China University of Technology is a private university in Taipei, Taiwan. The original campus is situated in Taipei's Wenshan District on five hectares of land. Since 2000, the owner has opened a second campus on 14 hectares of land in the Hukou township of Hsinchu County, Taiwan. Wikipedia.
China University of Technology | Date: 2015-06-16
Disclosed is a device for detecting wall abrasion of a solid-filling feeding well and a detection method thereof. The device comprises a well wall abrasion detector, a horizontal displacement meter, a vertical displacement monitor, and a limit guide rod. One end of the limit guide rod is connected to the well wall abrasion detector. The signal output terminal of the well wall abrasion detector is connected to the signal input terminal of the horizontal displacement meter, and the other end of the limit guide rod passes through the vertical displacement monitor for slidable setting. This disclosure mainly utilizes a resistance strain displacement sensor to detect the abrasion and deformation degree of the well wall, determines the position of damages with the vertical displacement monitor, and draws wall abrasion curves by using the obtained data. The device provided is easy to use, has low cost, has high reliability, and can effectively detect the wall abrasion condition of a solid-filling feeding well, thereby providing a basis for ensuring the working efficiency of the feeding well.
Zhou Y.,China University of Technology
Journal of Applied Geophysics | Year: 2017
A recently emerging seismic acquisition technology called simultaneous source shooting has attracted much attention from both academia and industry. The key topic in the newly developed technique is the removal of intense blending interferences caused by the simultaneous ignition of multiple airgun sources. In this paper, I propose a novel inversion strategy with multiple convex constraints to improve the deblending performance based on the projection onto convex sets (POCS) iterative framework. In the POCS iterative framework, as long as the multiple constraints are convex, the iterations are guaranteed to converge. In addition to the sparse constraint, I seek another important constraint from the untainted data. I create a blending mask in order to fully utilize the useful information hidden behind the noisy blended data. The blending mask is constructed by numerically blending a matrix with all its entries set to be one and then setting the non-one entries of the blended matrix zero. I use both synthetic and field data examples to demonstrate the successful performance of the proposed method. © 2017 Elsevier B.V.
Ying K.-C.,National Taipei University of Technology |
Cheng H.-M.,China University of Technology
Expert Systems with Applications | Year: 2010
Topics related to parallel machine scheduling problems have been of continuing interest for researchers and practitioners. However, the dynamic parallel machine scheduling problem with sequence-dependent setup times still remains under-represented in the research literature. In this study, an iterated greedy heuristic for this problem is presented. Extensive computational experiments reveal that the proposed heuristic is highly effective as compared to state-of-the-art algorithms on the same benchmark problem data set. © 2009 Elsevier Ltd. All rights reserved.
Lee J.,China University of Technology
Communications in Computer and Information Science | Year: 2017
The purpose of this study is to examine the relationships between four dimensions of user perceived interactivity: user control, responsiveness, connectedness, social interaction and consumers’ perceived imagination composed of reproductive and creative in web interaction environment, finally determining the level of overall satisfaction with ACG website engagements. The process of research aimed at developing a rigorous empirical measurement scale for attitudes toward social media that specifically applies to ACG websites, as suggested by expert interviews. Items covering engagement, satisfaction, and imagination were identified through literature review and expert interviews. Following the suggestion of Churchill (1979), Sethi and King (1994), and relevant literature on scale development, this research followed the process of building the initial pool, interviewing the experts, purifying the items, and assessing the reliability and validity through a pilot test and the formal survey. The final model delete “user control” and retain other three dimensions, “responsiveness”, “connectedness”, “social interaction” for the construct of perceived interactivity. And the measure scale consisted of 4 items for social interaction, 4 items for responsiveness, 4 items for connectedness, 5 items for engagement, 4 for satisfaction, and 6 for imagination dimensions, respectively. A four-phase method was performed and confirmatory factor analysis was verified. © Springer International Publishing AG 2017.
Chen C.-H.,China University of Technology
Expert Systems with Applications | Year: 2011
In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H ∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods. © 2010 Elsevier Ltd. All rights reserved.
Cheng Y.-M.,China University of Technology
International Journal of Project Management | Year: 2014
Construction cost overrun is a common problem in construction industries. The objective of this research is to extract the key cost-influencing factors with new concept and methods to help control the expenditure. Hence, this research adopts the Modified Delphi Method (MDM) with 2 groups and 2 rounds and Kawakita Jiro method (KJ) to consolidate the experts' opinions and identify and rank the key factors that affect project costs. Ninety cost-influencing factors are collected from literary review and interviews with experts with practical cost control experiences in the construction companies (Group 1). The KJ method is used to consolidate these factors into 4 categories and down to a total of 42 factors. 2 rounds of questionnaires are then conducted to filter the key factors. In order to verify views of those in the first group, Group 2 consists of experienced experts from the public sectors, consulting firms and construction companies as a comparison. Results of the analysis indicate that there are 16 key cost-influencing factors. Severity Index computation was then adopted to rank these key cost-influencing factors. The study renders that clearly defined scope of project in the contract and cost control are the major determinants for cost overrun. © 2013 Elsevier Ltd. APM and IPMA.
Lin S.-M.,China University of Technology
Neural Computing and Applications | Year: 2013
When constructing classification and prediction models, most researchers used genetic algorithm, particle swarm optimization algorithm, or ant colony optimization algorithm to optimize parameters of artificial neural network models in their previous studies. In this paper, a brand new approach using Fruit fly optimization algorithm (FOA) is adopted to optimize artificial neural network model. First, we carried out principal component regression on the results data of a questionnaire survey on logistics quality and service satisfaction of online auction sellers to construct our logistics quality and service satisfaction detection model. Relevant principal components in the principal component regression analysis results were selected for independent variables, and overall satisfaction level toward auction sellers' logistics service as indicated in the questionnaire survey was selected as a dependent variable for sample data of this study. In the end, FOA-optimized general regression neural network (FOAGRNN), PSO-optimized general regression neural network (PSOGRNN), and other data mining techniques for ordinary general regression neural network were used to construct a logistics quality and service satisfaction detection model. In the study, 4-6 principal components in principal component regression analysis were selected as independent variables of the model. Analysis results of the study show that of the four data mining techniques, FOA-optimized GRNN model has the best detection capacity. © 2011 Springer-Verlag London Limited.
Liu Y.,China University of Technology
Journal of Applied Polymer Science | Year: 2013
Thermosets, which have a highly crosslinked structure, play a pivotal role in high-performance composite materials because of their excellent mechanical properties, including their high modulus, high strength, and high glass-transition temperature. In general, however, thermosets are brittle materials with a toughness and elongation at break that are unsatisfactory for many applications, especially at high temperatures. The key factor that can greatly influence the toughness of a thermoset material is its cured microstructure or nanostructure. Recently, it has been revealed that the introduction of a reactive modifier into a multicomponent thermosetting prepolymer is a versatile way to finely tune the polymerization-induced phase separation (PIPS) and the microstructure and thermomechanical properties of the resulting thermosets. This review focuses first on the advancement of the methods used to study the PIPS of thermosetting prepolymers. I go on to discuss factors influencing the thermodynamic and the kinetic behavior of PIPS and the resulting morphology and thermomechanical properties of thermosetting blends obtained when nonlinear reactive modifiers are incorporated. Copyright © 2012 Wiley Periodicals, Inc.
Daroch M.,China University of Technology |
Geng S.,China University of Technology |
Wang G.,China University of Technology |
Wang G.,University of Hawaii at Manoa
Applied Energy | Year: 2013
Major challenges of the modern world: energy security, oil price, resources depletion and climate change, have prompted significant advances in research and development of biomass-derived energy and fuels. Algal biofuels are seen as one of the most promising solutions of global energy crisis and climate change for the years to come. Major advantages of algae are potentially high yield and no competition with food crops for arable land and fresh water resource. This review summarises recent advances in algal biofuel production and focuses on synthesis of transportation fuel rather than characterising algal feedstocks or their well-documented potential as bioenergy resource. The available literature covering production of bioethanol, biodiesel and other potential liquid fuels are evaluated. Overall finding from this study suggests that to date the most effective methods of producing biofuels from algal feedstocks are: fermentation of microalgae to bioethanol and production of biodiesel via in situ transesterification of microalgal biomass. The real breakthrough however is expected from metabolic engineering of photosynthetic organisms to produce and secrete biofuels that promises significant simplification of down-stream processing. © 2012 Elsevier Ltd.
Chen C.-H.,China University of Technology
Neural Computing and Applications | Year: 2012
In this study, a wavelet neural network (WNN)-based adaptive robust control (WARC) strategy is investigated to resolve the tracking control problem of a class of multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed control system comprises of an adaptive wavelet controller and a robust controller. The adaptive wavelet controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of a feedback linearization control (FLC) law. The proportional-integral (PI) adaptation laws of the MIMO control system are derived from the Lyapunov stability theorem, which are utilized to update the adjustable parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H ∞ control technique, the robust controller is developed to attenuate the effect of the approximation error caused by WNN approximator, so that the desired tracking performance can be achieved. Finally, two MIMO uncertain nonlinear systems, the ecological system and the unified chaotic system, are performed to verify the effectiveness and robustness of the proposed WARC strategy. Furthermore, the salient merits are also indicated in comparison with the FLC system. © 2010 Springer-Verlag London Limited.