Research on technical analysis of basketball match based on data mining

The aim of this paper is to preprocess basketball technology actions, to classify these actionswith data mining technology, to mine association rules among them. The main works are shown below:The common approaches of data mining are discussed, such as preprocessing technology, classification technology, clustering technology and mining rules technology. Both ID3 decision tree classification algorithm association and Apriori association rules algorithm are studied in detail.The paper discusses basketball technology actionsboth on a small scale and a large scale, J48 decision tree classification and Apriori association rules mining algorithm basketball are applied, all these research results should have useful instruction to team

Sliding Mode Control of Cable-Driven Redundancy Parallel Robot with 6 DOF Based on Cable-Length Sensor Feedback

The sliding mode control of the cable-driven redundancy parallel robot with six degrees of freedom is studied based on the cable-length sensor feedback. Under the control scheme of task space coordinates, the cable length obtained by the cable-length sensor is used to solve the forward kinematics of the cable-driven redundancy parallel robot in real-time, which is treated as the feedback for the control system. First, the method of forward kinematics of the cable-driven redundancy parallel robot is proposed based on the tetrahedron method and Levenberg-Marquardt method. Then, an iterative initial value estimation method for the Levenberg-Marquardt method is proposed. Second, the sliding mode control method based on the exponential approach law is used to control the effector of the robot, and the influence of the sliding mode parameters on control performance is simulated. Finally, a six-degree-of-freedom position tracking experiment is carried out on the principle prototype of the cable-driven redundancy parallel robot. The experimental results show that the robot can accurately track the desired position in six directions, which indicates that the control method based on the cable-length sensor feedback for the cable-driven redundancy parallel robot is effective and feasible

Site selection and scale measurement of regional coal reserves: taking Jiangsu Province as an example

AbstractThe frequent coal shortages have widely aroused attention to the construction of coal reserve system, some coal reserve bases have been established in many provinces. But researches are few on site selection and scale measurement. With references to the researches on oil reserves, the paper first explored the influential factors on the site selection of regional coal reserves, taking Jiangsu Province as an example, three coal reserve bases should be built in Nanjing, Xuzhou and Lianyungang to ensure the coal supply. Then, learning from the researches on coal resources reserves and grain reserves, a general approach to calculate the scale of regional coal reserves was given, then, taking the direct-supply power plant in Jiangsu as an example, the scale of coal reserves were est imated with the fluctuation range of stock volatility (0, 0), (+1%, -1%), (+5%, -5%), (+10%, -10%), suggesting that the biggest dynamic reserves of 301,800 tons be moderate, with the fluctuation range of stock volatility (+5%, -5%). The research has important significance to promote the construction of regional coal reserve system

DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Targeting to understand the underlying explainable factors behind observations and modeling the conditional generation process on these factors, we connect disentangled representation learning to Diffusion Probabilistic Models (DPMs) to take advantage of the remarkable modeling ability of DPMs. We propose a new task, disentanglement of (DPMs): given a pre-trained DPM, without any annotations of the factors, the task is to automatically discover the inherent factors behind the observations and disentangle the gradient fields of DPM into sub-gradient fields, each conditioned on the representation of each discovered factor. With disentangled DPMs, those inherent factors can be automatically discovered, explicitly represented, and clearly injected into the diffusion process via the sub-gradient fields. To tackle this task, we devise an unsupervised approach named DisDiff, achieving disentangled representation learning in the framework of DPMs. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of DisDiff.Comment: Accepted by NeurIPS 202

Empirical analysis on a keyword-based semantic system

Keywords in scientific articles have found their significance in information filtering and classification. In this article, we empirically investigated statistical characteristics and evolutionary properties of keywords in a very famous journal, namely Proceedings of the National Academy of Science of the United States of America (PNAS), including frequency distribution, temporal scaling behavior, and decay factor. The empirical results indicate that the keyword frequency in PNAS approximately follows a Zipf's law with exponent 0.86. In addition, there is a power-low correlation between the cumulative number of distinct keywords and the cumulative number of keyword occurrences. Extensive empirical analysis on some other journals' data is also presented, with decaying trends of most popular keywords being monitored. Interestingly, top journals from various subjects share very similar decaying tendency, while the journals of low impact factors exhibit completely different behavior. Those empirical characters may shed some light on the in-depth understanding of semantic evolutionary behaviors. In addition, the analysis of keyword-based system is helpful for the design of corresponding recommender systems.Comment: 9 pages, 1 table and 4 figure