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IEEE Transactions on Radar systems 2024年 2卷 778-790页

作者： Tsinos, Christos G. Arora, Aakash Tsiftsis, Theodoros A. National and Kapodistrian University of Athens Department of Digital Industry Technologies Evripus Campus Euboea 34400 Greece University of Twente Radio Systems Group Faculty of Electrical Engineering Mathematics and Computer Science AE Enschede7500 Netherlands Indian Institute of Technology Delhi Centre for Applied Research in Electronics New Delhi110016 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Jinan University Zhuhai Campus School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization-minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results. © 2023 IEEE.

关键词： Signal to noise ratio

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Recommender Diagnosis System with Fuzzy Logic in Cloud Environment 20

Recommender Diagnosis System with Fuzzy Logic in Cloud Envir...

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20th International Conference on Language engineering, ESOLEC 2022

作者： Aboghazalah, Maie Elnemr, Rasha Elsayed, Nedaa El-Sayed, Ayman El-Kafrawy, Passant Menoufia University Faculty of Science Math and Computer Science Department Egypt Climate Change Information Center Expert Systems Agriculture Research Center Giza Egypt Tanta University Faculty of Medicine Cardiology Department Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt Menoufia University Faculty of Science Egypt Effat Collage of Engineering Jeddah Saudi Arabia

ISBN: (纸本)9781665453226

Recommendation systems are now used in a wide range in many fields. In the medical field, recommendation systems have a great stature to both doctors and patients for its accurate prediction. It can reduce the time and efforts spent by doctors and patients. The present work introduces a simple and effective methodology for medical recommendation system based on fuzzy logic. Fuzzy logic is an important method to be used based on fuzzy input data. The input data for each patient are not the same, on which recommendation can differ. This work aims to develop techniques for h and ling the patient data to urge accurate lifestyle recommendations to the patient. Fuzzy logic is utilized to form different recommendations for the patient like lifestyle recommendations, medicine recommendations, and sports recommendations based on different patient factors like age, gender and patient diseases. After evaluating the system its efficiency reached 94%. This Experiment is the final module in a four modules recommendation system. The first one is responsible for diagnosing chest diseases using ECG signals. The second one makes diagnosis using X-ray images. The third is utilizing the security of the whole system through encryption when sending user data over the cloud. © 2022 IEEE.

关键词： Fuzzy logic

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AI for Closed-Loop Control systems New Opportunities for Modeling, Designing, and Tuning Control systems 2022

AI for Closed-Loop Control Systems New Opportunities for Mod...

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14th International Conference on Machine Learning and Computing, ICMLC 2022

作者： Schöning, Julius Riechmann, Adrian Pfisterer, Hans-Jürgen Faculty of Engineering and Computer Science Osnabrück University of Applied Sciences Germany Competence Centre of Electronics and Electric Drive Systems Science to Business GmbH Germany

ISBN: (纸本)9781450395700

Control systems, particularly closed-loop control systems (CLCS), are frequently used in production machines, vehicles, and robots nowadays. CLCS are needed to actively align actual values of a process to a given reference or set values in real-time with a very high precession. Yet, artificial intelligence (AI) is not used to model, design, optimize, and tune CLCS. This paper will highlight potential AI-empowered and -based control system designs and designing procedures, gathering new opportunities and research direction in the field of control system engineering. Therefore, this paper illustrates which building blocks within the standard block diagram of CLCS can be replaced by AI, i.e., artificial neuronal networks (ANN). Having processes with real-time contains and functional safety in mind, it is discussed if AI-based controller blocks can cope with these demands. By concluding the paper, the pros and cons of AI-empowered as well as -based CLCS designs are discussed, and possible research directions for introducing AI in the domain of control system engineering are given. © 2022 ACM.

关键词： Closed loop control systems

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Agent-based modeling (ABM) for urban neighborhood energy systems: literature review and proposal for an all integrative ABM approach

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Energy Informatics 2022年第Suppl 4期5卷 55页

作者： Vuthi, Petrit Peters, Irene Sudeikat, Jan Competence Center for Renewable Energy and Energy Efficiency Hamburg University of Applied Sciences Hamburg20099 Germany HafenCity University of Hamburg Technical Urban Infrastructure Systems Hamburg20457 Germany Department of Computer Science of the Faculty of Engineering and Computer Science Hamburg University of Applied Sciences Hamburg20099 Germany

Advancing the energy transition in real-world urban settings is attracting interest within interdisciplinary research communities. New challenges for local energy balancing arise particularly in urban neighborhoods where densely populated buildings are facing the needs of the heat transition, an increasing use of battery-electric vehicles and an expansion of renewable energies. Agent-based modeling (ABM) is a suitable approach for addressing various interlinked aspects like market mechanisms and processes, technology adoption, different stakeholder roles and the formulation of policy measures. In this work, we analyze peer-reviewed, open-access literature on ABM for energy neighborhoods and discuss key modeling aspects like model purpose and outcome, the logic of agents and decision-making, the treatment of space and time, and empirical grounding. These ABM allow the study of local market mechanisms, local renewable energy generation, microgrids, the unfolding of heat transition, neighborhood mobility and the evaluation of policies like regulation and financial incentives. We find a lack of integrated neighborhood energy assessments that simultaneously look at the different energy forms and applications: heating, electricity, and mobility. We present a consolidated ABM concept that integrates these sectors. Thus, our work contributes to the advancement of ABM and to the understanding of how to promote the transition to a decarbonized society in urban settings. © 2022, The Author(s).

关键词： Decision making

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Trajectory-based fast ball detection and tracking for an autonomous industrial robot system

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International Journal of Intelligent systems Technologies and Applications 2021年第2期20卷 126-145页

作者： AbdElKhalek, Youssef M. Awad, Mohammed Ibrahim Abd El Munim, Hossam E. Maged, Shady A. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Mechatronics Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Engineering Galala University Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt

Autonomising industrial robots is the main goal in this paper;imagine humanoid robots that have several degrees of freedom (DOF) mechanisms as their arms. What if the humanoid's arms could be programmed to be responsive to their surrounding environment, without any hard-coding assigned? This paper presents the idea of an autonomous system, where the system observes the surrounding environment and takes action on its observation. The application here is that of rebuffing an object that is thrown towards a robotic arm's workspace. This application mimics the idea of high dynamic responsiveness of a robot's arm. This paper will present a trajectory generation framework for rebuffing incoming flying objects. The framework bases its assumptions on inputs acquired through image processing and object detection. After extensive testing, it can be said that the proposed framework managed to fulfil the real-time system requirements for this application, with an 80% successful rebuffing rate. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Object detection

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Unveiling the Landscape of Machine Learning and Deep Learning Methodologies in Network Security: A Comprehensive Literature Review 2

Unveiling the Landscape of Machine Learning and Deep Learnin...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Saeed, Nouf Majid Sultan Eid Ibrahim, Amer Ali, Liaqat Al-Dmour, Nidal A. Mohammed, Abdul Salam Ghazal, Taher M. American University In The Emirates Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates College Of Engineering Mutah University Department Of Computer Engineering Jordan Skyline University College University City Sharjah Shariah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The dynamic nature of cyber threats offers a continual problem in the field of cybersecurity in the context of the expanding internet environment. This study provides an in-depth assessment of the literature on machine learning (ML) and deep learning (DL) methodologies for network analysis for intrusion detection. This review curates, assesses, and distils method-specific findings while considering temporal or thermal correlations. It provides a recognition of the importance of data in ML and DL approaches, and a comprehensive overview of frequently used network datasets in ML/DL applications, as well as the inherent challenges of adopting ML/DL in the cybersecurity field. The study concludes with well-informed recommendations for future areas of research in this critical domine. © 2024 IEEE.

关键词： Intrusion detection

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Thermodynamics in Stochastic Conway Game of Life

arXiv

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arXiv 2023年

作者： Pomorski, Krzysztof Kotula, Dariusz Cracow University of Technology Poland Faculty of Electrical and Computer Engineering Faculty of Computer Science and Telecommunications Quantum Hardware Systems

Cellular automata can simulate many complex physical phenomena using the power of simple rules. The presented methodological platform expresses the concept of programmable matter in which Newton’s laws of motion are one of examples. Energy has been introduced as the equivalent of the"Game of Life" mass, which can be treated as first level of approximation. The temperature presence and propagation was calculated for various lattice topology and boundary conditions by using the Shannon entropy measure. The conducted study provides strong evidence that despite not fulfillment the principle of mass and energy conservation, the entropy, mass distribution and temperatures approaches thermodynamic equilibrium. In addition, the described cellular automata system transits from positive to a negative temperatures that stabilizes and can be treated as a signature of system dynamical equilibrium. Furthermore, the system dynamics was presented in case of few species of cellular automata competing for maximum presence on given lattice with different boundary conditions. © 2023, CC0.

关键词： Cellular automata

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Real-time behavior recognition of animal: an IoT-based system design using acceleration data

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Multimedia Tools and Applications 2024年 1-26页

作者： Tran, Duc-Nghia Manh, Do Viet Van Thanh, Pham Shankar, A. Achyut Joshi, Kireet Tran, Duc-Tan Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam University of Fire Prevention and Fighting Hanoi100000 Viet Nam Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Center of Research Impact and Outcome Chitkara University Punjab Chandigarh India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam

By analyzing large amounts of animal behavior data, humans can assess cattle’s condition. It leads to research and development a field using accelerometers and machine learning algorithms to ‘study’ behavior from acceleration data. It is challenging to analyze animal behavior due to the activity complexity. For example, acceleration data of some cow behaviors (e.g., feeding and standing) are similar if these behaviors contain similar gestures. In practical, real-time applications even bring more challenging since it is necessary to minimize a data window for behavior consideration. Using short data window often reduces the performance of classification algorithms. This study proposes a new solution for the mentioned challenges by combining neck-mounted and leg-mounted accelerometers, then using the vector of the dynamic body acceleration (VeDBA) and Mean features of synchronized acceleration data for the Decision Tree algorithm. The proposed method is demonstrated that performs well (0.94 accuracy, 0.91 sensitivity, 0.90 positive predictive value) within a low computational complexity algorithm and short data window (3-second data window, at the rate of one sample per second). It is particularly suited for the case of a real-time IoT-based system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Accelerometers

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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Geographical Self-Organizing Map Clustering in Large-Scale Urban Networks for Perimeter Control 10

Geographical Self-Organizing Map Clustering in Large-Scale U...

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10th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2024

作者： Elouni, Maha Rakha, Hesham A. Menendez, Monica Abdelghaffar, Hossam M. Department of Computer Science Randolph-Macon College AshlandVA United States Charles E. Via Jr. Dept. of Civil and Environmental Engineering Virginia Tech BlacksburgVA United States Division of Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9789897587030

Traffic congestion in urban areas presents a major challenge to efficient transportation systems. Recent advancements in traffic management provide promising solutions, with perimeter control emerging as a technique to tackle network-wide congestion. However, it is crucial to identify geographically connected homogeneously congested areas for effective implementation. This research explores the application of clustering techniques, particularly geographical self-organizing maps (GeoSOM), to identify spatially connected and homogeneously congested areas within transportation networks. While GeoSOM has found applications across various domains, its adaptation to transportation networks for congestion clustering is novel. This study introduces and implements an adaptation of the GeoSOM algorithm tailored for the large-scale urban environment of downtown Los Angeles. Its performance is assessed through a comparative evaluation with two other clustering algorithms, namely DBSCAN and K-means. The results demonstrate that GeoSOM surpasses other clustering algorithms, exhibiting improvements of up to 43% in traffic density variance, up to 61% in the spatial quantization error, and 15% in the quantization error. This finding demonstrates that the proposed clustering algorithm is effective in identifying a spatially homogeneous congested area within a large-scale transportation network. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Traffic congestion

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