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Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

computers, Materials & Continua 2024年第8期80卷 2005-2030页

作者： Tareq Hamadneh Khalid Kaabneh Omar Alssayed Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Zoubida Benmamoun Zeinab Monrazeri Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Faculty of Engineering Liwa CollegeAbu Dhabi41009United Arab Emirates Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and *** address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making *** this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in *** theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy *** tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) *** function served as input for the SOA ***,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and *** extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the *** study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain *** simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

关键词： Optimization supply chain management sustainable lot size optimization bio-inspired metaheuristic stork

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Orbiting Optimization Model for Tracking Voltage Security Region Boundary in Bulk Power Grids

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CSEE Journal of Power and Energy systems 2022年第2期8卷 476-487页

作者： Xue Li Tao Jiang Linquan Bai Xiao Kou Fangxing Li Houhe Chen Guoqing Li the Department of Electrical Engineering Northeast Electric Power UniversityJilin 132012China the Department of Systems Engineering and Engineering Management University of North Carolina at CharlotteCharlotteNC28223 USA the Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996 USA

A voltage security region(VSR)is a powerful tool for monitoring the voltage security in bulk power grids with high penetration of *** can prevent cascading failures in wind power integration areas caused by serious over or low voltage *** bottlenecks of a VSR for practical applications are computational efficiency and *** bridge these gaps,a general optimization model for tracking a voltage security region boundary(VSRB)in bulk power grids is developed in this paper in accordance with the topological characteristics of the ***,the initial VSRB point on the VSRB is examined with the traditional OPF by using the base case parameters as initial ***,the rest of the VSRB points on the VSRB are tracked one after another,with the proposed optimization model,by using the parameters of the tracked VSRB point as the initial value to explore its adjacent VSRB *** proposed approach can significantly improve the computational efficiency of the VSRB tracking over the existing algorithms,and case studies,in the WECC 9-bus and the Polish 2736-bus test systems,demonstrate the high accuracy and efficiency of the proposed approach on exploring the VSRB.

关键词： Optimal power flow(OPF) stress direction voltage security critical point voltage security region voltage security region boundary

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Radioactive Metal Detection in Dustbin using Metaheuristic with Augmented Reality for Enhanced Safety and Security 6

Radioactive Metal Detection in Dustbin using Metaheuristic w...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Preethi, C. Hammed, S. Shahul Haripriya, K. Pavalarajan, S. Saran, V. S.Shree Meikannan, R. Pranesh Raj Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350394702

Enabling everyday objects to collect, exchange, and analyze data, the Internet of Things (IoT) is a technology paradigm that has the potential to revolutionize industries through data-driven decision-making, automation, and efficiency. IoT's fundamental principles and applications are succinctly summarized in this abstract, with emphasis on its role as a catalyst for the Fourth Industrial Revolution. Its ubiquity in various domains, such as smart homes and industrial automation, highlights its importance in shaping our interconnected world. With this in mind, we are encouraged to explore the vast opportunities and challenges presented by the IoT ecosystem. In industrial, medical, and research environments utilizing radioactive materials, ensuring radiation safety and security is essential. An important measure in preventing harm to staff and surroundings is identifying and segregating radioactive metal objects. Introducing an innovative camera-based approach for augmenting the detection of such objects is the aim of this *** order to develop a reliable and real-time detection system, our suggested Radioactive Metal Detection Bin (RMDB) integrates cutting-edge camera systems with powerful image processing *** acquire pictures of items moving across the detecting region, the RMDB system places high-resolution cameras strategically. These cameras have specific sensors and filters that are made to find gamma radiation coming from radioactive metals. © 2023 IEEE.

关键词： Automation

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Noureldeen, Rana M. Elshshtawy, Rahma A. Elgohary, Omnia A. Wahba, Fatma Alzahraa A. Attia, Marco K. Abo Elazm, Zeinab B. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis and Assessment of Wind Energy Potential of Almukalla in Yemen

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computers, Materials & Continua 2022年第8期72卷 3113-3129页

作者： Murad A.A.Almekhlafi Fahd N.Al-Wesabi Majdy M.Eltahir Anwer Mustafa Hilal Amin M.El-Kustaban Abdelwahed Motwakel Ishfaq Yaseen Manar Ahmed Hamza Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayelAseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayelAseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Electronic Department Faculty of EngineeringUniversity of Science and TechnologySana’aYemen

Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the *** Republic of Yemen has very good potential to use renewable ***,we find few studies on renewable wind energy in *** the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind ***,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and *** wind speeds were obtained only for the currently available data of five recent years,2005–*** study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and *** research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind *** Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind *** on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in Mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was *** to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.

关键词： Almukalla energy potential Rayleigh distribution Weibull distribution wind power density wind speed

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Reading Technology for Blind People Using OCR and Neura Machine Learning Method 6

Reading Technology for Blind People Using OCR and Neura Mach...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Preethi, C. Haripriya, K. Hammed, S. Shahul Pavalarajan, S. Priyadharshini, S. Varshambigai Achithra, B.R. Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350394702

This study focuses on the creation of a Neura Smart Book Reader that can help people who have visual impairments, especially those who are blind or have low vision and do not know Braille. The project uses IoT technology, which includes hardware, infrastructure, and services within the Internet of Things framework. The project primarily uses the energy-efficient Raspberry Pi device, which has machine-learning capabilities, making it adaptable. The Raspberry Pi computer is small and portable and operates on minimal power, showcasing advanced computing and Machine Learning algorithms. The system has a camera for capturing book pages, and the software performs Optical Character Recognition (OCR), combined with Machine Learning-driven character recognition. This improves the system's proficiency with each use. Once the image is recognized, the Neura Smart Book Reader converts it into audible text, allowing users to listen without tactile interaction. This proves how Machine Learning can improve accessibility. The device also offers online text-to-voice conversion via WiFi and 4G services and uses Machine Learning-based algorithms for enhanced linguistic articulation. This ensures a seamless reading experience for users across digital and hardcopy books. This amalgamation of IoT, Raspberry Pi, and Machine Learning technologies highlights the latest accessible reading solutions for visually impaired individuals,proving how Machine Learning can improve accessibility technology. © 2023 IEEE.

关键词： Machine learning

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Decentralized Security in IoT: Lightweight Blockchain with Optimized CA-LSTM for Improved Performance and Privacy 5

Decentralized Security in IoT: Lightweight Blockchain with O...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Selvakumarasamy, K. Kumar, T. Rajesh Meenambika, A. Balamanigandan, R. Mahaveerakannan, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Computer Science and Business Systems India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

An existing infrastructure can be linked to billions of devices, or "things,"through the Internet of Things (IoT). This allows for machine-to-machine communication as well as human-to-human communication. Massive volumes of data are being generated due to the proliferation of devices globally, which permeates every facet of everyday life. Thus, new problems are emerging as a result of the development and use of existing technologies, and these problems pertain to new applications, regulations, cloud computing, security, and privacy. With its decentralized nature, the blockchain has the potential to protect users' and data's privacy. This study introduces a novel method that makes use of lightweight blockchain technology to significantly lessen the computing load usually seen in traditional blockchain systems. Implementation time and computational complexity can be significantly reduced by connecting this lightweight blockchain with IoT devices. Improved safety is achieved by the use of CA-LSTM (channel attention long short-term memory) technology for attack detection. In order to improve the CA-LSTM algorithm's solution, the suggested system uses the RMSSO algorithm as a hyperparameter tuning technique. Similarly, to found very little power consumption and physical memory utilization;for example, Raspberry Pi devices used 0.2 GB of memory and NVIDIA Jetson devices used 0.42 GB. With the deployed model, power consumption increased by an average of 15% per device. This technology makes it easier to build certain types of business models and gives decentralized apps the ability to securely compute on encrypted data without compromising user privacy. The findings confirm that the proposed system is very secure, on par with conventional blockchain implementations. © 2024 IEEE.

关键词： Differential privacy

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Broken Rotor Bar Fault Detection in Induction Machines Based on Rectified Three-Phase Stator Currents 10

Broken Rotor Bar Fault Detection in Induction Machines Based...

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10th International Conference on Signal Processing and Intelligent systems, ICSPIS 2024

作者： Hajnayeb, Saeed Amerian, Fatemeh Marzebali, Mohammad Hoseintabar Abolghasemi, Vahid Seyyedbarzegar, Seyyed Meysam Shahrood University of Technology Faculty of Electrical Engineering Department Shahrood Iran School of Computer Science and Electronic Engineering Department University of ESSEX Colchester United Kingdom

ISBN: (纸本)9798331532543

Induction machines are widely used in industrial and household applications due to their efficiency in electromechanical energy conversion. However, broken rotor bars can significantly disrupt machine performance, typically manifesting as sideband harmonics near the fundamental frequency which can be utilized for fault detection. Nonetheless, frequency leakage can impede fault detection, concealing fault until substantial damage occurs. The proposed method, based on the rectified three-phase stator current (RTPSC), effectively reduces leakage frequency while maintaining the fault characteristic frequency (FCF) at its original amplitude. In addition, generate the fundamental harmonic at a frequency of 6 times. This approach aims to is to enhance diagnostic capabilities in the maintenance and monitoring of induction machines, improving operational efficiency and it also reduces the chance of catastrophic failures. The proposed methodology is tested on cases involving one, two, and three broken rotor bars, with the simulation results validated through experimental verification. © 2024 IEEE.

关键词： Stators

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