Chung Yuan Christian University

About: Chung Yuan Christian University is a education organization based out in Taoyuan City, Taiwan. It is known for research contribution in the topics: Membrane & Fuzzy logic. The organization has 9819 authors who have published 11623 publications receiving 213139 citations. The organization is also known as: Tiong-gôan-tāi-ha̍k & CYCU.

Electro-optical device based on photonic structure with a dual-frequency cholesteric liquid crystal

Abstract: An electrically tunable and polarizer-free photonic device is developed using a one-dimensional photonic crystal (PC) infiltrated with dual-frequency cholesteric liquid crystal (DFCLC) as a defect layer. The PC/DFCLC hybrid cell allows the employment of various frequency-modulated voltage pulses to regulate defect modes and switch between stable states. This device possesses many alluring features such as rapid bistable switching, intensity tunability, and wavelength tunability in the defect modes, and it requires no polarizers. It can be used as a filter, fast-speed shutter, or light-intensity modulator.

Evaluation of the diagnostic performance of infrared imaging of the breast: a preliminary study

Abstract: Background: The study was conducted to investigate the diagnostic performance of infrared (IR) imaging of the breast using an interpretive model derived from a scoring system. Methods: The study was approved by the Institutional Review Board of our hospital. A total of 276 women (mean age = 50.8 years, SD 11.8) with suspicious findings on mammograms or ultrasound received IR imaging of the breast before excisional biopsy. The interpreting radiologists scored the lesions using a scoring system that combines five IR signs. The ROC (receiver operating characteristic) curve and AUC (area under the ROC curve) were analyzed by the univariate logistic regression model for each IR sign and an age-adjusted multivariate logistic regression model including 5 IR signs. The cut-off values and corresponding sensitivity, specificity, Youden’s Index (Index = sensitivity+specificity-1), positive predictive value (PPV), negative predictive value (NPV) were estimated from the age-adjusted multivariate model. The most optimal cut-off value was determined by the one with highest Youden’s Index. Results: For the univariate model, the AUC of the ROC curve from five IR signs ranged from 0.557 to 0.701, and the AUC of the ROC from the age-adjusted multivariate model was 0.828. From the ROC derived from the multivariate model, the sensitivity of the most optimal cut-off value would be 72.4% with the corresponding specificity 76.6% (Youden’s Index = 0.49), PPV 81.3% and NPV 66.4%. Conclusions: We established an interpretive age-adjusted multivariate model for IR imaging of the breast. The cut-off values and the corresponding sensitivity and specificity can be inferred from the model in a subpopulation for diagnostic purpose. Trial Registration: NCT00166998.

Assembly line balancing in garment industry

Abstract: Garment manufacturing is a traditional industry with global competition. The most critical manufacturing process is sewing, as it generally involves a great number of operations. The aim of assembly line balance planning in sewing lines is to assign tasks to the workstations, so that the machines of the workstation can perform the assigned tasks with a balanced loading. Assembly line balancing problem (ALBP) is known as an NP-hard problem. Thus, the heuristic methodology could be a better way to plan the sewing lines within a reasonable time. This paper develops a grouping genetic algorithm (GGA) for ALBP of sewing lines with different labor skill levels in garment industry. GGA can allocate workload among machines as evenly as possible for different labor skill levels, so the mean absolute deviations (MAD) can be minimized. Real data from garment factories and experimental design are used to evaluate GGA's performance. Production managers can use the research results to quickly design sewing lines for important targets such as short cycle time and high labor utilization.

A Robust Automatic Merging Possibilistic Clustering Method

Abstract: Krishnapuram and Keller [IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98-110, May 1993] proposed a possibilistic c-means (PCM) clustering by relaxing the constraint in fuzzy c-means (FCMs) that the memberships of a data point across classes sum to 1. The PCM algorithm has a tendency to produce coincident clusters. This can be a merit of PCM as a good mode-seeking algorithm if initials and parameters are suitably chosen. However, the performance of PCM heavily depends on initializations and parameter selection. In this paper, we propose a mechanism of robust automatic merging. We then create an automatic merging possibilistic clustering method (AM-PCM), where the proposed algorithm does not only solve these parameter-selection and initialization problems but also obtains an optimal cluster number. The proposed AM-PCM algorithm first uses all data points as initial cluster centers and then automatically merges these surrounding points around each cluster mode such that it can self-organize data groups according to the original data structure. The AM-PCM can exhibit the robustness to parameter, noise, cluster number, different volumes, and initializations. The computational complexity of AM-PCM is also analyzed. Comparisons between AM-PCM and other clustering methods are made. Some numerical data and real datasets are used to show these good aspects of AM-PCM. Experimental results and comparisons actually demonstrate that the proposed AM-PCM is an effective and parameter-free robust clustering algorithm.