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automatic Emotion Recognition System: A cross Culture Study between Tamil and Russian Speaking Children 3

Automatic Emotion Recognition System: A cross Culture Study ...

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3rd international conference on Advanced Research in computing, ICARC 2023

作者： Nersisson, Ruban Bhuyan, Prithiraj Mekala, A. Mary Lyakso, Elena Vellore Institute of Technology School of Electrical Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India St. Petersburg State University The Child Speech Research Group Russia

ISBN: (纸本)9798350347371

Speech based communication contains both linguistic and paralinguistic information. It conveys emotions not only through its verbal communication, but also through voice characteristics such as pitch, volume, and stress. The objective of this study is to convey the child's emotion to a caretaker. To achieve this, a cross-cultural study of four emotions "joy - neutral (calm state) - sadness - anger"in children aged 8-12 years is considered. The data collection is carried out in multiple modes like acting speech in Russian and spontaneous and acting speech in Tamil language. SVM and MLP classitiers were used by the Russian specialist to train and validate speech data of both languages. The accuracy achieved by the Russian team is 84.1% and the Indian specialists used CNN and obtained the training and validation accuracy of 90% and >68% over 75 Epoch and used 7-layer CNN and obtained the training and validation accuracy of 95% and > 65% over 50 Epoch. The goal of the study is to see how well Russian and Indian specialists can recognise the emotional condition of Russian and Indian children based on their speech, and the results are correlated with computer-based emotion classification algorithms with various machine learning and deep net architectures. © 2023 IEEE.

关键词： Emotion Recognition

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Fuzzy control Strategy for Energy Allocation Optimization in Extended Range Mode 8

Fuzzy Control Strategy for Energy Allocation Optimization in...

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8th international conference on Image, Vision and computing, ICIVC 2023

作者： Jianling, Wang Xian, Yu Nan, Liu Detong, Cheng Xiamen University Tan Kah Kee College Department of Mechanical and Engineering Electrical Zhangzhou China

ISBN: (纸本)9798350335231

Based on the advantages of fuzzy control strategy, the energy management strategy of extended-range electric vehicles was selected as the research object, and the control parameters of extended-range electric vehicles were established by the two-dimensional fuzzy controller established by the FIS editor of MATLAB software, and the observation graph of fuzzy rules was observed, and the results of the control parameters were analyzed in detail. The Cruise software simulation software is used to establish the vehicle performance model of the extended-range electric vehicle, and the simulation analysis of the dynamic and economic performance is carried out. The multi-objective genetic optimization algorithm was used to optimize the working point of the range extender and the working speed of the engine, and the fuel consumption of the extended range electric vehicle was reduced by 22.9% after optimization. © 2023 IEEE.

关键词： Vehicle performance

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Study of Energy Capture with Photovoltaic Cell in Zinc Tile 7

Study of Energy Capture with Photovoltaic Cell in Zinc Tile

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7th international conference on Intelligent computing and control Systems, ICICCS 2023

作者： Nandankar, Praful V. Jeeva, C. Dattu, Vinjamuri S. N. Ch. Thangaraj, M. Udagani, Chikkappa Kirubakaran, D. Sudhakar, M. Gondwana University Department of Electrical Engineering Government College of Engineering Maharashtra Chandrapur442507 India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai602109 India Aditya Engineering College Department of Mechanical Engineering Andhra Pradesh Surampalem533437 India Department of Physics Tamil Nadu Dindigul624622 India University College of Science Department of Physics Tumkur University Karnataka Tumkur572103 India St. Joseph's Institute of Technology OMR Department of Electrical and Electronics Engineering Tamil Nadu Chennai600119 India Sri Sai Ram Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600044 India

ISBN: (纸本)9798350397253

This study presents a proposal to capture solar energy using photovoltaic cells on zinc tiles. In most existing photovoltaic projects, the implementation of photovoltaic modules requires a suitable roof and structure to house them. In this study, fasteners will be made directly to the tile, without the need for the structure to modify the roof. The most suitable methods for inserting photovoltaic cells into tiles were analyzed, evaluating aspects of layout, reliability, ease of implantation, and better energy capture. Roof prototypes were built with data collection directly from the roof. A structure called a photovoltaic rail has been produced. It can be loaded into most existing zinc roofs without having to insert a structure to protect it. After the production of photovoltaic busbars, their performance was analyzed by collecting voltage data. The results are analyzed by linking them with four seasons in a year, obtaining the results of energy production time, start time and end time of photovoltaic production. Photovoltaic rail structures were evaluated by analysis of possible changes during testing. Photovoltaic drains can be built to the required capacity by simply adding new drains without replacing the existing roof. The study showed satisfactory results, analyzing that in that year the drain structure did not change and the stress measurements remained constant and the study was conducted in four seasons in a year, taking into account various weather conditions. © 2023 IEEE.

关键词： Roofs

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Crop Yield Forecasting Based on Echo State Network Tuned by Crayfish Optimization Algorithm 2

Crop Yield Forecasting Based on Echo State Network Tuned by ...

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2nd IEEE international conference on Contemporary computing and Communications, InC4 2024

作者： Bacanin, Nebojsa Jovanovic, Luka Djordjevic, Marko Petrovic, Aleksandar Zivkovic, Tamara Zivkovic, Miodrag Antonijevic, Milos Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia School of Electrical Engineering University of Belgrade Belgrade Serbia

ISBN: (纸本)9798350383652

The agriculture sector, once reliant only on external inputs for increased yields, faces repercussions of diminished natural systems and significant environmental impact. High-precision agriculture aims to curb excessive usage of fertilizers and pesticides while prioritizing sustainability. Modern technologies can shape decisions toward the betterment of soil health, plant protection, and yield enhancement. Predictive models employing machine learning aid farmers in crop performance estimation, disease detection, soil health assessment, and yield prediction among others. These insights inform field operations, logistics, and contract negotiations. Leveraging machinery data, weather data, and drone imagery using machine learning can help us refine farming practices. Recurrent neural networks (RNN) can aid in various farming challenges like plant stress detection and irrigation. Yet, challenges persist in optimizing hyperparameters, a task we tackled using the nature-inspired crayfish optimization algorithm (COA) adapted to tune echo state networks (ESN) for multivariate time series forecasting of crop yield. This study demonstrates the utility of machine learning in agriculture, enabling yield assessment for improved sustainability. © 2024 IEEE.

关键词： Recurrent neural networks

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Design of A Real Time Remote Monitoring and controlling System Using LabVIEW 14

Design of A Real Time Remote Monitoring and Controlling Syst...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Rahman, Md Shovon Saha, Moutushi Hossain, Md Mehrab Nazrul Islam, A.K.M. Monir, Hasan Military Institute of Science and Technology Department of Electrical Electronic and Communication Engineering Dhaka1206 Bangladesh

ISBN: (纸本)9798350335095

This paper proposes a Virtual Instrumentation (VI) based graphical user interface (GUI) that is effective for Remote Monitoring and controlling for small scale electrical infrastructure using LabVIEW. The graphical user interface (GUI) is created utilizing LabVIEW, whereas the hardware is formed through voltage and current sensors, Arduino Uno and relay. Through USB communication, the program and device are interfaced. The monitoring interface utilizes real time measurement data to generate graphs displaying power, current, and voltage. Additionally, it has the capability to store and access a database file for the purpose of analyzing historical data. The control function involves the ability to activate or deactivate the system. Energy management of a building can be monitored and controlled by these GUI and prototype. This user interface can be used to demonstrate how to implement embedded systems projects, communicate through USB, acquire data, and monitor systems like distribution systems for commercial and industrial applications. © 2023 IEEE.

关键词： Graphical user interfaces

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A Survey on Security Issues in Cloud Systems 22

A Survey on Security Issues in Cloud Systems

引用

22nd RoEduNet conference: Networking in Education and Research, RoEduNet 2023

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Tapus, Nicolae Okumus, Ibrahim Taner Politehnica University of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Erbil Polytechnic University Soran Technical college Erbil Iraq Sutcu Imam University Computer Engineering Kahramanmaras Kahramanmaras Turkey

ISBN: (纸本)9798350393651

'The cloud' points out to servers, the databases and software that run on those servers. The Internet has always been constructed of infrastructure, servers and clients. Clients send their requests to servers, and servers reply, but cloud computing is distinct in the form that cloud servers are not just replying to requests with on-demand processing but they are saving data and running programs instead of the client. Cloud computing is a technology that is crossing a great expansion today. In cloud computing you can dynamically expand resources without knowledge of a new infrastructure, without developing new software or preparing new staff and to access such great technology the only things that are asked for are an Internet connection and an Internet browser. The goal of this survey is to present the different cloud security threats and recognize the proper security mechanism used to reduce them. © 2023 IEEE.

关键词： Cloud security

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Model Predictive control Based Frequency Regulation for Power Systems Containing Massive Energy Storage Clusters 5th

Model Predictive Control Based Frequency Regulation for Powe...

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5th international conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

作者： Lin, Yujun Yang, Qiufan Zhou, Jianyu Chen, Xia Wen, Jinyu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819910267

A large number of small-capacity distributed energy storage (ES) systems have been introduced to take an important part in grid frequency regulation. However, the accompanying high-order optimization problem causes an inevitable issue for both centralized and distributed control methods. Accordingly, based on the reduced-order predictive model, a two-layer control strategy of frequency support for power systems is proposed in this paper, in which the frequency regulation and economic dispatch are combined. The lower layer is implemented in a distributed framework which only requires communication between adjacent ESs, and the upper layer only utilizes the reduced-order information sent from the lower layer which lessens the computation burdens. The effectiveness of the entire control method is validated under different scenarios by simulations. As indicated in the simulation results, the proposed method achieves almost the same control effect in ES clusters as that using centralized control with a shorter computing time. And the proposed method incorporates the widespread multiple small-capacity ESs effectively for power system frequency control. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Model predictive control

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RESEARCH ON GAIT RECOGNITION BY USING 1DCNN BASED ON LOWER LIMB KINEMATIC POSTURE DATA 10

RESEARCH ON GAIT RECOGNITION BY USING 1DCNN BASED ON LOWER L...

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10th international Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Li, Mu Ma, Chao Wang, Shaohong Key Laboratory of Modern Measurement and Control Technology Ministry of Edu. Beijing Information Science and Technology University Beijing China Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781837242672

Gait recognition refers to the process of realizing the detection of human identity or health status and other information according to the characteristics of human plantar pressure, video images or human dynamics indicators. With the advantages of non-contact, non-aggression, no need for cooperation and easy to collect, gait recognition technology has been widely used in recent years in the field of public security criminal investigation, medical care, intelligent transportation and other fields. In this study, to meet the needs of gait recognition, IMU sensors are used to acquire human lower limb motion posture data and wavelet transform is used to decompose several components. Then the components were determined and the optimized 1DCNN model is used to carry out gait recognition research. The results showed that the optimized 1DCNN model for gait recognition task exhibits excellent performance with up to 95% accuracy, 96% recall and Fl value, as well as good computational efficiency, which is valuable for clinical automatic gait recognition analysis, rehabilitation engineering and criminal investigation work. Although there are some errors in some gait classifications, our study provides directions for further optimization of the algorithm to improve the accuracy and robustness of gait recognition. © The Institution of engineering & Technology 2024.

关键词： Crime

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Quantum-Enhanced Spiking Neural Networks for Closed-Loop Neuromodulation Systems: A Theoretically Advanced Framework

Quantum-Enhanced Spiking Neural Networks for Closed-Loop Neu...

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2024 international conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2024

作者： Ashar, Vallari Bansal, Sarthak Pandiyaraju, V. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (纸本)9798331543617

Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems currently available suffers from sensitivity, reaction time as well as energy consumption. The proposal in this research is to address these major issues in closed loop neuromodulation by using a Quantum enhanced Spiking Neural Network (QESNN) architecture. This paper represents the interfacing of two major fields: quantum sensing and neuromorphic computing. The QESNN architecture comprises three core components: This is implemented as an array of quantum sensors, a quantum classical hybrid interface, and a spiking neural network (SNN). Taking advantage of quantum superposition and entanglement principles, the quantum sensor array noninvasively images neural activity at the level of single action potentials using NV centers. These sensors work at ambient temperatures, which is unlike superconducting devices. For processing with neuromorphic processing, quantum-classical hybrid converts quantum sensor data into classical signals with advanced signal process such as quantum state estimation and noise reduction. By modeling biological neurons with leaky integrate and fire neurons, the SNN serves as a low power, timed neural dynamics modulation component that emulates biological event driven behavior. A key innovation in our architecture is adaptive thresholding, which dynamically adjusts detection thresholds based on signal distributions, improving sensitivity and reducing false positives by 45.6%. The system also achieves 20-30% higher power efficiency through techniques like adaptive sensor frequency control and low-power processing. Simulation results that show how the QESNN performs better than classical systems with less false positives and greater energy efficiency are presented. A new platform is demonstrated that integrates quantum sensing with neurom

关键词： Noise abatement

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Exploring Reproducibility of fMRI Analysis Using SPM and Nilearn Open-Source Tools

Exploring Reproducibility of fMRI Analysis Using SPM and Nil...

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international conference on electrical engineering, computing science and automatic control (CCE)

作者： Héctor G. Martínez-Fuentes Mario I. Gutierrez Jorge A. Mercado-Gutierrez Josefina Gutiérrez-Martínez Fisica Biomedica UMAM Facultad de Ciencias Mexico City Mexico Division de Inv. en Ing. Medica Inst. Nac. de Rehabilitacion “LGII” Mexico City Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Functional magnetic resonance imaging (fMRI) is a widely used technique for studying human brain activity. Various libraries and tools are available for performing the analysis and preprocessing of data. However, a significant challenge is that these different tools often yield varying results, which can complicate the interpretation and comparison of studies. In this context to contribute to the transparency of the used framework and reproducibility of the results in fMRI studies, this work presents a comparative analysis using Nilearn, a library available for Python, and SPM, a graphical interface available in MATLAB, along with preprocessing conducted with SPM in Nipype, also available for Python. Blood oxygen level- dependent (BOLD) contrast and anatomical volumes from one subject of the Flanker Task dataset were used. For this purpose, three distinct contrasts were established: Congruent > Incongruent, Congruent > Baseline, and Incongruent > Baseline. A point-by-point comparison of these masks was performed using a density plot and visualization of the different masks in the subject's anatomical volume. The results indicated that the design matrices of SPM and Nilearn are slightly different despite being configured with the same parameters, leading to significant differences in the final results.

关键词： Computational modeling Closed box Machine learning Functional magnetic resonance imaging electrical engineering computing Reproducibility of results Libraries MATLAB Python Programming environments

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