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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Jinjian Chen Zuoyong Li Taotao Lai Huosheng Xie College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Establishing dense correspondences between semantically similar images is a challenging task. Cost aggregation is a crucial step in finding correct dense correspondences, with the goal of optimizing the initial correlation map thereby removing the ambiguity of the correspondences. Current approaches use transformer architectures for cost aggregation, which lack local priors to adequately capture the local information contained in the correlation map. We propose to incorporate peripheral position coding into the transformer to explore the local information to obtain the matching set and call it the Peripheral Transformer Matcher (PTM). This coding technique partitions the overall receptive field of the self-attention mechanism into diverse peripheral regions, each with its own set of weights. By doing this, the proposed PTM gets a specific local prior by adding an inductive bias to the transformer models and making the initial correlation map less confusing. In addition, a local self-attention module is used to enhance the image features and obtain an enhanced initial correlation map. Comparisons of the experimental results with baselines on public datasets demonstrate the effectiveness of the proposed PTM.

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Torque Characteristic Analyses and Multi-Objective Optimisation of Multi-Layer Flux-Barrier Less-Rare-Earth Permanent Magnet Synchronous Machine

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IET Electric Power Applications 2025年第1期19卷

作者： Wang, Weinan Gong, Weize Zhu, Liangkuan Wei, Jian Wang, Mingqiao Liu, Yong Zheng, Ping College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

This paper investigates the torque characteristic and multi-objective optimisation (MOO) of the multi-layer flux-barrier less-rare-earth permanent magnet synchronous machine (MLFB-LRE-PMSM) used for the electric vehicles (EVs). This study explores the variation of torque characteristics with current angle under different winding current conditions, and thoroughly analyses the influence of permanent magnet (PM) structure parameters on reluctance torque, PM torque and the proportion of reluctance torque in the electromagnetic torque. On this basis, the sensitivity analysis method based on Sobol sequence and joint Sobol index is adopted, which not only simplifies the traditional analysis process, but also effectively considers the interaction effect between the optimisation objectives. Finally, a cooperative optimisation strategy of improved whale optimisation algorithm (WOA) and genetic algorithm (GA) is proposed, which is successfully applied to the MOO design of MLFB-LRE-PMSM. The results show that the improved WOA algorithm shows excellent optimisation performance and can be used as a new solution in the field of motor optimisation. At the same time, the engineering practicability of the proposed collaborative optimisation scheme is verified by finite element simulation. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Synchronous machinery

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Improving Electrically Evoked Compound Action Potential Based on Electrical Field Imaging and Electrode Interfaces

Improving Electrically Evoked Compound Action Potential Base...

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IEEE Conference on Electromagnetic Field Computation

作者： Charles T.M. Choi Chun Ting Ke Jelani Lawrence Alexander C. C. Wang Department of Electronics and Electrical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electrical and Control Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering & Computer Science International Graduate Program College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

The electrically evoked compound action potential (ECAP) has been used in various clinical studies and has become a key physiological signal for cochlear implants (CI). This study used four sensing electrodes to record ECAP signals based on the alternating polarity approach. An electrical field imaging (EFI) result based on the finite element method was used to obtain the interface impedance, then ECAP simulation results were computed and compared with a patient's clinical ECAP measurements. Preliminary modeling results show that the interface impedance obtained by this EFI-based technique can improve the simulation accuracy of the ECAP model. The ECAP modeling result will be compared with clinical ECAP measurements to validate the model in the full paper.

关键词： Electrodes Action potentials Impedance measurement Computational modeling Simulation Imaging Finite element analysis

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An Efficient Particle YOLO Detector for Urine Sediment Detection 4th

An Efficient Particle YOLO Detector for Urine Sediment Dete...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Chen, Zejian Hu, Rong Chen, Fukun Fan, Haoyi Ching, Fum Yew Li, Zuoyong Su, Shimei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China School of Electrical Engineering Zhengzhou University Henan Zhengzhou450001 China

ISBN: (纸本)9783031201011

Urine sediment detection is an essential aid in assessing kidney health. Traditional machine learning approaches treat urine sediment particle detection as an image classification task, segmenting particles for detection based on information such as edges or thresholds. However, the segmentation of sediment particles is complex due to the low contrast and weak edge characteristics of urine sediment images. In this paper, we consider urine sediment particle detection as a object detection task and propose the YOLOv5s-CBL, a detector dedicated to particle detection. Specifically, to mitigate the impact of background noise on detection accuracy, we inherit CBAM on the YOLOv5s model to help the network filter useless noise information and find regions of interest to extract target features. Then, we expand the original three-scale feature layer to improve the sensitivity of the model to larger-scale target sediment particles. Finally, we use the BiFPN structure instead of the original PANet combined with the FPN structure, which can more effectively fuse multiple different scales of information to improve the detection performance of the model. We compared state-of-the-art methods on two real-world datasets, and the experimental results demonstrated the effectiveness of YOLOv5s-CBL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Identification of Partial Discharge Fault Type and Sensitivity Analysis of Joint Detection Based on Clustering Algorithm 3

Identification of Partial Discharge Fault Type and Sensitivi...

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3rd International Conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Feng, Qi Shao, Zhenhua Wenzhou University College of Electrical and Electronic Engineering Wenzhou China Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9781665417327

Partial discharge (PD) is the initial manifestation of insulation deterioration in power equipment. When PD accumulates to a certain extent, it will lead to equipment damage. PD detection in advance is an effective method to prevent insulation deterioration. In this paper, AE method, TEV method and UHF method are used to study the internal discharge, suspension discharge and surface discharge respectively, pointing out the limitations of a single detection method, and using clustering method to extract the discharge characteristic parameters of different defect types to classify. The results of K-means clustering show that the accuracy of internal discharge is 83%, surface discharge is 65%, and suspension discharge is 66%. FCM clustering results show that the accuracy of internal discharge is 63%, surface discharge is 60%, and suspension discharge is 50%. The results of hierarchical clustering show that the accuracy of internal discharge is 73%, surface discharge is 70%, and suspension discharge is 67%. From the perspective of sensitivity, TEV method has the highest sensitivity, AE method and UHF method have their advantages in the sensitivity of different discharge defects. In order to overcome the limitation of a single detection method, combined with the experimental results and the actual noise interference and local location requirements, the application of multi-means combined detection is analyzed to provide some reference for the selection of joint detection method. UHF/AE method can avoid electromagnetic interference. UHF/TEV method can avoid acoustic interference;TEV/AE method can improve the localization accuracy. © 2021 IEEE.

关键词： Deterioration

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Partially Double-Pass Structure in Serial Hybrid Fiber Amplifier

Partially Double-Pass Structure in Serial Hybrid Fiber Ampli...

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2023 International Conference on engineering, Science and Advanced Technology, ICESAT 2023

作者： Ali, Ali Yaseen Salh Al-Mashhadani, Mohammed Kamil Al-Mashhadani, Thamer Fahad Hammudi, Rabi Noori Bouteraa, Yassine Hammudi, Rabi Noori Tunisia Tikrit University Department of Petroleum Systems Control Engineering Saladdin Iraq Tikrit University Department of Electrical Engineering Saladdin Iraq Kut Univeristy College Department of Laser Engineering and Optoelectronics Alkut Wasit52001 Iraq Prince Sattam Bin Abdulaziz University Department of Computer Engineering Al-Kharj11942 Saudi Arabia

ISBN: (纸本)9798350344400

In this paper, a partially double pass configuration in serial hybrid fiber amplifier is experimentally demonstrated. In the proposed design, a double pass erbium gain and single pass Raman gain are achieved serially. A total pump power of 450 mW (400 mW for 1495 nm Raman amplifier and 50 mW for1480 nm in erbium amplifier) were used. At -30 dBm input signal power and optimum pumps conditions, the achieved flatness bandwidth is 80 nm (1530-1610 nm) in the conventional and long bands (C+L) bands. In addition, the obtained average gain level is 33 dB. While the obtained flatness gain is 85 nm (1525-1610 nm) within the large input signal power region at -5 dBm. By choosing a proper pump wavelength that avoid the overlapping between Raman and erbium peaks gain, a wide flatness gain is obtained. © 2023 IEEE.

关键词： Raman scattering

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The Preparation Method, Resistive Switching Mechanism and Application Prospects of Memristor 7

The Preparation Method, Resistive Switching Mechanism and Ap...

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7th International Conference on Electrical, Mechanical and computer engineering, ICEMCE 2023

作者： Li, Jiao Zhao, Ximei Lei, Tengfei Chai, Lin Wang, Shi Yu, Yuna Collaborative Innovation Center of Memristive and Computing Application Qilu Institute of Technology Jinan250200 China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan250200 China College of Computer and Communication Engineering Qilu Institute of Technology Jinan250200 China

ISBN: (纸本)9798350382877

Memristors have also become one of the most promising research hotspots for solving data storage and computing problems faced in the big data era. As an emerging electronic device, memristor has potentially important applications in information storage and processing. This paper constructs a rod-shaped TiO2-x nanoscale memristor with FTO/TiO2-x/Cu structure, whose reliability is based on the memory behavior and its conversion mechanism. Research progress including materials, preparation methods, resistive switching mechanism and application prospects are described. First, the constituent materials of memristor are introduced, focusing on metal oxides and organic materials such as titanium oxide (TiO2). The choice of these materials is critical to memristor performance and applications. Secondly, the advantages and limitations of the preparation methods are discussed. Then the resistance switching mechanism of memristor was studied in depth, focusing on the generation principle and adjustment mechanism of memristor effect. These include important concepts such as charge migration, Schottky barrier formation and oxygen vacancy drift, which help to understand the electrical properties of memristors. In addition, we explore the prospects of memristors in practical applications. Finally, past and current work in this field is summarized, and the prospects and challenges of memristorrelated research are prospected. © 2023 IEEE.

关键词： Memristors

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Synergistically Learning Class-specific Tokens for Multi-class Whole Slide Image Classification

Synergistically Learning Class-specific Tokens for Multi-cla...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sun, Pengzhong Wang, Wei Li, Xiangyu Dong, Suyu Li, Shuo Wang, Kuanquan Luo, Gongning Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology Faculty of Computing Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Case Western Reserve University Case School of Engineering Cleveland United States

ISBN: (纸本)9798350337488

The application of transformer architecture in analyzing whole slide images (WSIs) has become increasingly popular due to its remarkable ability to learn complex associations. Nevertheless, a significant drawback emerges in the multiclass analysis of WSIs. The majority of the transformer-based methods available currently rely primarily on a single, class-agnostic token. This approach might not ideally capture the subtleties of class-discriminative information. To address this challenge, we present an innovative approach tailored for multi-class WSI analysis that harnesses the power of class-specific tokens. Central to our method is a novel attention mechanism designed to foster a synergistic learning relationship between patch and class tokens, enhancing the granularity of information captured and ensuring a more comprehensive representation of the WSI. Complementing this, we introduce a dynamic class-centric training strategy designed to optimize token representation learning, ensuring each token is informatively aligned with its corresponding class. Through extensive experimentation on three challenging multi-class WSI analysis datasets, our method consistently demonstrates superior performance, underscoring its potential as a robust solution for multi-class WSI analysis tasks. © 2023 IEEE.

关键词： Multi-class whole slide image analysis Multiple instance learning Transformer

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Underwater Target Detection Based on Dual Inversion Dehazing and Multi-Scale Feature Fusion Network 17th

Underwater Target Detection Based on Dual Inversion Dehazing...

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17th China Conference on Wireless Sensor Networks, CWSN 2023

作者： Wei, Lian-Suo Ma, Long-Yu Huang, Shen-Hao School of Information Engineering Suqian University Jiangsu Suqian223800 China College of Computer and Control Engineering Qiqihar University Heilongjiang Qiqihar161006 China

ISBN: (纸本)9789819710096

In response to the insufficient small-scale feature extraction capabilities of traditional SSD object detection algorithms, which lead to missed detections and false alarms in the detection of small underwater targets, this paper proposes a new underwater object detection algorithm. The algorithm first uses a Dual Reverse Dehazing (DRDM) module for image enhancement. Then, on the basis of the backbone network of the SSD algorithm, a channel attention mechanism and feature extraction convolution are introduced to construct a SE-Feature-Enhancement Module (SFEM) to integrate high-resolution shallow feature maps and deep feature maps with high semantic information. This fusion method preserves the context information of the feature maps, learns the necessary features, and suppresses redundant information, thereby improving the network’s learning ability. The experimental results show that the proposed algorithm achieves a detection accuracy of 94.62% on the Wilder_fish_new dataset. Compared with the current commonly used SSD (VGG) and SSD (Mobilenetv2) algorithms, the proposed algorithm improves the accuracy by 2.7 and 3.13 percentage points, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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