Reliability prediction has been studied in many industries for managing stocks and reducing quality assurance costs and production costs. Particularly, in the automotive industry, reliability prediction is performed based on two automobile reliability perspectives, time and mileage. To maximize cost savings, researchers attempted reliability prediction with short-term inputs. However, limited information on shortterm inputs resulted in unsatisfactory prediction results for the long warranty periods. Additionally, the overall evaluation metrics could not reflect the pattern-wise performance, such as the increasing failure patterns. This study proposes Complementary Reliability perspective Transformer (CRFormer) based on Transformer encoder to achieve enriched representations from a short-term input sequence. CRFormer fuses different automobile reliability perspective information and automobile features to compensate for the limited information on short-term input. The performance of CRFormer is evaluated based on automobile claim data accumulated over 16 years. Results showed that compared to previous methods in terms of overall, pattern-wise, and pattern similarity evaluation metrics, CRFormer achieved outstanding performance in time and mileage reliability prediction. Lastly, visualization results and survival analysis based on accurate model prediction can be used to support decision-making to reduce quality assurance costs and production costs.
BACKGROUND Existing bacterial culture test results for infectious diseases are written in unrefined text, resulting in many problems including typographical errors and stop words. Effective spelling correction processes are needed to ensure the accuracy and reliability of data for the study of infectious diseases, including medical terminology extraction. If a dictionary is established, spelling algorithms using edit distance are efficient. However, in the absence of dictionaries, traditional spelling correction algorithms that utilize only edit distances have limitations. OBJECTIVE In this research, we proposed a similarity-based spelling correction algorithm using pre-trained word embedding with the BioWordVec technique. This method uses a character-level N-grams-based distributed representation through unsupervised learning rather than the existing rule-based method. In other words, we propose a framework that detects and corrects typographical errors when a dictionary is not in place. METHODS For detected typographical errors not mapped to SNOMED clinical terms, a correction candidate group with high similarity considering the edit distance was generated using pre-trained word embedding from the clinical database. From the embedding matrix in which the vocabulary is arranged in descending order according to frequency, the grid search is used to search for candidate groups of similar words. Then, the correction candidate words are ranked in consideration of the frequency of the words, and the typos are finally corrected according to the ranking. RESULTS Bacteria identification words were extracted from 27,544 bacteria culture reports, and 16 types of 914 spelling errors were found. The similarity-based spelling correction algorithm using BioWordVec proposed in this research corrected 12 types of typographical errors and showed very high performance in correcting 99.45% of all spelling errors. CONCLUSIONS This tool corrected spelling errors effectively in the absence of a dictionary based on bacterial identification words in the bacteria culture reports. This method will help build a high-quality refined database of vast text data for electronic health records.
The proportion of the inventory range associated with spare parts is often considered in the industrial context. Therefore, even minor improvements in forecasting the demand for spare parts can lead to substantial cost savings. Despite notable research efforts, demand forecasting remains challenging, especially in areas with irregular demand patterns, such as military logistics. Thus, an advanced model for accurately forecasting this demand was developed in this study. The K-X tank is one of the Republic of Korea Army’s third generation main battle tanks. Data about the spare part consumption of 1,053,422 transactional data points stored in a military logistics management system were obtained. Demand forecasting classification models were developed to exploit machine learning, stacked generalization, and time series as baseline methods. Additionally, various stacked generalizations were established in spare part demand forecasting. The results demonstrated that a suitable selection of methods could help enhance the performance of the forecasting models in this domain.
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