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Optimal Allocation of DG Units in Radial Distribution systems

Optimal Allocation of DG Units in Radial Distribution System...

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international conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Elena Crenguţa Bobric Daniela Irimia Electrical Engineering and Computer Science Faculty Ştefan cel Mare University Suceava Romania

In the current context of increasing electricity consumption and at the same time reducing CO2 emissions, distributed renewable energy sources have gradually become a new trend in distribution network development. their widespread use can have a negative impact on the operation of the network. Arbitrary placement of these sources can cause both an increase in active power losses and a change in the voltage level in the nodes, with effects on the system’s stability. To address this issue, the paper uses the Grey Wolf Optimizer (GWO) algorithm to determine the optimal location and value of the DG that leads to minimal losses of active power, under the conditions of compliance with the restrictions regarding the voltage level in the nodes. the work takes into account DG that produces only active power (for example photovoltaic panels). ieee 33-bus and ieee 69-bus test distribution networks were used for the case study. the determinations were performed in MATLAB, by associating the GWO algorithm to calculating power losses in the analyzed network. the results demonstrate the importance of the optimal choice of both the DG location and their sizes for the efficient network operation. through the optimal placement of the distributed energy source according to the results of the applied algorithm, the benefit of using a green energy source is accumulated with the reduction of active power losses and the improvement of the bus voltage level.

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Personalized Recommendation: A novel approach based on Hybrid methods and fog computing architecture

Personalized Recommendation: A novel approach based on Hybri...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Noura Abdaoui Ismahene Hadj Khalifa Sami Faiz National School for Computer Science (ENSI) University of Manouba Manouba Tunisia Higher Institute of Multimedia Arts of La Manouba (ISAMM) University of Manouba Manouba Tunisia

A personalized recommender system meticulously analyzes users’ data encompassing their purchases, ratings, and interactions with other users. this thorough analysis allows the system to provide personalized and tailor-made recommendations aligned with users’ preferences. the advent of IoT-connected sensors has significantly augmented the availability of contextual information, prompting the rapid development of Context-Aware Recommendation systems. Consequently, we have embraced a cloud computing approach, specifically leveraging fog architecture, which optimizes shared resources and offers a stable environment for consistent performance measurement. the primary objective of this paper is to propose an intelligent context-aware recommender system based on fog architecture, employing hybrid machine learning approaches. this innovative system involves gathering data from fog nodes and employing a process that learns users’ profiles by screening and analyzing reflective feedback loops. Ultimately, this approach enables the prediction of personalized items.

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A multi-model Imputation Approach for Missing Data Based on Machine Learning 8

A multi-model Imputation Approach for Missing Data Based on ...

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8th Annual international conference on Network and Information systems for computers, ICNISC 2022

作者： Liu, Yong Gou, Xiangnan School of Computer and Intelligence Education Lingnan Normal University Zhanjiang524048 China Xiamen University Department of Software Engineering Xiamen361005 China

ISBN: (纸本)9781665453516

In the process of big data analysis, the consistency and integrity of the data are very important factors that affect the correctness of the analysis results in most cases. Usually before the data analysis process starts, data clustering should be made on the collected data sources to hold that subsequent analysis will not cause errors in the results due to data abnormalities. therefore, maintaining integrity in data clustering is one of those very important work. One principal reason for the incomplete data is that the collected data contains missing values, and the appearance of missing values comes from factors such as carelessness and instrument failure during the data collection process. At present, the common ways to deal with missing values are as follows: directly ignore the tuples with missing values, or use the median, mean etc. of the missing values attribute to impute. the simple application of these methods often results in the loss of the original features of the tuple, which affects the subsequent data analysis and application output, leading to incorrect results. In response to this problem, our research uses machine learning methods to impute missing values in a single field. We choose complete and consistent data samples as the training datasets, and divide the data into multiple clusters by K-means clustering to capture the associations which are not easily visible between the data. these clusters use multiple linear regression and artificial neural networks to establish predictive models. For the missing values that need to be predicted, first of all we use the KNN algorithm to obtain the cluster to which the data belongs, and then apply the cluster model to calculate the predicted value. In the experiment, it is proved that the multi-model imputation method proposed by this research is better than the existing imputation methods in the results of the statistical accuracy of Root Mean Squared Error (RMSE). © 2022 ieee.

关键词： K-means clustering

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A Novel Deep Q-Network-Based Scheme for Online Virtual Link Embedding in software Defined Networks

A Novel Deep Q-Network-Based Scheme for Online Virtual Link ...

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ieee international conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Xiaodong Tan Jinxin Du Lunde Chen Wanyu Liu School of Mechatronic Engineering and Automation of Shanghai University Shanghai China Sino-European School of Technology of Shanghai University (UTSEUS) Shanghai China

With the advent of software Defined Networking (SDN) and network virtualization, network systems are becoming increasingly more flexible and more scalable. With a global view and a centralized control, SDN can assign paths for individual virtual link (VL) requests, by employing classic graph algorithms (e.g. Dijkstra’s algorithm). However, this presumes that VL requests are independent of each other, which doesn’t hold true in real-world scenarios where VL requests can arrive randomly. In fact, previously instantiated VLs can take up resources (e.g. bandwidth) in the substrate network and render the residual resources unbalanced, leading to sub-optimal path assignment for subsequent VLs. this work investigates such scenarios and proposes a novel scheme based on Deep Q Network (DQN) and Link Feature Representation (LFR) for efficient online VL embedding. Experiments show that our proposed scheme (DQNLFR) can not only efficiently and effectively meet the routing requests of various VLs, but also ensure the balance of virtual network resources, leading to a higher acceptance rate. Moreover, the proposed solution has also the advantage of self-adaptability, scalability and generalization ability.

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RLHavoc:Enhanced Historical Data Orientation Enhances Code Coverage

RLHavoc:Enhanced Historical Data Orientation Enhances Code C...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Shihe Zhang Chaohao Liao Renhua Liu School of Computer Science and Engineering University of Electronic Science and Technology of China ChengDu China

Fuzzing is a common method in software test engineering, which tries to trigger potential vulnerabilities in the target program by continuously providing modified input to the test target. the traditional fuzzy test tools are white box test, gray box test, black box test. the mutation strategies used in these traditional testing tools have high randomness. As a result, the traditional test methods have high randomness in the selection of strategic actions, so effective strategic actions maybe be ignored. Aiming at improving the coverage rate of fuzzy test code based on variation, we implement RLHavoc to improve the efficiency of American fuzzy lop(AFL) based on Deep Q Network(DQN) algorithm by using DQN to prioritize the generation of test cases in AFL. RLHavoc combines AFL’s Havoc phase mutation strategy with DQN algorithm. DQN algorithm takes advantage of AFL’s Havoc strategy to conduct grey box testing for different binaries and use comprehensive historical data to guide the action selection of variation strategies in the fuzzy test vulnerability detection process, taking seeds as state and new the number of unique execution paths as reward, which enhances the action space orientation of the secondary fuzz target binaries, thus improving the code coverage. Compared to the traditional AFL fuzz effect, our experimental results show that the code coverage rate is improved by about 3%.

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Developing Module Generation for Odoo Using Concept of Low-Code Development Platform and Automation systems 8

Developing Module Generation for Odoo Using Concept of Low-C...

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8th ieee international conference on Industrial engineering and Applications, ICIEA 2021

作者： Pichidtienthum, Sopanawit Pugsee, Pakawan Cooharojananone, Nagul Program of Science for Industry Chulalongkorn University Faculty of Science Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand

ISBN: (纸本)9781665428958

In this study, a module generator for Odoo using a low-code development concept was proposed. Odoo is an open-source ERP software that brings together necessary modules for various business management and a Python web framework which allows developers to develop a module to extend the capability of Odoo. In recent years, software development on Odoo usually requires time to learn due to the complexity of the framework. therefore, a platform developed using low-code development-a concept of software creation with less code writing-was proposed. this platform is a web-based application tool that generates codes for developers to assist them in creating modules with less concern about errors and more focus on the logical side of programming. Developers can also use this application as a tool to validate and generate a source code for a ready-to-use module. this application was tested with non-Odoo users, Odoo users, and Odoo developers with various models containing various fields. Time efficiency between three types of users was compared, as well as time efficiency between generating a module using the conventional method and using the low-code concept module generator. the result revealed that using this application could reduce time by 20% on average. © 2021 ieee.

关键词： Automation

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DMCNET: Data-Driven Multi-Pilot Convolution Neural Network for Mimo-Ofdm Receiver

DMCNET: Data-Driven Multi-Pilot Convolution Neural Network f...

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ieee international conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Yutong Xin Jingyi Peng Zejian Lu Yong Lee Yang Yang School of Artificial Intelligence Beijing University of Posts & Telecommunications China China Industrial Control Systems Cyber Emergency Response Team China China Academic of Electronics and Information Technology China Collage of Electrical and Computer Engineering Sungkyunkwan University South Korea

this paper focuses on studying the deep learning (DL) application of neural networks to solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna scenarios, the channel model is more complex compared to single-antenna cases. By leveraging the characteristics of DL, such as automatic learning of parameters using deep neural networks, we treat the reception process of MIMOOFDM signals as a black box and utilize neural networks to accomplish the signal reception task. Moreover, we propose the data-driven multi-pilot convolution neural network for MIMO-OFDM receiver (DMCNet). By incorporating complex convolution and complex fully connected structures, we design a receiver network to recover the transmitted signals from the received signals. We validate the accuracy and robustness of DMCNet under different channel conditions, comparing the bit error rates with different schemes. Additionally, we discuss the factors influencing various channel effects. Experimental results demonstrate that the DL-based reception scheme exhibits promising feasibility in terms of accuracy and interference resistance when compared to traditional approaches.

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CVulDetector: Cross-domain Vulnerability Detection Based on Transfer Learning

CVulDetector: Cross-domain Vulnerability Detection Based on ...

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ieee international conference on Signal and Image Processing (ICSIP)

作者： Guoqiang Zeng Wen Zhou Zhongyuan Qin Xin Sun Wen Wang School of Cyber Science and Engineering Southeast University Nanjing China Internet Technology Center State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou China Internet Department State Grid Zhejiang Electric Power Co. Ltd Hangzhou China

software vulnerabilities pose a significant threat to computer security, and accurate identification of vulnerabilities has become a challenging task. Although previous work on deep learning-based vulnerability detection has achieved some success, it still faces issues such as 1) insufficient feature extraction capability of the models, and 2) unrealistic assumption of identical distribution between the source and target domains. To address these problems, we propose CVulDetector, a cross-domain vulnerability detection scheme based on transfer learning. Our approach incorporates both natural language characteristics and semantic syntax information of programs, enabling a more comprehensive description of program features. By employing two neural network models, we extract vulnerability-rich features and fine-tune the model using limited labeled data from the target domain after pre-training on a wide range of features from the source domain. Experimental evaluation on three CWE datasets shows an F1-score of 90.6%.

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An IDM-Based Two-Stage Robust Balance Analysis Model for High-proportion Renewable Energy Integration through HVDC System

An IDM-Based Two-Stage Robust Balance Analysis Model for Hig...

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ieee international conference on Power and Power and Renewable Energy (ICPRE)

作者： Jie Qi Jincheng Guo Yaodan Zhang School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland USA

Under the circumstance of the new-type power system, uncertainties on both sides of supply and demand lead to prominent characteristics of supply-demand probabilistic matching and multi-regional complementarity, which greatly increased the balance risk during system operation. In this regard, this paper proposes a short-term electric power balance analysis model for a renewable energy power base transmitted through a high-voltage direct current (HVDC) system. First, a detailed cascade hydropower system (CHS) and HVDC model are constructed , and different linearization techniques are applied to linearize different nonlinear constraints, reducing computational complexity. Second, to effectively capture the uncertainty of renewable energy, an Imprecise Dirichlet model (IDM) based method for constructing uncertainty sets is designed, greatly reducing the conservation of the robust optimization. Finally, a two-stage robust optimization model with mixed-integer recourse is established to analyze the potentially unbalanced state by minimizing the operational and curtailment costs. the proposed two-stage robust optimization model is a solved column-and-constraint generation (C&CG) algorithm by using customized nested. the result of the numerical verifies the effectiveness of the proposed model and algorithm.

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Optimal Siting and Sizing of Distributed Generation Under Uncertainties Using Point Estimate Method

Optimal Siting and Sizing of Distributed Generation Under Un...

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Technology and Energy Management (ICTEM), international conference on

作者： Ali Ashoornezhad Qasem Asadi Reza Saberi Hamid Falaghi Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran

Power distribution networks are mainly established as radial networks that causes a high level of losses and voltage drop. To overcome this issue, distributed generations (DGs) have been proposed in past decades. Such resources can bring several benefits to the system as well. In this paper, the optimal placement of DGs in distribution networks is suggested. Considered DGs include renewable resources such as photovoltaic (PV) and wind turbines (WT) as well as conventional resources. A point estimate method (PEM) is employed to catch the uncertainty of the problem. Using this scheme makes the model a mixed-integer nonlinear problem (MINLP), which is solved via evolutionary algorithms. In order to evaluate the effectiveness of the proposed method, PEM is implemented on an ieee 69-bus test system and results are discussed. Obtained results illustrate the best site and size of each type of DGs. In the proposed system, the Installed DGs based on the PEM lead to more than 2 percent loss reduction and voltage improvement.

关键词： Photovoltaic systems Renewable energy sources Uncertainty Voltage Evolutionary computation Probabilistic logic Distributed power generation

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