This paper introduces and analyses the method of applying Neuroscience methods to Boltzmann Machine, involving a combination of cognitive psychology, information theory, and dynamical systems. We utilized the emergent property of the probability of hidden layers to find the pattern of how units are behaving when stimulated by the visual layer and research into enhancing the predictive encoding capability of the encoding layer. We measure the connections and links between the units of the encoding layer by approximating it with the probability distribution of two units' activation behaviours. For example, the portion of the Auditory cortex responsible for processing auditory information, such as music, differs from the sections responsible for processing visual information, although they can still be linked and active concurrently. Besides, Neurons can modify their connections by learning new information and reinforcing the connections that have been utilized more frequently, and forgetting the connections if the probability distributions of two units diverge much. The Boltzmann machine is the probabilistic inference machine for ground truth using the free energy principle. The latter has stepped further from the concept to interpret cortical responses as a fundamental of intelligent agency. With simple and random interactions of each neuron, this 'intelligent agency' could achieve sophisticated functions in a specific area of a brain. Randomness is also a vital aspect of learning since it may achieve balance and embrace regularities according to Ramsey's Theory.
{"title":"Reinforcing feature distributions of hidden units of Boltzmann machine using correlations","authors":"Peixu Cai, W. Shen, Ruohan Yang, Qixian Zhou","doi":"10.1117/12.2672661","DOIUrl":"https://doi.org/10.1117/12.2672661","url":null,"abstract":"This paper introduces and analyses the method of applying Neuroscience methods to Boltzmann Machine, involving a combination of cognitive psychology, information theory, and dynamical systems. We utilized the emergent property of the probability of hidden layers to find the pattern of how units are behaving when stimulated by the visual layer and research into enhancing the predictive encoding capability of the encoding layer. We measure the connections and links between the units of the encoding layer by approximating it with the probability distribution of two units' activation behaviours. For example, the portion of the Auditory cortex responsible for processing auditory information, such as music, differs from the sections responsible for processing visual information, although they can still be linked and active concurrently. Besides, Neurons can modify their connections by learning new information and reinforcing the connections that have been utilized more frequently, and forgetting the connections if the probability distributions of two units diverge much. The Boltzmann machine is the probabilistic inference machine for ground truth using the free energy principle. The latter has stepped further from the concept to interpret cortical responses as a fundamental of intelligent agency. With simple and random interactions of each neuron, this 'intelligent agency' could achieve sophisticated functions in a specific area of a brain. Randomness is also a vital aspect of learning since it may achieve balance and embrace regularities according to Ramsey's Theory.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115818902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to meet the automatic injection and cleaning functions of digital PCR detector, a double-layer injection needle and its liquid path device were designed. Firstly, the finite element analysis of the injection needle was carried out based on ANSYS Workbench. Combined with the theoretical calculation, it is found that the stability is poor and the bending stiffness is insufficient. Then, to solve this problem, a spiral spring was added between the inner needle and the outer tube to optimize the injection needle, and the influence of spring position, length and pitch on the deformation of the injection needle was explored. Finally, considering the strength, deformation and cleaning effect, it is determined that the best scheme is that the spring is 5mm from the outlet of the outer tube, the length is 5mm and the pitch is 1mm. At this time, the maximum deformation caused by 0.1N lateral force is reduced from 0.527mm to 0.158mm, the maximum stress is reduced from 151.5MPa to 86.25MPa and the critical buckling pressure is increased from 4.276N to 28.468N. After optimization, the strength, stiffness and stability of the injection needle are guaranteed. The research results are of great significance to the design and optimization of the injection needle.
{"title":"Design and optimization of injection needle for digital PCR detector","authors":"Peiqi Zhang, Dongmei Li","doi":"10.1117/12.2671907","DOIUrl":"https://doi.org/10.1117/12.2671907","url":null,"abstract":"In order to meet the automatic injection and cleaning functions of digital PCR detector, a double-layer injection needle and its liquid path device were designed. Firstly, the finite element analysis of the injection needle was carried out based on ANSYS Workbench. Combined with the theoretical calculation, it is found that the stability is poor and the bending stiffness is insufficient. Then, to solve this problem, a spiral spring was added between the inner needle and the outer tube to optimize the injection needle, and the influence of spring position, length and pitch on the deformation of the injection needle was explored. Finally, considering the strength, deformation and cleaning effect, it is determined that the best scheme is that the spring is 5mm from the outlet of the outer tube, the length is 5mm and the pitch is 1mm. At this time, the maximum deformation caused by 0.1N lateral force is reduced from 0.527mm to 0.158mm, the maximum stress is reduced from 151.5MPa to 86.25MPa and the critical buckling pressure is increased from 4.276N to 28.468N. After optimization, the strength, stiffness and stability of the injection needle are guaranteed. The research results are of great significance to the design and optimization of the injection needle.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127492917","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}
Logo recognition technology can be used to identify the authenticity of logos, and logo substitution technology can be used to add watermarks to images, print anti-counterfeiting, effect generation, image composition, and even document signing. It can facilitate specific people and protect the rights of the author. This paper is a study of Logo recognition and substitution based on the SIFT algorithm, using the SIFT description to recognize the presence or absence of a pre-stored Logo in the Logo Library. The logo is replaced by a logo from the logo library.
{"title":"Logo detection and replacement method based on SIFT algorithm","authors":"Jiyuan Li, Fei Wang, Qianchuan Zhao, Yunhao Wu, Yimin Tian","doi":"10.1117/12.2671969","DOIUrl":"https://doi.org/10.1117/12.2671969","url":null,"abstract":"Logo recognition technology can be used to identify the authenticity of logos, and logo substitution technology can be used to add watermarks to images, print anti-counterfeiting, effect generation, image composition, and even document signing. It can facilitate specific people and protect the rights of the author. This paper is a study of Logo recognition and substitution based on the SIFT algorithm, using the SIFT description to recognize the presence or absence of a pre-stored Logo in the Logo Library. The logo is replaced by a logo from the logo library.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122827076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To solve the dangerous behavior of passengers, this paper studies a light warning device for escalators. First, the dangerous behaviors of passengers are graded, and then the dangerous behaviors of passengers are photographed and identified through the binocular cameras. Finally, the passengers are discouraged from the dangerous behaviors by the light warning. Through this device, the dangerous behaviors of passengers on escalator can be detected, prevented and dealt with as early as possible, and the occurrence of safety accident can be fundamentally eliminated.
{"title":"Design of light warning device for escalator","authors":"X. Chang","doi":"10.1117/12.2671855","DOIUrl":"https://doi.org/10.1117/12.2671855","url":null,"abstract":"To solve the dangerous behavior of passengers, this paper studies a light warning device for escalators. First, the dangerous behaviors of passengers are graded, and then the dangerous behaviors of passengers are photographed and identified through the binocular cameras. Finally, the passengers are discouraged from the dangerous behaviors by the light warning. Through this device, the dangerous behaviors of passengers on escalator can be detected, prevented and dealt with as early as possible, and the occurrence of safety accident can be fundamentally eliminated.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123029921","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}
Aspect-based sentiment analysis is crucial for Internet applications such as social networks and e-commerce, where the previous deep learning methods cannot process long-range semantic information in parallel. This paper proposes an aspectbased sentiment analysis method based on multiscale convolution and a double-layer attention mechanism. The technique uses pre-trained BERT to obtain the hidden semantic information of the context from the training set, then uses multiscale deep convolution and double-layer attention to process the long-distance semantic information between the target word and the context in parallel, and finally uses softmax for sentiment classification of the target word. In this paper, we use the public dataset of SemEval 2014 and the Twitter Dataset to validated the improved accuracy and F1 of the model.
{"title":"A parallel processing method for long-range contextual semantic information to sentiment analysis based on aspect","authors":"Lujunjie Gao, Xuhui Xiong, Dongni Ran","doi":"10.1117/12.2673054","DOIUrl":"https://doi.org/10.1117/12.2673054","url":null,"abstract":"Aspect-based sentiment analysis is crucial for Internet applications such as social networks and e-commerce, where the previous deep learning methods cannot process long-range semantic information in parallel. This paper proposes an aspectbased sentiment analysis method based on multiscale convolution and a double-layer attention mechanism. The technique uses pre-trained BERT to obtain the hidden semantic information of the context from the training set, then uses multiscale deep convolution and double-layer attention to process the long-distance semantic information between the target word and the context in parallel, and finally uses softmax for sentiment classification of the target word. In this paper, we use the public dataset of SemEval 2014 and the Twitter Dataset to validated the improved accuracy and F1 of the model.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127926704","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}
Power equipment failure prediction method has the problem of high cumulative deterioration, and a power equipment failure prediction method based on dynamic ontology modeling technology is designed to solve the above problem. It evaluates the health status of power equipment, clarifies the performance degradation range of equipment according to the characteristics reflected in different stages, constructs a residual life judgment model by combining the mechanism of reliability function, clarifies the performance degradation conditions and failure threshold of power equipment, and optimizes the fault prediction process by using dynamic ontology modeling technology. The test results showed that the mean values of cumulative degradation of the power equipment failure prediction method in the paper and three other power equipment failure prediction methods are 1.612, 3.263, 3.207, and 3.234, respectively, indicating that the power equipment failure prediction method designed after incorporating dynamic ontology modeling technique has higher use value.
{"title":"Application of dynamic ontology modeling techniques in power equipment fault prediction","authors":"Xinyao Feng, Yingwei Liang, Shaoguang Liu, Xiaolu Li, Hanyang Xie","doi":"10.1117/12.2671923","DOIUrl":"https://doi.org/10.1117/12.2671923","url":null,"abstract":"Power equipment failure prediction method has the problem of high cumulative deterioration, and a power equipment failure prediction method based on dynamic ontology modeling technology is designed to solve the above problem. It evaluates the health status of power equipment, clarifies the performance degradation range of equipment according to the characteristics reflected in different stages, constructs a residual life judgment model by combining the mechanism of reliability function, clarifies the performance degradation conditions and failure threshold of power equipment, and optimizes the fault prediction process by using dynamic ontology modeling technology. The test results showed that the mean values of cumulative degradation of the power equipment failure prediction method in the paper and three other power equipment failure prediction methods are 1.612, 3.263, 3.207, and 3.234, respectively, indicating that the power equipment failure prediction method designed after incorporating dynamic ontology modeling technique has higher use value.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"68 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593180","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}
Xiaomeng Xia, Bozhong Li, Hongbing Huang, Yize Tang, Wenjie Kong
The OSNR monitoring based on machine learning has achieved some results in coherent optical communication system, but it is not widely researched in intensity-modulation and direct detection system. In this paper, an electrical domain signal processing scheme based on deep neural network is proposed for monitoring link OSNR of intensity-modulation and direct detection system. We successfully estimate the OSNR of the 4GBaud OOK signal with the mean absolute error less than 0.81dB in the range of eight to 18 dB by a five layers deep neural network using 550,000 datasets.
{"title":"Research on OSNR monitoring technology based on deep neural network","authors":"Xiaomeng Xia, Bozhong Li, Hongbing Huang, Yize Tang, Wenjie Kong","doi":"10.1117/12.2672152","DOIUrl":"https://doi.org/10.1117/12.2672152","url":null,"abstract":"The OSNR monitoring based on machine learning has achieved some results in coherent optical communication system, but it is not widely researched in intensity-modulation and direct detection system. In this paper, an electrical domain signal processing scheme based on deep neural network is proposed for monitoring link OSNR of intensity-modulation and direct detection system. We successfully estimate the OSNR of the 4GBaud OOK signal with the mean absolute error less than 0.81dB in the range of eight to 18 dB by a five layers deep neural network using 550,000 datasets.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131744378","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 seed word-driven approach based on weakly supervised text classification (WTC) is the dominant approach. In existing seed word-driven methods，using metrics such as Term Frequency (TF), Inverse Document Frequency (IDF) and its combinations to update the seed words. the method assigns the same weight to all metrics, leading to the selection of common or poorly differentiated words as seed words; In addition most of the text classifiers used in the study have difficulty in capturing the correlation and global information between text information. In order to solve the above problems, Using Transformer as a text classifier first, The multi-headed self-attention mechanism allows capturing longrange dependencies while computing in parallel and fully learning the global semantic information of the input text. Then an improved TF-IDF method is proposed to increase the weight of IDF so that some common words that affect the classification can be filtered out. Its experimental results are improved on 20News and NYT datasets.
{"title":"Weakly supervised text classification method based on transformer","authors":"Ling Gan, aijun yi","doi":"10.1117/12.2672391","DOIUrl":"https://doi.org/10.1117/12.2672391","url":null,"abstract":"The seed word-driven approach based on weakly supervised text classification (WTC) is the dominant approach. In existing seed word-driven methods，using metrics such as Term Frequency (TF), Inverse Document Frequency (IDF) and its combinations to update the seed words. the method assigns the same weight to all metrics, leading to the selection of common or poorly differentiated words as seed words; In addition most of the text classifiers used in the study have difficulty in capturing the correlation and global information between text information. In order to solve the above problems, Using Transformer as a text classifier first, The multi-headed self-attention mechanism allows capturing longrange dependencies while computing in parallel and fully learning the global semantic information of the input text. Then an improved TF-IDF method is proposed to increase the weight of IDF so that some common words that affect the classification can be filtered out. Its experimental results are improved on 20News and NYT datasets.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121965205","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}
Diabetes is a very serious worldwide chronic disease that affects people's life and health. Patients require insulin injections to maintain blood sugar balance exogenously. Methods to detect diabetes are time-consuming and labor-intensive. With the popularity of machine learning algorithms, we expect to predict and analyze diabetes through deep learning methods. In this paper, we utilize machine learning methods for data analysis and prediction. Our method was tested on public datasets and found that the random forest algorithm performed best, and that BMI and gender were the most important factors affecting the prevalence of diabetes.
{"title":"Diabetes prediction and analysis using machine learning models","authors":"Yunjiu Li, Helin Wang, Zhirui Ye, Haina Zhou","doi":"10.1117/12.2672671","DOIUrl":"https://doi.org/10.1117/12.2672671","url":null,"abstract":"Diabetes is a very serious worldwide chronic disease that affects people's life and health. Patients require insulin injections to maintain blood sugar balance exogenously. Methods to detect diabetes are time-consuming and labor-intensive. With the popularity of machine learning algorithms, we expect to predict and analyze diabetes through deep learning methods. In this paper, we utilize machine learning methods for data analysis and prediction. Our method was tested on public datasets and found that the random forest algorithm performed best, and that BMI and gender were the most important factors affecting the prevalence of diabetes.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125935049","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}
Hui Zhang, Jian Zhao, Jiangshun Yu, Rong Yu, Wei Liu, Di Wang, Dan Liu
In view of the low efficiency and high cost of the current line patrol method, as well as the cumbersome technology and weak operability of the helicopter power patrol inspection, this paper describes the UAV system in detail. At the same time, combined with the application of UAV in the line operation and maintenance management, it introduces the process of UAV patrol inspection in detail, and focuses on the path planning, line fault detection and line evaluation and prediction in the transmission line patrol inspection. It is concluded that UAV inspection can effectively improve the efficiency of inspection and maintenance of transmission lines, and promote the process of intelligent operation and maintenance of transmission lines.
{"title":"Application of UAV in intelligent patrol inspection of transmission line","authors":"Hui Zhang, Jian Zhao, Jiangshun Yu, Rong Yu, Wei Liu, Di Wang, Dan Liu","doi":"10.1117/12.2672208","DOIUrl":"https://doi.org/10.1117/12.2672208","url":null,"abstract":"In view of the low efficiency and high cost of the current line patrol method, as well as the cumbersome technology and weak operability of the helicopter power patrol inspection, this paper describes the UAV system in detail. At the same time, combined with the application of UAV in the line operation and maintenance management, it introduces the process of UAV patrol inspection in detail, and focuses on the path planning, line fault detection and line evaluation and prediction in the transmission line patrol inspection. It is concluded that UAV inspection can effectively improve the efficiency of inspection and maintenance of transmission lines, and promote the process of intelligent operation and maintenance of transmission lines.","PeriodicalId":290902,"journal":{"name":"International Conference on Mechatronics Engineering and Artificial Intelligence","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130822079","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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