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551 results on '"Liu, Weibo"'

1. A Determination of the Local Gravitational Acceleration for the Tsinghua Tabletop Kibble Balance

Author

Liu, Weibo, Li, Nanjia, Ma, Yongchao, Hu, Ruo, Wu, Shuqing, Zhao, Wei, Huang, Songling, and Li, Shisong

Subjects
Physics - Instrumentation and Detectors
Abstract

The Kibble balance requires a measurement of the local gravitational acceleration, $g$, with a typical relative measurement uncertainty of $10^{-9}$. In this paper, the determination of $g$ for the Tsinghua tabletop Kibble balance is presented. A polynomial fitting method is proposed for blind transfers of the absolute gravitational acceleration using relative gravimeters, showing agreement with the value obtained by the tide correction within a few parts in $10^{9}$. Horizontal and vertical gravity gradients are extracted by mapping the gravity distribution at different heights. The self-attraction effect of major components in the experiment, as well as some time-varying systematic effects, are modeled. The final determination of the gravitational acceleration at the mass position, with an uncertainty of 5.4 $\mu$Gal ($k=2$), is achieved for the Tsinghua tabletop Kibble balance experiment., Comment: 11 figures, submitted to IEEE Trans. Instrum. Meas

Published
2024

6. Meta-analysis of the correlation between high expression of lncRNA NEAT1 in rectal cancer and pathological features and prognosis

Author

Lin Qiyi, Pan Jianpeng, Wang Huaishuai, Li Yinlin, Zhuang Yixiang, Cai Zhicong, Lin Gaofeng, Liu Weibo, and Xu Guoxi

Subjects
lncrna neat1, prognosis, rectal cancer, meta analysis, Biochemistry, QD415-436
Abstract

Background: To systematically evaluate the relationship between the expression level of long noncoding RNA NEAT1 and the clinical characteristics and prognostic value of rectal cancer patients. Methods: PubMed, EMBASE, Cochrane library database and case-control studies on the correlation between abnormal expression of lncRNA NEAT1 and prognosis of rectal cancer patients published by the American clinical trials registry before May 1, 2023 were searched. The search time was from the establishment of the database to May 30, 2023. Results: A total of 7 case-control studies were included, including 1063 cancer patients. The results of meta-analysis showed that the high expression of lncRNA NEAT1 was significantly correlated with the degree of differentiation [or=0.45, 95%CI=0.32-0.63, P

Published
2024
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17. Research on basketball game tactics based on multivariate statistical methods

Author

Liu Weibo

Subjects
multivariate statistics, cluster analysis, basketball tactics, mean square approximation theory, sum of squared errors, 97p20, Mathematics, QA1-939
Abstract

This paper analyzes and studies the tactics in basketball games using the main branches of cluster analysis using multivariate statistical methods. A brief overview of multivariate statistical analysis is summarized by analyzing the application of multivariate statistical methods. The main method for classifying basketball tactics is cluster analysis, and nonlinear programming functions with constraints are constructed using mean square approximation theory. The classification results are reasonable when combined with the least square error sum. According to the results, man-to-man defensive tactics were most frequently used in basketball, with 72 instances accounting for 75.19% of all defensive strategies. The most frequent offensive tactic in the basketball game was dribbling with a fast break, and 15 times succeeded in scoring during 20 dribbling with a fast break in the first half, with a success rate of 75%.

Published
2024
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27. Novel particle swarm optimization algorithms with applications to healthcare data analysis

Author

Liu, Weibo, Wang, Z., and Liu, X.

Subjects
006.3, Evolutionary computation, Deep learning, Data analysis, Clustering, Classification
Abstract

Optimization problem is a fundamental research topic which has been receiving increasing interest according to its application potential in almost all real-world systems including engineering systems, large-scaled complex networks, healthcare management systems and so on. A large number of heuristic algorithms have been developed with the purpose of effectively solving the optimization problems during the past few decades. Served as a powerful family of heuristic algorithms, the particle swarm optimization (PSO) algorithm has been successfully employed in a variety of practical applications in dealing with optimization problems. The PSO algorithm has exhibited more competitive performance than many popular evolutionary computation approaches because of its easy implementation, fast convergence and comprehensive ability of converging to a satisfactory solution. Nevertheless, there is still much room to improve the PSO algorithm in terms of both the convergence rate and the population diversity. To summarize, there are three challenging problems in developing new variant PSO algorithms with hope to further improve the convergence rate of the PSO algorithm and maintain the population diversity: 1) how to adjust the control parameters of the PSO algorithm; 2) how to achieve the balance between the local search and the global search during the evolution process; and 3) how to guarantee the search ability of the particles and avoid premature convergence. In this thesis, we address the above mentioned challenging problems and aim to design effective variant PSO algorithms with applications in intelligent data analysis. It should be pointed out that all the developed PSO algorithms in this thesis have been evaluated by comparing with some currently popular variant PSO algorithms. • With the aim to improve the convergence rate of the optimizer, an adaptive weighting PSO algorithm is put forward where a sigmoid-function-based weighting strategy is introduced to adjust the acceleration coefficients. With this weighting strategy, the distances from the particle to the global best position and from the particle to its personal best position are both taken into consideration, thereby having the distinguishing feature of enhancing the convergence rate. • As with other evolutionary computation approaches, the modification of parameters is an efficient method for improving the search ability of the algorithm. We present a randomised PSO algorithm where Gaussian white noise with adjustable intensity is utilized to randomly perturb the acceleration coefficients in order to explore and exploit the problem space thoroughly. • To further develop a novel PSO algorithm with promising search ability, we propose a randomly occurring distributedly delayed particle swarm optimization (RODDPSO) algorithm which demonstrates competitive performance in seeking the optimal solution. The randomly occurring distributed time delays not only contribute to a thorough exploration of the search space but also achieve a proper balance between the local exploitation and the global exploration. • To fully investigate the application potential of the developed PSO algorithms, we apply the RODDPSO algorithm to intelligent data analysis (including data clustering and classification problems). We optimize the initial cluster centroids of the K-means clustering algorithm and the hyperparameters of the deep neural network by using the RODDPSO algorithm. The developed PRODDPSO-based algorithms are successfully employed in patients’ triage categorization and patient attendance disposal problems with satisfactory performance.

Published
2020

29. A collaborative filtering model with heterogeneous neural networks for recommender systems

Author

Fan, Ge, Zeng, Wei, Sun, Shan, Geng, Biao, Wang, Weiyi, and Liu, Weibo

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

In recent years, deep neural network is introduced in recommender systems to solve the collaborative filtering problem, which has achieved immense success on computer vision, speech recognition and natural language processing. On one hand, deep neural network can be used to model the auxiliary information in recommender systems. On the other hand, it is also capable of modeling nonlinear relationships between users and items. One advantage of deep neural network is that the performance of the algorithm can be easily enhanced by augmenting the depth of the neural network. However, two potential problems may emerge when the deep neural work is exploited to model relationships between users and items. The fundamental problem is that the complexity of the algorithm grows significantly with the increment in the depth of the neural network. The second one is that a deeper neural network may undermine the accuracy of the algorithm. In order to alleviate these problems, we propose a hybrid neural network that combines heterogeneous neural networks with different structures. The experimental results on real datasets reveal that our method is superior to the state-of-the-art methods in terms of the item ranking., Comment: Some technical errors, such as the lack of computational complexity analysis

Published
2019

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