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Fault Detection and Classification for DC Microgrid using Binary Classification Models 5

Fault Detection and Classification for DC Microgrid using Bi...

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5th international conference on Control, Communication and computing, ICCC 2023

作者： Amal Krishna, T.S. Hari Kumar, R. College of Engineering Department of Electrical Engineering Kerala Trivandrum India

ISBN: (纸本)9798350334128

This paper presents a current and current derivative based fault detection and fault classification scheme. Current and current derivatives are taken and is processed through a binary classification based machine learning(ML) algorithm. A comparative study is done between binary classification models like Decision Tree, Naive Bayes and KNN algorithms. MATLAB/SIMULINK is used to simulate a 380 V DC Microgrid(DCMG). Fault classification is done for pole-pole(PP) and pole-ground(PG) faults under different fault resistances and varying load conditions. © 2023 IEEE.

关键词： Poles

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Software Startups Success Prediction: Machine learning Methods of Comparative Analysis 1

Software Startups Success Prediction: Machine Learning Metho...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Gracy, J Anitha Parthasarathy, S. Hirthick, S. Nandhini, P. Applied Mathematics and Computational Science Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798331518943

In recent years, there has been a significant rise in the number of software startups globally, driven by advances in technology and increasing reliance on digital solutions. These startups are crucial in shaping the future of industries. This study examines several machine learning models - Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and XGBoost - to predict the success of software startups based on key factors such as team dynamics and organizational characteristics. Using 473 data records and the top 19 features, the models were tested with an 80-20 train-test split, yielding strong performance in terms of accuracy, precision, recall, and F1 scores. Logistic Regression achieved the highest accuracy at 0.97, followed closely by KNN at 0.96. The findings reveal essential factors for startup success, such as team size, company age, and industry type, providing valuable insights for entrepreneurs, investors, and startup accelerators to adopt datadriven strategies that improve their chances of success. © 2024 IEEE.

关键词： Business Forecasting Data analysis machine learning software startups success prediction

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Integration of IoT-enabled UAVs in Smart Agriculture: Types, Applications, Trends, challenges 7

Integration of IoT-enabled UAVs in Smart Agriculture: Types,...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Prathyusha, Gajawada Krishnan, M.B.Mukesh Deepak, S.Prem Kumar SRM Institute of Science and Technology School of Computing Department of Networking and Communications India B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

Unmanned aerial vehicles (UAVs) have garnered increasing attention in recent years due to their utilization of artificial intelligence (AI) technologies and automation processes. These vehicles are being developed for various applications, including the military, smart agriculture, rescue operations, entertainment, monitoring, and medicine delivery. In the current era, UAVs extensively employ a wide range of technologies to transmit data to base stations. The collected data undergoes processing, producing remarkable outcomes through the integration of machine learning and deep learning technologies. This paper reviews disease identification and object detection techniques and their associated applications & challenges. Additionally, the research explores recent trends and technologies in the field of smart agriculture. This research emphasizes the use of sensors in unmanned aerial vehicles (UAVs) by analyzing the methods utilized for recognizing diseased areas and things while identifying insects and sick crops. This paper also addresses the challenges encountered by existing methodologies and proposes directions for future research and development. Furthermore, this review delves into suitable techniques for heterogeneous UAVs and examines IoT-related strategies. © 2024 IEEE.

关键词： Smart agriculture

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Enhancing Parallelization with OpenMP through Multi-Modal Transformer learning 5

Enhancing Parallelization with OpenMP through Multi-Modal Tr...

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5th international conference on Computer engineering and Application (ICCEA)

作者： Chen, Yuehua Yuan, Huaqiang Hou, Fengyao Hu, Peng Dongguan Univ Technol Dongguan Peoples R China Chinese Acad Sci Inst High Energy Phys Beijing Peoples R China Spallat Neutron Source Sci Ctr Dongguan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

The popularity of multicore processors and the rise of High Performance computing as a Service (HPCaaS) have made parallel programming essential to fully utilize the performance of multicore systems. OpenMP, a widely adopted shared-memory parallel programming model, is favored for its ease of use. However, it is still challenging to assist and accelerate automation of its parallelization. Although existing automation tools such as Cetus and DiscoPoP to simplify the parallelization, there are still limitations when dealing with complex data dependencies and control flows. Inspired by the success of deep learning in the field of Natural Language Processing (NLP), this study adopts a Transformer-based model to tackle the problems of automatic parallelization of OpenMP instructions. We propose a novel Transformer-based multimodal model, ParaMP, to improve the accuracy of OpenMP instruction classification. The ParaMP model not only takes into account the sequential features of the code text, but also incorporates the code structural features and enriches the input features of the model by representing the Abstract Syntax Trees (ASTs) corresponding to the codes in the form of binary trees. In addition, we built a BTCode dataset, which contains a large number of C/C++ code snippets and their corresponding simplified AST representations, to provide a basis for model training. Experimental evaluation shows that our model outperforms other existing automated tools and models in key performance metrics such as F1 score and recall. This study shows a significant improvement on the accuracy of OpenMP instruction classification by combining sequential and structural features of code text, which will provide a valuable insight into deep learning techniques to programming tasks.

关键词： component OpenMP Natural Language Processing Abstract Syntax Trees Parallelization

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Multibio Authentication of Online Users in Proctoring and E-learning 3

Multibio Authentication of Online Users in Proctoring and E-...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ajitha, P. Sadish Sendi, M. Navaneethakrishnan, Sundara Rajulu Sivasangari, A. Surendran, R. Sathyabama Institue of Science and Technology School of Computing Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Chennai India Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

online courses have become more liked recently as a brand-new method of instructing students in remoteness literacy environment. However, as request grows, educational accademies were faced with difficulty of determining how to verify that online scholars are the persons they say they are throughout learning ventures, particularly examinations. Mortal monitoring is an un-scalable method that calls for someone to oversee every student ever. On the computraized supervisors frequently focus on a certain kind of gadget and confirm the students' attendance without taking into account their interactions with the distance learning platform. In this study, present a multi- ralistic authentication system that is device/commerce independent and aims to constantly and translucent validate both existance and commerce. Through the use of a score-position emulsion of several realistic authentication response suc as facial, voice, touch based on the gadget. The system is suitable to certify to the scholars's identification all over the literacy occurrance depending on how it is used and the commerce that is conducted with it. Our donation has a fair chance of providing workable and dependable carry on a wider range of online system tests when compared to existing alternatives. © 2024 IEEE.

关键词： Students

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Exploration and Practice of Cultivating Methods of Undergraduates' Ability to Apply Knowledge: Taking engineering Drawing course as an example 23

Exploration and Practice of Cultivating Methods of Undergrad...

引用

8th international conference on Distance Education and learning, ICDEL 2023

作者： Wang, Xiaofei Wang, Jutao Zhang, Runfeng Yuan, Jingyao Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Natl. Demonstration Ctr. for Exp. Mech. and Elec. Eng. Education Tianjin University of Technology Tianjin University of Technolog China Tianjin University of Technology China

ISBN: (纸本)9798400700422

The ability to apply knowledge is one of the most important abilities for university students. In the paper, it introduces that how to foster the ability in the teaching of engineering drawing course, and presents some views about and exploration of the reform of the engineering drawing course for cultivating engineers with modern consciousness and innovation quality. In accordance with the standards of engineering education accreditation, based on the idea of Conceive-Design-Implement-Operate (CDIO), ideological and political of curriculum is merged into the course. The course reform better improve the quality of engineering education, lay a solid foundation of the specialty, and cultivate students' spatial thinking ability. At the same time, the course reform help students have a more comprehensive understanding to product-formation process, and then enhance students' sense of macro control of products. Combined with the characteristic of the engineering education accreditation and the CDIO in the teaching syllabus, curriculum structure, teaching methods and experiments are reformed. Following the pace of technological development, starting from stimulating students' interest in learning, integrating a 3D printing project into the teaching reform of engineering drawing is explored. There are two different level of introducing 3D printing into the course. They are shallow introduction and deep participation in producing a product by 3D printing. In the teaching mode of deep integration, students participate in the whole process of product design, modeling, 3D printing and all other 3D printing links. The implementation of this model is conducive to arousing students' interest in learning, giving free reign to their subjective initiative, enabling students to enjoy the learning process. Finally, the teaching objectives are realized, and their innovative ability are improved. © 2023 ACM.

关键词： Curricula

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Design and Implementation of Key Modules of English teaching System Based on J2EE 3rd

Design and Implementation of Key Modules of English Teaching...

引用

3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wang, Huijiao Sun, Yuheng Wu, Jianan Zhao, Wei Cao, Jingjie Wuhan Technology and Business University Hubei430065 China Xi’an Peihua University Institute of Media Shaanxi Xi’an710025 China

ISBN: (纸本)9783031631290

The user management module provides functions such as registration, login, and personal information management to achieve user authentication and personalized settings. The course management module supports course publishing, resource sharing, and homework management, providing rich learning resources and interactive communication platforms. The learning module integrates course content, listening, speaking, reading and writing training, and provides personalized learning recommendations and feedback through multimedia and artificial intelligence technology. The evaluation module achieves English proficiency testing, homework evaluation, and personal progress recording, helping students and teachers to achieve learning effectiveness and teaching management. Through the layered structure and componentization development of J2EE, the system has good scalability and maintainability. The experimental results indicate that the system has significant advantages in improving learning effectiveness and user experience. This study provides important reference and guidance for the design and implementation of English teaching systems.J2EE platform is an open, modular, distributed object-oriented framework for building applications. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： teaching

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Hybrid LQR Control Optimized with PSO and TLBO Algorithms for Single Area Load Frequency Control Incorporating Renewable Energy Systems

Hybrid LQR Control Optimized with PSO and TLBO Algorithms fo...

引用

2023 IEEE international conference on Modelling, Simulation and Intelligent computing, MoSICom 2023

作者： Pandey, Manish Kumar Mahia, Ram Niwash National Institute of Technology Hamirpur Department of Electrical Engineering Himachal Pradesh Hamirpur India

ISBN: (纸本)9798350393415

The recent shift towards renewable energy sources, such as wind and solar, has necessitated the development of more robust and adaptive load-frequency control (LFC) techniques. This study explores the application of Particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), and Auto-Tuned Linear quadratic regulator (LQR) controllers for LFC in a multi-source power system consisting of thermal, wind, and solar energy generation units. The system dynamics, given the intermittency of renewable sources and the unpredictable nature of step load disturbances, present challenges in maintaining frequency stability. In this context, a single test area combining various power-generation systems such as thermal, solar, and wind is taken. The simulation study shows that the PSO-based LQR gives more accurate results than the auto-Tuned and TLBO-Tuned LQR controllers regarding stability and settling time. © 2023 IEEE.

关键词： MATLAB

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Classification of Sleep Stages Using Single-Channel ECG Signals: A Comparative Analysis of Machine learning and Deep learning Methods 14

Classification of Sleep Stages Using Single-Channel ECG Sign...

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14th IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2024

作者： Ullah, Ahmad Khan, Muhammad Salman College of Engineering Qatar University Department of Computer Science and Engineering Doha Qatar College of Engineering Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350366556

In this paper, we provide an extensive evaluation of machine learning (ML) and deep learning (DL) methods for automatic sleep stage classification using a single-channel electrocardiogram (ECG) signal. To explore ML methods, we extracted 21 heart rate variability (HRV) features using the R-peak detection. These features were used to train and compare the performance of various models such as support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (K-NN), and Naïve Bayes (NB). Furthermore, we analyzed the DL methods to overcome the manual feature extraction. This method involved feeding raw time-series ECG signal to a 1-dimensional convolutional neural network (CNN) and short-time Fourier transform (STFT) based spectrogram to a 2D CNN. The overall evaluation was performed using 15-subject data each from two publicly available datasets. To process the data for analysis, resampling, bandpass filtering, baseline correction, 30-second interval extraction from whole-night ECG recordings, and z-score normalization techniques were applied. We conducted experiments on one dataset and then replicated the experiments on the other dataset to assess the robustness and generalizability of the methods. Through a comparative analysis, the spectrogram-based 2D CNN emerged as the effective method with the highest test accuracy and k-score of 87.65% and 0.82 respectively. This study contributes to ECG-based sleep classification research, highlighting the effectiveness of deep learning for effective classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

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Joint 13th international conference on Soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive learning

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