2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)最新文献

Design and Implementation of Fractional Proportional-Integral Control For Hybrid Solar and Wind System 分式比例积分控制的太阳能与风能混合系统设计与实现

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201766

Abhisek Gantayat, S. Behera, J. K. Pradhan, A. Naik

引用次数: 0

Output Voltage Regulated Multiple Output Flyback Converter using PIC and STPIC 使用PIC和STPIC的输出稳压多输出反激变换器

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201662

A. Mishra, P. K. Nanda, Debiprasanna Das, A. Patra, Narayan Nahak, Lalit M. Sathapathy

{"title":"Output Voltage Regulated Multiple Output Flyback Converter using PIC and STPIC","authors":"A. Mishra, P. K. Nanda, Debiprasanna Das, A. Patra, Narayan Nahak, Lalit M. Sathapathy","doi":"10.1109/APSIT58554.2023.10201662","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201662","url":null,"abstract":"This work focuses on the process of designing and the utilization of DC to DC fly back converter in closed loop system. Form six decagons conversion of DC to DC has found to be one of the major research areas in the field of power electronics engineering. For getting different levels of output voltage with single input, the advance systems like computer, telecommunication use single input single output dc to dc converter. This system has certain disadvantages like it is less efficient, low power density and the whole system becomes costly. In order to obtained the characteristics of being highly efficient and high power density the power converter with multiple output features are gaining attention. By using this multiple output converter the output is to be regulated for any type of load and source side disturbances. A Multiple Output Flyback Converter (MOFC) is designed, modeled and simulated in Simulink for controlling the output voltage as the desired value. Self Tuned Proportional Integral Controller (STPIC) or Conventional Proportional Integral Controller (PIC) are the two different methods which are utilized for getting the reference signal to obtain the switching pulse of the converter. Different parameters such as Settling Time (Ts), Rise Time (Tr) and Overshoot (OS) are obtained to analyze the performances. The responses of the used method are depicted for comparison of the outputs.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Optimized Ensemble Decision Tree models for MT-VSC-HVDC Transmission Line Protection MT-VSC-HVDC输电线路保护的贝叶斯优化集成决策树模型

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201799

Abha Pragati, D. A. Gadanayak, S. Hasan, Manohar Mishra

{"title":"Bayesian Optimized Ensemble Decision Tree models for MT-VSC-HVDC Transmission Line Protection","authors":"Abha Pragati, D. A. Gadanayak, S. Hasan, Manohar Mishra","doi":"10.1109/APSIT58554.2023.10201799","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201799","url":null,"abstract":"Over the last few decades, the High Voltage Direct Current (HVDC) technology has experienced significant growth. HVDC grid technologies are increasingly being employed for strengthening transmission systems and improving connectivity. In cases of long-range and bulk power transmission, HVDC systems have proven to be an attractive option compared to HVAC systems. HVDC grids exhibit reduced power loss and almost negligible lines reactive power. Faults must be fixed promptly, regardless of any challenges. This study presents a fault detection and classification method based on Bayesian optimized decision tree classifiers for an MT-VSC-HVDC transmission system. The primary objective of this research is to extract the DC voltage and current signal through the relays installed in the HVDC network. Afterward, fourteen features are formulated using these signals for the experimentation. Based on these features, Bayesian-optimized decision tree classifier is used to identify and differentiate the faults events. The proposed approach enables rapid identification, faster detection, and fixation of both internal and external faults. The proposed approach is rigorously assessed for various probable fault circumstances simulated with varying transmission system operating parameters. This experimental approach considerably reduces the complexity and time required to identify faults at various points on the HVDC transmission grids with high precision.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117169641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Tuning of Fractional Order PID controller using Nelder-Mead Algorithm for DC Motor Speed Control 基于Nelder-Mead算法的分数阶PID控制器的最优整定

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201735

Devee Dutta Mishra, Pratiti Padhi, Ankit Aniket Tripathy, S. Patnaik, P. Sahoo

引用次数: 0

Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms 基于机器学习的天气预报:回归与分类算法的比较研究

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201679

Sonal Wadhwa, R. Tiwari

{"title":"Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms","authors":"Sonal Wadhwa, R. Tiwari","doi":"10.1109/APSIT58554.2023.10201679","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201679","url":null,"abstract":"Accurate weather forecasting is essential in many industries, including agriculture, transportation, and disaster management, making it a prime use case for machine learning algorithms. In this study, we investigate how to forecast several types of weather, including rain, sunshine, clouds, fog, drizzle, and snow, using a variety of fundamental machine learning methods and boosting algorithms. To train and evaluate the various algorithms, we utilized a dataset made up of historical meteorological data, including characteristics like temperature, humidity, wind speed, and pressure. We performed tests on many machine learning methods, some of which you may be familiar with: decision trees, random forests, naive bayes, k-nearest neighbors, and support vector machines. We also used boosting techniques like XGBoost and AdaBoost to further enhance the precision of our forecasts. Our results indicated that XGBoost and AdaBoost, two popular boosting algorithms, achieved the highest levels of accuracy (87.86% and 87.33%) compared to the other algorithms we tested. The findings were verified using ROC Curve Analysis and Lift Curve Analysis, which demonstrated that the XGBoost and AdaBoost models performed better in terms of true positive rate, false positive rate, and lift.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125280193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early Stage Ovarian Cancer Prediction using Machine Learning 使用机器学习预测早期卵巢癌

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201764

C. Nayak, A. Tripathy, Manoranjan Parhi, S. Barisal

引用次数: 1

A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing 自然语言处理中关键词提取与同义词生成的比较研究

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201796

Rasmi Rani Dhala, A.V.S Pavan Kumar, S. Panda

引用次数: 0

Features of Low and Highly Susceptible Individuals in Retail Investment Fraud: A Machine Learning – Based Analysis 零售投资欺诈中低易感和高易感个体的特征:基于机器学习的分析

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201693

Princess Elmalyn B. Malik, Wen James P. Bulasa, Gernel S. Lumacad, Lester Dave T. Dagtay, Cookie J. Fajardo

{"title":"Features of Low and Highly Susceptible Individuals in Retail Investment Fraud: A Machine Learning – Based Analysis","authors":"Princess Elmalyn B. Malik, Wen James P. Bulasa, Gernel S. Lumacad, Lester Dave T. Dagtay, Cookie J. Fajardo","doi":"10.1109/APSIT58554.2023.10201693","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201693","url":null,"abstract":"Investment fraud/scam is defined as the intentional misinterpretation, concealment, or omission of facts regarding promised goods, services, or other expectations by putting funds into investments that are not real, unnecessary, never intended to be fulfilled, or intentionally distorted for the purpose of monetary gain. We present in this paper, an analysis of individuals' features/characteristics of those who are highly susceptible to retail investment scamming using machine learning (ML) methods. Purposive sampling is applied in data collection, asking only those who've at least experienced being scammed in a retail investment. Participants' demographic profile, emotional intelligence scores, personality traits scores and financial literacy levels are collected as parameters for the analysis. The data (N = 177) is first submitted to a Boruta algorithm for feature selection and out of nineteen (19) input features, only seven (7) features are confirmed to be important in determining low or high likelihood of susceptibility in retail investment scamming. Afterwards, a 2 - cluster solution is revealed using the $k$ - means clustering. Cluster 1 is composed of individuals having higher number of times being scammed - characterized by higher social class, higher income, higher emotional intelligence scores, higher levels of agreeableness, openness and extraversion, and lower financial knowledge. Cluster 2 is composed of individuals having lesser number of times being scammed - characterized by lower social class, lower income, lower emotional intelligence scores, lower levels of agreeableness, openness and extraversion and higher financial knowledge. Findings of this study may serve as basis for prevention, protection and enforcement against retail investment frauds.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"43 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129611737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biometric Cryptosystem with Deep Learning: A New Frontier in Security 深度学习的生物识别密码系统:安全领域的新前沿

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201772

Prabhjot Kaur, N. Kumar

引用次数: 0

Environmental Economic Dispatch of Hybrid Renewable Energy using PBMWOA 基于PBMWOA的混合可再生能源环境经济调度

2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) Pub Date : 2023-06-09 DOI: 10.1109/APSIT58554.2023.10201795

S. Kar, D. Dash, Renu Sharma

引用次数: 0