Pub Date : 2022-06-06DOI: 10.54614/electrica.2022.22020
Ali Ağçal, Tugba Halime Dogan, Galip Aksu
{"title":"The Effects of Operating Frequency on Wireless Power Transfer System Design and Human Health in Electric Vehicles","authors":"Ali Ağçal, Tugba Halime Dogan, Galip Aksu","doi":"10.54614/electrica.2022.22020","DOIUrl":"https://doi.org/10.54614/electrica.2022.22020","url":null,"abstract":"","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47572805","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 : 2022-06-06DOI: 10.54614/electrica.2022.21136
Humirah Majeed, Vikram Singh
This paper presents the circuit of a low-noise amplifier (LNA) using active inductor (AI) input matching with common gate (CG) current-reused technique. This configuration is implemented in 90 nm CMOS and enables to achieve high power-gain (S 21 ) with ultra-wideband (UWB) input matching at low power levels. Utilization of modified high-Q AI at the input side of the proposed LNA reduces the number of inductors and achieves UWB from only two inductors. Proposed LNA dissipates 10.4 mW from 1.0 V supply and exhibits an S 21 response of 18.0 ± 0.8 dB for 3.1–10.6 GHz with a maximum and average S 21 of 18.8 dB and 18.22 dB, respectively. The proposed LNA has noise-figure (NF) equal to 3.36–4.68 dB, with input (S 11 ) and output (S 22 ) reflection coefficients of less than − 9.3 dB and − 11.35 dB, respectively across the entire UWB range.
{"title":"A Common-Gate, gm-boosting LNA Using Active Inductor-Based Input Matching for 3.1–10.6 GHz UWB Applications","authors":"Humirah Majeed, Vikram Singh","doi":"10.54614/electrica.2022.21136","DOIUrl":"https://doi.org/10.54614/electrica.2022.21136","url":null,"abstract":"This paper presents the circuit of a low-noise amplifier (LNA) using active inductor (AI) input matching with common gate (CG) current-reused technique. This configuration is implemented in 90 nm CMOS and enables to achieve high power-gain (S 21 ) with ultra-wideband (UWB) input matching at low power levels. Utilization of modified high-Q AI at the input side of the proposed LNA reduces the number of inductors and achieves UWB from only two inductors. Proposed LNA dissipates 10.4 mW from 1.0 V supply and exhibits an S 21 response of 18.0 ± 0.8 dB for 3.1–10.6 GHz with a maximum and average S 21 of 18.8 dB and 18.22 dB, respectively. The proposed LNA has noise-figure (NF) equal to 3.36–4.68 dB, with input (S 11 ) and output (S 22 ) reflection coefficients of less than − 9.3 dB and − 11.35 dB, respectively across the entire UWB range.","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42162815","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 : 2022-06-06DOI: 10.54614/electrica.2022.21152
A. A. Ivanov, A. V. Demakov, M. Komnatnov, T. Gazizov
{"title":"Semi-analytical Approach for Calculating Shielding Effectiveness of an Enclosure with a Filled Aperture","authors":"A. A. Ivanov, A. V. Demakov, M. Komnatnov, T. Gazizov","doi":"10.54614/electrica.2022.21152","DOIUrl":"https://doi.org/10.54614/electrica.2022.21152","url":null,"abstract":"","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43575728","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}
{"title":"Parameter Calculation and Structural Electric Field Optimization of Stress Cone for Main Insulation Layer of a 10 kV Cable","authors":"Jian Wu, Jing Xu, Weixun Qin, Jianfeng Wang, Mugen Wu","doi":"10.54614/electrica.2022.210134","DOIUrl":"https://doi.org/10.54614/electrica.2022.210134","url":null,"abstract":"","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44082374","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 : 2022-06-06DOI: 10.54614/electrica.2022.210083
Priya R. Pattanaik, B. Panigrahi, Subhendu Pati
{"title":"An Improved Cumulative Sum-Based Fault Detector for Power Network Protection","authors":"Priya R. Pattanaik, B. Panigrahi, Subhendu Pati","doi":"10.54614/electrica.2022.210083","DOIUrl":"https://doi.org/10.54614/electrica.2022.210083","url":null,"abstract":"","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45638378","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 : 2022-06-06DOI: 10.54614/electrica.2022.210123
F. Yucel
{"title":"A New Simulated Grounded Inductor with Two-Terminal Active Devices","authors":"F. Yucel","doi":"10.54614/electrica.2022.210123","DOIUrl":"https://doi.org/10.54614/electrica.2022.210123","url":null,"abstract":"","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46208582","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 : 2022-06-06DOI: 10.54614/electrica.2022.210055
Qiao Ai, Yongjun Chen
{"title":"Research on NFTSMC Speed Control of Improved Terminal Attractor of Permanent Magnet Synchronous Motor Based on SM-MRAS","authors":"Qiao Ai, Yongjun Chen","doi":"10.54614/electrica.2022.210055","DOIUrl":"https://doi.org/10.54614/electrica.2022.210055","url":null,"abstract":"","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44034743","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 : 2022-06-06DOI: 10.54614/electrica.2022.210005
Ugur Alcan, F. Kaçar
With the internet brought along by technology, people have started to produce data in almost all their jobs. We create a huge data source with many activities we cannot count, such as sending messages on Whatsapp, sharing photos on Instagram, searching in Google, and sending electronic mails (email) and this process is repeated every single day. Such dense and different data also lead to information garbage. Analyzing this dump with traditional technologies has been another problem. Big companies that are interested to analyze this mass information, analyze the behavior of their customers, and determine their strategies according to the results obtained have come up with the concept of big data. Big data are the form of the data we obtain from different sources such as social media shares, sensor data, photo archives, call records obtained from Global System for Mobile Communications (GSM) operators, and search engine statistics, into a meaningful and processable form [1]. In this study, the effect of the coronavirus disease 2019 pandemic, which is an important problem of today, on Istanbul traffic has been examined by using the power of big data technologies. In this context, the hourly traffic index of the 2020 dataset which has openly been published by Istanbul Metropolitan Municipality [2], and the curfew time dataset is discussed. Apache Spark, a new generation data processing tool, has been used in the analysis of these datasets. With Apache Spark, first, general analysis of the Istanbul traffic index data for 2020 has been carried out, and then, the data obtained have been checked whether it is associated with the curfew time dataset and impact analysis has been performed. Elasticsearch has been utilized to keep the processed data, and Kibana has been used for data visualization. At the end of the study, machine learning applications on traffic density have been enhanced using Apache Spark's machine learning library, Application Programming Interface (API) with logistic regression, decision trees, random forest, gradient-boosted tree-based OneVsRest, and linear support vector machine-based OneVsRest methods.
{"title":"Impact Analysis of COVID-19 Pandemic on Istanbul Traffic with Big Data Tools","authors":"Ugur Alcan, F. Kaçar","doi":"10.54614/electrica.2022.210005","DOIUrl":"https://doi.org/10.54614/electrica.2022.210005","url":null,"abstract":"With the internet brought along by technology, people have started to produce data in almost all their jobs. We create a huge data source with many activities we cannot count, such as sending messages on Whatsapp, sharing photos on Instagram, searching in Google, and sending electronic mails (email) and this process is repeated every single day. Such dense and different data also lead to information garbage. Analyzing this dump with traditional technologies has been another problem. Big companies that are interested to analyze this mass information, analyze the behavior of their customers, and determine their strategies according to the results obtained have come up with the concept of big data. Big data are the form of the data we obtain from different sources such as social media shares, sensor data, photo archives, call records obtained from Global System for Mobile Communications (GSM) operators, and search engine statistics, into a meaningful and processable form [1]. In this study, the effect of the coronavirus disease 2019 pandemic, which is an important problem of today, on Istanbul traffic has been examined by using the power of big data technologies. In this context, the hourly traffic index of the 2020 dataset which has openly been published by Istanbul Metropolitan Municipality [2], and the curfew time dataset is discussed. Apache Spark, a new generation data processing tool, has been used in the analysis of these datasets. With Apache Spark, first, general analysis of the Istanbul traffic index data for 2020 has been carried out, and then, the data obtained have been checked whether it is associated with the curfew time dataset and impact analysis has been performed. Elasticsearch has been utilized to keep the processed data, and Kibana has been used for data visualization. At the end of the study, machine learning applications on traffic density have been enhanced using Apache Spark's machine learning library, Application Programming Interface (API) with logistic regression, decision trees, random forest, gradient-boosted tree-based OneVsRest, and linear support vector machine-based OneVsRest methods.","PeriodicalId":36781,"journal":{"name":"Electrica","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43411222","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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