Ibrahim Hamdan, Marwa M. M. Youssef, Omar Noureldeen
This study proposes the integration of a supercapacitor (SC) with the DC link of a three-phase four-wire active power filter (APF) by using an interfaced three-level bidirectional buck-boost converter controlled by the fuzzy control approach. APF is a flexible alternating current transmission system (FACTS) device that enhances power quality in the electrical network by reducing the load current harmonics and compensating the reactive power. Regulating the DC voltage of the APF’s DC link and absorbing fluctuations in compensated reactive power during disturbances are the major objectives of the integration of an SC circuit to APF. The studied model is a wind farm of Gabal El-Zeit with a total capacity of 580 MW, connected to unbalanced nonlinear loads. The Gabal El-Zeit wind farm is divided into three projects with a capacity of 240 MW, 220 MW, and 120 MW. The model is simulated by the MATLAB/SIMULINK program, and the effectiveness of the proposed methodology is proved by applying different types of faults such as single-line-to-ground, double-line-to-ground, and three-line-to-ground faults. In addition, an additional SC circuit with a two-level converter is connected to the generators coupled to the wind turbines to enhance the performance of the wind farm during disturbances. The results show that SC-integrated APF can reduce the harmonic distortion and compensate the reactive power for high or low inductive loads. Also, it can regulate the DC voltage and absorb the fluctuations in the reactive power during faults. Finally, the performance and the stability of the overall electric system are improved.
{"title":"A Proposed Supercapacitor Integrated with an Active Power Filter for Improving the Performance of a Wind Generation System under Nonlinear Unbalanced Loading and Faults","authors":"Ibrahim Hamdan, Marwa M. M. Youssef, Omar Noureldeen","doi":"10.1155/2023/2863528","DOIUrl":"https://doi.org/10.1155/2023/2863528","url":null,"abstract":"This study proposes the integration of a supercapacitor (SC) with the DC link of a three-phase four-wire active power filter (APF) by using an interfaced three-level bidirectional buck-boost converter controlled by the fuzzy control approach. APF is a flexible alternating current transmission system (FACTS) device that enhances power quality in the electrical network by reducing the load current harmonics and compensating the reactive power. Regulating the DC voltage of the APF’s DC link and absorbing fluctuations in compensated reactive power during disturbances are the major objectives of the integration of an SC circuit to APF. The studied model is a wind farm of Gabal El-Zeit with a total capacity of 580 MW, connected to unbalanced nonlinear loads. The Gabal El-Zeit wind farm is divided into three projects with a capacity of 240 MW, 220 MW, and 120 MW. The model is simulated by the MATLAB/SIMULINK program, and the effectiveness of the proposed methodology is proved by applying different types of faults such as single-line-to-ground, double-line-to-ground, and three-line-to-ground faults. In addition, an additional SC circuit with a two-level converter is connected to the generators coupled to the wind turbines to enhance the performance of the wind farm during disturbances. The results show that SC-integrated APF can reduce the harmonic distortion and compensate the reactive power for high or low inductive loads. Also, it can regulate the DC voltage and absorb the fluctuations in the reactive power during faults. Finally, the performance and the stability of the overall electric system are improved.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"119 1","pages":"2863528:1-2863528:17"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77475312","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}
YiZhe Zhang, Yunyi Zhang, Guohui Zhou, W. Zhang, Kangle Li, Quanqi Mu, W. He, Kai Tang
Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.
{"title":"A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule","authors":"YiZhe Zhang, Yunyi Zhang, Guohui Zhou, W. Zhang, Kangle Li, Quanqi Mu, W. He, Kai Tang","doi":"10.1155/2023/8987461","DOIUrl":"https://doi.org/10.1155/2023/8987461","url":null,"abstract":"Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"136 1","pages":"8987461:1-8987461:15"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86431033","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}
In order to solve the problem of low image identification accuracy of Trichagalma glabrosa insect gall pests in a complex natural environment, an image identification method of Trichagalma glabrosa insect gall pests based on YOLOv5s was designed and introduced in this study. The original images were preprocessed with the grayscale maximum method and different gradients of noise, which reduced the color difference interference with complex backgrounds and improved the image identification rate. A total of 6090 images of insect gall pests under opposite light, back light, and complex backgrounds were constructed, which were divided into a training set and a test set with a ratio of 7 : 3. The results showed that the precision, recall, and mean average precision of YOLOv5s were 94.35%, 95.42%, and 95.8%, respectively. YOLOv5s, YOLOv4, and Faster-RCNN were compared and analyzed under the same test conditions. The identification accuracy of YOLOv5s was higher than that of YOLOv4 and Faster-RCNN, and its model size was only 13.8 MB. It was considered that the designed YOLOv5s method could help accurately and quickly identify Trichagalma glabrosa insect gall pests with high identification accuracy and a small model capacity, which was more conducive to the migration application of the model, and provide a new method for the rapid identification of Trichagalma glabrosa insect gall pests in a complex natural environment.
{"title":"Identification Research of Trichagalma Glabrosa Insect Gall Pests Based on YOLOv5s","authors":"Tianpeng Zhang, Wei Wang","doi":"10.1155/2023/4011188","DOIUrl":"https://doi.org/10.1155/2023/4011188","url":null,"abstract":"In order to solve the problem of low image identification accuracy of Trichagalma glabrosa insect gall pests in a complex natural environment, an image identification method of Trichagalma glabrosa insect gall pests based on YOLOv5s was designed and introduced in this study. The original images were preprocessed with the grayscale maximum method and different gradients of noise, which reduced the color difference interference with complex backgrounds and improved the image identification rate. A total of 6090 images of insect gall pests under opposite light, back light, and complex backgrounds were constructed, which were divided into a training set and a test set with a ratio of 7 : 3. The results showed that the precision, recall, and mean average precision of YOLOv5s were 94.35%, 95.42%, and 95.8%, respectively. YOLOv5s, YOLOv4, and Faster-RCNN were compared and analyzed under the same test conditions. The identification accuracy of YOLOv5s was higher than that of YOLOv4 and Faster-RCNN, and its model size was only 13.8 MB. It was considered that the designed YOLOv5s method could help accurately and quickly identify Trichagalma glabrosa insect gall pests with high identification accuracy and a small model capacity, which was more conducive to the migration application of the model, and provide a new method for the rapid identification of Trichagalma glabrosa insect gall pests in a complex natural environment.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"180 1","pages":"4011188:1-4011188:7"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86227750","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}
Reliable operation of energy management systems, grid stability, and managing energy demand responses are becoming challenging because of the flickering nature of solar irradiance. Accurate forecasting of global solar irradiance, i.e., global horizontal irradiance (GHI), plays a significant role in energy policy-making and the energy market. This paper proposes a novel global solar irradiance forecasting model based on the ensemble recursive radial basis function neural networks (ERRBFNNs). The various atmospheric inputs based on the built ensemble recursive radial basis function neural networks make the network more stable and robust to climatic uncertainty. This paper statistically investigates the performance of novel feed-forward neural networks based on forecasting models with various hidden nodes for global solar irradiance forecasting applications. We validated the proposed ERRBFNN global solar irradiance forecasting model using real-time data sets. The simulation results confirm that the proposed ensemble recursive radial basis function neural network based on global solar irradiance forecasting improves the accuracy, generalization, and network stability. Furthermore, the proposed ERRBFNN lowers the forecasting error to the least compared to other state-of-the-art forecasting models.
{"title":"Statistical Analysis of Novel Ensemble Recursive Radial Basis Function Neural Network Performance on Global Solar Irradiance Forecasting","authors":"M. Madhiarasan, M. Louzazni, Brahim Belmahdi","doi":"10.1155/2023/2554355","DOIUrl":"https://doi.org/10.1155/2023/2554355","url":null,"abstract":"Reliable operation of energy management systems, grid stability, and managing energy demand responses are becoming challenging because of the flickering nature of solar irradiance. Accurate forecasting of global solar irradiance, i.e., global horizontal irradiance (GHI), plays a significant role in energy policy-making and the energy market. This paper proposes a novel global solar irradiance forecasting model based on the ensemble recursive radial basis function neural networks (ERRBFNNs). The various atmospheric inputs based on the built ensemble recursive radial basis function neural networks make the network more stable and robust to climatic uncertainty. This paper statistically investigates the performance of novel feed-forward neural networks based on forecasting models with various hidden nodes for global solar irradiance forecasting applications. We validated the proposed ERRBFNN global solar irradiance forecasting model using real-time data sets. The simulation results confirm that the proposed ensemble recursive radial basis function neural network based on global solar irradiance forecasting improves the accuracy, generalization, and network stability. Furthermore, the proposed ERRBFNN lowers the forecasting error to the least compared to other state-of-the-art forecasting models.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"35 1","pages":"2554355:1-2554355:10"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86865089","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}
Rajesh Singh, A. Gehlot, P. Kuchhal, S. Choudhury, S. Akram, Neeraj Priyadarshi, B. Khan
Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance’s applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances.
目前，由于人口的广泛增长，人们普遍担心电力短缺。因此，由于电力短缺，智能设备已经发展并获得了极大的关注，以减少家用电器的电力消耗。但是，它缺乏可以根据环境条件控制家电的通用遥控器。为了克服这些挑战，本研究提出了一种基于硬件的远程控制系统，该系统可以在自主和半自主模式下运行，根据环境条件控制家用电器。在自主模式下，接收器部分通过调光器改变器具的施加电压水平来调节环境条件下的参数。半自治中的参数由用户通过不同级别的远程控制进行监控。使用2.4 GHz射频调制解调器在远端和接收机之间建立无线个人网络WPAN (wireless personal network)通信。此外，接收器内置了Wi-Fi调制解调器，使基于互联网的移动应用程序能够操作设备。通过MATLAB分析，发现采用粒子群优化(PSO)方法的比例积分导数(PID)控制器是在适当环境条件下控制家电的较好方法。通过MATLAB研究得出，PSO-PID控制器与传统电器相比，加热器节能14.88%，排风机节能36.9%，灯泡节能37.49%。
{"title":"Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller","authors":"Rajesh Singh, A. Gehlot, P. Kuchhal, S. Choudhury, S. Akram, Neeraj Priyadarshi, B. Khan","doi":"10.1155/2023/9611321","DOIUrl":"https://doi.org/10.1155/2023/9611321","url":null,"abstract":"Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance’s applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"55 1","pages":"9611321:1-9611321:25"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81421119","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}
To address the diversity of user preferences and dynamic changes of interests in the personalized recommendation scenario, a personalized recommendation model based on the improved gated recurrent unit (GRU) network in a big data environment is proposed. First, in order to deal with outliers in sequence recommendation, context awareness sequence recommendation is introduced, and the dynamic changes of users’ interests are modeled by redefining the update gate and the reset gate of the GRU. Then, the duration information about how long users browse each item is processed and transformed to obtain the duration attention factor of each recommended item. And the duration attention factors and the item information are together used as the input of the proposed model for training and prediction. Finally, the auxiliary loss function is introduced to make up for the shortcomings of the traditional negative logarithmic likelihood function, and a super-parameter is applied to combine the auxiliary loss function with the negative logarithmic likelihood function so as to enhance the relationship between the interest representation and the accuracy of recommendation. Experiments show that the root mean square error (RMSE) of the proposed method in the Criteo dataset and MovieLens-1M dataset is 0.7257 and 0.7869, respectively, and the mean absolute error (MAE) is 0.5147 and 0.5893, respectively, which are better than those of the comparison methods. Therefore, the proposed method significantly outperforms the comparison methods in improving the accuracy of personalized recommendation in the system.
{"title":"Personalized Recommendation Model Based on Improved GRU Network in Big Data Environment","authors":"Hui Guo, Zheng Guo, Zhihong Liu","doi":"10.1155/2023/3162220","DOIUrl":"https://doi.org/10.1155/2023/3162220","url":null,"abstract":"To address the diversity of user preferences and dynamic changes of interests in the personalized recommendation scenario, a personalized recommendation model based on the improved gated recurrent unit (GRU) network in a big data environment is proposed. First, in order to deal with outliers in sequence recommendation, context awareness sequence recommendation is introduced, and the dynamic changes of users’ interests are modeled by redefining the update gate and the reset gate of the GRU. Then, the duration information about how long users browse each item is processed and transformed to obtain the duration attention factor of each recommended item. And the duration attention factors and the item information are together used as the input of the proposed model for training and prediction. Finally, the auxiliary loss function is introduced to make up for the shortcomings of the traditional negative logarithmic likelihood function, and a super-parameter is applied to combine the auxiliary loss function with the negative logarithmic likelihood function so as to enhance the relationship between the interest representation and the accuracy of recommendation. Experiments show that the root mean square error (RMSE) of the proposed method in the Criteo dataset and MovieLens-1M dataset is 0.7257 and 0.7869, respectively, and the mean absolute error (MAE) is 0.5147 and 0.5893, respectively, which are better than those of the comparison methods. Therefore, the proposed method significantly outperforms the comparison methods in improving the accuracy of personalized recommendation in the system.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"27 1","pages":"3162220:1-3162220:9"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83847649","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}
Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the “gain rate” optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms.
{"title":"Intrusion Detection-Data Security Protection Scheme Based on Particle Swarm-BP Network Algorithm in Cloud Computing Environment","authors":"Zhun Wang, Xue Chen","doi":"10.1155/2023/1128545","DOIUrl":"https://doi.org/10.1155/2023/1128545","url":null,"abstract":"Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the “gain rate” optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"101 1","pages":"1128545:1-1128545:10"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88969452","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}
Arindom Chakraborty, Monirul Islam, Fahim Shahriyar, S. Islam, Hasan U. Zaman, Mehedi Hasan
Smart home is a habitation that has been outfitted with technological solutions that are intended to provide people with services that are suited to their needs. The purpose of this article is to perform a systematic assessment of the latest smart home literature and to conduct a survey of research and development conducted in this field. In addition to presenting a complete picture of the current smart home system’s (SHS) development and characteristics, this paper provides a deep insight into latest hardware and trends. The research then moves on to a detailed discussion of some of the important services provided by the SHS and its advantages. The paper also statistically discusses the current and future research trends in the SHS, followed by a detailed portrayal of the difficulties and roadblocks in implementing them. The comprehensive overview of the SHS presented in this paper will help designers, researchers, funding agencies, and policymakers have a bird’s-eye view of the overall concept, attributes, technological aspects, and features of modern SHSs.
{"title":"Smart Home System: A Comprehensive Review","authors":"Arindom Chakraborty, Monirul Islam, Fahim Shahriyar, S. Islam, Hasan U. Zaman, Mehedi Hasan","doi":"10.1155/2023/7616683","DOIUrl":"https://doi.org/10.1155/2023/7616683","url":null,"abstract":"Smart home is a habitation that has been outfitted with technological solutions that are intended to provide people with services that are suited to their needs. The purpose of this article is to perform a systematic assessment of the latest smart home literature and to conduct a survey of research and development conducted in this field. In addition to presenting a complete picture of the current smart home system’s (SHS) development and characteristics, this paper provides a deep insight into latest hardware and trends. The research then moves on to a detailed discussion of some of the important services provided by the SHS and its advantages. The paper also statistically discusses the current and future research trends in the SHS, followed by a detailed portrayal of the difficulties and roadblocks in implementing them. The comprehensive overview of the SHS presented in this paper will help designers, researchers, funding agencies, and policymakers have a bird’s-eye view of the overall concept, attributes, technological aspects, and features of modern SHSs.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"42 1","pages":"7616683:1-7616683:30"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81825469","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}
B. Al-Nashy, B. T. S. Al-Mosawi, M. Oleiwi, A. Al-khursan
This work studies the thallium halogenide TlBr/BrCl quantum dot (QD) semiconductor structure and specifies its optical properties. This QD structure is poorly studied. High gain is obtained, with two peaks at 800 and 3000 n m . Doping is shown to increase the gain by one order. Then, TlBr QD semiconductor optical amplifier (SOA) characteristics are studied. High dB gain is shown mainly at the doped structure, which can be used in various inline applications.
本文研究了卤化铊TlBr/BrCl量子点(QD)的半导体结构，并对其光学性质进行了表征。对这种量子点结构的研究很少。获得了高增益，在800和3000 n m处有两个峰值。掺杂可以使增益增加一个数量级。然后，研究了TlBr QD半导体光放大器(SOA)的特性。高dB增益主要表现在掺杂结构上，可用于各种内联应用。
{"title":"Gain of TlBr/BrCl Quantum Dot Semiconductor Optical Amplifier","authors":"B. Al-Nashy, B. T. S. Al-Mosawi, M. Oleiwi, A. Al-khursan","doi":"10.1155/2023/1941232","DOIUrl":"https://doi.org/10.1155/2023/1941232","url":null,"abstract":"This work studies the thallium halogenide TlBr/BrCl quantum dot (QD) semiconductor structure and specifies its optical properties. This QD structure is poorly studied. High gain is obtained, with two peaks at \u0000 \u0000 800\u0000 \u0000 and \u0000 \u0000 3000\u0000 \u0000 n\u0000 m\u0000 \u0000 . Doping is shown to increase the gain by one order. Then, TlBr QD semiconductor optical amplifier (SOA) characteristics are studied. High dB gain is shown mainly at the doped structure, which can be used in various inline applications.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"100 1","pages":"1941232:1-1941232:7"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76368089","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}
A smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The “Eigen-trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle’s score, and the CH is selected based on the maximum vehicle’s score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay.
{"title":"EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET","authors":"M. Jabbar, H. Trabelsi","doi":"10.1155/2023/6327247","DOIUrl":"https://doi.org/10.1155/2023/6327247","url":null,"abstract":"A smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The “Eigen-trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle’s score, and the CH is selected based on the maximum vehicle’s score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":"16 1","pages":"6327247:1-6327247:15"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87981162","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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