The variable signal patterns and extremely high data rate of phased array radar greatly increase the complexity of electromagnetic environment, which makes the traditional method of radar working mode identification face great challenges. In this paper, a network structure based on temporal convolutional network (TCN) and Bi-directional long short-term memory (Bi-LSTM) parallel fusion processing is proposed. Depending on the advantages of TCN in depth temporal feature extraction and Bi-LSTM in global time series feature extraction, the typical working mode of phased array radar is accurately recognized. The experimental results show that under the condition of complex parameter interleaving, the recognition accuracy of the network for typical operating modes of phased array radar reaches 96.77%, which proves the feasibility of the method.
{"title":"Working modes recognition method of phased array radar based on TCN-BiLSTM parallel processing","authors":"Hongxing Wang, Zhengyun Jiang, Lushan Ding","doi":"10.1117/12.2671073","DOIUrl":"https://doi.org/10.1117/12.2671073","url":null,"abstract":"The variable signal patterns and extremely high data rate of phased array radar greatly increase the complexity of electromagnetic environment, which makes the traditional method of radar working mode identification face great challenges. In this paper, a network structure based on temporal convolutional network (TCN) and Bi-directional long short-term memory (Bi-LSTM) parallel fusion processing is proposed. Depending on the advantages of TCN in depth temporal feature extraction and Bi-LSTM in global time series feature extraction, the typical working mode of phased array radar is accurately recognized. The experimental results show that under the condition of complex parameter interleaving, the recognition accuracy of the network for typical operating modes of phased array radar reaches 96.77%, which proves the feasibility of the method.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114974882","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 cell microinjection task in space requires the operator on the ground hold the handle of haptic device to control the remote dexterous manipulator in Space Cabin to needle into the cell for gene injection or nucleus extraction. To prevent the failure of punctures, the reliable force feedback plays a key role to adjust the position and velocity of the needle of manipulator in the process. In this paper, a feasible haptic rendering approach is presented to carry out the cell microinjection teleoperation. The cell is modeled as a sphere-tree adjacently connected with deformed springs with its cytomembrane and inner nucleus physical properties. A configuration-based constrained optimization method is performed to calculate the feedback force. We also propose a locking method to maintain the force feedback stable when the needle passes through cell boundaries with different physical properties. Finally, three sets of experiments are designed to validate the efficiency and stability of our method in cell microinjection.
{"title":"Interactive haptic simulation of cell microinjection task","authors":"Mingzhen Li, Ge Yu, Meiqi Zhao","doi":"10.1117/12.2671081","DOIUrl":"https://doi.org/10.1117/12.2671081","url":null,"abstract":"The cell microinjection task in space requires the operator on the ground hold the handle of haptic device to control the remote dexterous manipulator in Space Cabin to needle into the cell for gene injection or nucleus extraction. To prevent the failure of punctures, the reliable force feedback plays a key role to adjust the position and velocity of the needle of manipulator in the process. In this paper, a feasible haptic rendering approach is presented to carry out the cell microinjection teleoperation. The cell is modeled as a sphere-tree adjacently connected with deformed springs with its cytomembrane and inner nucleus physical properties. A configuration-based constrained optimization method is performed to calculate the feedback force. We also propose a locking method to maintain the force feedback stable when the needle passes through cell boundaries with different physical properties. Finally, three sets of experiments are designed to validate the efficiency and stability of our method in cell microinjection.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"157 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120863832","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}
Reliability Centered Maintenance (RCM) technology can improve the reliability of equipment maintenance, but it still has problems such as low analysis efficiency and poor description completeness. We propose to construct a RCM maintenance strategy model based on logical reasoning. This model uses Answer Set Programming (ASP), a non-monotone logic programming language, to realize the theoretical modeling of RCM in the form of logical rules. We use preference optimization to improve the RCM analysis method and integrate CWA (Closed World Assumption) and NAF (Negation As Failure) into the ASP program. Practicality and generality are the main core objectives of this model. Finally, the turbine engine failure of aircraft is taken as the main research example. The effectiveness and efficiency of the model are verified by a comparison of model conclusion consistency. Experimental results show that compared with other RCM systems, this model has good efficiency, reliability, and completeness.
以可靠性为中心的维护(RCM)技术可以提高设备维护的可靠性，但仍存在分析效率低、描述完整性差等问题。提出了一种基于逻辑推理的RCM维护策略模型。该模型采用非单调逻辑编程语言回答集编程(Answer Set Programming, ASP)，以逻辑规则的形式实现RCM的理论建模。我们利用偏好优化改进RCM分析方法，并将CWA (Closed World Assumption)和NAF (Negation As Failure)整合到ASP程序中。实用性和通用性是该模型的主要核心目标。最后以飞机涡轮发动机故障为主要研究实例。通过对模型结论一致性的比较，验证了模型的有效性和有效性。实验结果表明，与其他RCM系统相比，该模型具有良好的效率、可靠性和完整性。
{"title":"RCM maintenance strategy modeling based on logic language","authors":"Wen-Chong Wang, Zhipeng Wang, Xingyue Su, X. Wang, Suhao Zheng, Qinzhou Niu","doi":"10.1117/12.2671087","DOIUrl":"https://doi.org/10.1117/12.2671087","url":null,"abstract":"Reliability Centered Maintenance (RCM) technology can improve the reliability of equipment maintenance, but it still has problems such as low analysis efficiency and poor description completeness. We propose to construct a RCM maintenance strategy model based on logical reasoning. This model uses Answer Set Programming (ASP), a non-monotone logic programming language, to realize the theoretical modeling of RCM in the form of logical rules. We use preference optimization to improve the RCM analysis method and integrate CWA (Closed World Assumption) and NAF (Negation As Failure) into the ASP program. Practicality and generality are the main core objectives of this model. Finally, the turbine engine failure of aircraft is taken as the main research example. The effectiveness and efficiency of the model are verified by a comparison of model conclusion consistency. Experimental results show that compared with other RCM systems, this model has good efficiency, reliability, and completeness.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125023770","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}
Isonicotinic acid is used as a pharmaceutical intermediate, mainly for the production of the anti-tuberculosis drug isoniazid. Prediction of isonicotinic acid yield using data from the production process is helpful to ensure product quality and improve production efficiency. Traditional BP neural networks have lots of disadvantages such as slow convergence, easy to fall into local minima and sensitive to the selection of initial weights and thresholds. In order to predict isonicotinic acid yield efficiently and accurately, a prediction model of isonicotinic acid yield based on the Grey Wolf Optimizer (GWO) optimized BP (GWO-BP) neural network was proposed. The prediction model was used to predict the historical production data of isonicotinic acid in a plant, and the experimental results showed that the accuracy of the proposed GWO-BP prediction model was higher compared with the traditional BP and GA-BP prediction models.
{"title":"Isonicotinic acid yield prediction by BP neural network based on optimization of grey wolf algorithm","authors":"Zhenyuan Li, Guo Ru, P. Sheng","doi":"10.1117/12.2672167","DOIUrl":"https://doi.org/10.1117/12.2672167","url":null,"abstract":"Isonicotinic acid is used as a pharmaceutical intermediate, mainly for the production of the anti-tuberculosis drug isoniazid. Prediction of isonicotinic acid yield using data from the production process is helpful to ensure product quality and improve production efficiency. Traditional BP neural networks have lots of disadvantages such as slow convergence, easy to fall into local minima and sensitive to the selection of initial weights and thresholds. In order to predict isonicotinic acid yield efficiently and accurately, a prediction model of isonicotinic acid yield based on the Grey Wolf Optimizer (GWO) optimized BP (GWO-BP) neural network was proposed. The prediction model was used to predict the historical production data of isonicotinic acid in a plant, and the experimental results showed that the accuracy of the proposed GWO-BP prediction model was higher compared with the traditional BP and GA-BP prediction models.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126767597","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 the expressway network, a large number of abnormal vehicle overtime behaviors occur every day. Currently, there is no efficient detection method. To solve this problem, this paper presents a vehicle full probability prediction optimization model, which can calculate the abnormal probability of other vehicles affected by the event after one vehicle is identified as abnormal vehicle. Further on, this paper establishes multiple events linear estimation model for two probability vehicle types. Finally, this paper presents a probability algorithm of abnormal overtime driving behavior to calculate the abnormal probability of various probability vehicle types.
{"title":"Research on prediction of abnormal overtime behavior of expressway vehicles","authors":"Lingyu Huo, YuYang Lei, Jinsong Ye, Lei Zhou","doi":"10.1117/12.2671258","DOIUrl":"https://doi.org/10.1117/12.2671258","url":null,"abstract":"In the expressway network, a large number of abnormal vehicle overtime behaviors occur every day. Currently, there is no efficient detection method. To solve this problem, this paper presents a vehicle full probability prediction optimization model, which can calculate the abnormal probability of other vehicles affected by the event after one vehicle is identified as abnormal vehicle. Further on, this paper establishes multiple events linear estimation model for two probability vehicle types. Finally, this paper presents a probability algorithm of abnormal overtime driving behavior to calculate the abnormal probability of various probability vehicle types.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123012072","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}
Chenhuan Tang, Shiran Zhu, Meng Zhang, Jie Chen, Xingyi Guo
Based on YOLOv4-tiny, A lightweight mask detection algorithm is presented. By replacing the CBL module in the backbone feature extraction network (CSPdarknet-tiny) and Yolo Head with Ghost module that reduces the parameters of the network model. By the combination of Ghost module, CBAM attention, SMU activation function, and BN layer, a lightweight attention mechanism residual module (GCS_Block) is designed, which is embedded into the backbone feature extraction network, improving the model extract mask feature level. The Kmeans++ method is used to perform anchor box clustering on the dataset in this thesis. The experimental results show that compared with YOLOv4-tiny, the MAP has increased from 74.02% to 86.77%, the parameter has decreased from 6,056,606 to 1,657,828. The memory size of the model is 5.6MB.
{"title":"Mask detection algorithm based on the improved YOLOv4 - tiny","authors":"Chenhuan Tang, Shiran Zhu, Meng Zhang, Jie Chen, Xingyi Guo","doi":"10.1117/12.2671703","DOIUrl":"https://doi.org/10.1117/12.2671703","url":null,"abstract":"Based on YOLOv4-tiny, A lightweight mask detection algorithm is presented. By replacing the CBL module in the backbone feature extraction network (CSPdarknet-tiny) and Yolo Head with Ghost module that reduces the parameters of the network model. By the combination of Ghost module, CBAM attention, SMU activation function, and BN layer, a lightweight attention mechanism residual module (GCS_Block) is designed, which is embedded into the backbone feature extraction network, improving the model extract mask feature level. The Kmeans++ method is used to perform anchor box clustering on the dataset in this thesis. The experimental results show that compared with YOLOv4-tiny, the MAP has increased from 74.02% to 86.77%, the parameter has decreased from 6,056,606 to 1,657,828. The memory size of the model is 5.6MB.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114549893","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}
When it is used to cluster datasets with complex structure, the Affinity Propagation (AP) algorithm faces a number of problems such as excessive local clustering, low accuracy, and invalid clustering evaluation results of some internal evaluation indexes due to excessive clustering. In view of this, this paper proposes an algorithm designed to determine the optimal clustering number. In this paper, the methods of coarse clustering and merging similar clusters are adopted to reduce the clustering number and optimize the maximum clustering number (Kmax), and new calculation methods for intra-cluster compact density, inter-cluster relative density and cluster separation are provided, based on which a new internal evaluation index is designed. The experimental results regarding UCI and NSL-KDD datasets show that the proposed model can provide correct clustering partitioning and accurate clustering range and can well outperform the other three improved algorithms in relevant detection indexes such as detection rate and false alarm rate.
{"title":"Improved affinity propagation optimal clustering number algorithm based on merging similar clusters","authors":"Gui-jiang Duan, Chensong Zou","doi":"10.1117/12.2671395","DOIUrl":"https://doi.org/10.1117/12.2671395","url":null,"abstract":"When it is used to cluster datasets with complex structure, the Affinity Propagation (AP) algorithm faces a number of problems such as excessive local clustering, low accuracy, and invalid clustering evaluation results of some internal evaluation indexes due to excessive clustering. In view of this, this paper proposes an algorithm designed to determine the optimal clustering number. In this paper, the methods of coarse clustering and merging similar clusters are adopted to reduce the clustering number and optimize the maximum clustering number (Kmax), and new calculation methods for intra-cluster compact density, inter-cluster relative density and cluster separation are provided, based on which a new internal evaluation index is designed. The experimental results regarding UCI and NSL-KDD datasets show that the proposed model can provide correct clustering partitioning and accurate clustering range and can well outperform the other three improved algorithms in relevant detection indexes such as detection rate and false alarm rate.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122162720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A chain pitch measurement method of scraper conveyor based on speckle structured light is proposed to improve security and automation. Two speckle structured light cameras are used to collect point clouds on the chain surface from different angles, and the point clouds are transformed to a common reference coordinate system by rotating and translation matrix with calibration. The chain point cloud is preprocessed by plane model segmentation and radius filtering, the main direction of the point cloud is calculated by point cloud principal component analysis, the key points of measurement are detected by neighbors within radius search of the point cloud, and finally, the chain pitch is solved by Euclidean distance. The actual measurement error of the measurement method proposed in this paper is less than 2%, which can meet the needs of coal mining.
{"title":"Scraper conveyor chain pitch measurement based on speckle structured light","authors":"Junsheng Zhang, Honglei Wang, Jiacheng Li","doi":"10.1117/12.2671210","DOIUrl":"https://doi.org/10.1117/12.2671210","url":null,"abstract":"A chain pitch measurement method of scraper conveyor based on speckle structured light is proposed to improve security and automation. Two speckle structured light cameras are used to collect point clouds on the chain surface from different angles, and the point clouds are transformed to a common reference coordinate system by rotating and translation matrix with calibration. The chain point cloud is preprocessed by plane model segmentation and radius filtering, the main direction of the point cloud is calculated by point cloud principal component analysis, the key points of measurement are detected by neighbors within radius search of the point cloud, and finally, the chain pitch is solved by Euclidean distance. The actual measurement error of the measurement method proposed in this paper is less than 2%, which can meet the needs of coal mining.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128723556","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}
At present, the data cleaning method based on time series realizes the data cleaning by classifying the data in the time series. Due to the lack of dimensionality reduction, the cleaning efficiency is low. For this reason, this paper proposes a method for rapid cleaning of smart grid data based on sparse self-coding. In this paper, the encoder neural network is constructed to reduce the dimension of the data, and Logsf algorithm is used to obtain the optimal weight of the data, obtain the main characteristics of the data, and achieve clustering cleaning of the data. In the experiment, the cleaning efficiency of the proposed method was verified. The experimental results show that the method proposed in this paper has a short time delay and high cleaning efficiency for smart grid data cleaning.
{"title":"Fast cleaning method for smart grid data based on sparse self-coding","authors":"Peiyao Xu, Jianyong Wang, Fengtao Huang, Chao Lin, Chennan Zhou","doi":"10.1117/12.2671323","DOIUrl":"https://doi.org/10.1117/12.2671323","url":null,"abstract":"At present, the data cleaning method based on time series realizes the data cleaning by classifying the data in the time series. Due to the lack of dimensionality reduction, the cleaning efficiency is low. For this reason, this paper proposes a method for rapid cleaning of smart grid data based on sparse self-coding. In this paper, the encoder neural network is constructed to reduce the dimension of the data, and Logsf algorithm is used to obtain the optimal weight of the data, obtain the main characteristics of the data, and achieve clustering cleaning of the data. In the experiment, the cleaning efficiency of the proposed method was verified. The experimental results show that the method proposed in this paper has a short time delay and high cleaning efficiency for smart grid data cleaning.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125138103","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}
This paper proposes an electrical capacitance tomography algorithm based on an elastic network. To obtain feasible solutions, the L1 and L2 norms are used as the regular terms of the objective function, so that the solution has both the feature selection characteristics of the L1 norm and the image smoothing characteristics of the L2 norm. And utilize the normalized Laplacian as the weight of the elastic network, perform edge detection, and identify the dominance of L1 and L2. This algorithm makes the imaging region smooth, preserves the edge details of the image, and increases the accuracy of the image.
{"title":"Research on image reconstruction algorithm of capacitance imaging based on elastic network","authors":"Xinyu Zhang, Shuai Chen, Xia Li, Yang Lou, Z. Kan","doi":"10.1117/12.2671070","DOIUrl":"https://doi.org/10.1117/12.2671070","url":null,"abstract":"This paper proposes an electrical capacitance tomography algorithm based on an elastic network. To obtain feasible solutions, the L1 and L2 norms are used as the regular terms of the objective function, so that the solution has both the feature selection characteristics of the L1 norm and the image smoothing characteristics of the L2 norm. And utilize the normalized Laplacian as the weight of the elastic network, perform edge detection, and identify the dominance of L1 and L2. This algorithm makes the imaging region smooth, preserves the edge details of the image, and increases the accuracy of the image.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129650636","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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