Search

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [ABSTRACT FROM AUTHOR]

The global economy appears the trend of anti-globalization under the influence of COVID-19. Based on the input-output table of lead database from 2006 to 2020, this paper divides the factors that affect the development of financial industry in China, the United States and Russia into six aspects: price, intermediate input, household consumption, government consumption, export and import. ADGA-BP neural network model is proposed in this paper, which is based on six aspects of price, intermediate input, consumer, government consumption, export and import. The intermediate input is decomposed from the perspective of industrial structure to study the interrelationship between financial industry and other industries in the three countries. The results show that the intermediate input is the main factor in the development of financial industry in the three countries, but the source industries of the intermediate input are not the same; the two factors of household consumption and price are closely related to the development of financial industry in the three countries, and they all play a role in promoting China, while the relationship between household consumption and the United States and between price and Russia is reverse; Government consumption only has a significant impact on Russia; from the perspective of mutual influence, the mutual investment between the financial industry of China and the United States is relatively large, while the relationship between the Russian financial industry and the two countries is relatively weak. It shows that under the background of covid-19, the development of financial industry is affected. [ABSTRACT FROM AUTHOR]

This paper attempts to apply recurrent neural networks (RNN) to price forecasts and financial trading. Compared with previous neural networks models, the recurrent neural network can better use the previous information to infer subsequent events, which is more suitable for price time series analysis. Long Short-Term Memory (LSTM) has made structural changes to the RNN to avoid long-term dependency problems. The empirical research uses the 2010–2017 price panel data of four kinds of soybean futures in China's futures market, and confirms the model's improved predictive ability through statistical tests. The empirical analysis of futures trading verifies the practice of these model strategies in terms of risked return. This paper improves and expands the application of recurrent neural networks model, and provides a new idea for applying artificial neural network algorithm to futures trading. [ABSTRACT FROM AUTHOR]

In the new normal period, the trend changes and adjustments of the environment such as international trade, production capacity, labor supply and resource constraints have put forward new requirements for China's industrial development, which have brought new challenges and given new opportunities. In the new normal stage where economic growth continues to decline, industrial growth is still an important support for economic growth. The advancement of industrial technology is the main driving force for improving the total factor productivity of the industrial industry. Therefore, the most important thing to promote industrial growth is to upgrade the level of industrial technology. In response to the above-mentioned problems, this paper analyzed the relationship between industrial technology and industrial output in the new normal environment by using the BP neural network (BPNN) algorithm. The connection between the two has been found, which provided a clear direction for the functional adjustment of economic law. Experimental studies have shown that there is a positive relationship between industrial technological progress and industrial output. When other conditions are the same, and when the non-new normal is selected, industrial output increases by about 0.36% for every 1% increase in industrial technological progress. When choosing to be in the new normal, industrial technological progress has a higher impact on industrial output. For every 1% increase in technological progress, industrial output increases by about 0.39%. [ABSTRACT FROM AUTHOR]

Extreme value theory can analyze the extreme changes of financial market returns, and it is an effective financial risk management model. The distribution characteristics of financial assets' returns are the core content of all financial models. The assumptions about the fluctuation behavior of returns and its distribution characteristics are of great theoretical and practical significance for the measurement and management of financial risks. We compare the estimates of value at risk by the peak over threshold model with that by the traditional variance-covariance method. During downturns, the peak over threshold model based value at risk estimates is higher relative to that by the traditional variance-covariance approach. At the same time, this paper conducts statistical analysis of basic data through data collection and combines the establishment of statistical methods to study the practical effects of various factors on carbon productivity in China. In addition, this paper constructs a low-carbon economic neural network model based on particle swarm optimization to study carbon emissions. Finally, on the basis of analysis and research, this paper puts forward policy recommendations for China's future low-carbon development model, and then provides reference for guiding the development of China's future low-carbon model and provides theoretical basis for subsequent related research. [ABSTRACT FROM AUTHOR]

As we know, AI is deeply integrated with all walks of life. While upgrading traditional products and transforming traditional industries, a large number of new products, new industries and new formats have emerged in large numbers, and new industrial space has been further expanded. The potential for steady economic growth remains huge. However, the influence of merelygrowing capital asset and labor force dimension in terms of boosting economic progress has been waning globally. Considering the China's data panel data outlying areas during 1978–2017 as the research sample, possible economic growing rate of Chinahas been calculated in detail according to the labor-intensive structural time-varying elastic model. It shows a U-shaped development track during 2018–2027, with an average growth rate between 5.00%and 6.00%. There are also great variances in the probable economy growing rates in different regions in China. It shows that relying solely on traditional factors of production to drive potential economic growth, China has been unable to maintain the prosperity of stable development over the past few decades. Based on the experience of typical representative countries, this paper also puts forward some relevant countermeasures and suggestions including focusing on a new round of technological revolutions such as big data, new generation AI, 5 G, IOT, cloud computing, robotics, and blockchain to progress economical growing rate of China in the future. [ABSTRACT FROM AUTHOR]

The construction of construction projects is an important industry of national social and economic development, and price management control is an important part of construction projects, and has become an important factor for major construction companies in China to manage construction projects. At present, the internal construction price management is not the best, nor the most ideal. Few investments exceed the budget, mainly due to defects in effective construction price management, lack of advanced technology and lack of prospects for prepayment, which make it difficult to match the actual and expected results of construction project price management. The actual results are always unsatisfactory. In this paper, the engineering cost estimation model is studied, and the neural network comprehensive prediction model is established to improve the accuracy and application technology of the prediction model. By using the building of BIM technology and neural network model, and effectively using the price advantage of ICT, it is used in the construction industry, and the cost is strictly controlled, so as to bring huge profits to the enterprise and promote the development of the enterprise. [ABSTRACT FROM AUTHOR]

Climate warming has triggered a series of external uneconomic problems that have had a tremendous impact on human production and life. At the same time, the current intelligent models for low-carbon economic research have some drawbacks. Therefore, in order to explore a more scientific low-carbon development model, based on the analysis of low-carbon development at home and abroad, this study combines particle swarm optimization and neural network to analyze China's carbon productivity through multidimensional analysis, determine multiple factors and select more important data as factor analysis objects.In this paper, the authors analyze the neural network model for new energy industry economy based on particle swarm optimization. The result shows that the particle swarm algorithm has a very fast convergence rate and can avoid falling into local minimum values in the later stage and finally achieve higher solution precision. The development of new energy industry is an important strategy and means for implementing sustainable development and adjusting the industrial structure. This article, from the perspective of industrial economics, uses genetic neural networks and fuzzy uncertainty to explore how to effectively combine the capital market with the development of new energy industries. Bring into play the role of the capital market in supporting the development of new energy industries. [ABSTRACT FROM AUTHOR]

The development of Daqing Oilfield in China has entered the middle and late stages of high water cut. At this time, oil-water two-phase flow is ubiquitous, and its flow rate is very difficult to measure accurately. Addressing this issue, the measurement model and simulation model of electromagnetic flow transducer (EFT) with saddle excitation structure is designed in this paper. Then the distribution characteristics of magnetic flux density of different excitation structures are analyzed by finite element simulation. Furthermore, the prediction model between the parameters of different excitation structures and the performance evaluation indexes is established based on RBF neural network. Through normalization and weight assignment on the output of neural network model, the structure optimization factor is constructed. Then the optimum solution of this factor is gotten, and the optimum parameters of EFT's excitation structure are obtained. In addition, an EFT with the optimum structure is developed and tested in Daqing oilfield, and the experiment results show that the EFT has high precision, especially in the high viscosity wells. [ABSTRACT FROM AUTHOR]

The modeling and prediction of short-term traffic flow can reflect the prediction results of the traffic state and traffic flow data. In this paper, first, we use a high-dimensional tensor to represent the multi-mode characteristics of traffic flow data, and we make use of the basic operations properties of tensors, such as Tucker decomposition, to study the methods for filling in data, such as ITRM. Additionally, we preprocess the lost traffic flow and abnormal data. At the same time, we study the short-term traffic flow based on the "week-day-time" multi-mode of the traffic flow data. Using the grey model (GM (1, 1)) to predict the same period of the weekly mode, the scrolling grey model (SGM) of the same time period is predicted. For the time mode, a neural network time series of wavelet analysis is used to predict the traffic flow forecast during the same period. Then, the prediction results of the three different models are weighted by the grey correlation analysis method, and then, the coupling prediction model of the three models is obtained. In the end, according to the traffic flow data of the main road of Shaoshan road in Changsha, Hunan, China, we first preprocess the lost data by using the filling algorithm for the tensor data, and then, we make the traffic flow data complete, use the three tensor data modes of traffic flow, and analyze the results. The experimental results show that the coupling prediction model with the tensor model is much better than the single GM (1, 1) model, the SGM and the neural network prediction model. [ABSTRACT FROM AUTHOR]

How to analyze and predict the consumption behavior of Chinese residents is of great significance based on the existing consumption theory research and the actual situation in China. This study studies the factors influencing the consumption of urban and rural residents in China through factor analysis and builds a model of urban and rural residents' consumption expenditure based on BP neural network. At the same time, through the data collection, the consumption data of urban and rural residents in China from 1986 to 2017 were collected. The BP neural network prediction model was used to predict the consumption expenditure of urban and rural residents, and the absolute error and relative error were obtained. In addition, this study establishes a combined forecasting model based on Lasso regression analysis and BP neural network to predict the consumption expenditure of urban and rural residents and compares its prediction results with BP neural network prediction model. The research shows that the combined prediction model based on Lasso regression analysis and BP neural network comprehensively surpasses BP neural network prediction model in prediction accuracy, and it has certain effectiveness and puts forward practical suggestions, which provides theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Based on the climatic, surface and human factors of desertification, an index system and an intelligent model of desertification forewarning are established for the downstream of Tarim River. Based on the remote sensing, climate, land surface, and human data in 1990, 2000, and 2006, the ArcGIS software is used to quantify and rasterize the data of each factor. The forewarning model reveals the distribution of desertification degree in the three study periods on a scale of 30×30 m and the results are used to conduct the correction and verification of model parameters. The development trend of desertification in the study area in 2015 is then forecasted under the conditions of "intermittent water conveyance" and "no water conveyance" respectively. Through the parameter correction, the accuracy of the simulated distribution of desertification degree can be more than 90%, showing a good performance of the model. The intermittent water conveyance plays a certain role in the reversal of desertification in the downstream of Tarim River, which is a necessary measure to prevent a complete desertification. However, the reversal effect is only confined to a limited extent along the river channel and cannot thoroughly change the current situation of desertification in the study area. [ABSTRACT FROM AUTHOR]