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

共 2101 条记录，以下是411-420 订阅

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The Traffic Violation detection system using YoloV7 3

The Traffic Violation Detection System using YoloV7

引用

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

作者： Harini, S. Suguna, M. Subramani, A T Venkata Krishna, Gokila Harini Thiagarajar College of Engineering Cse Department Madurai15 India Thiagarajar College of Engineering It Department Madurai15 India

ISBN: (纸本)9798350336238

In recent time surveys, the deaths and injuries due to traffic violations have increased chiefly in Indian roads. So, this needed the assistance of an automated computer vision-based object detection model, as manually identifying the vehicles violating traffic is hectic. The principle of this paper is to detect multiple violations using single video frames. The input video stream obtained from the surveillance camera is processed and annotated to carry out multiple processes. The dataset used for red-light jumping is COCO and the dataset for over boarding is created by annotating the images obtained from google. The model is trained and the output is visualized using tensorboard. The parameters used are Precision, Recall, F-measure and P-measure. The accuracy for red light skipping is 93% and the mAP value for over boarding is 0.5:0.95. This system utilizes the video stream at its maximum to detect various violations. © 2023 IEEE.

关键词： Object detection

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Intelligent Monitoring and Control of IOT technology in Greenhouse Farming 3

Intelligent Monitoring and Control of IOT Technology in Gree...

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3rd international conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Chen, Rumin Zhao, Xiaojing Zhao, Mi'ao Zhang, Hongbo Department of Information Engineering Tianjin College University of Science and Technology Tianjin Beijing China

ISBN: (纸本)9781510668867

Intelligent agriculture is the product of the combination of technology and traditional agriculture in the computer era, mainly using IOT (Internet of Things technology), artificial intelligence and computer technology to reform and innovate the traditional agricultural production and management methods, and realize the informationization and intelligence of agricultural production management. In this context, how to use technology to improve crop yield and management efficiency has become an urgent social issue. This paper mainly discusses the application of IOT technology in greenhouse planting from two aspects, which is also the innovation point of this paper. Through the intelligent detection of soil and soil-less environment in greenhouses, it plays an important role in ensuring the normal and healthy growth of crops and increasing production and income. © 2023 SPIE.

关键词： Artificial intelligence

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Driver Assistance systems with Driver Drowsiness detection Using Haar-Cascade Algorithm 3

Driver Assistance Systems with Driver Drowsiness Detection U...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Gaikwad, Sujata Patil, Upendra Subhedar, Mansi Hoc College of Engineering and Technology Department of Electronics and Telecommunication Engineering Pillai Rasayani Tal. Khalapur Maharashtra Dist. Raigad India

ISBN: (纸本)9798350330724

The proposed work is developed to avoid accidents caused due to different parameters and aims to build an effective system. Design modifications are not required in vehicle, this work can be externally placed in vehicle so all old vehicles can be part of this system. Emphasizing the aspects that make commuting a laborious job and providing appropriate solutions to make the journey safe and reliable is crucial in improving the overall commuting experience. The proposed system covers two aspect - First aspect determine driver's drowsiness which will determine the sleeping and yawning state of the driver. The system automatically detects driver fatigue based on visual information and artificial intelligence. Identifying, tracking and analyzing both the driver's face and eyes and using softmax of neuron transfer functions measures PERCLOS (percentage of eyes closed).Second aspect includes adaptive cruise system which includes sensors. The sensor used in this system is not reliant on human visual perception, distance judgment, or directional awareness. Instead, it monitors the driving environment and identifies potential dangerous situations. It handles various tasks such as preventing collisions while reversing, maintaining a safe distance from the vehicle in front, avoiding collisions at intersections, avoiding obstacles on the road, and keeping the vehicle within its lane. To achieve real-time monitoring, the system utilizes the Raspberry Pi Camera. The methods proposed in this work, such as feature extraction and neural networks, help to overcome the drawbacks of the current approaches, increase the effectiveness of traffic sign detection, and decrease the number of accidents on the road. © 2023 IEEE.

关键词： Cameras

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Service Resilience Optimization Algorithm for Intelligent Space-based system with Minimum Delay 3

Service Resilience Optimization Algorithm for Intelligent Sp...

引用

3rd international conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Qin, Junxiang Li, Xuan Ma, Xiaotian Yang, Jun College of Intelligence Science and Technology The National University of Defense Technology China

ISBN: (纸本)9798350335972

Space-based systems have enormous economic, social and military values that make them vulnerable to interference and even attack. With the gradual development of networked and intelligent space-based systems, intelligent space-based systems can still maintain a certain service performance without paralysis when confronted with interference or attack, that is, flexible services. However, when a traditional space-based system encounters node failure, the service is directly interrupted or needs ground re-planning, and it does not have the ability of flexible service. To solve this problem, this paper presents a service resilience recovery algorithm for minimum latency. Experiments show that in the service elasticity evaluation of smart space-based systems based on real networks, the more satellite nodes there are, the better the service elasticity, and the recovery time is 42.3% shorter than the traditional random algorithm, which is 3 times higher than the nearest strategy. © 2023 IEEE.

关键词： Elasticity

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ESSCIRC 2022 - IEEE 48th European Solid State Circuits conference, Proceedings

ESSCIRC 2022 - IEEE 48th European Solid State Circuits Confe...

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3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

ISBN: (纸本)9798350320749

The proceedings contain 128 papers. The topics discussed include: brain tumor segmentation using deep learning techniques: comparative analysis;imperceptibility and robustness study in different transform domain for copyright protection in digital image watermarking in hybrid SVD-domain;optimized detection of continuous gravitational-wave signals using convolutional neural network;automated method for calibration findings of auto slide scanner using automated digital microscopy;implementation of perovskite solar cells using GPVDM;mangoes taste detection system using deep learning;bird species recognition using deep learning;optimization of power and delay in VLSI circuits using hybrid flip-flop circuit;ellipse shaped graphene metamaterial absorber for terahertz applications;design and development of a microgripper for use in pipeline inspection robot;and screening of citrus diseases using deep learning embedders and machine learning techniques.

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A Data Analysis Method for Abnormal Operation of Intelligent IoT system Based on Federated Learning and Deep Learning 3

A Data Analysis Method for Abnormal Operation of Intelligent...

引用

3rd Asian conference on Frontiers of Power and Energy

作者： Zhong, Jiayong Gao, Jin Wang, Xuewen Liu, Dinghao Shen, Zhenghua Wu, Chongchong State Grid Chongqing Elect Power Res Inst Chongqing Peoples R China State Grid Chongqing Elect Power Co Informat & Commun Branch Chongqing Peoples R China

ISBN: (纸本)9798350378467;9798350367676

Data friendly interaction is a key link in ensuring the smooth and reliable operation of the Power Internet of Things (pIoT), and accurate and efficient identification and analysis of abnormal network data is essential. In response to this, this article proposes an anomaly data identification method that combines federated learning and deep learning. This article uses the Long Short Term Memory Network (BiLSTM) to perform forward and backward learning training on the data features of the pIoT network, deeply and accurately mining and learning the information features of the data. Furthermore, horizontal federated learning is introduced to achieve adaptive parameter adjustment and precise model fitting of the network model, achieving accurate identification and analysis of data states. The simulation experiment used the CIC-ToN-IoT dataset to simulate the complex situation of the intelligent IoT system in the power grid. The results showed that the proposed method had identification indicators RMSE and MAE of over 91.18% and 91.34% for complex datasets, respectively, and had excellent ability to identify and analyze abnormal data.

关键词： Power IoT Smart IoT system BiLSTM Federated Learning Anomaly detection

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Design of Carbon Footprint Intelligent detection and Management system Based on Internet of Things technology 3

Design of Carbon Footprint Intelligent Detection and Managem...

引用

3rd international conference on Robotics and Artificial intelligence, JCRAI 2023

作者： Lei, Wenfeng Zhong, Da Cheng, Guosong Jiangxi Sgit-smart Cloud Digital Technology Co. Ltd. 344000 China

ISBN: (纸本)9798400707704

Currently, the dual threats of global climate change and environmental pollution are becoming increasingly prominent, and people urgently need to explore effective energy management solutions to reduce carbon emissions and energy consumption. This paper proposed a design scheme of carbon footprint intelligent detection and management system based on Internet of Things (IoT) technology. This plan optimized energy utilization by intelligently monitoring and controlling the energy consumption of household appliances, thereby improving energy efficiency and reducing carbon emissions. The experimental results showed that after one year of use, the energy consumption of air conditioning equipment using IoT technology decreased by 35.7%, and carbon dioxide emissions decreased by 31.6%;the energy consumption of refrigerators using IoT technology decreased by 30.1%, and carbon dioxide emissions decreased by 29.2%;the energy consumption of washing machines using IoT technology was reduced by 41.2%, and carbon dioxide emissions was reduced by 31.1%. The design scheme of carbon footprint intelligent detection and management system using IoT technology is feasible. Through the comparison of experimental data, it was found that it can significantly optimize the energy utilization of household appliances, reduce unnecessary carbon emissions, and make an important contribution to environmental protection and sustainable development. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Carbon footprint

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Analysis of unsupervised-learning based approach for the hazard prediction along with the application of the Gaussian method of anomaly detection to reduce the false alarm scenarios in underground coal mines 3

Analysis of unsupervised-learning based approach for the haz...

引用

3rd IEEE international conference on technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Sharma, Mayank Maity, Tanmoy Banerjee, Soumyadip Dhanbad Deparment of Electrical Engineering Jharkhand India

ISBN: (纸本)9798350397819

Safety is a prime concern in the underground mining industry. The wireless sensor networks of hybrid architectures are proposed for monitoring underground mines. Very little work is observed in the field to make an intelligent monitoring system;many are supervised learning-based approaches. Unsupervised and reinforcement learning approaches are significantly fewer in number, and these areas are still unexplored for the underground mine scenarios. This paper proposes the analysis of the K-means clustering algorithm for an intelligent gas hazard monitoring system based on an unsupervised learning method. However, before hazard detection, an anomaly detection algorithm is applied to remove anomalies so there will be fewer chances of false alarms. The F 1 score metric is used to verify the anomaly detection algorithm. Principal component analysis (PCA) is used to visualize the multidimensional gas data. After training the model in various scenarios, it is observed that the model is capable of detecting a hazardous situation with a significant accuracy level. © 2023 IEEE.

关键词： Unsupervised learning

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Fault Diagnosis in Dynamic systems Using Distributed Neural Networks 3

Fault Diagnosis in Dynamic Systems Using Distributed Neural ...

引用

3rd IEEE international conference on Electronic Engineering, ICEEM 2023

作者： Fkirin, M.A. Elhag, Rania Menofia University Faculty of Electronic Engineering Industrial and Control Engineering Department Egypt

ISBN: (纸本)9798350323511

This paper presents a novel approach for Fault detection and diagnosis (FDD) using distributed Neural Networks (DNN) to enhance system performance. FDD plays an important role in industrial applications, to guarantee system performance by detecting and analyzing faults to prevent system failures. Various approaches have been adopted for FDD, among which Artificial intelligence (AI) has shown promising results. Artificial Neural Networks (ANN), a key AI approach, are extensively utilized nowadays in FDD to monitor system efficiency. This paper introduces a DNN approach that determines the model structure and estimates the results using the Back Propagation (BP) Algorithm. The DNN where used in this work not only to identify multiple faults for DC motors (e.g., broken bar, dynamic strangeness... etc.) but also to determine fault type and location inside a distributed drive network. Several tests of DC engine faults were conducted to assess the execution of the proposed approach. The results revealed a significant improvement in system performance using the proposed approach compared to the multilayer perceptron (MLP) method. The proposed approach enhanced the system's stability and performance by reducing faults from the default value. © 2023 IEEE.

关键词： Fault detection

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iicteam@LT-EDI: Leveraging pre-trained Transformers for Fine-Grained Depression Level detection in Social Media 3

iicteam@LT-EDI: Leveraging pre-trained Transformers for Fine...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Vajrobol, Vajratiya Singh, Karanpreet Aggarwal, Nitisha Institute of Informatics and Communication University of Delhi India

ISBN: (纸本)9789544520847

Depression is a significant mental illness characterized by feelings of sadness and a lack of interest in daily activities. Early detection of depression is crucial to prevent severe consequences, making it essential to observe and treat the condition at its onset. At ACL-2022, the DepSign-LT-EDI project aimed to identify signs of depression in individuals based on their social media posts, where people often share their emotions and feelings. Using social media postings in English, the system categorized depression signs into three labels:"not depressed,""moderately depressed," and"severely depressed." To achieve this, our team has applied MentalRoBERTa, a model trained on big data of mental health. The test results indicated a macro F1-score of 0.439, ranking the fourth in the shared task. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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