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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss dep.oyment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Comparison of Milk Quality Grades Using the Logistic Model Tree Algorithm

Comparison of Milk Quality Grades Using the Logistic Model T...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Phanintorn Suaprae Thanakrit Janchidfah Brijesh Bhatt Dep. Of Business Information Technology Management Phibulsongkram Rajabhat University Phitsanulok Thailand Dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Dep. Of Computer Engineering Faculty of Technology Dharmsinh Desai University India

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This study aims to track and categorize milk quality grades using the Logistic Model Tree (LMT) algorithm, based on the analysis of 1,059 milk samples. The focus of the research is to evaluate key factors such as pH value, taste, smell, fat content, concentration, and color, which play a crucial role in determining the overall quality of milk. The goal is to create an efficient and standardized process for sorting cow's milk, which can save significant time and resources in the dairy industry. To identify the most suitable algorithm for this task, the research team initially tested 12 different algorithms, assessing their performance in predicting milk quality. Among these, the Logistic Model Tree (LMT) emerged as the most effective model, achieving an impressive accuracy rate of 99.76%. The LMT algorithm, known for combining the advantages of decision trees and logistic regression, demonstrated its ability to classify milk quality grades with high precision. The results of this study indicate that the LMT algorithm can be a valuable tool for automating the milk quality grading process. By leveraging key attributes such as pH, fat content, and color, it is possible to efficiently sort and grade milk, reducing the need for manual inspection and improving overall productivity in the dairy industry. This method offers a significant improvement over traditional quality assessment techniques and has the potential to be widely adopted for milk sorting and quality control applications.

关键词： Analytical models Dairy products Accuracy Standardization Prediction algorithms Data models Classification algorithms Quality assessment Logistics Sorting

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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Using A novel noisy deep reinforcement learning approach for detecting the cyberattacks in stored hydrogen fuel based AC-DC networks

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International Journal of Hydrogen Energy 2025年 135卷 457-476页

作者： Oveysikian, Ali Abed, Azher M. Davatgar, Shiva Mosallaei, Behrooz Basem, Ali Habib Ali, Sadek Ahmed, Mohsen Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Mechanical Power Technical Engineering Department College of Engineering and Technology Al-Mustaqbal University Hilla Babylon51001 Iraq Department of Electrical Engineering Islamic Azad University Ahvaz Branch Ahvaz Iran The Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM88003 United States Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Dep. of Electrical Engineering Techniques Al-Amarah University College Maysan Iraq Imam Abdulrahman Bin Faisal University P.O. Box 1982 Eastern Province Dammam31441 Saudi Arabia

Rising hydrogenous renewable energy and distributed storage integration in hybrid AC-DC networks present new challenges to grid reliability and security. Existing detection methods struggle with the complex dynamics of these systems, particularly when faced with sophisticated cyber threats. Voltage attacks, capable of causing widespread outages and equipment damage, are a significant threat. This paper proposes a novel deep reinforcement learning (DRL) approach using Ornstein-Uhlenbeck noise for enhanced voltage attack detection. Our approach addresses the limitations of conventional detection methods that suffer from low accuracy and poor convergence in complex hybrid environments. The Noisy Deep Q-Network (NDQN) leverages this noise exploration technique to achieve efficient state space exploration and smoother convergence during training. An advanced attack model specifically designed to target vulnerabilities in stored hydrogen fuel-based AC-DC systems based on hydrogen fuel resources is also presented. The NDQN approach's effectiveness is evaluated in a realistic and complex hybrid AC-DC environment under the proposed attack model. The results demonstrate significant improvements in detection accuracy (62.44 % higher average return compared to traditional DQN) and robustness compared to existing methods. Additionally, the approach helped maintain system integrity while reducing generation from the electrical grid by 42.7 %, leading to substantial cost savings. This work showcases the combined efficacy of the novel attack model and the NDQN approach, offering a promising solution for bolstering power grid security and reliability. © 2025 Hydrogen Energy Publications LLC

关键词： Reinforcement learning

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A Multivariate Functional Analysis of Inter-Hands Spatiotemporal Coordination in Human Bimanual Tasks and its Implications for Robotics

A Multivariate Functional Analysis of Inter-Hands Spatiotemp...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Marco Baracca Andre Meixner Giuseppe Averta Paolo Salaris Tamim Asfour Matteo Bianchi Dept. of Information Engineering Research Center “E. Piaggio” University of Pisa Pisa Italy Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Dep. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coordination between the motion of the hands required for this type of actions makes their planning not trivial. A proper definition of these coordination patterns moving from the human example could simplify their translation on the robot side, fostering the generation of effective bimanual tasks. In this work, we propose Multivariate functional Principal Component Analysis (MfPCA) as a mathematical tool to encode inter-hands temporal kinematic covariations in terms of principal spatiotemporal coordination patterns in the Cartesian domain. We compared these patterns extracted from a dataset of human bimanual tasks with those resulting from the usage of classical fPCA, applied indep.ndently to each hand (univariate fPCA). We found that MfPCA allows for a better classification of the tasks, with respect to a state of the art taxonomy. For what concerns motion planning, MfPCA and fPCA yield similar accuracy in the reconstruction of the motion, but with a smaller number of principal components needed in the MfPCA case. These results, although preliminary, can open interesting perspectives for the usage of MfPCA for human-like bimanual motion planning and control of robotic manipulators, as well as for action recognition, to enable a more effective human-robot interaction.

关键词： Hands Translation Shape Robot kinematics Taxonomy Human-robot interaction Functional analysis Planning Spatiotemporal phenomena Principal component analysis

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Wavelet-based acoustic seabed discrimination system

Wavelet-based acoustic seabed discrimination system

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International Conference on Image Processing Theory, Tools and Applications

作者： Javidan, Reza Dep. of Computer Engineering and Information Technology Shiraz University of Technology Iran

ISBN: (纸本)9781424472482

In almost any underwater operation that takes place on or under the seafloor, it is necessary to have an understanding of the makeup of the seabed below the silt and sediment. Sonar is an acoustic system extensively used for underwater inspection as well as seabed classification. In this paper, the problem of automatic segmentation and classification of seafloor using automatic acoustic seabed discrimination systems is discussed and a new split and merge algorithm based on the concept of standard wavelet transform is presented. Experimental results on true prototype data indicate the robustness and effectiveness of the proposed algorithm. ©2010 IEEE.

关键词： Wavelet transforms

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Sensitivity analysis of the multiuser offset QAM multicarrier systems to carrier frequency and timing offsets

Sensitivity analysis of the multiuser offset QAM multicarrie...

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International Symposium on Telecommunications

作者： Saeedi-Sourck, Hamid Sadri, Saeed Farhang-Boroujeny, Behrouz Dep. of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 Iran Dep. of Electrical and Computer Engineering University of Utah UT United States

ISBN: (纸本)9781424481835

We analyze of the effects of carrier frequency offset (CFO) and timing offset in multiuser offset QAM (OQAM) filter bank multicarrier (FBMC) systems. These effects are quantified by signal-to-interference ratio (SIR) of a desired user. Expressions for SIRs are derived. Numerical results show that the multiuser OQAM multicarrier system is more robust to CFO than orthogonal frequency division multiple access (OFDMA) systems. On the other hand, while, because of the presence cyclic prefix (CP), OFDMA can tolerate some level of timing offset, multiuser OQAM multicarrier system may degrade for any deviation of timing offset from its optimum value. © 2010 IEEE.

关键词： Quadrature amplitude modulation

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Robust watermarking scheme based on RDWT-SVD: Embedding data in all subbands

Robust watermarking scheme based on RDWT-SVD: Embedding data...

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作者： Lagzian, Samira Soryani, Mohsen Fathy, Mahmood Dep. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781424498345

In this paper, a new method for non-blind image watermarking presented that is robust against affine transformation and ordinary image manipulation. The proposed method presents a watermarking scheme based on redundant discrete wavelet transform (RDWT) and Singular Value Decomposition (SVD). After applying RDWT to cover image, we apply SVD to each subband. Then we modify singular values of the cover image using singular values of the visual watermark. The advantage of the proposed technique is its robustness against most common attacks. Analysis and experimental results show higher performance of the proposed method in comparison to the DWT-SVD method. © 2011 IEEE.

关键词： Singular value decomposition

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Predicted vector spectral search algorithm in motion estimation

Predicted vector spectral search algorithm in motion estimat...

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Canadian Conference on Electrical and computer engineering 2005

作者： Tafazoli, Hassan Reza SoroushMehr, S.M. Samavi, Shadrokh Dep. of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

In this paper a new method for determining the search area for motion estimation algorithm based on block matching is suggested. In the proposed method the search area is adaptively found for each block of a frame. The designated search area is similar to that of the full search algorithm but smaller for most blocks of a frame. Therefore, the proposed algorithm is like FS in terms of regularity but requires less computational complexity. To find the search area the temporal and spatial correlation among the motion vectors of blocks are exploited. Based on this, the matched block is chosen from a rectangular area that the prediction vectors set out. Simulation results indicate speedup ratio of proposed algorithm more than 85.8% as compared to FS algorithm. © 2005 IEEE.

关键词： Motion estimation

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Speed sensorless vector controlled induction motor drive based stator and rotor resistances estimation taking core losses into account 19

Speed sensorless vector controlled induction motor drive bas...

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19th International Middle-East Power Systems Conference, MEPCON 2017

作者： Aziz, Ahmed G. Mahmoud A. Diab, Ahmed A. Zaki Sattar, Montaser Abd El Dep. of Electrical and Computer Engineering High Institute for Engineering and Technology Minia Egypt Dep. of Electrical Engineering Faculty of Engineering Mina University Minia Egypt

ISBN: (纸本)9781538609903

In this paper, a proposed sensorless vector controlled induction motor drive using a modified full order observer taking core losses in to account has been presented. The full order observer is designed to simultaneous estimation of stator and rotor resistance and rotor speed. Matlab /Simulink package has been used to simulate the suggested scheme. The simulation results prove the effectiveness of the proposed induction motor drive. Moreover, a comparison between the convential full order observer and the proposed one with core losses compensation has been carried out. © 2017 IEEE.

关键词： Induction motors

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