Scientific and accurate prediction of high-tech industries is of great practical significance for government departments to grasp the future economic operation and formulate development strategies. In this paper, aiming at some shortcomings of neural network (NN) applied in economic forecasting, GANN was introduced to construct the economic forecasting model of high-tech industry. Genetic algorithm (GA) has simple calculation and strong robustness and can generally ensure convergence to the global optimum, which effectively overcomes the shortcomings of NN using gradient descent method. In order to verify the feasibility of the economic forecasting model in this paper, the comparative experiments of different models are carried out in this paper. Experimental results show that the proposed algorithm has faster convergence speed and greater generalization ability, and the average error rate is reduced to about 1%. The prediction accuracy of this model reached 95.14%, which was about 11.93% higher than the previous model. Applying the economic forecasting model in this paper to the economic forecasting of high-tech industries can provide the means and reference value for the government to formulate regional future economic development plans, forecast, and control the economic growth and development direction.

Establishment of Economic Forecasting Model of High-tech Industry Based on Genetic Optimization Neural Network

With the continuing increase of people’s living standards, tourism has now become the primary mode of holiday and leisure activity. Scientific and reasonable route planning can best meet people’s essential needs, provide detailed planning in terms of travel time and cost, resolve the impact of various external factors, and realize the complementary benefits of scenic tourism while ensuring that each factor configuration is optimized. External factors such as trip time, cost, travel date, and transport mode all have an impact on route planning. In order to develop a mathematical model and gather other information, it is frequently essential to determine two or more of these influencing elements. The ideal plan for the influencing elements is to first develop an optimal self-driving trip route that meets all of the requirements. This strategy is becoming increasingly popular among the general public, and it focuses on how to spend the least amount of money in order to visit more tourist attractions, construct the most reasonable routes, and provide circumstances for tourists to save time and money. The purpose of this study is to examine the topic of self-driving travel route planning and to suggest the creation of a route planning model. In this paper, a self-driving journey route planning model is built using a recurrent neural network model. The driving route is designed with a high-speed priority strategy, and the travel route selection problem takes into account the consumption condition as well as the quantity of scenic sites. The developed model is used to optimize self-driving trip routes, and the results are promising.

/pdf/self-driving-tour-travel-route-planning-model-construction-saboo09w.pdf

Self-Driving Tour Travel Route Planning Model Construction Using Recurrent Neural Network

Moving target recognition and analysis is an important research direction in the field of computer vision, which is widely used in our life, such as intelligent robot, video surveillance, medical education, sports competition, and national defense security. By analyzing the video of weightlifting, this paper extracts the key postures of athletes’ training, so as to assist coaches to train athletes more professionally. Based on DL (Deep Learning), a key pose extraction method of sports video (RoI_KP for short) based on classified learning of regions of interest is proposed. By fine-tuning CNN (Convolutional Neural Network), a network model suitable for video classification of weightlifting in the region of interest is obtained. Finally, according to the classification results, the selection strategy of classification results is designed to extract key poses. According to the characteristics of different modal information, different DNN (deep neural network) is adopted, and various depth networks are combined to mine the multi-modal spatio-temporal depth features of human movements in video. Experimental results show that the method proposed in this paper is very competitive.

https://downloads.hindawi.com/journals/misy/2022/5168898.pdf

A Method of Key Posture Detection and Motion Recognition in Sports Based on Deep Learning

As a revolutionary education model, the flipped classroom teaching model has unique advantages over the traditional education model. How to change the teaching method of flipped classroom into a teaching method suitable for college English courses is a problem. The goal of this research is to investigate how to use data mining and few-shot learning technology to investigate the impact of MOOC and flipped classroom task-based college English teaching modes. This work provides a data mining-based decision tree algorithm and examines the enhanced decision tree method. The experimental results of this study demonstrate that two students in each of the two groups believe that the English teaching mode, which is mostly taught by traditional teachers, is very favorable to a thorough understanding of basic knowledge, accounting for 4% of the total. There are 17 students that believe the new teaching methodology is really beneficial, accounting for 34% of the total. This mode was determined to be effective for in-depth knowledge of the basics by a total of 25 students. It can be seen that the flipped classroom model is more popular with students.

https://downloads.hindawi.com/journals/cin/2022/9770747.pdf

MOOC and Flipped Classroom Task-Based English Teaching Model for Colleges and Universities Using Data Mining and Few-Shot Learning Technology

The impact of big data technology on the Chinese Men’s Professional Basketball League (CBA) is studied in this study. With the growth of the CBA league in recent years, teams from the western region have emerged. Although there is a trend of “multipolarization” in the competition pattern, it overall depicts a situation in which the east is strong and the west is weak. In addition, the method of recruiting foreign aid workers has improved over time, as has the foreign aid system. The number of foreign aids has increased from one in 1995 to a high of 47, and the quality of foreign aid has improved over time. This not only strengthens the club team, but it also has a significant impact on the CBA league’s competition patterns. The basic factors that affect the changes in the competition pattern are institutional factors, coaching factors, star player factors, foreign aid factors, regional economic factors, reserve talent factors, team style factors, player security factors, and so on, according to research and analysis of the CBA league’s development and changes. Six teams have won the CBA league championship in its 22 seasons. The Bayi and Guangdong teams each won eight times, accounting for more than 70% of the total number of championships.

/pdf/chinese-professional-basketball-league-using-mobile-2z09kpve.pdf

Chinese Professional Basketball League Using Mobile Computing and Big Data Technology

Underwater image enhancement method with light scattering characteristics

Underwater images suffer color distortions and low contrast. This is because the light is absorbed and scattered when it travels through water. Different underwater scenes result in different color deviations and levels of detail loss in underwater images. To address these issues of color distortion and low contrast, an underwater image enhancement method that includes two-level wavelet decomposition maximum brightness color restoration, and edge refinement histogram stretching is proposed. First, according to the Jaffe-McGlamery underwater optical imaging model, the proportions of the maximum bright channel were obtained to correct the color of underwater images. Then, edge refinement histogram stretching was designed, and edge refinement and denoising processing were performed while stretching the histogram to enhance contrast and noise removal. Finally, wavelet two-level decomposition of the color-corrected and contrast-stretched underwater images was performed, and the decomposed components in equal proportions were fused. The proposed method can restore the color and detail and enhance the contrast of the underwater image. Extensive experiments demonstrated that the proposed method achieves superior performance against state-of-the-art methods in visual quality and quantitative metrics.

Underwater image enhancement via two-level wavelet decomposition maximum brightness color restoration and edge refinement histogram stretching.

An efficient nanodiamond-based monolithic foam catalyst prepared by a facile thermal impregnation strategy for direct dehydrogenation of ethylbenzene to styrene

Sustainable nanocarbon catalysts have made measurable progress in ethylbenzene direct dehydrogenation (DDH). Thanks for the distinctive physical and chemical properties of Nanodiamond (ND), the considerable attention have been paid to diverse catalytic transform processes involving the DDH reaction. However, the powder-form carbon cannot be directly used in fixed-bed reactors owing to the resulting huge pressure drop. Herein, inspired by the ship-in-boat strategy, we present an ingenious assembly-in-foam approach for the preparation of a novel hybrid structured monolithic catalyst featuring an aerogel of sulfur-doped nanodiamonds/carbon nanotubes hybrid within the macropores of SiC foam (ND−CNT−SDS/SiC) by heating the freeze-dried macroporous hybrid gels composed of CNT, ND, and SDS in N2 atmosphere. Owing to the promoted NDs dispersion, strengthened mass transfer, and sulfur-doping, the nanodiamonds/carbon nanotube hybrid foam monolithic catalyst exhibits a terrific catalytic performance with high stability for energy-saving styrene production from DDH of ethylbenzene reaction, as well as overcoming the shortcomings of application of powder-formed nanocarbon in heterogeneous catalysis. Furthermore, the emerging Assembly-in-Foam way for preparing nanocarbons-based foam monolithic catalyst towards diverse chemical processes has been paved. 

Assembly‐in‐Foam Approach to Construct Nanodiamond/Carbon Nanotube Hybrid Monolithic Carbocatalysts for Direct Dehydrogenation of Ethylbenzene to Styrene

Self-powered flexible transparent ultraviolet (UV) photodetectors (PDs) have aroused great attention for the next-generation portable and wearable optoelectronic devices. However, it is still a challenge to fabricate high performance self-powered...

Xiaojing Wei

Papers

Underwater image enhancement method with light scattering characteristics

Underwater image enhancement via two-level wavelet decomposition maximum brightness color restoration and edge refinement histogram stretching.

An efficient nanodiamond-based monolithic foam catalyst prepared by a facile thermal impregnation strategy for direct dehydrogenation of ethylbenzene to styrene

Assembly‐in‐Foam Approach to Construct Nanodiamond/Carbon Nanotube Hybrid Monolithic Carbocatalysts for Direct Dehydrogenation of Ethylbenzene to Styrene

Fast-response self-powered flexible transparent ultraviolet photodetectors based on CuO/ZnO nanowire array heterojunction