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Online Trained Model of Tweets Expertise 10

Online Trained Model of Tweets Expertise

引用

10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Mozaidze, Elena Zuev, Sergei Kabalyants, Petr Belgorod State Technological University Named After V.G. Shukhov Dept. Of Computer Software And Automated Systems Belgorod Russia V.I. Vernadsky Crimean Federal University Dept. Of Computer Engineering And Modelling Simferopol Russia

ISBN: (纸本)9798350372939

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. © 2024 IEEE.

关键词： Expert systems

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A Novel DB CASE Tool for Relational Databases 31

A Novel DB CASE Tool for Relational Databases

引用

31st Symposium of Advanced Database systems, SEBD 2023

作者： Avignone, Andrea Chiusano, Silvia Fiori, Alessandro Garza, Paolo Marchetta, Emanuele Dept. of Control and Computer Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy

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Security in remote access, based on Zero Trust Model concepts and SSH authentication with signed certificates 11

Security in remote access, based on Zero Trust Model concept...

引用

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI 2022

作者： Iță, Cristina-Raluca Constantinescu, Rodica-Claudia Vlădescu, Alexandru Alexandrescu, Bogdan Information and Computer Systems Engineering POLITEHNICA University of Bucharest Romania Applied Electronics and Information Engineering Dept. POLITEHNICA University of Bucharest Romania

ISBN: (纸本)9781510660939

This paper aims to resume the information on the various ways in which security can be achieved when it comes to accessing external resources from the outside through company devices or personal devices. When planning security policies, it is essential to start with the idea that anything outside is exposed to the risk of infection. We will analyze the Zero Trust model and other security concepts in order to use them for remote access. The model has been designed to adapt to the modern needs of the workforce and employers, including remote work in terms of maximum cyber security, minimizing the risk of resource exploitation (people, devices, applications or data). In order to make the transition to such a model, this paper will implement and test a solution for remote management of resources (applications, servers), which implements some of the principles of Zero Trust architectures, starting from the classic version SSH (Secure Shell) access. The solution involves accessing, securing and authorizing a secure virtual machine via SSH, and then securing access to production resources. The technical mechanism by which these operations are performed presupposes that after authentication on the buffer machine a user certificate is automatically requested which will be signed by a certification authority. This certificate contains the group to which the operator belongs so that, by presenting this certificate, it is allowed to authenticate, authorize and audit access to the resources that will be managed by the user in that session. © 2023 SPIE.

关键词： Authentication

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Time-series analysis and Flood Prediction using a Deep Learning Approach 7

Time-series analysis and Flood Prediction using a Deep Learn...

引用

7th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2022

作者： Selva Jeba, G. Chitra, P. Rajasekaran, Uma Maheswari Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India

ISBN: (纸本)9781665496483

Deep neural networks have been used successfully to solve time series prediction problems. Given their ability to automatically understand the temporal connections found in time series, they have shown to be an effective solution. In this proposed research, a Deep Learning (DL) based flood prediction model is explored and utilized for interpretation and prediction using meteorological data to reduce computational and time complexity with high accuracy. Gated Recurrent Networks (GRU) a variant of recurrent neural network model which can effectively use past data information for prediction and is faster in terms of training speed is the deep learning architecture deployed. Correlation analysis was performed on the weather parameters and the appropriate parameters were chosen. The dataset compromises 52 years (19022 records) of weather data in which 80% is used for training 20% for testing. The predictive modeling of rainfall associated with the South-west monsoon can guide the prediction of flood occurrence. The model deployed was evaluated with the performance metrics such as RMSE, MAE against LSTM model. The deployed RNN-GRU model had relatively low RMSE and MAE values when compared with LSTM architecture with improved prediction accuracy. © 2022 IEEE.

关键词： Time series analysis

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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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control of Deterministic Chaotic Modes in Power systems

Control of Deterministic Chaotic Modes in Power Systems

引用

2023 IEEE International Conference on Smart Information systems and Technologies, SIST 2023

作者： Beisenbi, Mamyrbek Satpayeva, Ainur Mussabayeva, Shugla Mussina, Gulnara Tlegen, Meruert L.N. Gumilyov Eurasian National University Dept. of System Analysis and Control Astana Kazakhstan Astana It University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350335040

The paper examines a new approach to controlling deterministic dynamical chaotic mode of power system with two electric generate-sources. A control law is synthesized in the class of one-parameter structurally stable mappings from catastrophe theory. Analytical calculations and a numerical experiment in MatLab showed that the proposed system excludes the generation of a deterministic chaotic mode for any changes in the uncertain system parameters. The study of the control system is carried out by the gradient-velocity method Lyapunov vector functions © 2023 IEEE.

关键词： Uncertainty analysis

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Power Loss Analysis of Energy Management System with Model Predictive control in Mae Hong Son Microgrid 3

Power Loss Analysis of Energy Management System with Model P...

引用

3rd IEEE International Conference on Power Electronics, Intelligent control and Energy systems, ICPEICES 2024

作者： Prapanwattana, Nattakit Dechawatanapaisal, Donlapat Banjerdpongchai, David Rajan, Abhishek Center of Excellence in Intelligent Control Automation for Process Systems Chulalongkorn University Faculty of Engineering Dept. of Electrical Engineering 254 Phayathai Rd Bangkok Pathumwan10310 Thailand National Institute of Technology Sikkim Dept. of Electrical Engineering South Sikkim Ravangla737139 India

ISBN: (纸本)9798350387193

Energy management system (EMS) is an important tool for energy efficiency and reliability of the power system. The optimal power dispatch of energy resources can be obtained using the nonlinear model predictive control (NMPC). It is formulated as an optimization problem subject to linear and nonlinear constraints. Although the existing system works well under the current conditions, power loss has not been considered. Power loss in transmission lines occurs due to their resistance and a high amount of power flow to loads connecting to the system. The power loss leads to decreased efficiency in the electrical system and reduces the lifespan of equipment because of the high temperature from loss. This paper aims to analyze the power loss in the Mae Hong Son (MHS) microgrid. To achieve the research objectives, we employ the EMS of MHS with NMPC and calculate power loss in transmission lines for a period of 7 days. EMS considers three objective functions, namely, total operating cost, total carbon dioxide emission, and combined economic and emission dispatch and take into account of three seasons consisting of rainy, winter, and summer. © 2024 IEEE.

关键词： Predictive control systems

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Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

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Slotted ALOHA with Code Combining for IoT Networks 3

Slotted ALOHA with Code Combining for IoT Networks

引用

3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Tyrovolas, Dimitrios Chrysologou, Athanasios Chondrogiannis, Georgios Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Chatzidiamantis, Nestor Karagiannidis, George Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece Technical University of Chania Dept. of Electrical and Computer Engineering Chania Greece University of Ioannina Computer Science Engineering Department Ioannina Greece Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350333732

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network's outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network's performance when the sensors' access is based on a random access scheme. © 2023 IEEE.

关键词： Wireless sensor networks

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Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK 12

Predicting Optimal Placement of Electric Vehicle Charge Stat...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Mousaei, Arash Naderi, Yahya University of Tabriz Dept. Computer and Electrical Engineering Tabriz Iran Ricardo Aea Sustainable Energy Systems Department Glasgow United Kingdom

ISBN: (纸本)9798331525132

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R2, Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development. © 2025 IEEE.

关键词： Linear regression

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