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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Lakshmi, Kanchi Lohitha Shobana, S. Kumar, Bathula Prasanna Glory, K.B. Kumar, Bonda Kiran Rani, Sandya Vasireddy Venkatadri Institute of Technology Department of Computer Science and Engineering Guntur India R.M.K Engineering College Department of It India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science and Engineering AP Guntur India Koneru Lakshmaiah Education Foundation Department of Engineering English Andhra Pradesh State Vaddeswaram522302 India Kl Deemed to Be University Kl School of Architecture Department of Architecture Guntur District Vaddeswaram522302 India St. Martin's Engineering College Dept. of It Telangana Secunderabad India

ISBN: (纸本)9798350396638

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks. © 2023 IEEE.

关键词： Network security

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Thermal Simulation for Magnetic Coupler of Wireless Power Transfer Electric Vehicles by Using Heat Sink and Thermoelectric Cooler

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Progress In Electromagnetics Research M 2024年 126卷 89-98页

作者： Farooq, Umar Shafique, Shahryar Asif, Muhammad Arslan, Muhammad Wongjom, Poramed Ullah, Rizwan Zhilenkov, Anton Mobayen, Saleh Pijitrojana, Wanchai Electrical and Computer Engineering Department Thammasat School of Engineering Thammasat University Rangsit Campus Klong Luang Pathum Thani 12120 Thailand Ministry of IT & Telecommunications Islamabad Pakistan Department of Electrical Engineering University of Science and Technology Main Campus Bannu23200 Pakistan Department of Mechanical Engineering University of Engineering and Technology Lahore Pakistan Division of Physics Faculty of Science and Technology Thammasat University Rangsit Campus Klongluang Pathum Thani 12120 Thailand Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan

In challenging operational environments such as underground buildings beneath roadways, reliability and performance of wireless power transfer (WPT) systems for electric vehicles (EVs) heavily hinge on the operating temperature of the magnetic couplers. Addressing this, this study introduces a novel approach employing heat sink and thermoelectric cooler technologies to mitigate temperature rise in magnetic couplers, which is particularly crucial for high-power applications. Utilizing ANSYS simulation, the study evaluates a WPT high-power application coil model with a total output power of 2 KW and an 18 cm air gap, with a 3.5 cm adjacent alignment to enhance thermal performance on both transmitter and receiver sides. Results demonstrate significant thermal enhancement, reducing the temperature of coils from 63◦ C to 54◦ C solely with the heat sink and further down to 48◦ C with the combined implementation of both heat sink and thermoelectric cooler. These measures effectively dissipate heat from the coils into the surrounding air, ensuring system efficiency and stability while facilitating functionality of system components. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Heat sinks

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Dielectric behaviours of bio-derived epoxy resins from cashew nutshell liquid

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High Voltage 2021年第2期6卷 255-263页

作者： Mulan Mu Alun Vaughan Department of Electronics and Computer Science Tony Davies High Voltage LaboratoryUniversity of SouthamptonSouthamptonUK Novel Polymer Technology Section TWI LtdCambridgeUK

In this study,four commercially available bio-derived epoxy systems(extracted from cashew nutshell liquid)were prepared and *** glass transition temperature(Tg),dielectric spectroscopy,DC conductivity and breakdown properties of these epoxy resins were *** scanning calorimetry(DSC)demonstrated that the T_(g) of the investigated systems ranged from 67 to 122°*** DC conductivity was very low(<10^(-16) S cm^(-1))and comparable to the conventional dielectrics at room temperature(RT).However,all systems showed a strong temperature dependence of the electrical conductivity and exhibited sharp increase around their respective T_(g).Arrhenius analysis led to activation energy,E_(a),values around 1 eV;higher E_(a) values were observed in systems with a lower T_(g).Dielectric spectroscopy revealed a flat and low response at temperature below T_(g).However,both the real and imaginary permittivity increased with decreasing frequency at mid to low frequencies as the temperatures approached T_(g).The variations of AC breakdown strength of all samples were not statistically significant,but the DC breakdown strength of sample 2503Aþ2002B was higher than the others,which might be due to reduced charge transport in this *** results indicate that novel bio-derived epoxy systems from renewable sources are potential alternatives for traditional petroleum-based epoxy systems in certain insulation applications.

关键词： conductivity resins liquid

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A computer-aided speech analytics approach for pronunciation feedback using deep feature clustering

A computer-aided speech analytics approach for pronunciation...

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作者： Nazir, Faria Majeed, Muhammad Nadeem Ghazanfar, Mustansar Ali Maqsood, Muazzam Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Data Science University of the Punjab Lahore Pakistan School of Architecture Computing and Engineering University of East London London United Kingdom Department of Computer Science COMSATS University Islamabad Attock Campus Pakistan

Nowadays, the demand for language learning is increasing because people need to communicate with other people belonging to different regions for their business deals, study, etc. During language learning, a lot of pronunciation mistakes occur due to unfamiliarity with a new language and differences in accent. In this paper, we perform speech mistakes analysis using deep feature-based clustering. We proposed two novel methods for speech analysis, one to deal with phonemic errors (confusing phonemes) and the other to deal with the prosodic errors (partially changed pronunciation variation of phones). For accurate and efficient language learning, it is important to learn both phonemic as well as prosodic error corrections. In our first method, we perform speech analysis by combining deep CNN features and clustering algorithm to detect the phonemic errors. We classify the phonemes using K-nearest neighbor, Naïve Bayes, and support vector machine (SVM). We perform experiments on the six most frequently mispronounced confusing pairs of Arabic to handle phonemic errors and achieve an accuracy of 94%. In our second method, we proposed the unsupervised phone variation model (PVM) to detect prosodic errors. In PVM, each phone is extended to represent the different types of pronunciation variation of that phone with different proficiency levels. We use an Arabic dataset of 28 individual phones for speech analysis and provide feedback based on the variation of each phone and achieves an accuracy of 97%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Error correction

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network 3

Deep Learning based vehicle image detection using Yolo V5 wi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rani, S. Swapna Mudigonda, Aditya Hemanth, S.V. Sundararajan Reddy, G. Vinoda Amirthayogam, G. Engineering College Department of Electronics and Communication Engineering Hyderabad India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Telangana Hyderabad501401 India Hindustan Institute of Technology and Science Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique - a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image's quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region's borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image's most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms. © 2024 IEEE.

关键词： Vehicles

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A New Methodology for Software-Simulation of Membrane Systems Using a Multi-Thread Programming Model

SSRN

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SSRN 2024年

作者： Cascado-Caballero, Daniel Diaz-Del-Rio, Fernando Cagigas-Muñiz, Daniel Orellana-Martín, David Pérez-Hurtado, Ignacio Department of Computer Architecture and Technology Universidad de Sevilla Seville Spain Research Group on Natural Computing Department of Computer Science and Artificial Intelligence Universidad de Sevilla Seville Spain

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a methodology for simulating P systems using a multi-threaded methodological approach in a multi-core processor. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a mul-tiprocessor. The results obtained suggest that the methodology is valid and that it is worth testing it with more complex systems to find the limits of the methodology. © 2024, The Authors. All rights reserved.

关键词： Program processors

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Serverless Computation and Related Analysis Based on Matrix Multiplication 1

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2nd International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2022

作者： Gong, Jiajun Sun, Haotian Wang, Yilin College of Architecture and Urban Planning Shenyang Jianzhu University Shenyang110000 China College of Stomatology ChongQing Medical University Chengdu610066 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9789819945542

ISBN: (纸本)9789819945535

This paper investigates the effect of the change in matrix dimensionality and the relationship between function communication time and execution time, as well as the relationship between memory parameters and execution time, on the efficiency and economic cost of computation when using a serverless platform for high-dimensional matrix data. AWS Lambda was used to create the functions, AWS S3 to store the data, perform the high-dimensional matrix fork multiplication and matrix convolution operations, respectively, and S3 to design the monitoring and data analysis interface, and finally, analyze the statistical data. According to the analysis, the average communication time consumption ratio decreases gradually as the number of matrix dimensions increases. The computation time decreases by increasing the number of nodes as far as concurrency allows. As the memory parameter increases, the computation time decreases, and the total cost decreases and then increases. The experimental results show that the impact of communication costs on serverless computing decreases as the size of the data increases. When the concurrency limit is not reached, there is a significant increase in computational efficiency, reducing the time consumed by individual nodes and the overall computing process. Increased memory improves computational efficiency but does not necessarily result in total cost savings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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Transportation of Hybrid MoS₂ – SiO₂ /EG Nanofluidic System Toward Radially Stretched Surface

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Arabian Journal for science and Engineering 2023年第1期48卷 953-966页

作者： Ahmad, Iftikhar Hussain, Syed Ibrar Raja, Muhammad Asif Zahoor Shoaib, Muhammad Qurratulain Department of Mathematics University of Gujrat Gujrat 50700 Pakistan Department of Mathematics and Computer Science University of Palermo Via Archirafi 34 Palermo 90123 Italy Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Mathematics COMSATS University Islamabad Attock Campus Attock 43600 Pakistan

A stochastic computing approach is implemented in the present work to solve the nonlinear nanofluidics system that occurs in the model of atomic physics. The process converts the partial differential nanofluidics system with suitable level of similarities transformation into nonlinear systems of differential equations. For the construction of datasets, finite difference scheme (Lobatto IIIA) is applied through different selection of collocation points for nonlinear nanofluidics system having accuracy of order four. Lobatto IIIA has a strong point to tackle extremely nonlinear systems of ordinary differential equations in smooth way. For different scenarios, datasets are well trained through computing scheme to investigate the heat transfer and thermal performance of nanofluidic transportation system of nanofluids and hybrid nanofluids toward stretching surfaces with variation of Biot number, Nusselt number and skin fraction. Furthermore, the reliability, accuracy and efficiency are endorsed through various statistical analysis and graphical illustrations of proposed computing scheme. © 2022, The Author(s).

关键词： Biot number Finite difference technique Nanofluidic system Skin friction Stochastic computing

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Eight-direction least-square phase unwrapping algorithm for optical three-dimensional measurement 3

Eight-direction least-square phase unwrapping algorithm for ...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： He, Yantao Liu, Sijia Pan, Minling Wang, Rongfu Li, Xueri Zhang, Rong Department of Computer Science Guangdong University of Science and Technology No. 2 Songshan Lake Section 523668 China School of Mechanical Engineering Hunan University of Science and Technology Taoyuan Road 411201 China School of Electronics and Information Engineering Beibu Gulf University No. 12 Binhai Avenue 535011 China

ISBN: (纸本)9781510668522

The least-square method is widely used in the fast phase unwrapping of optical three-dimensional measurement due to its high-speed and comprehensive characteristics. In order to solve the problem of measurement error propagation in the phase unwrapping algorithm based on least-square method, an improved eight-direction least-square phase unwrapping algorithm is proposed in this paper. This algorithm not only researches the gradient difference between the target phase and its eight-neighbor phases, improving measurement accuracy and reducing measurement errors. The superiority and robustness of this algorithm have been demonstrated through simulation data and measurement of industrial bearing roller end faces © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Measurement errors

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Distributed Learning by Local Training ADMM 63

Distributed Learning by Local Training ADMM

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Kth Royal Institute of Technology 2School of Electrical Engineering and Computer Science and Digital Futures Stockholm Sweden Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Trieste Italy

ISBN: (纸本)9798350316339

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments. © 2024 IEEE.

关键词： Peer to peer networks

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