Innovation is the engine and accelerator that drives high-quality economic and enterprise development. In recent years, the output of scientific and technological innovation in China has been high, but the phenomenon of low efficiency and low quality of innovation occurs frequently. In this study, first, technological innovation efficiency (TIE) was measured. Then, a dynamic evaluation and analysis of spatial-temporal characteristics of efficiency were performed. Lastly, the driving factors of innovation efficiency were explored. TIE was calculated dynamically in 30 provinces of China from 2011 to 2019 based on the improved super-efficiency SBM-DEA model. Then, the kernel density estimation method was adopted to analyse the spatial-temporal differentiation characteristics and dynamic evolution process of provincial efficiency. The findings confirm that from 2011 to 2019, the top five provinces for TIE in China were Beijing (1.0), Shanghai (0.96), Hainan (0.96), Jilin (0.94) and Tianjin (0.91). The provinces with lowest average efficiency were Qinghai (0.77), Ningxia (0.73) and Inner Mongolia (0.73). The significant differences in the level of technological innovation in different regions were caused by the long-term and in-depth implementation of the government’s strategy of revitalising science and driving innovation in parts of areas. The findings of kernel function confirm that the TIE in most parts of China was gradually polarised. Furthermore, the results show that for every 1 unit of government R&D funding support, the average marginal utility of the expected TIE will reach 0.192, which is more significant in the central and western regions. On this basis, combined with environmental factors of innovation market, infrastructure, financing and enterprise innovation potential, the article also extracts the driving factors that affect the differences in provincial efficiency. The findings provide a reference for guiding provinces to carry out innovation activities independently and improve innovation quality and efficiency.
In this paper, we present a definition on the degree of overlap between two clusters and develop an algorithm for calculating the overlap rate. Using this theory, we also develop a new hierarchical cluster merging algorithm for image segmentation and apply it to the ship detection in high resolution image. In our experiment, we compare our method with several existing popular methods. Experimental results demonstrate the effectiveness of the overlap rate measuring method and the new ship detection method.
Common methods for matching multivariate time series such as the Euclid method and PCA method have difficulties in taking advantage of the global shape of time series. The Euclid method is not robust, while the PCA method is not suitable to deal with the small-scale multivariate time series. This paper proposes a pattern matching method based on point distribution for multivariate time series, which is able to characterize the shape of series. Local important points of a multivariate time series and their distribution are used to construct the pattern vector. To match pattern of multivariate time series, the Euclid norm is used to measure the similarity between the pattern vectors. The global shape characteristic is used in the method to match patterns of series. The results of experiments show that it is easy to characterize the shape of multivariate time series with this method, with which various scales can be dealt with in series data.
Stocks are a common kind of financial time series. In this paper we present a new similarity measure for time series clustering, and then select a set of stocks to create efficient portfolio, which is of crucial importance in the process of creating efficient portfolio. We largely reduce the efficient times of portfolio using clustering-based selection, and only select a subset of stocks from different groups to create efficient portfolio each time, then it is easy to get the portfolio with the lowest risk at a given level of return. A set of 100 stocks were utilized for experiments, and compared with other selection methods, the results show that our method could largely reduce the efficient times of portfolio. Group-ward hierarchical cluster was used to cluster stocks.
In this paper, a color texture retrieval system using multiresolution mosaic for flexible image classification is developed. First, a representation of color texture image is investigated. The texture can he characterized by features such as shape,, structure, color and randomness. The features of texture are extracted by using operators. Then the feature images are transformed to lower dimension feature vectors using multi-resolution mosaic processing. Next, a similarity function is calculated for an unknown input color texture image. Finally, a new distance with weight functions is calculated by using similarity functions and the results are sorted. By selecting weight functions, we can reflect the impression of texture features flexibly in retrieval. The effectiveness of retrieval is demonstrated with several color texture image database in the simulations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.