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TERRAIN ADAPTIVE HUMAN WALKING ANIMATION

SYSTEMS AND COMPUTERS IN JAPAN 1995年第5期26卷 79-88页

作者： TSUTSUGUCHI, K SAKAINO, H WATANABE, Y Member NTT Human Interface Laboratories Yokosuka Japan 238 Hidetorno Sakaino received his B.E. degree in 1986 and his M.E. degree in 1988 from Hokkaido Univ. Hokkaido Japan. He is a Research Engineer in NTT HI Labs. From 1988 to 1990 he worked as an engineer at the Visual Media Division in NYT HI Labs. In 1993 he began to work in the Advanced Video Processing Laboratory HI Labs NTT. His current research interests are representations of elastic objects and their transformations given the interaction between fluid dynamics and moving objects. Yashuhiko Watanabe received his Bachelor's degree from Niigata Univ. Niigata Japan in 1981. He is the Senior Research Engineer in the Advanced Video Processing Laboratory of NIT HI Labs. He is presently researching 3D volume processinghisualization systems. Since joining the Electrical Communications Laboratories NTT in 1981 he has been working on facsimile communication systems and Videotex communication systems. He is a member of the Institute of Image Electronic Engineers of Japan.

This paper describes a method for automatically generating human walking sequences that match the path set to the given terrain. The Environment Adaptive Human Walking Animation (EAW) module using this method offers to users an interface that can unite human walking motion with the environment in virtual worlds. The EAW module has a 4-layer structure comprising the GUI level, the control level, the motion generation level and the rendering level. The EAW module realizes the adaptation to the 3D terrain by using ''area divisions'' and ''step divisions'' for the walking path in the control level, and generates the walking motion sequences by using the ''E-KLAW'' method that uses dynamics and kinematics in the motion generation level. By using this module, the user's tasks are made significantly simpler and the user can incorporate human motion into various scenes.

关键词： COMPUTER ANIMATION ENVIRONMENT ADAPTIVE HUMAN WALKING EAW MODULE E-KLAW DYNAMICS KINEMATICS USER INTERFACE

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13.2% EQE near-infrared TADF OLED with emission peak at 761 nm

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Chemical Engineering Journal 2023年 452卷

作者： Tai, Jing-Wen Tang, Yukun Zhang, Kai Yang, Chen-Zong Pan, Ze-Hui Lin, Yu-Ching Shih, Yu-Wei Chen, Chia-Hsun Chiu, Tien-Lung Lee, Jiun-Haw Wang, Chuan-Kui Wu, Chung-Chih Fan, Jian Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China Department of Electrical Engineering Graduate Institute of Electronics Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei10617 Taiwan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan32003 Taiwan Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan State Key Laboratory of Structural Chemistry Fujian Institute of Research on Structure of Matter Chinese Academy of Sciences Fujian Fuzhou35002 China

Near infrared (NIR) emitters have attracted much attention due to their advanced application in biomedical sensing/imaging, and telecommunications. However, the development of efficient NIR thermally activated delayed fluorescence (TADF) emitters with the emission over 750 nm is still a challenge. Herein two Y-shaped NIR TADF molecules (Py-TPA and Py-CN-TPA) were introduced. The inclusion of pyridine unit in these two emitters not only extended the conjugated system but also increased the molecular aspect ratios, thereby leading to their high horizontal ratio of emitting dipole orientation (Θ//). In addition, the NIR emitter showed a low Huang-Rhys (HR) factor in thin film relative to that in solution, indicating a low non-radiative transition rate and a high photoluminescent quantum yield (PLQY). Consequently, TADF OLED based on Py-TPA demonstrated a record-high external quantum efficiency (EQE) of 22.2% at 726 nm, 16.3% at 747 nm, and 13.2% at 761 nm. © 2022 Elsevier B.V.

关键词： Aspect ratio

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Meineng Wang Luping Zhang Yu-An Huang Yangyuan Li Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Mathematics and Computer Science Yichun University Yichun China School of Information Engineering East China University of Technology Nanchang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets.

关键词： Sequential analysis Perturbation methods Clustering methods RNA Noise Clustering algorithms Sparse matrices Gene expression Unsupervised learning Periodic structures

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Correction to: Primary health information standard system based on semantic interoperability

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BMC medical informatics and decision making 2019年第1期19卷 144页

作者： Xia Zhao Xiaohua Li Wei Yang Qianjin Feng Yi Zhou Qiong Wang Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China. General Hospital of Guangzhou Military Command of PLA Guangzhou China. Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China. deepai.yang@***. Sun Yat-sen University Guangzhou China. The Third Affiliated Hospital of Guangzhou Medical University Guangzhou China.

After publication of this supplement article [1], it was brought to our attention that the first and correspondence authors' affiliation information was incorrectly spelt. The original spelling was written as '...

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有向动态网络的自发恢复（英文）

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Engineering 2024年第6期 208-214页

作者：刘雪明闫贤 H.Eugene Stanley School of Artificial Intelligence and Automation & The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems & the Key Laboratory of Image Processing Huazhong University of Science and Technology Center for Polymer Studies Department of Physics Boston University

Complex networked systems, which range from biological systems in the natural world to infrastructure systems in the human-made world, can exhibit spontaneous recovery after a failure; for example, a brain may spontaneously return to normal after a seizure, and traffic flow can become smooth again after a jam. Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks. However, most real-world networks are directed. To fill this gap, we build a model in which nodes may alternately fail and recover, and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks. We find that the tool can accurately predict the final fraction of active nodes, and the prediction accuracy decreases as the fraction of bidirectional links in the network increases, which emphasizes the importance of directionality in network dynamics. Due to different initial states, directed dynamical networks may show alternative stable states under the same control parameter, exhibiting hysteresis behavior. In addition, for networks with finite sizes, the fraction of active nodes may jump back and forth between high and low states, mimicking repetitive failure-recovery processes. These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

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Consensus algorithms for biased labeling in crowdsourcing

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Information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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Impulsive positive observers and dynamic output feedback stabilization of positive linear continuous systems

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International Journal of Robust and Nonlinear Control 2016年第13期27卷

作者： Jiang-Wen Xiao Meng-Jie Hu Yan-Wu Wang Wu-Hua Chen School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China College of Mathematics and Information Science Guangxi University Nanning 530004 China

This paper is concerned with the design and the synthesis of the impulsive positive observer (IPO) for positive linear continuous systems. The IPO can estimate the states for positive systems even when the measured output is only available at discrete-time instants. In this paper, a time-varying weighted copositive Lyapunov function is constructed, and the upper and lower bounds of impulsive intervals method combined with the convex combination technique are used to establish sufficient conditions for the existence of the IPO. Furthermore, the design of the dynamic output feedback controller based on the IPO is addressed to positively stabilize positive linear systems. Algorithms are given to design the IPO and the controller, respectively. Finally, two numerical examples are provided to show the effectiveness of the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： impulsive positive observer dynamic output feedback controller time-varying weighted copositive Lyapunov function positive system upper and lower bounds of impulsive intervals method convex combination technique

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一种实用的二维工业CT重建算法

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中国体视学与图像分析 2005年第3期10卷 189-192页

作者：李慧田捷张兆田 Department of Education Technology Capital Normal University Beijing 100037 China Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences P. O. Box 2728 Beijing 100080 China Department of Information Science National Natural Science Foundation of China Beijing 100085 China

Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to look inside the scanned object and to analysis its inner structures. A non-destructive testing software have been developed to efficiently detect inner flaws of space industrial components. As the core of our software, reconstruction algorithms including preprocess of raw data, re-arrange algorithm and filtered back-projection algorithms have been described in detail in this article. With real raw data from CASC of China, experimental results verified the applied reconstruction algorithm in our software. Furthermore, forward algorithms simulating generation of fan-beam raw data are also presented in this article.

关键词：二维工业CT 图象重建计算方法计算机系统

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Predictive protocol of flocks with small-world connection pattern

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Physical Review E 2009年第1期79卷 016113-016113页

作者： Hai-Tao Zhang Michael Z. Q. Chen Tao Zhou The Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 People's Republic of China Department of Engineering University of Cambridge Cambridge CB2 1PZ United Kingdom Department of Engineering University of Leicester Leicester LE1 7RH United Kingdom Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong SAR People's Republic of China Department of Modern Physics University of Science and Technology of China Hefei 230026 People’s Republic of China Department of Physics University of Fribourg Chemin du Muse 3 Fribourg CH-1700 Switzerland

By introducing a predictive mechanism with small-world connections, we propose a new motion protocol for self-driven flocks. The small-world connections are implemented by randomly adding long-range interactions from the leader to a few distant agents, namely, pseudoleaders. The leader can directly affect the pseudoleaders, thereby influencing all the other agents through them efficiently. Moreover, these pseudoleaders are able to predict the leader’s motion several steps ahead and use this information in decision making towards coherent flocking with more stable formation. It is shown that drastic improvement can be achieved in terms of both the consensus performance and the communication cost. From the engineering point of view, the current protocol allows for a significant improvement in the cohesion and rigidity of the formation at a fairly low cost of adding a few long-range links embedded with predictive capabilities. Significantly, this work uncovers an important feature of flocks that predictive capability and long-range links can compensate for the insufficiency of each other. These conclusions are valid for both the attractive and repulsive swarm model and the Vicsek model.

关键词： pattern formation protocols random processes 3-DIMENSIONAL LATTICE MODEL DYNAMICAL COMPLEX NETWORKS REPULSIVE INTERACTIONS STABILITY ANALYSIS ATTRACTIVE FORCES SENSOR NETWORKS ANIMAL GROUPS AGENTS TRANSITIONS CONSENSUS

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Predicting the risk of pancreatic cancer with a CT-based ensemble AI algorithm

arXiv

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arXiv 2020年

作者： Zhou, Chenjie Ma, Jianhua Xu, Xiaoping Feng, Lei Yimamu, Adilijiang Wang, Xianlong Li, Zhiming Mo, Jianhua Huang, Chengyan Kong, Dexia Gao, Yi Li, Shulong Department of Hepatobiliary Surgery II Guangdong Provincial Research Center for Artificial Organ and Tissue Engineering Institute of Regenerative Medicine ZhuJiang Hospital Southern Medical University Guangzhou Guangdong Province510515 China State Key Laboratory of Organ Failure Research Southern Medical University Guangzhou510515 China School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou Guangdong Province510515 China Radiology department ZhuJiang Hospital Southern Medical University Guangzhou Guangdong Province510515 China Department of radiology 2nd affiliated hospital of Guangzhou Medical University Guangzhou Medical University Guangzhou Guangdong Province510515 China

Objectives: Pancreatic cancer is a lethal disease, hard to diagnose and usually results in poor prognosis and high mortality. Developing an artificial intelligence (AI) algorithm to accurately and universally predict the early cancer risk of all kinds of pancreatic cancer is extremely important. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT. Methods: Our algorithm combines the radiomics method and a support tensor machine (STM) by the evidence reasoning (ER) technique to construct a binary classifier, called RadSTM-ER. RadSTM-ER takes advantage of the handcrafted features used in radiomics and learning features learned automatically by the STM from the CTs for presenting better characteristics of lesions. The patient cohort consisted of 135 patients with pathological diagnosis results where 97 patients had malignant lesions. Twenty-seven patients were randomly selected as independent test samples, and the remaining patients were used in a 5-fold cross validation experiment to confirm the hyperparameters, select optimal handcrafted features and train the model. Results: RadSTM-ER achieved independent test results: an area under the receiver operating characteristic curve of 0.8951, an accuracy of 85.19%, a sensitivity of 88.89%, a specificity of 77.78%, a positive predictive value of 88.89% and a negative predictive value of 77.78%. Conclusions: These results are better than the diagnostic performance of the five experimental radiologists, four conventional AI algorithms, which initially demonstrate the potential of noncontrast CT-based RadSTM-ER in cancer risk prediction for all kinds of pancreatic lesions. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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