• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.103-109
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• 2018

In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.119-129
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• 2023

Purpose: This study proposes a classification model for implementing condition-based maintenance (CBM) by monitoring the real-time status of a machine using acceleration sensor data collected from a vehicle. Methods: The classification model's performance was improved by applying Fourier transform to convert the acceleration sensor data from the time domain to the frequency domain. Additionally, the Generative Adversarial Network (GAN) algorithm was used to augment images and further enhance the classification model's performance. Results: Experimental results demonstrate that the GAN algorithm can effectively serve as an image augmentation technique to enhance the performance of the classification model. Consequently, the proposed approach yielded a significant improvement in the classification model's accuracy. Conclusion: While this study focused on the effectiveness of the GAN algorithm as an image augmentation method, further research is necessary to compare its performance with other image augmentation techniques. Additionally, it is essential to consider the potential for performance degradation due to class imbalance and conduct follow-up studies to address this issue.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.87-93
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• 2012

This paper discusses a new weighting method for text analyzing from the view point of supervised learning. The term frequency and inverse term frequency measure (tf-idf measure) is famous weighting method for information retrieval, and this method can be used for text analyzing either. However, it is an experimental weighting method for information retrieval whose effectiveness is not clarified from the theoretical viewpoints. Therefore, other effective weighting measure may be obtained for document classification problems. In this study, we propose the optimal weighting method for document classification problems from the view point of supervised learning. The proposed measure is more suitable for the text classification problem as used training data than the tf-idf measure. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of newspaper article and the customer review which is posted on the web site.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.92-93
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• 2004

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.421-426
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• 2001

The pneumatic classification and acidic leaching behaviors of phosphor sludge have been examined to establish the recycling system of rare earth components contained in waste fluorescent lamp. At first, separation characteristic of rare earth components and calcium phosphate in phosphor sludge was investigated by pneumatic classification. After pneumatic classification of phosphor sludge, rare earth components were leached in various acidic solutions and sodium hydroxide solution. For recovery of soluble component in leaching solution, rare earth components were separated as hydroxide and oxalate precipitations. The experimental results obtained are summarized as follows: (1) In classification process, rare earth components in phosphor sludge were concentrated to 29.3% from 13.3%, and its yield was 32.9%. (2) In leaching process, sulfuric acid solution was more effective one as a leaching solvent of rare earth component than other solutions. Y and Eu components in phosphor sludge were dissolved in sulfuric acid solution of 1.5 k㏖/㎥, and other rare earth components were rarely dissolved in leaching solution. Leaching degrees of Y and Eu were respectively 92% and 98% in the following optimum leaching conditions; sulfuric acid concentration is 1.5 k㏖/㎥ , leaching temperature 343 K, leaching time 3.6 ks and pulp concentration 30 kg/㎥. (3) Y and Eu components of phosphor sludge contained in waste fluorescent lamp were, effectively recovered by three processes of pneumatic classification, sulfuric acid leaching and oxalate precipitation methods. Their recovery was finally about 65 %, and its purity was 98.2%.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.358-366
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• 2011

Multi-criteria ABC inventory classification, which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. The weighted linear optimization (WLO) model proposed by Ramanathan (2006) solves the problem of multi-criteria ABC inventory classification by generating a set of criterion weights for each inventory item and assigning a normalized score to the item for ABC analysis. However, the WLO model has some limitations. First, many inventory items can share the same optimal score, which can hinder a precise classification of inventory items. Second, the model allows too much flexibility in weighting multiple criteria; each item is allowed to choose its own weights so that it can maximize its score. As a result, if an item dominates the others in terms of a certain criterion, it may be classified into a higher class regardless of other criteria by assigning an overwhelming weight to the criterion. Consequently, an item with a high value in an unimportant criterion and low values in others may be inappropriately classified as class A, leading to an inaccurate classification of inventory items. To overcome these shortcomings, we extend the WLO model by using the cross-efficiency method in data envelopment analysis. We claim that the proposed model can provide a more reasonable and accurate classification of inventory items by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.39-45
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• 2022

For national industrial development in the Fourth Industrial Revolution, it is necessary to provide researchers with appropriate job information. This can be achieved by interconnecting the National Science and Technology Standard Classification System used for management of research activity with the Korean Employment Classification of Occupations used for job information management. In the present study, an automatic linkage model of classification systems is introduced based on a pre-trained language model for interconnecting science and technology information with job information. We propose for the first time an automatic model for linkage of classification systems. Our model effectively maps similar classes between the National Science & Technology Standard Classification System and Korean Employment Classification of Occupations. Moreover, the model increases interconnection performance by considering hierarchical features of classification systems. Experimental results show that precision and recall of the proposed model are about 0.82 and 0.84, respectively.

• Journal of Industrial Technology
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• v.19
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• pp.403-408
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• 1999

An efficient method is developed for classifying fingerprint data based on 2-D discrete wavelet transform. Fingerprint data is first converted to a binary image. Then a multi-level 2-D wavelet transform is performed. Vertical and horizontal subbands of the transformed data show typical energy distribution patterns relevant to the fingerprint categories. The proposed method with moderate level of wavelet transform is successful in classifying fingerprints into 5 different types. Finer classification is possible by higher frequency subbands and closer analysis of energy distribution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.1
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• pp.87-92
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• 2004

This review focuses on the developed and the being developed environmental sensors in particular biological sensors. As well as discussing the classification and some main principles, presenting current trend of the environmental sensors is given. Two main categories are immunosensors and catalytic sensors. In addition to those. DNA or RNA sensors or protein based sensors are discussed. Some crucial examples of the applications of such sensors are given to show how the sensor technology it used for environmental and biological monitoring, biomarkers of exposure.