Syndrome differentiation (SD) is a basic task in TCM (Traditional Chinese Medicine) diagnosis and treatment. TCM syndrome differentiation is very complex and time-consuming. Meanwhile, the accuracy of the results depends on the experience of TCM practitioners. To help TCM practitioners differentiate syndrome more quickly, we propose a syndrome differentiation method of deep learning based on multi-feature fusion. We extracted char, word and POS (Part of Speech) from TCM diagnosis and treatment records. The vector representation of char feature is obtained by ZY-BERT (Zhong Yi BERT), ZY-BERT was pre-trained on large datasets of TCM-SD (TCM Syndrome Differentiation). The vector representation of word and POS is obtained by Word2vec (Word to vector). We construct text graphs of char, word and POS according to context. GCN (Graph Convolutional Networks) is used to extract spatial structure information between multiple features to achieve multi-feature fusion. The experiment was carried out on TCM-SD. The experimental results showed that the accuracy of the proposed method was 81.52%, which was better than the comparison method. This method is helpful in the development of TCM modernization.
辨证论治是中医诊治的一项基本任务。中医辨证过程复杂，耗时长。同时，结果的准确性取决于中医从业人员的经验。为了帮助中医更快地辨证，我们提出了一种基于多特征融合的深度学习辨证方法。从中医诊疗记录中提取字符、单词和词类。通过ZY-BERT (Zhong Yi BERT)获得字符特征的向量表示，ZY-BERT在TCM- sd (TCM Syndrome Differentiation)大数据集上进行预训练。word和POS的向量表示由Word2vec (word to vector)获得。我们根据上下文构造char、word和POS的文本图。采用GCN(图卷积网络)提取多个特征之间的空间结构信息，实现多特征融合。实验采用中药- sd进行。实验结果表明，该方法的准确率为81.52%，优于对比方法。这种方法有助于中医现代化的发展。
{"title":"Research on TCM syndrome differentiation based on multi-feature fusion and GCN","authors":"Boting Liu, Weili Guan, Zhijie Fang","doi":"10.1117/12.2682399","DOIUrl":"https://doi.org/10.1117/12.2682399","url":null,"abstract":"Syndrome differentiation (SD) is a basic task in TCM (Traditional Chinese Medicine) diagnosis and treatment. TCM syndrome differentiation is very complex and time-consuming. Meanwhile, the accuracy of the results depends on the experience of TCM practitioners. To help TCM practitioners differentiate syndrome more quickly, we propose a syndrome differentiation method of deep learning based on multi-feature fusion. We extracted char, word and POS (Part of Speech) from TCM diagnosis and treatment records. The vector representation of char feature is obtained by ZY-BERT (Zhong Yi BERT), ZY-BERT was pre-trained on large datasets of TCM-SD (TCM Syndrome Differentiation). The vector representation of word and POS is obtained by Word2vec (Word to vector). We construct text graphs of char, word and POS according to context. GCN (Graph Convolutional Networks) is used to extract spatial structure information between multiple features to achieve multi-feature fusion. The experiment was carried out on TCM-SD. The experimental results showed that the accuracy of the proposed method was 81.52%, which was better than the comparison method. This method is helpful in the development of TCM modernization.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"6 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132236365","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 a multi-group end-to-end path optimization method based on genetic algorithm(MEEPOGA). Under the condition of meeting the bandwidth requirements and delay requirements of data transmission, in a network with limited link capacity and given delay, MEEPOGA arranges data transmission paths for multiple groups of source nodes to destination nodes. These paths achieve the goal of minimizing overall cost while avoiding link congestion. Considering that the genetic algorithm can provide stable and efficient search in the complex problem space, we solve the above optimization problem by making appropriate improvements to the genetic algorithm. It mainly includes modification of encoding strategy, fitness function and genetic operator. At the same time, we conducted comparative experiments with other algorithms. The optimization method proposed in this paper is mainly divided into two steps: First, MEEPOGA finds a set of possible solutions for each pair of source nodes and destination nodes under the conditions of bandwidth and delay. Then the combination evaluation is carried out through the genetic algorithm to find the optimal solution. For evaluation on a collection of paths, an objective-based penalty function is proposed. Simulation experiments show that our algorithm has good performance.
{"title":"Multi-group end-to-end path optimization algorithm based on genetic algorithm","authors":"Hui Liu, Hao Xu, Xianglin Wan","doi":"10.1117/12.2682366","DOIUrl":"https://doi.org/10.1117/12.2682366","url":null,"abstract":"This paper proposes a multi-group end-to-end path optimization method based on genetic algorithm(MEEPOGA). Under the condition of meeting the bandwidth requirements and delay requirements of data transmission, in a network with limited link capacity and given delay, MEEPOGA arranges data transmission paths for multiple groups of source nodes to destination nodes. These paths achieve the goal of minimizing overall cost while avoiding link congestion. Considering that the genetic algorithm can provide stable and efficient search in the complex problem space, we solve the above optimization problem by making appropriate improvements to the genetic algorithm. It mainly includes modification of encoding strategy, fitness function and genetic operator. At the same time, we conducted comparative experiments with other algorithms. The optimization method proposed in this paper is mainly divided into two steps: First, MEEPOGA finds a set of possible solutions for each pair of source nodes and destination nodes under the conditions of bandwidth and delay. Then the combination evaluation is carried out through the genetic algorithm to find the optimal solution. For evaluation on a collection of paths, an objective-based penalty function is proposed. Simulation experiments show that our algorithm has good performance.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117338743","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 article presents a machine learning-based method for detecting micro-sleep. The method is simple, efficient, and can be applied in practical scenarios without the need for large-scale equipment such as servers. We recorded the physiological characteristics of 16 young adults in a driving simulation laboratory, mainly consisting of electroencephalogram (EEG) and driver behaviour videos, and used machine learning to detect micro-sleep events. We compared different machine learning algorithms (SVM, KNN, ANN) and ultimately adopted a combination of ANN and SVM algorithms (pre-processing small-scale data), which reduced the recognition error rate from an initial 4.5% to 0.2%. This combination accelerated the recognition speed and improved the accuracy, making it a practical approach.
{"title":"A new and efficient method for detecting micro-sleep based on machine learning","authors":"Xuebin Zhu, Zhoulin Wang, Zhenghong Yu, Ying-Jia Lin, Haijie Feng","doi":"10.1117/12.2682363","DOIUrl":"https://doi.org/10.1117/12.2682363","url":null,"abstract":"This article presents a machine learning-based method for detecting micro-sleep. The method is simple, efficient, and can be applied in practical scenarios without the need for large-scale equipment such as servers. We recorded the physiological characteristics of 16 young adults in a driving simulation laboratory, mainly consisting of electroencephalogram (EEG) and driver behaviour videos, and used machine learning to detect micro-sleep events. We compared different machine learning algorithms (SVM, KNN, ANN) and ultimately adopted a combination of ANN and SVM algorithms (pre-processing small-scale data), which reduced the recognition error rate from an initial 4.5% to 0.2%. This combination accelerated the recognition speed and improved the accuracy, making it a practical approach.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126574714","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 this paper, a new method is proposed for the intelligent prediction of the thermal insulation performance of vacuum glass, i.e., the use of cascade forest algorithm to detect the heat transfer coefficient (U-value) of vacuum glass. By constructing different intelligent algorithm models, random forest, extreme random forest and cascade forest algorithms are used. By evaluating the proposed method using mean absolute error (MAE), mean square error (MSE) and R-squared value, the cascade forest was evaluated with values of 0.0401, 0.0035 and 0.9896, respectively, and the predicted value curve was very close to the true value curve, so it was concluded that the cascade forest algorithm was superior to the random forest and extreme random forest algorithms in predicting the heat transfer coefficient of vacuum glass. In order to avoid the risk of overfitting, k-fold cross-validation was also added to each random forest in the cascade forest during the training process, and the accuracy of the cross-validated data was improved by 1% as shown by the data. It is known from the experimental results that the algorithm with cascade forest gives a new idea for the work of fast detection of heat transfer characteristics of vacuum glass based on small samples.
{"title":"Prediction of insulation performance of vacuum glass based on cascade forest model","authors":"Xin Fang, Yanggang Hu, Lei Wang","doi":"10.1117/12.2682452","DOIUrl":"https://doi.org/10.1117/12.2682452","url":null,"abstract":"In this paper, a new method is proposed for the intelligent prediction of the thermal insulation performance of vacuum glass, i.e., the use of cascade forest algorithm to detect the heat transfer coefficient (U-value) of vacuum glass. By constructing different intelligent algorithm models, random forest, extreme random forest and cascade forest algorithms are used. By evaluating the proposed method using mean absolute error (MAE), mean square error (MSE) and R-squared value, the cascade forest was evaluated with values of 0.0401, 0.0035 and 0.9896, respectively, and the predicted value curve was very close to the true value curve, so it was concluded that the cascade forest algorithm was superior to the random forest and extreme random forest algorithms in predicting the heat transfer coefficient of vacuum glass. In order to avoid the risk of overfitting, k-fold cross-validation was also added to each random forest in the cascade forest during the training process, and the accuracy of the cross-validated data was improved by 1% as shown by the data. It is known from the experimental results that the algorithm with cascade forest gives a new idea for the work of fast detection of heat transfer characteristics of vacuum glass based on small samples.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740496","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}
Dot matrix invisible code is widely used in anti-counterfeiting and traceability of goods, dot matrix invisible code is a kind of technology that "disappears" in the package decoration. This kind of code will neither destroy the overall effect of packaging decoration nor affect the function of barcode, it provide technical support for anti-counterfeit traceability means, which is difficult to be seen by the naked eye and needs to be read under special lighting conditions, merchants and consumers can access product information by identifying the code, it often has the problem of poor contrast due to light intensity, shooting angle and other reasons. The image enhancement technology is used to improve the image quality and lay the foundation for the subsequent work. This paper proposes an improved γ-CLAHE image enhancement algorithm for dot matrix invisible code, which converts the image into the color space with color and brightness separation, performs histogram equalization enhancement processing on the brightness components, combines with Gamma correction, so that the enhanced image quality is significantly improved. In this study, the CLAHE algorithm and the improved algorithms on LAB, HSV and YCrCb color spaces are compared separately, the experimental results show that the improved algorithm is much more effective than the CLAHE algorithm, and the improved algorithm in YCrCb color space is more suitable for image enhancement of dot matrix invisible codes than other color and bright separation color spaces, It has obvious superiority in indicators such as information entropy, mean gradient, standard deviation, etc., and can effectively improve the contrast of low-quality dot matrix invisible codes, at the same time, this study provide ideas for similar invisible code image enhancement.
{"title":"An improved γ-CLAHE image enhancement algorithm for dot matrix invisible code","authors":"Mingyang Ren, Jiangfeng Xu","doi":"10.1117/12.2682517","DOIUrl":"https://doi.org/10.1117/12.2682517","url":null,"abstract":"Dot matrix invisible code is widely used in anti-counterfeiting and traceability of goods, dot matrix invisible code is a kind of technology that \"disappears\" in the package decoration. This kind of code will neither destroy the overall effect of packaging decoration nor affect the function of barcode, it provide technical support for anti-counterfeit traceability means, which is difficult to be seen by the naked eye and needs to be read under special lighting conditions, merchants and consumers can access product information by identifying the code, it often has the problem of poor contrast due to light intensity, shooting angle and other reasons. The image enhancement technology is used to improve the image quality and lay the foundation for the subsequent work. This paper proposes an improved γ-CLAHE image enhancement algorithm for dot matrix invisible code, which converts the image into the color space with color and brightness separation, performs histogram equalization enhancement processing on the brightness components, combines with Gamma correction, so that the enhanced image quality is significantly improved. In this study, the CLAHE algorithm and the improved algorithms on LAB, HSV and YCrCb color spaces are compared separately, the experimental results show that the improved algorithm is much more effective than the CLAHE algorithm, and the improved algorithm in YCrCb color space is more suitable for image enhancement of dot matrix invisible codes than other color and bright separation color spaces, It has obvious superiority in indicators such as information entropy, mean gradient, standard deviation, etc., and can effectively improve the contrast of low-quality dot matrix invisible codes, at the same time, this study provide ideas for similar invisible code image enhancement.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126925103","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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