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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Li, Siyuan Li, Xinde School of Cyber Science and Engineering Southeast University Jiangsu Nanjing211100 China Key Laboratory of Measurement and Control of CSE School of Automation Southeast University Jiangsu Nanjing210018 China Southeast University Shenzhen Research Institute Shenzhen518063 China Nanjing Center for Applied Mathematics Nanjing211135 China

ISBN: (纸本)9781510681279

Incomplete multi-view clustering (IMVC) for liver tumor CT images provide common and complementary information, that essential to support liver tumor diagnosis of without labeling. These IMVC techniques still have two problems, though: 1) they only heed the following to the resemblance between the same samples of different views, ignoring the resemblance between different samples of the same view, which can provide more information for liver tumor diagnose;2) most of the existing diagnostic processes are staged, greatly increasing the cost of time. We suggest a solution to these problems by a dual contrastive learning framework to learning mutual information from cross-view contrast learning and intra-view contrast learning at the same time. On this basis, the cosine similarity between views is constructed using positive and negative samples from these dual contrastive learning, and the whole process is carried out synchronously. In this paper, two multi-view liver tumor data sets are compared with 4 methods in recent years, indicating that this method is the most advanced. © 2024 SPIE.

关键词： Diagnosis

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An Improved YOLOv5 Detection Algorithm with Pruning and OpenVINO Quantization

An Improved YOLOv5 Detection Algorithm with Pruning and Open...

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2023 China automation Congress, CAC 2023

作者： Chen, Cheng Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

In order to address the problem of current object detection models being too large to be deployed on robot controllers, this paper proposes improvements to YOLOv5 for real-time detection. The YOLOv5s model is pruned at the Batch Normalization (BN) layer, and further optimized using the OpenVINO tool for quantization. The results demonstrate that our improvements effectively improve the model's inference speed while maintaining its accuracy. Compared to the original YOLOv5 model, the pruned model achieves the same accuracy without degradation, with a 36.6% reduction in parameter count and a 35% reduction in weight storage file size. Furthermore, after optimization with OpenVINO, the final model achieves a FPS of 56. © 2023 IEEE.

关键词： Object detection

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Open-circuit fault diagnosis method for inverters using deep learning and the evidence reasoning rule

Open-circuit fault diagnosis method for inverters using deep...

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2022 International Conference on Smart Energy and Electrical engineering, SEEE 2022

作者： Yu, Hang Gao, Haibo He, Yelan Lin, Zhiguo Xu, Xiaobin Pan, Zhiqiang School of Marine and Energy Power Engineering Wuhan University of Technology Wuhan China School of Computer Science and Technology Wuhan University of Technology Wuhan China Institute of System Science and Control Engineering Hangzhou Dianzi University Hangzhou China Shanghai Marine Diesel Engine Research Institute Shanghai China

Inverters having high voltage levels, high power density, and high integration are widely used. However, many high-frequency switch units also increase the probability of failure. Therefore, developing an accurate and stable fault diagnosis method is necessary. This paper proposes a fault diagnosis algorithm based on deep learning and the evidence reasoning (ER) rule. It not only ensures high diagnostic accuracy, but also enhances the stability of the diagnostic results. The algorithm takes the three-phase voltage source inverter as the research object and extracts the three-phase current signals with different types of faults as features. First, Convolutional and Deep Neural Network methods were utilized independently to determine a preliminary diagnosis. Second, the softmax functions of the Convolutional and Deep Neural Network outputs provided the probability distribution of the fault category, which was used as the evidence body for the ER rule to construct the fusion diagnosis. In addition, a new method of determining the reliability and the importance factors of the evidence was proposed in which the evaluation index of the deep-learning diagnosis result was applied. Finally, the final classification result was obtained using the ER rule. The proposed method can effectively enhance the accuracy and robustness compared with a single classifier. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Multi-parameter load sensing pump model simulation and flow rate characteristics research

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Chinese Journal of Aeronautics 2022年第12期35卷 296-308页

作者： ZongXia JIAO Zhenyu WANG Xiaochao LIU HuJiang WANG Pengyuan QI Weizhi QIAO School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Research Institute for Frontier Science Beihang UniversityBeijing100083China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100083China Science and Technology on Vehicle Transmission Laboratory China North Vehicle Research InstituteBeijing 100072China Ningbo Institute of Technology Beihang UniversityNingbo 315800China

Load sensing pumps have been widely used in diverse hydraulic *** show that structural parameters have undeniable impacts on the characteristics and efficiency of the load sensing *** main purpose of this article is to study the influence of load sensing pump structure parameters on flow *** the present study,a nonlinear multi-parameter model is proposed for this type of *** this model,different parameters,including spool clearance,spool covering amount,internal leakage are considered to reflect the displacement adjustment process of the load sensing ***,a frequency sweep method is proposed to analyze the frequency domain of the nonlinear mathematical *** experiment rig was built to study the influence of key structural parameters on the dynamic follow-up characteristics of the pump *** obtained results show that the diameter of the orifice d can significantly affect the working characteristics of the *** is found that a large diameter of the orifice d can improve the phase following ability of the system,while a small diameter of the orifice d can reduce the bypass flow rate and increase the amplitude following *** paper provides a new consideration to study the dynamic follow-up characteristics of the load sensing pump.

关键词： Flow rate characteristics Frequency analysis Hydraulic Load sensing pump Nonlinear

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Parameter Design Method of the Magnetic Coupler for Wireless Charging System Using Metamodel of Optimal Prognosis

Parameter Design Method of the Magnetic Coupler for Wireless...

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China International Electrical and Energy Conference (CIEEC)

作者： Jun Ma Jiafang He Zhenjie Li Shuai Dong Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology Harbin China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

This paper addresses the parameter design problem of magnetic couplers and proposes a multi-objective optimization design method based on the Metamodel of Optimal Prognosis (MOP). The method involves mathematically fitting optimization objectives and design parameters using the optimal predictive metamodel, thereby establishing response surfaces for multi-objective optimization. By transforming finite element calculations of the magnetic coupler into numerical computations through fitting, the optimization process is accelerated. The parameters ultimately selected are verified through simulation, thus validating the effectiveness of the proposed optimization method.

关键词： Design methodology Fitting Optimization methods Couplers Wireless power transfer Surface charging Response surface methodology

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Aerial-ground collaborative routing with time constraints

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Chinese Journal of Aeronautics 2023年第2期36卷 270-283页

作者： Jiahao XING Lichen SU Wenjing HONG Lu TONG Renli LYU Wenbo DU School of Electronics and Information Engineering Beihang UniversityBeijing 100091China School of Future Aerospace Technology/Shen Yuan Honors College Beihang UniversityBejing 100091China School of Automation Science and Electrical Engineering Beihang UniversityBejing 100091China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Shenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Research Institute of Frontier Science Beihang UniversityBeijing 100083China CAAC Key Laboratory of General Aviation Operation Civil Aviation Management Institute of ChinaBeijing 100102China

The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery *** reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily *** this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion ***,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality *** numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.

关键词： Aerial-ground collaborative delivery Mixed-integer programming Space-time hybrid heuristic algorithm Vehicle routing problem with drones Time-dependent travel times

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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A Study of Encapsulation Temperature Field of Dry-type Air-core Reactor with the Structure of Equivalent and Aluminum Wire-Insulation

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Distributed Generation and Alternative Energy Journal 2022年第5期37卷 1665-1680页

作者： Wan, Xiaorong Wang, Han Li, Yingna Li, Chuan Zhao, Zhengang Cui, Zhigang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China Electric Power Research Institute of Yunnan Power Grid Co. Ltd Kunming650217 China

During the operation of the Dry-type Air-core Reactor (DAR), the insulation material deteriorates due to local overheating of the encapsulation, and the reactor is burned in severe conditions. Therefore, studying the temperature rise of the reactor plays an important role in the reliability of its operation and the economy of design and production. According to the structural characteristics of the reactor, the three-dimensional thermal field model of the aluminum wire-insulation structure of the reactor is established, and the encapsulation temperature field distribution of the reactor under steady state is obtained. Compare the simulation result with the encapsulation temperature field distribution of the equivalent structure. The results show that the highest temperature of the aluminum wire-insulation structure encapsulation rises to 73.51◦C, which is located in the middle of the reactor. Each encapsulation hotspot is located about 20% from the upper edge of the encapsulation. In the temperature rise test, a Fiber Bragg Grating (FGB) temperature sensor is installed about 20% from the upper edge of the encapsulation. The test shows that the most significant value of temperature rise of the reactor is 73◦C, which is located in the middle of the reactor. The temperature rise test results verify the accuracy of the simulated calculation value of the temperature field of the aluminum wire-insulated reactor and provide a reference for the temperature rise calculation and temperature rise monitoring of the DAR. © 2022 River Publishers.

关键词： Fiber Bragg gratings

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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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Science China Earth Sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV Qinjun QIU Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph Representation model Spatiotemporal features Spatiotemporal relationships

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

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2023 China automation Congress, CAC 2023

作者： Zhong, Tao Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments. © 2023 IEEE.

关键词： Object detection

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