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Simulink Integration with OPC for PLC Management in a Microgrid 7

Simulink Integration with OPC for PLC Management in a Microg...

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7th IEEE international Humanitarian Technologies conference, IHTC 2024

作者： Savastio, Luigi Pio Maula, Giovanni Amato, Giulia De Tuglie, Enrico Elio Polytechnic University of Bari Dept. of Electrical and Information Engineering Bari Italy

ISBN: (纸本)9798350354645

this paper presents a methodology that allows the implementation of any control strategy developed in MATLAB/Simulink into a real system using OPC communication. the main focus of the study is the control of a microgrid, supervised by a SCADA system, using MATLAB/Simulink. To achieve this, communication between platforms via OPC is necessary. the use of the OPC protocol between the programming software and the SCADA supervisory system significantly simplifies the work of researchers, eliminating the need to implement controls on traditional devices such as PLCs, which would not offer the same level of flexibility. Students particularly benefit from this methodology as they can leverage advanced software tools, gaining practical experience in controlling microgrids and complex energy systems, without the complexity associated with traditional hardware programming. this paper discusses the control of the microturbine in the PrInCE microgrid at Politecnico di Bari, with the aim of compensating for the electrical load and eliminating power exchange with the external grid via the TieLine. the programming of MATLAB code and the configuration of control in Simulink is demonstrated, which can communicate with the SCADA system via OPC, allowing students to engage with a versatile and educationally valuable platform. © 2024 IEEE.

关键词： SCADA systems

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741243

the proceedings contain 157 papers. the special focus in this conference is on Innovative Computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated Learning and programming Learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge Computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of Intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis Technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in Automated Mechanical Design.

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741922

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741816

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741205

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Merge Conflict Resolution: Classification or Generation? 38

Merge Conflict Resolution: Classification or Generation?

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38th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Dong, Jinhao Zhu, Qihao Sun, Zeyu Lou, Yiling Hao, Dan Peking Univ Sch Comp Sci MoE Key Lab High Confidence Software Technol Peking U Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9798350329964

Collaborative development is critical to improve the productivity. Multiple contributors work simultaneously on the same project and might make changes to the same code locations. this can cause conflicts and require manual intervention from developers to resolve them. To alleviate the human efforts of manual conflict resolution, researchers have proposed various automatic techniques. More recently, deep learning models have been adopted to solve this problem and achieved state-of-the-art performance. However, these techniques leverage classification to combine the existing elements of input. the classification-based models cannot generate new tokens or produce flexible combinations, and have a wrong hypothesis that fine-grained conflicts of one single coarse-grained conflict are independent. In this work, we propose to generate the resolutions of merge conflicts from a totally new perspective, that is, generation, and we present a conflict resolution technique, MergeGen. First, we design a structural and fine-grained conflict-aware representation for the merge conflicts. then, we propose to leverage an encoder-decoder-based generative model to process the designed conflict representation and generate the resolutions auto-regressively. We further perform a comprehensive study to evaluate the effectiveness of MergeGen. the quantitative results show that MergeGen outperforms the state-of-the-art (SOTA) techniques from both precision and accuracy. Our evaluation on multiple programming languages verifies the good generalization ability of MergeGen. In addition, the ablation study shows that the major component of our technique makes a positive contribution to the performance of MergeGen, and the granularity analysis reveals the high tolerance of MergeGen to coarse-grained conflicts. Moreover, the analysis on generating new tokens further proves the advance of generative models.

关键词： Merge Conflict Resolution generative Models Conflict Representation

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Multiagent Reinforcement Learning for Efficient Cyber Attack Detection on the Internet of Medical things using tSNE- ZOA based Dimensionality Reduction 7

Multiagent Reinforcement Learning for Efficient Cyber Attack...

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7th international conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Deepa Rajan, S. Manikandan, A. VISTAS School of Engineering Department of CSE Tamil Nadu Chennai600117 India

ISBN: (纸本)9798350372816

Technological developments in information and communication (ICT) have completely changed a paradigm in computing. the creation of several new communication channels, including the Internet of things (IoT), is a consequence of these advancements. Sensitive data can be transmitted between medical devices using the Internet of Medical things (IoMT), it serves as a component of the Internet of things. However potential dangers such as internet hijackings, impersonation, lack of service violence, password inference, and man-in-the-middle make security a problem, IoT technology for healthcare improves patient contact and treatment. Safeguarding IoMT ecosystem against malware attacks is crucial for patient safety and maintaining high touch and care. this paper presents a method to detect cyber-attacks in the medical field using Multiagent Reinforcement learning. Initially, data for processing is collected from the NSL-KDD and preprocessed using Improved generative Adversarial Imputation Network (I-GAIN) for missing value replacement. Ordered Quantile normalization (ORQN) is used to converts an arbitrary vector into a normal (Gaussian) vector. then, t-distributed stochastic neighbor embedding (t-SNE) is then used to reduce the dimensionality of the dataset. Number of components in t-SNE is optimally selected using the Zebra Optimization Algorithm (ZOA). Multiagent Reinforcement learning (MARL) is a technique utilized to detect data attacks based on the simulated environment. According to simulated research, the proposed approach achieves 95.3% accuracy, 4.7% error, and 93.5% precision. the developed technique outperforms existing methods in performance, resulting in a better prediction to detect cyber-attack in medical data. © 2024 IEEE.

关键词： generative adversarial networks

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Optimizing the Identification of Suitable Congregations for Preachers Using a GMM-PCA-BIC Hybrid Clustering Approach 7

Optimizing the Identification of Suitable Congregations for ...

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7th international conference of Computer and Informatics engineering, IC2IE 2024

作者： Kurniawan, Rahmad Ibnu Daqiqil, I.D. Fatayat Sukamto Fitriansyah, Aidil Lestari, Fitra Husti, Ilyas Universitas Riau Faculty of Mathematics and Natural Sciences Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Industrial Engineering Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331505530

the assignment of preachers to mosques in Riau Province faces significant challenges due to the geographic dispersion of mosques and inconsistent scheduling practices. Preachers often travel long distances, and mismatched assignments lead to inefficiencies and reduced effectiveness in delivering sermons. this study addresses these issues by leveraging advanced clustering techniques to optimize preacher assignments. Specifically, we applied Gaussian Mixture Models (GMM) to cluster mosque and preacher data based on geolocation, activity, and profiles. We integrated Principal component Analysis (PCA) for dimensionality reduction to enhance the clustering performance, forming a GMM-PCA hybrid approach. Data was obtained from the Indonesian Ulema Council Riau Province Chapter, which consists of 185 mosques and 250 preachers, totaling 435 data points. the results indicate that using GMM alone, the optimal clustering was achieved with 10 clusters, yielding a Silhouette Score of 0.31. the GMM-PCABIC hybrid approach improved clustering quality. It achieved optimal results with 9 clusters, a significantly higher Silhouette Score of 0.48, and a lower BIC Score of 2696.18. these findings demonstrate the novelty of the GMM-PCA-BIC hybrid method in effectively capturing the complex characteristics of mosque and preacher data, ensuring efficient and relevant preacher assignments. Implementing this optimized clustering approach allows mosque administrators to make data-driven decisions, enhancing the effectiveness of religious activities. © 2024 IEEE.

关键词： Dimensionality reduction

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Using the GAN Method, Analysis Several Characteristics of the ECG Signal in Order to Detect Cardiac Arrhythmia 7

Using the GAN Method, Analysis Several Characteristics of th...

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7th IEEE international conference for Convergence in Technology, I2CT 2022

作者： Sanamdikar, Sanjay T. Borawake, Madhuri P. Bamanikar, Ashvini A. PDEAs College of Engineering Instrumentation Dept Manjari Pune India PDEAs College of Engineering Computer Dept Manjari Pune India

ISBN: (纸本)9781665421683

Around the world, a large majority of individuals suffer from multiple cardiac diseases. As a consequence, understanding how the ECG signal operates is crucial for detecting a variety of heart conditions. the electrocardiogram (ECG) is a test that determines how strong the heart's electrical system is. PQRST waves are a series of pulses that constitute a heartbeat in an ECG signal. In the characteristic extraction of ECG signals, the amplitude and time intervals of PQRST waves are determined for the learning of ECG signals. the PQRST segment's amplitudes and time intervals can be utilized to determine the human heart's proper functionality. In recent years, the bulk of methodologies and research for analyzing the ECG signal have been developed. Support Vector Regression, Kernel Principal component Analysis, General Sparse Neural Network, and GAN techniques are compared in this article. Both of the other methods are outperformed by the generative Adversarial Network method. However, each of the aforementioned approaches and strategies has its own set of advantages and disadvantages. the proposed system was created using Matlab software. In this study, the proposed technique was demonstrated using the MIT-BIH Arrhythmia record, which was personally annotated and validated. © 2022 IEEE.

关键词： generative adversarial networks

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6th international School on engineering Trustworthy Software Systems, SETSS 2024

6th International School on Engineering Trustworthy Software...

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6th international School on engineering Trustworthy Software Systems, SETSS 2024

ISBN: (纸本)9789819646555

the proceedings contain 7 papers. the special focus in this conference is on engineering Trustworthy Software Systems. the topics include: From Logic to programming;digital Twin Tutorial: the Incubator Case Study;AI components for High Integrity, Safety-Critical Human-Cyber-Physical Systems: A Challenge for Formal Methods;Testing and Design of Uniform CNF Samplers: A Virtuous Cycle Enabled by Distribution Testing;software engineering Experiences of an Optimist;Automating component-Based Embedded Software Construction via Formal Synthesis and LLMs.

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