent Manufacturing Tianjin Sino-German University of Applied Technology Tianjin300350 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

In the context of nonlinear and uncertain manipulator systems, achieving fast and accurate desired trajectories for each joint of the manipulator is crucial. Our study aims to improve current control methods by combining sliding mode control and neural network techniques. Specifically, we leverage the neural networks’ approximation capabilities to estimate both the modeling error and external disturbance in our research. These estimates are then integrated into the synovial control as additional gains. This approach allows for the sliding mode controller’s switch gain to adapt to changing modeling error and external disturbance, thereby avoiding the vibration production typically associated with traditional sliding mode controllers. It can be observed from the simulation results that the combination of the proposed nonsingular terminal sliding mode controller and neural network achieves fast and precise tracking of desired trajectories. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sliding mode control

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Remolding Semantic Focus with Dual Attention Mechanism for Aspect-based Sentiment Analysis 8

Remolding Semantic Focus with Dual Attention Mechanism for A...

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8th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2022

作者： Li, Xingda Bao, Yanwei Hu, Min Ren, Fuji Hefei University of Technology Hefei Anhui230009 China Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine China University of Electronic Science and Technology of China Chengdu Chengdu611730 China

ISBN: (纸本)9781665477352

Aspect-based sentiment analysis (ABSA) is an NLP task that classify fine-grained sentiment towards one specific aspect from the same text. While attention mechanism has achieved great success, attaching aspects to abstract sentiment remains challenging. In this paper, we propose dual attention mechanism, a novel method to re-weight the distribution of attention between stack BERT layers, in prompt learning way with pretrained language model BERT. Specifically, after obtaining the most attractive words, the method raises weight of other possible corresponding words and makes model consider more comprehensively. To introduce more aspect information, we classify the sentiment in improved prompt learning way. Note that the overfitting using BERT on ABSA, we utilize the approach of staged loss that restrict the training not too small. Finally, the experiment results demonstrate the effectiveness and the stability of dual attention and provide a good insight of attention mechanism. © 2022 IEEE.

关键词： Sentiment analysis

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Safety-Aware Human-Lead Vehicle Platooning by Proactively Reacting to Uncertain Human Behaving

arXiv

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arXiv 2024年

作者： Hu, Jia Wang, Shuhan Zhang, Yiming Wang, Haoran Liu, Zhilong Cao, Guangzhi Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University Shanghai201804 China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle China Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University 4800 Cao’an Road Shanghai China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Dazhuo Intelligent Technology Company Shanghai China ZDrive.ai Shanghai China

Human-Lead Cooperative Adaptive Cruise Control (HL-CACC) is regarded as a promising vehicle platooning technology in real-world implementation. By utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces the cost and enhances the reliability of perception and decision-making. However, state-of-the-art HL-CACC technology still has a great limitation on driving safety due to the lack of considering the leading human driver’s uncertain behavior. In this study, a HL-CACC controller is designed based on Stochastic Model Predictive Control (SMPC). It is enabled to predict the driving intention of the leading Connected Human-Driven Vehicle (CHV). The proposed controller has the following features: i) enhanced perceived safety in oscillating traffic;ii) guaranteed safety against hard brakes;iii) computational efficiency for real-time implementation. The proposed controller is evaluated on a PreScan&Simulink simulation platform. Real vehicle trajectory data is collected for the calibration of the simulation. Results reveal that the proposed controller: i) improves perceived safety by 19.17% in oscillating traffic;ii) enhances actual safety by 7.76% against hard brakes;iii) is confirmed with string stability. The computation time is approximately 3.2 milliseconds when running on a laptop equipped with an Intel i5-13500H CPU. This indicates the proposed controller is ready for real-time implementation. © 2024, CC BY.

关键词： Stochastic models

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Accurate MTPA Control Based on Ferrari Method for IPMSM with Partial Derivative Term Calculation 27

Accurate MTPA Control Based on Ferrari Method for IPMSM with...

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27th International Conference on Electrical Machines and Systems, ICEMS 2024

作者： Li, Yiyang Gu, Xin Wang, Zhiqiang Guo, Liyan Chen, Wei Li, Xinmin Li, Chen School of Electronics and Information Engineering Tiangong University Tianjin China School of Electrical Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin China National Local Joint Engineering Research Center of Electrical Machine System Design and Manufacturing China Zhejiang University Advanced Electrical Equipment Innovation Center Hangzhou China

ISBN: (纸本)9784886864406

Even if the exact motor parameters are known, the MTPA strategy cannot obtain precise maximum torque per ampere (MTPA) points under large current amplitude because the partial derivative of inductance to current is ignored in the modeling process. In view of this, the MTPA mathematical model considering the partial derivative of inductance to current is established in this paper, and the MTPA point tracking error caused by ignoring the partial derivative in the modeling process based on Ferrari method is analyzed. Then, an offline-online combined method for calculating the partial derivative term is proposed to compensate the MTPA tracking error. On this basis, an MTPA control strategy based on Ferrari method considering the partial derivative of inductance to current is proposed. Experiment to verify the feasibility and effectiveness of the proposed method. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Permanent magnets

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Hierarchically porous Cu current collector with lithiophilic CuxO interphase towards high-performance lithium metal batteries

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Journal of Energy Chemistry 2021年第7期30卷 292-299页

作者： Yaya Wang Zexu Zhao Wei Zeng Xingbo Liu Lei Wang Jian Zhu Bingan Lu State Key Laboratory for Chemo/Biosensing and Chemometrics and College of Chemistry and Chemical EngineeringHunan Key Laboratory of Two-Dimensional MaterialsHunan UniversityChangsha 410082HunanChina State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan Provincial Key Laboratory of Intelligent Laser ManufacturingHunan UniversityChangsha 410082HunanChina School of Materials and Engineering Hunan UniversityChangsha 410082HunanChina School of Physics and Electronics Hunan UniversityChangsha 410082HunanChina

Lithium metal is one of the most promising anode materials for next-generation electrochemical energy storage due to low electrochemical potential and high specific ***,large volume change and uncontrollable formation of lithium dendrite during cycling severely hinder the practical application of rechargeable Li metal ***,we report a hierarchically porous Cu covered with lithiophilic CuxO(HPCu-CuxO) via femtosecond laser strategy in about 2 min as current collector for highperformance Li metal *** precisely tunable pore volume and depth as well as lithiophilic CuxO interphase,the HPCu-CuxO not only guides homogeneous Li nucleation,resulting in a smooth and dendrite-free lithium surface,but also provides space to alleviate the volume expansion of Li metal anode,achieving excellent structure ***,highly stable Coulombic efficiency and ultralow overpotential of 15 mV even up to 1000 h were achieved at the current density of 1 mA cm^(-2).Moreover,the resultant Li@HPCu-CuxO//LiFePO_(4) full battery delivered outstanding cycle stability and rate *** results offer a pathway toward high-energy-density and safe rechargeable Li metal batteries.

关键词： Lithium metal batteries Femtosecond laser strategy Hierarchically porous structure Lithiophilic CuxO interphase

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

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Implementing a modified Smith predictor using chemical reaction networks and its application to protein translation 4

Implementing a modified Smith predictor using chemical react...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Xiao, Yijun Lv, Hui Wang, Xing'an School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Liaoning Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110004 China College of Environmental and Chemical Engineering Dalian University Liaoning Dalian116622 China Dalian Chivy Biotechnology Co. Ltd Liaoning Dalian116622 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this article, a special attention is paid to the biochemical controller synthesis for time delay systems and try to implement the well-established Smith predictor approach in the context of biochemical systems. Then, chemical reaction networks (CRNs) are adopted to construct a modified Smith predictor scheme (integrating Smith predictor and feedback compensation controllers) for the first time. Taking a delayed protein translation model as the background, the CRNs-based proposed scheme has access to a method that can solve the effect of co-translated mRNA decay in protein translation. In addition, considering that the decay of mRNA affects mRNA stability and protein production, the co-translated mRNA degradation is treated as an interference input of the protein translation process. Our results show that the impact of a disturbance input (mRNA degradation) is restrained by the modified control strategy. The CRNs-based modified Smith predictor makes the protein translation process more robust and achieves protein output quickly and stably. © 2022 IEEE.

关键词： Time delay

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BContext2Name: Naming Functions in Stripped Binaries with Multi-Label Learning and Neural Networks

BContext2Name: Naming Functions in Stripped Binaries with Mu...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Bing Xia Yunxiang Ge Ruinan Yang Jiabin Yin Jianmin Pang Chongjun Tang Key Laboratory of Mathematical Engineering and Advanced Computing dept.ZhongYuan University of Technology Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis dept.ZhongYuan University of Technology Zhengzhou China dept.ZhongYuan University of Technology Zhengzhou China dept.Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Conducting binary function naming helps reverse engineers understand the internal workings of the code and perform malicious code analysis without accessing the source code. However, the loss of debugging information poses the challenge of insufficient high-level semantic information description for stripping binary code function naming. Meanwhile, the existing binary function naming scheme has one function label for only one sample. The long-tail effect of function labels for a single sample makes the machine learning-based prediction models face the challenge. To obtain a function correlation label and improve the propensity score of uncommon tail labels, we propose a multi-label learning-based binary function naming model BContext2Name. This model automatically generates relevant labels for binary function naming by function context information with the help of PfastreXML model. The experimental results show that BContext2Name can enrich function labels and alleviate the long-tail effect that exists for a single sample class. To obtain high-level semantics of binary functions, we align pseudocode and basic blocks based on disassembly and decompilation, identify concrete or abstract values of API parameters by variable tracking, and construct API-enhanced control flow graphs. Finally, a seq2seq neural network translation model with attention mechanism is constructed between function multi-label learning and enhanced control flow graphs. Experiments on the dataset reveal that the F1 values of the BContext2Name model improve by 3.55% and 15.23% over the state-of-the-art XFL and Nero, respectively. This indicates that function multi-label learning can provide accurate labels for binary functions and can help reverse analysts understand the inner working mechanism of binary code. Code and data for this evaluation are available at https://***/CSecurityZhongYuan/BContext2Name.

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静电驱动下指数型变截面梁式陀螺仪的非线性特性与分析

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Acta Mechanica Sinica 2023年第3期39卷 168-184页

作者：张昆鹏畅兆敏郝淑英张琪昌冯晶晶 Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Schoolof MechanicalEngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin 300384China Tianjn Key Laorary of Noninearynamics and Conto Soolof MhancalEngineeringTinjin UnverTianjn 30002China

本文设计一类新型的指数型变截面梁式微陀螺,变截面指数梁的宽度与长度的关系为指数函数变化的形式,且指数梁的截面变化因子小于等于零,梁的厚度随梁长仍保持不变.考虑了曲率非线性和惯性非线性对系统的影响,利用扩展哈密顿原理、单模... 详细信息

本文设计一类新型的指数型变截面梁式微陀螺,变截面指数梁的宽度与长度的关系为指数函数变化的形式,且指数梁的截面变化因子小于等于零,梁的厚度随梁长仍保持不变.考虑了曲率非线性和惯性非线性对系统的影响,利用扩展哈密顿原理、单模近似法和拉格朗日微分方程,建立了指数型变截面梁式非线性陀螺仪的振动控制方程、边界条件和非线性离散化模型,且考虑了陀螺仪驱动和检测两个方向上直流电压和交流电压对系统响应的影响,采用ADM法求解陀螺仪系统在不同截面变化因子下的静态响应,利用多尺度法对系统的非线性离散化模型进行摄动求解,分析各个参数对陀螺仪动态响应的影响规律.结果表明,随着截面变化因子的增大,指数梁陀螺仪的吸合电压和一阶固有频率逐渐增加,并呈现近似线性的变化规律.通过调节指数梁截面变化因子,可以控制科氏力响应峰值频率和检测峰值频率的差值.利用指数梁陀螺仪系统的硬化非线性特征,当交流电压施加在指数梁厚度方向时,选择合适的指数截面变化因子、阻尼比和交流电压,可以使指数梁式陀螺仪系统获得更好的带宽性能及线性可测范围;当交流电压施加在指数梁宽度方向,选取合适的截面变化因子,不仅可以获得较高的系统灵敏度性能,同时增加陀螺仪对外部输入角速度的线性可测范围.

关键词： Exponential beam Micro-gyroscope Nonlinear characteristics Bandwidth Sensitivity

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