K. Selvakumar, D. Selvabharathi, R. Palanisamy, T. M. Thamizh Thentral
The increasing concerns about greenhouse gas emissions have made it necessary to incorporate environmental constraints in the operation of power systems. The CO2 emission-constrained short-term unit commitment problem (CSCUCP) is a multiobjective optimization problem that involves minimizing both the cost of operation and the CO2 emissions. This paper proposes an integer-coded shuffled frog-leaping algorithm (SFLA) to minimize both total CO2 emissions and operating costs for the unit commitment problem (UCP) over a one-day scheduling period. The SFLA is inspired by the natural food-searching behavior of frogs. The proposed method aims to determine the optimal start-up and shut-down times for generating units to meet fluctuating loads while minimizing operating costs and CO2 emissions. The method takes into account fuel costs, start-up and shut-down costs, and maintenance costs while satisfying various constraints. The study uses the IEEE 39 bus with a 10-unit test system, and the results are related to conventional methods. The proposed method consistently produces lower CO2 emissions and total operating costs compared to the existing methods.
{"title":"CO2 Emission-Constrained Short-Term Unit Commitment Problem Using Shuffled Frog Leaping Algorithm","authors":"K. Selvakumar, D. Selvabharathi, R. Palanisamy, T. M. Thamizh Thentral","doi":"10.1155/2023/2336689","DOIUrl":"https://doi.org/10.1155/2023/2336689","url":null,"abstract":"The increasing concerns about greenhouse gas emissions have made it necessary to incorporate environmental constraints in the operation of power systems. The CO2 emission-constrained short-term unit commitment problem (CSCUCP) is a multiobjective optimization problem that involves minimizing both the cost of operation and the CO2 emissions. This paper proposes an integer-coded shuffled frog-leaping algorithm (SFLA) to minimize both total CO2 emissions and operating costs for the unit commitment problem (UCP) over a one-day scheduling period. The SFLA is inspired by the natural food-searching behavior of frogs. The proposed method aims to determine the optimal start-up and shut-down times for generating units to meet fluctuating loads while minimizing operating costs and CO2 emissions. The method takes into account fuel costs, start-up and shut-down costs, and maintenance costs while satisfying various constraints. The study uses the IEEE 39 bus with a 10-unit test system, and the results are related to conventional methods. The proposed method consistently produces lower CO2 emissions and total operating costs compared to the existing methods.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136019591","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}
N. Suresh Babu, B. Ravivarma, E. M. Elsayed, K. G. Sreekumar
Rank distance codes are known to be applicable in various applications such as distributed data storage, cryptography, space time coding, and mainly in network coding. Rank distance codes defined over finite fields have attracted considerable attention in recent years. However, in some scenarios where codes over finite fields are not sufficient, it is demonstrated that codes defined over the real number field are preferred. In this paper, we proposed a new class of rank distance codes over the real number field . The real array rank distance (RARD) codes we constructed here can be used for all the applications mentioned above whenever the code alphabet is the real field . From the class of RARD codes, we extract a subclass of equidistant constant rank codes which is applicable in network coding. Also, we determined an upper bound for the dimension of RARD codes leading the way to obtain some optimal RARD codes. Moreover, we established examples of some RARD codes and optimal RARD codes.
{"title":"Construction of a Class of Real Array Rank Distance Codes","authors":"N. Suresh Babu, B. Ravivarma, E. M. Elsayed, K. G. Sreekumar","doi":"10.1155/2023/9952813","DOIUrl":"https://doi.org/10.1155/2023/9952813","url":null,"abstract":"Rank distance codes are known to be applicable in various applications such as distributed data storage, cryptography, space time coding, and mainly in network coding. Rank distance codes defined over finite fields have attracted considerable attention in recent years. However, in some scenarios where codes over finite fields are not sufficient, it is demonstrated that codes defined over the real number field are preferred. In this paper, we proposed a new class of rank distance codes over the real number field <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"> <mi mathvariant=\"double-struck\">R</mi> </math> . The real array rank distance (RARD) codes we constructed here can be used for all the applications mentioned above whenever the code alphabet is the real field <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\"> <mi mathvariant=\"double-struck\">R</mi> </math> . From the class of RARD codes, we extract a subclass of equidistant constant rank codes which is applicable in network coding. Also, we determined an upper bound for the dimension of RARD codes leading the way to obtain some optimal RARD codes. Moreover, we established examples of some RARD codes and optimal RARD codes.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"27 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136232747","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}
Noise in a communication system degrades the signal level at the receiver, and as a result, the signal is not properly recovered or eliminated at the receiver side. To avoid this, it is necessary to modify the signal before transmission, which is achieved using channel coding. Channel coding provides an opportunity to recover the noisy signal at the receiver side. The low-density parity-check (LDPC) code is an example of a forward error correcting code. It offers near Shannon capacity approaching performance; however, there is a constraint regarding high-girth code design. When the low-girth LDPC code is decoded using conventional methods, an error floor can occur during iterative decoding. To address this issue, a neural network (NN)-based decoder is utilized to overcome the decoding problem associated with low-girth codes. In this work, a neural network-based decoder is developed to decode audio samples of both low- and high-girth LDPC codes. The neural network-based decoder demonstrates superior performance for low-girth codes in terms of bit error rate (BER), peak signal-to-noise-ratio (PSNR), and mean squared error (MSE) with just a single iteration. Audio samples sourced from the NOIZEUS corpus are employed to evaluate the designed neural network. Notably, when compared to a similar decoder, the decoder developed in this study exhibits an improved bit error rate for the same signal-to-noise ratio.
{"title":"Performance Evaluation of Conventional and Neural Network-Based Decoder for an Audio of Low-Girth LDPC Code","authors":"Dharmeshkumar Patel, Ninad Bhatt","doi":"10.1155/2023/1071142","DOIUrl":"https://doi.org/10.1155/2023/1071142","url":null,"abstract":"Noise in a communication system degrades the signal level at the receiver, and as a result, the signal is not properly recovered or eliminated at the receiver side. To avoid this, it is necessary to modify the signal before transmission, which is achieved using channel coding. Channel coding provides an opportunity to recover the noisy signal at the receiver side. The low-density parity-check (LDPC) code is an example of a forward error correcting code. It offers near Shannon capacity approaching performance; however, there is a constraint regarding high-girth code design. When the low-girth LDPC code is decoded using conventional methods, an error floor can occur during iterative decoding. To address this issue, a neural network (NN)-based decoder is utilized to overcome the decoding problem associated with low-girth codes. In this work, a neural network-based decoder is developed to decode audio samples of both low- and high-girth LDPC codes. The neural network-based decoder demonstrates superior performance for low-girth codes in terms of bit error rate (BER), peak signal-to-noise-ratio (PSNR), and mean squared error (MSE) with just a single iteration. Audio samples sourced from the NOIZEUS corpus are employed to evaluate the designed neural network. Notably, when compared to a similar decoder, the decoder developed in this study exhibits an improved bit error rate for the same signal-to-noise ratio.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136234555","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}
Samuel Ndegwa, Kumbirayi Nyachionjeka, Edwell T. Mharakurwa
Vertical handover management plays an essential role in wireless network technologies, mainly due to the rapid development of various radio access technologies (RATs) that require users to connect seamlessly from one RAT to another. However, in multiple RAT environments, vertical handover management encounters different challenges, including unnecessary handovers, handover failures, ping-pong handovers, and unsuitable access network selection. Essential in vertical handover management is maintaining the desired quality of service (QoS) by the mobile device user. The seamless movement of mobile device users as they run various applications depends on a well-performing vertical handover decision-making algorithm. This bears special significance in a heterogeneous network environment. This paper proposes a vertical handover algorithm that considers user preferences (i.e., a vertical handover algorithm that evaluates the application currently running on a user device). The main objective of the algorithm is to determine when it is necessary to perform the handover, depending on the applications running on the mobile device. The proposed algorithm utilizes a fuzzy logic system to assess whether the handover is necessary and a multiattribute decision-making (MADM) method to select the best available radio access network. A simulation scenario involving different applications at various mobile device velocities was developed. The results proved the algorithm’s effectiveness compared to some of the earlier proposed vertical handover algorithms. At velocities below 10 m/s and 30 m/s, the proposed algorithm had 0% and 15.02% unnecessary handovers, respectively, while the technique for order preference by similarity to ideal solution (TOPSIS) utility’s function-based algorithm obtained 12.38% and 23.24% at the same velocities, respectively. In addition, compared to TOPSIS, the obtained results of the proposed algorithm demonstrated a lower handover failure rate and ping-pong rate for a velocity span of 1–30 m/s for the considered user applications.
{"title":"User Preference-Based Heterogeneous Network Management System for Vertical Handover","authors":"Samuel Ndegwa, Kumbirayi Nyachionjeka, Edwell T. Mharakurwa","doi":"10.1155/2023/5551773","DOIUrl":"https://doi.org/10.1155/2023/5551773","url":null,"abstract":"Vertical handover management plays an essential role in wireless network technologies, mainly due to the rapid development of various radio access technologies (RATs) that require users to connect seamlessly from one RAT to another. However, in multiple RAT environments, vertical handover management encounters different challenges, including unnecessary handovers, handover failures, ping-pong handovers, and unsuitable access network selection. Essential in vertical handover management is maintaining the desired quality of service (QoS) by the mobile device user. The seamless movement of mobile device users as they run various applications depends on a well-performing vertical handover decision-making algorithm. This bears special significance in a heterogeneous network environment. This paper proposes a vertical handover algorithm that considers user preferences (i.e., a vertical handover algorithm that evaluates the application currently running on a user device). The main objective of the algorithm is to determine when it is necessary to perform the handover, depending on the applications running on the mobile device. The proposed algorithm utilizes a fuzzy logic system to assess whether the handover is necessary and a multiattribute decision-making (MADM) method to select the best available radio access network. A simulation scenario involving different applications at various mobile device velocities was developed. The results proved the algorithm’s effectiveness compared to some of the earlier proposed vertical handover algorithms. At velocities below 10 m/s and 30 m/s, the proposed algorithm had 0% and 15.02% unnecessary handovers, respectively, while the technique for order preference by similarity to ideal solution (TOPSIS) utility’s function-based algorithm obtained 12.38% and 23.24% at the same velocities, respectively. In addition, compared to TOPSIS, the obtained results of the proposed algorithm demonstrated a lower handover failure rate and ping-pong rate for a velocity span of 1–30 m/s for the considered user applications.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135567613","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":"Retracted: 3D Reconstruction and Intelligent Digital Conservation of Ancient Buildings Based on Laser Point Cloud Data","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9798476","DOIUrl":"https://doi.org/10.1155/2023/9798476","url":null,"abstract":"<jats:p />","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824818","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}
Yibeltal Abebaw, Rajeev K. Shakya, Demissie Jobir Gelmecha, Eshetu Tessema Ware
In multicell massive MIMO system, the maximum limit on area throughput can be achieved by improving spectral efficiency and cell density, as well as bandwidth. In order to evaluate the area throughput for such scenarios, the spectral efficiency (SE) that utilizes the linear zero forcing uplink combining scheme, can be modeled under the Rician fading channel and the BS in case of up-links, is responsible to estimate the channel. Different from existing work, the proposed model incorporates various estimators such as minimum mean square error (MMSE), element-wise minimum mean square error estimators under Rician fading. The multicell scenarios with uplink (UL) massive MIMO has been analyzed using the proposed model under different cases such as pilot reuse factor, coherence block length, different number of antennas, and different estimators. The simulation results and analysis are presented based on these parameters. It is found that the average summation of SE per cell can be improved by optimizing MMSE channel estimation using ZF UL combiner, installing multiple BS antennas, serving multiple number of UEs per cell, and using efficient pilot reuse factor. The MMSE and ZF uplink combining are found to be more suitable in improving SE as compared to MMSE-MR. For example, the uplink SE of MMSE channel estimator for pilot reuse factors, 1, 3, and 4, is calculated as 22.5 bit/s/Hz/cell, 22.3 bit/s/Hz/cell, and 21 bit/s/Hz/cell, respectively. The uplink SE for EW-MMSE channel estimator with pilot reuse factors, 1, 3, and 4, is calculated as 22.5 bit/s/Hz/cell, 22 bit/s/Hz/cell, and 22 bit/s/Hz/cell, respectively. For the uplink SE of LS channel estimators, it can be 17.9 bit/s/Hz/cell, 20.2 bit/s/Hz/cell, and 20 bit/s/Hz/cell with pilot reuse factors as f = 1, 3, and 4, respectively. So, for f = 3, the maximum calculated uplink SE for MMSE, EW-MMSE, and LS is 17.6 bit/s/Hz/cell, 17.8 bit/s/Hz/cell, and 13 bit/s/Hz/cell, respectively. It can be concluded that the improved performance is obtained by reducing the pilot contamination at a pilot reuse factor f = 3 with different values of SNR, coherence block length, number of UEs, and number of BS antennas. There is also trade-off between the pilot contamination mitigation and the larger SE. However, there is not much effect on coherence block as when it increases, then the SE increases as well.
{"title":"Spectral Efficiency Analysis for Uplink Multicell Massive MIMO Cellular Communication System under Fading Channels","authors":"Yibeltal Abebaw, Rajeev K. Shakya, Demissie Jobir Gelmecha, Eshetu Tessema Ware","doi":"10.1155/2023/6623938","DOIUrl":"https://doi.org/10.1155/2023/6623938","url":null,"abstract":"In multicell massive MIMO system, the maximum limit on area throughput can be achieved by improving spectral efficiency and cell density, as well as bandwidth. In order to evaluate the area throughput for such scenarios, the spectral efficiency (SE) that utilizes the linear zero forcing uplink combining scheme, can be modeled under the Rician fading channel and the BS in case of up-links, is responsible to estimate the channel. Different from existing work, the proposed model incorporates various estimators such as minimum mean square error (MMSE), element-wise minimum mean square error estimators under Rician fading. The multicell scenarios with uplink (UL) massive MIMO has been analyzed using the proposed model under different cases such as pilot reuse factor, coherence block length, different number of antennas, and different estimators. The simulation results and analysis are presented based on these parameters. It is found that the average summation of SE per cell can be improved by optimizing MMSE channel estimation using ZF UL combiner, installing multiple BS antennas, serving multiple number of UEs per cell, and using efficient pilot reuse factor. The MMSE and ZF uplink combining are found to be more suitable in improving SE as compared to MMSE-MR. For example, the uplink SE of MMSE channel estimator for pilot reuse factors, 1, 3, and 4, is calculated as 22.5 bit/s/Hz/cell, 22.3 bit/s/Hz/cell, and 21 bit/s/Hz/cell, respectively. The uplink SE for EW-MMSE channel estimator with pilot reuse factors, 1, 3, and 4, is calculated as 22.5 bit/s/Hz/cell, 22 bit/s/Hz/cell, and 22 bit/s/Hz/cell, respectively. For the uplink SE of LS channel estimators, it can be 17.9 bit/s/Hz/cell, 20.2 bit/s/Hz/cell, and 20 bit/s/Hz/cell with pilot reuse factors as f = 1, 3, and 4, respectively. So, for f = 3, the maximum calculated uplink SE for MMSE, EW-MMSE, and LS is 17.6 bit/s/Hz/cell, 17.8 bit/s/Hz/cell, and 13 bit/s/Hz/cell, respectively. It can be concluded that the improved performance is obtained by reducing the pilot contamination at a pilot reuse factor f = 3 with different values of SNR, coherence block length, number of UEs, and number of BS antennas. There is also trade-off between the pilot contamination mitigation and the larger SE. However, there is not much effect on coherence block as when it increases, then the SE increases as well.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136115415","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}
The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed.
{"title":"Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces","authors":"Rishi Gupta, Amit Kumar Gupta, Deepak Panwar, Ashish Jain, Partha Chakraborty","doi":"10.1155/2023/4284045","DOIUrl":"https://doi.org/10.1155/2023/4284045","url":null,"abstract":"The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135854288","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}
Rong Chang, Peng Xiao, Hongqiang Wan, Songlin Li, Chengjiang Zhou, Fei Li
In order to prevent the economic losses caused by large-scale power outages and the life safety losses caused by circuit failures, the main purpose of this paper is to improve the efficiency, accuracy, and reliability of transmission line defect detection, and the main innovation is to propose a transmission line defect detection method based on YOLOv7 and the multi-UAV collaboration platform. First, a novel multi-UAV collaboration platform is proposed, which improved the search range and detection efficiency for defect detection. Second, YOLOv7 is used as a detector for multi-UAV collaboration platform, and several improvements improved the efficiency of defect detection under complex backgrounds. Finally, a complete transmission line defect images dataset is constructed, and the introduction of several defect images such as insulator self-blast and cracked insulators avoids the problem of low application value of single defect detection. The results indicate that the proposed method not only enhances the detection range and efficiency but also improves the detection accuracy. Compared with YOLOv5-S, which has good detection performance, YOLOv7 improves accuracy by 1.2%, recall by 4.3%, and mAP by 4.1%, and YOLOv7-Tiny achieves the fastest speed 1.2 ms and the smallest size 11.7 Mb. Even if the images contain complex backgrounds and noises, a mAP of 0.886 can still be obtained. Therefore, the proposed method provides effective support for transmission line defect detection and has broad application scenarios and development prospects.
{"title":"A Transmission Line Defect Detection Method Based on YOLOv7 and Multi-UAV Collaboration Platform","authors":"Rong Chang, Peng Xiao, Hongqiang Wan, Songlin Li, Chengjiang Zhou, Fei Li","doi":"10.1155/2023/9943589","DOIUrl":"https://doi.org/10.1155/2023/9943589","url":null,"abstract":"In order to prevent the economic losses caused by large-scale power outages and the life safety losses caused by circuit failures, the main purpose of this paper is to improve the efficiency, accuracy, and reliability of transmission line defect detection, and the main innovation is to propose a transmission line defect detection method based on YOLOv7 and the multi-UAV collaboration platform. First, a novel multi-UAV collaboration platform is proposed, which improved the search range and detection efficiency for defect detection. Second, YOLOv7 is used as a detector for multi-UAV collaboration platform, and several improvements improved the efficiency of defect detection under complex backgrounds. Finally, a complete transmission line defect images dataset is constructed, and the introduction of several defect images such as insulator self-blast and cracked insulators avoids the problem of low application value of single defect detection. The results indicate that the proposed method not only enhances the detection range and efficiency but also improves the detection accuracy. Compared with YOLOv5-S, which has good detection performance, YOLOv7 improves accuracy by 1.2%, recall by 4.3%, and mAP by 4.1%, and YOLOv7-Tiny achieves the fastest speed 1.2 ms and the smallest size 11.7 Mb. Even if the images contain complex backgrounds and noises, a mAP of 0.886 can still be obtained. Therefore, the proposed method provides effective support for transmission line defect detection and has broad application scenarios and development prospects.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098072","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}
Lighting has emerged as a central concern in the domain of city planning and design in recent decades. Better lighting does more than just make cities safer and more secure; it also makes them more aesthetically pleasing and easier to live in. A single type of optical sensor is no longer sufficient to meet the needs of intelligent lighting for urban roads, and as such, there is a growing demand for cutting-edge control systems that can adapt to the dynamic lighting needs in urban environments. This paper’s goal is to create an intelligent urban lighting control system by integrating optical multisensor technology and the gray model (GM model). Programmable logic controller (PLC) serves as the system’s central processing unit, with light intensity sensors and color sensor-detecting devices placed strategically throughout each city and linked directly to the controller. Each road streetlight is equipped with a motion sensor detection device that is tasked with identifying the presence of vehicles and pedestrians within its field of view. Data fusion technology is utilized to process the environmental data gathered by optical multisensors, the collected data are then used to control and predict outcomes using the robust prediction capability of the GM model, and the result is a lighting control strategy that is both efficient and intelligent. In the end, the strategy presented in this paper is applied to improving the management of an industrial park lighting system’s energy consumption. The results of the evaluations show that the fresh method is successful in dimming, prediction, and control. This conclusively demonstrates the efficacy of the paper’s proposed design solution, which integrates optical multisensor technology with sophisticated control algorithms and data analysis to improve the quality of life in urban areas by boosting the efficiency and sustainability of the urban lighting system.
{"title":"Design of the Urban Lighting Control System Based on Optical Multisensor Technology and the GM Model","authors":"Weili Wu, Xiang Tang","doi":"10.1155/2023/8883914","DOIUrl":"https://doi.org/10.1155/2023/8883914","url":null,"abstract":"Lighting has emerged as a central concern in the domain of city planning and design in recent decades. Better lighting does more than just make cities safer and more secure; it also makes them more aesthetically pleasing and easier to live in. A single type of optical sensor is no longer sufficient to meet the needs of intelligent lighting for urban roads, and as such, there is a growing demand for cutting-edge control systems that can adapt to the dynamic lighting needs in urban environments. This paper’s goal is to create an intelligent urban lighting control system by integrating optical multisensor technology and the gray model (GM model). Programmable logic controller (PLC) serves as the system’s central processing unit, with light intensity sensors and color sensor-detecting devices placed strategically throughout each city and linked directly to the controller. Each road streetlight is equipped with a motion sensor detection device that is tasked with identifying the presence of vehicles and pedestrians within its field of view. Data fusion technology is utilized to process the environmental data gathered by optical multisensors, the collected data are then used to control and predict outcomes using the robust prediction capability of the GM model, and the result is a lighting control strategy that is both efficient and intelligent. In the end, the strategy presented in this paper is applied to improving the management of an industrial park lighting system’s energy consumption. The results of the evaluations show that the fresh method is successful in dimming, prediction, and control. This conclusively demonstrates the efficacy of the paper’s proposed design solution, which integrates optical multisensor technology with sophisticated control algorithms and data analysis to improve the quality of life in urban areas by boosting the efficiency and sustainability of the urban lighting system.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135044831","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 wireless communication, the channel function estimated commonly has errors due to the influence of noise, so traditional channel estimation methods cannot accurately estimate the real channel function. Aiming at this problem, we propose a channel estimation method that combines sounding reference signal (SRS) remapping with the deep-learning network BRD_ESRNet. BRD_ESRNet consists of image denoising using a deep convolutional neural network with batch renormalization (BRDNet) and an expanded superresolution convolutional neural network (ESRCNN). At the transmitter side, we first map the SRS into four-box structures, and then, the four-box structures are scattered distribution throughout the time-frequency resource block. At the receiver side, we first perform the modified least squares (LS) estimation based on the four-box structure and place the result into the top-left resource unit of the four box. Then, we perform linear interpolation for the whole resource block. Finally, we equate the estimated channel matrix to a low-resolution image containing noise and input it to BRD_ESRNet. Thus, we obtain data with high resolution and achieve the purpose of reducing the estimation error of the channel function. The experimental results show that the proposed method in this paper has a significant improvement in performance compared to the methods of Soltani et al. and Nithya et al. In this paper, the methods of Soltani et al. and Nithya et al. are referred to as methods 1 and 2, respectively.
在无线通信中，由于噪声的影响，估计的信道函数通常存在误差，传统的信道估计方法无法准确估计真实的信道函数。针对这一问题，我们提出了一种将探测参考信号(SRS)重映射与深度学习网络BRD_ESRNet相结合的信道估计方法。BRD_ESRNet由深度卷积神经网络(BRDNet)和扩展超分辨率卷积神经网络(ESRCNN)进行图像去噪。在发射端，我们首先将SRS映射成四盒结构，然后将四盒结构分散分布在整个时频资源块中。在接收端，我们首先基于四盒结构执行修正最小二乘(LS)估计，并将结果放入四盒的左上角资源单元中。然后，我们对整个资源块执行线性插值。最后，我们将估计的通道矩阵等同于含有噪声的低分辨率图像，并将其输入BRD_ESRNet。从而获得高分辨率的数据，达到减小信道函数估计误差的目的。实验结果表明，与Soltani et al.和Nithya et al.的方法相比，本文提出的方法在性能上有显著提高。本文将Soltani et al.和Nithya et al.的方法分别称为方法1和方法2。
{"title":"BRD_ESRNet and SRS-Based Channel Estimation","authors":"Rifei Yang, Guohua Yao, Zhuhua Hu","doi":"10.1155/2023/6660715","DOIUrl":"https://doi.org/10.1155/2023/6660715","url":null,"abstract":"In wireless communication, the channel function estimated commonly has errors due to the influence of noise, so traditional channel estimation methods cannot accurately estimate the real channel function. Aiming at this problem, we propose a channel estimation method that combines sounding reference signal (SRS) remapping with the deep-learning network BRD_ESRNet. BRD_ESRNet consists of image denoising using a deep convolutional neural network with batch renormalization (BRDNet) and an expanded superresolution convolutional neural network (ESRCNN). At the transmitter side, we first map the SRS into four-box structures, and then, the four-box structures are scattered distribution throughout the time-frequency resource block. At the receiver side, we first perform the modified least squares (LS) estimation based on the four-box structure and place the result into the top-left resource unit of the four box. Then, we perform linear interpolation for the whole resource block. Finally, we equate the estimated channel matrix to a low-resolution image containing noise and input it to BRD_ESRNet. Thus, we obtain data with high resolution and achieve the purpose of reducing the estimation error of the channel function. The experimental results show that the proposed method in this paper has a significant improvement in performance compared to the methods of Soltani et al. and Nithya et al. In this paper, the methods of Soltani et al. and Nithya et al. are referred to as methods 1 and 2, respectively.","PeriodicalId":46573,"journal":{"name":"Journal of Electrical and Computer Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135251939","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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