• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Applied Chemistry for Engineering
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• v.29 no.3
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• pp.283-288
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• 2018

As the use of environmentally friendly and non-disease natural pigments grows, various methods for extracting natural pigments have been studied. The natural color was extracted from parsley, a vegetable ingredient containing natural dyes. Target color codes of green series of natural dyes extracted as variables #50932C (L = 55.0, a = -40.0, b = 46.0) were set with the pH and temperature of extracted natural color coordinates (of the extracted), and the quantitative intensities of natural dyes were analyzed. During the colorimetric analysis predicted by the reaction surface analysis method, a color coordinate analysis was conducted under the optimal conditions of pH 8.0 and extraction temperature of $60.9^{\circ}C$. Under these conditions, predicted figures of L, a, and b were 55.0, -36.3, and 36.8, respectively, while actual experimental ones confirmed were 69.0, -35.9, and 31.4, respectively. In these results, the theory accuracy and actual error rate were confirmed to be 73.0 and 13.8%, respectively. The theoretical optimization condition of the color difference (${\Delta}E$) was at the pH of 9.2 and extraction temperature of $55.2^{\circ}C$. Under these conditions the predicted ${\Delta}E$ figure was 12.4 while the experimental one was 13.0. The difference in color analysis showed 97.5% of the theoretical accuracy and 4.5% of the actual error rate. However, the combination of color coordinates did not represent a desired target color, but rather close to the targeted color by means of an arithmetic mean. Therefore, it can be said that when the reaction surface analysis method was applied to the natural dye extraction process, the use of color coordinates as a response value can be a better method for optimizing the dye extraction process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.3
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• pp.71-86
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• 1983

Current R.C. retaining wall design is bared on WSD, but the reliability based design method is more rational than the WSD. For this reason, this study proposes a reliability based design criteria for the cantilever retaining wall, which is most common type of retaining wall, and also proposes the theoretical bases of nominal safety factors of stability analysis by introducing the reliability theory. The limit state equations of stability analysis and design of each part of cantilever retaining wall are derived and the uncertainty measuring algorithms of each equation are also derived by MFOSM using Coulomb's coefficient of the active earth pressure and Hansen's bearing capacity formula. The levels of uncertainties corresponding to these algorithms are proposed appropriate values considering our actuality. The target reliability indices (overturning: ${\beta}_0$=4.0, sliding: ${\beta}_0$=3.5, bearing capacity: [${\beta}_0$=3.0, design for flexure: [${\beta}_0$=3.0, design for shear: ${\beta}_0$=3.2) are selected as optimal values considering our practice based on the calibration with the current R.C. retaining wall design safety provisions. Load and resistance factors are measured by using the proposed uncertainties and the selected target reliability indices. Furthermore, a set of nominal safety factors, allowable stresses, and allowable shear stresses are proposed for the current WSD design provisions. It may be asserted that the proposed LRFD reliability based design criteria for the R.C. retaining wall may have to be incorporated into the current R.C. design codes as a design provision corresponding to the USD provisions of the current R.C. design code.