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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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High-Speed CBJT Operational Amplifier Based on 'Folded' Cascode with a Differentiating Transient Correction Circuit

High-Speed CBJT Operational Amplifier Based on 'Folded' Casc...

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2024 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2024

作者： Titov, A.E. Kuznetsov, D.V. Denisenko, D.Y. Alferova, I.A. Southern Federal University Dept. Of Automatic Control Systems Rostov-on-Don Russia Don State Technical University Dept. Of Scientific Research Dept. Of Information Systems And Radio Engineering Rostov-on-Don Russia Don State Technical University Dept. Of Scientific Research Rostov-on-Don Russia Don State Technical University Dept. Of Scientific Research Dept. Of Cyber Security Of Information Systems Rostov-on-Don Russia

ISBN: (纸本)9798350364989

A circuit design of a high-speed CBJT operational amplifier (OpAmp) based on an 'folded' cascode is proposed, in which a differentiating transient correction circuit is introduced to increase the maximum slew rate of the output voltage (SR). The considered OpAmp is intended for use in a subclass of so-called discrete-to-analog SC filters on switched capacitors, where (in some important cases) higher SR values of the OpAmp are required. In addition, such high-speed OpAmps are in demand in drivers of high-speed analog-to-digital converters and other devices of automatics and computer engineering. computer simulation in LTSpice environment shows that the proposed CBJT OpAmp circuit designs for micron silicon technological processes provide SR increase from 45.8 V/µs to 18000 V/µs. © 2024 IEEE.

关键词： Operational amplifiers

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Access Control in Cloud Storage through Sanitizable Access Control System

Enhancing Access Control in Cloud Storage through Sanitizabl...

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Computational Intelligence for security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Kolli Naveena M Suleman Basha Chanda Sai Sathvik Tadikamalla Revanth Mahajan Polepaka Nithin Emmanuel CSE (Cyber Security) Institute of Aeronautical Engineering Hyderabad India Dept. of CSE (Data Science) RGM College of Engineering and Technology (Autonomous) Nandyal Andhra Pradesh India Dept. of CSE (Cyber Security) Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

There is a need to help a persuading public taking a gander at with showing which beats the check of the expected to inspect plan. The proposed structure is made to genuinely take a gander at the exactness of cloud information through Organized able, strangely or on request without recovering the whole information or without changing extra electronic issue with the cloud clients and cloud servers. It guarantee that no information content is spilled to Instructive during the inspecting structure. It stays mindful of cutoff rightness of information, unsurprising quality and security of managed information. The proposed imagine combines three key parts; they are information proprietor, cloud server cutoff and Specialist.

关键词： Access control Cloud computing Privacy security Servers Synchronization Cryptography Sustainable development Homomorphic encryption Edge computing

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High-Speed Operational Amplifier with a Differentiating Circuit of Transient Correction

High-Speed Operational Amplifier with a Differentiating Circ...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Titov, Alexey E. Alferova, Irina A. Kuznetsov, Dmitry V. Southern Federal University Dept. of Automatic Control Systems Rostov-on-Don Russia Don State Technical University Dept. of Scientific Research Rostov-on-Don Russia Don State Technical University Dept. of Cyber Security of Information Systems Rostov-on-Don Russia Don State Technical University Dept. of Information Systems and Radio Engineering Rostov-on-Don Russia

ISBN: (纸本)9798350360646

The relevance of the present research is related to the search for promising ideas for increasing the maximum slew rate of the output voltage (SRM) of new-generation operational amplifiers (OpAmps). Proposed circuit techniques that provide (with simultaneous use) an increase in SRM by more than 2 orders of magnitude. Such high-speed operational amplifiers are in demand today for application in drivers of high-speed ADCs (AD9208, AD9691, etc.), and as active elements in circuits of analog-to-discrete filters on switched capacitors. It is proposed to introduce (according to the developed rules) a nonlinear differentiating RC-circuit realized based on an additional differential stage and a differentiating capacitor of small capacitance into the classical OpAmps with one integrating correction capacitance. Such circuit "additives"practically do not change the low-signal dynamic and static parameters of the OpAmp, and at a large signal during the transient front, when the input stage enters the nonlinear mode, form a large current overcharge of the integrating correction capacitor. Consequently, the SRM of the OpAmp is significantly increased. © 2024 IEEE.

关键词： analog-to-digital converter analog-to-discrete filter maximum slew rate of output voltage nonlinear correction operational amplifier

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A Performance Comparison of Commercial Speech Recognition APIs in Noisy Environments

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1266-1273页

作者： Lee, Geonhui Lee, Sanghwa Ji, Suhwan Kim, Auk Im, Hyeonseung Dept. of Computer Science and Engineering Dept. of Convergence Security Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Dept. of Convergence Security Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of

This paper compares the performance of five commercial speech recognition APIs under noisy environments, namely those provided by Amazon AWS, Microsoft Azure, Google, Kakao, and Naver. To this end, we used an open dataset for development and evaluation of multi-channel noise processing technology provided in AI Hub. We tested each API's performance with respect to the speaker's gender and location and the speech content, and measured their error rate using both word error rate (WER) and character error rate (CER). Except for the AWS API, the error rate was higher when tested with female's data than male's one, and when tested with the data recorded from the side than the front. The error rate was also relatively high when the test sentences contained proper nouns such as person's names and local names, and the shorter the sentences, the higher the error rate. Moreover, the Google API outperformed all the others in terms of both WER and CER, with 53% and 18% of error rate, respectively. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Speech recognition

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Protecting Sensitive Data in the Cloud: A Network security Approach 15

Protecting Sensitive Data in the Cloud: A Network Security A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohammed, Abdul Sajid Sreeramulu, Madhusudhan Dasari Gopal, Santhosh Kumar Gupta, Ketan Anand Kumar, V. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States American Express Dept of Cyber Security United States Charlotte NC United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350370249

Cloud Computing adoption has increased dramatically over the past few years, making the protection of sensitive data on the cloud a major concern. In this paper network security is presented as an effective way to ensure the security of data on the cloud using methods like encryption, access control, and DLP. The method concentrates on the security of the network infrastructure, and also the internal cloud environment security controls. This methodology combines internal and external security measures to provide an overall protection of data housed in the cloud. Conclusions The work is mainly intended to increase the understanding of the need for network security and shielding data in the cloud, and someways to enhance data security in the cloud for businesses and organizations. © 2024 IEEE.

关键词： Cloud Computing Data Privacy Encryption Network security Protection

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Beam Profiling and Beamforming Modeling for mmWave NextG Networks 32

Beam Profiling and Beamforming Modeling for mmWave NextG Net...

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Fathalla, Efat Samir Zargarzadeh, Sahar Xin, Chunsheng Wu, Hongyi Jiang, Peng Santos, Joao F. Kibilda, Jacek Da Silva, Aloizio Pereira Old Dominion University Dept. Electrical Computer Engineering NorfolkVA23529 United States University of Arizona Dept. Electrical Computer Engineering TucsonAZ85721 United States Commonwealth Cyber Initiative Virginia Tech ArlingtonVA22203 United States

ISBN: (纸本)9798350336184

This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks. We have used two commercial-grade mmWave testbeds with operational frequencies on the 27 Ghz and 71 GHz, respectively, for beam profiling. The obtained datasets were used to train the machine learning model to estimate the received downlink signal power, and data rate at the receivers (user equipment with different geographical locations in the range of a transmitter (base station). The results have showed high prediction accuracy with low mean square error (loss), indicating the model's ability to estimate the received signal power or data rate at each individual receiver covered by a beam. The dataset and the machine learning based beamforming model can assist researchers in optimizing various network design problems for mmWave networks. © 2023 IEEE.

关键词： Beamforming

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Construction Site Modeling Objects Using Artificial Intelligence and BIM Technology: A Multi-Stage Approach

Construction Site Modeling Objects Using Artificial Intellig...

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

作者： Dolhopolov, Serhii Honcharenko, Tetyana Savenko, Volodymyr Balina, Olena Bezklubenko, Iryna Liashchenko, Tamara Kyiv National University of Construction and Architecture Dept. of Cyber Security and Computer Engineering Kyiv Ukraine Kyiv National University of Construction and Architecture Dept. of Information Technologies of Design and Applied Mathematics Kyiv Ukraine Kyiv National University of Construction and Architecture Dept. of Organization and Management of Construction Kyiv Ukraine Kyiv National University of Construction and Architecture Dept. of Information Technologies Kyiv Ukraine

ISBN: (纸本)9798350335040

This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an 'evolutionary' digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site. © 2023 IEEE.

关键词： Architectural design

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods 4

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Zaiets, Nataliia Vlasenko, Lidiia Lutska, Nataliia Zhyltsov, Andrii Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350395532

The paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis;binary classification decision tree;feedforward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes. © 2023 IEEE.

关键词： Decision trees

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