Pub Date : 2022-01-01DOI: 10.3934/electreng.2022009
O. Mahela, Pappu Ram Bheel, M. K. Bhaskar, B. Khan
This manuscript has introduced an algorithm based on current signals and frequency rate change (ROCOF) to identify islanding events. Current is analyzed by the use of Stockwell transform (ST) at 3.84 kHz sampling frequency (SF) and a median of absolute values of every column of output matrix (CSIRI) is computed. Rate of change of CSIRI (ROCOCSIRI) is computed. Proposed current based islanding recognition index (IRIC) is computed by multiplying ROCOF with CSIRI & ROCOCSIRI and a weight factor (WC). Threshold values THI1 & THI2 are selected 100 and 3000 for IRIC for identifying the Islanding condition. These are also effective to differentiate islanding conditions from non-islanding events which include both the faulty and operational events. Magnitude of IRIC is greater than 3000 for the faulty events and lower than 100 for operational events. For islanding events magnitude of IRIC falls in between the 100 and 3000. Algorithm is effective to identify and classify the events in three categories which are islanding events, faulty events and operational events effectively. Study is realized in MATLAB/Simulink scenario.
{"title":"Islanding detection in utility grid with renewable energy using rate of change of frequency and signal processing technique","authors":"O. Mahela, Pappu Ram Bheel, M. K. Bhaskar, B. Khan","doi":"10.3934/electreng.2022009","DOIUrl":"https://doi.org/10.3934/electreng.2022009","url":null,"abstract":"This manuscript has introduced an algorithm based on current signals and frequency rate change (ROCOF) to identify islanding events. Current is analyzed by the use of Stockwell transform (ST) at 3.84 kHz sampling frequency (SF) and a median of absolute values of every column of output matrix (CSIRI) is computed. Rate of change of CSIRI (ROCOCSIRI) is computed. Proposed current based islanding recognition index (IRIC) is computed by multiplying ROCOF with CSIRI & ROCOCSIRI and a weight factor (WC). Threshold values THI1 & THI2 are selected 100 and 3000 for IRIC for identifying the Islanding condition. These are also effective to differentiate islanding conditions from non-islanding events which include both the faulty and operational events. Magnitude of IRIC is greater than 3000 for the faulty events and lower than 100 for operational events. For islanding events magnitude of IRIC falls in between the 100 and 3000. Algorithm is effective to identify and classify the events in three categories which are islanding events, faulty events and operational events effectively. Study is realized in MATLAB/Simulink scenario.","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222286","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 : 2022-01-01DOI: 10.3934/electreng.2022013
S. Singh, Tripurari Sharan, Arvind P. Singh
This article aims to examine the |S11| parameter of a multiband Coplanar Waveguide (CPW)-fed antenna. The proposed square-shaped antenna-1 (Ant.1) and antenna-2 (Ant. 2) are primarily composed of three ground terminal stubs: Terminal-1 (T1), Terminal-2 (T2), and Terminal-3 (T3), all of which have an inverted L-shaped radiating patch. The proposed antennas' resonance frequencies (fr) can be adjusted by the electrical dimension and length of the stub resonators, the dielectric constant (εr) of substrate materials, and their appropriate thicknesses. It will have an impact on their return loss (|S11|), Impedance Bandwidth (IBW), radiation pattern, and antenna performance in terms of frequency characteristics, as demonstrated in this article. The proposed structure based on Flame-Retardant fiber glass epoxy (FR4) substrate covered a wideband frequency range from 1.5 to 3.2 GHz, (IBW = 1.7 GHz) and from 3.4 to 3.65 GHz (IBW = 0.25 GHz). The total IBW is 1.95 GHz, at S11 ≤ −10 dB with three resonance frequencies of values fr1 = 1.75, fr2 = 2.65, and fr3 = 3.50 GHz) for triple-band applications. The results are compared with the research work reported earlier. The proposed Ant.1 ensured, dual and triple band applications whereas the proposed Ant. 2 ensured dual, triple and quad bands applications with reasonable antennas' sizes similar to the earlier reported works. Furthermore, the design technique as well as the impacts of various substrate materials and multi-stub resonator lengths on the operating bands and resonance frequency are thoroughly explored and analyzed.
{"title":"Investigating the S-parameter (|S11|) of CPW-fed antenna using four different dielectric substrate materials for RF multiband applications","authors":"S. Singh, Tripurari Sharan, Arvind P. Singh","doi":"10.3934/electreng.2022013","DOIUrl":"https://doi.org/10.3934/electreng.2022013","url":null,"abstract":"<abstract> <p>This article aims to examine the |S<sub>11</sub>| parameter of a multiband Coplanar Waveguide (CPW)-fed antenna. The proposed square-shaped antenna-1 (Ant.1) and antenna-2 (Ant. 2) are primarily composed of three ground terminal stubs: Terminal-1 (T1), Terminal-2 (T2), and Terminal-3 (T3), all of which have an inverted L-shaped radiating patch. The proposed antennas' resonance frequencies (<italic>f<sub>r</sub></italic>) can be adjusted by the electrical dimension and length of the stub resonators, the dielectric constant (ε<italic><sub>r</sub></italic>) of substrate materials, and their appropriate thicknesses. It will have an impact on their return loss (|S<sub>11</sub>|), Impedance Bandwidth (IBW), radiation pattern, and antenna performance in terms of frequency characteristics, as demonstrated in this article. The proposed structure based on Flame-Retardant fiber glass epoxy (FR4) substrate covered a wideband frequency range from 1.5 to 3.2 GHz, (IBW = 1.7 GHz) and from 3.4 to 3.65 GHz (IBW = 0.25 GHz). The total IBW is 1.95 GHz, at S<sub>11</sub> ≤ −10 dB with three resonance frequencies of values <italic>f<sub>r1</sub></italic> = 1.75, <italic>f<sub>r2</sub></italic> = 2.65, and <italic>f<sub>r3</sub></italic> = 3.50 GHz) for triple-band applications. The results are compared with the research work reported earlier. The proposed Ant.1 ensured, dual and triple band applications whereas the proposed Ant. 2 ensured dual, triple and quad bands applications with reasonable antennas' sizes similar to the earlier reported works. Furthermore, the design technique as well as the impacts of various substrate materials and multi-stub resonator lengths on the operating bands and resonance frequency are thoroughly explored and analyzed.</p> </abstract>","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222393","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 : 2022-01-01DOI: 10.3934/electreng.2022021
A. Amer, Tamanna Siddiqu
Sarcasm means the opposite of what you desire to express, particularly to insult a person. Sarcasm detection in social networks SNs such as Twitter is a significant task as it has assisted in studying tweets using NLP. Many existing study-related methods have always focused only on the content-based on features in sarcastic words, leaving out the lexical-based features and context-based features knowledge in isolation. This shows a loss of the semantics of terms in a sarcastic expression. This study proposes an improved model to detect sarcasm from SNs. We used three feature set engineering: context-based on features set, Sarcastic based on features, and lexical based on features. Two Novel Algorithms for an effective model to detect sarcasm are divided into two stages. The first used two algorithms one with preprocessing, and the second algorithm with feature sets. To deal with data from SNs. We applied various supervised machine learning (ML) such as k-nearest neighbor classifier (KNN), na?ve Bayes (NB), support vector machine (SVM), and Random Forest (RF) classifiers with TF-IDF feature extraction representation data. To model evaluation metrics, evaluate sarcasm detection model performance in precision, accuracy, recall, and F1 score by 100%. We achieved higher results in Lexical features with KNN 89.19 % accuracy campers to other classifiers. Combining two feature sets (Sarcastic and Lexical) has shown slight improvement with the same classifier KNN; we achieved 90.00% accuracy. When combining three feature sets (Sarcastic, Lexical, and context), the accuracy is shown slight improvement. Also, the same classifier we achieved is a 90.51% KNN classifier. We perform the model differently to see the effect of three feature sets through the experiment individual, combining two feature sets and gradually combining three feature sets. When combining all features set together, achieve the best accuracy with the KNN classifier.
{"title":"A novel algorithm for sarcasm detection using supervised machine learning approach","authors":"A. Amer, Tamanna Siddiqu","doi":"10.3934/electreng.2022021","DOIUrl":"https://doi.org/10.3934/electreng.2022021","url":null,"abstract":"Sarcasm means the opposite of what you desire to express, particularly to insult a person. Sarcasm detection in social networks SNs such as Twitter is a significant task as it has assisted in studying tweets using NLP. Many existing study-related methods have always focused only on the content-based on features in sarcastic words, leaving out the lexical-based features and context-based features knowledge in isolation. This shows a loss of the semantics of terms in a sarcastic expression. This study proposes an improved model to detect sarcasm from SNs. We used three feature set engineering: context-based on features set, Sarcastic based on features, and lexical based on features. Two Novel Algorithms for an effective model to detect sarcasm are divided into two stages. The first used two algorithms one with preprocessing, and the second algorithm with feature sets. To deal with data from SNs. We applied various supervised machine learning (ML) such as k-nearest neighbor classifier (KNN), na?ve Bayes (NB), support vector machine (SVM), and Random Forest (RF) classifiers with TF-IDF feature extraction representation data. To model evaluation metrics, evaluate sarcasm detection model performance in precision, accuracy, recall, and F1 score by 100%. We achieved higher results in Lexical features with KNN 89.19 % accuracy campers to other classifiers. Combining two feature sets (Sarcastic and Lexical) has shown slight improvement with the same classifier KNN; we achieved 90.00% accuracy. When combining three feature sets (Sarcastic, Lexical, and context), the accuracy is shown slight improvement. Also, the same classifier we achieved is a 90.51% KNN classifier. We perform the model differently to see the effect of three feature sets through the experiment individual, combining two feature sets and gradually combining three feature sets. When combining all features set together, achieve the best accuracy with the KNN classifier.","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222576","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 : 2022-01-01DOI: 10.3934/electreng.2022022
K. Lakshmi, P. Panigrahi, R. Goli
In the last decade, research has been started due to accelerated growth in power demand has mainly concentrated on the large power production and quality of power. After the digital revolution, non-conventional energy sources, many state-of-art equipment, power electronics loads, reactive power compensating devices, sophisticated measuring devices, etc., entered the power industry. The reactive power compensating devices, connected electrical equipment, renewable energy sources can be anticipated/unanticipated action can cause considerable reactions may be failure issues to power grids. To deal with these challenges, the power sector crucially needs to design and implement new security systems to protect its systems. The Internet-of-Things (IoT) is treated as revolution technology after the invention of the digital machine and the internet. New developments in sensor devices with wireless technologies through embedded processors provide effective monitoring and different types of faults can be detected during electric power transmission. The wavelet (WT) is one of the mathematical tools to asses transient signals of different frequencies and provides crucial information in the form of detailed coefficients. Machine learning (ML) methods are recommended in the power systems community to simplify digital reform. ML and AI techniques can make effective and rapid decisions to improve the stability and safety of the power grid. This recommended approach can contribute critical information about symmetrical or asymmetrical faults through machine learning assessment of IoT supervised microgrid protection in the presence of SVC using the wavelet approach covers diversified types of faults combined with fault-inception-angles (FIA).
{"title":"Machine learning assessment of IoT managed microgrid protection in existence of SVC using wavelet methodology","authors":"K. Lakshmi, P. Panigrahi, R. Goli","doi":"10.3934/electreng.2022022","DOIUrl":"https://doi.org/10.3934/electreng.2022022","url":null,"abstract":"In the last decade, research has been started due to accelerated growth in power demand has mainly concentrated on the large power production and quality of power. After the digital revolution, non-conventional energy sources, many state-of-art equipment, power electronics loads, reactive power compensating devices, sophisticated measuring devices, etc., entered the power industry. The reactive power compensating devices, connected electrical equipment, renewable energy sources can be anticipated/unanticipated action can cause considerable reactions may be failure issues to power grids. To deal with these challenges, the power sector crucially needs to design and implement new security systems to protect its systems. The Internet-of-Things (IoT) is treated as revolution technology after the invention of the digital machine and the internet. New developments in sensor devices with wireless technologies through embedded processors provide effective monitoring and different types of faults can be detected during electric power transmission. The wavelet (WT) is one of the mathematical tools to asses transient signals of different frequencies and provides crucial information in the form of detailed coefficients. Machine learning (ML) methods are recommended in the power systems community to simplify digital reform. ML and AI techniques can make effective and rapid decisions to improve the stability and safety of the power grid. This recommended approach can contribute critical information about symmetrical or asymmetrical faults through machine learning assessment of IoT supervised microgrid protection in the presence of SVC using the wavelet approach covers diversified types of faults combined with fault-inception-angles (FIA).","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222589","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 : 2022-01-01DOI: 10.3934/electreng.2022015
Umesh Agarwal, Naveen Jain, M. Kumawat
Over the last decade, automated distribution networks have grown in importance since traditional distribution networks are insufficiently intelligent to meet the growing need for reliable electricity supplies. Because the distribution network is the least reliable and the sole link between the utility and its customers, it is critical to improve its reliability. The remote-controlled switch (RCS) is a viable choice for boosting system reliability. It shortens the interruption period, which also minimizes the expected interruption cost and the amount of energy not served. Using the greedy search algorithm, this research expands the current reliability evaluation technique to include RCSs in distribution networks. The optimal location and numbers of RCSs have been evaluated with compromised cost. This study simultaneously takes into account the effects of load growth on system reliability indices, the impact of age on equipment failure rates and the hidden failure rate of fuses. The Roy Billinton test system's distribution network connected at bus 2 and bus 5 has been used to test the effectiveness of the suggested approach. The outcomes demonstrate that effective RCS deployment improves the radial distribution network's reliability indices significantly.
{"title":"Reliability enhancement of distribution networks with remote-controlled switches considering load growth under the effects of hidden failures and component aging","authors":"Umesh Agarwal, Naveen Jain, M. Kumawat","doi":"10.3934/electreng.2022015","DOIUrl":"https://doi.org/10.3934/electreng.2022015","url":null,"abstract":"Over the last decade, automated distribution networks have grown in importance since traditional distribution networks are insufficiently intelligent to meet the growing need for reliable electricity supplies. Because the distribution network is the least reliable and the sole link between the utility and its customers, it is critical to improve its reliability. The remote-controlled switch (RCS) is a viable choice for boosting system reliability. It shortens the interruption period, which also minimizes the expected interruption cost and the amount of energy not served. Using the greedy search algorithm, this research expands the current reliability evaluation technique to include RCSs in distribution networks. The optimal location and numbers of RCSs have been evaluated with compromised cost. This study simultaneously takes into account the effects of load growth on system reliability indices, the impact of age on equipment failure rates and the hidden failure rate of fuses. The Roy Billinton test system's distribution network connected at bus 2 and bus 5 has been used to test the effectiveness of the suggested approach. The outcomes demonstrate that effective RCS deployment improves the radial distribution network's reliability indices significantly.","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222418","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 : 2022-01-01DOI: 10.3934/electreng.2022004
Abhijeet Kumar, Arpit Kumar
This work eases the feasibility of infrared thermometer application and reliability to introduce a novel design with upgraded applications & functions. The custom-designed compact device "Badge" structured comprises the operative methods through the electronic packages of an optimal level. The physical and social distance measured by the ToF (Time of Flight) infrared laser sensor within 1 m from the subject and the measuring equipment (MLX90632 SMD QFN and VL530LX ToF). When the distance is not maintained, or the physical distance condition is not met, the flashing LED, or vibration should trigger an indication (warning for physical distancing and alteration for pyrexia warning, respectively). Statistical analysis and simulation-based studies criticized the accuracy of ±0.5°F and relational model of the independent and dependent variable for this device with significant R2 = 0.99 and P < = 1; values with the lowest accuracy error of ±0.2°F and least residual sum of squares 0.01462 values. The portable, lightweight, and dynamic body temperature monitoring altered the application from static to continuous, complete structural design. This alternative provides the best technique to combine worn (personnel) medical devices with primary healthcare instruments to help body temperature measurements that are not contactable, fast, and accurate. It builds a way of processing through the protocol Covid-19.
这项工作简化了红外测温仪应用的可行性和可靠性，引入了一种具有升级应用和功能的新颖设计。定制设计的紧凑型装置“Badge”结构包括通过最佳水平的电子封装的操作方法。ToF (Time of Flight)红外激光传感器测量的距离被测者和测量设备(MLX90632 SMD QFN和VL530LX ToF)在1 m内的物理和社交距离。当未保持距离或未达到物理距离条件时，LED闪烁或振动应触发指示(分别为物理距离警告和发热警告变化)。基于统计分析和模拟的研究批评了该设备±0.5°F的准确性以及自变量和因变量的相关模型，R2 = 0.99, P < = 1;精度误差最小为±0.2°F，残差平方和最小为0.01462值。便携、轻便、动态的体温监测改变了从静态应用到连续、完整的结构设计。这种替代方案提供了将穿戴式(人员)医疗设备与初级保健仪器相结合的最佳技术，以帮助进行非接触式、快速和准确的体温测量。它通过Covid-19协议建立了一种处理方式。
{"title":"Contactless temperature and distance measuring device: A low-cost, novel infrared -based","authors":"Abhijeet Kumar, Arpit Kumar","doi":"10.3934/electreng.2022004","DOIUrl":"https://doi.org/10.3934/electreng.2022004","url":null,"abstract":"This work eases the feasibility of infrared thermometer application and reliability to introduce a novel design with upgraded applications & functions. The custom-designed compact device \"Badge\" structured comprises the operative methods through the electronic packages of an optimal level. The physical and social distance measured by the ToF (Time of Flight) infrared laser sensor within 1 m from the subject and the measuring equipment (MLX90632 SMD QFN and VL530LX ToF). When the distance is not maintained, or the physical distance condition is not met, the flashing LED, or vibration should trigger an indication (warning for physical distancing and alteration for pyrexia warning, respectively). Statistical analysis and simulation-based studies criticized the accuracy of ±0.5°F and relational model of the independent and dependent variable for this device with significant R2 = 0.99 and P < = 1; values with the lowest accuracy error of ±0.2°F and least residual sum of squares 0.01462 values. The portable, lightweight, and dynamic body temperature monitoring altered the application from static to continuous, complete structural design. This alternative provides the best technique to combine worn (personnel) medical devices with primary healthcare instruments to help body temperature measurements that are not contactable, fast, and accurate. It builds a way of processing through the protocol Covid-19.","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222607","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 : 2022-01-01DOI: 10.3934/electreng.2022005
J. Rajeshwari, M. Sughasiny
The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB).
{"title":"Dermatology disease prediction based on firefly optimization of ANFIS classifier","authors":"J. Rajeshwari, M. Sughasiny","doi":"10.3934/electreng.2022005","DOIUrl":"https://doi.org/10.3934/electreng.2022005","url":null,"abstract":"The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB).","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222158","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 : 2022-01-01DOI: 10.3934/electreng.2022012
K. Rao, B. K. Reddy, C. R. Reddy, K. Kumar, Jakka Yeshwanth Reddy
High precision oscillators became a significant call for both designer and testing engineers. Modern vibrators are being utilized in a variety of circuits, and accessibility to a wide range of frequencies is of the utmost importance in all research establishments. To produce various frequencies, utilizing a single gadget is very challenging for the designers. This article aims to provide the low frequency (RC) oscillator and high frequency (LC) oscillators with various output frequencies on a single chip. The use of both oscillators is necessary due to the fact that there are currently no such devices on the market, which makes it necessary to avoid using bulky recurrence generator hardware in order to facilitate rapid exploration and plausibility research. Here, a RC oscillator with high current accuracy and a LC oscillator with low force have been used to design a voltage controlled oscillator (VCO) IC by utilizing the Cadence 45 nm technology. This particular VCO IC is able to obtain two different frequencies with reasonable precision. Further, execution is completed by utilizing exclusive requirement inconsistent message format designing. This proposed work can be used at both audio frequency and radio frequency ranges from megahertz (MHz) to gigahertz (GHz).
{"title":"Implementation of on-chip high precision oscillators with RC and LC using digital compensation technique","authors":"K. Rao, B. K. Reddy, C. R. Reddy, K. Kumar, Jakka Yeshwanth Reddy","doi":"10.3934/electreng.2022012","DOIUrl":"https://doi.org/10.3934/electreng.2022012","url":null,"abstract":"High precision oscillators became a significant call for both designer and testing engineers. Modern vibrators are being utilized in a variety of circuits, and accessibility to a wide range of frequencies is of the utmost importance in all research establishments. To produce various frequencies, utilizing a single gadget is very challenging for the designers. This article aims to provide the low frequency (RC) oscillator and high frequency (LC) oscillators with various output frequencies on a single chip. The use of both oscillators is necessary due to the fact that there are currently no such devices on the market, which makes it necessary to avoid using bulky recurrence generator hardware in order to facilitate rapid exploration and plausibility research. Here, a RC oscillator with high current accuracy and a LC oscillator with low force have been used to design a voltage controlled oscillator (VCO) IC by utilizing the Cadence 45 nm technology. This particular VCO IC is able to obtain two different frequencies with reasonable precision. Further, execution is completed by utilizing exclusive requirement inconsistent message format designing. This proposed work can be used at both audio frequency and radio frequency ranges from megahertz (MHz) to gigahertz (GHz).","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222346","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 : 2022-01-01DOI: 10.3934/electreng.2023005
J. Rajeshwari, M. Sughasiny
Skin cancer is a pandemic disease now worldwide, and it is responsible for numerous deaths. Early phase detection is pre-eminent for controlling the spread of tumours throughout the body. However, existing algorithms for skin cancer severity detections still have some drawbacks, such as the analysis of skin lesions is not insignificant, slightly worse than that of dermatologists, and costly and time-consuming. Various machine learning algorithms have been used to detect the severity of the disease diagnosis. But it is more complex when detecting the disease. To overcome these issues, a modified Probabilistic Neural Network (MPNN) classifier has been proposed to determine the severity of skin cancer. The proposed method contains two phases such as training and testing the data. The collected features from the data of infected people are used as input to the modified PNN classifier in the current model. The neural network is also trained using Spider Monkey Optimization (SMO) approach. For analyzing the severity level, the classifier predicts four classes. The degree of skin cancer is determined depending on classifications. According to findings, the system achieved a 0.10% False Positive Rate (FPR), 0.03% error and 0.98% accuracy, while previous methods like KNN, NB, RF and SVM have accuracies of 0.90%, 0.70%, 0.803% and 0.86% correspondingly, which is lesser than the proposed approach.
{"title":"Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique","authors":"J. Rajeshwari, M. Sughasiny","doi":"10.3934/electreng.2023005","DOIUrl":"https://doi.org/10.3934/electreng.2023005","url":null,"abstract":"Skin cancer is a pandemic disease now worldwide, and it is responsible for numerous deaths. Early phase detection is pre-eminent for controlling the spread of tumours throughout the body. However, existing algorithms for skin cancer severity detections still have some drawbacks, such as the analysis of skin lesions is not insignificant, slightly worse than that of dermatologists, and costly and time-consuming. Various machine learning algorithms have been used to detect the severity of the disease diagnosis. But it is more complex when detecting the disease. To overcome these issues, a modified Probabilistic Neural Network (MPNN) classifier has been proposed to determine the severity of skin cancer. The proposed method contains two phases such as training and testing the data. The collected features from the data of infected people are used as input to the modified PNN classifier in the current model. The neural network is also trained using Spider Monkey Optimization (SMO) approach. For analyzing the severity level, the classifier predicts four classes. The degree of skin cancer is determined depending on classifications. According to findings, the system achieved a 0.10% False Positive Rate (FPR), 0.03% error and 0.98% accuracy, while previous methods like KNN, NB, RF and SVM have accuracies of 0.90%, 0.70%, 0.803% and 0.86% correspondingly, which is lesser than the proposed approach.","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222429","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 : 2022-01-01DOI: 10.3934/electreng.2023002
D. Swain, S. S. Biswal, P. Rout, P. K. Ray, R. Jena
The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.
{"title":"Optimal fractional sliding mode control for the frequency stability of a hybrid industrial microgrid","authors":"D. Swain, S. S. Biswal, P. Rout, P. K. Ray, R. Jena","doi":"10.3934/electreng.2023002","DOIUrl":"https://doi.org/10.3934/electreng.2023002","url":null,"abstract":"The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.","PeriodicalId":36329,"journal":{"name":"AIMS Electronics and Electrical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70222319","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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