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Taoyuan, Taiwan

Lunghwa University of Science and Technology is a private university of science and technology in the Taiwanese vocational education system, originally based in Guishan District, Taoyuan City, Taiwan.As its spirit of fundamental of initial creation in its school song, "a technical skill can be valued unlimitedly; both hands can be creating infinitely for country." The LHU is a multi-technology institution that has a good open-mined system and universal spirit of root cause studying architecture among structured cells.Popular undergraduate majors at LHU include electronics, business, mechanic, engineering, finance, philology, international business, industrial management, multi-media and gaming science, tourism and leisure science and computer science. Popular fields of study among graduate students include electronics, engineering, computer science and finance.LHU claims to provide HTC, TSMC, UMC and other famous firms with more engineering, computer science and business graduates than any other college or university, and philanthropic support of LHU is among the highest in the Taiwanese vocational education system and technical education system. Wikipedia.

Liu F.-H.,Lunghwa University of Science and Technology
Ceramics International | Year: 2011

This article describes a novel layer manufacturing process for forming a ceramic part with porous multi-channel architecture by laser gelling under low laser energy. The process involves bonding silica powder by gelled silica sol after exposure by a CO2 laser. Lower laser energy density of 0.8 J/mm2 is required to produce ceramic parts by "gelling effect". Therefore, the geometrical deflection and thermal distortion can be reduced after laser scanning. The inner porous structures were supported by ceramic slurries to prevent sagged deflection and to enhance dimensional accuracy due to optimal slurry suspension. The flexural strength of the green specimen was 4.7 MPa, while that of the gelled specimen was 12.5 MPa after heat-treatment at 1200 °C for 1.5 h. The proposed process has potential for fabricating complex interconnected porous ceramics for tissue engineering applications. © 2011 Elsevier Ltd and Techna Group S.r.l. All rights reserved. Source

Wu J.-Y.,Lunghwa University of Science and Technology
Applied Soft Computing Journal | Year: 2011

Developing an efficient classification method is a challenge task in many research domains, such as neural network (NN) classifiers, statistical classifiers and machine learning. This study focuses on NN classifiers, which are data-driven analytical techniques. This study presents a cerebellar model articulation controller NN (CMAC NN) classifier, which has the advantages of very fast learning, reasonable generalization ability and robust noise resistance. To increase the accuracies of training and generalization, the CMAC NN classifier is designed with multiple-input and multiple-output (MIMO) network topology. The performance of the proposed MIMO CMAC NN classifier is evaluated using PROBEN1 benchmark datasets (such as for diabetes, cancer and glass) taken from the UCI Machine Learning Repository. Numerical results indicate that the proposed CMAC NN classifier is efficient for tested datasets. Moreover, this study compares the experimental results of the CMAC NN classifier with those in the published literature, indicating that the CMAC NN classifier is superior to some published classifiers. Therefore, the CMAC NN classifier can be considered as an analytical tool for solving classification tasks, such as medical decision making. © 2010 Elsevier B.V. All rights reserved. Source

Tseng M.-L.,Lunghwa University of Science and Technology
Journal of Cleaner Production | Year: 2013

Measuring sustainable production indicators (SPIs) is becoming an important environmental activity due to government directives and increasing awareness among the populous to protect the environment and reduce waste. For printed circuit board manufacturers, measuring SPIs that are dedicated to sustainable activities usually require a multi-criteria structure with driving and dependence power for tracking. Hence, the measures of SPIs are always based on subjective perceptions and interactive relations in nature. A hybrid method for composing a hierarchical structure based on linguistic preferences and that concurrently applies perception judgment is lacking. This study proposes a novel approach in which fuzzy set theory and interpretive structural modeling are adopted to address the analytical objective. The proposed criteria are categorized into a hierarchical structure and arranged into visual quadrants on a graph according to their driving and dependence powers. The insights graph and hierarchical structure could assist mangers in strategic planning related to improving their firms' environmental activities. Managerial implications and concluding remarks are addressed. © 2010 Elsevier Ltd. All rights reserved. Source

Lin R.-J.,Lunghwa University of Science and Technology
Journal of Cleaner Production | Year: 2013

Green supply chain management (GSCM) has become a proactive approach to enhance environmental performance. Under stakeholder pressures and regulations, firms need to enhance GSCM practice, which are influenced by practices such as green purchasing, green design, product recovery, and collaboration with customers and suppliers. As proactive firms adopt GSCM, their economic performance and environmental performance will be improved. Hence, this study aims to examine the influential factors among eight criteria of three main GSCM practices, namely practices, performances, and external pressures. To deal with the vagueness of human being's perceptions, this study utilizes the fuzzy set theory and decision making trial and evaluation laboratory method to form a structural model to find out the cause and effect relationships among criteria. The results and managerial implications are discussed. © 2010 Elsevier Ltd. All rights reserved. Source

Wen W.,Lunghwa University of Science and Technology
Expert Systems with Applications | Year: 2010

This paper presents an intelligent traffic management expert system with RFID technology. The system provides both practically important traffic data collection and control information and can trace criminal or illegal vehicles such as stolen cars or vehicles that evade tickets, tolls or vehicle taxes. The system architecture consists of an RFID reader, a passive tag, a personal computer, a pair of infrared sensors, and a high-speed server with a database system. Based on RFID technology, the system collects and calculates average speed and average flow information on each road of a district area in a city. It then transmits the messages from all the congested roads in a district area to the server in the district center via a communication program. Through a flooding algorithm, each server in a district center exchanges and updates information with all neighbor servers in other district centers so all that the servers in various district centers can get all the latest congestion messages in a city. Therefore, a dynamic navigation system can find the shortest path that avoids congested roads. Meanwhile, we compare three types of tags for choosing a better solution for e-plates in the future. We also adopt infrared sensors for detecting cars that do not have a tag. © 2009 Elsevier Ltd. All rights reserved. Source

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