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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Analysis of Intelligent Research and Development system of Power Communication Equipment Based on Artificial intelligence technology 3

Analysis of Intelligent Research and Development System of P...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Qin, Weiwei Luo, Pengyuan Wang, Kaixing Liu, Yulong Luo, Jing Qin, Hao Nanning Power Supply Bureau Guangxi Nanning China

ISBN: (纸本)9798350317022

The research and development of electric power communication equipment is a heavy task. Traditional research and development systems have problems such as inconsistent communication protocols and interface standards between devices and poor compatibility. This paper studies the intelligent research and development system of power communication equipment based on artificial intelligence, and tests and evaluates its performance. In this paper, the response time of the system is tested by simulating the load in real scenarios. Secondly, the throughput of the system is evaluated by sending a large number of requests and recording the number of requests processed by the system. At the same time, we conducted a concurrency capability test to simulate concurrent requests and observe the response time and throughput of the system. In addition, stability tests were performed to evaluate how the system would perform over long periods of time and under sustained load. The research results show that the artificial intelligence-based intelligent R&D system for power communication equipment shows obvious advantages in terms of throughput, stability and response time. The system can efficiently process large-scale data and requests to improve throughput. At the same time, through learning and pattern recognition, the system can improve stability, prevent and repair potential anomalies. © 2023 IEEE.

关键词： Artificial intelligence Communication Equipment Electric Power Project Intelligent Research and Development system

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Non-line-of-sight Human Vital Sign detection Method Based on Multipath Signals Fusion 3

Non-line-of-sight Human Vital Sign Detection Method Based on...

引用

3rd international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Liu, Xingyao Chen, Jiahui Guo, Shisheng Liang, Jingjing University of Electronic Science and Technology School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350302356

This paper considers the problem of human vital sign detection in a non-line-of-sight scenario by exploiting a millimeter-wave radar system. Specifically, first, the multipaths induced by wall reflections and corner diffraction are separated in the range-Angle domain. Then, a path association method is proposed to match different multipaths with different targets to obtain the locations of the targets. After estimating the targets' range cells, the time series signs with respect to multipath are formed and used to estimate the respiration and heartbeat information of the human targets by exploiting the cross-power spectrum. Finally, the proposed method is verified to get high-precision location information and vital sign values by both simulations and experiments. © 2023 IEEE.

关键词： Millimeter waves

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A Hybrid Learning Approach for Automatic Data Labelling and Anomaly detection in IoT Networks 3

A Hybrid Learning Approach for Automatic Data Labelling and ...

引用

3rd IEEE international conference on Artificial intelligence, ICAI 2023

作者： Kanwal, Rimsha Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Computer Science Islamabad Pakistan College of Engineering Technology Management Economics and Policy Program Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798350322125

Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%. © 2023 IEEE.

关键词： Classification (of information)

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Deep Learning Based Lumpy Skin Disease (LSD) detection 3

Deep Learning Based Lumpy Skin Disease (LSD) Detection

引用

3rd international conference on Smart Data intelligence, ICSMDI 2023

作者： Dommeti, Dhiren Nallapati, Siva Ramakrishna Lokesh, Chalamalasetti Bhuvanesh, Singasani P Vara Prasad Padyala, Venkata Srinivas, P.V.V.S. Koneru Lakshmaiah Education Foundation Department of Cse Guntur India

ISBN: (纸本)9781665464871

The emergence of the lumpy skin disease has become a major threat to the livestock industry in recent years, causing high economic losses and health risks to both animals and humans. This virus is difficult to detect due to its complexity, making the early detection and accurate diagnosis of this virus essential. This study will explore the utilization of convolutional neural networks (CNNs) to efficiently and accurately detect and identify the LSDV than traditional methods. Further, the advantages of using CNNs for this purpose has been discussed and some of the applications of this new technology has also been explored. Additionally, the future potential of using CNNs to perform virus detection is also discussed. However, Lumpy disease is classified differently based on its severity. To determine the extent to which the animal is impacted by lumpy skin disease, it is necessary to recognize various stages of the disease. This research study referred to the use of several CNN architectures and Regression algorithms to detect the Lumpy skin disease virus as early as possible. The architectures explored are and the EfficientNet-EfficientNetB7 architecture, MobileNetV2, EfficientNet-EfficientNetB3 architecture, VGG16, InceptionV3, ResNet50, VGG19, Xception and DenseNet201. The paper thoroughly describes all of the steps required to carry out the disease detection model, from data collection to process and outcome. © 2023 IEEE.

关键词： Viruses

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Computer Vision-Based Roof detection using YOLOv4 3

Computer Vision-Based Roof Detection using YOLOv4

引用

3rd international conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Santos, Cyd Laurence B. Medina, Ruji P. Taylar, Jonathan V. Technological Institute of the Philippines Quezon City Quezon City Philippines

ISBN: (纸本)9798350394559

Over the last few decades, there has been a shift of focus in urban planning in which artificial intelligence (AI) is being integrated to make informed decisions and predictions through modelling and simulation. Among all AI applications, computer vision (CV) plays a crucial task because of the role of visual data in the field of urban planning. Application of CV like object detection specifically roof detection has been very useful for the data collection and interpretation process of urban planning. At the top of this technology is the YOLO model which is an object detection model optimized to accurately detect objects based on the dataset trained within it. This research utilized a YOLOv4 model to detect roofs based on Google Earth satellite images. A total of 500 ground truth images were extracted across different regions of the Philippines and were annotated using Computer Vision Annotation Tool (CVAT) which were then pre-processed and augmented resulting to a total of 4,802 images. Using a custom dataset, the YOLOv4 model demonstrated superior performance with a mean average precision (mAP) of 92%, outperforming alternatives such as Single Shot detection (SSD) MobileNet and Detectron 2 Faster R-CNN, which achieved maximum mAP values of 83% and 79%, respectively. This highlights YOLOv4's accuracy in roof detection from Google Earth satellite images and proving its potential in urban planning applications. © 2023 IEEE.

关键词： Object detection

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Research on Innovative Artificial intelligence Education Assisted by Big Data 3

Research on Innovative Artificial Intelligence Education Ass...

引用

3rd international conference on Engineering Education and Information technology, EEIT 2023

作者： Hong, Lei Zhang, Dongyang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350326840

Artificial intelligence (AI) is an important driving force leading the new round of technological and industrial revolution, and the construction of AI major has also entered a period of rapid development since the attention of colleges. However, limited by the lack of development time, regarding to the cultivation of innovative talents in the field of AI, the problems of incomplete curriculum construction, insufficient innovation spirit and poor engineering ability are more prominent, along with the increased demand for superior AI talents from employer. Thus, this paper deeply analyzes the defects of current AI teaching, aiming at exploring talent cultivating path assisted by education big data from students. Furthermore, a novel five-stage model for AI major education is proposed to guide students to reinforce the elementary knowledge, foster the spirit of innovation and address the practical issues. The comprehensive analysis indicates that the proposed teaching model is conducive to stimulating learning enthusiasm of students, and significantly bringing the talent cultivation system to a higher level. © 2023 IEEE.

关键词： Big data

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Research on Face Image Recognition system Based on Computer Artificial intelligence technology 3

Research on Face Image Recognition System Based on Computer ...

引用

3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Xiong, Fangfang You, Zhenyu Nantong Institute of Technology School of Computer and Information Engineering Nantong226002 China Nantong CIMC Energy Equipment Co. Ltd Technology Center Nantong226003 China

ISBN: (纸本)9798350398410

This paper develops a new facial recognition technology based on Python+ OpenCV technology. This study includes three modules: face recording, face training and face recognition. A sparse representation classification technique based on Gabor low - order restoration is presented to recognize face images. By Gabor transformation, the images of each training sample and their corresponding error images are transformed to obtain the feature vector of Gabor. The feature vectors of Gabor are combined into a dictionary of Gabor characteristics. The experimental results show that the algorithm can suppress the interference of variable light and preserve the facial expression well. This method can not only improve the brightness contrast of the image, but also filter out the light component of the face effectively. The accuracy of face recognition under different illumination conditions is 98.56%. © 2023 IEEE.

关键词： Face recognition

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A bullet hole recognition method based on visual detection 3

A bullet hole recognition method based on visual detection

引用

3rd international conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Yu, Fengjie Song, Shibin Zhu, Jie Cheng, Weihao Wu, Wenshuo Zhang, Luwen Jiang, Xiaojie Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China Yantai Tulan Electronic Technology Co. Ltd Yantai City China

ISBN: (纸本)9798350360813

Shooting Intelligent detection system has important applications in the military industry. Existing target shooting systems mainly face problems such as high cost, complex operation, and poor environmental adaptability. Considering the above limitations, in this paper we propose a fast, low-cost, and easy-to-deploy bullet hole recognition algorithm based on visual detection, which mainly consists of four parts: image preprocessing, extraction of target center, bullet hole extraction and ring value determination. In the process of bullet hole extraction, a method for segmenting effective target surface is proposed to cope with the problem of off-target bullet holes. Our method can not only accurately localize the bullet holes, but also obtain the exact ring value at that location. In addition, due to the robust adaptability of our method, it can even be applied to outdoor scenarios. © 2023 IEEE.

关键词： Image processing

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Expand Object detection Dataset Automatically via Deep Learning Neural Networks 3

Expand Object Detection Dataset Automatically via Deep Learn...

引用

3rd international conference on Computing and Information technology, ICCIT 2023

作者： Liu, Bing Ganesan, Subramaniam Oakland University Department of Electrical and Computer Engineering RochesterMI United States

ISBN: (纸本)9798350321487

Object detection is an important research field in image processing. The state-of-the-art machine learning models are trained with huge number of samples to solve this problem. Manually labeling objects on images is labor intensive, expensive, and time consuming. It is desirable to create an automatic labeling system to reduce the cost and the time needed. A deep learning neural networks based automatic labeling system is proposed in this paper to expand Microsoft Common Objects in Context 2017 (COCO 2017) dataset from 80 classes to 81 classes by adding labels for the new class, human faces, automatically. The system contains an Auto-Labeling Model (ALM) which is trained using Web Image Dataset for Event Recognition FACE (WIDER FACE) dataset. ALM detects faces in COCO 2017 dataset and provides bounding boxes automatically with very high accuracy. The generated new labels by ALM are merged to the existing labels in COCO 2017 dataset automatically. 4,110 hours human effort is saved using this system. The expanded COCO 2017 dataset is evaluated quantitatively and visually to ensure the efficiency of the proposed auto-labeling system. © 2023 IEEE.

关键词： Object detection

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Data Flow Analysis in Automotive Engine Underpower Fault detection 3

Data Flow Analysis in Automotive Engine Underpower Fault Det...

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3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Liu, Jianke Ming, Zhikun Yu, Tianxi Dongfeng Commercial Vehicle Co. Ltd Shiyan442000 China Department of Sanitary Inspection and Quarantine Kunming Medical University Kunming650500 China

ISBN: (纸本)9781510663473

Due to the relative maturity of data flow(DF) analysis and research technology as well as the increasing number and development of diagnostic instruments, the automotive engine fault detection and diagnosis system is developing in the direction of precision, intelligence and multi-module diagnosis. In this paper, the fault detection system is designed for the problem of insufficient power of automobile engine. The ECU of automobile engine has the function of collecting the engine DF under various conditions in real time, and then reading out the relevant data during the driving process with the automobile fault diagnostic instrument, and the maintenance personnel combine them according to different requirements to form different data sets. The fault detection system also has the function of fault identification and prediction, and the best result of PSO-ELM algorithm is found by comparing the prediction accuracy of different algorithms. Here, it is hoped that the research in this paper will provide a reliable basis for analyzing and researching automotive engine faults. © 2023 SPIE.

关键词： Forecasting

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