• Journal of Korean Society for Quality Management
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• v.37 no.2
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• pp.32-45
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• 2009

The importance of quality logistics service has kept growing in fierce competitions. Accordingly, many have tried to assess the logistics service quality and attempted to propose the strategic ways to manage it. However, it has been known that service quality is a multi-dimensional construct and not all quality attributed are viewed as equally important to customers. In other words, each quality attribute has different implications for customer satisfaction. To this respect, the current study attempted to identify the satisfying/dissatisfying quality factor through the Kana approach. In so doing, several satisfying and dissatisfying quality factors in logistics service quality were identified. Further, several academic and practical implications for logistics service quality were proposed.

• Journal of Korean Academy of Nursing
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• v.30 no.5
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• pp.1243-1253
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• 2000

The purpose of this study was to verify the validity of the Patient Severity Classification Tool by examining the correlations between the APACHE Ⅲ and the Patient Severity Classification Tool and to propose admission criteria to the ICU. The instruments used for this study were the APACHE Ⅲ developed by Knaus and the Patient Severity Classification Tool developed by Korean Clinical Nurses Association. Data was collected from the 156 Medical ICU patients during their first 24 hours of admission at the Seoul National University Hospital by three trained Medical ICU nurses from April 20 to August 31 1999. Data were analyzed using the frequency, $x^2$, Wilcoxon rank sum test, and Spearman rho. There was statistically significant correlations between the scores of the APACHE III and the Patient Severity Classification Tool. Mortality rate was increased as patients classification of severity in both the APACHE III and the Patient Severity Classification Tool scored higher. The Patient Severity Classification Tool was proved to be a valid and reliable tool, and a useful tool as one of the severity predicting factors, ICU admission criteria, information sharing between ICUs, quality evaluations of ICUs, and ICU nurse staffing.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.7
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• pp.1202-1208
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• 2020

Objective: The aim of this study was to identify a distribution pattern of meat quality grade (MQG) as a function of carcass yield index (CYI) and the gender of Hanwoo (bull, cow, and steer) to determine the optimum point between both yield and quality. We also attempted to identify how pre- and post-deboning variables affect the gender-specific beef quality of Hanwoo. Methods: A total of 31 deboning variables, consisting of 7 pre-deboning and 24 post-deboning variables from bulls (n = 139), cows (n = 69), and steers (n = 153), were obtained from the National Institute of Animal Science (NIAS) in South Korea. The database was reconstructed to be suitable for a statistical significance test between the CYI and the MQG as well as classification of meat quality. Discriminant function analysis was used for classifying MQG using the deboning parameters of Hanwoo by gender. Results: The means of CYI according to 1+, 1, 2, and 3 of MQG were 68.64±2.02, 68.85±1.94, 68.62±5.88, and 70.99±3.32, respectively. High carcass yield correlated with low-quality grade, while high-quality meat most frequently was obtained from steers. The classification ability of pre-deboning parameters was higher than that of post-deboning parameters. Moisture and the shear force were the common significant parameters in all discriminant functions having a classification accuracy of 80.6%, 71%, and 56.9% for the bull, cow, and steer, respectively. Conclusion: This study provides basic information for predicting the meat quality by gender using pre-deboning variables consistent with the actual grading index.

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.704-716
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• 2018

The aim of this study is to examine the characteristics analysis of the discharge and water quality based on TPLMS (Total Pollution Load Management System) in the Jinwi River unit basin, and to propose a management plan by selecting the point that needs improvement of water quality in order to achieve the target water quality. We evaluated the discharge and water quality characteristics, statistical analysis, daily delivery load and daily delivery density, grade classification, for 14 total pollution load site's from 2014 to 2016 year in Jinwi river unit basin. The average discharge of Jinwi river unit basin is $22.411m^3/s$ and discharge of Hwangguji River is 32.8% and the water quality characteristics along the tributary river were clarified spatially. As the result, it was analyzed that Seongeun River is an indirect indicator of organic pollutants, Gwanri River is a seasonal factor, Osan River and Hwangguji River both affect water quality. Estimation of delivered pollutant loads at the HG-3 site was 6,470.4 BOD kg/day, 6,846.7 TN kg/day and delivered pollutant loads density increased to $220.9BOD\;kg/day/km^2$, $22.4TP\;kg/day/km^2$ at the HG-4 site. This result demonstrates that the total pollution load site needed to improve water quality of the Jinwi River unit basin was HG-3 site.

• Journal of Korean Elementary Science Education
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• v.26 no.4
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• pp.395-406
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• 2007

The purpose of this study was to investigate the characteristics of 2nd grade students' interaction in the classification activities of LTTS. For the purposes of this study, three heterogeneous groups, chosen by cognitive level, were selected. The students' interactions were audio/video taped and classified as either cognitive or affective interaction. The results of this study are as follows. In the cognitive interactions, the frequency and quality of the functions of 'questions' and 'making suggestions' were higher than those of 'Responses' and 'Receiving opinions'. In the affective interactions, the frequency of 'induction' and 'dissatisfaction' was higher than that of the other types. The frequency and quality of interactions of students in both the early and mid concrete stage were higher than those of students in the transitional stage. Qualitatively higher-level interactions such as 'making suggestions' and positive interactions such as 'induction' to induce students who were passive in activities were made by the students at higher cognitive levels. However, the low-level of interaction in suggesting their opinion to the constituent's suggestion and 'dissatisfaction' with student in transition period who were passive in activity influenced group working negatively.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2580-2582
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• 2004

In this paper, a Neural Network(NN) based approach for classification of two dimensions images. The proposed algorithm is able to apply in the actual industry. The described diagnostic algorithm is presented to defect surface failures on tiles. A way to get data for a digital image process is several kinds of it. The tiles are scanned and the digital images are preprocessed and classified using neural networks. It is important to reduce the amount of input data with problem specific preprocessing. The auto-associative neural network is used for feature generation and selection while the probabilistic neural network is used for classification. The proposed algorithm is evaluated experimentally using one hundred of the real tile images. Sample image data to preprocess have histogram. The histogram is used as input value of probabilistic neural network. Auto-associative neural network compress input data and compressed data is classified using probabilistic neural network. Classified sample images are determined by human state. So it is intervened human subjectivity. But digital image processing and neural network are better than human classification ability. Therefore it is very useful of quality control improvement.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.421-431
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• 2020

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis. Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naïve Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study. Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms. Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.

• Journal of Information Technology Services
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• v.18 no.4
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• pp.135-149
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• 2019

Recently, there are many researches for extracting meaningful information from large amount of text data. Among various applications to extract information from text, topic modeling which express latent topics as a group of keywords is mainly used. Topic modeling presents several topic keywords by term/topic weight and the quality of those keywords are usually evaluated through coherence which implies the similarity of those keywords. However, the topic quality evaluation method based only on the similarity of keywords has its limitations because it is difficult to describe the content of a topic accurately enough with just a set of similar words. In this research, therefore, we propose topic keywords reorganizing method to improve the identifiability of topics. To reorganize topic keywords, each document first needs to be labeled with one representative topic which can be extracted from traditional topic modeling. After that, classification rules for classifying each document into a corresponding label are generated, and new topic keywords are extracted based on the classification rules. To evaluated the performance our method, we performed an experiment on 1,000 news articles. From the experiment, we confirmed that the keywords extracted from our proposed method have better identifiability than traditional topic keywords.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.21-33
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• 2022

Purpose: As most defense technologies are classified as confidential, the corresponding International Patent Classifications (IPCs) require special attention. Consequently, the list of defense-related IPCs has been managed by the government. This paper aims to evaluate the defense-related IPCs and propose a methodology to revalidate and improve the IPC classification scheme. Methods: The patents in military technology and their corresponding IPCs during 2009~2020 were utilized in this paper. Prior to the analysis, patents are divided into private and public sectors. Social network analysis was used to analyze the convergence structure and central defense technology, and association rule mining analysis was used to analyze the convergence pattern. Results: While the public sector was highly cohesive, the private sector was characterized by easy convergence between technologies. In addition, narrow convergence was observed in the public sector, and wide convergence was observed in the private sector. As a result of analyzing the core technologies of defense technology, defense-related IPC candidates were identified. Conclusion: This paper presents a comprehensive perspective on the structure of convergence of defense technology and the pattern of convergence. It is also significant because it proposed a method for revising defense-related IPCs. The results of this study are expected to be used as guidelines for preparing amendments to the government's defense-related IPC.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.