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Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shanghai Land & Resources / Shanghai Guotu Ziyuan is the property of Editorial Board of Shanghai Land & Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Recognition of building group patterns is an important part of the building generalization. Efficient methods for the recognition of building group patterns can effectively improve the quality of automated map generalization. Traditional recognition methods mainly include geometric methods and traditional machine learning methods, which are limited by the complex rule definitions and huge feature engineering. The graph convolution neural network (GCN) can overcome the limitations of traditional methods to some extent and has been successfully applied to the pattern analysis of buildings. However, there are few methods to recognize multiple building group patterns using GCN, and the existing GCN methods are almost based on spectral graph convolution, which do not consider local spatial information fully. Methods: As the spatial GCN is more efficient than the spectral GCN, this paper introduces a spatial GCN method DGCNN to recognize three building group patterns, including linear pattern, grid pattern and irregular pattern. The first step is to cluster the buildings into different groups, then some indices are selected to construct the feature vector of each group according to the Gestalt principles. Second, Delaunay triangulation (DT) and minimum spanning tree (MST) are chosen to construct graph structures of building groups. Finally, graph structures are used as input for the DGCNN model, and building group patterns are obtained after training. Results: The experiment selects the urban areas in Shanghai and we compare the accuracies with traditional spectral GCN method. The results show that the accuracy for the DGCNN model in the train set can reach 97.06%, and 95% for the test set. And the accuracies for the spectral GCN model in the train set is 90.90%, and 86.05% in the test set. Conclusions: The proposed method overcomes the limitations of spectral GCN, and it does not need graph Fourier transform. It can improve the recognition accuracy significantly and faces less difficulty in explaining the process of feature extraction. Hence it is an effective method for the recognition of building group patterns. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Donghua University (Natural Science Edition) is the property of Journal of Donghua University (Natural Science) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multiple random empirical kernel learning machine(MREKLM) selects a small number of samples to construct the empirical feature space, but does not take into account the distribution of data when projecting, and takes a long time to select samples. In order to utilize the distribution information of samples, this paper introduced the intraclass dispersion matrix, and proposed an intra-class variance based multiple empirical kernel learning(ICVMEKL), which enabled samples to take into account the intra-class information when projecting. The method enhanced the classification boundary and improved the classification accuracy. Further, in order to reduce the selection time of samples, this paper proposed an improved ICVMEKL(ICVMEKL＿I) based on locality-sensitive hashing algorithm by using the border point extraction based on locality-sensitive hashing(BPLSH) to select samples, so that the samples for constructing the empirical kernel no longer need to be obtained from the candidate set, and the method reduced the training time. Experiments on multiple datasets show that ICVMEKL can effectively improve the accuracy, ICVMEKL＿I can significantly reduce the training time, both of which show good generalization performance. [ABSTRACT FROM AUTHOR]

Online status monitoring and fault recognition of photoelectric composite submarine cables (hereinafter referred to as submarine cables) offers an effective approach for early warning of faults in submarine cables. In order to improve the speed and accuracy of this practice, this paper proposes a submarine cable vibration signal recognition method based on the sparrow search algorithm (SSA) optimized support vector machine (SSA-SVM). Firstly, the submarine cable fault signal was decomposed by the ensemble empirical module decomposition (EEMD) method, allowing for extraction of the kurtosis and energy entropy combination of each component as the training feature set, so as to avoid any potential signal distortion caused by direct noise reduction that may affect the extraction of target features. Then, the SSA was used to optimize the penalty factor and kernel function parameters of the SVM, thereby improving the recognition accuracy. Finally, through the submarine cable vibration signal simulation system based on Brillouin optical time domain analysis (BOTDA), 500 groups of submarine cable vibration signals under anchor smashing, erosion and friction conditions were obtained, and 3 types of noisy signals were decomposed by EEMD, to extract the feature data set of each component, in which 80% is taken as the training set, and the remaining 20% as the test set. The EEMD-SSA-SVM algorithm proposed in this paper was compared with the EEMD-PSO-SVM and SVM algorithms. The results show that the EEMD-SSA-SVM algorithm exhibits higher accuracy and superior optimization ability. Specifically, the accuracy of the test set reaches 95%, surpassing that achieved by other algorithm models.

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Changes in the structure and function of female pelvic floor organs, muscles and surrounding connective tissue are the main causes of pelvic floor disorders (PFD), and a detailed understanding of the spatial anatomy of the pelvic floor under physiological and pathological conditions can help clinicians make accurate diagnoses and optimize treatment strategies. The widespread use of magnetic resonance imaging (MRI) technology has improved clinicians' understanding of the pelvic floor structure, but presentation of the pelvic floor structure is still limited to two-dimensional planar images and the three-dimensional (3D) spatial structure cannot be visualized. The 3D reconstruction technology based on MRI can transform the structure of the female pelvic floor organs into digital models for observation, measurement and even 3D printing for inverse analysis, which greatly promotes the clinical understanding of the pathogenesis and treatment of PFD and provides basis for individualized treatment. However, the existing MRI-based 3D reconstruction technology is not yet mature, for example different advantages and disadvantages of different commercial softwares, different algorithms of different image recognition techniques, lack of uniform evaluation criteria for 3D reconstruction models, and difficulty of balancing modeling efficiency and quality, which limit it as an effective medical research tool for PFD. This paper reviews the research of 3D digital model reconstruction of female pelvic floor tissue and organs based on MRI, aiming to provide reference for clinical diagnosis, treatment and scientific research of PFD. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous increase of import and export of various countries, people have put forward higher requirements on the efficiency and accuracy of meat and meat products safety indicators detection. Since electronic nose technology is simple to operate and allows rapid and nondestructive testing, it can meet today's need for efficient test of meat and meat products. In this paper, the detection principle of electronic nose technology was introduced firstly, and its development process was described from two aspects of hardware and software system. Then, the application research progress of electronic nose technology in meat and meat products detection in recent years from the aspects of freshness, adulteration, flavor evaluation and microbial contamination of meat and meat products was analyzed. Different electronic nose instruments and equipment or different pattern recognition algorithms result in different analysis results. Therefore, it highlighting the feasibility and advancement of electronic nose technology application in various aspects of meat and meat products detection. At the same time, in view of the application research results of electronic nose technology in the detection of meat and meat products, the paper pointed out the shortcomings of electronic nose technology, for example: The analysis effect of electronic nose technology was uneven, the price of electronic nose equipment was relatively expensive, and the application range of large electronic nose equipment was limited. Therefore, there were still some difficulties and problems of electronic nose technology in the aspects of universality and popularization. Finally, in view of the shortcomings of the current electronic nose technology, the development and application prospects of the electronic nose technology in the future were prospected. In terms of hardware system, with the research and development continuously of new gas sensitive materials, the durability and sensitivity to smell recognition of the electronic nose by improving the performance of the electrode film material of the electronic nose sensor array was enhanced. In terms of software system, with the upgrading continuously of computer systems, a supporting platform for the emerging and complex pattern recognition algorithms was provided. New pattern recognition algorithms in the pattern recognition system of electronic nose technology were explored and introduced, so that electronic nose technology can achieve faster and more accurate recognition and analysis of odors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To accurately recognize and evaluate tennis actions, by combining computer vision with tennis related knowledge, this paper proposes a tennis action recognition and evaluation method based on PoseC3D. Firstly, a pose estimation model based on resnet-50 is used to detect human targets in tennis video and extract bone key points. Secondly, the PoseC3D model is trained through the video data set collected in the professional tennis court, so that it can classify the sub actions of tennis. Thirdly, the dynamic time warping algorithm is used to evaluate the classified actions. Finally, based on the collected video data set, a large number of experiments are carried out. The results show that the Top1 accuracy of the proposed tennis action recognition method based on PoseC3D can reach 90.8%. Compared with the methods based on graph convolution network, such as AGCN and ST-GCN, it has stronger generalization ability. Moreover, the proposed scoring algorithm based on dynamic time warping can give real-time and accurate evaluation scores for corresponding actions after action classification, reducing the work intensity of tennis teachers and effectively improving the quality of tennis teaching. [ABSTRACT FROM AUTHOR]

With the continuous increase of import and export of various countries, people have put forward higher requirements on the efficiency and accuracy of meat and meat products safety indicators detection. Since electronic nose technology is simple to operate and allows rapid and nondestructive testing, it can meet today's need for efficient test of meat and meat products. In this paper, the detection principle of electronic nose technology was introduced firstly, and its development process was described from two aspects of hardware and software system. Then, the application research progress of electronic nose technology in meat and meat products detection in recent years from the aspects of freshness, adulteration, flavor evaluation and microbial contamination of meat and meat products was analyzed. Different electronic nose instruments and equipment or different pattern recognition algorithms result in different analysis results. Therefore, it highlighting the feasibility and advancement of electronic nose technology application in various aspects of meat and meat products detection. At the same time, in view of the application research results of electronic nose technology in the detection of meat and meat products, the paper pointed out the shortcomings of electronic nose technology, for example: The analysis effect of electronic nose technology was uneven, the price of electronic nose equipment was relatively expensive, and the application range of large electronic nose equipment was limited. Therefore, there were still some difficulties and problems of electronic nose technology in the aspects of universality and popularization. Finally, in view of the shortcomings of the current electronic nose technology, the development and application prospects of the electronic nose technology in the future were prospected. In terms of hardware system, with the research and development continuously of new gas sensitive materials, the durability and sensitivity to smell recognition of the electronic nose by improving the performance of the electrode film material of the electronic nose sensor array was enhanced. In terms of software system, with the upgrading continuously of computer systems, a supporting platform for the emerging and complex pattern recognition algorithms was provided. New pattern recognition algorithms in the pattern recognition system of electronic nose technology were explored and introduced, so that electronic nose technology can achieve faster and more accurate recognition and analysis of odors.

In the process of industrial production of sapphire crystals, the seeding step plays a vital role. Seeding needs to be performed under conditions where the temperature gradient is small and the temperature distribution tends to be stable. At present, the industrial production of sapphire mainly relies on manual experience to control the seed rod to achieve the crystal pilot operation, but the low accuracy of manual pilot operation will lead to poor quality of finished products and waste of resources. Therefore, this paper proposes a method based on sapphire visual spokes pattern recognition method to detect the free liquid surface state of sapphire melt, so as to realize an efficient crystal induction mechanism. In this method, classic skeletoning algorithm was used to refine the spoke pattern, and Harris operator was used to extract the characteristic information. The extracted characteristic information was put into the motion trajectory model to judge the stability of the melt, and the stability of the temperature distribution of the liquid surface was analyzed to achieve crystal induction. Experimental results show that this algorithm is effective, the efficiency of sapphire crystal seeding is greatly improved, and the yield of the finished product is also improved, which can effectively guide the industrial production of sapphire. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Optical detection of partial discharge (PD) is an important way to reflect the insulation status of equipment. C4F7N/CO2 gas mixture is one of the most potential substitutes for SF6 at present, but there is a lack of research on its optical PD characteristics and diagnostic methods. In this paper, a PD multispectral array detection platform that can collect 7 characteristic bands is constructed, and 4 kinds of PD defects are produced. The similarities and differences of the PD multispectral characteristics in phase distribution, energy distribution, and feature stacking map under the conditions of 5 different ratios of C4F7N/CO2 gas mixture and pure SF6 gas are analyzed. Finally, a novel method of PD diagnosis based on multispectral features (MF) and k-nearest neighbors (KNN) is proposed. The experimental results show that the fault recognition accuracy in pure SF6 can reach 96.2%. The recognition rate of C4F7N/CO2 gas mixture is above 88%, and the highest accuracy rate is 91.1%. This method has a guiding significance for the PD diagnosis of environmentally friendly gas-insulated equipment, and provides a new route for traditional PD detection and diagnosis.

Fermentation is the key to the formation of characteristics and quality of black tea. It is very important to evaluate the fermentability of black tea quickly and accurately. In this paper, the application of artificial sensory evaluation technology, biochemical component detection technology, intelligent biomimetic technology, data fusion technology and intelligent biomimetic equipment in the evaluation of black tea fermentation quality in recent years was reviewed, and the development trend of intelligent biomimetic technology in the evaluation of black tea fermentation in the future was prospected.

Time series signals are widely used in various pattern recognition applications.In order to solve the problem of low pattern recognition rate of time series signals for a large number of targets,this article uses a variety of transform methods to convert time series signals into images,and performes pattern recognition using image classification algorithms.In the experiment,the mechanomyography (MMG) corresponding to 30 sign languages on the forearm are collected and converted into diffe-rent image styles,and a convolution neural network (CNN) framework is designed to establish pattern recognition classification models for the images.The models are optimized twice with the application of transfer learning algorithm,and the recognition rate of the best classification model reaches 98.7%,which is much higher than the recognition rate of traditional machine learning algorithms.The experimental results imply that the image processing of time series signals can effectively improve the recognition rate of multi-target pattern recognition of MMG.This paper can provide references for pattern recognition of other time series signal.

The kernel method maps the original spatial data to a high-dimensional Hilbert space by nonlinear mapping and hides the mapping in the linear learner. The kernel function is used to replace the complex inner product operation in high-dimensional space, which can effectively avoid the ‘curse of dimensionality’ caused by high-dimensional space calculation. The kernel method has the advantages of learnability, efficient calculation, linearization and good generalization performance, which provides a new effective way to solve the problem of nonlinear target tracking. The traditional target tracking methods often use the tracking model to predict the current motion state of the target and ensure the accuracy and real-time tracking. The kernel method provides a general way of linearization and can be independent of the specific model with efficient computing. Introducing the kernel learning method into target tracking is expected to improve environmental adaptability. In this paper, based on the idea of kernel method, the current research progress of kernel learning target tracking is presented, including target detection method based on kernel learning, generative and discriminative target tracking method, and multi-kernel learning method with different kernel functions. Further research on kernel learning target tracking for kernel function optimization, long-term tracking, feature extraction and target occlusion are prospected.

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)