Ta Hwa University of Science and Technology

Hsinchu, Taiwan

Ta Hwa University of Science and Technology

Hsinchu, Taiwan
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Hsieh C.-S.,Ta Hwa University of Science and Technology
Automatica | Year: 2017

This paper presents a system reformation based unbiased minimum-variance input and state estimation for systems with unknown inputs which can be reconstructed with a one-step delay. It is shown that, within this new filtering approach the optimal unknown input and state estimation can be simultaneously achieved through the filter developed by Gillijns and De Moor. An illustrative example is given to show the effectiveness of the proposed results. Moreover, under some additional assumptions the proposed system reformation can be easily extended to consider a multi-step delayed input estimation. © 2017 Elsevier Ltd.


Liu P.-L.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2011

The rapid development of information technology and the emergence of the Internet have created a borderless business environment and intensified market competition. Riding on the globalization trend, high-tech companies have been gradually leveraging information technology in order to shorten their manufacturing processes, enhance productivity with lower costs and prompt delivery to meet the customers' needs. To achieve these targets and maintain competitive advantages, companies have been introducing enterprise resource planning (ERP) and knowledge management (KM). This paper finds, via literature review, that most scholars focus only on the deployment of ERP systems and improvement of flows. Few have introduced the KM concept into ERP systems. This paper collated the literature relevant to ERP and KM and integrates the findings to introduce the ERP KM concept. The most important thing is to establish a detailed introduction plan and a prior understanding of the critical success factors (CSFs) for ERP KM introduction. This paper summarizes the CSFs for ERP KM introduction via literature review and examines the influence of these CSFs on management performance. A questionnaire survey is conducted to collect the relevant data and SPSS 10.0 (statistics software) is run for statistical and multiple regression analyses. Among these CSFs, support from senior managers, corporate vision, reengineering of corporate flows and project management, selection of appropriate consulting firms and software suppliers, the identification of suitable employees to take part in ERP introduction and the proper training and education programs have positive influences on management performance. In the multiple regression analysis, all of the individual constructs are positively and significantly correlated. The explanatory power of individual variables is high. It is hoped that the research finding can serve as a reference for ERP KM introduction to corporations. © 2011 Published by Elsevier Ltd.


Wu W.-W.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2011

Enterprise Resource Management (ERP) systems are viewed as a promising and powerful information technology solution for dealing with the impact of competition advancements and enabling corporations to improve productivity and to operate more efficiently. Although implementations of ERP are complex and costly, corporations may actively adopt and engage in such ERP implementations if perceived benefits exceed perceived risks and costs. A number of studies have contributed to discussion of important factors related to ERP introduction or implementation. Other studies have listed various potential benefits which may be obtained when implementing ERP systems. However, few studies attempt to deepen the analyses of the ERP users' perceived benefits in order to gain meaningful findings for promoting ERP implementations. Typically, elements of a set of ERP benefits do not necessarily share the same importance. Moreover, a given ERP benefit may be accorded a variety of very different levels of importance by different corporations. This paper attempts to segment the ERP users into two subgroups according to the notion of Herzberg's Motivation-Hygiene theory, and further, to uncover imperative perceived benefits for distinct subgroups of ERP users employing the rough set theory. The results of this study should provide better understanding and knowledge of strategic implications for both ERP system adopters and vendors, and thus advance the scope of ERP implementations. © 2010 Elsevier Ltd. All rights reserved.


Wu W.-W.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2011

Among several types of "cloud services", the Software as a Service (SaaS) solution is promising. The Technology Acceptance Model (TAM) and its modified versions have been popularly utilized for examining how users come to accept a new technology, but have not yet been employed to handle issues regarding SaaS adoption. This paper attempts to develop an explorative model that examines important factors affecting SaaS adoption, in order to facilitate understanding with regard to adoption of SaaS solutions. An explorative model using partial least squares (PLS) path modeling is proposed and a number of hypotheses are tested, which integrate TAM related theories with additional imperative constructs such as marketing effort, security and trust. Thus, the findings of this study can not only help enterprise users gain insights into SaaS adoption, but also help SaaS providers obtain inspiration in their efforts to discover more effective courses of action for improving both new product development and marketing strategy. © 2011 Elsevier Ltd. All rights reserved.


Wu W.W.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2010

Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed. © 2009 Elsevier Ltd. All rights reserved.


Hsieh C.-S.,Ta Hwa University of Science and Technology
Automatica | Year: 2010

In this paper, a globally optimal filtering framework is developed for unbiased minimum-variance state estimation for systems with unknown inputs that affect both the system state and the output. The resulting optimal filters are globally optimal within the unbiased minimum-variance filtering over all linear unbiased estimators. Globally optimal state estimators with or without output and/or input transformations are derived. Through the global optimality evaluation of this research, the performance degradation of the filter proposed by Darouach, Zasadzinski, and Boutayeb [Darouach, M., Zasadzinski, M., & Boutayeb, M. (2003). Extension of minimum variance estimation for systems with unknown inputs. Automatica, 39, 867-876] is clearly illustrated and the global optimality of the filter proposed by Gillijns and De Moor [Gillijns, S., & De Moor, B. (2007b). Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 43, 934-937] is further verified. The relationship with the existing literature results is addressed. A unified approach to design a specific globally optimal state estimator that is based on the desired form of the distribution matrix from the unknown input to the output is also presented. A simulation example is given to illustrate the proposed results. © 2010 Elsevier Ltd. All rights reserved.


Tsao C.C.,Ta Hwa University of Science and Technology
International Journal of Machine Tools and Manufacture | Year: 2012

During final assembly operations, hole quality is a key aspect when making holes in composite-based components, which can affect the in-service life under fatigue loads. The step drill, however, was widely used in the aircraft, automotive and machine tool industries to produce a step, a countersink or counterbore holes in a single operation. On the other hand, delamination caused by drilling thrust has been recognized as one of the most problematic defects after drilling. The present study presents a comprehensive model of critical thrust force for step drill with and without the effect of induced bending moment (IBM) considered. In this analysis, the critical thrust force associated with the effect of IBM, which causes the onset of delamination when using the step drill, is predicted and discussed. © 2012 Elsevier Ltd. All rights reserved.


Wu W.-W.,Ta Hwa University of Science and Technology
Applied Soft Computing Journal | Year: 2012

Knowledge is a key source of sustainable competitive advantage. In response to increasingly drastic and competitive environments, many organizations wish to better utilize and manage knowledge for business success. For the purpose to execute formal knowledge management (KM) effectively, some works have suggested several critical factors of KM implementations. However, in a strategic view, such a list of critical factors must be further honed to increase practical usefulness, as not all critical factors necessarily share the same importance. Moreover, assessing the importance of critical factors inevitably involves the vagueness of human judgment. Hence, this study presents a favorable method combining fuzzy set theory and the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to segment the critical factors for successful KM implementations. Also, an empirical study is presented to illustrate the proposed method and to demonstrate its usefulness. © 2011 Elsevier B.V. All rights reserved.


Hsieh C.-S.,Ta Hwa University of Science and Technology
Automatica | Year: 2013

This paper addresses the optimal unbiased minimum-variance state estimation of descriptor systems in the framework of unknown input filtering. It is shown that any descriptor system can be equivalently transformed into a standard state-space system with unknown inputs; the existing globally optimal state estimators can then be readily used to facilitate the optimal filter design. This research highlights the relationship between descriptor state estimation and unknown input filtering for standard state-space systems with unknown inputs. A direct application to the state estimation of descriptor systems with unknown inputs via the proposed results is also addressed. An illustrative example is given to show the novelty of the proposed results. © 2013 Elsevier Ltd. All rights reserved.


Hsieh C.-S.,Ta Hwa University of Science and Technology
Automatica | Year: 2011

This paper addresses globally optimal unbiased minimum-variance state estimation for systems with unknown inputs that affect both the system and the output with the descriptor Kalman filtering method. It is shown that directly applying the conventional descriptor Kalman filter (DKF) to the considered problem may not yield the globally optimal solution because the unknown input vector may not be estimable. To remedy this problem, three approaches are proposed to facilitate optimal filter design: the transformed approach uses some input and output transformations, the untrammeled approach does not require any transformations, and the augmented approach reconstructs the unknown input dynamics. Then, three "5-block" forms of the extended DKF (5-block EDKF) are derived as globally optimal state estimators in the sense that the first two filters are equivalent to the recently developed extended recursive three-step filter and the third is equivalent to the conventional augmented state Kalman filter. The relationship between the proposed EDKFs and the existing results in the literature is addressed. Simulation results are given to illustrate the usefulness of the proposed filters. © 2011 Elsevier Ltd. All rights reserved.

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