Optimization of the Optical Whiteness Ratio for Flexible Display by Using Taguchi's Dynamic Approach

Su, Chao‐Ton; Lin, Chia-Ming; Hsu, Chun-Chin

doi:10.1109/tsm.2011.2170184

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…The parameter design takes advantage of experiments to determine the combination of the controlling factor and level; it is one of the many quality control methods proposed by Taguchi, and it has achieved the greatest contribution in terms of enhancing quality [20, 21]. The design takes advantage of the principle of the orthogonal array, using fewer experiments and a simple configuration of the experiment by reducing the sensitivity of the system to the noise factor.…”

Section: The Proposed Model and Research Methodsmentioning

confidence: 99%

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters

Shie

Lin

et al. 2019

BioMed Eng OnLine

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Background Hemodialysis mainly relies on the “artificial kidney,” which plays a very important role in temporarily or permanently substituting for the kidney to carry out the exchange of waste and discharge of water. Nevertheless, a previous study on the artificial kidney has paid little attention to the optimization of factors and levels for reducing the solidification of the artificial kidney during the hemodialysis procedure. Thus, this study proposes an integrated model that uses the Taguchi method, omega formula, and back-propagation network to determine the optimal factors and levels for addressing this issue. Methods First, we collected the recommendations of medical doctors and nursing staff through a small group discussion, and used the Taguchi method to analyze the key factors at different levels. Next, the omega formula was used to convert the analysis results from the Taguchi method to assess the defect rate. Finally, we utilized back-propagation network algorithms to predict the optimal factors and levels for artificial kidney solidification, in order to confirm that the key factors and levels identified can effectively improve the solidification rate of the artificial kidney and thereby enhance the effect of hemodialysis. Results The research finding proposes the following as the optimal factors and levels for artificial kidney solidification: the amount of anticoagulation should be set at 500 units, the velocity of blood flow at 300 ml/min, the dehydration volume at 2.5 kg, and the vascular access type as autologous blood vessels. We obtained 270 sets of data from the patients of End Stage Renal Disease (ESRD) under the setting of the optimal combination of the factors at different levels; the defect rate of artificial kidney solidification is 12.9%, which is better than the defect rate of 32% in the original experiment. Meanwhile, the patient characteristics for physiological status in BMI, serum calcium, hematocrit, ferritin, and transferrin saturation percentage are improved by this study. Conclusion This conclusion validates the ability of the proposed model in this study to improve the solidification rate of the artificial kidney, thereby confirming the model’s use as a standard operation procedure in the hemodialysis experiment. The ideas behind and the implications of the proposed model are further discussed in this study.

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Section: The Proposed Model and Research Methodsmentioning

confidence: 99%

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters

Shie

Lin

et al. 2019

BioMed Eng OnLine

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“…The final conclusion will be a protein function, which will be considered as the function of p . The conclusion is valid, if it has been deducted from all previous premises [30]. Table 2 presents the standard inference rules for predicate logic.…”

Section: Methodsmentioning

confidence: 99%

Predicting protein functions by applying predicate logic to biomedical literature

Taha¹,

Iraqi²,

Aamri³

2019

BMC Bioinformatics

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BackgroundA large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts. However, they consider only explicitly mentioned terms that co-occur with proteins in texts. We observe that some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts.ResultsTo overcome the limitations of methods that rely solely on explicitly mentioned terms in texts to predict protein functions, we propose in this paper an Information Extraction system called PL-PPF. The proposed system employs techniques for predicting the functions of proteins based on their co-occurrences with explicitly and implicitly mentioned biological molecule terms that pertain functional categories in biomedical literature. That is, PL-PPF employs a combination of statistical-based explicit term extraction techniques and logic-based implicit term extraction techniques. The statistical component of PL-PPF predicts some of the functions of a protein by extracting the explicitly mentioned functional terms that directly describe the functions of the protein from the biomedical texts associated with the protein. The logic-based component of PL-PPF predicts additional functions of the protein by inferring the functional terms that co-occur implicitly with the protein in the biomedical texts associated with it. First, the system employs its statistical-based component to extract the explicitly mentioned functional terms. Then, it employs its logic-based component to infer additional functions of the protein. Our hypothesis is that important biological molecule terms pertaining functional categories of proteins are likely to co-occur implicitly with the proteins in biomedical texts. We evaluated PL-PPF experimentally and compared it with five systems. Results revealed better prediction performance.ConclusionsThe experimental results showed that PL-PPF outperformed the other five systems. This is an indication of the effectiveness and practical viability of PL-PPF’s combination of explicit and implicit techniques. We also evaluated two versions of PL-PPF: one adopting the complete techniques (i.e., adopting both the implicit and explicit techniques) and the other adopting only the explicit terms co-occurrence extraction techniques (i.e., without the inference rules for predicate logic). The experimental results showed that the complete version outperformed significantly the other version. This is attributed to t...

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“…The Taguchi Method therefore has been utilized for improving the industry and optimizing parameter design. For instance, Su et al [10] effectively enhanced optical whiteness with Taguchi's Dynamic Approach, Hong [11] identified the key factors in the market segment with the Taguchi Method, and Souza et al [12] applied the Taguchi Method to improving the quality of hemodialysis. By computing the target function and transforming it into a Signal-to-Noise ratio, the Signal-to-Noise ratio of the quality characteristics is divided into the Larger the Better, Nominal the Better, and the Smaller the Better in the definition of Taguchi Method.…”

Section: Taguchi Methodsmentioning

confidence: 99%

“…The total variability of the S/N ratio was measured by the sum of the squared deviations from the total mean S/N ratio. Su et al [10], Chen and Ou [24], and Souza et al [12] applied P test values to proceed with the decision-making process. The P value was calculated for each design parameter.…”

Section: Anovamentioning

confidence: 99%

Integrating the Taguchi Method and Response Surface Methodology for Process Parameter Optimization of the Injection Molding

Jou¹,

Lin²,

Lee³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:The outer coating of an optical fiber cable protects the optical fiber, and the contraction rate of the outer coating can affect the communication capacity and distance, so the contraction rate is often regarded as the key factor in defining the quality of an optical fiber cable. For this reason, using the example of the circular single-pin optical fiber cable manufacturing process of the largest optical communication manufacturer in the world, the Taguchi Method (TM) is used to screen the variables that have significant effects on the contraction rate of the outer coating. Furthermore, the optimization engineering of Response Surface Methodology (RSM) is utilized for the empirical research to acquire a prediction model that can be used to optimize the optical fiber outer coating injection molding process. The research results show that the contraction rate predicted by the integration of the Taguchi Method and RSM is 2.28%. The empirical results reveal that the contraction rate of the outer coating indeed decreased from the original 3.83% down to 2.16%, indicating that the integration of the Taguchi Method and RSM could effectively improve the quality.

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Optimization of the Optical Whiteness Ratio for Flexible Display by Using Taguchi's Dynamic Approach

Cited by 8 publications

References 10 publications

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters

Predicting protein functions by applying predicate logic to biomedical literature

Integrating the Taguchi Method and Response Surface Methodology for Process Parameter Optimization of the Injection Molding

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