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IEEE Open Access Journal of Power and Energy 2025年 12卷 341-352页

作者： Marvin Dorn Jonas Lotze Uwe Kuehnapfel André Weber Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute for Automation and Applied Informatics TransnetBW GmbH Stuttgart Germany Institute for Applied Computer Science Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

Due to the energy transition, which involves phasing out base load power plants such as coal, there is a need to establish storage systems within the energy system to compensate for fluctuations of renewable energies. Batteries are suitable for day-night cycles and particularly for short-cycle applications. To address the problem of dark-doldrums, when neither wind nor solar energy is available, gas and, in the more distant future, hydrogen power plants are to be used. By combining batteries and hydrogen power plants in a hybrid energy storage system, further advantages and application possibilities arise regarding grid stability and system design. This work illustrates interrelationships between the subsystems, optimizes proportions, and demonstrates logical system sizes, technologies, and their costs. A central part of the work are the self-derived methods for system design and the justification of these. Storage pressure, running times, availability time, annual cycles and design of the subsystems are described. Systems of this scale are difficult to imagine. A program developed as part of this work to implement the methods, visualizes the system, displays the system parameters, and shows the best-case and worst-case capital expenditures. An optimized system design is presented. Different combinations in the system design show the effects on capital expenditures. Starting from 2 to 4 hours of availability time, the hybrid system becomes cheaper than a pure battery system in terms of capital expenditures.

关键词： Costs Hydrogen Batteries System analysis and design Power generation Hydrogen storage Optimization High-voltage techniques Renewable energy sources Photovoltaic systems

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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Mining Road Traffic Accident Data for Prediction of Accident Severity

Mining Road Traffic Accident Data for Prediction of Accident...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bahiru, Tadesse Kebede Manjula, V.S. Akele, Tadesse Birara Tesfaw, Engdaw Ayalew Belay, Tadesse Destaw Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information Technology Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Pg Dept of Computer Science Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information System Amhara Kombolcha Ethiopia Wachamo University School of Computing and Informatics College of Engineering and Technology Dept. of Information Technology SSNPR Hosaena Ethiopia

ISBN: (纸本)9781665474511

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities. © 2023 IEEE.

关键词： Support vector machines

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Decentralized control in active distribution grids via supervised and reinforcement learning

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Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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Gamification of cyber ranges in cybersecurity education 20

Gamification of cyber ranges in cybersecurity education

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Jelo, Martin Helebrandt, Pavol Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

There is a lack of cybersecurity experts, and this problem is getting worse with the increasing number and importance of IT systems together with rising number and sophistication of attacks. Rapid development in cybersecurity necessitates constant training and practice to keep up with the latest threats. Studying IT, and especially cybersecurity is considered difficult and there is an urgent need to encourage interest of students in this field. One promising avenue to improving the situation is gamification. Gamification has been used in education to improve enthusiasm of students, increasing their numbers, information uptake and retention. Leveraging social connections and human competitiveness can further motivate students to take part and engage in the learning activities they would otherwise not consider. Virtualized environments offer means to scale up the practical training as well as share latest teaching methods. Cyber ranges used by educational institutions often are such open platforms that provide such practical cybersecurity training. Moreover, with some thoughtful planning they can prove to be popular serious games. In this paper, we explore opportunities for hands-on cybersecurity education by using gamified scenarios in cyber range virtualized environment. We focus on cyber testing environments, what their types are, and what it includes. We place emphasis on creating basic game scenario close to real-world situations. © 2022 IEEE.

关键词： Students

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Covert Channel Detection Methods 20

Covert Channel Detection Methods

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Ondov, Adrian Helebrandt, Pavol Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

The modern networking world is being exposed to many risks more frequently every day. Most of systems strongly rely on remaining anonymous throughout the whole endpoint exploitation process. Covert channels represent risk since they ex-ploit legitimate communications and network protocols to evade typical filtering. This firewall avoidance sees covert channels frequently used for malicious communication of intruders with systems they compromised, and thus a real threat to network security. While there are commercial tools to safeguard computer networks, novel applications such as automotive connectivity and V2X present new challenges. This paper focuses on the analysis of the recent ways of using covert channels and detecting them, but also on the state-of-the-art possibilities of protection against them. We investigate observing the timing covert channels behavior simulated via injected ICMP traffic into standard network communications. Most importantly, we concentrate on enhancing firewall with detection and prevention of such attack built-in features. The main contribution of the paper is design for detection timing covert channel threats utilizing detection methods based on statistical analysis. These detection methods are combined and implemented in one program as a simple host-based intrusion detection system (HIDS). As a result, the proposed design can analyze and detect timing covert channels, with the addition of taking preventive measures to block any future attempts to breach the security of an end device. © 2022 IEEE.

关键词： Statistical methods

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EAMSA 512: New 512 Bits Encryption Algorithm Based on Modified SALSA20

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2023年第2期4卷 131-142页

作者： Naif, Jolan Rokan Ahmed, Israa S. Zaki, Nashwan Dheyaa Hoomod, Haider K. Informatic Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Information Technology and Communications Baghdad Iraq Computer Science Department College of Education Mustanisiryah University Baghdad Iraq

This paper presents the 512-bit encryption algorithm based on modified SALSA20 using an 11-dimensional chaotic system used for generating the keys for the proposed encryption algorithm. Chaotic keys are derived from a combination of two systems, one with six dimensions and the other with five. These keys are used in different operations like shifting, Xoring, and encryption steps in the new proposed algorithm. The proposed algorithm consists of a combination of two parallel parts: Salsa20 and S boxes with chaos keys. The proposed system takes 512 bits of the plaintext, which will be split into two 256-bit parts, the left part encrypted with modified salas20 and the chaos keys, and the right 256-bit part encrypted using eight 8x8 s-boxes and the chaos keys. The results are swapped and combined, and this operation is repeated for 16 rounds to get the cipher text. The testing of EAMSA 512bit using various tests demonstrates the algorithm's strength and security, as well as its ability to avoid many attacks with lightweight processing. © 2023 International Journal of Chemical and Biochemical Sciences. All rights reserved.

关键词： Chaotic systems

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Sentiment Analysis of Game Reviews on STEAM using BERT, BiLSTM, and CRF

Sentiment Analysis of Game Reviews on STEAM using BERT, BiLS...

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2023 International Conference on Electrical Engineering and informatics, ICEEI 2023

作者： Al Mursyidy Fadhlurrahman, Jalaluddin Herawati, Neng Ayu Widya Aulya, Hayyu Rachma Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides information on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error and inaccurate results. To help gamers get the desired game information and increase customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its performance based on the f1 score evaluation results. The proposed model that we created is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and fine-tuning Bidirectional Encoder Representations from Transformer (BERT). We then propose another model, BiLSTM-CRF, by examining the effect of using the last four hidden states of BERT and comparing it with the last one hidden state of BERT to be used in the BiLSTM-CRF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CRF on the last four hidden states yielded the best performance, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CRF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context. © 2023 IEEE.

关键词： Sentiment analysis

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Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

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2023 International Conference on Electrical Engineering and informatics, ICEEI 2023

作者： Parameswara, Made Arbi Mahendra, Farrel Satya Putra Mardika, Muhammad Randy Cahya Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question format and susceptibility to manipulation on this research, we propose an approach that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to create a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro performance of 98%. © 2023 IEEE.

关键词： Machine learning

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Apply Combined Model Between VGG16 and LightGBM on Weather Image Recognition

Apply Combined Model Between VGG16 and LightGBM on Weather I...

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2023 RIVF International Conference on Computing and Communication Technologies, RIVF 2023

作者： Quy Nam, Tran Syamsuddin, Irfan Posts and Telecommunications Institute of Technology Hanoi Viet Nam Politeknik Negeri Ujung Department of Informatics & Computer Engineering Pandang Indonesia

ISBN: (纸本)9798350315844

This research implements hybrid deep learning network models for weather image classification. The study proposes to apply a combined model, namely VGG16-LightGBM. In its architecture model, a pre-train convolutional neural network (CNN) name as VGG16 is employed for feature extraction of images and the LightGBM algorithm is used to make classification. The results on accuracy of proposed models were compared with four other models, namely Xception, Inception V3,Vgg19, Vgg16 which are all implemented by transfer learning mechanism on the same dataset. The experimental results proved that the VGG16-LightGBM gives the best performance with the highest accuracy of 81,28%, outperforms the transfer learning technique of other 4 pre-train models in the problem of weather image classification. © 2023 IEEE.

关键词： Image classification

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