Pub Date : 2022-08-30DOI: 10.1080/21642583.2022.2112317
Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang
To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate ( ) and the rejection rate ( ) are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.
{"title":"Study on egg sorting model based on visible-near infrared spectroscopy","authors":"Xiaoping Han, Yan-Hong Liu, Xuyuan Zhang, Zhiyong Zhang, Hua Yang","doi":"10.1080/21642583.2022.2112317","DOIUrl":"https://doi.org/10.1080/21642583.2022.2112317","url":null,"abstract":"To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate ( ) and the rejection rate ( ) are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44140736","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 : 2022-08-19DOI: 10.1080/21642583.2022.2110541
C. Liang, Yanwen Li, Yan-Hong Liu, Pengchen Wen, Hua Yang
ABSTRACT In this paper, the segment and weight prediction problems are investigated for ear of grape based on deep learning technologies. The image datum is collected from ZaoHeiBao grape in a greenhouse by camera. The grape ear target segmentation model is constructed by cross combining three backbone networks (ResNet18, ResNet50, and ResNet101) and four deep learning semantic segmentation networks (SFNet, GCNet, EMANet, and Deeplabv3). The experimental results show that for the SFNet-ResNet18 model, whose structural size is 52.68MB, the mean Intersection over Union (mIoU) is , the mean Pixel Accuracy (mPA) is , and the average segmentation speed of the image ( ) is 0.217s. Therefore, the performance of the SFNet-ResNet18 model outperforms other combined network models and is selected to segment grape ears. Furthermore, on the basis of the segmentation results of grape ears by using the SFNet-ResNet18 model, the grape ear weight is predicted by adopting fractional regression model. The value of is 0.8903, which means that the selected model could accurately predict the weight of grape ears. The proposed method can not only segment the grape ears and accurately predict the weight of the grape ears, but also provide theoretical and technical support for grape yield prediction.
{"title":"Segmentation and weight prediction of grape ear based on SFNet-ResNet18","authors":"C. Liang, Yanwen Li, Yan-Hong Liu, Pengchen Wen, Hua Yang","doi":"10.1080/21642583.2022.2110541","DOIUrl":"https://doi.org/10.1080/21642583.2022.2110541","url":null,"abstract":"ABSTRACT In this paper, the segment and weight prediction problems are investigated for ear of grape based on deep learning technologies. The image datum is collected from ZaoHeiBao grape in a greenhouse by camera. The grape ear target segmentation model is constructed by cross combining three backbone networks (ResNet18, ResNet50, and ResNet101) and four deep learning semantic segmentation networks (SFNet, GCNet, EMANet, and Deeplabv3). The experimental results show that for the SFNet-ResNet18 model, whose structural size is 52.68MB, the mean Intersection over Union (mIoU) is , the mean Pixel Accuracy (mPA) is , and the average segmentation speed of the image ( ) is 0.217s. Therefore, the performance of the SFNet-ResNet18 model outperforms other combined network models and is selected to segment grape ears. Furthermore, on the basis of the segmentation results of grape ears by using the SFNet-ResNet18 model, the grape ear weight is predicted by adopting fractional regression model. The value of is 0.8903, which means that the selected model could accurately predict the weight of grape ears. The proposed method can not only segment the grape ears and accurately predict the weight of the grape ears, but also provide theoretical and technical support for grape yield prediction.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44151483","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 : 2022-08-06DOI: 10.1080/21642583.2022.2102550
Paweł Moszczyński, Maciej Moszczyński, Patrycja Bryczek-Wróbel, M. Kapalka, Tomasz Drozdowski, Jarosław Wróbel
This article presents the concept of improving the security of information systems intended for storing knowledge in organizations. It is designed to protect knowledge from leakage, theft or destruction, but without denying access to its resources to employees. It is possible thanks to the introduction of the so-called security and sharing groups (S&S groups) that have two attributes at the same time – the level of knowledge security and the level of knowledge sharing. The proposed concept also assumes assigning knowledge categories to data and enabling the organization to manage the allocation of operations on data from individual knowledge categories to S&S groups. As a result, the system enables the protection of the most valuable knowledge resources, without restricting access to its other categories. The developed concept makes it easier to find a compromise between the strength of security and the free flow of knowledge at the time of system implementation. In addition, the dynamic ability to choose the level of security and the level of access to knowledge in the system allows adaptation to changes taking place in the environment of the organization and increases its resilience.
{"title":"The concept of improving the security of IT systems supporting the storage of knowledge in organizations","authors":"Paweł Moszczyński, Maciej Moszczyński, Patrycja Bryczek-Wróbel, M. Kapalka, Tomasz Drozdowski, Jarosław Wróbel","doi":"10.1080/21642583.2022.2102550","DOIUrl":"https://doi.org/10.1080/21642583.2022.2102550","url":null,"abstract":"This article presents the concept of improving the security of information systems intended for storing knowledge in organizations. It is designed to protect knowledge from leakage, theft or destruction, but without denying access to its resources to employees. It is possible thanks to the introduction of the so-called security and sharing groups (S&S groups) that have two attributes at the same time – the level of knowledge security and the level of knowledge sharing. The proposed concept also assumes assigning knowledge categories to data and enabling the organization to manage the allocation of operations on data from individual knowledge categories to S&S groups. As a result, the system enables the protection of the most valuable knowledge resources, without restricting access to its other categories. The developed concept makes it easier to find a compromise between the strength of security and the free flow of knowledge at the time of system implementation. In addition, the dynamic ability to choose the level of security and the level of access to knowledge in the system allows adaptation to changes taking place in the environment of the organization and increases its resilience.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42301054","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 : 2022-07-27DOI: 10.1080/21642583.2022.2102551
Yujing Shi, Lulu Yao, Shanqiang Li
In this paper, the problem of the adaptive synchronization control is studied for a class of stochastic complex networks with unknown nonlinear coupling strength and derivative coupling. First, in order to deal with the unknown nonlinear coupling strength, Takagi–Sugeno (T–S) fuzzy method is used to transform the network model into a T–S fuzzy complex network model. Then,a fuzzy adaptive controller and the corresponding adaptive parameter update rate are designed. Subsequently, a new Lyapunov function is constructed, which is related to the derivative coupling. By employing the stochastic analysis technique and Lyapunov stability theory, a sufficient condition is given for exponential stabilization in mean square of the synchronization error system. Finally, the effectiveness of the obtained theoretical results is verified through a simulation.
{"title":"Adaptive synchronization control for stochastic complex networks with derivative coupling","authors":"Yujing Shi, Lulu Yao, Shanqiang Li","doi":"10.1080/21642583.2022.2102551","DOIUrl":"https://doi.org/10.1080/21642583.2022.2102551","url":null,"abstract":"In this paper, the problem of the adaptive synchronization control is studied for a class of stochastic complex networks with unknown nonlinear coupling strength and derivative coupling. First, in order to deal with the unknown nonlinear coupling strength, Takagi–Sugeno (T–S) fuzzy method is used to transform the network model into a T–S fuzzy complex network model. Then,a fuzzy adaptive controller and the corresponding adaptive parameter update rate are designed. Subsequently, a new Lyapunov function is constructed, which is related to the derivative coupling. By employing the stochastic analysis technique and Lyapunov stability theory, a sufficient condition is given for exponential stabilization in mean square of the synchronization error system. Finally, the effectiveness of the obtained theoretical results is verified through a simulation.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48150996","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 : 2022-07-26DOI: 10.1080/21642583.2022.2102552
Hualong Du, Qiuyue Cui, Peng Liu, Xinyu Ma, He Wang
To reduce the effect of non-linearity in air gap control in active magnetic bearings (AMB). The PID controller for the AMB is proposed in this study, which is optimized with a reformative artificial bee colony (RABC) algorithm. The RABC algorithm balances the exploitation and exploration capabilities of the ABC algorithm by introducing globally optimal solutions and improved food source probabilities. Simulation with six benchmark functions validates the proposed algorithm, and the results reveal that the RABC algorithm has higher search accuracy and faster search speed than previous ABC algorithm versions. The experimental results show that RABC-PID outperforms the other four approaches and has greater robustness when compared to traditional PID, PSO-PID, DE-PID, and GA-PID. Meanwhile, the RABC-PID controller makes the AMB system more stable.
{"title":"PID controller enhanced with artificial bee colony algorithm for active magnetic bearing","authors":"Hualong Du, Qiuyue Cui, Peng Liu, Xinyu Ma, He Wang","doi":"10.1080/21642583.2022.2102552","DOIUrl":"https://doi.org/10.1080/21642583.2022.2102552","url":null,"abstract":"To reduce the effect of non-linearity in air gap control in active magnetic bearings (AMB). The PID controller for the AMB is proposed in this study, which is optimized with a reformative artificial bee colony (RABC) algorithm. The RABC algorithm balances the exploitation and exploration capabilities of the ABC algorithm by introducing globally optimal solutions and improved food source probabilities. Simulation with six benchmark functions validates the proposed algorithm, and the results reveal that the RABC algorithm has higher search accuracy and faster search speed than previous ABC algorithm versions. The experimental results show that RABC-PID outperforms the other four approaches and has greater robustness when compared to traditional PID, PSO-PID, DE-PID, and GA-PID. Meanwhile, the RABC-PID controller makes the AMB system more stable.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44923081","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}
Wireless sensor network (WSN) coverage problem is to think about how to maximize the network coverage to obtain reliable monitoring and tracking services with guaranteed quality of service. In this paper, a simplified slime mould algorithm (SSMA) for solving the WSN coverage problem is proposed. In SSMA, we mainly conducted 13 groups of WSNs coverage optimization experiments and compared them with six well-known meta-heuristic optimization algorithms. The experimental results and Wilcoxon rank-sum test show that the proposed SSMA is generally competitive, outstanding performance and effectiveness. We proposed SSMA algorithm could be helpful to effectively control the network nodes energy, improve the perceived quality of services and extend the network survival time.
{"title":"SSMA: simplified slime mould algorithm for optimization wireless sensor network coverage problem","authors":"Yuanye Wei, Xiuxi Wei, Huajuan Huang, Jian Bi, Yongquan Zhou, Yanlian Du","doi":"10.1080/21642583.2022.2084650","DOIUrl":"https://doi.org/10.1080/21642583.2022.2084650","url":null,"abstract":"Wireless sensor network (WSN) coverage problem is to think about how to maximize the network coverage to obtain reliable monitoring and tracking services with guaranteed quality of service. In this paper, a simplified slime mould algorithm (SSMA) for solving the WSN coverage problem is proposed. In SSMA, we mainly conducted 13 groups of WSNs coverage optimization experiments and compared them with six well-known meta-heuristic optimization algorithms. The experimental results and Wilcoxon rank-sum test show that the proposed SSMA is generally competitive, outstanding performance and effectiveness. We proposed SSMA algorithm could be helpful to effectively control the network nodes energy, improve the perceived quality of services and extend the network survival time.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48667602","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 : 2022-07-06DOI: 10.1080/21642583.2022.2096149
Zhongqi Miao, Wenxuan Huang
Aiming at the portfolio problem of gold and bitcoin with a given linear trading commission, this paper puts forward the stage implementation forecast and optimal portfolio model. In the aspect of data prediction, SMA is used to predict the initial data, LSTM is used to predict the price trend of long-term data, and daily updated real-time price data is predicted. Considering the risk aversion of investors, the heuristic algorithm is used to solve the daily trading strategy of maximizing utility from September 12th, 2016 to September 12th, 2021. The simulation analysis of the sliding window shows that the algorithm can realize reasonable prediction, which verifies the effectiveness of the algorithm.
{"title":"An optimal portfolio method based on real time prediction of gold and bitcoin prices","authors":"Zhongqi Miao, Wenxuan Huang","doi":"10.1080/21642583.2022.2096149","DOIUrl":"https://doi.org/10.1080/21642583.2022.2096149","url":null,"abstract":"Aiming at the portfolio problem of gold and bitcoin with a given linear trading commission, this paper puts forward the stage implementation forecast and optimal portfolio model. In the aspect of data prediction, SMA is used to predict the initial data, LSTM is used to predict the price trend of long-term data, and daily updated real-time price data is predicted. Considering the risk aversion of investors, the heuristic algorithm is used to solve the daily trading strategy of maximizing utility from September 12th, 2016 to September 12th, 2021. The simulation analysis of the sliding window shows that the algorithm can realize reasonable prediction, which verifies the effectiveness of the algorithm.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42402406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.1080/21642583.2022.2092782
H. Tran, T. Nguyen, T. V. Huynh, Nhiem Quoc Tran
Recent innovations in Light-emitting diode (LED) technology and Internet of Things applications have promoted the development of visible light communication and localization applications. LED-based indoor positioning application has been a potential topic attracting the attention of many researchers because this positioning technique provides high accuracy, low cost, simple operation, and medium complexity. This paper focuses on analyzing the positioning quality with different LED layout structures. Furthermore, we consider the influence of noise in these models through the ensemble learning algorithm. We also combine the ensemble learning method with the trilateration algorithm in the proposed solution. The numerical simulation results show that the proposed solution respectively achieved a positioning accuracy of 0.023, 0.011, and 0.009 m when we considered the negative effect of all noises in 3 distinct layouts: 3 LEDs, 4LEDs, and 5 LEDs.
{"title":"Improving accuracy of indoor localization system using ensemble learning","authors":"H. Tran, T. Nguyen, T. V. Huynh, Nhiem Quoc Tran","doi":"10.1080/21642583.2022.2092782","DOIUrl":"https://doi.org/10.1080/21642583.2022.2092782","url":null,"abstract":"Recent innovations in Light-emitting diode (LED) technology and Internet of Things applications have promoted the development of visible light communication and localization applications. LED-based indoor positioning application has been a potential topic attracting the attention of many researchers because this positioning technique provides high accuracy, low cost, simple operation, and medium complexity. This paper focuses on analyzing the positioning quality with different LED layout structures. Furthermore, we consider the influence of noise in these models through the ensemble learning algorithm. We also combine the ensemble learning method with the trilateration algorithm in the proposed solution. The numerical simulation results show that the proposed solution respectively achieved a positioning accuracy of 0.023, 0.011, and 0.009 m when we considered the negative effect of all noises in 3 distinct layouts: 3 LEDs, 4LEDs, and 5 LEDs.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42242297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-23DOI: 10.1080/21642583.2022.2089930
S. Dobrišek, Ziga Golob, Jerneja Žganec Gros
Finite-state transducers have been proven to yield compact representations of pronunciation dictionaries used for grapheme-to-phoneme conversion in speech engines running on low-resource embedded platforms. However, for highly inflected languages even more efficient language resource reduction methods are needed. In the paper, we demonstrate that the size of finite-state transducers tends to decrease when the number of word forms in the modelled pronunciation dictionary reaches a certain threshold. Motivated by this finding, we propose and evaluate a new type of finite-state transducers, called ‘finite-state super transducers’, which allow for the representation of pronunciation dictionaries by a smaller number of states and transitions, thereby significantly reducing the size of the language resource representation in comparison to minimal deterministic final-state transducers by up to 25%. Further, we demonstrate that finite-state super transducers exhibit a generalization capability as they may accept and thereby phonetically transform even inflected word forms that had not been initially represented in the original pronunciation dictionary used for building the finite-state super transducer. This method is suitable for speech engines operating on platforms at the edge of an AI system with restricted memory capabilities and processing power, where efficient speech processing methods based on compact language resources must be implemented.
{"title":"Finite-state super transducers for compact language resource representation in edge voice-AI","authors":"S. Dobrišek, Ziga Golob, Jerneja Žganec Gros","doi":"10.1080/21642583.2022.2089930","DOIUrl":"https://doi.org/10.1080/21642583.2022.2089930","url":null,"abstract":"Finite-state transducers have been proven to yield compact representations of pronunciation dictionaries used for grapheme-to-phoneme conversion in speech engines running on low-resource embedded platforms. However, for highly inflected languages even more efficient language resource reduction methods are needed. In the paper, we demonstrate that the size of finite-state transducers tends to decrease when the number of word forms in the modelled pronunciation dictionary reaches a certain threshold. Motivated by this finding, we propose and evaluate a new type of finite-state transducers, called ‘finite-state super transducers’, which allow for the representation of pronunciation dictionaries by a smaller number of states and transitions, thereby significantly reducing the size of the language resource representation in comparison to minimal deterministic final-state transducers by up to 25%. Further, we demonstrate that finite-state super transducers exhibit a generalization capability as they may accept and thereby phonetically transform even inflected word forms that had not been initially represented in the original pronunciation dictionary used for building the finite-state super transducer. This method is suitable for speech engines operating on platforms at the edge of an AI system with restricted memory capabilities and processing power, where efficient speech processing methods based on compact language resources must be implemented.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48941475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-23DOI: 10.1080/21642583.2022.2087786
Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li
Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.
{"title":"Surface microseismic data denoising based on sparse autoencoder and Kalman filter","authors":"Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li","doi":"10.1080/21642583.2022.2087786","DOIUrl":"https://doi.org/10.1080/21642583.2022.2087786","url":null,"abstract":"Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44308083","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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