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Reinforcement Learning Based Collaborative Perception for Vehicular Networks

Reinforcement Learning Based Collaborative Perception for Ve...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Hongyi Lin, Zhiping Zhu, Yunjun Li, Jieling Xiao, Liang Tang, Yuliang Zhang, Yanyong Institute of Artificial Intelligence Xiamen University China Xiamen University Dept. Information and Communication Engineering China University of Science and Technology of China School of Computer Science and Technology China

ISBN: (纸本)9798350351255

Reinforcement learning (RL)-based collaborative perception in vehicular networks chooses the sub-frame of radio channel resources for connected autonomous vehicles (CAVs) to exchange sensing data to enhance the perception performance, but leads to inaccurate detection in the light detection and ranging (LiDAR)-based object detection due to the asynchronous scan period of the LiDAR point clouds. This paper proposes a RL-based collaborative perception scheme to choose the transmit power and sub-frame to share the feature maps extracted from point clouds. Based on the estimated packet timestamp, the network topology and the channel gains among CAVs, this scheme enhances the perception accuracy and latency against path-loss and interference. The collaborative risk in the policy distribution is formulated as a weighted sum of the perception latency and packet loss rate to avoid the time asynchronization and information loss of the feature map exchange. The performance bound of the perception accuracy and latency is provided based on a Nash equilibrium of the cooperative game among CAVs. Simulation results based on five CAVs show the performance gain of the perception accuracy and latency over the benchmarks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Design of a Smart and Green Industry 4.0 Architecture 24

Design of a Smart and Green Industry 4.0 Architecture

引用

24th International Arab Conference on information Technology, ACIT 2023

作者： Hamdan, Khaled Kazar, Okba College of It Uaeu Dept. of Computer Science and Software Engineering United Arab Emirates College of It Uaeu Dept. of Information Systems & Security United Arab Emirates

ISBN: (纸本)9798350384307

The industrial sector has entered a phase of profound change which sees digital technologies being integrated into the heart of industrial processes. This fourth industrial era gives birth to a new generation of factory called 'Digital Factory', 'Industry 4.0'. This industrial change is the result of the integration of new digital technologies into manufacturing processes and the optimization of energy consumption. The commitment to the environment is also a key and success factor of Industry 4.0 and green in particular the reduction of carbon emissions and pollution. This is how smart green industry try to take in charge the problem of energy consumption and to reduce the Co2 pollution. The goal of this paper is to present the new model of smart green industry using distributed artificial intelligence based on multi-agent system (MAS). The motivation to use the intelligent approach based on MAS is based on the similarity between the fundamental concepts of the cyber-physical system and those contained in the architecture and operation of MAS such as communication, coordination, decision and cooperation. © 2023 IEEE.

关键词： environmental impact Industry 4.0 smart and green Industry 4.0

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Efficient Lane Detection based on Feature Aggregation for Advanced Driver Assistance Systems

Efficient Lane Detection based on Feature Aggregation for Ad...

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2023 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2023

作者： Jhong, Sin-Ye Ko, Ching-Hsiang Su, Yi-Feng Hua, Kai-Lung Chen, Yung-Yao National Cheng Kung University Dept. of Engineering Science Tainan Taiwan National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Automotive Research and Testing Center Research and Development Division Changhua Taiwan National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350302714

As we enter the era of smart cars and electric vehicles, the significance of autonomous driving systems, especially crucial components like lane detection technology, increases dramatically. However, advanced solutions often face daunting challenges, especially under adverse environmental and lighting conditions, yielding varying detection results. This variability prompts automakers to enforce numerous restrictions on auxiliary functions, often deterring potential customers from upgrading relevant services. This study addresses these limitations and introduces a sophisticated deep learning-based lane detection model to supersede existing technologies. The model fuses an efficient feature extraction network with a Recurrent Feature-Shift Aggregator (RESA) and a Bilateral Up-Sampling Decoder (BUSD). These elements work harmoniously, enabling enhanced global information extraction and fortifying detection capability under challenging conditions. The methodology of this study is segregated into three parts: Efficient Backbone and Neck for feature extraction, Global Feature Enhancement Decoder for maximizing feature map details, and the actual implementation of a Lane Assist System. These modules collectively help us calculate high-accuracy lane coordinates, thereby overcoming the limitations of previous lane detection methods. Following the successful creation and validation of the model, the study extends the implementation to the Hexagon vehicle control platform, where the Lane Departure and Lane Keeping assistance systems are deployed. The proposed technology is validated through rigorous experiments and analyses, utilizing the highly recognized TuSimple dataset, custom-collected Taiwanese scene data, and actual road tests. Results illustrate high and consistent detection performance under diverse environmental conditions, validating the technology's effectiveness and feasibility. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Comparison of the Current Harmonic Pollution of Asynchronous Motor Drives With Field Oriented Control and Direct Torque Control

Comparison of the Current Harmonic Pollution of Asynchronous...

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2023 International Symposium on Electromagnetic Compatibility - EMC Europe, EMC Europe 2023

作者： Nuca, Iurie Kostic, Dusan Nicolae, Petre-Marian Nicolae, Ileana-Diana University of Craiova Dept. of Electrical Engineering Aeronautics and Energetics Craiova Romania University of Craiova Computer Science and Information Tech. Dept. Craiova Romania

ISBN: (纸本)9798350324006

Asynchronous motor drives are the most common form of controlled electromechanical conversion, including railway traction. While many studies compared the dynamic performance of different control methods, a comparison on the impact of power quality and electromagnetic compatibility is often lacking. This paper compares the two most common control methods for traction application: field-oriented control, and direct torque control, by their impact on harmonic distortions of current. The comparison is done on simulations of mathematical models in steady state for the harmonic decomposition via the Fast Fourier Transform to determine the total harmonic distortion. © 2023 IEEE.

关键词： Induction motors

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Generalization Evaluation of Seq2seq based Fog Computing Application Placement Algorithm on Limited Dataset 3

Generalization Evaluation of Seq2seq based Fog Computing App...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Pakpahan, Michael S. M. Nugroho, Lukito Edi Widyawan, Widyawan Wardhana, Ajie Kusuma Muhammad Ardian, R.A. Astagenta, Rangga Satria Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Dept. Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network's edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model's performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?;2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios. © 2023 IEEE.

关键词： Fog computing

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A Real-time Data Analysis Approach for Predicting COVID-19 Outcomes using Heterogeneous Ensemble Learning 9

A Real-time Data Analysis Approach for Predicting COVID-19 O...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Azam, Muhammad Yousaf, Adnan Zafar, Fatima Munir, Muhammad Saad Saeed, Muhammad Imran The Superior University Dept. of Computer Science and Information Technology Lahore54000 Pakistan The Superior University Dept. of Electrical Engineering Lahore54000 Pakistan The Superior University Dept. of Information Engineering Lahore54000 Pakistan Riyadh11564 Saudi Arabia

ISBN: (纸本)9798350316926

COVID-19, caused by the SARS-CoV-2 virus, has resulted in over 5.3 million deaths worldwide, significantly impacting global demographics and causing severe acute respiratory syndrome. This study presents a novel approach to predicting COVID-19 outcomes using a heterogeneous ensemble learning methodology. By combining multiple machine learning models trained on different portions of the data, the approach aims to create a more accurate and robust prediction model. The study utilizes various classifiers, including K-Nearest Neighbors (KNN), Logistic Regression, Decision Tree, Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN). Individual accuracies for these models range from 74.58% to 82.29%. By employing the effective majority voting method of heterogeneous ensemble learning, an impressive overall accuracy of 82.29% is achieved. The findings indicate that this approach outperforms standalone models and other ensemble techniques in predicting COVID-19 outcomes, making it a valuable tool for healthcare decision-makers. The proposed strategy has significant policy implications for managing and detection of critical COVID-19 patients with limited provided attributes. © 2023 IEEE.

关键词： COVID-19

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Disease Detection In Pomegranate Plant Using Image Processing Techniques 9

Disease Detection In Pomegranate Plant Using Image Processin...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Sahebgouda Sumana, M. B.L.D.E.AsV.P.Dr.P.G.Halakatti College of Engineering and Technology Dept. of Computer Science and Engineering Vijayapur India Msrit Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350334395

The majority of the people in India is dependent on agriculture. Every crop the farmers cultivate has some sort of illness. The most successfully adopted subtropical fruit crop in India is the pomegranate. Pomegranates have the highest economic value of all the fruits. Pomegranate has very little immunity to infection during cultivation, especially in the early stages. As a result, it is particularly susceptible to the sickness. The detection of plant diseases is a beneficial use of image processing. Picture acquisition, picture pre-processing, Image segmentation, feature extraction, object recognition, and classification are all steps within the infection discovery handle. © 2023 IEEE.

关键词： Image segmentation

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RoseVision: An Android Application To Detect Rose Leaf Diseases Using Modified Convolutional Neural Network 26

RoseVision: An Android Application To Detect Rose Leaf Disea...

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26th International Conference on computer and information Technology, ICCIT 2023

作者： Nayan, Saidul Islamm Rahman, Syed Mahfuzur Mahbub, Nosin Ibna University of Global Village Dept. of Computer Science and Engineering Barishal8200 Bangladesh Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh

ISBN: (纸本)9798350359015

Every year 28% of the global cut rose production is hampered by various rose plant diseases. Early detection and classification could diminish the cost of cultivation lost. However, without proper knowledge and expert assistance, farmers are facing challenges to identify the diseases in time to take action. Previously, a few works have been conducted on this. So far, no study has been done to develop an efficient automatic mobile application detection system with faster, lighter, and more accurate predictions. In this study, we presented an artificially intelligent mobile application using image processing to identify rose leaf diseases. The primary goal of this work is to create a simple but exact identification with minimal computing power and time. We proposed a Modified Convolutional Neural Network (MCNN) to classify different rose leaf diseases. The proposed MCNN model outperforms the famous pre-trained models including VGG16, VGG19, DenseNet121, MobileNetV2, Resnet50, InceptionV3, and Extreme Inception (Xception) by achieving 97.94% test accuracy. Additionally, based on the trained MCNN model, we developed an android-based mobile application that will allow the farmers to quickly identify the disease using their android phones. © 2023 IEEE.

关键词： Convolutional neural networks

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An Overview of Protocols-Based Security Threats and Countermeasures in WLAN 4

An Overview of Protocols-Based Security Threats and Counterm...

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4th International Conference for Emerging Technology, INCET 2023

作者： Ojha, Nitish Kumar Baray, Elyas Indian Institute of Information Technology Dept. of Information Technology Allahabad211012 India Sharda University Knowledge Park - III Dept. of Computer Science and Engineering Greater Noida201310 India

ISBN: (纸本)9798350335750

WLAN security protocols are the most important section for wireless networks which controls all the security-related issues by securing the network with some pre-defined rules made by wireless-protected access organizations, further which makes it the main target of hacking and threats by interested parties and to be safe from such threats. There should be countermeasures for such threats where there will be a solution for any threat encountered and to mention. WIFI or wireless local area Network is a cost-effective easy deployment Network that makes users take advantage of vast data shared on the internet and by the network without the need for any cable for connections. Here in this overview paper by reviewing many researchers' techniques and works on the WLAN security topic, there are shared threats to WLAN and the ways of taking precautions against those threats as countermeasures and for which the tables are used. For comparison of techniques used by authors and some famous and highly dangerous threats are also defined further. WPA3's new security protocol is also defined with its newly found vulnerabilities and countermeasures except there is a conclusion on the downgrade attack problem with WPA3. Further, there are also countermeasures for that as well, nowadays people think the network is secured with standards defined by the IEEE organization but some know this is not the case so it makes it a more important area for research. © 2023 IEEE.

关键词： Wireless local area networks (WLAN)

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Fault Analysis and Identification in Ubiquitous Networks using Cognitive Agents Approach

Fault Analysis and Identification in Ubiquitous Networks usi...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Ayyannavar, Vasudha V. Bhajantri, Lokesh B. Basaveshwar Engineering College Dept. Computer Science and Engineering Bagalkot India Basaveshwar Engineering College Dept. Information Science and Engineering Bagalkot India

ISBN: (纸本)9781665484251

The ubiquitous network has set of heterogeneous networks such as wireless sensor networks, cellular networks, mobile adhoc networks, Bluetooth networks, etc. Each heterogeneous network communicates with other to perform ubiquitous computation, but there is a need of a fault tolerance mechanism. This kind of mechanism is used to provide continuous services to users in the presence of faults. The faults may be node, link, and memory level faults in the ubiquitous networks. Such levels faults are lead to the failure of the networks. However, there is a need for substantial time and sufficient resources for providing reliability in fault tolerance mechanisms. In this work, it has been proposed that cognitive agent based fault analysis and identification of types of faults using support vector machine algorithm. Also, the various types of cognitive agents are defined as network cognitive agents, heterogeneous node cognitive agents, gateway cognitive agents, ubiquitous cognitive agents, and fault analysis and identification cognitive agents. Each agent is communicating with other for analyzing the types of faults. Finally, the proposed work is simulated in terms of delay, probability analysis and identification, throughput, packet delivery ratio, and agent computation overhead. © 2022 IEEE.

关键词： Heterogeneous networks

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