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1,133 results on '"Wang, Kaijun"'

2. Integrated Design and Optimization of SSPC Current Measurement Module Based on AMR

Author

Xu, Feiran, Wang, Li, Wang, Kaijun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, Li, Zewen, editor, and Luo, An, editor

Published
2024
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16. 中国污水处理概念厂

Author

Qu, Jiuhui, primary, Ren, Hongqiang, additional, Wang, Hongchen, additional, Wang, Kaijun, additional, Yu, Gang, additional, Ke, Bing, additional, Yu, Han-Qing, additional, Zheng, Xingcan, additional, and Li, Ji, additional

Published
2023
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21. Fast and Robust Bin-picking System for Densely Piled Industrial Objects

Author

Guo, Jiaxin, Fu, Lian, Jia, Mingkai, Wang, Kaijun, and Liu, Shan

Subjects
Computer Science - Robotics
Abstract

Objects grasping, also known as the bin-picking, is one of the most common tasks faced by industrial robots. While much work has been done in related topics, grasping randomly piled objects still remains a challenge because much of the existing work either lack robustness or costs too much resource. In this paper, we develop a fast and robust bin-picking system for grasping densely piled objects adaptively and safely. The proposed system starts with point cloud segmentation using improved density-based spatial clustering of application with noise (DBSCAN) algorithm, which is improved by combining the region growing algorithm and using Octree to speed up the calculation. The system then uses principle component analysis (PCA) for coarse registration and iterative closest point (ICP) for fine registration. We propose a grasp risk score (GRS) to evaluate each object by the collision probability, the stability of the object, and the whole pile's stability. Through real tests with the Anno robot, our method is verified to be advanced in speed and robustness.

Published
2020

28. Predicting the EQ-5D utilities from the Kansas City Cardiomyopathy Questionnaire in patients with heart failure

Author

Thomas, Merrill, Jones, Philip G, Cohen, David J, Suzanne, Arnold V, Magnuson, Elizabeth A, Wang, Kaijun, Thourani, Vinod H, Fonarow, Gregg C, Sandhu, Alexander T, and Spertus, John A

Subjects
Cardiovascular, Clinical Research, Heart Disease, Good Health and Well Being, Cardiomyopathies, Heart Failure, Humans, Kansas, Quality of Life, Stroke Volume, Surveys and Questionnaires, Health economics, Health status, Utilities
Abstract

IntroductionEvaluation of health status benefits, cost-effectiveness, and value of new heart failure therapies is critical for supporting their use. The Kansas City Cardiomyopathy Questionnaire (KCCQ) measures patients' heart failure-specific health status but does not provide utilities needed for cost-effectiveness analyses. We mapped the KCCQ scores to EQ-5D scores so that estimates of societal-based utilities can be generated to support economic analyses.MethodsUsing data from two US cohort studies, we developed models for predicting EQ-5D utilities (3L and 5L versions) from the KCCQ (23- and 12-item versions). In addition to predicting scores directly, we considered predicting the five EQ-5D health state items and deriving utilities from the predicted responses, allowing different countries' health state valuations to be used. Model validation was performed internally via bootstrap and externally using data from two clinical trials. Model performance was assessed using R2, mean prediction error, mean absolute prediction error, and calibration of observed vs. predicted values.ResultsThe EQ-5D-3L models were developed from 1000 health status assessments in 547 patients with heart failure and reduced ejection fraction (HFrEF), while the EQ-5D-5L model was developed from 3925 patients with HFrEF. For both versions, models predicting individual EQ-5D items performed as well as those predicting utilities directly. The selected models for the 3L had internally validated R2 of 48.4-50.5% and 33.7-45.6% on external validation. The 5L version had validated R2 of 57.7%.ConclusionMappings from the KCCQ to the EQ-5D can yield the estimates of societal-based utilities to support cost-effectiveness analyses when EQ-5D data are not available.

Published
2021

29. Breiman's 'Two Cultures' Revisited and Reconciled

Author

Subhadeep, Mukhopadhyay, and Wang, Kaijun

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Economics - Econometrics, Statistics - Methodology
Abstract

In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The cultural division between these two statistical learning frameworks has been growing at a steady pace in recent years. What is the way forward? It has become blatantly obvious that this widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole. This article presents a solution by establishing a link between the two cultures. Through examples, we describe the challenges and potential gains of this new integrated statistical thinking., Comment: This paper celebrates the 70th anniversary of Statistical Machine Learning--- how far we've come, and how far we have to go. Keywords: Integrated statistical learning theory, Exploratory machine learning, Uncertainty prediction machine, ML-powered modern applied statistics, Information theory

Published
2020

30. On The Problem of Relevance in Statistical Inference

Author

Mukhopadhyay, Subhadeep and Wang, Kaijun

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract

This paper is dedicated to the "50 Years of the Relevance Problem" - a long-neglected topic that begs attention from practical statisticians who are concerned with the problem of drawing inference from large-scale heterogeneous data., Comment: Revised (much-improved) version. The procedure (including all the datasets) is implemented in the R-package LPRelevance

Published
2020

42. Cardiovascular, Kidney Failure, and All-Cause Mortality Events in Patients with Focal Segmental Glomerulosclerosis in a U.S. Real-World Database

Author

Velez, Juan Carlos Q., primary, Thakker, Kamlesh M., additional, Bensink, Mark E., additional, Lerma, Edgar V., additional, Lieblich, Richard, additional, Bunke, C. Martin, additional, Gong, Wu, additional, Wang, Kaijun, additional, Rava, Andrew R., additional, Amari, Diana T., additional, Oliveri, David, additional, Murphy, Michael V., additional, and Cork, David M.W., additional

Published
2024
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47. Spectral Graph Analysis: A Unified Explanation and Modern Perspectives

Author

Mukhopadhyay, Subhadeep and Wang, Kaijun

Subjects
Mathematics - Statistics Theory, Statistics - Computation
Abstract

Complex networks or graphs are ubiquitous in sciences and engineering: biological networks, brain networks, transportation networks, social networks, and the World Wide Web, to name a few. Spectral graph theory provides a set of useful techniques and models for understanding `patterns of interconnectedness' in a graph. Our prime focus in this paper is on the following question: Is there a unified explanation and description of the fundamental spectral graph methods? There are at least two reasons to be interested in this question. Firstly, to gain a much deeper and refined understanding of the basic foundational principles, and secondly, to derive rich consequences with practical significance for algorithm design. However, despite half a century of research, this question remains one of the most formidable open issues, if not the core problem in modern network science. The achievement of this paper is to take a step towards answering this question by discovering a simple, yet universal statistical logic of spectral graph analysis. The prescribed viewpoint appears to be good enough to accommodate almost all existing spectral graph techniques as a consequence of just one single formalism and algorithm., Comment: The first draft of the paper was written in June 2015

Published
2019

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