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IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： N. Mazibuko K. T. Akindeji K Moloi Department of Electrical Power Engineering Department of electronics and computer engineering Smartgrid research center Durban University of Technology Durban South Africa

ISBN: (数字)9798350389388

ISBN: (纸本)9798350389395

Compensated transmission lines are employed in power system networks to increase energy transmission capacity, improve system stability, decrease transmission losses, and provide more flexibility in power management. However, the effectiveness of protection relays in detecting, classifying, and locating faults in such networks is significantly impacted. This is due to changes in impedance within the fault loop caused by devices used in compensated networks, which alters the apparent impedance seen by the relay. This study introduces three different fault detection and classification schemes for compensated transmission lines. Initially, an IEEE 9-bus system with series compensation at line 4–6 is utilized for data collection. Feature extraction using Principal Component Analysis (PCA) is then conducted, followed by the implementation of fine k-nearest neighbors (KNN), Medium Nuero network (MNN), and SQVM classifiers. A comprehensive performance analysis is carried out, revealing that SQVM achieves the highest overall accuracy of 99.3%, while the fine tree outperforms SQVM in single-line fault detection.

关键词： Power transmission lines Fault detection Power system stability Nearest neighbor methods Propagation losses Stability analysis Impedance Relays Protection Principal component analysis

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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

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computer Systems Science & engineering 2023年第8期46卷 1533-1547页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Civil Engineering Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop *** agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental *** control has become one of the significant problems in the agricultural *** traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same *** more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed *** may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable *** study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision *** HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision *** image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise *** addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature *** detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection *** experimental results of the HHOGCN-WD technique are investigated under the benchmark *** results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

关键词： Weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning

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Optimizing Antenna Design for 6G Applications Using Machine Learning Techniques

Optimizing Antenna Design for 6G Applications Using Machine ...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Praveen Kumar Yadav, Vivek Prakash Mukesh, Aashiwal Nirmal Jawale, Sourabh Kumar, Ankit Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul01811 Korea Republic of FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Pilani India FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Mumbai India Medicaps University Department of Computer Science and Engineering Indore India Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9798350379716

The emergence of 6G networks requires innovative antenna designs that operate at extremely high frequencies. The proposed paper explores the application of advanced machine learning techniques to optimize the design of antennas for 6G applications at 15 GHz. Traditional design methods are often time-consuming and depend on expert knowledge, whereas machine learning can automate and expedite this process. We developed a dataset incorporating critical antenna design parameters such as geometric dimensions and applied algorithms to predict key performance metrics like return loss, gain, and bandwidth. Our results demonstrate that neural networks, in particular, offer superior predictive accuracy, significantly reducing design iteration times. This integration of machine learning into antenna design not only improves performance but also facilitates the development of adaptive antenna systems, meeting the stringent demands of 6G networks. This research highlights the potential of machine learning to revolutionize antenna design, enabling rapid and efficient development of next-generation communication systems. © 2024 IEEE.

关键词： Smart antennas

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Distributed Control for a Microgrid Cluster: Implementation and Experimental Validation

Distributed Control for a Microgrid Cluster: Implementation ...

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International Conference on Environment and electrical engineering (EEEIC)

作者： David Carrasco-González Raúl Sarrias-Mena Pablo Horrillo-Quintero Ehsan Hosseini Francisco Llorens-Iborra Luis M. Fernández-Ramírez SURET Research Group Department of Electrical Engineering University of Cadiz Algeciras Spain SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks University of Cadiz Algeciras Spain

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

In response to growing energy demands and concerns about climate change, microgrid clusters (MGCs) are gaining widespread attention. Their ability to integrate technologies for energy consumption, generation, and storage in AC, DC or a compound of both offer a high degree of flexibility and resiliency. This research introduces a new control strategy for an MGC composed of two MGs interconnected to a local grid via a point of common coupling. The first one is a DC MG comprising an ultracapacitor (UC), wind turbine (WT), DC loads and hydrogen system. The second is an AC MG with a battery bank, AC loads and photovoltaic (PV) generator. The control strategy employs local controllers for each device and distributed control via two control agents, coordinating the power distribution among the energy storage systems (ESSs) of the MGC. This system is tested and validated on a real-time experimental setup, using two Raspberry Pi microcontrollers and an OPAL-RT unit, under different working conditions. The results obtained in this work show that the control strategy implemented in the MGC works correctly.

关键词： Energy consumption Microcontrollers Wind speed Decentralized control Hydrogen Microgrids Supercapacitors Real-time systems Wind turbines Solar radiation

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Efficient Virtual Network Embedding with Hierarchical and Cooperative Multi-Agent Reinforcement Learning 3

Efficient Virtual Network Embedding with Hierarchical and Co...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Ullah, Ihsan Lim, Hyun-Kyo Seok, Yeong-Jun Khan, Muhammad Sajjad Han, Youn-Hee University of Technology and Education Advanced Technology Research Center Cheonan Korea Republic of Future Convergence Engineering Korea University of Technology and Education Cheonan Korea Republic of International Islamic University Department of Electrical & Computer Engineering Pakistan

ISBN: (纸本)9798350332391

Virtual network embedding is a process that allows for the creation of multiple virtual networks, each specifically designed to meet the unique requirements of various services, on a shared physical network infrastructure. 5G network virtualization requires an efficient embedding algorithm to maximize the resource utilization of the substrate network. In this paper, we propose a hierarchical cooperative multi-agent reinforcement learning scheme to provide a virtual network embedding solution for effective network resource utilization, maximum revenue, and minimum cost. The proposed scheme applies 1) hierarchical reinforcement learning for efficient exploration by dividing the problem into sub-steps, and 2) multiagent-based cooperative reinforcement learning to improve algorithm performance through the cooperation of multiple agents. The simulation results show that the proposed scheme outperforms the comparative schemes in terms of revenue, cost, revenue-to-cost ratio, and acceptance ratio. © 2023 IEEE.

关键词： 5G mobile communication systems

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Inhibition of CD82 improves colitis by increasing NLRP3 deubiquitination by BRCC3

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Cellular & Molecular Immunology 2023年第2期20卷 189-200页

作者： Jae-Sung Kim Hyo Keun Kim Joongho Lee Sein Jang Euni Cho Seok-Jun Mun Seokhyun Yoon Chul-Su Yang Department of Bionano Technology Hanyang UniversitySeoul04673Korea Institute of Natural Science&Technology Hanyang UniversityAnsan15588Korea Department of Molecular and Life Science Hanyang UniversityAnsan15588Korea Center for Bionano Intelligence Education and Research Ansan15588Korea Department of Computer Science College of SW ConvergenceDankook UniversityYongin16890Korea Department of Electronics&Electrical Engineering College of EngineeringDankook UniversityYongin16890Korea

CD82 is a transmembrane protein that is involved in cancer suppression and activates immune cells;however, information on the NLRP3 inflammasome is limited. Herein, we show that although CD82 suppressed the activation of the NLRP3 inflammasome in vivo and in vitro, CD82 deficiency decreased the severity of colitis in mice. Furthermore, two binding partners of CD82, NLRP3 and BRCC3, were identified. CD82 binding to these partners increased the degradation of NLRP3 by blocking BRCC3-dependent K63-specific deubiquitination. Previous studies have shown that CD82-specific bacteria in the colon microbiota called Bacteroides vulgatus (B. vulgatus) regulated the expression of CD82 and promoted the activation of the NLRP3 inflammasome. Accordingly, we observed that B. vulgatus administration increased mouse survival by mediating CD82 expression and activating NLRP3 in mice with colitis. Overall, this study showed that CD82 suppression reduced the pathogenesis of colitis by elevating the activation of the NLRP3 inflammasome through BRCC3-dependent K63 deubiquitination. Based on our findings, we propose that B. vulgatus is a novel therapeutic candidate for colitis.

关键词： CD82 NLRP3 BRCC3 Colitis Inflammation

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A comparison study between Modbus and IEC 61850 MMS Protocols

A comparison study between Modbus and IEC 61850 MMS Protocol...

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Power engineering Conference (SAUPEC), Southern African Universities

作者： Rayner Johnson Senthil Krishnamurthy Haltor Mataifa Mohammed Esmail Department of Electrical Electronic and Computer Engineering Center for Substation Automation and Energy Management Systems Cape Peninsula University of Technology Cape Town South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

The growing adoption of renewable energy primary plants has been seen globally. South Africa has seen a significant development in utility-scale photovoltaic (PV) farms in recent years due to the alleviated regulations stipulated in the latest Electricity Regulation Amendment Act. Connecting the PV plants to the power grid has also undergone a technical evolution by incorporating modern embedded computers known as Intelligent Electronic Devices (IEDs). The International Electrotechnical Committee (IEC) 61850 standard defines a suite of protocols used in modern power systems. This study compares the IEC 61850 Manufacturing Messaging Specification (MMS) with the well-known MODBUS protocol. The comparison is achieved by simulating a solar farm's Power Plant Controller (PPC).

关键词： Photovoltaic systems Renewable energy sources Power engineering Protocols Inverters Regulation Real-time systems Power grids IEC Standards Standards

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Surgical Tool Occlusion Detection and Segmentation Using Deep Transfer Learning: A Machine Vision Approach

Surgical Tool Occlusion Detection and Segmentation Using Dee...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Sevilla, Rovenson Alon, Alvin Sarraga Evangelista, Ryan S. Delos Santos Ligayo, Michael Angelo Arago, Ryan Flores Delos Santos, Ronnel Cabral Cunanan, Christopher Austria, Yolanda D. University of the Philippines Department of Electrical Engineering Technological Manila Philippines Center for AI Research Department of Trade and Industry Makati City Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Engineering Quezon City University Quezon City Philippines College of Computing Studies Don Honorio Ventura State University Pampanga Philippines Bulacan State University Computer Engineering Department Bulacan Philippines Adamson University Department of Computer Engineering Manila Philippines

ISBN: (纸本)9798350369106

The combination of deep transfer learning and mask region-based convolutional neural network (Mask-RCNN) technology offers a groundbreaking approach to enhancing machine vision for surgical robots. The objective of this work is to enhance the accuracy and precision of identifying tools in surgical environments by investigating the use of deep transfer learning for detecting and segmenting occluded surgical tools. Our investigation exhibits the model's robustness in successfully recognizing and segmenting obstructed surgical tools, as seen by an exceptional Mean Average Precision (mAP) score of 93.8%. By conducting thorough testing and inference studies, the model demonstrated an outstanding ability to accurately identify various instruments, such as 'mayo,' 'clamp,' and 'scalpel,' even in difficult situations when objects were partially occluded. The use of Mask-RCNN enhances our investigation, representing a crucial advancement in enhancing surgical robotics with sophisticated segmentation skills. The research underscores the potential of Mask-RCNN in the field of machine vision for surgical robots, underlining the necessity for ongoing progress to tackle complexities for improved real-world implementation. © 2024 IEEE.

关键词： Machine vision

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Outage-Minimization Power and Spectrum Allocation for Terahertz Communications Systems 58

Outage-Minimization Power and Spectrum Allocation for Terahe...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Uchimura, Sota Ishibashi, Koji Jornet, Josep Miquel The University of Electro-Communications The Advanced Wireless and Communication Research Center (AWCC) Japan Institute for the Wireless Internet of Things Northeastern University Department of Electrical and Computer Engineering United States

We propose joint transmit power and spectrum allocation schemes for terahertz (THz) communication systems with multi-connectivity technologies to overcome random path blockage effects and molecular absorption losses. The proposed approaches focus on quality of service (QoS) guarantees for instantaneous throughput fluctuated by the blockage dynamics and enhancement of the average total system throughput over time. To this end, a sum-of-outage-probability minimization problem and a geometric mean maximization problem for the throughput are formulated for the former and latter concepts, respectively. Moreover, a regularized sum-of-outage-probability minimization problem in which a regularizer suppresses throughput losses is formulated to deal with both the QoS requirement and the throughput enhancement. Stochastic learning and convex optimization mechanism are introduced to solve those problem. Numerical results confirm that the proposed approaches show the better tradeoff between the outage probability and the total system throughput than the conventional approaches. © 2024 IEEE.

关键词： molecular absorption random path blockage stochastic learning Terahertz (THz) communications

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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