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The Effect of Photovoltaic Plant Integration on Power Flow Based on The Newton Raphson Method on The Krueng Raya Feeder

The Effect of Photovoltaic Plant Integration on Power Flow B...

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computer System, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Anggy Prayoga Ira Devi Sara Melinda Melinda Graduate Program of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia dept. of Electrical Engineering and Computer Universitas Syiah Kuala Banda Aceh Indonesia

Due to the intermittent characteristics of solar radiation, the integration of large-scale photovoltaic (PV) systems into the power grid raises many power quality problems such as voltage drop, unstable grid frequency, and harmonics. Despite these drawbacks, the integration of large-scale PV systems into the grid definitely has several advantages. This paper investigates the positive impact of integrating large-scale PV systems into the electric power grid. In this study, the PV system contributes 10%-20% of the power to the grid. Load flow analysis based on the Newton Raphson model is used to calculate grid losses when the PV system is connected at different positions along the feeder line. The results showed that there was a decrease in power losses that occurred in the network from 29.4 kW to 29.2 kW when the PV system was injected into the network at the start of the power line. Then losses can be reduced from 29.4 kW to 29.1 kW when the PV system is integrated in the midfield. The reduction in power losses becomes more significant when the PV system is injected at the end of the line from 29.4 to 28.6 kW. In conclusion, integration of the PV system into the power grid can improve the power loss and voltage profile.

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Improving E-commerce Fraud Detection via Machine Learning: Comparative Evaluation of Model Effectiveness

Improving E-commerce Fraud Detection via Machine Learning: C...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Gangalakshmi, T. Sathya, V. Vinodhini, S. Chandralekha, E. Usha, V. Ravikumar, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept.of CSE Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Dept.of Computer Science and Business Systems Chennai India Velammal Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350319200

In the fast-growing E-commerce world, fraud has become a major concern, causing economic losses and eroding consumer trust. This work aimed to detect E-commerce fraud using machine learning on a customer dataset. The dataset contained transactional and customer data, requiring preprocessing to handle missing values and categorical features. Four models - Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Classifier - were trained and evaluated for fraud detection accuracy using metrics like Accuracy, Precision, Recall, and F1-Score. Results revealed distinct model strengths and weaknesses. Logistic Regression achieved balanced performance, while Random Forest and Gradient Boosting excelled in certain aspects. Support Vector Classifier also demonstrated competitive results. This work highlights the potential of machine learning in E-commerce fraud prevention, enhancing transaction security and trust. Further research could refine and advance fraud detection techniques for online platforms. © 2023 IEEE.

关键词： E-commerce fraud Fraud detection Hybrid approach Machine learning

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POCS-based Image Compression: An Empirical Examination

POCS-based Image Compression: An Empirical Examination

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International Conference on Green Technology and Sustainable Development (GTSD)

作者： Truong-Dong Do Le-Anh Tran Thanh-Dat Nguyen Nghe-Nhan Truong Dong-Chul Park My-Ha Le Dept. of Aerospace Systems Engineering Sejong University Seoul South Korea Dept. of Electronics Engineering Myongji University Yongin South Korea Dept. of Research and Development OCST Co. Ltd. Seoul South Korea Dept. of Computer Science & Engineering Sejong University Seoul South Korea Dept. of Electrical & Electronics Engineering HCMC University of Technology & Education Ho Chi Minh City Vietnam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

This paper investigates the applicability of the Pro-jection onto Convex Set (POCS)-based clustering algorithm to image compression tasks. The POCS-based clustering approach treats all data points in a given dataset as non-intersecting convex sets and performs POCS-based parallel projections from each cluster prototype onto corresponding member data points to minimize an objective function and update cluster prototypes. The POCS-based clustering algorithm has been proven to be able to yield promising results against other prevailing clustering approaches in terms of convergence time and clustering error on general clustering tasks. In this study, a comparison of various clustering schemes for image compression applications has been conducted. The evaluations and analyses on various standard test images verify that the POCS-based clustering algorithm can perform competitively against other conventional clustering methods in image compression problems.

关键词： Image coding Clustering methods Clustering algorithms Prototypes Linear programming Sustainable development Standards

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Machine Learning Models for Cardiovascular Disease Prediction: A Comparative Study 7

Machine Learning Models for Cardiovascular Disease Predictio...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Yan, Chao Xing, Yiluan Liu, Sensen Gao, Erdi Wang, Jinyin Northeastern University Department of Electrical and Computer Engineering Sunnyvale United States Indiana University Bloomington Economics Department Bloomington United States Washington Univ. in St. Louis Dept. Electr. and Systems Eng St. LouisMO United States New York University Tandon School of Engineering New York United States Stony Brook University Department of Computer Science New York United States

ISBN: (纸本)9798350352009

Cardiovascular diseases (CVDs) pose a significant threat to global public health, affecting individuals across various age groups. Factors such as cholesterol levels, smoking, alcohol consumption, and physical inactivity contribute to their onset and progression. Enhancing our understanding of CVD etiology and informing targeted interventions for disease prevention and management remains a critical challenge. In this study, we address the task of predicting the likelihood of individuals developing CVDs using machine learning techniques. Specifically, we explore three approaches: the k-nearest neighbors (KNN) algorithm, logistic regression, and the random forest algorithm. Leveraging a comprehensive dataset sourced from Kaggle, encompassing 11 relevant factors, we conduct a series of experiments to identify the most influential predictors of CVDs. Our analysis aims not only to forecast disease occurrence but also to elucidate the primary determinants contributing to its manifestation. Through comparative analysis of the three methodologies, we demonstrate that the random forest algorithm exhibits superior performance in terms of predictive accuracy. This research represents a significant step towards leveraging machine learning techniques to enhance our understanding of CVD dynamics and inform targeted interventions for disease prevention and management. © 2024 IEEE.

关键词： Contrastive Learning

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Artificial Intelligence Assisted IoT Data Intrusion Detection 4

Artificial Intelligence Assisted IoT Data Intrusion Detectio...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Shukla, Amogh Ahamad, Shahanawaj Rao, G.Nageswara Al-Asadi, Avein Jabar Gupta, Ankur Kumbhkar, Makhan Research Assistant Vellore Institute of Technology Vellore India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Lakireddy Bali Reddy College of Engineering Dept. of EEE Mylavaram India Computer Institute of Sulaimani Dept. of Computer Program Iraq Vaish College of Engineering Dept. of Computer Science and Engineering Rohtak India Christian Eminent College Dept. of Computer Science Indore India

ISBN: (纸本)9781665414470

It has become increasingly important since the introduction of Internet of Things (IoT) technology to ensure the security of IoT networks. Network anomalies and threats can be identified and predicted using a variety of intrusion detection systems (IDS). Despite the fact that the Internet of Things is vulnerable to attacks, early detection of malicious behavior can prevent data from being compromised. In this work, the primary goal is to develop machine artificial intelligence energy efficient models that can be used to detect attacks on the Internet of Things network. Normal and attack data from the IoT environment must be collected in order to construct a model. The Bayesian Network, the Artificial Neural Network, and the Support Vector Machine are considered to have the most promise. Using roundtrip time and power consumption data, a traditional three-layer Artificial Neural Network is put through its paces in the real world. © 2021 IEEE.

关键词： Internet of things

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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Online Trained Model of Tweets Expertise

Online Trained Model of Tweets Expertise

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Information Technology and Nanotechnology (ITNT), International Conference on

作者： Elena Mozaidze Sergei Zuev Petr Kabalyants Dept. of computer software and automated systems Belgorod State Technological University named after V.G. Shukhov Belgorod Russia Dept. of computer engineering and modelling V.I. Vernadsky Crimean Federal University Simferopol Russia

ISBN: (数字)9798350372939

ISBN: (纸本)9798350372946

The paper proposes a model for the examination of tweets (brief texts and messages) in Russian. The model is based on the authors' explorations on the identification of tweets meanings. The main idea is to apply the latest developed quantum machine learning method to the hierarchical classification models of texts. This approach avoids the text corpus necessity and supports the online learning due to the very design. The model proposed is tested in the experiment. The target content was given in the experiment and the model is used to measure the correspondence between the target topics and the test messages. The result of the experiment shows that the proposed method allows to distinguish the target content in the test text.

关键词： Natural languages Machine learning Information technology Nanotechnology

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

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

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International Conference on Energy Conservation and Efficiency (ICECE)

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

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.

关键词： Costs Estimation Microgrids Reinforcement learning Mathematical models Power system reliability Smart grids

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Strategies for Optimizing Time Series Visual Data Exploration

Strategies for Optimizing Time Series Visual Data Exploratio...

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ACS/IEEE International Conference on computer systems and Applications

作者： Heba Helal Mohamed A. Sharaf College of Information Technology United Arab Emirates University UAE Dept. of Computer Engineering and Systems College of Engineering Mansoura University Egypt

Dealing with high-dimensional time series data makes the process of recommending visualizations with "interesting" insights difficult. The challenge originates from finding a way to obtain the recommended visualizations efficiently without compromising their quality. Identifying such visualizations manually is considered a labor-intensive and time-consuming process. In response, this paper introduces different techniques designed to optimize the automated recommendation process. These techniques are entirely based on the concept of computation sharing and pruning. Furthermore, we provide a glimpse into our future research works in PhD thesis. The objective is to broaden the scope of our current work and enhance the generality of our problem statement.

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Empirical Characterization of Doppler in Industrial Wireless Channels

Empirical Characterization of Doppler in Industrial Wireless...

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European Conference on Antennas and Propagation, EuCAP

作者： Dreyelian Morejón Jon Montalbán Eneko Iradier Mohamed Kashef Hany Richard Candell Pablo Angueira Dept. Communication Engineering UPV/EHU Bilbao Spain Dept. Electronics Technology UPV/EHU Bilbao Spain Dept. Computer Languages and Systems UPV/EHU Bilbao Spain NIST: National Institute of Standards and Technology Gaithersburg USA

This paper analyzes the temporal variation of the radio propagation channel in industrial environments. The results have been obtained by processing empirical data from the National Institute of Standards and Technology (NIST) in a field measurement campaign, at different industrial premises, with various dimensions and clutter densities. The paper analyzes the shape of the Doppler spectra and related parameters such as Doppler bandwidth and coherence time. The results are supplemented with maximum Delay Times and RMS Delay Spread data. The influence of factors such as the operational frequency band, environment, and polarization is also analyzed.

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