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Software engineering and Computer Science (CSECS), International Conference on

作者： Chikwendu Ijeoma Amuche Xiaoling Zhang Chiagoziem Chima Ukwuoma Chikwendu Okechuwku Chinedum Ejiyi Chukwuebuka Joseph School of Information and Communication Engineering dept. University of Electronic Science and Technology of China Chengdu China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan P.R. China Department of Biochemistry Federal University of Technology Owerri. Ihiagwa Nigeria College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan P.R. China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Detecting fraud in multi-relational graphs is a significant difficulty due to the complex nature of fraudulent activities and the inadequacies of conventional Graph Neural Networks (GNNs) in managing heterophilic relationships and long-range dependencies. Current GNN-based models experience over-smoothing and over-squashing, which constrains their capacity to differentiate between fraudulent and legitimate interactions. To tackle these difficulties, we offer MR-GT, a Graph Transformer-based model that integrates multi-relational attention and edge-aware techniques to enhance fraud detection. MR-GT implements edge-aware message passing and edge-based attention bias, enabling the model to concentrate on significant transactional links while disregarding less pertinent interactions. The approach utilizes relation-aware aggregation to accurately identify fraud-related patterns in diverse interactions. Experimental findings on the Yelp and Amazon datasets indicate that MR-GT significantly surpasses state-of-the-art approaches, attaining an AVC of 92.33 on Yelp and 98.25 on Amazon. The ablation study further illustrates the significance of each component, indicating that MR-GT provides a scalable and efficient approach for identifying fraud in extensive, multi-relational graphs.

关键词： Measurement Computer science Attention mechanisms Image edge detection Message passing Focusing Transformers Graph neural networks Fraud Software engineering

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Disorders in fluid filled pipeline structure with elastic helmholtz resonators 29

Disorders in fluid filled pipeline structure with elastic he...

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2022 29th International Conference on nuclear engineering, ICONE 2022

作者： Zeng, Qingna Wang, Donghui Zang, Fenggang Zhang, Yixiong Wang, Bihao Yuan, Zhihao Science and Technology on Reactor System Design Technology Laboratory Chengdu610213 China Nuclear Power Institute of China Chengdu610213 China College of Shipbuilding Engineering Harbin Engineering University Harbin150001 China

ISBN: (纸本)9780791886359

This paper studies the influence of hybrid disorders on acoustic transmission characteristic in pipeline with periodic elastic HRs. Band gap structures (BGs) for infinite period are investigated by transfer matrix method and finite element method respectively, and fit well with the corresponding transmission loss (TL) for finite regular periods. By introducing a certain degree of uncertainty level, Interval analysis method is used to convert uncertain parameter problems into two deterministic models. Frequency response function (FRF) for near-periodic structure with single disorder and BGs for quasiperiodic structure with multiple disorders considering cavity radius and periodic length are investigated. The existence of disorders in periodic structure would always reduce attenuation interval and intensity, some disturbance would even change the original acoustic transmission characteristic and excite completely new attenuation zones. The sensitivity of pipeline acoustic vibration transfer characteristic to disorder parameter depends on attenuation mechanism, as for periodic elastic HRs pipeline, acoustic propagation stability is more sensitive to characteristic of locally resonant unit. The work in this paper enriches the stability analysis of pipeline with elastic HRs, which would provide theoretical reference for noise and vibration reduction design in practical engineering applications. © 2022 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Transfer matrix method

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Intelligent Strawberry Ripeness and Disease Detection Classification System Based on Deep Learning

Intelligent Strawberry Ripeness and Disease Detection Classi...

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International Conference on Electronics and Information technology (EIT)

作者： Jiajun Li Xin Yang Songcheng Qian Junjie Xu Lei Sun Zhaoning Zeng School of Automation Engineering University of Electronic Science and Technology of China Chengdu China College of Energy Chengdu University of Technology Chengdu China School of Earth Science Yunnan University China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

In modern agricultural production, timely detection of crop diseases and accurate assessment of ripeness are crucial for reducing yield loss and enhancing product quality. Strawberries, being a widely popular and economically valuable fruit, are particularly sensitive to diseases and strict in ripeness requirements, making quality control during their production critical. This paper develops a classification system for strawberry ripeness and pest disease detection based on the YOLOv5 deep learning model and automated robotic arm technology. The YOLOv5 model, known for its high efficiency, accurately identifies strawberries and assesses their ripeness and pest conditions, thereby enhancing the precision and speed of detection. The automated robotic arm technology utilizes the detection results from the YOLOv5 model to perform efficient and accurate automated sorting and grading of strawberries, significantly reducing labor costs and human error. By integrating deep learning and automation technologies, this system substantially increases strawberry yield and quality, alleviates labor burdens, and enhances agricultural production efficiency.

关键词： YOLO Deep learning automation Costs Accuracy Production Manipulators Robots Diseases Sorting

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Improved Blending Attention Mechanism in Visual Question Answering

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Computer Systems Science & engineering 2023年第10期47卷 1149-1161页

作者： Siyu Lu Yueming Ding Zhengtong Yin Mingzhe Liu Xuan Liu Wenfeng Zheng Lirong Yin School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325000China School of Public Affairs and Administration University of Electronic Science and Technology of ChinaChengdu611731China Department of Geography and Anthropology Louisiana State UniversityBaton Rouge70803LAUSA

Visual question answering(VQA)has attracted more and more attention in computer vision and natural language *** are committed to studying how to better integrate image features and text features to achieve better results in VQA *** of all features may cause information redundancy and heavy computational *** mechanism is a wise way to solve this ***,using single attention mechanism may cause incomplete concern of *** paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism *** the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as *** the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall *** results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.

关键词： Visual question answering spatial attention mechanism channel attention mechanism image feature processing text feature extraction

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Research on Precision Positioning Control System of Punching Machine Feeding Device 6

Research on Precision Positioning Control System of Punching...

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6th IEEE Information technology and Mechatronics engineering Conference, ITOEC 2022

作者： Pang, Dangfeng Cui, Shigang Zhang, Yongli College of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China College of Mechanical Engineering Tianjin Sino-German University of Applied Sciences Tianjin China

ISBN: (纸本)9781665431859

In order to improve the working efficiency and safety of the stainless steel spoon punching, after analyzing the punching and cutting process and the production requirements of enterprise, a precision positioning control system for the automatic feeding device based on integral separation PID algorithm is designed. The system uses Siemens S7-200SMART PLC as the master station. Design parameters and display data through the touch screen, and use DC motors and pneumatic circuits to achieve coordinated control of the punch and feeder. Through actual production verification, the system has high relocation accuracy and reliable operation, the efficiency increased by 40%. At the same time, it reduces safety hazards and labor costs, provides a reference and theoretical basis for the automation upgrade and transformation of stamping feeding. © 2022 IEEE.

关键词： Efficiency

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Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal Carcinoma Radiotherapy with a Multidimensional Information Fusion Network

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal...

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International Conference on Computer Applications technology (ICCAT)

作者： Xin Jiang Zhi-Chao Chen Zheng Sun Ming-Zhe Liu School of Artificial Intelligence Chongqing University of Technology Chongqing China Department of Radiology Chongqing University Cancer Hospital Chongqing China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China

ISBN: (数字)9798331539795

ISBN: (纸本)9798331539801

X-ray-induced acoustic computed tomography (XACT) imaging is an emerging noninvasive imaging technique with wide applications in various clinical medicine fields. However, traditional XACT imaging methods often suffer from low imaging quality and increased noise artifacts, particularly notable under sparse sampling conditions. To overcome these challenges, we propose a hybrid supervised reconstruction method based on deep learning. Specifically, we extract and integrate information from XACT thermoacoustic signals and XACT images reconstructed by traditional methods to leverage complementary feature information from both modalities. To adapt to imaging under sparse sampling, we define multiple sparse conditions based on real-world imaging environments and validate them using a real radiotherapy dataset of head and neck cancer patients. The results demonstrate the adaptability of our method to sparse conditions and its capability to enhance XACT imaging quality under such circumstances. Additionally, we validate the feasibility of X-ray thermoacoustic imaging for 3D dose monitoring.

关键词： Deep learning Three-dimensional displays Imaging Feature extraction Acoustics Robustness Radiation therapy Data mining X-ray imaging Image reconstruction

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Analysis and Design of Finite Input Inductance Class-E Inverters with Variable Complex Loads

Analysis and Design of Finite Input Inductance Class-E Inver...

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IEEE Workshop on Computers in Power Electronics

作者： Yufei Wang Min Sun Dongsheng Cai Jian Li Qi Huang Siming Pan School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Southwest University of Science and Technology Mianyang China

This paper presents a detailed analysis and design method of the Class-E inverter with finite input inductance under arbitrary complex loads based on the first harmonic approximation. The analytic results show that the inverter can achieve ZVS with a wide range of complex loads with proper resonant network design. The model established allows designers to plot the theoretical ZVS load impedance region for specific design parameters directly in the complex plane. Subsequently a design method for the inverter to achieve high efficiency operation under variable complex loads is developed. This method thus allows the Class-E inverter to be used in an application in which the load resistance varies as well as the reactance. The effectiveness of the proposed method is verified by a 13.56 MHz Class-E inverter prototype.

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Convolution-Transformer for Image Feature Extraction

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Computer Modeling in engineering & Sciences 2024年第10期141卷 87-106页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

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Recent progress of radionuclides separation by porous materials

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Science China Chemistry 2024年第11期67卷 3515-3577页

作者： Yinghui Xie Long Yu Long Chen Cheng Chen Li Wang Fenglei Liu Yun Liao Peng Zhang Tao Chen Yihui Yuan Yuexiang Lu Boyang Huang Hui Yang Suhua Wang Shuao Wang Lijian Ma Feng Luo Yunhai Liu Baowei Hu Hongqing Wang Duoqiang Pan Wenkun Zhu Ning Wang Zhe Wang Liang Mao Shengqian Ma Xiangke Wang Guangdong Provincial Key Laboratory for Green Agricultural Production and Intelligent Equipment School of Environmental Science and EngineeringGuangdong University of Petrochemical TechnologyMaoming 525000China School of Life Science Shaoxing UniversityShaoxing 312000China College of Environmental Science and Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Radiation Medicine and Protection School for Radiological and Interdisciplinary Sciences(RAD-X)and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education InstitutionsSoochow UniversitySuzhou 215123China College of Chemistry Sichuan UniversityChengdu 610064China School of Chemistry and Materials Science East China University of TechnologyNanchang 330013China School of Chemistry and Chemical Engineering University of South ChinaHengyang 421001China Frontiers Science Center for Rare Isotopes Lanzhou UniversityLanzhou 730000China State Key Laboratory of Environment-friendly Energy Materials National Co-innovation Center for Nuclear Waste Disposal and Environmental SafetySichuan Civil-military Integration InstituteSchool of National Defence&Nuclear Science and TechnologySouthwest University of Science and TechnologyMianyang 621010China State Key Laboratory of Marine Resource Utilization in South China Sea Collaborative Innovation Center of Marine Science and TechnologyHainan UniversityHaikou 570228China Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China State Key Laboratory of Pollution Control and Resource Reuse School of the EnvironmentNanjing UniversityNanjing 210093China Department of Chemistry University of North TexasDentonTX 76201USA

The separation of radionuclides is critical for the sustainable development of nuclear energy. It is urgent to design and prepare functionalized materials for efficient radionuclides separation. Porous materials are considered excellent candidates for the separation of radionuclides under complex conditions due to their high specific surface areas, tunable pore structures and controllable functionalities. In this review, we summarized the design, preparation and functionalization of porous materials and their application for separation of radionuclides in the past five years, discussed the separation performance and analyzed the structure-activity relationship between various radionuclides and porous materials, and systematically clarified their characterization and mechanism of different type porous materials. We also introduced the detection, irradiation and chemical toxicity of different reflective radionuclides.

关键词： porous materials radionuclides detection separation radiotoxicity interaction mechanism

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MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation

arXiv

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arXiv 2024年

作者： Yang, Qihang Zhao, Yang Cheng, Hong Glasgow College University of Electronic Science and Technology of China Chengdu611731 China School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China

Autonomous driving necessitates advanced object detection techniques that integrate information from multiple modalities to overcome the limitations associated with single-modal approaches. The challenges of aligning diverse data in early fusion, and the complexities along with overfitting issues introduced by deep fusion underscore the efficacy of late fusion at the decision level. Late fusion ensures seamless integration without altering the original detector’s network structure. This paper introduces a pioneering Multi-modal Multi-class Late Fusion (MMLF) method, which is designed for late fusion to enable multi-class detection. Fusion experiments conducted on the KITTI validation and official test datasets illustrate substantial performance improvements, presenting our model as a versatile solution for multi-modal object detection in autonomous driving. Moreover, our approach incorporates uncertainty analysis into the classification fusion process, which renders our model more transparent and trustworthy, providing more reliable insights into category predictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Object recognition

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