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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Ahmed, Ali Saadoon Hamdi, Mustafa Maad Abood, Mohammed Salah Khaleel, Arshad Mohammed Fathy, Maha Khaleefah, Shihab Hamad Al-Maarif University College Department of Computer Science Al-Anbar Iraq Al-Maarif University College Department of Computer Engineering Techniques Al-Anbar Iraq Beijing Institute of Technology School of Information and Electronics Engineering Beijing China Altinbas Universiti Department of Electrical and Computer Engineering Istanbul Turkey Al-Maarif University College Faculty of Computer Science Anbar31001 Iraq

ISBN: (纸本)9781665468350

The conventional strategies utilized for channel assessment don't exploit the multipath lack. In MIMO-OFDM frameworks, channel assessment is critical for computing framework execution. There are pre-codes and books higher than obscure channel boundaries using channel status data (CSI). Multiple Input Multiple Output (MIMO) systems are practical innovations that can be used to meet the growing demand for remote communication and fast data transfers. To predict the transmission and gathering of data between radio wires, various procedures for channel assessment can be used. The article focuses on a channel estimation technique that employs LS and MMSE algorithms and is based on pilot-assisted block type training symbols. The results for the BER and SNR scenarios were presented using the LS (least squares) and MMSE assessment techniques. The bit blunder rate (BER) is tested for various computerized tweak plans with various sign to-commotion (SNR) values in the OFDM framework. This resulted in fantastic results. Because of this, it can be concluded that the least-squares assessment strategy is a valuable method for assessing channels. © 2022 IEEE.

关键词： Orthogonal frequency division multiplexing

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A Hybrid Three-Port Topology for Urban Charging Stations 14

A Hybrid Three-Port Topology for Urban Charging Stations

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Klidbari, Mohammadreza Khodaparast Souri, Naser Habibolahi, Zahra Sadat Montazeri Hedeshi, Hamid K. N. Toosi University of Technology Faculty of Electrical Engineering Tehran Iran Virginia Tech The Bradley Department of Electrical and Computer Engineering United States Iran University of Science and Technology Department of Electrical Engineering Tehran Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331516116

Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid multiport isolated DC-DC converter for urban charging stations, incorporating fast and slow charging ports with a fixed switching frequency. It provides isolation and enables soft switching on the primary side of the converter without circulating current on its secondary side. The primary side does not need feedback, which reduces complexity. The second stage generates a wide output voltage range to charge the electric vehicle battery by employing a switch. In addition, the proposed topology offers reduced component count and simple control with fixed-frequency operation. This paper provides the concept and the operation modes. Experimental results are provided to validate its features. The prototype converter achieves 96% peak efficiency. © 2025 IEEE.

关键词： Topology

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A Review of IoT-Driven Innovations in Supply Chain Management

A Review of IoT-Driven Innovations in Supply Chain Managemen...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Arpit Chandankhede Palash Gourshettiwar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Supply Chain Management (SCM) worldwide makes major advancements through the Internet of Things (IoT) by enabling real-time monitoring and prediction analytics and automatic decision capabilities in worldwide deliver chains. Through IoT technology integration with Artificial Intelligence (AI) and blockchain and cloud computing the supply chain solves major operational problems including monitoring difficulties and waste reduction and regulatory compliance requirements and operation leadership deficits. The implementation of IoT for supply chain management encounters major adoption barriers from high installation costs combined with security risks and operability complications as well as limited scalability capacity that affects especially small to medium enterprises (SMEs) and developing markets. The evaluation paper examines the shifting impact of IoT in supply chain management which enhances performance metrics and brings greater clarity and strengthens supply chain resilience. A scientist studied existing papers and case studies to reveal several essential advantages which include better inventory management, predictive analysis systems and more effective supply-chain collaborations. The analysis emphasizes effective solutions for security privacy and moral and environmental issues to guarantee sustainable expanded IoT adoption. This paper delivers practical knowledge alongside best practices to help organizations adopt IoT systems which create adaptable efficient deliver chains with sustainability at their core thus achieving long-term market benefits in today’s complex global marketplace.

关键词： Technological innovation Privacy Supply chain management Scalability Supply chains Organizations Internet of Things Security Sustainable development Artificial intelligence

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The Role of Machine Learning in Optimizing Industrial Communication Protocols

The Role of Machine Learning in Optimizing Industrial Commun...

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Vikas Kumar Kharbas Mahesh TR Satish Upadhyay Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore India Department of uGDX ATLAS SkillTech University Mumbai India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Due to the complexity and urgency of information exchange in modern companies, optimizing communication protocols in the workplace is essential. Using methods from machine learning, this research takes a fresh tack toward optimizing the performance of communication protocols. The suggested approach integrates Reinforcement Learning (RL), Convolutional Neural Networks (CNNs), and Genetic Algorithms (GAs) to provide a self-optimizing system with dynamic behavior. The RL feature facilitates this protocol's flexibility in the face of dynamic network circumstances by providing rapid feedback. CNNs are used for real-time anomaly detection to improve safety and dependability. GAs methodically explores combinations of protocol parameters to determine the optimal collection that matches specified performance criteria. The recommended technique was compared to the usual methodology using a variety of efficiency and reliability measures. The findings demonstrate that the proposed approach frequently outperforms the state-of-the-art methods in a broad range of commercial settings with regards to efficiency, dependability, and adaptability. Understanding how to enhance industrial communication protocols is crucial in the age of Industry 4.0 and the IIoT. The recommended solution offers an autonomous, adaptable, and secure tool for boosting communication in dynamic production situations.

关键词： Protocols Industrial communication Reinforcement learning Production Real-time systems Reliability Vehicle dynamics Optimization Genetic algorithms Industrial Internet of Things

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Can't Take My Eyes off You: Increasing Task Involvement in Cooperative Driving With Natural Habituated Interfaces

Can't Take My Eyes off You: Increasing Task Involvement in C...

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International Conference on Intelligent Transportation

作者： Jürgen Pichen Nikol Figalova Martin Baumann Department of Human Factors Faculty of Engineering Computer Science and Psychology Ulm University Ulm Germany Department of Clinical and Health Psychology Faculty of Engineering Computer Science and Psychology Ulm University Germany

Vehicle automation is one of the challenges for future transportation, and the question of how to design future interfaces remains unanswered. The goal of this study is to compare two cooperative approaches using (a) a natural habituated steering wheel and (b) a touchscreen interface with (c) an SAE L3 manual take-over approach. We conducted a driving simulator experiment with 26 participants who experienced several overtaking scenarios. Our findings suggest that the manual take-over approach requires more attentional resources than cooperative approaches. Moreover, we observed higher task engagement in the safety-critical phases of the overtaking manoeuvre when using the steering wheel, which could there-after result in safer driving behaviour. Additionally, participants reported that they prefer the steering wheel interface over the touchscreen interface. In conclusion, the natural habituated approach and using the steering wheel interface should be considered when designing the driver-vehicle interaction for SAE L3 vehicles.

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A model to detect fake profile on Instagram using rule-based approach

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CSI Transactions on ICT 2024年第4期12卷 95-105页

作者： Sonowal, Gunikhan Balaji, V. Kumar, Naresh Faculty of Computer Technology Assam down town University Guwahati India Department of Computer Science and Engineering GITAM University Bengaluru India Computer Science DMPS University of Nizwa Sultanate of Oman Birkat Al Mawz Oman

Online organizations or businesses have always been concerned about fake accounts, but the latest Instragram deal has brought attention to their severe consequences. A fake Instagram profile contains false or misleading information, usually to deceive others. These profiles may impersonate a real person or business by using stolen images, fictitious names, and other manufactured details. Fake profiles can be used for a variety of goals, including cyberbullying, fraud, and the dissemination of fake information. The number of fake profiles is continually growing despite the efforts of many researchers to address the problem. This paper attempts to detect fake profiles using a rule-based approach. A rule-based approach is a technique for classifying data according to a set of criteria. In this method, a set of if–then rules are developed to categorize the data according to specific standards. Initially, the rules were analyzed using 4 plot techniques and found that the rules satisfied the randomness, fixed distribution, fixed variation, and normal distribution. After the rules are selected, machine learning and Artificial Neural Network (ANN) are tested with a variety of evaluation matrices. The results of the tests indicate that the Artificial Neural Network (ANN) provides superior accuracy with a figure of 94.25 percent. The model was also contrasted with other machine learning methods, and the results demonstrate that the suggested model can recognize fake profiles.

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Poisoning Attack Mitigation for Privacy-Preserving Federated Learning-Based Energy Theft Detection

Poisoning Attack Mitigation for Privacy-Preserving Federated...

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IEEE International Conference on Communications (ICC)

作者： Mahmoud Srewa Michaela F. Winfree Mohamed I. Ibrahem Mahmoud Nabil Rongxing Lu Ahmad Alsharif Department of Computer Science University of Alabama AL USA Department of Computer Science University of Kentucky KY USA School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Egypt Electrical and Computer Engineering University of North Carolina A&T NC USA Faculty of Computer Science University of New Brunswick Fredericton NB Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In federated learning (FL) based electricity theft detection, detection nodes (DNs) locally train deep learning models on consumers' data and share only the local model parameters with an aggregation server (AS) to generate a global model shared by all nodes for better detection accuracy. However, several privacy concerns should be addressed including membership and inference attacks. To mitigate these attacks, several privacy-preserving aggregation schemes have been introduced. Nevertheless, existing FL detectors often overlook the threat of poisoning attacks, in which certain DNs hold maliciously labeled, i.e., poisoned, data during the training. This manipulated data can subsequently be exploited to introduce backdoors into the global model after its deployment. This paper introduces a novel approach that enhances privacy and resilience against poisoning attacks in FL-based electricity theft detection within smart grids. Our approach enables encrypting local parameters before sending them to the AS, thus safeguarding consumers' privacy. Additionally, it utilizes a cosine similarity test over encrypted data to detect and mitigate poisoning attacks by filtering out malicious local gradients from being considered in the global model computation. Through extensive evaluations, we demonstrate the effectiveness of our FL-based detector in substantially reducing the poisoning attack success rate even when 50% of DNs train their local models with malicious targeted power consumption data, all while preserving consumers' privacy.

关键词： Privacy Data privacy Power demand Federated learning Electricity Computational modeling Detectors

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Light-weight Sketch Recognition with Knowledge Distillation 6

Light-weight Sketch Recognition with Knowledge Distillation

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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Huynh, Viet-Tham Nguyen, Tam.V Tran, Minh-Triet Software Engineering Laboratory Faculty of Information Technology University of Science VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam University of Dayton Department of Computer Science United States

ISBN: (纸本)9798350327410

Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concept arts. In addition, by understanding drawings, we can generate 3D models that can be used for further design and development. Thus, in this paper, we aim to develop a novel lightweight network that can accurately recognize sketch drawings. We propose a lightweight-yet-efficient neural network based on MobileNetV2 for sketch recognition and employ knowledge distillation to train the proposed model from EfficientNet-B4. To evaluate the accuracy of the proposed method, we collect a dataset of sketches comprising 1800 drawings in 12 categories, ranging from furniture to animals. The experimental results show that our network model achieves an accuracy of 96.7%, with 96.9% precision, 96.7% recall, and 96.7% F1-score. These results demonstrate that the proposed approach has great potential for practical sketch recognition applications, such as interior design or VR scene generation. © 2023 IEEE.

关键词： Distillation

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Minimal Input Structural Modifications for Strongly Structural Controllability

Minimal Input Structural Modifications for Strongly Structur...

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IEEE Conference on Decision and Control

作者： Geethu Joseph Shana Moothedath Jiabin Lin Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft CD The Netherlands Department of Electrical and Computer Engineering Iowa State University IA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable. We focus on the generalized structured systems that rely on zero/nonzero/arbitrary structure, i.e., some entries of system matrices are zeros, some are nonzero, and the remaining entries can be zero or nonzero (arbitrary). We derive the feasibility conditions of the problem, and if it is feasible, we reformulate it into another equivalent problem. This new formulation leads to a greedy heuristic algorithm. However, we also show that the greedy algorithm can give arbitrarily poor solutions for some special systems. Our alternative approach is a randomized Markov chain Monte Carlo-based algorithm. Unlike the greedy algorithm, this algorithm is guaranteed to converge to an optimal solution with high probability. Finally, we numerically evaluate the algorithms on random graphs to show that the algorithms perform well.

关键词： Greedy algorithms Monte Carlo methods Heuristic algorithms Controllability Computational complexity Time-varying systems

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IoT and AI Synergy in Society: Robotics and Automation Perspectives 7

IoT and AI Synergy in Society: Robotics and Automation Persp...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Geetha, B.T. Jothi, J. Salomi Backia Pal, Shaunak Islam, Imam Taqi Deen Raghuram, K.S. Soni, Mukesh Simats Saveetha University Saveetha School of Engineering Department of Ece Tamil Nadu India Srm Institute of Science and Technology Faculty of Science and Humanities Department of Commerce Ramapuram campus Chennai600089 India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India University of Electrical Science and Technology of China Sichuan Chengdu China Department of Mechanical Engineering Visakhapatnam India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science& Technology Tathawade Pune India

ISBN: (纸本)9798350350067

The Internet of Things, or IoT, is a relatively new Internet revolution that is being successfully adopted by the corporate, industrial, healthcare, and economic sectors as well as other areas of the modern information society. Specifically, IoT with artificial intelligence (AI) support improves a great deal the performance in a wide range of daily applications with a focus on automation systems, robotics, transportation, and business. Smart automation in the manufacturing sector is now possible thanks to the development of Industry 4.0. The BRDT algorithm is used in this work to track how much energy appliances and machinery use, forecast behaviour, and identifies unusual conduct. When the suggested system's efficiency is compared to current approaches, it provides an efficiency of 85%. The application of this technology raises a number of standardisation, security, resource planning, legal, and paradigm-shifting business challenge problems. The whole supply chain and all of its actors, from producers to end users, are crucial to the success or failure of Industry 4.0 deployment. © 2024 IEEE.

关键词： Supply chains

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