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International Carpathian Control Conference (ICCC)

作者： Patrik Grosinger Jan Rybář Stanislav Ďuriš Institute of Applied Mechanics and Mechatronics Faculty of Mechanical Engineering Slovak University of Technology Bratislava Slovakia Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology Bratislava Slovakia

In this paper, a complete design of discrete low-pass, high-pass, band-pass, band-stop filters based on an analog model is performed. This approach combines the advantages of designing continuous-time filters, which are real electrical circuits, whit digital filters in the form of a program running in an infinite loop. Unlike the standard approach of a filter design directly in the discrete-time, in this case, the filter is designed as an analog and subsequently is transformed into a digital form while maintaining all the properties.

关键词： Band-pass filters Filtering Filtering algorithms Microcomputers Data models Real-time systems Digital filters

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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Electrical and computer engineering Researches (ICECER), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Tahir Abbas Karamath Ateeq College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Science TIMES Institute Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah UAE

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems.

关键词： Renewable energy sources Accuracy Load forecasting Machine learning Predictive models Power system stability Data models Forecasting Sustainable development Load modeling

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Deep Learning and CNN-Based Approach for Efficient Detection of Potato Leaf Diseases

Deep Learning and CNN-Based Approach for Efficient Detection...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing technology (AGERS)

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Muhammad Salah-ud-Din Iqbal Waqas Iqbal Munir Ahmad Muhammad Saleem Faculty of Information Science and Technology College of Arts & Science Applied Science University Manama Bahrain & Center for Cyber Security Universiti Kebangsaan Malaysia Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain School of Computer Science Minhaj University Lahore Pakistan Department of forestry and Range Management University of Agriculture Faisalabad Pakistan College of Informatics Korea University Seoul Republic of Korea

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

Potato is a maj or food crop worldwide, providing essential nutrients to millions. However, various diseases can severely affect potato crops, leading to reduced yield and quality. Early disease detection is crucial for effective disease management and control. This study presents a deep learning-based approach using CNN to autonomously diagnose potato leaf diseases. The model leverages the Plant Village dataset, which contains images of potato leaves in both healthy and diseased states. The proposed method demonstrates high accuracy in identifying multiple potato leaf diseases, offering promising results for application in precision agriculture. By utilizing CNN s, the model effectively detects and classifies disease symptoms, providing a reliable tool for early intervention in agricultural practices. The approach's potential impact on improving crop yield and minimizing losses due to diseases is highlighted, making it a significant contribution to smart farming techniques. The study underscores the importance of deep learning in modern agricultural practices and its role in enhancing disease management strategies.

关键词： Deep learning Precision agriculture Smart agriculture Plants (biology) Geoscience Nutrients Reliability Remote sensing Diseases Testing

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Parallel algorithm for the unsupervised binning of metagenomic sequences 21

Parallel algorithm for the unsupervised binning of metagenom...

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5th International Conference on Machine Learning and Soft Computing, ICMLSC 2021

作者： Hoang, Vu Vinh, Le Van Hoai, Tran Van Lang, Tran Van Bao, Huynh Quang Faculty of Information Technology HCMC University of Technology and Education Viet Nam Faculty of Computer Science and Engineering Ho Chi Minh City Viet Nam University of Technology VNU-HCM Viet Nam Institute of Applied Mechanics and Informatics VAST Viet Nam

ISBN: (纸本)9781450387613

The binning of metagenomic sequences is one of crucial steps in metagenomic projects which allow the study of uncultured organisms. Although the projects need to analyze a huge amount of data, most available binning methods run in single mode, and thus require much processing time. This paper proposes a parallel binning algorithm for metagenomic sequences without reference databases. The method is able to utilize the strength of computing clusters and shared-memory multiprocessing systems by using MPI and OpenMP techniques. Experimental results show that the proposed algorithm outperforms a single-mode binning algorithm in the aspect of computational performance while still achieving similar classification quality. The source codes and datasets used in this work can be downloaded from https://***/BiMetaPL. © 2021 ACM.

关键词： Multiprocessing systems

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Theoretical and Numerical Investigation of Acoustic Cavitation Bubble Based on the Impact of Ultrasound Frequency

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Bulletin of the Russian Academy of Sciences: Physics 2023年第Suppl 3期87卷 S341-S347页

作者： Ahmed K. Abu-Nab Abu-Bakr, Ali F. Zain Abu Shaeer Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow oblast Dolgoprudny141700 Russia Department of Basic Science Higher Institute of Engineering and Technology Kafr El Sheikh33511 Egypt

Abstract: Acoustic cavitation is the expansion and contraction of existing microbubbles in liquids brought on by an ultrasonic field. The dynamics of oscillations at higher pressures and temperatures when the cavitation bubbles collapse can be used to characterize cavitation dynamical behavior. This work deals with the theoretical and numerical investigation of acoustic cavitation in viscoelastic media considering the influence of ultrasound frequency. An acoustic pressure equation combined with a modified Rayleigh–Plesset equation forms the theoretical framework for ultrasound cavitation dynamics. The numerical solutions of the given cavitation bubble model were obtained taking into account the effect of acoustic pressure, using the exponential B-Spline collection method. The dynamics of the cavitation bubbles’ radius and the internal pressure of acoustic cavitation were examined at different values of viscosity and the impact of physical parameters on the monatomic, diatomic, and adiabatic gas-filled spherical bubble’s collapse dynamics for (k = 7/5, 5/3, 1) respectively. © Pleiades Publishing, Ltd. 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023, Vol. 87, Suppl. 3, pp. S341–S347. Pleiades Publishing, Ltd., 2023. ISSN 1062-8738, Bulletin of the Russian Academy of Sciences: Physics, 2023. Pleiades Publishing, Ltd., 2023.

关键词： Dynamics

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An Attention-Based Deep Network for Plant Disease Classification

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Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 institute of Information technology, Warsaw university of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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On the existence of highly organized communities in networks of locally interacting agents

arXiv

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arXiv 2023年

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Kostakioi Arta47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece Center for Applied Optimization University of Florida Gainesville United States

In this paper we investigate phenomena of spontaneous emergence or purposeful formation of highly organized structures in networks of related agents. We show that the formation of large organized structures requires exponentially large, in the size of the structures, networks. Our approach is based on Kolmogorov, or descriptional, complexity of networks viewed as finite size strings. We apply this approach to the study of the emergence or formation of simple organized, hierarchical, structures based on Sierpinski Graphs and we prove a Ramsey type theorem that bounds the number of vertices in Kolmogorov random graphs that contain Sierpinski Graphs as subgraps. Moreover, we show that Sierpinski Graphs encompass close-knit relationships among their vertices that facilitate fast spread and learning of information when agents in their vertices are engaged in pairwise interactions modelled as two person games. Finally, we generalize our findings for any organized structure with succinct representations. Our work can be deployed, in particular, to study problems related to the security of networks by identifying conditions which enable or forbid the formation of sufficiently large insider-subnetworks with malicious common goal to overtake the network or cause disruption of its operation. © 2023, CC BY.

关键词： Graphic methods

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Digital Twins for Energy-Efficient Manufacturing

Digital Twins for Energy-Efficient Manufacturing

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Annual IEEE Systems Conference

作者： Nader Mohamed Sanja Lazarova-Molnar Jameela Al-Jaroodi Department of Computer Science and Information Systems Pennsylvania Western University California PA USA Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Faculty of Engineering University of Southern Denmark Odense Denmark Department of Engineering Robert Morris University Pittsburgh PA USA

Some manufacturing industries consume massive energy for their manufacturing and logistics processes. The costs of manufacturing can increase significantly as the energy cost increases. Such increase amplifies the final cost of the created products on the consumers. This makes finding different approaches that utilize innovative technologies to improve energy efficiency in manufacturing extremely important. This paper investigates how digital twins can be utilized to enable improved energy efficiency in manufacturing. Different applications of digital twins for energy-efficient manufacturing are investigated. In addition, the paper discusses some challenges of utilizing digital twins for this purpose. A framework for utilizing digital twins for energy-efficient manufacturing is also discussed.

关键词： Manufacturing industries Costs Manufacturing processes Buildings Energy efficiency Raw materials Manufacturing

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Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

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International computer Sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Jamiu R. Olasina Obiseye Obiyemi Dept. of Electrical and Information Engineering Covenant Applied Informatics and Communication Africa Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal Polytechnic Ilaro Ilaro Nigeria Dept. of Electrical Power Engineering Space Science Centre Durban University of Technology Durban South Africa

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain.

关键词： Wireless communication Technological innovation Transfer learning Stochastic processes Rician channels Market research Natural language processing Optimization Residual neural networks Radio spectrum management

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