Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776765
Joshua S. Gulmatico, Julie Ann B. Susa, M. A. Malbog, Aimee G. Acoba, Marte D. Nipas, Jennalyn N. Mindoro
Music consumption patterns could alter due to digitization, and music popularity was redefined in the streaming era. The number of people using Spotify is constantly growing. It has risen to become one of the most popular internet music providers in recent years. People have been listening to my favorite performers and receiving new song recommendations via the Spotify app for the past year. The research looks at the relationship between song data – audio attributes from the Spotify database (for example, key and tempo) – and song popularity, as measured by the number of Spotify streams a song has. To develop a high accuracy model for predicting hit songs, the researcher investigates four machine learning algorithms (MLAs): Linear Regression, Random Forest Classifier, and K-means Clustering. This study presents a prediction model for determining whether a piece of music is popular in the mainstream and using machine learning to classify songs based on their popularity.
{"title":"SpotiPred: A Machine Learning Approach Prediction of Spotify Music Popularity by Audio Features","authors":"Joshua S. Gulmatico, Julie Ann B. Susa, M. A. Malbog, Aimee G. Acoba, Marte D. Nipas, Jennalyn N. Mindoro","doi":"10.1109/ICPC2T53885.2022.9776765","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776765","url":null,"abstract":"Music consumption patterns could alter due to digitization, and music popularity was redefined in the streaming era. The number of people using Spotify is constantly growing. It has risen to become one of the most popular internet music providers in recent years. People have been listening to my favorite performers and receiving new song recommendations via the Spotify app for the past year. The research looks at the relationship between song data – audio attributes from the Spotify database (for example, key and tempo) – and song popularity, as measured by the number of Spotify streams a song has. To develop a high accuracy model for predicting hit songs, the researcher investigates four machine learning algorithms (MLAs): Linear Regression, Random Forest Classifier, and K-means Clustering. This study presents a prediction model for determining whether a piece of music is popular in the mainstream and using machine learning to classify songs based on their popularity.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127862648","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776676
Rohan Gawhade, Lokesh Ramdev Bohara, Jesvin Mathew, Poonam Bari
Machine Learning (ML) has seen a sudden exponential rise in past decades. Numerous resources and documentation allow people to become ML practitioners. Companies make huge profits out of the analysis and predictions they make. ML Engineers are highly paid for their knowledge in this domain. It has become prevalent and much more comprehensible. One best out of the important stages in ML is Data preprocessing, and feature extraction. In Data Preprocessing itself, there are various tasks one needs to perform accurately to make the data provided. From handling missing values to encoding and normalization, each step has its importance and hence a professional must be adept with each of these steps. Data Preprocessing steps depend upon the type of data provided i.e. categorical data, continuous data, an array of images' pixels or even images themselves. With the requirement to deal with all the cleaning steps, it becomes quite strenuous to learn and become an expert. Moreover, it is time-consuming and does not guarantee expected results. Hence, there is a need to handle this issue. We aim to automate this complete process to ease the work of Machine Learning Engineers and make it more productive. Any user will only have to provide the dataset and does not have to manually select the processing techniques as provided by the latest Data Mining tools. The application will observe the dataset and apply the suitable techniques on its own. Since all the steps will be automated and the user will only have to provide the dataset, even the people who are not familiar with concepts of Machine Learning can pre-process the dataset. This allows the opening of opportunities for people from various domains who desire to perform Machine Learning operations.
{"title":"Computerized Data-Preprocessing To Improve Data Quality","authors":"Rohan Gawhade, Lokesh Ramdev Bohara, Jesvin Mathew, Poonam Bari","doi":"10.1109/ICPC2T53885.2022.9776676","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776676","url":null,"abstract":"Machine Learning (ML) has seen a sudden exponential rise in past decades. Numerous resources and documentation allow people to become ML practitioners. Companies make huge profits out of the analysis and predictions they make. ML Engineers are highly paid for their knowledge in this domain. It has become prevalent and much more comprehensible. One best out of the important stages in ML is Data preprocessing, and feature extraction. In Data Preprocessing itself, there are various tasks one needs to perform accurately to make the data provided. From handling missing values to encoding and normalization, each step has its importance and hence a professional must be adept with each of these steps. Data Preprocessing steps depend upon the type of data provided i.e. categorical data, continuous data, an array of images' pixels or even images themselves. With the requirement to deal with all the cleaning steps, it becomes quite strenuous to learn and become an expert. Moreover, it is time-consuming and does not guarantee expected results. Hence, there is a need to handle this issue. We aim to automate this complete process to ease the work of Machine Learning Engineers and make it more productive. Any user will only have to provide the dataset and does not have to manually select the processing techniques as provided by the latest Data Mining tools. The application will observe the dataset and apply the suitable techniques on its own. Since all the steps will be automated and the user will only have to provide the dataset, even the people who are not familiar with concepts of Machine Learning can pre-process the dataset. This allows the opening of opportunities for people from various domains who desire to perform Machine Learning operations.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939108","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776880
Sheikh Suhail Mohammad, Sheikh Javed Iqbal
Electric vehicles are currently acting as a replacement for fossil fuel-based vehicles. Electric vehicles are environment friendly, and energy efficient. However, electric vehicles demand research attention to improve system modelling, design, reliability, stability and control issues. Power-sharing is critical for electric vehicles reliable and economical operation; hence, they need to improve the power-sharing techniques and algorithms. A supercapacitor assisted fuel cell-based micro-power system is proposed and studied in this work. A power-sharing technique is proposed to control the power flow between fuel cell and supercapacitor during different vehicle operating modes to improve system reliability, stability, and vehicle dynamics. Supercapacitor state of the charge & voltage, fuel cell response time and motor power demand are critical variables for power-sharing and decision making. The design details give information about the system component types their advantages and disadvantages. An extended discussion is carried out that explains how the motors power rating is selected subjected to road dynamics. Time-domain simulations are performed in MATLAB/Simulink that validate the effectiveness of the proposed power-sharing and control technique during different operating modes.
{"title":"Power Management, Control and Design of Supercapacitor Assisted Fuel Cell-based Micro Power System for Electric Vehicles","authors":"Sheikh Suhail Mohammad, Sheikh Javed Iqbal","doi":"10.1109/ICPC2T53885.2022.9776880","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776880","url":null,"abstract":"Electric vehicles are currently acting as a replacement for fossil fuel-based vehicles. Electric vehicles are environment friendly, and energy efficient. However, electric vehicles demand research attention to improve system modelling, design, reliability, stability and control issues. Power-sharing is critical for electric vehicles reliable and economical operation; hence, they need to improve the power-sharing techniques and algorithms. A supercapacitor assisted fuel cell-based micro-power system is proposed and studied in this work. A power-sharing technique is proposed to control the power flow between fuel cell and supercapacitor during different vehicle operating modes to improve system reliability, stability, and vehicle dynamics. Supercapacitor state of the charge & voltage, fuel cell response time and motor power demand are critical variables for power-sharing and decision making. The design details give information about the system component types their advantages and disadvantages. An extended discussion is carried out that explains how the motors power rating is selected subjected to road dynamics. Time-domain simulations are performed in MATLAB/Simulink that validate the effectiveness of the proposed power-sharing and control technique during different operating modes.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994304","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776979
Soumya Shastry, P. Dewangan
This paper discusses about order reduction of linear time invariant (LTI) systems based on error minimization by Genetic algorithm. The coefficients of the state space modelmatrices of reduced dimensions are obtained by the proposed method. The reduction procedure is simple and computer oriented. An example system is considered to show the efficacy of the proposed method. The step responses of the higher order system and its models, and validation parameters areused for performance comparison. The results obtained confirm the superiority of the proposed technique.
{"title":"Order Reduction of Linear Time Invariant Systems Using Genetic Algorithm","authors":"Soumya Shastry, P. Dewangan","doi":"10.1109/ICPC2T53885.2022.9776979","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776979","url":null,"abstract":"This paper discusses about order reduction of linear time invariant (LTI) systems based on error minimization by Genetic algorithm. The coefficients of the state space modelmatrices of reduced dimensions are obtained by the proposed method. The reduction procedure is simple and computer oriented. An example system is considered to show the efficacy of the proposed method. The step responses of the higher order system and its models, and validation parameters areused for performance comparison. The results obtained confirm the superiority of the proposed technique.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"95 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130565071","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776894
Shreya Chakravarty, Shardul Fating, Ishita Jain, Ishika Varun, R. Khandelwal
The all-embracing use of digital images has revamped the quality of life and security to a great extent. Right from finding an item on online shopping websites through a clicked picture, to CCTV cameras being used for road traffic control, the users have learnt to appreciate the existence of technology being as advanced. However, one cannot overlook the gravity of this technology being misused. Although, the digitization has incorporated advanced concepts like Computer Vision and Deep Learning for security-check and crowd control, this has encouraged the advancement of courtroom discussions. Framing people for wrongdoings they are not involved with, on the basis of a fake “digital proof,” is one of the newly faced muddles. False allegations on a person, on the basis of a picture or a video, can potentially put a question on the existence of a person. The need to find the legitimacy of a produced image is therefore, of utmost importance. There have been various studies over the years, wherein a lot of methods were proposed to develop a system that identifies the camera model. Through this paper, we aim to produce a comparative study between four leading architectures, DenseNet, Inception V3, MobileNetV2 and Exception(XCeption), and suggest a the most competent architecture for commercialization of this system.
{"title":"Comparative Study between Leading Transfer Learning Architectures for Source Camera Identification","authors":"Shreya Chakravarty, Shardul Fating, Ishita Jain, Ishika Varun, R. Khandelwal","doi":"10.1109/ICPC2T53885.2022.9776894","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776894","url":null,"abstract":"The all-embracing use of digital images has revamped the quality of life and security to a great extent. Right from finding an item on online shopping websites through a clicked picture, to CCTV cameras being used for road traffic control, the users have learnt to appreciate the existence of technology being as advanced. However, one cannot overlook the gravity of this technology being misused. Although, the digitization has incorporated advanced concepts like Computer Vision and Deep Learning for security-check and crowd control, this has encouraged the advancement of courtroom discussions. Framing people for wrongdoings they are not involved with, on the basis of a fake “digital proof,” is one of the newly faced muddles. False allegations on a person, on the basis of a picture or a video, can potentially put a question on the existence of a person. The need to find the legitimacy of a produced image is therefore, of utmost importance. There have been various studies over the years, wherein a lot of methods were proposed to develop a system that identifies the camera model. Through this paper, we aim to produce a comparative study between four leading architectures, DenseNet, Inception V3, MobileNetV2 and Exception(XCeption), and suggest a the most competent architecture for commercialization of this system.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123106977","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776982
Vijay Mohale, T. Chelliah
The extra high voltage transmission line 765k V, connected to a doubly-fed induction machine (DFIM) for variable speed pumped storage plant prone to sub-synchronous oscillation (SSO). Hence, the cost-effective and most popular approach to increase the power transfer ability in long transmission lines is to use series capacitive compensation. However, SSO is a significant problem that can cause electrical instability and generator shaft failure. The main motive of this paper is to investigate sub-synchronous oscillations caused by series compensation in a transmission line connected to a DFIM. The simulation is carried out and results are validated in MATLAB/Simulink to analyze SSO in case of different series compensation levels of 30%, 50%, and 90%. The experimental validation is obtained in the laboratory on scale down model of Tehri (PSPP to be commissioned) to Meerut EHV transmission line.
{"title":"Impact of Series Compensated High Voltage Transmission Lines in the Operation of DFIM Based Hydro Unit","authors":"Vijay Mohale, T. Chelliah","doi":"10.1109/ICPC2T53885.2022.9776982","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776982","url":null,"abstract":"The extra high voltage transmission line 765k V, connected to a doubly-fed induction machine (DFIM) for variable speed pumped storage plant prone to sub-synchronous oscillation (SSO). Hence, the cost-effective and most popular approach to increase the power transfer ability in long transmission lines is to use series capacitive compensation. However, SSO is a significant problem that can cause electrical instability and generator shaft failure. The main motive of this paper is to investigate sub-synchronous oscillations caused by series compensation in a transmission line connected to a DFIM. The simulation is carried out and results are validated in MATLAB/Simulink to analyze SSO in case of different series compensation levels of 30%, 50%, and 90%. The experimental validation is obtained in the laboratory on scale down model of Tehri (PSPP to be commissioned) to Meerut EHV transmission line.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596136","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776999
S. V. Krishna
Grid connected PV solar generates DC power utilizing the solar energy as input and the generated DC power is converted to AC power using DC-DC converter and DC-AC Inverter. The output AC current from inverter consists of harmonic currents along with fundamental current. This current is fed to Grid through Inverter Duty Transformer (IDT) and Power Transformer (PT). The harmonics generated by solar plant are measured at LV & HV of IDT by using a harmonic analyser at site and presented in this paper. Apart from it, simulation model of PV solar generation with DC-DC converter & Inverter is also developed in PSCAD and the generated harmonics are compared with the measured harmonics and presented in this paper.
{"title":"Power Quality Analysis of Solar Plant by Measurement at Site and Through Simulation in PSCAD","authors":"S. V. Krishna","doi":"10.1109/ICPC2T53885.2022.9776999","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776999","url":null,"abstract":"Grid connected PV solar generates DC power utilizing the solar energy as input and the generated DC power is converted to AC power using DC-DC converter and DC-AC Inverter. The output AC current from inverter consists of harmonic currents along with fundamental current. This current is fed to Grid through Inverter Duty Transformer (IDT) and Power Transformer (PT). The harmonics generated by solar plant are measured at LV & HV of IDT by using a harmonic analyser at site and presented in this paper. Apart from it, simulation model of PV solar generation with DC-DC converter & Inverter is also developed in PSCAD and the generated harmonics are compared with the measured harmonics and presented in this paper.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134137109","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777008
Alimurtaza Merchant, Naveen Shenoy, Abhinav Bharali, M. A. Kumar
The Open University (OU), one of the largest public research universities, provides a wide range of data from its distance learning courses. Hence, the Open University Learning Analytics Dataset (OULAD) allows predicting student academic performance in online learning programs. The dataset consists of demographic features such as gender, disability, education level, and behavioural features, which depict engagement levels of students in courses. This paper predicts student academic performance in online learning programs using machine learning and statistical values. We train multi-class classifiers on the preprocessed dataset after feature selection and removing noisy data. Decision Tree, Random Forest, Gradient Boosting and KNN classifiers are trained on both demographic data alone and including virtual learning environment (VLE) data with it. Each classifier shows greater accuracy with the VLE data included. All classifiers achieve accuracies above 92%, with gradient boosting achieving the maximum accuracy of 97.5%.
{"title":"Predicting Students' Academic Performance in Virtual Learning Environment Using Machine Learning","authors":"Alimurtaza Merchant, Naveen Shenoy, Abhinav Bharali, M. A. Kumar","doi":"10.1109/ICPC2T53885.2022.9777008","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777008","url":null,"abstract":"The Open University (OU), one of the largest public research universities, provides a wide range of data from its distance learning courses. Hence, the Open University Learning Analytics Dataset (OULAD) allows predicting student academic performance in online learning programs. The dataset consists of demographic features such as gender, disability, education level, and behavioural features, which depict engagement levels of students in courses. This paper predicts student academic performance in online learning programs using machine learning and statistical values. We train multi-class classifiers on the preprocessed dataset after feature selection and removing noisy data. Decision Tree, Random Forest, Gradient Boosting and KNN classifiers are trained on both demographic data alone and including virtual learning environment (VLE) data with it. Each classifier shows greater accuracy with the VLE data included. All classifiers achieve accuracies above 92%, with gradient boosting achieving the maximum accuracy of 97.5%.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124330465","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9776836
Srinjoy Ganguly, Sai Nandan Morapakula, Luis Miguel Pozo Coronado
Sentiment classification is one of the best use cases of classical natural language processing (NLP). We witness its power in various domains such as banking, business, and the marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology that can provide a quantum advantage for NLP tasks. In this paper, we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and decent accuracy for experiments run on a noisy quantum device. We utilize the lambeq QNLP toolkit and t|ket > by Cambridge Quantum (Quantinuum) to produce the results.
情感分类是经典自然语言处理(NLP)的最佳用例之一。我们在银行、商业和营销行业等各个领域见证了它的力量。我们已经知道经典的人工智能和机器学习如何改变和改进技术。量子自然语言处理(Quantum natural language processing, QNLP)是一项新兴的技术，可以为自然语言处理任务提供量子优势。在本文中，我们展示了QNLP在情感分析中的首次应用，并在三种不同类型的模拟中实现了完美的测试集准确性，并在噪声量子设备上运行的实验中实现了不错的准确性。我们利用lambeq QNLP工具包和剑桥量子(Quantum)的t|ket >来产生结果。
{"title":"Quantum Natural Language Processing Based Sentiment Analysis Using Lambeq Toolkit","authors":"Srinjoy Ganguly, Sai Nandan Morapakula, Luis Miguel Pozo Coronado","doi":"10.1109/ICPC2T53885.2022.9776836","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9776836","url":null,"abstract":"Sentiment classification is one of the best use cases of classical natural language processing (NLP). We witness its power in various domains such as banking, business, and the marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology that can provide a quantum advantage for NLP tasks. In this paper, we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and decent accuracy for experiments run on a noisy quantum device. We utilize the lambeq QNLP toolkit and t|ket > by Cambridge Quantum (Quantinuum) to produce the results.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132102807","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}
Pub Date : 2022-03-01DOI: 10.1109/ICPC2T53885.2022.9777031
Harin M. Mohan, Santanu Kumar Dash
Solar photovoltaic (PV) systems are becoming increasingly popular across the world, and the Modular Multilevel Converter (MMC) architecture emerged as an appealing option for PV integration. The MMC topology has been conceived to alleviate the inconveniences of traditional multilevel converters, such as the higher-order level capacitor balance problem, the necessity for power filters and interface transformers. Because of its inherent benefits, MMC is used as the interface between diverse energy resources. Here, the PV is integrated with maximum power point tracking into the three phase three level MMC. When it comes to maintaining output power quality, modulation methods are significant. The proposed capacitor voltage balancing algorithms use phase disposition pulsewidth modulation (PDPWM) and space vector modulation (SVM) schemes to ensure the balancing of the capacitors in each sub-module. The MATLAB/Simulink simulation is performed and the effectiveness of the control approaches is evaluated.
{"title":"Performance assessment of a solar-powered three-phase modular multilevel converter with capacitor voltage balancing","authors":"Harin M. Mohan, Santanu Kumar Dash","doi":"10.1109/ICPC2T53885.2022.9777031","DOIUrl":"https://doi.org/10.1109/ICPC2T53885.2022.9777031","url":null,"abstract":"Solar photovoltaic (PV) systems are becoming increasingly popular across the world, and the Modular Multilevel Converter (MMC) architecture emerged as an appealing option for PV integration. The MMC topology has been conceived to alleviate the inconveniences of traditional multilevel converters, such as the higher-order level capacitor balance problem, the necessity for power filters and interface transformers. Because of its inherent benefits, MMC is used as the interface between diverse energy resources. Here, the PV is integrated with maximum power point tracking into the three phase three level MMC. When it comes to maintaining output power quality, modulation methods are significant. The proposed capacitor voltage balancing algorithms use phase disposition pulsewidth modulation (PDPWM) and space vector modulation (SVM) schemes to ensure the balancing of the capacitors in each sub-module. The MATLAB/Simulink simulation is performed and the effectiveness of the control approaches is evaluated.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132420407","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}
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