• 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.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.110-114
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• 2014

Purpose: The advancement in PET/CT test devices has decreased the test time and popularized the test, and PET/CT tests have continuously increased. However, this increases the exposure dose of radiation workers, too. This study aims to measure the radiation shielding rate of $^{18}F-FDG$ with a strong energy and the shielding effect when worker wore an apron during the PET/CT test. Also, this study compared the shielding rate with $^{99m}TC$ to minimize the exposure dose of radiation workers. Materials and Methods: This study targeted 10 patients who visited in this hospital for the PET/CT test for 8 days from May 2nd to 10th 2013, and the $^{18}F-FDG$ distribution room, patient relaxing room (stand by room after $^{18}F-FDG$ injection) and PET/CT test room were chosen as measuring spots. Then, the changes in the dose rate were measured before and after the application of the APRON. For an accurate measurement, the distance from patients or sources was fixed at 1M. Also, the same method applied to $^{99m}TC's$ Source in order to compare the reduction in the dose by the Apron. Results: 1) When there was only L-block in the $^{18}F-FDG$ distribution room, the average dose rate was $0.32{\mu}Sv$, and in the case of L-blockK+ apron, it was $0.23{\mu}Sv$. The differences in the dose and dose rate between the two cases were respectively, $0.09{\mu}Sv$ and 26%. 2) When there was no apron in the relaxing room, the average dose rate was $33.1{\mu}Sv$, and when there was an apron, it was $22.3{\mu}Sv$. The differences in the dose and dose rate between them were respectively, $10.8{\mu}Sv$ and 33%. 3) When there was no APRON in the PET/CT room, the average dose rate was $6.9{\mu}Sv$, and there was an APRON, it was $5.5{\mu}Sv$. The differences in the dose and dose rate between them were respectively, $1.4{\mu}Sv$ and 25%. 4) When there was no apron, the average dose rate of $^{99m}TC$ was $23.7{\mu}Sv$, and when there was an apron, it was $5.5{\mu}Sv$. The differences in the dose and dose rate between them were respectively, $18.2{\mu}Sv$ and 77%. Conclusion: According to the result of the experiment, $^{99m}TC$ injected into patients showed an average shielding rate of 77%, and $^{18F}FDG$ showed a relatively low shielding rate of 27%. When comparing the sources only, $^{18F}FDG$ showed a shielding rate of 17%, and $^{99m}TC$'s was 77%. Though it had a lower shielding effect than $^{99m}TC$, $^{18}F-FDG$ also had a shielding effect on the apron. Therefore, it is considered that wearing an apron appropriate for high energy like $^{18}F-FDG$ would minimize the exposure dose of radiation workers.