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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Ikram, Muhammad Ullah, Zakir Nasir, Muhammad Ahmed, Salman Marwat, Safdar Nawaz Khan University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan University of Engineering and Technology Dept. of Electrical Power Engineering Peshawar Pakistan Allama Iqbal Open University Deptment of Physics Islamabad Pakistan

ISBN: (纸本)9781665476010

The significant penetration of renewable energy sources (RES) in the smart grid (SG) provides a new landscape for researchers to develop an optimal energy management model. The households with RES become prosumers to provide the surplus energy in the local community. The coordination and control of such RES prosumers are important to estimate the available energy in the system and the total demand required. In this paper, we proposed a decentralized coordination control approach for transactive energy systems (TES). The proposed TES is capable of coordinating and controlling a complex network of prosumers with limited control information. The consensusability and graph-theoretic schemes are used for prosumer RES nodes. In the proposed model, energy trading among large-scale prosumers has been demonstrated. The algorithm is capable to provide the total surplus energy available across the network and total demand in an autonomous fashion. The coordination control among prosumers is achieved through a computationally efficient approach without sharing the prosumer's profile to preserve the security and privacy of the prosumers. To validate the proposed scheme, we perform extensive simulations on decentralized 450 prosumers. The resiliency of the proposed scheme is confirmed through dynamic and contingent communication topologies for energy trading ratios, cumulative returns, and convergence ratios. The cumulative returns improved from 0.447% to 24.8% while the energy trade among the prosumers recorded from -0.89 to 2.09. The final return is enhanced from -1.95% to 22.3% due to internal and external coordination control of nonlinear loads. © 2023 IEEE.

关键词： Multi agent systems

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Multi-agent and Reinforcement Learning Schemes for Demand Response Estimation in Distributed Microgrids 6

Multi-agent and Reinforcement Learning Schemes for Demand Re...

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6th International Conference on Energy Conservation and Efficiency, ICECE 2023

作者： Ikram, Muhammad Ahmed, Salman Marwat, Safdar Nawaz Khan Nasir, Muhammad University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan Allama Iqbal Open University Department of Physics Islamabad Pakistan

ISBN: (纸本)9798350332193

Demand response (DR) is an approach that encourages consumers to shape consumption patterns in peak demands for the reliability of the power system and cost minimization. The optimal DR scheme has not only leverages the distribution system operators (DSOs) but also the consumers in the energy network. This paper introduced a multi-agent coordination control and reinforcement learning approach for optimal DR management. Each microgrid is considered an agent for the state and action estimation in the smart grid and programmed rewards and incentive plans. In this regard, the Multi-agent Markov game (MAMG) is utilized for the state and action. At the same time, the reward is articulated through reinforcement learning deep Q-network (DQN) and deep deterministic policy gradient (DDPG) schemes. The proposed DR model also encourages consumer participation for long-term incentivized benefits through integrating battery energy storage systems (BESS) in the SG network. The reliability of DQN and DDPG schemes is demonstrated and observed that the dynamically changing electricity cost is reduced by 19.86%. Moreover, the controllability of complex microgrids is achieved with limited control information to ensure the integrity and reliability of the network. The proposed schemes were simulated and evaluated in MATLAB and Python (PyCharm IDE) environments. © 2023 IEEE.

关键词： Smart power grids

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Condition monitoring of industrial exhaust motor-fan assembly using statistical feature optimization with nature-inspired optimizers 3

Condition monitoring of industrial exhaust motor-fan assembl...

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3rd IEEE International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Das, Abhrodeep Agrawal, Ayushman Kundoliya, Nishkarsh Dutta, Ananta Pal, Surjya K. Kumar, Rishi Jalpaiguri Government Engineering College Dept. of Computer Science and Engineering WB India Indian Institute of Technology Dept. of Mechanical Engineering WB Kharagpur India Indian Institute of Technology Dept of Ocean Engg & Naval Architecture WB Kharagpur India Research & Development Centre for Iron & Steel Jharkhand Ranchi India

ISBN: (纸本)9798350313703

Exhausters are very crucial components in sinter plant of steel manufacturing industries. Problems in fan or motor side of the exhausters lead to machine stoppages or reduced speed, resulting in stoppage of the system and decreased sinter production. Common issues include imbalance, misalignments, looseness, eccentricity, and motor bearing failure. The proposed IoT-based system aims to continuously analyze vibration signals, predict issues, and prevent sudden stoppages and exhauster tripping, thereby enhancing overall plant efficiency. This study focuses on online monitoring and health prediction of industrial exhausters by selection of statistical features extracted from triaxial time-series vibration data by using multiple optimizers, followed by implementation of machine learning classifiers. By incorporating a novel feature selection process based on nature-inspired optimizers, this methodology aims to enhance the overall generalization performance of classification. The retrained models with cross-validation were evaluated with real-time industrial data, which shows 99.5% cross-validation accuracy and 95.83% real-time prediction accuracy. The proposed methodology holds great potential for detecting machine faults in industrial applications. © 2024 IEEE.

关键词： Forecasting

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Knowledge Transfer Challenges: A Practical Study of Yemen Public Telecommunication Corporation 1

Knowledge Transfer Challenges: A Practical Study of Yemen Pu...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Taweel, Sadik Ali, Ghassan Ahmed Alhazbi, Saleh Yousef, Walid Al-Fuhaidi, Belal Albater, Thamer Hezzam, Essa Ghallab, Abdullatif Department of Information Systems University of Science and Technology Sana’a Yemen Faculty of Islamic Technology Universiti Islam Sultan Sharif Ali Brunei Darussalam Brunei Dept. of Computer Science & Eng. Qatar University Doha Qatar Dept. of Information Technology University of Science and Technology Sana’a Yemen Department of Computer Science University of Science and Technology Sana’a Yemen Faculty of Business Open University Malaysia Sana’a Yemen Department of Information Systems Taibah University Medina Saudi Arabia

ISBN: (纸本)9798331533557

The success of organizations today relies heavily on their intellectual capital, as knowledge is a key resource for value creation. Sharing and transferring knowledge enhances its value, embedding it within an organization’s processes and boosting competitiveness. This study focuses on the Yemen public telecommunication corporation (YPTC), which has adopted a knowledge-focused management approach. This study aims to explore challenges related to knowledge transfer in Yemen’s public sector, where such research is lacking. Using a descriptive and analytical method, data were collected via a questionnaire from 200 employees, yielding a response rate of 88%. The findings indicate a high level of knowledge transfer at YPTC, with individual, organizational, and technological factors identified as significant influences. The key barriers include trust, information technology support, motivation, and social networks. © 2024 IEEE.

关键词： Knowledge Transfer Yemen Public Telecom

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Autonomous Social Amenity, Pothole Detection and Reporting

Autonomous Social Amenity, Pothole Detection and Reporting

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International Multidisciplinary Information technology and Engineering Conference (IMITEC)

作者： David Mosela Moses Olaifa Pius Owolawi Vusi Malele Dept. Computer Systems Engineering Tshwane University of Technology Tshwane South Africa Dept. of Computer Systems Engineering Tshwane University of Technology Tshwane South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. This study provides an in-dept. exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing.

关键词： YOLO computer vision Roads Computational modeling Machine learning Hardware Acoustics Road safety Software tools Surface treatment

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Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification

Neural Network-Based Analysis of Forest Fire Aftermath in Cl...

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2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Hnatushenko, Viktoriia Hnatushenko, Volodymyr Soldatenko, Dmytro Dept. Information Technologies and Systems Ukrainian State University of Science and Technologies Dnipro Ukraine Dept. Information Technologies and Computer Engineering Dnipro University of Technology Dnipro Ukraine Institute of Photogrammetry and GeoInformation Leibniz Universität Hannover Germany

Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated. The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure. © Author(s) 2024.

关键词： Semantic Segmentation

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Acceleration and energy consumption optimization in cascading classifiers for face detection on low-cost ARM *** asymmetric architectures

arXiv

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arXiv 2024年

作者： Corpas, Alberto Costero, Luis Botella, Guillermo Igual, Francisco D. García, Carlos Rodríguez, Manuel Dept. of Architecture and Computer Technology ETSIIT University of Granada Granada18071 Spain Dept. Computer Architecture and Automation Complutense University of Madrid Madrid28040 Spain

This paper proposes a mechanism to accelerate and optimize the energy consumption of a face detection software based on Haar-like cascading classifiers, taking advantage of the features of low-cost Asymmetric Multicore Processors (AMPs) with limited power budget. A modelling and task scheduling/allocation is proposed in order to efficiently make use of the existing features on *** ARM processors, including: (I) source-code adaptation for parallel computing, which enables code acceleration by applying the OmpSs programming model, a task-based programming model that handles data-dependencies between tasks in a transparent fashion;(II) different OmpSs task allocation policies which take into account the processor asymmetry and can dynamically set processing resources in a more efficient way based on their particular features. The proposed mechanism can be efficiently applied to take advantage of the processing elements existing on low-cost and low-energy multi-core embedded devices executing object detection algorithms based on cascading classifiers. Although these classifiers yield the best results for detection algorithms in the field of computer vision, their high computational requirements prevent them from being used on these devices under real-time requirements. Finally, we compare the energy efficiency of a heterogeneous architecture based on asymmetric multicore processors with a suitable task scheduling, with that of a homogeneous symmetric architecture. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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CDP: Selective Influential Nodes Based on Cliques in the Diffusion Process in Social Networks 9

CDP: Selective Influential Nodes Based on Cliques in the Dif...

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9th International Conference on Web Research, ICWR 2023

作者： Yazdanifar, Fatemeh Beni, Hamid Ahmadi Salimi, Nikoo Bouyer, Asgarali Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Madani University Dept. Software Engineering Azarbijan Shahid Tabriz Iran University of Genoa Dept. of the Elec. Electronics Electronics and Telecommunication Eng. and Naval Architecture Genoa Italy Azarbaijan Shahid Madani University Dept. of Computer Engineering Tabriz Iran

ISBN: (纸本)9798350399691

Influence maximization techniques emphasize selecting a set of influential nodes in order to maximize influence. Because the algorithms presented in this field ignore the topology of cliques for diffusion, there are two major challenges in influence maximization algorithms: optimal diffusion and computational overhead reduction. As a result, the CDP algorithm is presented in this article to address these issues. This algorithm first selects suitable cliques for diffusion based on their position and strategy in social networks. Furthermore, for each clique, a score is calculated based on the topological criteria of the clique, and suitable cliques are selected for diffusion by applying a threshold limit. The seed nodes are chosen from the cliques in the second step. To avoid the rich club phenomenon, only a few nodes from each clique are chosen as seed candidate nodes. Finally, the seed nodes are chosen based on the node's topology and the strength of the node's level one neighbor. In the experiment section, the CDP algorithm significantly outperforms the best algorithms presented in recent years in terms of influence spread rate and execution time. © 2023 IEEE.

关键词： Diffusion

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Optimizing Latency and Communication in Federated Edge Computing with LAFEO and Gradient Compression for Real-Time Edge Analytics 6

Optimizing Latency and Communication in Federated Edge Compu...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Namratha, P. Chaganti, Koushik Reddy Elicherla, Sivananda Lahari Reddy Guddati, Surekha Swarna, Akarapu Thiramal Reddy, P. Department of CSE GATES Institute of Technology and Science Anantapur India Dept. of Computer and Information Systems University of the Cumberlands KY United States Dept of CSE Dayananda Sagar University Bangalore India Dept of CSE Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

The increasing need for real-time data analytics in edge computing is furthered by the latency pressures from computational and communication bottlenecks, thus demanding novel solutions. Fundamental approaches to federated learning and distributed machine learning do not accommodate changes in dynamic latency conditions very well, thereby being inefficient in terms of synchronization, communication overhead, and delay in task execution. This paper presents a holistic framework consisting of three innovative approaches to solve these problems: Latency-Aware Federated Edge-Aware Optimization (LAFEO), Latency-Driven Gradient Compression with Adaptive Sparsity (LAGCAS), and Predictive Latency Compensation with Dynamic Replication (PLCDR). The LAFEO framework consists of a double-layer optimization framework, combining latency-weighted scheduling with reinforcement learning to dynamically adapt the synchronization frequencies. This results in 25-30% end-to-end latency reduction with an accuracy deviation that is capped at 2% centralized models. LAGCAS reduces communication overhead by applying adaptive gradient scarification methods, bringing compressions of gradients under real-time accuracy thresholds: it reduces 40% communication volume and accelerates convergence by 20%. PLCDR uses predictive modelling to predict latency bottlenecks and dynamically replicates critical computations across the edge nodes. It hence offers 15-25% decrease in the completion time of tasks and up to 30% enhancement in the resilience of system sets. The existing methods will be addressed with the key limitations due to the introduction of latency-aware mechanisms that adapt to real-time edge conditions along with high accuracy and efficiency. Simulation results have shown marked scalability improvements, latency reductions, and resilience;in its positioning, this work's effort is a transformative advancement in the latency-sensitive edge computing process. © 2025 IEEE.

关键词： Federated learning

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A Comprehensive Threat Model for Autonomous Vehicle Communication Security 6

A Comprehensive Threat Model for Autonomous Vehicle Communic...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Meegammana, Niranjan W. Fernando, Harinda Shilpa Sayura Foundation Cyber Security Research Lab Kandy Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The rapid advancement of autonomous vehicles (AVs) promises to enhance transportation efficiency, reliability, and safety. However, these systems, particularly multi-channel vehicular communications, are vulnerable to cyber attacks that threaten authenticity, authorization, confidentiality, integrity, availability, and non-repudiation. The 2014 Chrysler Jeep hack highlights the urgent need for robust security measures in vehic-ular communications. This study utilizes the STRIDE, DREAD, and MITRE ATT&CK security frameworks to develop a comprehensive threat model that identifies and assesses critical threats and risks to AV communications. By detailing specific attack vectors and their multidimensional impacts and criticality, the study establishes a baseline of threats for communication security in vehicular networks. It produces visual threat maps, impact graphs of various attacks, and a color-coded threat grid to guide stakeholders in developing effective cyber security measures for protecting AV communications, thereby help stakeholders to improve the reliability and safety of AV navigation for fostering growth in the AV industry. © 2024 IEEE.

关键词： Cyber attacks

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