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检索条件"任意字段=7th International Conference on Computer and Information Processing Technology, ISCIPT 2022"

共 4197 条记录，以下是121-130 订阅

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7th EAI international conference on Advanced Hybrid information processing, ADHIP 2023

7th EAI International Conference on Advanced Hybrid Informat...

引用

7th EAI international conference on Advanced Hybrid information processing, ADHIP 2023

ISBN: (纸本)9783031505485

the proceedings contain 108 papers. the special focus in this conference is on Advanced Hybrid information processing. the topics include: A Method of Resolving the Conflict of Shared Resources in Online Teaching of Design Professional Artworks Based on Feedback Integration;design of Substation Battery Remote Monitoring System Based on LoRa technology;optimization Scheduling Algorithm of Logistics Distribution Vehicles Based on Internet of Vehicles Platform;design of Logistics information Tracking System for Petrochemical Enterprises Under the Background of Intelligent Logistics;real Time Tracking of the Position of Intelligent Logistics Cold Chain Transportation Vehicles Based on Wireless Sensor Networks;a Real Time Tracking Method for Intelligent Logistics Delivery Based on Recurrent Neural Network;a Real Time Tracking Method for Unmanned Traffic Vehicle Paths Based on Electronic Tags;error Motion Tracking Method for Athletes Based on Multi Eye Machine Vision;research on Real Time Tracking Method of Multiple Moving Objects Based on Machine Vision;a Method for Identity Feature Recognition in Wireless Visual Sensing Networks Based on Convolutional Neural Networks;feature Recognition of Rural Household Domestic Waste Based on ZigBee Wireless Sensor Network;a Method of Recognizing Specific Movements in Children’s Dance Teaching Video Based on Edge Features.

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Design of Pig Farm Environment Regulation and Video Monitoring System Based on Livestock Internet of things Orbital Inspection Robot 7

Design of Pig Farm Environment Regulation and Video Monitori...

引用

7th international conference on Communication Engineering and technology, ICCET 2024

作者： Sun, Hanqing Palaoag, thelma D. Quan, Qingle University Of The Cordilleras College Of Information Technology And Computer Science Baguio City Philippines

ISBN: (纸本)9798350373271

Livestock Internet of things is the application of Internet of things (IOT) to the animal husbandry to realize the collection, transmission, and calculation processing of animal husbandry information to achieve smart breeding, promote the development of the breeding industry, and improve breeding efficiency. Pigs are an important component and pillar industry of the livestock breeding industry [1]. Temperature, humidity, CO2, NH3, H2S and other malodorous gases in the pig house seriously affect the health of pigs [2]. Current pig house environmental regulation is based on fixed-point sensor data collection and cannot collect environmental information of the entire pig house. In order to avoid the spread of diseases caused by contact with the outside, the farms adopt closed management. Farm owners, investors and other stakeholders can only view the internal situation by installing fixed cameras. this method can only view the situation of the pig house from a distance. And there are blind spots in video surveillance. In order to achieve uniform regulation of the pig house environment and mobile video monitoring to achieve 'cloud farming', this article designs a track inspection robot based on the livestock Internet of things to achieve uniform regulation of environmental information and mobile video monitoring in the pig farm. It aims to use the Internet of things and sensors, robots, image processing and other technologies realize uniform control of the pig house environment and unmanned breeding, save breeding costs, improve breeding efficiency, and promote the development of smart animal husbandry. © 2024 IEEE.

关键词： Environmental regulations

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7th EAI international conference on Advanced Hybrid information processing, ADHIP 2023

7th EAI International Conference on Advanced Hybrid Informat...

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7th EAI international conference on Advanced Hybrid information processing, ADHIP 2023

ISBN: (纸本)9783031505454

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Human Pose Estimation Based on Lite HRNet with Coordinate Attention 7

Human Pose Estimation Based on Lite HRNet with Coordinate At...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Li, Ruoxi Huang, Hongbo Zheng, Yaolin Beijing Information Science and Technology University Computer School Beijing China

ISBN: (纸本)9781665478571

Human pose estimation is an essential task in computer vision, applied in motion recognition, motion capture, augmented reality, etc. the emergence of Lite HRNet balances computational complexity with high precision, using channel attention mechanism instead of complex convolution while maintaining high resolution, thus possessing less computation and higher precision. the use of the channel attention mechanism results in the loss of the position information that is crucial to generating spatially selective attention maps. Accordingly, in this paper, we implement an improved pose estimation method based on the coordinate attention mechanism and Lite HRNet. Our method adds the coordinate information embedding to the original approach instead of expensive point-by-point convolutions. In this way, more information can be reserved in the case of less computation. the proposed method is evaluated on the COCO dataset, and the experiments show that this method can achieve a better balance in terms of accuracy of calculation quantities. © 2022 IEEE.

关键词： Convolution

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Enforcing Helmet Compliance Via WhatsApp using Clustering Algorithms 7

Enforcing Helmet Compliance Via WhatsApp using Clustering Al...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Swaroop, P. Chaitanya Sri, G. Hruthi Teja, V. Jahnavi, V. Kumar, C. S. Pavan Velagapudi Ramakrishna Siddhartha Engineering College Department of Information Technology Vijayawada India

ISBN: (纸本)9798350359299

this initiative aims to quickly identify individuals who violate traffic laws and notify them of their violations via What's App, reducing the burden on law enforcement officers."the major goal is to improve traffic law enforcement. the rise in road accidents is mostly due to widespread noncompliance with traffic rules, which includes failure to wear a helmet, exceeding speed limits, and taking the wrong route. By integrating cutting-edge approaches in computer vision, image processing, and machine learning, the system overcomes considerable problems connected with traffic rule breaches. the primary goals include accurately identifying individuals operating automobiles, validating helmet usage, and recognizing license plates using image processing techniques. Following that, automatic traffic challans are issued in compliance with the violations. In essence, the Intelligent Traffic Management System combines these technological breakthroughs to provide a complete strategy to improving traffic safety, reducing traffic offenses, and optimizing traffic law enforcement. this groundbreaking technology announces a path towards safer and better organized streets, representing a significant advancement in the field of intelligent traffic management. © 2024 IEEE.

关键词： Machine learning

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A fatigue driving detection approach based on TPU computing device 7

A fatigue driving detection approach based on TPU computing ...

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7th international conference on Electronic information technology and computer Engineering (EITCE)

作者： Tang, Qi Ren, Xianping Tao, Ningguo Mi, Qiwei Jianghan Univ Wuhan Hubei Peoples R China

ISBN: (纸本)9798400708305

Fatigue driving is a longstanding hazard in the field of road traffic safety. the factors such as longtime driving, lack of sleep, and work pressure result in driver fatigue which impair driver ability to detect and respond to sudden situations, and increase the risk of serious traffic accidents. this paper proposes a novel method for driver fatigue detection based on YOLOv5 algorithm. Firstly, a fatigue detection dataset is collected and created for this purpose. the YOLOv5 algorithm is then utilized to detect driver multiple facial fatigue features such as yawning, closed eyes, and head nodding, and the driver's fatigue status is determined based on the statistical frequency of the three fatigue characteristics. Furthermore, the YOLOv5 trained model is deployed on the Tensor processing Unit (TPU) computing device. the experimental results demonstrated that the YOLOv5 algorithm model achieved a mAP@0.5 of 98.51% when evaluated on the validation sets. the driver fatigue detection system deployed on the TPU computing device can perform at 25 fps with an average accuracy of 93.8%. this system can monitor the driver's status in real time in practical applications, timely remind drivers to pay attention to safety, and help reduce the risk of traffic accidents caused by fatigue driving.

关键词： Fatigue driving detection YOLOv5,Tensor processing Unit (TPU)

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Uncertain Data processing Algorithm for Base Station Energy Consumption 7

Uncertain Data Processing Algorithm for Base Station Energy ...

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7th international conference on Electronic information technology and computer Engineering (EITCE)

作者： Zuo, Xinyu Nie, Zhangzhen Liu, Wenwen Wang, Nan Tianjin Normal Univ Tianjin Peoples R China

ISBN: (纸本)9798400708305

the current base station management faces challenges such as imprecise information perception, a lack of precise prediction techniques for load and energy consumption, and the absence of refined optimization methods for multi-source comprehensive scheduling. It can only achieve a quantitative complementarity of energy on the supply side, the grid side, and the load side, without considering the differences in energy quality of various forms of energy and their optimal scheduling in conversion, transmission, storage, and utilization. Simultaneously, there is a severe deficiency in crowss-temporal and spatial allocation and utilization of energy, as well as the use of edge computing and big data analytics for precise prediction and optimization scheduling. this has resulted in low overall energy utilization efficiency, high carbon emissions, and other issues. there is an urgent need to break through the key technologies of accurate perception, precise prediction, precise scheduling, and fine control in the energy Internet of things system for base stations. the project team has put forth a scientifically sound solution, addressing issues related to precise perception of base station status, accurate load prediction, fine optimization of energy management, and precise control of comprehensive energy systems. they have proposed the "Uncertain Data processing Algorithm for Base Station Energy Consumption" to tackle and solve the challenge of precise load prediction in energy IoT based on high-noise, low-quality data.

关键词： Nearest neighbor algorithms Base station Energy consumption reduction Base station status awareness

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A High Gain Omnidirectional Dipole Array Antenna for WiFi-6E 7

A High Gain Omnidirectional Dipole Array Antenna for WiFi-6E

引用

7th IEEE international conference on Electronic information and Communication technology, ICEICT 2024

作者： Gu, Wenzhe Feng, Botao Luo, Yiyuan Zhu, Xuexin Lu, Yun Huizhou University School of Computer Science and Engineering Huizhou China Shenzhen University College of Electronics and Information Engineering Shenzhen China Xiangtan University School of Electronic Information Engineering Xiangtan China

ISBN: (纸本)9798350384437

A high gain vertical polarization omnidirectional antenna is proposed, with overall size of 250mm×16mm×1mm. A double U-shaped symmetrical dipole oscillator is used as radiation oscillator, and then four radiation elements are formed into a series-fed antenna array. Meanwhile, two four-element series-fed arrays are connected in parallel, feeding in the parallel center. All antenna elements are direct connected by microstrip lines. through adding protrusions or adjusting their widths on microstrip lines, impedance transformation can be achieved, and standing wave and gain can be *** and physical testing results show that the antenna can effectively cover the WiFi-6E frequency range (5.9GHz-7.125GHz), with |S11| ≤ -15dB. In the horizontal direction, the antenna exhibits low gain variations ( © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Research on image object fault detection based on deep learning 7

Research on image object fault detection based on deep learn...

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7th international conference on Automation Electronics and Electrical Engineering

作者： Shi, Leilei Shandong Vocat Coll Light Ind Zibo Shandong Peoples R China

ISBN: (纸本)9798350377040;9798350377033

In recent years, with the rapid development of deep learning, many computer vision tasks have achieved breakthroughs again and again. In many computer vision tasks, fault detection has been widely concerned because of its great application value. Compared with traditional image target fault detection methods, the image target fault detection method based on deep learning digs image features layer by layer through deep neural networks, and can learn more advanced abstract features without manual feature design, and has better generalization. Based on deep learning technology, this paper studies the algorithm of image target fault detection. In this paper, a fault detection method based on attention mechanism is proposed to solve the problem that the fault detection based on object detection cannot distinguish the fault with not obvious features. this method uses the attention mechanism to assign different weights to each feature graph when the network performs multi-scale feature fusion, so that the network selectively pays attention to the higher-order semantic information and lower-order detail information that are more helpful for fault identification, and improves the recognition ability of the network for the faults with less obvious features. In the experimental verification stage, the performance of the proposed method is higher than that of the benchmark method on both private and public datasets. Among them, 93.28% mAP@all is achieved on the private dataset, which is 1.16% higher than that of the benchmark method (92.04%). 78.55% mAP@0.5 was achieved on the publicly available VOC dataset, an improvement of 1.48% compared to 77.07% for the benchmark method. At the same time, FPS of this method can be maintained at 74,mAP@0.5 up to 99.55%, which can fully meet the real-time requirements and achieve high detection accuracy, which proves that the algorithm in this paper has reached the advanced level.

关键词： Deep learning image processing anomaly detection fault detection attention mechanism

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Research and Application of a Multi-Modal information Scheduling Method Based on Large Language Models in Intelligent Transportation 24

Research and Application of a Multi-Modal Information Schedu...

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2024 7th international conference on computer information Science and Artificial Intelligence, CISAI 2024

作者： Luo, Junshao Lin, Hongjun Qiu, Jiandong Shenzhen Urban Transport Planning Center Co. Ltd. Guangdong Shenzhen China

ISBN: (纸本)9798400707254

As intelligent transportation systems evolve, traffic management faces increasing challenges in processing vast amounts of heterogeneous data and supporting complex decision-making processes. To address these challenges, a multi-modal information scheduling method based on large language models is proposed, enabling efficient parsing of natural language inputs, cross-database queries, and automated scheduling through functionalities such as intelligent POI scheduling, associated information query, and precise camera scheduling. the findings demonstrate that this approach significantly enhances decision-support capabilities in complex scenarios and offers valuable insights for applications within the intelligent transportation domain. © 2024 Copyright held by the owner/author(s).

关键词： Query languages

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