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9th International Conference on Multimedia Systems and Signal processing, ICMSSP 2024

作者： Wang, Zihan Zhang, Yunxi Ma, Yuqi Zhao, Ya Liu, Jia School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798400716911

This paper proposes a novel potato detection algorithm YOLOv8-P, based upon the improved YOLOv8 framework to achieve enhanced detection accuracy. The algorithm incorporates the BiFormer attention mechanism and introduces an additional detection layer specifically designed for large targets, thereby significantly improving the model's performance in identifying larger potatoes. Additionally, a comprehensive dataset named potato-2024 was developed to address the limitations of existing public datasets in potato detection. To validate the effectiveness of YOLOv8-P, comparative experiments were conducted with YOLOv5 and YOLOv8 on the potato-2024 dataset. The results demonstrate that, under identical experimental conditions and parameters, YOLOv8-P exhibits superior recognition accuracy in potato identification, thus establishing a new benchmark in potato detection technology. © 2024 Copyright held by the owner/author(s).

关键词： YOLOv8

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A review of bearing fault diagnosis based on weakly supervised learning

A review of bearing fault diagnosis based on weakly supervis...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Zhao, Bichuang Cen, Jian Si, Weiwei Huang, Hankun Yang, Zhuohong Chen, Honghua Guangdong Polytechnic Normal University Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory GuangZhou China

ISBN: (纸本)9798350337754

In the era of big data, mechanical intelligent fault diagnosis is more intelligent due to the large-scale collection of data. However, fault data is often difficult to obtain, resulting in small or even no samples. This article reviews the research on weakly supervised learning in the field of fault diagnosis. For few shot learning methods, it is divided into data augmentation method and feature extraction and transfer method. Metric-based zero-shot fault diagnosis using pre-trained word vectors to calculate similarity. Finally, this paper summarizes and prospects the future research of weakly supervised learning. © 2023 IEEE.

关键词： Failure analysis

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Blood pressure monitoring via double sandwich-structured triboelectric sensors and deep learning models

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Nano Research 2022年第6期15卷 5500-5509页

作者： Xu Ran Fangyuan Luo Zhiming Lin Zhiyuan Zhu Chuanjun Liu Bin Chen Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China Research Laboratory U.S.E.Co.Ltd.Shibuya-kuTokyo 150-0013Japan

Real-time blood pressure monitoring is essential for the timely diagnosis and treatment of cardiovascular *** traditional prediction methods estimate blood pressure by measuring multiple sets of physiological signals with energyconsuming ***,a continuous,cuffless and self-powered blood pressure monitoring system was developed based on a new double sandwich-structured triboelectric sensor and a novel blood pressure method estimation.A pyramid-patterned sensor based on the double sandwich structure realizes a sensitivity of 0.89 V/kPa in a linear range of 0–35 kPa,which is more than twice of the conventional single electrode *** sensor processes a low pressure detection limit of 1 g andfast response time of 32 ***,it can easily capture the pulse signal at the radial ***,a novel method for estimating blood pressure using pulse waves accompanied by the user’s background information was *** method measures only one set of pulse signals and is portable.A deep learning model with multi-network structures was developed to improve the estimation *** mean absolute error and standard deviation of error for systolic and diastolic blood pressure(SBP and DBP)estimations were 3.79±5.27 and 3.86±5.18 mmHg,*** work reveals a new sensing structure of triboelectric sensors and offers a novel method for blood pressure estimation.

关键词： triboelectric nanogenerator self-powered sensor blood pressure monitoring deep learning

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Fall Detection System Based on YOLO Algorithm and MobileNetV2 Model 10

Fall Detection System Based on YOLO Algorithm and MobileNetV...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Han, Chunxiao Zhang, Jingyan Wu, Han Tianjin Key Laboratory of Information Sensing & Intelligent Control China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9798331510138

Accidental falls are the major causes of injury among elderly people living alone. However, traditional fall detection algorithms usually have missed or false detections problems. In this paper, we design a fall detection system based on YOLO algorithm and MobileNetV2 model. First, detection frame coordinates and joint coordinates of the human body are fused as feature information, and background information is filtered out. A lightweight deep learning model, MobileNetV2, is then used for classification. Compared with commonly used lightweight classification models such as ShuffleNet and EfficientNet, the MobileNetV2 model performs better in recognizing fall behavior. Our system can effectively distinguish fall behavior from daily activities, meeting the requirements for fall detection. © 2024 IEEE.

关键词： Fall detection

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Experiment on protection performance of plasma array to 6GHz high-power microwave

Experiment on protection performance of plasma array to 6GHz...

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2023 Advanced Fiber Laser Conference, AFL 2023

作者： Liu, Yang Lv, Shouye Li, Jinzhou Meng, Gang Liao, Shuai Shi, Jiaming Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote Sensing Information Beijing100192 China State Key Laboratory of Pulsed Power Laser Technology National University of Defense Technology Hefei230037 China

ISBN: (纸本)9781510677661

For purpose of the electromagnetic pulse (EMP) protection research, a double-layer barrier of cylindrical plasma array was designed, and its protective performance to 6GHz high-power microwave (HPM) was conducted experimentally. Combining the distribution of the discharge plasma array, the shielding effectiveness of the double-layer plasma on 6GHz HPM pulse was studied. The main research contents and conclusions are as follows: The transmission energy of HPM decreased as the increase of plasma electron density, indicating an improved protection effect, with both double-layer and single-layer plasma array. Besides, the results showed that the transmission attenuation of HPM in double-layer structure was greater than that in single-layer structure because of the interlayer reflection. In addition, an interesting phenomenon was discovered during the experiment, which was that the boundary plasma units had a significant impact on the HPM transmission. The shielding effect of HPM was better with opening seven plasma units than that with opening whole plasma array. Finally, the spectral characteristics and transmission coefficients of the HPM were presented by Fourier transform, and the results of the protective performance were consistent with the above-mentioned time-domain analysis. The experiment results in this paper are of great significance in protecting against HPM based on plasma. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Carrier concentration

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Fundamental Limits of Multiple-Access Integrated Sensing and Communication Systems

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IEEE Transactions on information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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Multi-Channel Based on Attention Network for Infrared Small Target Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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Python-based Remote Server Data Collection Program for VASP 4

Python-based Remote Server Data Collection Program for VASP

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4th IEEE International Conference on Electronic Technology, Communication and information, ICETCI 2024

作者： Huo, Shengjun Hou, Meiling Xiong, Zhiwei Hebei Normal University College of Engineering Shijiazhuang050024 China Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control Shijiazhuang050024 China

ISBN: (纸本)9798350361643

In today's data-driven world, the ability to collect and analyze data from remote servers has become an essential task for various industries. With the exponential growth of data, organizations require efficient and reliable methods to extract and process data from remote locations. This program design and development aims to address this need by leveraging the Python language and the Paramiko and Openpyxl libraries. The program's functional design consists of two primary modules: remote server interaction and Excel workbook generation. It features seven key function points, including logging in to the remote server, browsing the file system, searching for specific files, saving data, displaying reminder messages, forming predefined designs, and filling in information data. By utilizing this program, users can efficiently collect specific energy values from remote servers and organize them into Excel workbooks, facilitating data analysis and visualization. The program's ability to ensure data accuracy by checking for changes in the last half minute further enhances its reliability. Overall, this program provides a robust solution for data collection and analysis, making it an invaluable tool for organizations seeking to harness the power of remote data. © 2024 IEEE.

关键词： Data visualization

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MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China School of Computer Science and Engineering North Minzu UniversityYinchuan750021China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided ***,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are *** solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung *** pixel dependence relationship is established in local and non-local fields to improve the model perception ***,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained ***,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic ***,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image *** results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this *** method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

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Unveiling High-speed Follow-up Consultation for Chronic Disease Treatment: A Pediatric Hospital Case in China

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Proceedings of the ACM on Human-Computer Interaction 2022年第CSCW2期6卷 1-29页

作者： Fu, Jiaojiao Zhou, Yangfan Wang, Xin School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing 220 Handan Rd. Shanghai200433 China

In developing countries suffering from a severe shortage of physicians, the follow-up clinical consultation of long-term chronic care becomes a high-speed collaborative work, which has to be conducted in a few minutes. Although much existing work studies how time factors affect physicians' workflows and requirements for information systems, high-speed chronic care is yet to be well investigated. This work bridges the gap by presenting a case of a pediatric hospital in China. We focus on the processes of follow-up consultations, the factors enabling physicians to complete consultation in several minutes, as well as the challenges faced by physicians and patients. Through observations and interviews, we find that physicians conduct multiple tasks (information acquisition, patient-provider communication, and medical data documentation) simultaneously to reduce the consultation duration. Adopting an information summary alternative is the key to fast information acquisition. Templates and references in EMR contribute to rapid documentation and prescription. However, multitasking brings physicians a heavy cognitive load. It also severely compresses the duration of patient-provider communication. As a result, some of the patients' needs, especially emotional ones, are neglected. Based on these findings, we discuss the characteristics and requirements of high-speed chronic care and accordingly propose design suggestions. © 2022 ACM.

关键词： Hospitals

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