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Automobile Spare Part Classification via AI Image Processing Techniques, a Comparison Study

Automobile Spare Part Classification via AI Image Processing...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Boon Hoe Lee Muhammed Basheer Jasser Bayan Issa Samuel-Soma M. Ajibade Hui Na Chua Richard T.K. Wong Anwar Abdul P.P. Majeed School of Engineering and Technology Sunway University Bandar Sunway Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Artificial intelligence (AI) and image recognition technologies are developing rapidly, creating new opportunities for process improvement in industry, especially in spare parts classification. Spare parts classification using images has not been thoroughly researched and utilized in the car repair industry, especially in Malaysia. This project seeks to create a reliable and effective system that uses AI to identify and classify spare parts from user-uploaded images precisely. The main goals are to simplify inventory management and improve the precision and speed of part identification in diverse industrial environments. In the end, an accuracy of 98.70% was achieved for spare part type classification and $\mathbf{8 9. 7 0 \%}$ for both type and car brand classification with the use of transfer learning, finetuning, and object detection techniques.

关键词： Industries Accuracy Transfer learning Object detection Classification algorithms Automobiles Reliability Artificial intelligence Particle swarm optimization Image classification

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Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada Rhazes Lab Mayo Clinic Department of Artificial Intelligence and Informatics RochesterMN United States

ISBN: (纸本)9781665430654

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Artificial Neural Network (ANN) Coordinate Search Expensive Optimization Gradient-free Large-Scale Optimization Stochastic Gradient Descent (SGD)

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Study and Simulation of Wind Farms Based on Squirrel Cage Induction Generator in Electrical Distribution System 17

Study and Simulation of Wind Farms Based on Squirrel Cage In...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Zanjani, Sayed Mohammad Ali Moazzami, Majid Honarvar, Mohammad Amin Mosavi, Amir Fathollahi, Arman Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

Wind power sources are evolving and becoming more prevalent in energy systems, which has implications for a variety of operational metrics, including stability and frequency regulation. To guarantee the stability and reliability of the modern power system, it is crucial that grid-connected wind turbines operate consistently and reliably. It is still challenging to accurately and reliably characterize these generators and their components to analyze sequestered operational possibilities like short-circuit faults, soft-starting, and generator switching. To comprehensively understand the stability interactions within distribution grids or localized networks, an accurate simulation is essential. The current study investigates the impact of a static synchronous compensator (STATCOM) on a distribution system that incorporates an induction generator wind farm. Specifically, we analyze and simulate the effect of STATCOM on a 25 kV distribution system, which delivers the power generated by a 9 MW wind farm to the grid via a 25 kV line. The outcomes demonstrate that STATCOM plays a pivotal role in regulating the voltage changes at the common connection point between the distribution system and the wind farm during both normal and faulty conditions. In addition to improving the stability and efficiency of the distribution network, STATCOM can also provide active power during short-circuit fault conditions. Based on the outcomes, the effectiveness of the proposed approach is highly dependent on the wind energy system's parameters. Due to the inherent parameter uncertainty, non-stationarity, and nonlinearity in the wind power system, the use of a parameter prediction approach based on artificial intelligence (AI) is recommended for future research. © 2023 IEEE.

关键词： Static synchronous compensators

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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Forgive and forget: Self-stabilizing swarms in spite of Byzantine robots

Forgive and forget: Self-stabilizing swarms in spite of Byza...

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作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Graduate School of Science and Technology Nara Institute of Science and Technology Nara Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Tokyo Japan

In this article, we consider the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the program/policy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to forgive and forget, otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. We demonstrate the need to periodically forgive and forget to realize swarm several tasks including patrolling/cleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrol/clean the plane. © 2020 John Wiley & Sons, Ltd.

关键词： Robots

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The Impact of Collusive Interest Flooding Attack on NDN Network Performance

The Impact of Collusive Interest Flooding Attack on NDN Netw...

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International Conference on Wireless and Telematics (ICWT)

作者： Jupriyadi Ridha Muldina Negara Eueung Mulyana Nana Rachmana Syambas School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Collusive Interest Flooding Attack (CIFA) is a denial of service (DoS) attack in Named Data Networking (NDN) networks. Using a fake server, this attack develops the Interest Flooding Attack (IFA) attack. The attacker sends a large number of requests to the fake server. Sending requests to fake servers is intended to avoid existing flood attack detection systems. In this paper, an analysis of the impact of CIFA attacks is carried out by carrying out attacks and varying network topology configurations. This is done to get a more comprehensive idea of the possible impact of the attack. The performance metric used is the interest satisfaction ratio (ISR) value, which describes the ratio of the number of data packets consumers receive to the total interest packet requests sent. The results of the simulations show that CIFA attacks can reduce the performance of the NDN network. This can be seen based on the ISR value, which decreases when a CIFA attack occurs. By increasing the size of the content store (CS) on the router, you can increase the ISR value so that the impact of attacks can be minimized.

关键词： Wireless communication Measurement Network topology Telematics Servers Floods

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Overview of Load Disaggregation Models, Methods and Hardware

Overview of Load Disaggregation Models, Methods and Hardware

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International Universities Power engineering Conference (UPEC)

作者： Mircea N. Lancranjan Alexandru Muresan Dacian I. Jurj Alexandru Berciu Alexis Polycarpou Levente Czumbil Dan D. Micu Department Electrical Engineering Technical University of Cluj-Napoca Cluj-Napoca Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9798350379730

ISBN: (纸本)9798350379747

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated.

关键词： Load monitoring Costs Scalability Signal processing algorithms Hardware Smart meters Energy efficiency Transient analysis Time complexity Load modeling

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Acoustic cavitation bubble under influence of frequency ultrasound: Numerical analysis 3

Acoustic cavitation bubble under influence of frequency ultr...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Abu-Nab, A.K. Abu-Bakr, A.F. Abushaeer, Zain Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Moscow Institute of Physics and Technology Phystech School of Applied Mathematics and Informatics Moscow141700 Russia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Higher Institute of Engineering and Technology Department of Basic Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350323511

This work presents a numerical investigation of acoustic cavitation in Newtonian fluid based on frequency ultrasound. The model of ultrasonic cavitation dynamics is formulated by a modified Rayleigh-Plesset equation and an acoustic pressure equation. The exponential B-Spline collection method was employed to obtain the numerical solutions of the given cavitation bubble model, considering the effect of acoustic pressure. The behaviour of the cavitation microbubbles' radius and the internal pressure of acoustic cavitation were examined at different values of frequencies. The obtained results reveal the importance of frequencies in ultrasound, which play a vital role in changing the behaviour of cavitation bubbles. © 2023 IEEE.

关键词： Numerical methods

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TPC-H Benchmark Q3, Q6 and Q12 Sequential, OpenMP Parallel and CUDA Parallel Implementation 44

TPC-H Benchmark Q3, Q6 and Q12 Sequential, OpenMP Parallel a...

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44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Mitrovski, Jovan Djinevski, Leonid Gusev, Marjan Arsenovski, Sime Sts. Cyril and Methodius University in Skopje Faculty of Computer Science and Engineering Macedonia American University of Europe - FON Faculty of Informatics Skopje Macedonia University of Tourism and Management Faculty of Informatics Skopje Macedonia

ISBN: (纸本)9789532331011

In the last decade or two, parallel computing has solved a myriad of problems when it comes to speeding up the problem-solving process. To deliver the necessary acceleration of their product, application developers have turned to CUDA-powered GPU parallel processing. One of the many uses of the well-known NVIDIA parallel computing platform CUDA is to enhance the performance of problems whose root comes from speeding up database queries. The fundamental focus of this paper is evaluating the performance of different parallelizing techniques using the TPC-H benchmark. This is achieved by parallelizing the 'SELECT' queries that the benchmark offers using the Open Multi-Processing (OpenMP) API (Q3, Q6, and Q12 in particular), then parallel on the GPU using the previously mentioned CUDA platform. Lastly, comparing the GPU's performance with both sequential instruction execution and parallel instruction execution on a CPU. This implementation resulted in speedup ratios ranging from 2 to 4 when analyzing the CPU-parallel code and ranging from 10 up to 558 when analyzing the GPU - parallel code. © 2021 Croatian Society MIPRO.

关键词： Electrocardiograms

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Transformer-Based Indonesian Language Model for Emotion Classification and Sentiment Analysis

Transformer-Based Indonesian Language Model for Emotion Clas...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Ahmadian, Hendri Abidin, Taufik Fuadi Riza, Hammam Muchtar, Kahlil Graduate School of Mathematics and Applied Sciences Universitas Syiah Kuala Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Jakarta Indonesia Universitas Syiah Kuala Faculty of Engineering Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350359633

The rapid development of social networks has made much user-generated data accessible for public evaluation. These data can be used for multiple purposes, such as textual analysis of comments and reviews. This article employs a variant of the bidirectional encoder representations from Transformer (BERT) model designed explicitly for Bahasa Indonesia (called IndoBERT model) to enhance the performance of the Indonesian natural language understanding benchmark in tasks, such as sentiment analysis and emotion classification. The two tasks were tested using a hybrid method, combining the IndoBERT model's last hidden layer summation with a neural network model. The performance of the resulting model was assessed using the F1-score metric. The experimental results show that the proposed model attains an accuracy of 0.92 and 0.76 for sentiment analysis and emotion classification, respectively. © 2023 IEEE.

关键词： Sentiment analysis

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