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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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Conspiracy in the Stealing of Electricity detection Through the IOT 3

Conspiracy in the Stealing of Electricity Detection Through ...

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3rd international conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Mohanraj, C. Pushpalatha, N. Muthumanidevi, B. Amirtha Varsheni, S. Sivakumar, R. Sharma, Vandana Alkhayyat, Ahmed Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641 202 India Sri Krishna College of Technology Dept of Mechanical Engineering Tamilnadu Coimbatore India Amity Institute of Information Technology Amity University UP India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9798350336238

One of the primary difficulties the distribution network needs to solve is power loss. The 'power mismatch' between generation and metered consumption may be employed to describe it. This power larceny has a major influence on distribution utilities. The goal of our proposed system is to design a system to monitor the electricity used by each load and to identify and eradicate electricity theft from current lines and meters. The proposed method also addresses the issue of overloading. It also entails reporting to the Electricity Board representatives the theft that was committed via IoT. The ATmega328 microcontroller in this instance is utilised to detect energy larceny and transmit the data to the IoT module, which then transmits the data to the EB. The practical application of this proposed method results in significant electricity savings, which boosts the economy of the nation. © 2023 IEEE.

关键词： Internet of things

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Improved the detection Algorithm of Safety Helmet Wearing Based on YOLOv8 3

Improved the Detection Algorithm of Safety Helmet Wearing Ba...

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3rd international conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Chen, Luyao Mao, Yihai Zhang, Hongyi Luan, Shen School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350359961

To settle challenges with low detection accuracy of small and strong objects, as well as complicated settings in practical construction scenarios like tunnels, coal mining operations, and building locations, an improved YOLOv8 safety helmet wearing detection algorithm has been established. To begin, the transformer part is the central component of the system. It uses the contextual knowledge stored in the feature diagram to help the model learn on its own and improve the linking of global feature information. Additionally, the bidirectional feature pyramid network (BiFPN) is employed to integrate characteristic data across various dimensions. Finally, important nodes in the network have the 3D weighted attention mechanism SimAM installed. This gets rid of unnecessary background details and focuses more on the unique features of the hard helmet. The experimental findings reveal that on the SHWD dataset, the revised algorithm's mAP (IOU = 0.5) increased by 3.8% to 95%, and the detection speed reached 127 FPS. In crowded, complex, and tiny object instances, detection increased significantly. It meets precision and real-Time safety helmet detection standards and introduces a new solution for challenging construction situations. © 2023 IEEE.

关键词： BiFPN Component safety helmet wearing detection SimAM YOLOv8

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Face detection and Classification using Convolutional Neural Network with Channel Attention Module 3

Face Detection and Classification using Convolutional Neural...

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

作者： Almoussawi, Zainab Abed Kumar, K Sudheer Veena, T. Adnan, Myasar Mundher Jabar, Muhammed Basim College of Islamic Science Ahl Al Bayt University Karbala Zainb Iraq School of Computer Science & Artificial Intelligence SR University Telangana Warangal India S.A. Engineering College Department of Information Technology Chennai India The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350317022

Automatic face detection is one of the most challenging in computer vision, FER is a wide range of application in human- computer interaction, behavior, and human expression. However, most of these related researches use general image classification network, which lead inadaptability while applying to face detection. This paper proposed a deep learning using Convolution Neural Network (CNN) with Channel Attention Module. The FER 2013 dataset is utilized in this research for effective classification of face detection. The Data Augmentation is used in this experiment for data pre-processing and feature extraction using high feature generation pyramid (HFGP) and low feature generation pyramid (LFGP). Face detector using Single-Short multi biox detector (SSD) and ResNet 10 based face detection. Then, face detector are given imput to channel attention module classifer which utlized Deep Learning (DL) for effective classification. The obtained result show that the proosed DL using CNN model achieves better accuracy of 99.90% on FER 2013 dataset which ensure accurate classification compared to other existing methods like Engagement Index, Deep Neural Net and Zoning based model. © 2023 IEEE.

关键词： Channel Attention module Convolution Neural Network Deep Learning ResNet 10 and Single- Short multi box Detector

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FallGuardian: Wear OS-Based Machine Learning Fall detection Framework 3rd

FallGuardian: Wear OS-Based Machine Learning Fall Detection ...

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3rd international conference on Applied intelligence and Informatics (AII)

作者： Michaelides, Alexandros Hameed, Nazia Walker, Adam Univ Nottingham Sch Comp Sci Jubilee Campus Nottingham NG8 1BB England

ISBN: (纸本)9783031686382;9783031686399

Falls among elderly individuals pose a significant health risk, and fast detection is crucial to prevent injuries. This paper presents FallGuardian, a fall detection system using machine learning and an IoT smartwatch powered by Google's Wear OS, which works by communicating with a server hosting the fall detection machine learning model. This work explores the different methodologies of currently available solutions to fall detection using IoT devices. Publicly available datasets are evaluated using machine learning models which power fall detection system behind FallGuardian. A range of machine learning models are evaluated, including k-Nearest Neighbors, Support Vector Machines (SVM) with both linear and Radial Basis Function kernels, Random Forest, Naive Bayes, and a deep learning approach employing a Convolutional Neural Network (CNN). The experiments produced models with high evaluation metrics. Notably, a CNN achieves an outstanding 0.99 accuracy on test data. However, it is observed that these models face challenges in maintaining high performance levels when tested in real-time. Our research highlights the need for ongoing refinement and adaptation of fall detection systems to bridge this gap between testing performance and practical application. Our work advances the development of robust IoT-based fall detection systems, aiming to enhance safety in practical scenarios.

关键词： fall detection machine learning IoT Smartwatch healthcare deep learning real-time fall detection Wear OS

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Deep Learning based Dynamic Object Probability with Long Short-Term Memory for Drowsiness detection 3

Deep Learning based Dynamic Object Probability with Long Sho...

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

作者： Sudheer Kumar, K. Radha, K. Alkhafaji, Mohammed Ayad Al-Jawahry, Hassan M. Almoussawi, Zainab Abed School of Computer Science & Artificial Intelligence SR University Telangana Warangal India Vivekananda College of Technology for Women Department of AI & DS Namakkal India National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq College of Islamic Science Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Driver Drowsiness detection (DDD) is crucial for preventing road accidents and serves as a pivotal element for shift from semi-autonomous to fully autonomous driving. Various Deep Learning (DL) models leveraging video sequences, offer sophisticated motion sensing capabilities. In this paper, the potential of identifying the regions of the eyes and mouth to capture yawning behavior, serving as a reliable indicator of drowsiness. The proposed Dynamic Object Probability with Long Short-Term Memory (DOP-LSTM) technique is employed to specifically detect the driver within the video sequence. Subsequently, the Dense Scale-Invariant Feature Transform (D-SIFT) technique is utilized to extract the regions of the driver's eyes and mouth. The features extracted from both the eyes and mouth are fused using Multi-Scale Feature Fusion (MSFF) techniques to determine the driver's state. LSTM is implemented for classification and detection and it demonstrates superior performance, achieving Precision of 99.69%, F-Measure of 99.02%, Recall of 98.30%, and Accuracy of 98.81%, when compared to existing models such as Inception V3-lstm, Hybrid-DDD, and Multi-scale Convolutional Neural Network-Flamingo Search Algorithm (MCNN-FSA). © 2023 IEEE.

关键词： Deep Learning Dense Scale-Invariant Feature Transform Drowsiness Dynamic Object detection Long Short-Term Memory

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The Application of Transfer Learning to the detection of Signals in Wireless Networks 3

The Application of Transfer Learning to the Detection of Sig...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Saket Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

In this research, we provide a machine learning strategy for detecting flaws during production runs. As an example of a scenario where sensors are spread out over a large industrial plants, the Tennessee Eastman system is employed. A filter-wrapper hybrid feature choosing approach called the Hybrid Fisher Wrapper approach was created to choose the most representative sensors to achieve the greatest detecting quality for fault detection. The proposed method provides a wide variety of options, each with its own level of sensing difficulty, computational load, and detectability. It's an alternative to the conventional Industry 4.0 strategy, in which a distributed network of sensors sends data to a consolidated cloud. The proposed solution is decentralized compared to centralized servers so that processing may take place close to the sensors that produce the data or at the periphery of the Internet of Things. The connectivity, privacy, and latency of this technique are all better than those of centralized alternatives. The experimental results show that the proposed method produces fault identification solutions that are at par with or better than state-of-the-art. The produced solutions are 37.5 times faster and require 1.99 times fewer characteristics than performing with the highest detection quality. The scalability of the framework provides an architectural framework from which the best possible implementation may be chosen. © 2023 IEEE.

关键词： Edge Computing Fault detection Feature Selec tion Industry 4.0 Machine Learning

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Research on Safety Monitoring and Defect Identification Method of Oil and Gas Pipeline Based on Faster R-CNN technology 3

Research on Safety Monitoring and Defect Identification Meth...

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2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Xin, Hang Du, Xiaoran Sino-Pipeline International Company Limited Mansion HengYi No.5 Anding Road Chaoyang District Beijing China

ISBN: (纸本)9798350341546

For the problems of oil and gas pipelines in complex environments, such as difficulty in defect detection, high false alarm rate, and inaccurate positioning, this paper proposes a defect recognition algorithm based on Faster R-CNN. First, according to the Faster R-CNN model, RPN is established to obtain the confidence and coordinate system number of image features and effective anchor points. The information of the prediction box and target box is generated. Second, an ROI Pooling strategy is proposed to eliminate the size inconsistency between the prediction and target boxes. Third, concerning the defect characteristics of oil and gas pipelines, a classification regression model considering the influence of category and location is constructed to describe the defects more accurately. Finally, an oil and gas pipeline safety monitoring system based on the Faster R-CNN algorithm is built. Simulation and experiment prove the effectiveness of the proposed defect recognition algorithm. © 2023 IEEE.

关键词： Defect recognition Faster R-CNN Oil and gas pipeline Region proposal network

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Research on a Mobile Hydrological detection system Based on BDS 3

Research on a Mobile Hydrological Detection System Based on ...

引用

3rd international conference on Electronic Informationtechnology and Smart Agriculture, ICEITSA 2023

作者： Ning, Jing Wang, Renwei Zhou, Jieyang School of Communication and Electronic Engineering Jishou University Hunan China School of Computer Science and Engineering Jishou University Hunan China

ISBN: (纸本)9798400716775

The present buoy is advancing towards increased intelligence and automation, instigating interest in the evolution of intelligent buoys. The effective detection of water quality data holds the potential to alleviate workload and facilitate real-time data acquisition and anomaly recording in scenarios of unmanned supervision. This paper introduces a buoy system designed for hydrological detection, providing a succinct overview of the design, elucidating the operation of diverse subsystems, and delineating the establishment of the hardware platform. The system underwent corresponding tests in a lake, enabling remote access during the testing phase. The experiment sensor data could be accessed in the cloud, and no anomalies were detected in the environment during the testing period. Furthermore, we bolstered control and detection reliability through the strategic application of data fusion, deliberately progressing toward the advancement of the Internet of Underwater Things (IoUT). © 2023 ACM.

关键词： Buoys

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Efficient Pavement Crack detection in Drone Images using Deep Neural Networks

Efficient Pavement Crack Detection in Drone Images using Dee...

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2023 international conference on Electronics, Information, and Communication, ICEIC 2023

作者： Kim, Heegwang Sung, Junghoon Kim, Mingi Park, Chanyeong Paik, Joonki Chung-Ang University Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Korea Republic of Chung-Ang University Image Processing and Intelligent System Laboratory Graduate School of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350320213

Since the drone input images are usually filmed at very high altitudes and with various viewing angles, cracks appear in many different sizes and shapes. A commonly used crack detection dataset has a small size such as 480x320, and it is important that how well it detects the prominent continuous cracks. To detect pavement cracks, we proposed an efficient crack detection network. The proposed network achieved better performance than the conventional network. © 2023 IEEE.

关键词： crack detection sigmoid fusion module YOLOX

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Positioning Method for Covered Vehicles Based on Neutrino detection and Reinforcement Learning 3

Positioning Method for Covered Vehicles Based on Neutrino De...

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3rd international conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Han, Mingren Gao, Jiashan Zhou, Peng Zhang, Yixin Beijing Institute of Control Engineering China Academy of Space Technology Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350357707

In complex environments like underwater or underground, traditional navigation technologies face limitations due to the restricted penetration of optical and radio signals. Neutrino signals, which have extremely strong penetration capabilities, offer a new possibility. This study explores the challenges of navigation and positioning of covered vehicles and proposes a neutrino-based navigation and positioning technology to overcome the limitations of traditional navigation technologies in complex environments. This research combines reinforcement learning methods and Kalman filters, using the Proximal Policy Optimization (PPO) algorithm to tune the parameters of the Kalman filter to improve navigation and positioning accuracy. In the study, a Kalman filter estimation system model for vehicles in navigation signal denied environment environments was first established, then a navigation and positioning method based on neutrino detection was designed, and finally, a parameter adaptive PPO Extended Kalman Filter (PPOEKF) state estimation method was proposed. Through mathematical simulation and comparative analysis, the research results show that using reinforcement learning methods to tune the parameters of the Kalman filter can improve the accuracy of navigation and positioning, especially in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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