Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046561
Chenxi Jiang, Yan Yang, Baoping Han
This paper describes the design and implementation of smart classroom control system based on Android in detail. The hardware part of the project is programmed with Keil software; the software part is developed based on Android Studio integrated development environment, using Bluetooth communication protocol to communicate with hardware. You can use the Android Application Control Panel to view indoor temperature values and light intensity. Can control the projector curtain rise and fall, front and rear window curtain rise and fall, air conditioning on and off, front and rear LED lights on and off. The light can be adjusted intelligently according to the value of light intensity.
{"title":"Design and Implementation of Android-based Smart Classroom Control System","authors":"Chenxi Jiang, Yan Yang, Baoping Han","doi":"10.1109/ICARCE55724.2022.10046561","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046561","url":null,"abstract":"This paper describes the design and implementation of smart classroom control system based on Android in detail. The hardware part of the project is programmed with Keil software; the software part is developed based on Android Studio integrated development environment, using Bluetooth communication protocol to communicate with hardware. You can use the Android Application Control Panel to view indoor temperature values and light intensity. Can control the projector curtain rise and fall, front and rear window curtain rise and fall, air conditioning on and off, front and rear LED lights on and off. The light can be adjusted intelligently according to the value of light intensity.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129615213","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046475
G. Shi, Peng Hu, Jinzhong Chen, Chunyu Li, Hanquan Zhou, Yilai Ma
As an important national energy infrastructure, the oil and gas pipeline is known as the “lifeline project”. Magnetic flux leakage (MFL) testing is currently the most widely used inline inspection method for oil and gas steel pipelines, which can realize the identification, quantification and positioning of pipeline metal loss and other defects. MFL testing is equivalent testing. Eliminating noise in MFL signal plays a vital role in correctly extracting information from signal and realizing correct defect identification. In this paper, a wavelet de-noising method of MFL signal based on alternating coefficient is proposed. The method uses signal-to-noise ratio (SNR), root mean square error (RMSE), smoothness, for comprehensive evaluation. Combined with the time consumption, the appropriate wavelet denoising parameters are selected. The application of engineering inspection data shows that the method has a good application effect.
{"title":"Wavelet De-noising Method Analysis of Pipeline Magnetic Flux Leakage In-line Inspection Based on Coefficient of Variation","authors":"G. Shi, Peng Hu, Jinzhong Chen, Chunyu Li, Hanquan Zhou, Yilai Ma","doi":"10.1109/ICARCE55724.2022.10046475","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046475","url":null,"abstract":"As an important national energy infrastructure, the oil and gas pipeline is known as the “lifeline project”. Magnetic flux leakage (MFL) testing is currently the most widely used inline inspection method for oil and gas steel pipelines, which can realize the identification, quantification and positioning of pipeline metal loss and other defects. MFL testing is equivalent testing. Eliminating noise in MFL signal plays a vital role in correctly extracting information from signal and realizing correct defect identification. In this paper, a wavelet de-noising method of MFL signal based on alternating coefficient is proposed. The method uses signal-to-noise ratio (SNR), root mean square error (RMSE), smoothness, for comprehensive evaluation. Combined with the time consumption, the appropriate wavelet denoising parameters are selected. The application of engineering inspection data shows that the method has a good application effect.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116350278","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046437
Qiang He, Kangli Xia, Hui Pan
Because of aging and very low birthrate, agricultural labor force has become seriously insufficient. In order to solve this problem, researchers have developed a series of agricultural robots for different purposes, including fruit picking robots. For fruit picking robots, detection and recognition of fruits is an important task. Here a fruit recognition technique based on the statistical characteristics of HSV color was developed. First, the RGB color fruit images were converted into HSV color. Then the hue distribution of HSV color of fruit is approximated with a Laplace distribution. Further, this Laplace distribution can be adopted as the characteristic description of this fruit. The fruit was segmented out of the input image. If the segmented fruit image falls in some Laplace distribution with 90% confidence interval, then input fruit belongs to this special fruit. In practice, the Mahalanobis distance (MD) corresponding to the 90% confidence interval of the Laplace distribution for each fruit class was set as the reference evaluation. If the input fruit data has a smaller Mahalanobis distance than the reference evaluation, the input belongs to this type fruit. The experimental results have shown the good performance for this fruit recognition technique on different kinds of fruits.
{"title":"Fruit Recognition Using Color Statistics","authors":"Qiang He, Kangli Xia, Hui Pan","doi":"10.1109/ICARCE55724.2022.10046437","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046437","url":null,"abstract":"Because of aging and very low birthrate, agricultural labor force has become seriously insufficient. In order to solve this problem, researchers have developed a series of agricultural robots for different purposes, including fruit picking robots. For fruit picking robots, detection and recognition of fruits is an important task. Here a fruit recognition technique based on the statistical characteristics of HSV color was developed. First, the RGB color fruit images were converted into HSV color. Then the hue distribution of HSV color of fruit is approximated with a Laplace distribution. Further, this Laplace distribution can be adopted as the characteristic description of this fruit. The fruit was segmented out of the input image. If the segmented fruit image falls in some Laplace distribution with 90% confidence interval, then input fruit belongs to this special fruit. In practice, the Mahalanobis distance (MD) corresponding to the 90% confidence interval of the Laplace distribution for each fruit class was set as the reference evaluation. If the input fruit data has a smaller Mahalanobis distance than the reference evaluation, the input belongs to this type fruit. The experimental results have shown the good performance for this fruit recognition technique on different kinds of fruits.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131832019","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046515
Hui Chen, Xiangfu Wang
Objective: To review and evaluate the technical advantages, disadvantages and research progress of robotic navigation technology in pedicle screw fixation. Methods: An extensive review of domestic and international literature on robotic navigation technology in pedicle screw fixation was conducted to summarize the advantages and disadvantages of this technology and its clinical application, as well as to provide an outlook on its future development. Results: Robotic nailing has the advantages of improved accuracy, reduced intraoperative radiation and minimally invasive surgery compared to freehand nailing. However, as the application of robotic navigation technology is still in its infancy, there are disadvantages in terms of low coverage, high cost, unstable accuracy and long operative time. Conclusion: The application of robotic navigation technology in pedicle screw fixation is a combined solution that requires not only the improvement of robotic shortcomings, but also a good surgical foundation, strict control of the surgical indications and rational selection of the surgical approach in order to adequately ensure the effectiveness and safety of the procedure.
{"title":"Advances in the Study and Application of Robotic Navigation Technology in Pedicle Screw Fixation","authors":"Hui Chen, Xiangfu Wang","doi":"10.1109/ICARCE55724.2022.10046515","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046515","url":null,"abstract":"Objective: To review and evaluate the technical advantages, disadvantages and research progress of robotic navigation technology in pedicle screw fixation. Methods: An extensive review of domestic and international literature on robotic navigation technology in pedicle screw fixation was conducted to summarize the advantages and disadvantages of this technology and its clinical application, as well as to provide an outlook on its future development. Results: Robotic nailing has the advantages of improved accuracy, reduced intraoperative radiation and minimally invasive surgery compared to freehand nailing. However, as the application of robotic navigation technology is still in its infancy, there are disadvantages in terms of low coverage, high cost, unstable accuracy and long operative time. Conclusion: The application of robotic navigation technology in pedicle screw fixation is a combined solution that requires not only the improvement of robotic shortcomings, but also a good surgical foundation, strict control of the surgical indications and rational selection of the surgical approach in order to adequately ensure the effectiveness and safety of the procedure.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229504","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046611
Shuangqing Lin, Kui Liang, Na An, Shiyu Peng
With the rapid development of high-end Computer Numerical Control (CNC) machine tools, aeroengines and other large-scale mechanical equipment towards high precision and intelligence, it is an extremely important task to carry out health management of equipment and ensure the equipment can work in safety and stability. The essential part of mechanical equipment are bearings, whose performance will directly determine the health of the equipment. Predicting the remaining life of bearings can provide effective decision support for equipment maintenance plans, so as to avoid safety accidents, which is significant for the health management of mechanical equipment. Currently, signal processing methods and data-driven methods are widely used in bearing life prediction. However, mechanical equipment has been in the background of strong noise for a long time, and its feature signal extraction is difficult, and the traditional regression prediction accuracy is low. Aiming at the above problems, a bearing residual life method based on Improved Parameter Adaptive Variational Mode Decomposition-Long Short Term Memory Networks (IPVMD-LSTM) model is proposed. IPVMD-LSTM has two characteristics: (1) Fully considering the characteristics of bearing cyclostationarity and impulsiveness, a synthetic index is constructed and used as the objective function, the parameters of VMD are optimized by Particle Swarm Optimization (PSO), so as to reduce noise effect influence. (2) Fully consider the temporal characteristics of the actual working condition data, and use the LSTM to extract the temporal characteristics for prediction. The experimental results show that the IPVMD-LSTM method in this paper has a significant improvement in the prediction accuracy, and its Root Mean Square Error (RMSE) is reduced by 2.81% compared with the traditional method.
{"title":"A Novel RUL Prediction Method for Bearing Using IPVMD-LSTM","authors":"Shuangqing Lin, Kui Liang, Na An, Shiyu Peng","doi":"10.1109/ICARCE55724.2022.10046611","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046611","url":null,"abstract":"With the rapid development of high-end Computer Numerical Control (CNC) machine tools, aeroengines and other large-scale mechanical equipment towards high precision and intelligence, it is an extremely important task to carry out health management of equipment and ensure the equipment can work in safety and stability. The essential part of mechanical equipment are bearings, whose performance will directly determine the health of the equipment. Predicting the remaining life of bearings can provide effective decision support for equipment maintenance plans, so as to avoid safety accidents, which is significant for the health management of mechanical equipment. Currently, signal processing methods and data-driven methods are widely used in bearing life prediction. However, mechanical equipment has been in the background of strong noise for a long time, and its feature signal extraction is difficult, and the traditional regression prediction accuracy is low. Aiming at the above problems, a bearing residual life method based on Improved Parameter Adaptive Variational Mode Decomposition-Long Short Term Memory Networks (IPVMD-LSTM) model is proposed. IPVMD-LSTM has two characteristics: (1) Fully considering the characteristics of bearing cyclostationarity and impulsiveness, a synthetic index is constructed and used as the objective function, the parameters of VMD are optimized by Particle Swarm Optimization (PSO), so as to reduce noise effect influence. (2) Fully consider the temporal characteristics of the actual working condition data, and use the LSTM to extract the temporal characteristics for prediction. The experimental results show that the IPVMD-LSTM method in this paper has a significant improvement in the prediction accuracy, and its Root Mean Square Error (RMSE) is reduced by 2.81% compared with the traditional method.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321869","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046631
Zelin Yao, Can Liu, Yu Wei, Xinyu Lian, Zehua Yang
To solve the problems that ant colony algorithm (ACO) has long iterations, slow convergence, and is difficult to find the optimum, an ACO based on the annealing tempering coefficient (AHACO) is proposed, which can speed up convergence and improve the ability to find optimum. According to the distribution characteristics of path, an adaptive state transition probability (APACO) is introduced, and two types of adaptive coefficient are given. Subsequently, an adaptive evaporation coefficient is introduced to optimize convergence (AEACO). enhanced adaptive combined ACO is introduced to combine all advantages. Finally, parameters selection and simulation experiments are designed and executed. The results indicate that the effectiveness of EACACO.
{"title":"Enhanced Adaptive Combined Ant Colony Algorithm","authors":"Zelin Yao, Can Liu, Yu Wei, Xinyu Lian, Zehua Yang","doi":"10.1109/ICARCE55724.2022.10046631","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046631","url":null,"abstract":"To solve the problems that ant colony algorithm (ACO) has long iterations, slow convergence, and is difficult to find the optimum, an ACO based on the annealing tempering coefficient (AHACO) is proposed, which can speed up convergence and improve the ability to find optimum. According to the distribution characteristics of path, an adaptive state transition probability (APACO) is introduced, and two types of adaptive coefficient are given. Subsequently, an adaptive evaporation coefficient is introduced to optimize convergence (AEACO). enhanced adaptive combined ACO is introduced to combine all advantages. Finally, parameters selection and simulation experiments are designed and executed. The results indicate that the effectiveness of EACACO.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127329449","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046592
Yu Chen, Liping Chen, J. Ding
In this paper, we propose an obstacle avoidance algorithm, which selects a point of the obstacle avoidance path as the chromosome, constructs the fitness function together with the path length, joint angle increment, and movement time as evaluation indexes, and performs scale transformation on the fitness to improve the competitiveness of the population. The algorithm cycles through the process of optimizing the velocity term in the chromosome in the first step with a particle swarm algorithm; selection in the second step; and crossover and mutation operations on individuals in the third step, in order to avoid the population falling into premature maturity, where the crossover and mutation probabilities vary adaptively with the results of the previous generation. The final smooth and continuous obstacle avoidance trajectory is obtained.
{"title":"Adaptive Genetic Algorithm Based Particle Swarm Optimization for Industrial Robotic Arm Obstacle Avoidance Trajectory Optimization","authors":"Yu Chen, Liping Chen, J. Ding","doi":"10.1109/ICARCE55724.2022.10046592","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046592","url":null,"abstract":"In this paper, we propose an obstacle avoidance algorithm, which selects a point of the obstacle avoidance path as the chromosome, constructs the fitness function together with the path length, joint angle increment, and movement time as evaluation indexes, and performs scale transformation on the fitness to improve the competitiveness of the population. The algorithm cycles through the process of optimizing the velocity term in the chromosome in the first step with a particle swarm algorithm; selection in the second step; and crossover and mutation operations on individuals in the third step, in order to avoid the population falling into premature maturity, where the crossover and mutation probabilities vary adaptively with the results of the previous generation. The final smooth and continuous obstacle avoidance trajectory is obtained.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129748746","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046434
Chuang Liu, Qiong Li, Zhiwei You
With the emergence of deep learning, speech recognition research for languages with a wide speaker base, such as English and Chinese, has become reasonably mature. However, Uyghur speech recognition research has only developed slowly, because the modeling is subpar since Uighur is a low-resource language. To solve the aforementioned problem, we propose an end-to-end Uyghur speech recognition model based on multi-task learning with Branchformer. Branchformer can capture both global and local contexts, allowing it to learn richer features from low volumes of data to improve model performance in resource-constrained situations. The multi-task learning model can fully utilize the data through multiple related tasks, enhancing the model’s generalizability under low resources. In this paper, the multi-task learning model is trained and decoded using modeling units including phoneme, sub-word, and word. The performance of the model is then evaluated using various datasets. The results show that the proposed model outperforms the mainstream models in terms of recognition, in the self-built database and open-source corpus Thuyg-20, respectively, the word accuracy of the proposed model reaches 94.48% and 91.16%, which is a significant improvement compared with each benchmark model.
{"title":"An End-to-End Uyghur Speech Recognition Based on Multi-task Learning","authors":"Chuang Liu, Qiong Li, Zhiwei You","doi":"10.1109/ICARCE55724.2022.10046434","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046434","url":null,"abstract":"With the emergence of deep learning, speech recognition research for languages with a wide speaker base, such as English and Chinese, has become reasonably mature. However, Uyghur speech recognition research has only developed slowly, because the modeling is subpar since Uighur is a low-resource language. To solve the aforementioned problem, we propose an end-to-end Uyghur speech recognition model based on multi-task learning with Branchformer. Branchformer can capture both global and local contexts, allowing it to learn richer features from low volumes of data to improve model performance in resource-constrained situations. The multi-task learning model can fully utilize the data through multiple related tasks, enhancing the model’s generalizability under low resources. In this paper, the multi-task learning model is trained and decoded using modeling units including phoneme, sub-word, and word. The performance of the model is then evaluated using various datasets. The results show that the proposed model outperforms the mainstream models in terms of recognition, in the self-built database and open-source corpus Thuyg-20, respectively, the word accuracy of the proposed model reaches 94.48% and 91.16%, which is a significant improvement compared with each benchmark model.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115859020","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046610
Min Gao, Yuanyuan Fei, Zhou Wang, Chunming Ma, Li Luo
Many applications of location-based indoor navigation services require precise location information of a user. While Global Positioning System (GPS) loses reliability indoors, fingerprints-based localization technology (FBLT) embodies superiority regarding accuracy and robustness. In a Bluetooth-based fingerprint localization system, a radio map is constructed offline and used as a reference for subsequent real-time localization tasks. However, the quality of the fingerprint radio map could be problematic when it comes to a large, broad space with low beacon density. Data collection in such a space could be exhausting as well. Another main issue is that different mobile devices receive heterogeneous signal strength at the same location. In this article, we propose a highly practical localization system with a semi-supervised learning fingerprints construction method that provides an efficient solution for a large-scale localization system in a complex indoor environment. We also conducted a series of experiments to evaluate the performance of this system.
{"title":"Semi-supervised Fingerprint Construction and Localization System For Large Indoor Area","authors":"Min Gao, Yuanyuan Fei, Zhou Wang, Chunming Ma, Li Luo","doi":"10.1109/ICARCE55724.2022.10046610","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046610","url":null,"abstract":"Many applications of location-based indoor navigation services require precise location information of a user. While Global Positioning System (GPS) loses reliability indoors, fingerprints-based localization technology (FBLT) embodies superiority regarding accuracy and robustness. In a Bluetooth-based fingerprint localization system, a radio map is constructed offline and used as a reference for subsequent real-time localization tasks. However, the quality of the fingerprint radio map could be problematic when it comes to a large, broad space with low beacon density. Data collection in such a space could be exhausting as well. Another main issue is that different mobile devices receive heterogeneous signal strength at the same location. In this article, we propose a highly practical localization system with a semi-supervised learning fingerprints construction method that provides an efficient solution for a large-scale localization system in a complex indoor environment. We also conducted a series of experiments to evaluate the performance of this system.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124992100","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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