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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

共 1533 条记录，以下是891-900 订阅

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Design LCL Filter for Single Phase Grid Tie Inverter Using Firefly Algorithm 8

Design LCL Filter for Single Phase Grid Tie Inverter Using F...

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8th international conference on Information Technology, computer and Electrical engineering, ICITACEE 2021

作者： Irkham Setiawan, Iwan Facta, Mochammad Andromeda, Trias Hermawan Diponegoro University Electrical Engineering Department Semarang Indonesia

ISBN: (纸本)9781665439985

this paper establishes a design approach of LCL filter topology to reduce high order current harmonic single phase grid tie inverter based on Firefly Algorithm (FA) as one meta-heuristic method to optimization process. LCL filter topology is chosen as case studied because it can be eliminated current harmonic better than L filter topology and LC filter topology. the optimization process by FA proposed to finding the optimum values of filter components that result in good harmonic attenuation, minimum value components, and meet the regulation of interconnecting distributed energy resources with electric power system as the ieee 1547 standard. Objective function an optimization process consists of three terms, minimum harmonic attenuation rate, minimum value of inductors, and minimum value of capacitor. the optimization process and grid tie inverter systems has been developed in MATLAB software. Design LCL filter topology for single phase grid tie inverter based FA method succeed by reduced harmonic current meet the ieee 1547 standard, and compared with traditional design method, the LCL filter topology based FA method results more optimize numerical values of filter components. © 2021 ieee.

关键词： Optimization

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Low-Voltage Ride-through Strategy for Doubly-Fed Induction Generator Using Dual Series Dynamic Braking Resistors 8th

Low-Voltage Ride-Through Strategy for Doubly-Fed Induction G...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Sun, Tingting School of Electrical and Energy Engineering Nantong Institute of Technology Nantong226002 China

ISBN: (纸本)9789819602247

With the series dynamic braking resistor (SDBR) applied to assist the low-voltage ride-through (LVRT) of the doubly-fed induction generator (DFIG), its fixed resistance value fails to achieve satisfactory LVRT performances under different fault scenarios. In this paper, a novel dual SDBR scheme is designed to adapt to different scenarios and achieve optimal overall LVRT performance. Two different resistance values of the SDBR are selected and applied separately based on switching criterions considering voltage drop depth. With this scheme, the large SDBR resistance is applied under comparatively severe voltage drops to limit the inrush stator current, while the small SDBR resistance is applied under comparatively minor voltage drops to support the grid frequency during the LVRT. Feasibility and effectiveness of the dual SDBR scheme is verified based on simulation results of a DFIG-integrated power system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Asynchronous generators

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Analysis of an IoT-Based SDN Smart Health Monitoring System 8th

Analysis of an IoT-Based SDN Smart Health Monitoring System

引用

8th international conference on Microelectronics, Circuits, and systems, Micro 2021

作者： Tejeswara Kumar, M. Vikram G, N.V.R. Patel, Punyaban Andhra Pradesh Guntur District India Department of Computer Science and Engineering CMR Technical Campus UGC Autonomous Telangana Kandlakoya Medchal Hyderabad501401 India

ISBN: (纸本)9789819904112

Health specialists and scientists advocated for the use of IoT-based remote health monitoring devices to track the health of the senior citizens and critically challenged patients. However, such technologies may generate massive amounts of data, necessitating the security and privacy of that data. Various health monitoring system's (HMS) safety and privacy issues propose the management of a safety IoT-based health monitoring system, privacy, and dependable service delivery to patients and seniors to minimize and avoid health risk. High quality of life is dependent on good health. A continuous health care monitoring system is required for healthcare, and the data is stored in the cloud via a network, allowing doctors and nurses to provide patients with rapid treatment. IoT is the most recent developing technology for healthcare that is associated with electronic devices such as sensors and smart phones that have become part of everyday life. Sensors and wireless sensor networks (WSN) are used by the Internet of things to interact with patients to recognize medical concerns and communicate with them. Sensors collect a vast amount of data that is physically linked to the patient. Integrate data in the cloud with the aid of software Defined Networks (SDNs). SDN keeps track of sensitive information and ensures the safety of medical records. It will assist not just patients but also health care professionals in safeguarding particular patient data. SDN not only secures data but also prevents traffic from flowing from source to destination. this study intends to build a smart health care system with SDN by utilizing an IoT application to collect data via sensors and then stores it in the cloud. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health risks

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Optimal Design of 20 KW Grid-Connected Solar Power Plant for Maximizing Solar Radiation in Tabriz utilizing PV syst software

Optimal Design of 20 KW Grid-Connected Solar Power Plant for...

引用

Technology and Energy Management (ICTEM), international conference on

作者： Ebrahim Soltani Mohammad Mohsen Hayati Ali Aminlou Kazem Zare Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

In this paper, an optimal method for technical studies and feasibility studies for the construction of a photovoltaic power plant connected to the grid with a capacity of 20 kW is presented to design an optimal angle according to the geographical coordinates of the power plant construction site. Photovoltaics (PV) arrays and inverters are one of the main pieces of equipment. are in this field. Choosing the right capacity, layout and angle increases the efficiency of the power plant and also reduces the cost and losses. Based on the geographical location of the solar power plant and also the placement of PV arrays for ease of maintenance and creating the optimal angle of PVs, received the maximum power from sunlight is realized.

关键词： Photovoltaic systems Meters Costs Layout Voltage Solar energy Maintenance engineering

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Performance Evaluation of Xception Networks and Short-Time Fourier Transform Spectrograms for Motor Imagery Classification

Performance Evaluation of Xception Networks and Short-Time F...

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Recent Advances and Innovations in engineering (ICRAIE)

作者： Cagatay Murat Yilmaz Bahar Hatipoglu Yilmaz Department of Software Engineering Karadeniz Technical University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

this study seeks to explore the success of the Xception convolutional neural network and short-time Fourier transform in classifying EEG signals with limited training samples using pre-trained deep transfer learning. So far, most deep learning research has concentrated on one-dimensional time series inputs. Two-dimensional inputs are another excellent way of giving EEG signals to deep learning. this study used a two-dimensional STFT-based method to transform EEG signals into images and Xception for classification. BCI Competition IV dataset 2b, which includes nine participants, was used to evaluate the performance. this study is the first to report the STFT + Xception approach results for classifying motor imagery signals. It achieved good results from a small number of MIEEG data, averaging over 80% for nine subjects with a very low standard deviation of 2.9% between subjects. Applying data augmentation and training from scratch with more data can show more successful results in the future.

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Knowledge-Based Medical Tourism Recommender System

Knowledge-Based Medical Tourism Recommender System

引用

international conference for Convergence of Technology (I2CT)

作者： Aishwarya Harinivas Bharathi R Chetan A Gowda Priya Mohata R Sharmila Department of Computer Science and Engineering PES University Bengaluru India

Medical tourism refers to people travelling to other countries to access cost-effective and high-quality medical treatment. Over the past few years, the need for medical tourism has increased due to a lack of infrastructure in the places they reside or financial aid. Although there are websites that provide information regarding hospitals and their treatments, they employ basic filtration methods to present the results to the user. In order to address this problem, a medical tourism recommendation system was *** medical tourism recommendation system is knowledge-based. It leverages the data available to offer the best results for each treatment. Based on the user’s requirements, the top-k recommendations are given using an aggregated score. the results have been validated and found to be satisfactory. the recommendation system would definitely benefit users who need to travel for their medical assistance and cater to their budget-friendliness and high-quality treatment requirements.

关键词： Hospitals Filtration Knowledge based systems Recommender systems Optimization Convergence

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Scientific Paper Recommendation System

Scientific Paper Recommendation System

引用

international conference for Convergence of Technology (I2CT)

作者： Rajeev Singh Gaurav Gaonkar Vedant Bandre Nishant Sarang Sachin Deshpande Computer Engineering Vidyalankar Institute of Technology University of Mumbai Mumbai India

Advancements in research and technology have resulted in a flood of information in scientific research areas. It is challenging to find research papers of interest in today’s scenario, where hundreds of articles are published daily. It makes it impossible for the researcher and other readers to keep abreast of the research in their domain. Previously keyword matching techniques and recommender systems were used for finding relevant research papers. But these approaches were time-consuming and failed in scaling for a large dataset containing millions of articles. In this research, we propose a scalable end-to-end content-based scientific paper recommendation system capable of recommending research papers from the abstract or the context of the research paper for which we want to find the recommendation. We also evaluate our system using the average co-citation metric from [1]. Our end-to-end system performs with a average co-citation score of 14.88 and has an average response time of 1.4 seconds.

关键词： Measurement Time factors Floods Recommender systems Convergence

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Emotion and Mentality Monitoring Assistant (EMMA)

Emotion and Mentality Monitoring Assistant (EMMA)

引用

international conference on Communication and Electronics systems (ICCES)

作者： Ishini Kiridena Dayan Marasinghe Ruvini Karunarathne Kavindu Wijethunga Harinda Fernando Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Human emotions, psychology, and social well-being are all parts of mental health. It has an impact on how people feel, think, and act. It aids in figuring out how individuals act under pressure, interact with each other, and arrive at decisions. Every period of life, from childhood and adolescence to maturity, is vital for mental health. there are several elements that affect mental health issues, such as experiences with trauma or abuse in life, work-life stress, relationship difficulties, etc. Help is available for many of the mental health issues that are identified within a person. the proposed solution is implementing a friendly chatbot that is capable of capturing human emotional states (happy, angry, sad, etc.) and mental health states (depression, anxiety, stress, etc.) using human computer interaction and health record management. the proposed solution has been developed by considering both regular users and mental health practitioners. the user can communicate with the proposed assistant using text or voice to identify their emotions and mental health based on keywords, particular characteristics of voice and facial recognition used during the conversation. Once the mental health and emotion statuses are analyzed the electronic health records will be readily available on the blockchain as blockchain tokens, this could be shared with the practitioners securely.

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Study on the Performance of Amorphous IGZO TFT thin-Film Transistor with Ti/Au Stacking as the Source/Drain

Study on the Performance of Amorphous IGZO TFT Thin-Film Tra...

引用

ieee international conference on Signal and Image Processing (ICSIP)

作者： Qingru Lu Bing Li Rui Chen Xiaodong Huang School of Electron and Computer Southeast University Cheng Xian College Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Computer and Software Engineering Nanjing Vocational University of Industry Technology Nanjing China Key Laboratory of MEMS of the Ministry of Education Southeast University Nanjing China

through magnetron sputtering, a Ti/Au stack was adopted as the source/drain of the transistor, and Amorphous indium-gallium-zinc-oxide (α-IGZO or IGZO) was utilized as the active layer (150μm×150μm) to fabricate a bottom-gate contact thin-film transistor (TFT) with a structure of Ti/Au/IGZO (150μm×150μm)/SiO2/P. the electrical characteristics of this device were studied. the experiment showed that by depositing Ti/Au electrodes as the source/drain, and generating a TiOx oxide layer at the interface between the IGZO layer and the Ti/Au electrodes after annealing, the threshold voltage of the device was 4.7V, and the sub-threshold swing was relatively small. the gate bias voltage had excellent regulation on the leakage current of the device, and the device exhibited good cut-off characteristics.

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A Distributed Privacy-Preserving Framework for Deep Learning with Edge-Cloud Computing 15

A Distributed Privacy-Preserving Framework for Deep Learning...

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2022 ieee Congress on Cybermatics: 15th ieee international conferences on Internet of things, ithings 2022, 18th ieee international conferences on Green Computing and Communications, GreenCom 2022, 2022 ieee international conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th ieee international conference on Smart Data, SmartData 2022

作者： Dai, Fei Liu, Guozhi Huang, Bi Xu, Xiaolong Chen, Chaochao Zhou, Zhangbing Zhou, Xiaokang Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Zhejiang University College of Computer Science and Technology Zhejiang China China University of Geosciences School of Information Engineering Beijing China Shiga University Faculty of Data Science Shiga Japan

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Industrial Internet of things (IIoT) systems can leverage deep learning (DL) models to provide intelligent applications. However, the training process of such DL models tends to leak privacy when the training data contain sensitive operational and management information. Most studies about privacy-preserving DL require a trusted data collector and thus are not suitable in an untrusted environment. In this paper, we propose a distributed privacy-preserving framework for DL with edge-cloud computing, where direct privacy leakage and indirect privacy leakage of training data are both considered. First, a pre-trained convolutional neural network (CNN) is partitioned into two parts for limiting direct privacy leakage of raw data, namely the feature module and the classification module. the feature module consisting of the lower layers of the CNN is deployed on an edge server, which is used to attract the feature of raw data. the feature data is fed to the classification module consisting of the higher layers of the CNN, which is deployed on the cloud server to compute image classification tasks. Second, a perturbation module between the feature module and the classification module is designed for limiting indirect privacy leakage during feature data transmission. the perturbation module uses local differential privacy (LDP) to produce noise in the feature data. third, to further improve the accuracy, we retrain the classification module with the randomized feature data from edge servers. Experimental results show that the proposed framework achieves high accuracy under low privacy budgets. © 2022 ieee.

关键词： Classification (of information)

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