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Hsing Kuo University

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Chen C.-L.,Chaoyang University of Technology | Lee C.-C.,Fu Jen Catholic University | Hsu C.-Y.,Hsing Kuo University
International Journal of Communication Systems | Year: 2012

Various user authentication schemes with smart cards have been proposed. Generally, researchers implicitly assume that the contents of a smart card cannot be revealed. However, this is not true. An attacker can analyze the leaked information and obtain the secret values in a smart card. To improve on this drawback, we involve a fingerprint biometric and password to enhance the security level of the remote authentication scheme Our scheme uses only hashing functions to implement a robust authentication with a low computation property. Copyright © 2011 John Wiley & Sons, Ltd.


Sung T.-W.,National Sun Yat - sen University | Sung T.-W.,Hsing Kuo University | Yang C.-S.,National Cheng Kung University
International Journal of Ad Hoc and Ubiquitous Computing | Year: 2010

The deployment of wireless sensor devices is one of the most fundamental and important issues in wireless sensor network applications, and coverage is a chief consideration in deployment requirements. This paper proposes a hexagonal cell-based sensor deployment strategy that adopts a mobility-assisted hybrid wireless sensor network. Calculation of coverage hole size, the corresponding hole-healing process and discovery of nearly redundant sensors are based on the hexagonal cells into which the sensing field is virtually divided. Simulations show that the proposed algorithm achieves significantly improved field coverage, and indicates both the number and the influence of nearly redundant sensors in the wireless sensor network. Copyright © 2010 Inderscience Enterprises Ltd.


Huang Y.-M.,National Cheng Kung University | Huang Y.-M.,Chia Nan University of Pharmacy and Science | Liu C.-H.,Hsing Kuo University | Tsai C.-C.,National Taiwan University of Science and Technology
Interactive Learning Environments | Year: 2013

Web-based self-learning (WBSL) has received a lot of attention in recent years due to the vast amount of varied materials available in the Web 2.0 environment. However, this large amount of material also has resulted in a serious problem of cognitive overload that degrades the efficacy of learning. In this study, an information graphics method is proposed to resolve this problem. This method is based on social tagging, which is used to visualize the relationships among materials and can thus assist learners in facilitating learning. To examine the feasibility of the proposed method for managing cognitive load, an experimental model was designed in which cognitive load theory was adopted as the theoretical framework. A total of 60 university students participated in the experiment, and the partial least squares method was used to verify the experimental model. The results show that the information graphics method has a positive impact on three types of cognitive load, namely intrinsic, extraneous, and germane. Furthermore, intrinsic and germane cognitive load have a positive influence on perceived learning effectiveness, while extraneous cognitive load does not have a significant influence. One possible reason for this outcome is that the problem of visual load was not considered in the design of this study. The overall summary of the findings is that the use of social tagging can effectively manage cognitive load and positively links to perceived learning effectiveness. © 2013 Copyright Taylor and Francis Group, LLC.


Wu T.-T.,Chia Nan University of Pharmacy and Science | Sung T.-W.,Hsing Kuo University
CIN - Computers Informatics Nursing | Year: 2014

In recent years, mobile device-assisted clinical education has become popular among nursing school students. The introduction of mobile devices saves manpower and reduces errors while enhancing nursing students' professional knowledge and skills. To respond to the demands of various learning strategies and to maintain existing systems of education, the concept of Cloud Learning is gradually being introduced to instructional environments. Cloud computing facilitates learning that is personalized, diverse, and virtual. This study involved assessing the advantages of mobile devices and Cloud Learning in a public health practice course, in which Google+ was used as the learning platform, integrating various application tools. Users could save and access data by using any wireless Internet device. The platform was student centered and based on resource sharing and collaborative learning. With the assistance of highly flexible and convenient technology, certain obstacles in traditional practice training can be resolved. Our findings showed that the students who adopted Google+ were learned more effectively compared with those who were limited to traditional learning systems. Most students and the nurse educator expressed a positive attitude toward and were satisfied with the innovative learning method. Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins.


Kuo Y.,Hsing Kuo University
Computers and Industrial Engineering | Year: 2010

The vehicle routing problem (VRP) has been addressed in many research papers. Only a few of them take time-dependent travel speeds into consideration. Moreover, most research related to the VRP aims to minimize total travel time or travel distance. In recent years, reducing carbon emissions has become an important issue. Therefore, fuel consumption is also an important index in the VRP. In this research a model is proposed for calculating total fuel consumption for the time-dependent vehicle routing problem (TDVRP) where speed and travel times are assumed to depend on the time of travel when planning vehicle routing. In the model, the fuel consumption not only takes loading weight into consideration but also satisfies the "non-passing" property, which is ignored in most TDVRP-related research papers. Then a simulated annealing (SA) algorithm is proposed for finding the vehicle routing with the lowest total fuel consumption. An experimental evaluation of the proposed method is performed. The results show that the proposed method provides a 24.61% improvement in fuel consumption over the method based on minimizing transportation time and a 22.69% improvement over the method based on minimizing transportation distances. © 2010 Elsevier Ltd. All rights reserved.


Chiu K.-C.,Hsing Kuo University
2011 IEEE International Conference on Quality and Reliability, ICQR 2011 | Year: 2011

Over the last two decades, various software reliability growth models (SRGM) have been proposed, and there has been a gradual but marked shift in the balance between software reliability and software testing cost in recent years. Chiu and Huang (2008) provided a Software Reliability Growth Model from the Perspective of Learning Effects, which is able to reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and offers better performance when fitting different data with consideration of the learning effects. However, this earlier model assumes that the learning effects are constant. In contrast, this paper discusses a software reliability growth model with time-dependent learning effects. © 2011 IEEE.


Kuo Y.,Hsing Kuo University | Wang C.-C.,Feng Chia University
Expert Systems with Applications | Year: 2012

The purpose of this paper is to propose a variable neighbourhood search (VNS) for solving the multi-depot vehicle routing problem with loading cost (MDVRPLC). The MDVRPLC is the combination of multi-depot vehicle routing problem (MDVRP) and vehicle routing problem with loading cost (VRPLC) which are both variations of the vehicle routing problem (VRP) and occur only rarely in the literature. In fact, an extensive literature search failed to find any literature related specifically to the MDVRPLC. The proposed VNS comprises three phases. First, a stochastic method is used for initial solution generation. Second, four operators are randomly selected to search neighbourhood solutions. Third, a criterion similar to simulated annealing (SA) is used for neighbourhood solution acceptance. The proposed VNS has been test on 23 MDVRP benchmark problems. The experimental results show that the proposed method provides an average 23.77% improvement in total transportation cost over the best known results based on minimizing transportation distance. The results show that the proposed method is efficient and effective in solving problems. © 2011 Elsevier Ltd. All rights reserved.


Kuo Y.,Hsing Kuo University | Wang C.-C.,Feng Chia University
Management of Environmental Quality | Year: 2011

Purpose: In recent years, people have started to realize the importance of environmental protection, and in particular the problem of global warming. Consequently, many governments have started to view decreasing carbon emissions as a priority. Green transportation is one of the policies that is relevant to these efforts. This research aims to optimize the routing plan with minimizing fuel consumption. Design/methodology/approach: In this research, a model is proposed for calculating the total fuel consumption when given a routing plan. Three factors which greatly affect fuel consumption of transportation - transportation distance, transportation speed and loading weight - are taken into consideration. Then a simple Tabu Search is used to optimize the routing plan and an experimental evaluation of the proposed method is performed. Findings: It is shown that the proposed method provides substantial improvements over a method based on minimizing transportation distances. Originality/value: The experimental results show that the routing plans found by the proposed method require less fuel consumption than that found by optimizing methods in which the distance travelled was minimized. That means that, if the distribution center can transport goods using vehicles with better fuel consumption, and the drivers can drive in the such a way as to reduce the discharge of carbon, then the proposed method can be a strategy for the continuous improvement of fuel consumption. © Emerald Group Publishing Limited.


Wang C.-C.,Hsing Kuo University
International Communications in Heat and Mass Transfer | Year: 2010

This article uses the concept of differential equation maximum principle as well as the technique of virtual time to establish the solution's monotonic relation with residual of forced convection. To obtain error bounds of approximate solution, the article first uses cubic spline approximation to discretize the differential equation, then applies "residual correction method" newly put forth by it to convert the once complex inequation constraint mathematical programming problem into a simple problem of equation iteration. What is more, not only the obtained upper and lower approximate solutions of the differential equation can correctly analyze error range, but also it is found that the new method helps increase the accuracy of mean approximate solutions. © 2009 Elsevier Ltd. All rights reserved.


Chiu K.-C.,Hsing Kuo University
IEEE International Conference on Industrial Engineering and Engineering Management | Year: 2012

This paper considered time-dependent learning effects in the software reliability growth model which Chiu et al. (2008) provided from the perspective of learning effects and would be able to reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and had better performance in fitting different data with consideration of a constant learning effect to enhance the model. This study assumed learning effects were depend on the process time and improved the model with linear-learning effect and exponential-learning effect to discuss when and what learning effects would occur in the software development process. This paper also verified the effectiveness of the proposed model with R square (Rsq) and compared with other models by using the comparison criteria with real data set. The results revealed that the proposed model shows good fitting in the data set which software development process exists time-dependent learning effects. © 2012 IEEE.

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