COMPUTER GRAPHIC ASSEMBLY TECHNOLOGY FOR COMPOUND-BODY

ＣＯＭＰＵＴＥＲＧＲＡＰＨＩＣＡＳＳＥＭＢＬＹＴＥＣＨＮＯＬＯＧＹＦＯＲＣＯＭＰＯＵＮＤ－ＢＯＤＹ￥ＳｈａｎｇＪｉａｎｚｈｏｎｇ；ＰａｎＣｕｎｙｕｎ（ＤｅｐａｒｔｍｅｎｔｏｆＰｒｅｃｉｓｉｏｎＭａｃｈｉｎａｒｙａｎｄｉｎｓｔｒｕｍｅｎｔＮａｔｉｏｎａ...     

ICARCV＇96 FOURTH INTERNATIONAL CONFERENCE ON CONTROL，AUTOMATION，ROBOTICS AND VISION

ＩＣＡＲＣＶ＇９６ＦＯＵＲＴＨＩＮＴＥＲＮＡＴＩＯＮＡＬＣＯＮＦＥＲＥＮＣＥＯＮＣＯＮＴＲＯＬ，ＡＵＴＯＭＡＴＩＯＮ，ＲＯＢＯＴＩＣＳＡＮＤＶＩＳＩＯＮ￥／／３－６Ｄｅｃｅｍｂｅｒ１９９６ＷｅｓｔｉｎＳｔａｍｆｏｒｄ，Ｓｉｎｇａｐｏｒｅ，ＣＡＬＬＦＯ...     

A FEATURE－BASED PRODUCT MODELLING SYSTEM ON PC （CASE STUDY OF PARTS DESIGN FOR PRESS MACHINE）

ＡＦＥＡＴＵＲＥ－ＢＡＳＥＤＰＲＯＤＵＣＴＭＯＤＥＬＬＩＮＧＳＹＳＴＥＭＯＮＰＣ（ＣＡＳＥＳＴＵＤＹＯＦＰＡＲＴＳＤＥＳＩＧＮＦＯＲＰＲＥＳＳＭＡＣＨＩＮＥ）ＹｏｎａｓＨａｇｏｓ；ＴｏｎｇＢｉｎｇｓｈｕＡｂｓｔｒａｃｔ：Ｔｈｉｓａｒｔｉｃｌｅｒｅｐｏ...     

MOSCAD智能型遥测遥控系统简介

ＭＯＳＣＡＤ智能型遥测遥控系统简介ＭＯＳＣＡＤＩｎｔｅｌｉｇｅｎｃｅＳｙｓｔｅｍｆｏｒＴｅｌｅｍｅｎｉｎｇａｎｄＲｅｍｏｔｅＣｏｎｔｒｏｌ●杜金茹邱燕ＤｕＪｉｎｒｕＱｉｕＹｉａｎ１概述ＭＯＴＯＲＯＬＡ公司生产的ＳＣＡＤＡ系统（以下简称为ＭＯＳＣＡＤ）...     

COMPUTER AIDED INSPECTION PLANNING

ＣＯＭＰＵＴＥＲＡＩＤＥＤＩＮＳＰＥＣＴＩＯＮＰＬＡＮＮＩＮＧＴａｎｇＸｉａｏｑｉｎｇＣＯＭＰＵＴＥＲＡＩＤＥＤＩＮＳＰＥＣＴＩＯＮＰＬＡＮＮＩＮＧ￥ＴａｎｇＸｉａｏｑｉｎｇＡｂｓｔｒａｃｔＣｏｍｐｕｔｅｒａｉｄｅｄｉｎｓｐｅｃｔｉｏｎｐｌａｎｎｉｎ...     

业界性能最高的路由器

思科系统推出两款业界性能最高的路由器──Ｃｉｓｃ１２４１０和Ｃｉｓｃｏ１２４１６。基于这两款路由器,服务供应商可以将其网络扩展到帽子光纤网的速度,为下一代ＩＰ服务提供足够的带宽。Ｃｉｓｃｏ１２４００系列路由器采用业界独一无二的分布式体系结构,可以提供ＩＰ＋光纤的网络基础和服务构建模块,是构建１０ＧｂｐｓＩＰ基础设施最佳的平台。 思科还为这两款路由器配套推出了单端口ＯＣ－１９２ｃ／ＳＴＭ－６４ｃ ＰＯＳ（Ｐａｃｋｅｔ ｏｖｅｒＳＯＮＥＴ／ＳＤＨ）线路接口卡、四端口ＯＣ－４８ｃ／ＳＴＭ－１６ｃ ＰＯＳ线…     

VERSION CONTROL OF DISTRIBUTED EDBMS FOR CSCW

ＶＥＲＳＩＯＮＣＯＮＴＲＯＬＯＦＤＩＳＴＲＩＢＵＴＥＤＥＤＢＭＳＦＯＲＣＳＣＷ￥ＷａｎｇＱｉａｎｐｉｎｇ；ＬｉｎＺｏｎｇｋａｉ；ＧｕｏＹｕｃｈａｉ（ＣＡＤＬａｂ．，ＩｎｓｔｉｔｕｔｅｏｆＣｏｍｐｕｔｉｎｇＴｅｃｈｎｏｌｏｇｙＡｃａｄｅｍｉａＳｉｎｉｃ...     

Feature-based Integrated CAD/CAPP/CAM System For Concurrent Engineering

Ｆｅａｔｕｒｅ＿ｂａｓｅｄＩｎｔｅｇｒａｔｅｄＣＡＤ／ＣＡＰＰ／ＣＡＭＳｙｓｔｅｍＦｏｒＣｏｎｃｕｒｅｎｔＥｎｇｉｎｅｒｉｎｇＷａｎｇＨｕｉｃｈｅｎｇＺｈａｎｇＸｉｎｆａｎｇＺｈｏｕＪｉ（ＣＡＤＣｅｎｔｅｒｏｆＨ．Ｕ．Ｓ．Ｔ）ＡｂｓｔｒａｃｔＴｈｉｓ...     

实验曲线的自动分段拟合

实验曲线的自动分段拟合陈金友（酒泉卫星发射中心）ＡＵＴＯＭＡＴＩＣＳＥＣＴＩＯＮＡＬＦＩＴＴＩＮＧＯＦＴＨＥＭＥＡＳＵＲＥＤＤＡＴＡ￥ＣｈｅｎＪｉｎ－ｙｏｕ（ＣｈｉｎａＪｉｕｑｕａｎＳａｔｅｌｌｉｔｅＬａｕｎｃｋＣｅｎｔｅｒ）Ａｂｓｔｒａｃｔ：Ｔｈｉ...     

COMPUTER AIDED DESIGN OF AIRCRAFT CABIN LAYOUT

ＣＯＭＰＵＴＥＲＡＩＤＥＤＤＥＳＩＧＮＯＦＡＩＲＣＲＡＦＴＣＡＢＩＮＬＡＹＯＵＴ￥ＹａｎｇＧｕｏｎｇ；ＨｕａｎｇＹｕｙｕ（ＢｅｉｊｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＡｅｒｏｎａｕｔｉｃｓａｎｄＡｓｔｒｏｎａｕｔｉｃｓＢｅｉｊｉｎｇ．１０００８３）Ｐ．Ｒ．...     

Development of Tactile Mapping system for the stiffness characterization of tissue slice using novel tactile sensing technology

In this study, we modified our previously developed Micro-Tactile Sensor (MTS) not only to measure elasticity with high sensitivity but also to detect the instant of contact. First, modal analysis of the MTS was undertaken using finite element analysis to find a frequency where longitudinal and rotational vibration modes exist. Second, the MTS oscillated using these two types of vibration, and its basic performance was assessed. While the resonance mode of major longitudinal vibration shifted in an indentation and elasticity dependent manner, a disappearance of the rotational vibration upon contact resulted in abrupt change in resonance frequency which was used to signal touch detection. Last, using the modified MTS, we developed Tactile Mapping technology and used it to obtain a contour image and topographical Young's modulus information for a slice of porcine heart with coronary artery.     

On determinism in modal transition systems

Modal transition system (MTS) is a formalism which extends the classical notion of labelled transition systems by introducing transitions of two types: must transitions that have to be present in any implementation of the MTS and may transitions that are allowed but not required.The MTS framework has proved to be useful as a specification formalism of component-based systems as it supports compositional verification and stepwise refinement. Nevertheless, there are some limitations of the theory, namely that the naturally defined notions of modal refinement and modal composition are incomplete with respect to the semantic view based on the sets of the implementations of a given MTS specification. Recent work indicates that some of these limitations might be overcome by considering deterministic systems, which seem to be more manageable but still interesting for several application areas.In the present article, we provide a comprehensive account of the MTS framework in the deterministic setting. We study a number of problems previously considered on MTS and point out to what extend we can expect better results under the restriction of determinism.     

基于2DSVD的多变量时间序列半监督分类

目前时间序列半监督分类研究主要集中在单变量时间序列,由于多变量时间序列（MTS）变量之间存在复杂关系,MTS的半监督分类研究比较少.针对这种情况,提出一种基于二维奇异值分解的MTS半监督分类方法,该方法首先计算行-行以及列-列协方差矩阵的特征向量,然后从MTS样本中提取特征矩阵;特征矩阵的行数以及列数不仅比原MTS样本低,而且还清晰地考虑了MTS样本的二维特性.在10个MTS数据集上的实验结果表明,该方法的分类性能显著地好于使用扩展Frobenius范数、中心序列、以及基于一维奇异值分解的半监督分类方法.     

Variable grouping in multivariate time series via correlation

The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the "variable groupings" problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping method.     

Evolutionary learning of dynamic probabilistic models with large time lags

In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc.     

A Face Identification System Based on the Mahalanobis–Taguchi System

In this paper, we propose a face identification system based on the Mahalanobis–Taguchi System (MTS). The MTS is one of the pattern recognition methods frequently used in quality engineering, and can perform robust pattern recognition by using training data, including noise. It is likely that this advantage will allow the effective implementation of a robust face identification system against lighting and face position fluctuations. Moreover, the MTS can optimize the number of attributes required for identification by using the orthogonal array and the signal/noise (SN) ratio. The face identification system has to deal with many users and the amount of data in a facial image is large. Therefore, the time required for identification can be decreased and the amount of data in the facial image database can be reduced by performing the optimization. We confirmed the effectiveness of the proposed system through practical experiments. The experimental results revealed that the MTS was an effective method for robust face identification, and could effectively reduce the number of attributes required for identification.     

基于MTS/COM+和Windows DNA的三层企业级应用研究

阐述MTS/COM+技术以及WindowsDNA结构的概念和特点,并通过开发实例,对基于Client/Server两层和三层结构模式进行分析。最后,以Delphi6.0作为开发平台,成功实现了基于MTS/COM+中间件的目标系统。     

Predicting the financial crisis by Mahalanobis–Taguchi system – Examples of Taiwan’s electronic sector

In the past researches of financial crisis early-warning model, multiple regression, linear probability model, and multiple discriminate analysis are commonly adopted, all of which have generated good discrimination effects, with over 90% accuracy. Dr. Taguchi, well known for his robust design, has lately brought up a new method – Mahalanobis–Taguchi System (MTS), which is mainly used to conduct multivariate diagnoses and forecasts. This study attempts to use MTS to build up a financial crisis early-warning model for Taiwan’s companies. It chooses both in financial sound judgment and in financial trouble TSE- and OTC-listed electronic companies in 2005 as training set and uses both in financial sound judgment and in financial trouble TSE- and OTC-listed electronic companies in 2006 as testing set to verify the accuracy of this model. There are two phases in our research, in which we firstly use MTS, logistic regression and neural network to establish the financial crisis early-warning model, followed by a comparative analysis of average accuracy rate of financial prediction in the second phase. The result of experiment shows that the accuracy rate of financial crisis early-warning system established by MTS, logistic regression and neural network are 96.1%, 92.3%, and 96.1%, respectively, indicating that MTS provides greater application effect in predicting financial crisis.     

An Evaluation of the Robustness of MTS for Imbalanced Data

In classification problems, the class imbalance problem will cause a bias on the training of classifiers and will result in the lower sensitivity of detecting the minority class examples. The Mahalanobis-Taguchi System (MTS) is a diagnostic and forecasting technique for multivariate data. MTS establishes a classifier by constructing a continuous measurement scale rather than directly learning from the training set. Therefore, it is expected that the construction of an MTS model will not be influenced by data distribution, and this property is helpful to overcome the class imbalance problem. To verify the robustness of MTS for imbalanced data, this study compares MTS with several popular classification techniques. The results indicate that MTS is the most robust technique to deal with the classification problem on imbalanced data. In addition, this study develops a "probabilistic thresholding method" to determine the classification threshold for MTS, and it obtains a good performance. Finally, MTS is employed to analyze the radio frequency (RF) inspection process of mobile phone manufacturing. The data collected from the RF inspection process is typically an imbalanced type. Implementation results show that the inspection attributes are significantly reduced and that the RF inspection process can also maintain high inspection accuracy.     

Kernel-based sparse representation for gesture recognition

In this paper, we propose a novel sparse representation based framework for classifying complicated human gestures captured as multi-variate time series (MTS). The novel feature extraction strategy, CovSVDK, can overcome the problem of inconsistent lengths among MTS data and is robust to the large variability within human gestures. Compared with PCA and LDA, the CovSVDK features are more effective in preserving discriminative information and are more efficient to compute over large-scale MTS datasets. In addition, we propose a new approach to kernelize sparse representation. Through kernelization, realized dictionary atoms are more separable for sparse coding algorithms and nonlinear relationships among data are conveniently transformed into linear relationships in the kernel space, which leads to more effective classification. Finally, the superiority of the proposed framework is demonstrated through extensive experiments.