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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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RADAR: a rapid discovery algorithm for routes in SDN-based IoV

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Digital Communications and Networks 2024年

作者： Zahid Khan Nauman Khan Anis Koubaa Adel Ammar Wadii Boulila College of Computer and Information Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Information Technology University of Malakand Chakdara (Dir lower) KPK 18800 Pakistan

Link failures are a critical issue that disrupts consistent communications in the fast-evolving Internet-of-Vehicles ( IoV ) environment. In response to the aforementioned limitation, we introduce the rapid discovery algorithm for routes ( RADAR ), a novel scheme designed for rapid route discovery in the event of abrupt link failures or topological changes. Built on a heuristic T h e t a ⁎ algorithm, RADAR operates within a three-layered Software-Defined Networking ( SDN ) architecture to ensure seamless communications. The first layer collects vital vehicle information, including location, speed, direction, and vehicle ID . The second layer uses an SDN edge controller to carry out local and intra-zone path discovery with the T h e t a ⁎ heuristic algorithm. The third layer introduces a global controller that oversees inter-zone route discovery, thereby expanding the network's reach and capabilities. To validate RADAR 's effectiveness, we compare its performance to those of Dijkstra -based and A ⁎ path planning algorithms across key metrics—Route Discovery Messages ( RDM ), Path Length ( PL ), and Route Discovery Time ( RDT ). Our analysis shows significant improvements with RADAR over Dijkstra , achieving up to an 80% increase in RDM , 20% in PL , and 70% in RDT . It also surpasses A ⁎ by up to 70% in RDM and 60% in RDT . These results indicate that RADAR enhances routing reliability in IoV systems, thereby improving overall network performance.

关键词： Route discovery Software defined networking (SDN) Link failure Intra-zone communications Inter-zone communications T h e t a ⁎

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Two-Layer SVM, Towards Deep Statistical Learning

Two-Layer SVM, Towards Deep Statistical Learning

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Alireza Kazemi Reza Boostani Mahmoud Odeh Mohammad Rasmi AL-Mousa Mathematics Department Faculty of Science Shiraz University Shiraz Fars Iran CSE&IT Dept. Electrical and Computer Engineering School Shiraz University Shiraz Fars Iran Department of Management Information System Zarqa University Jordan Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K*(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs).

关键词： Support vector machines Electric potential Statistical learning Neural networks Transforms Nonhomogeneous media Robustness

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Adaptive neural output constrained control for an unmanned surface vessel with coupled input constraint

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Ocean Engineering 2025年 335卷

作者： Yuncheng Ouyang Yang Bai Linghuan Kong Xuerao Wang School of Artificial Intelligence the Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Anhui University Hefei China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macao China

In this paper, we focus on the tracking control problem of an uncertain unmanned surface vessel (USV) system with coupled input constraint and output constraint. Considering the issue of coupled input constraint, a transformation matrix is proposed to decouple the constrained inputs. Concerning output constraint occurring in a limited time interval (OCOLT) of the USV system, a novel shifting function with a new barrier function is introduced to transform the constrained system into an unconstrained one. Furthermore, neural network technology is employed to tackle the problems of system uncertainty. Through the Lyapunov stability theory, the stability analysis of the USV system is given to demonstrate the feasibility of the proposed control method. Finally, effectiveness of the proposed control method is verified by simulations.

关键词： Adaptive control systems

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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Smart Lighting system for Children's Therapy to Prevent Nyctophobia Syndrome at Bedtime

Smart Lighting System for Children's Therapy to Prevent Nyct...

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International Conference on Cybernetics and Intelligent system (ICORIS)

作者： Andi Pramono Badrul Munir Muchammad Farchan Satrio Arif Budiman Baskoro Azis Wahyu Waskito Putra Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Departemen of Neurology Brawijaya University Malang Indonesia Department of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Information System Computer Science faculty Brawijaya University Malang Indonesia

ISBN: (纸本)9781665453967

Everyone has a fundamental desire for sleep. By sleeping, humans can conserve energy and replenish their stamina to optimal levels. Lighting is one component that influences the quality of a person's sleep. One of them is a person who sleeps with the lights on, suffers from nyctophobia, has poor sleep quality, and may be responsible for several illnesses. This study aims to develop a smart lighting device that will aid in treating youngsters with nyctophobia to get used to sleeping with the lights off to improve their sleep quality. The case study methodology used in this study entails the development of a prototype. This method employs an oximeter as a heart rate detector and a sensor comparison as the measurement device. Arduino Uno circuit and heart rate sensor are the main devices in this research. At the same time, the NRF 24L01 is a communication medium between the two Arduino Uno boards. The experimental measurements in normal conditions indicate that the heart rate sensor was nearly the same as the value displayed by the oximeter. Similarly, after running for 1 minute, both devices produced almost identical results, with an average difference of 1 Beats Per Minute (BPM). According to experiments with a heart rate sensor, the switch-off condition can be activated if the heart rate hits 90 BPM. In this case, the light will switch off once the heart rate hits 70 BPM, which might be used for sleeping individuals.

关键词： Heart rate Phase measurement Prototypes Medical treatment Switches Turning Nanoscale devices

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Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

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Intelligent Automation & Soft Computing 2024年第3期39卷 417-436页

作者： Muhammad S.Alam Farhan B.Mohamed Ali Selamat Faruk Ahmed AKM B.Hossain Department of Emergent Computing School of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Malaysia-Japan International Institute of Technology(MJIIT) Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia Media and Game Innovation Centre of Excellence(MaGICX) Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Engineering Technology University of MemphisMemphisTN38152USA Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Information System and Cyber Security College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance *** scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and *** research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image *** annotated image dataset trains the proposed system and predicts the camera pose in *** system mainly improved the localization performance of indoor cameras by more accurately predicting the camera *** also recognizes the scene changes during the sequence and evaluates the effects of these *** system achieved high accuracy and real-time *** scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact ***,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

关键词： Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)

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Research on Compression Optimization Algorithm for Super-resolution Reconstruction Network

Research on Compression Optimization Algorithm for Super-res...

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Electrical Engineering and Automation (IFEEA), International Forum on

作者： Xiaodong Zhao Yanfang Fu Feng Tian Xunying Zhang Unmanned System Research Institute Northwestern Polytechnical University Xi’an China School of Computer Science and Engineering Xi’an Technological University Xi’an China Faculty of Science & Technology Bournemouth University UK School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781665464222

Under the condition of limited resources of embedded systems, the paper proposes a compression optimization algorithm based on pruning and quantization, so that the computational requirements of the super-resolution reconstruction algorithm based on a Convolutional Neural Network (CNN) can be met. First, a multiple regularization pruning optimization algorithm based on an attention module and a BatchNorm layer is proposed. Then, a coordination optimization algorithm of INT8 training and quantization for FPGA architecture is proposed. The performance of the pruning optimization algorithm was verified for the Super-Resolution CNN (SRCNN), the Fast Super-Resolution CNN (FSRCNN), and the Very Deep Super-resolution CNN (VDSRCNN). As for SRCNN, the performance of the quantization optimization algorithm was verified on the FPGA EC2 hardware simulation platform. The results show that the proposed compression optimization algorithm can achieve a good balance between network accuracy and inference speed.

关键词： Training Quantization (signal) Embedded systems Superresolution Reconstruction algorithms Inference algorithms Hardware

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Fault Diagnosis Model of Switching Power Supply Based on Improved DBN

Fault Diagnosis Model of Switching Power Supply Based on Imp...

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International Conference on Intelligent system and Knowledge Engineering, ISKE

作者： Lei Zhou Cheng Yu Hu Liu Xin Li Fenfen Li Haiyan Zhang Faculty of Computer and Software Engineering Research Center for logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Huai’an Jiangsu China College of Computer Science and Technology Nanjing University of Technology Nanjing Jiangsu China Faculty of Chemical Engineering Huaiyin Institute of Technology Huai’an Jiangsu China

ISBN: (纸本)9781665405546

Switching power supply has wide application in various electrical equipment. The failure of high-voltage filter capacitors is the main cause of frequent faults, accounting for more than 60% of switching power supply failures. In order to realize the real-time detection of switching power supply failure without disassembling the power supply, a fault diagnosis model for improving the deep belief network (DTDBN) is proposed. The model uses the data acquisition device to isolate, filter, and collect data on the grid voltage of the switching power supply, judge the failure ratio of the filter capacitor according to the degree of voltage distortion of the power supply. Experimental results show that the proposed fault detection method achieves a 100% recognition rate for the filter capacitor normal state, 50% and 90% state recognition rate is 90%. The superiority of this method for the classification and early warning of power capacitor faults is proved.

关键词： Fault diagnosis Power supplies Fault detection Capacitors Switches Voltage Distortion

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Non-volatile optical switch based on Sb2Se3-loaded three-mode interference

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Optics Letters 2025年第12期50卷 4022-4025页

作者： Xinxin Zhao Xiaolong Liu Yujiao Tao Lei Yang Kai Xu Hongtao Lin Peipeng Xu Wei Zhang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China Key Laboratory of Photoelectric Detecting Materials and Devices of Zhejiang Province Ningbo 315211 China The State Key Lab of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

In this Letter, we propose a non-volatile switch based on multimode interference in an Sb 2 Se 3 -loaded waveguide. By implementing low-loss phase control within a multimode interference coupler, this design effectively manages the self-imaging phase shift length during a single phase change cycle. The fabricated multiplexed optical switch exhibits impressive performance characteristics, including a low insertion loss of ∼ 1.5 dB, a compact length of ∼35 μm, and minimal cross talk below −6 dB over a 35 nm bandwidth for both the amorphous and crystalline states. The ability to reconfigure light routing via Sb 2 Se 3 phase transitions opens new avenues for optical field-programmable gate arrays and high-density photonic processors.

关键词： Crosstalk Effective refractive index Field programmable gate arrays Multimode interference Optical neural systems Phase shift

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