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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Satya Prakash Yadav Amit Gupta Caio Dos Santos Nascimento Victor Hugo C. de Albuquerque Mahaveer Singh Naruka Sansar Singh Chauhan Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) GreaterNoida U.P. India Graduate Program in Telecommunications Engineering. (PPGET) Federal Institute of Education Science and Technology of Ceará (IFCE) Fortaleza-CE Brazil Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Teleinformatics Engineering Federal University of Ceará Fortaleza Fortaleza/CE Brazil Computer Science & Engineering GL Bajaj Institute of Technology & Management Greater Noida India

Virtual assistants are improving and providing consumers with greater advantages. The comprehension and fulfilment of requests by virtual assistants will increase as voice recognition and natural language processing continue to grow. Virtual assistants are projected to be employed in more commercial activities as speech recognition technology advances. The main goal of developing personal assistant software (virtual assistant) is to use web-based semantic data sources, user-generated content, and knowledge from knowledge libraries. Basically, main objective of making this Voice-Based Virtual Assistant is to make life easier and having a personal assistant to everyone which can perform many tasks. As the end user interacts with a virtual assistant, the AI programming learns from the data provided and improves its ability to forecast the end user's needs. Virtual assistants are often used to do things like add tasks to a calendar, provide information that would normally be found in a website, and operate and monitor Smart Home devices like lighting and cameras and thermostats. Massive volumes of data are required to fuel virtual assistant technologies, which feed Artificial Intelligence (AI) platforms such as machine learning, natural language processing, and speech recognition. Speech recognition has a lengthy history and has seen several key advancements. On smartphones and wearable devices, speech recognition for dictation, search, and voice commands has become a standard feature. Design of a small, large vocabulary speech recognition system that can run quickly, accurately, and with minimum latency on mobile devices.

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

Parallel Approximations for High-Dimensional Multivariate No...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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The Performance Investigation of Smart Diagnosis for Bearings Using Mixed Chaotic Features with Fractional Order

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Sensors 2023年第8期23卷 3801-3801页

作者： Li, Shih-Yu Tam, Lap-Mou Wu, Shih-Ping Tsai, Wei-Lin Hu, Chia-Wen Cheng, Li-Yang Xu, Yu-Xuan Cheng, Shyi-Chyi Graduate Institute of Manufacturing Technology National Taipei University of Technology Taipei10608 Taiwan Institute for the Development and Quality 999078 China Department of Electromechanical Engineering Faculty of Science and Technology University of Macau 999078 China Master Program Graduate Institute of Mechatronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Mechanical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung City202301 Taiwan

This article presents a performance investigation of a fault detection approach for bearings using different chaotic features with fractional order, where the five different chaotic features and three combinations are clearly described, and the detection achievement is organized. In the architecture of the method, a fractional order chaotic system is first applied to produce a chaotic map of the original vibration signal in the chaotic domain, where small changes in the signal with different bearing statuses might be present;then, a 3D feature map can be obtained. Second, five different features, combination methods, and corresponding extraction functions are introduced. In the third action, the correlation functions of extension theory used to construct the classical domain and joint fields are applied to further define the ranges belonging to different bearing statuses. Finally, testing data are fed into the detection system to verify the performance. The experimental results show that the proposed different chaotic features perform well in the detection of bearings with 7 and 21 mil diameters, and an average accuracy rate of 94.4% was achieved in all cases. © 2023 by the authors.

关键词： Fault detection

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A peristaltic soft, wearable robot for compression and massage therapy

arXiv

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arXiv 2022年

作者： Zhu, Mengjia Ferstera, Adrian Dinulescu, Stejara Kastor, Nikolas Linnander, Max Hawkes, Elliot W. Visell, Yon Media Arts and Technology Program Department of Electrical and Computer Engineering Department of Mechanical Engineering California NanoSystems Institute University of California Santa Barbara United States Mechanical Engineering Program Department of Mechanical Engineering ETH Zurich Switzerland

Soft robotics is attractive for wearable applications that require conformal interactions with the human body. Soft wearable robotic garments hold promise for supplying dynamic compression or massage therapies, such as are applied for disorders affecting lymphatic and blood circulation. In this paper, we present a wearable robot capable of supplying dynamic compression and massage therapy via peristaltic motion of finger-sized soft, fluidic actuators. We show that this peristaltic wearable robot can supply dynamic compression pressures exceeding 22 kPa at frequencies of 14 Hz or more, meeting requirements for compression and massage therapy. A large variety of software-programmable compression wave patterns can be generated by varying frequency, amplitude, phase delay, and duration parameters. We first demonstrate the utility of this peristaltic wearable robot for compression therapy, showing fluid transport in a laboratory model of the upper limb. We theoretically and empirically identify driving regimes that optimize fluid transport. We second demonstrate the utility of this garment for dynamic massage therapy. These findings show the potential of such a wearable robot for the treatment of several health disorders associated with lymphatic and blood circulation, such as lymphedema and blood clots. © 2022, CC BY.

关键词： Blood

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Affecting Factors of Efficiency in Photovoltaic Energy Systems and Productivity-Enhancing Suggestions

Affecting Factors of Efficiency in Photovoltaic Energy Syste...

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2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

作者： Ay, Ilker Kademli, Murat Karabulut, Sener Savas, Serkan Resources Technology Program Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School Hacettepe University Department of Alternative Energy Ankara Turkey Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School Hacettepe University Department of Mechanical Program Ankara Turkey Kirikkale University Faculty of Engineering and Architecture Department of Computer Engineering Kirikkale Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

In recent years, hazardous gases emission from fossil fuels has attracted public concerns due to its worse effects on the ecosystem and living conditions not only mankind but also all creatures living on earth. That's why solar energy has a vital role in alternative energy resources. Solar energy sources will gain more importance in the future. As it is known, the most needed type of energy today is electrical energy. Thus, in this study, the necessary conditions for the photovoltaic (PV) systems used in solar energy production to operate at maximum performance and which parameters are required to control these conditions are examined. The results show that four parameters that we need to measure. These are: maximum operating current of a panel/cell (Impp), maximum operating voltage of a panel/cell (Vmpp), panel surface temperature and light intensity falling on the panel. Except for the panel surface temperature, the rest of the parameters can be measured directly. However, affecting the panel surface temperature;we must not ignore parameters such as ambient temperature, wind speed, humidity and light intensity. Therefore, while determining the panel surface temperature, these parameters should also be measured and a surface temperature should be determined accordingly. © 2022 IEEE.

关键词： Surface temperature

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Adaptive Fuzzy Sliding Mode Control With Logarithmic Sliding Surface for Active Suspension Systems

Adaptive Fuzzy Sliding Mode Control With Logarithmic Sliding...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Andika Aji Wijaya Fitri Yakub Winda Astuti Mahmud Iwan Solihin S. Syafiie Department of Mechanical Engineering University of Business and Technology (UBT) Jeddah Saudi Arabia Department of Electronic Systems Engineering University of Technology Malaysia (UTM) Kuala Lumpur Malaysia Department of Computer Engineering BINUS ASO School of Engineering Automotive and Robotics Program Bina Nusantara University (BINUS) Jakarta Indonesia Department of Mechanical and Mechatronics Engineering UCSI University Kuala Lumpur Malaysia Department of Chemical and Materials Engineering Faculty of Engineering-Rabigh King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper introduces a hybrid control strategy that integrates the unique features of natural logarithm sliding mode control (lnSMC) and fuzzy logic systems. The lnSMC control approach ensures that the vibration amplitude remains within a bound, as determined by the parameters of the lnSMC switching function. Furthermore, the fuzzy logic system is employed to approximate the nonlinearities and uncertainties inherent in the suspension system components, such as the spring and damper. The stability of the closed-loop system is analyzed using the Lyapunov stability theorem, guaranteeing convergence within a finite time. To validate the effectiveness of the proposed controller, a simulation study was carried out using a quarter-car suspension model, considering the system’s nonlinearities, parameter uncertainties, and various external disturbances. The results indicate that the proposed controller significantly improves ride comfort while maintaining suspension deflection and road holding within critical safety limits.

关键词： Fuzzy logic Vibrations Uncertain systems Uncertainty Roads Switches Stability analysis Vehicle dynamics Springs Sliding mode control

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Ivan Alexander Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Departement Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers.

关键词： Training Sentiment analysis Accuracy Social networking (online) Text categorization Nearest neighbor methods Naive Bayes methods Random forests Robots Airline industry

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Multicast Routing Technique for Augmenting Routing Efficiency in Mobile WSN

Multicast Routing Technique for Augmenting Routing Efficienc...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： K. Anand M. Rajkumar Srikanth MS S. Radhika Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India School of Information Technology and Engineering VIT University Vellore Tamil Nadu India Program Specialist TNS India Foundation Bangalore Karnataka India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Mobile Wireless Sensor Network (MWSN) is an active network completely self-governing from a fixed structure. Also, MWSN has small channel bandwidth, great node movement, and inadequate battery energy. In WSN, flooding raises the reliability regarding successful communication of a packet with greater overhead. The flooding consumes the network's resources quickly, especially in terms of the lifetime of the node and energy, etc. Unicast routing creates an extra overhead and increases energy consumption because unicast routing mainly focuses on only minimum distance. But, multicasting is used when the desired destination is at maximum distance and increases the network's reliability in terms of throughput. This paper's objectives are to develop an efficient and reliable protocol using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Multicast Routing Technique for Augmenting (MRTA) routing efficiency in MWSN is introduced. This method aims to enhance the link stability of the route for raising the route lifetime and diminishing the routing load. This method discovers the stable path from several ways to decline the overhead and delay between the sender and receiver. Simulation results demonstrate that this approach improves the network lifetime increases the throughput, and minimizes the routing load in the MWSN.

关键词： Wireless sensor networks Unicast Receivers Multicast communication Routing Throughput Stability analysis

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Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise

arXiv

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arXiv 2024年

作者： Boukanjime, Brahim Maama, Mohamed Laboratory of Mathematical Modeling and Economic Calculations Hassan First University of Settat Morocco Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young’s inequality and Chebyshev’s inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describes scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system’s state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model’s behavior under different parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models

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Efficient Automatic External Defibrillator Using the Slope of Hilbert Transformed ECG Signals

Efficient Automatic External Defibrillator Using the Slope o...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Samir A. Elsagheer Mohammed S. Sayed Mohsen A. Hassan Computer Science and Information Technology Egypt-Japan University of Science and Technology Alexandria Egypt Facurlty of Engineering Aswan Universty Aswan Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Department of Electronics and Communications Engineering Zagazig University Zagazig Egypt Materials science and engineering program IDE Egypt-Japan University of Science and Technology Alexandria Egypt

Sudden cardiac arrest (SCA) is a life-threatening condition that demands immediate intervention. The key to survival often lies in the timely and precise analysis of electrocardiogram (ECG) signals to determine the necessity of a shock from an Automatic External Defibrillator (AED). In this paper, we address the critical need for improved accuracy in AED decision-making by presenting a novel approach that leverages the Hilbert Transform to calculate the slope of ECG signals by harnessing the analytical power of Hilbert Transformed ECG signals. By scrutinizing the heart's electrical activity through this method, we aim to enhance the AED's ability to differentiate between cases of ventricular fibrillation and other non-shockable rhythms, ultimately leading to more efficient and effective treatment. Our research explores two distinct approaches for signal analysis and correlates their findings to achieve higher precision in defibrillation decisions. To validate the effectiveness of our approach, we employ a diverse and publicly available dataset containing various heart conditions, allowing us to demonstrate the robustness of our method across a wide spectrum of cases. This study represents a significant advancement in the field of automatic external defibrillation, with the potential to save countless lives through more accurate and timely interventions.

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