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International Crimean Conference on Microwave & Telecommunication Technology

作者： K. A. Aksyonov I. A. Spitsina O. P. Aksyonova Philipp Schaible Ural'skij federal'nyj universitet imeni pervogo Prezidenta Rossii B N El'cina Ekaterinburg Sverdlovskaa ooblast' RU Hof University of Applied Science Enoch-Widman-Str 84 95028 Germany

The present paper concerns the analysis of computer aided software engineering Tools (such as IBM Rational, CA ERwin Modeling Suite, Aris Toolset, BizAgi, Elma, Power Designer, BPsim ??? Borland Together Designer) for automation of a development process and change management of typical enterprise business process. The enterprise information system development is based on Big Data technology. The simulation is used as a basis for enterprise business process improvement tools.

关键词： computer aided software engineering

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On Fuzzy Expert System Development Using computer-aided software engineering Tools 12

On Fuzzy Expert System Development Using Computer-Aided Soft...

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Proceedings of IEEE East West Design and Test Symposium ( EWDTS 2014)

作者： Polkovnikova, N. A. Kureichik, V. M. Fed State Owned Auton Educ Estab Higher Vocat Edu Rostov Na Donu Russia

ISBN: (纸本)9781479976300

The purpose of this article is to demonstrate a way of intellectualization of automated information and diagnostic systems using knowledge bases, databases and algorithms for the formalization of procedures in terms of the development of an expert system for marine diesel engines. The aim of this work is to develop an expert system's architecture with data mining tools for solving the problem of technical exploitation of marine diesel engines based on fragmented, unreliable and possibly inaccurate information. The architecture of such expert system allows moving from normal monitoring to "information monitoring" in the specialized intelligent human-machine systems. Application of data mining technology allows optimizing database processing queries that retrieve the required information from the actual data in order to detect important patterns. An approach based on data mining and fuzzy logic in the expert system is shown on an example of solving technical exploitation of marine diesel engines problem.

关键词： computer aided software engineering data mining diesel engines expert systems fuzzy logic man-machine systems marine engineering mechanical engineering computing program diagnostics query processing software tools automated diagnostic systems automated information systems computer-aided software engineering tools data mining technology data mining tools database processing queries expert system architecture fuzzy expert system development information monitoring knowledge bases marine diesel engines specialized intelligent human-machine systems Diesel engines Expert systems Fuzzy logic Monitoring Pragmatics Expert System computer aided software engineering Fuzzy logic Marine diesel engines Diesel engines Man-Machine Systems mechanical engineering computing Marine engineering analysis programme Knowledge Bases data mining Pragmatics query analysis software Tools diagnostic system

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Exploring Multivariate Dynamics of Emotions Through Time-Varying Self-Assessed Arousal and Valence Ratings

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2025年第1期16卷 333-345页

作者： Gargano, Andrea Nardelli, Mimma Scilingo, Enzo Pasquale Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Univ Pisa Res Ctr E Piaggio I-56126 Pisa Italy

Emotions arise from a complex interplay of various factors, including conscious experience, physiological processes, and contextual elements. Although emotions are inherently dynamic processes, this aspect is oftentimes neglected in experimental protocols. In this study, we employed dynamical systems theory to investigate the time-varying self-assessed emotion ratings. We used the continuous ratings of the publicly available CASE dataset, in which thirty individuals rated their level of arousal and valence while watching videos designed to evoke four different emotions. First, we analyzed the univariate dynamics by reconstructing the phase space from the arousal and valence series separately, and quantified their regularity and spatial complexity by using three metrics: Fuzzy, Sample, and Distribution Entropy. Then, we combined the arousal and valence series and proposed a novel index, the Multichannel Distribution Entropy (MDistEn), to estimate the complexity of the bivariate phase space. By coupling the two dimensions, we found that MDistEn resulted as an effective marker of fear, showing patterns statistically different from all of the other stimuli (p-value $\leq$<= 0.001). These findings support the investigation of the time-varying dynamics of annotated emotion ratings as a promising pathway to discriminate the onset of fear-related pathological states.

关键词： Entropy Videos Physiology Indexes Annotations Time series analysis computer aided software engineering Affective computing fear arousal valence fuzzy entropy distribution entropy multivariate multichannel distribution entropy

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Emotion Classification Through Optimal Segments of EDA and Texture Analysis of Time-Encoded Images With Artificial Intelligence

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kumar, P. Sriram Ronickom, Jac Fredo Agastinose Indian Inst Technol BHU Sch Biomed Engn Varanasi 221005 India

In this study, we focus on investigating the time-encoded images of electrodermal activity (EDA) segments to identify significant patterns for an emotion recognition system. Initially, the EDA signals were procured from two openly accessible datasets, namely, continuously annotated signals of emotions (CASE) and wearable stress and affect detection (WESAD). These signals were then preprocessed and decomposed into phasic signals through a convex optimization approach. Subsequently, we divided the phasic signals into two equal segments, each further subdivided into nine equal windows with a 50% overlap. Moreover, we generated time-encoded image representations of these windowed phasic signals using a Gramian angular summation field (GASF), Markov transition field (MTF), recurrence plot (RP), and a fusion of these images. In addition, we extracted 85 textural features based on the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), fractal dimension texture analysis (FDTA), Zernike's moments (ZMs), Hu's moments (HMs), and first-order statistics (FOSs). Four machine learning (ML) models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and multilayer perceptron (MLP), were developed to classify two-class emotions associated with arousal and valence from CASE, as well as three-class emotions (amusement, neutral, and stress) from WESAD, considering three different approaches: the first half, the second half, and the whole phasic signal. The highest classification accuracy achieved was 79.79% for two-class arousal and 71.71% for two-class valence on the CASE. In contrast, our models demonstrated an outstanding emotional classification accuracy of 98.4% for the three-class emotion in the WESAD. These outcomes highlight the potential of our proposed methodology for analyzing emotions in healthcare, with the ability to accurately classify emotions holding promising implications for improving patient care, menta

关键词： Emotion recognition Physiology Feature extraction Image segmentation computer aided software engineering Anxiety disorders Spectrogram Convolutional neural networks Brain modeling Accuracy Electrodermal activity (EDA) emotion detection feature extraction machine learning (ML) optimal segments time-encoded images

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A Techno-Economic Modeling Approach to 6G Network Deployment: Exploring Costs and Use Case Feasibility

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IEEE ACCESS 2025年 13卷 35597-35608页

作者： Kokkinis, Dimitris Ioannou, Nikolaos Katsianis, Dimitrios Varoutas, Dimitris Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 16122 Greece Natl & Kapodistrian Univ Athens Dept Digital Ind Technol Psachna 34400 Evia Greece

In recent years, significant research has explored 6G vision, enabling technologies, business models, and potential applications. However, the economic feasibility of these advanced and intelligent services must be addressed before standardization. To support the successful realization of 6G promises, we quantified the technical requirements and assessed the economic viability of the proposed solutions using current 5G data and appropriate multipliers. An example application of our analysis shows that the required performance improvements and network densification lead to significantly higher infrastructure costs, with 6G investments estimated to be 200%-840% higher than those of 5G, depending on the use case. In addition, both revenue and Average Revenue Per User (ARPU) are projected to increase considerably. For example, implementing 6G in the case of Pervasive Connectivity & Smart Cities requires an ARPU growth of 10% and a 479% increase in revenue compared with today's 5G, making it highly feasible. However, use cases such as AR/VR applications present challenges, with ARPU requiring a 582% increase to achieve 407% revenue growth. The proposed approach represents a significant contribution that offers economic insights to stakeholders by quantifying and assessing various 6G deployment scenarios. It does so in a structured and replicable manner, fostering an open dialogue on the economic potential of 6G within a well-defined framework.

关键词： 6G mobile communication 5G mobile communication Costs Reliability Business Investment Biological system modeling Solid modeling Smart cities computer aided software engineering 5G 6G business models standardization techno-economic use cases wireless

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Spatially Variant Filtering Network Based on Generalized Optimal Constraints for Sidelobe Suppression in SAR Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Suo, Yuxi Fu, Kun Wu, Youming Meng, Qingbiao Miao, Tian Diao, Wenhui Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Network Informat Syst Technol NIST Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100190 Peoples R China

Sidelobes commonly disturb synthetic aperture radar (SAR) image understanding and interpretation. Traditional spatially variant filtering algorithms achieve a superior tradeoff between sidelobe suppression and resolution preservation by means of adaptively calculating filtering parameters under some specific restrictions, such as filter design restriction and minimum amplitude constraint (MAC). These restriction aims to obtain an efficient analytical solution for filters, which is easy to calculate under unsupervised conditions. However, the restriction scope is so narrow that the suppression performance achieved by these filters is limited. Also, since the unsupervised optimization based on MAC indiscriminately minimizes amplitude, the main-lobe loss is unavoidable. To further improve the performance, a spatially variant convolution neural network (SVNN) is proposed, which consists of two core modules. One is the spatially variant filter generation (SVFG) module, adaptively generating superior spatially variant filters under more relaxed restrictions. The other is a paralleled shifted convolution (PSC) module, converting the signal format to achieve a fast and parallel spatially variant filtering process. Benefiting from more relaxed filter restrictions, the novel network successfully achieves better performance on sidelobe suppression. In addition, with supervised optimization based on another more accurate restriction, namely, minimum error constraint (MEC), the proposed algorithm also achieves superior main-lobe maintenance. All of them are validated by comparative experiments based on satellite data from GaoFen-3 and TerraSAR-X, and our proposed method achieves state-of-the-art performance. The entire project is available at https://***/suoyuxi/SVNN.

关键词： Optimization Signal processing algorithms Radar polarimetry Maintenance Synthetic aperture radar Signal resolution Convolutional neural networks computer aided software engineering Spatial resolution Solids Deep learning sidelobe suppression spatially variant filtering synthetic aperture radar (SAR)

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Analysis of Magnetic Field Errors From Coil Displacement for the Material Plasma Exposure Experiment

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2025年第5期35卷

作者： Burkhardt, Earle E. Duckworth, Robert C. Kaufman, Michael Rapp, Juergen Ferguson, Phillip Keys, Simon Warner, Rory van Oort, Johannes Oak Ridge Natl Lab Oak Ridge TN 37830 USA Tesla Engn Ltd Storrington RH20 3EA England

The magnet system for the Material Plasma Exposure eXperiment (MPEX) provides the necessary field profile to enable RF source and heating along the length to meet the desired key performance parameters (KPPs) at the target area. The magnet system consists of six superconducting magnets and one room-temperature magnet. While each magnet has been designed with mechanical supports to maintain magnetic field alignment due to electromagnetic mechanical loading and cyclic operating modes, the positions of coil windings could drift or shift over each magnet subsystem. The magnetic field requirements for the plasma are that at any on-axis location over the length of the plasma volume, the off-axis axial field shall not vary more than 1% relative to the on-axis field, and the radial field component shall be less than 1% of the on-axis field. Because the diameter of the coils is much larger than the diameter of the plasma, the off-axis axial field requirement is not an issue;however, if the coils are shifted or tilted, the radial field component can exceed the allowable limit. Given that the specific amount of drift varies significantly depending on the global magnetic field strength and complexity of the magnet winding geometry, two analyses were carried out. This paper covers the amount of shift or tilt that the coil windings within each magnet subsystem can tolerate while still meeting the design requirements with respect to magnetic field. The allowable shift of each subsystem varies from 2.6 mm to 29.1 mm, and the tilt varies from 1.7 mm to 32.8 mm.

关键词： Superconducting magnets Coils Magnetic fields Plasmas Windings Magnetic analysis Superconductivity computer aided software engineering Government Superconducting coils Fusion magnets plasma applications super- conducting magnets

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Passive Beamforming for RIS-aided Multi-Pair Transmission Using Partial Elements

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IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2764-2768页

作者： Xu, Yicheng Gao, Ruifeng Hu, Yingdong Li, Ye Wang, Jue Quek, Tony Q. S. Nantong Univ Schoolof Informat Sci & Technol Nantong 226019 Peoples R China Nantong Univ Sch Transportat & Civil Engn Nantong 226019 Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore

In current reconfigurable intelligent surface (RIS)-aided systems, adding a new pair of transceivers often requires the RIS phases to be entirely re-calculated for all elements, inducing great overhead, especially for large-scale RISs. In this letter, we propose to only adjust part of RIS elements to serve the newly added pair, while the other elements stay unchanged, providing more flexibility to balance performance and overhead. Simulation results show that the method, with modifying only a small portion of elements, can achieve most of the RIS gain that would be achieved if optimizing phases of all the elements.

关键词： Array signal processing Resource management OFDM Linear programming Wireless communication Transceivers Simulation Receiving antennas Indexes computer aided software engineering Reconfigurable intelligent surface multi-pair phase design interference suppression resource allocation

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A Bayesian Filtering Approach for Tracking Sympathetic Arousal and Cortisol-Related Energy From Marked Point Process and Continuous-Valued Observations

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IEEE ACCESS 2023年 11卷 137204-137247页

作者： Wickramasuriya, Dilranjan S. Khazaei, Saman Kiani, Roozbeh Faghih, Rose T. Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA NYU Tandon Sch Engn Dept Biomed Engn New York NY 10010 USA NYU Dept Psychol New York NY 10003 USA

Multiple state variables governed by internal processes within the human body remain unobserved. On a number of occasions, these states are linked to point process bioelectric and biochemical observations coupled together with continuous-valued variables. These observations provide a means to estimate the latent states of interest. We develop a state-space method to estimate unobserved sympathetic arousal and energy production states from skin conductance and cortisol data respectively, comprising of a marked point process and a continuous-valued observation. The method involves Bayesian filtering applied within an expectation-maximization (EM) framework for state estimation and model parameter recovery. Results are evaluated on both simulated and experimental data. On experimental skin conductance data, high arousal levels are generally detected during cognitive stress periods and lower values are detected during relaxation. Results are also in conformity with general physiology for cortisol data. On separate experimental data, skin conductance-based estimates are validated/cross-checked with functional Near Infrared Spectroscopy features. Estimation is also performed on heart rate and skin conductance data to illustrate the method's wider applicability. We also compare the method with earlier approaches. We show how it outperforms a previous method for cortisol-based energy estimation, and its superiority to earlier methods for estimating sympathetic arousal. The EM approach is thus able to estimate latent physiological states within the body from point process bioelectric and biochemical phenomena. The method could be applied in wearable monitoring and automated closed-loop therapy delivery for patients diagnosed with certain types of neuropsychiatric or hormone disorders.

关键词： Emotion recognition Physiology Blood Estimation computer aided software engineering Bayes methods Biomedical signal processing State-space methods Kalman filters state-space methods estimation emotion recognition

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The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English

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IEEE ACCESS 2023年 11卷 93381-93392页

作者： Imam, Ayad Tareq Isra Univ Fac Informat Technol Amman 11622 Jordan

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.

关键词： Automatic programming computer aided software engineering decision support systems feature extraction knowledge discovery requirements engineering semantic search semiotics

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