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The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

With the improvement of our country's economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system. [ABSTRACT FROM AUTHOR]

Distributed Virtual Reality (DVR) is a combination of network and virtual reality technology, it could facilitate to construct a uniformly shared Distributed Virtual Environment (DVE) by using network to connect geographically distributed multiplayers. This paper concentrates on the theoretical research and practical development about Multiplayer Virtual Intelligent System (MVIS), and the main contribution could be summarized as two points. (1) Based on the DVR technology, this paper presented some theoretical research on MVIS, including the classification of virtual entities, communication pattern of entities, and the behavioral consistency research. Furthermore, a Multiplayer Earliest Deadline First (MEDF) program was proposed in order to guarantee the consistency of entities. (2) A prototype algorithm experiment system, called Multiplayer Graph-algorithm Intelligent System (MGIS), was designed. MGIS not onlyefficiently solves many problems in traditional computer algorithm teaching, such as high-abstraction, difficulty to understand, and lack of interaction mechanism; but also extends the application of DVR to cultural tourism, because MGIS is developed on the 3D scene of Lushan Mountain, which is one of the notable tourist attractions in China, and was included in the UNESCO World Heritage list in 1996. What i's more, MGIS illustrates the ability of expression, applicability and generality of the theoretical research about MVIS. [ABSTRACT FROM AUTHOR]

China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants' location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants' positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control. [ABSTRACT FROM AUTHOR]

With the steady growth of the economy and the rapid development of modern industrial technology, the problem of environmental pollution has increased. To continue to develop, it is necessary to thoroughly implement the sustainable development strategy, and we must pay more attention to environmental issues. One of the important management tools implemented in China for environmental management is environmental quality monitoring and evaluation. Environmental quality monitoring can scientifically evaluate the environmental quality of a region, scientifically evaluate and forecast the environmental management and environmental engineering, and provide scientific basis for environmental management, environmental engineering, formulation of environmental standards, environmental planning, comprehensive prevention and control of environmental pollution, and ecological environment construction. This paper will discuss the basic principles of neural network and the implementation process of MATLAB and in the MATLAB software implementation and display process. At the same time, the results of different parameters are analyzed through experiments, and the network parameters are constantly adjusted to improve the accuracy of the evaluation results. Taking the regional environment as an example, two monitoring methods are proposed, and a variety of neural network models are used to analyze each prediction method. Case study results show that the latter method has a better prediction effect. [ABSTRACT FROM AUTHOR]

Crew scheduling of the passenger dedicated line is the daily work plan of train attendants, and crew scheduling problem is one of the key problems of the train crew management. Considering the actual condition of China's railway and crew rules, a crew scheduling model is established in this paper to minimize rest time between two consecutive shifts in all roundtrips. In order to solve the scheduling model, in this paper a serial-parallel ant colony optimization algorithm (SP-ACO) is provided. The SP-ACO proposed two improvements: (1) a serial-parallel ant grouping strategy, it means that we let every group of ants find the solution one group by one group for reflecting the positive feedback in a generation; (2) the transition method based on the roulette wheel selection, it means that we randomly select between the certain search and the roulette wheel to make the diversity of solutions for avoiding all ants toward the same part of the search space. Finally, two application cases of the scheduling model are represented. The two cases results show that the methods can not only automatically set roundtrips, but also decrease the number of roundtrips, and the results also show that the feasibility of model and the validity of improved ant colony algorithm for crew scheduling. [ABSTRACT FROM AUTHOR]

Model accuracy and running speed are the two key issues for flood warning in urban areas. Traditional hydrodynamic models, which have a rigorous physical mechanism for flood routine, have been widely adopted for water level prediction of rainwater pipe network. However, with the amount of pipes increasing, both the running speed and data availability of hydrodynamic models would be decreased rapidly. To achieve a real-time prediction for the water level of the rainwater pipe network, a new framework based on a machine learning method was proposed in this paper. The spatial and temporal autocorrelation of water levels for adjacent manholes was revealed through theoretical analysis, and then a support vector machine (SVM)-based machine learning model was developed, in which the water levels of adjacent manholes and rivers-near-by-outlets at the last time step were chosen as the independent variables, and then the water levels at the current time step can be computed by the proposed machine learning model with calibrated parameters. The proposed framework was applied in Fuzhou city, China. It turns out that the proposed machine learning method can forecast the water level of the rainwater pipe network with good accuracy and running speed. [ABSTRACT FROM AUTHOR]

In recent years, with the growth of China's economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people's lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide. [ABSTRACT FROM AUTHOR]

This paper presents a new segmentation method based on division of space which chooses principal curves to extract strokes of characters and form the initial stroke set. The strokes in the initial set are disposed by the fuzzy theorems and grouped based on the confidence of the classifiers. The training sample space is constructed by the Affinity Propagation (AP) algorithm and the Biomimetic Pattern Recognition (BPR) theory. The strokes are arranged in a sequence divided by calculating the distance of each subsequence to the relative subspace. Finally, with the maximum posterior (MAP) criterion, the optimal segmentation hypotheses described in a probabilistic model is found. Our system is validated by experimental results from samples of real Chinese bank checks, achieving a correct rate of without 95.23% rejection. [ABSTRACT FROM AUTHOR]

One of the biggest difficulties, which China's power infrastructure is now experiencing, is the capacity to estimate demand of short-term electricity. It can accurately estimate changes in the total power load in particular locations. Accurate forecasting results, which also serve as a dependable guide for power system operation, may increase the flexibility and resource usage of the contemporary power market. Power load characteristics are impacted by several things. This study presents an estimate method for short-term demand of electricity based on WOA-BILSTM in order to comprehensively evaluate the time series features presented in the power load data and improve the accuracy of power load forecasting. After extracting features for the variables that influence power load, this method uses the bidirectional long-term and short-term memory (BILSTM) neural network layer for bidirectional time series feature learning. Using the local electricity load data from Quanzhou from 2018 as the data set, the prediction model is constructed by screening the multi-dimensional input parameters and selecting the feature vectors with good association as the input carefully. By comparing the outcomes of three popular load forecasting models, including LSTM network, BILSTM network, and WOA-BILSTM, it is demonstrated that the WOA-BILSTM neural network method is more accurate and effective than others two. By building an optimal combination model, this technique could increase the accuracy of power load data's short-term predictions. At the same time, it also reduces the time of personnel debugging. [ABSTRACT FROM AUTHOR]

Peking opera is one of the national cultural heritages in China. However, it is difficult for people to learn the gestures in Peking opera performance, which limits the spread of this traditional culture. To address this issue, we propose a Peking opera gesture training system using infrared sensors. Specifically, we build a character avatar for demonstrating the gestures in Peking opera in the proposed system. Based on the data collected by infrared sensors, a method for calculating gesture similarity is proposed and is applied for the training of Peking opera gestures, which allows natural interactions and provides interactive feedback for user gestures. We conducted multiple experiments to verify the feasibility and effectiveness of the training system. The experimental results showed that the proposed system can overcome the difficulties in the traditional learning process of Peking opera gestures, which helps users to achieve the goal of learning standard Peking opera gestures. The proposed training system greatly eases the learning of Peking opera gestures, adding vitality into the culture of traditional Peking opera. [ABSTRACT FROM AUTHOR]

The intelligent regulation and control strategies for rice and wheat combine harvesters' operation are lacking and the rule of parameter matching is fuzzy in China, around these issues. The dynamic correlation control law among the parameters of rice and wheat, cleaning operation parameters of combine harvesters, the cleaning loss rate and impurity rate, and so on are studied. The intelligent control model for the rice and wheat combine harvester is established based on case-based reasoning (CBR). According to different rice and wheat varieties, water content and other rice and wheat properties, the control scheme of cleaning fan speed, air distributor plate angle and upper sieve opening with low cleaning impurity rate and cleaning loss rate is provided. Through the development of web-based cleaning intelligent control expert system and experimental evaluation, the feasibility and effectiveness of the CBR method in the intelligent control filed of rice and wheat combine harvesters are verified. [ABSTRACT FROM AUTHOR]

Scene text detection is an important research branch of artificial intelligence technology whose goal is to locate text in scene images. In the Tibetan areas of China, scene images containing both Tibetan and Chinese texts are ubiquitous. Thus, detecting bilingual Tibetan-Chinese scene texts is important in promoting intelligent applications for minority languages. In this study, a scene text detection database for bilingual Tibetan-Chinese is constructed using a manually labeled method and an automatic synthesis method, and a text detection method is proposed. First, we predict a text rectangular region and the text center region for each text instance and simultaneously learned the expansion distance of the text center region. Second, based on the classification score of the text center region and the text rectangular region, we obtain the final confidence of each text instance and then filter out the text center region with lower confidence. Third, through the learned expansion distance, the full-text instance from the remaining text center region is recovered. The results show that our method obtains good detection performance; it achieves an accuracy of up to 75.47% during the text detection phase, laying the foundation for scene text recognition in the subsequent step. [ABSTRACT FROM AUTHOR]

Lighting energy consumption occupies a large proportion in the building electricity consumption. It cannot only save energy, but also reduce the uneven and constant illumination of the working face, which makes full use of the natural lighting and combines with the intelligent lighting control. An office building in Zhengzhou of China has been selected as an example for analyzing the climatic factors that influence building daylighting and the factors of the building itself, the illuminance of working faces in different sky models and at different times has been simulated and calculated to analyze the illuminance variation law in the direction of room depth and parallel direction of side Windows. Partition and point combined constant illumination control strategy for the lamps in different areas has been put forward and realized by BP neural network algorithm. By controlling the dimming of artificial light source and adjusting the curtain opening degree intelligently, uniform and constant illumination has been achieved. Energy saving effect in combination with natural lighting has been evaluated to prove that the control strategy cannot only maintain constant illumination in every working face but also significantly reduce the electric energy consumption and carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Recent research has shown that lumbar disease has become common in China. Since the structure of the lumbar spine is extremely complex, a finite element analysis method was used to perform biomechanical simulation and analysis of stress and strain on the L3–L4 lumbar segment to provide both a scientific and theoretical basis for clinical diagnosis and medical research. The MC volume-rendering 3D reconstruction method was the first step to accurately constructing the finite element model of the L3–L4 lumbar sacral segment, which was simulated prior to the addition of the ligaments, fibrous ring, and other major spinal tissue. The finite element model network was classified and the material properties of the corresponding parts were described. According to the normal model, careful simulation and deformation were performed, in addition to intervertebral disc degeneration in various cases. We have provided a detailed and professional analysis of the biomechanical properties, providing a powerful biomechanical basis for the diagnosis of intervertebral disc bulge and degeneration. [ABSTRACT FROM AUTHOR]