• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.169-177
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• 2011

As demand for efficiency in handling dynamic XML data grows, new dynamic XML labeling schemes have been researched. The key idea of the dynamic XML labeling scheme is to find ancestor-descendent-sibling relationships and to minimize memory space to store total label, response time and range of relabeling incurred by update operations. The prime number labeling scheme is a representative scheme which supports dynamic XML documents. It determines the ancestor-descendant relationships between two elements by a simple divisibility test of labels. When a new element is inserted into the XML data using this scheme, it does not change the label values of existing nodes. However, since each prime number must be used exclusively, labels can become significantly large. Therefore, in this paper, we introduce a novel technique to effectively reduce the problem of label overflow. The suggested idea is based on tree decomposition. When label overflow occurs, the full tree is divided into several sub-trees, and nodes in each sub-tree are separately labeled. Through experiments, we show the effectiveness of our scheme.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• The Korean Journal of Community Living Science
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• v.21 no.4
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• pp.581-593
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• 2010

This study was performed to find out the correct recognition and stabilization of the food labeling system, and the dietary behavior of college students for processed food as well as their recognition of the food labeling system as observed according to their residence type. A questionnaire was composed with three divisions containing general items, the dietary behavior for processed food and the effectiveness of the nutrition labeling system. The data was analyzed by SPSS WIN 17.0 and the results are as follows; The findings showed differences according to residence type were the intake frequency of processed food, the ways of preservation and the reasons of returning or exchange of the purchased processed food. And there was no difference in the recognition of dietary behavior for processed food and food nutrition labeling system. For overall perception of the dietary behavior and the recognition of the food labeling system, the ratio of college students who considered selection standard related to health when purchasing processed food was low. And the ratio of those who checked the label for milk and dairy products or instant food which is concerned with decomposition was high. On the contrary, the ratio of checking was relatively low for beverages, noodles and cookies. Many answered that the reason for having checked the food label was to find out the safety of the food and that of having read the nutrition label was to control weight and to check the nutrition ingredients. In general, many answered positively for the recognition and the necessity of food labeling system, but the actual practice of selecting and managing processed food was poor.

• Journal of the Korean Dietetic Association
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• v.23 no.1
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• pp.94-105
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• 2017

A menu labeling initiative is a lawful regulation with an aim to promote public health by providing customers the right to make informed menu choices. As college years are a critical period in which students form dietary habits, which are sustained throughout their lives, provision of nutritional information at the university dining services is important to students' health and life. Due to the lack of research on menu labeling at university dining services, the purpose of this study was to examine college students' attitudes and motivations toward menu labeling at university dining services, as well as their use intentions toward nutrition information at university dining services. Data were collected from a self-administered survey distributed to 484 college students who had experienced university dining services. Motivations of university students toward menu labeling were categorized into 'knowledge pursuit' and 'health pursuit'. Students' attitudes toward menu labeling had a positive effect on their intention to use menu labeling at university dining services. The findings of the study indicated that female students, or those who frequently used nutrition information, tended to have higher attitudes, motivations, and use intentions toward nutrition information. The study results suggest that facilitation of healthy eating environments at university dining services by offering nutrition information, and nutrition and health education is necessary.

• Journal of Korean Society on Water Environment
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• v.38 no.5
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• pp.231-239
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• 2022

In this study, we applied ENERWATER to evaluate the energy consumption labeling of wastewater treatment plants in Korea using the Korea sewerage statistics data. The results showed that the energy label status was excellent in the SBR process for small and medium-scale wastewater treatment plants and the A2O process for large-scale wastewater treatment plants. The energy labeling of wastewater treatment plants of 50,000 tons capacity was excellent. The statuses of metropolitan cities and Jeollanam-do province were excellent. We analyzed the effects of renewable energy on wastewater treatment plants' energy consumption and found out that digestion gas for large-scale plants and photovoltaic energy for small-scale plants were effective in improving energy labeling. In addition, we compared the energy labels of four wastewater treatment plants in "Z" city and wastewater treatment plant "X" had the best energy label, and the wastewater treatment plants "V" and "Y" had to be selected as priorities for the energy diagnosis and improvement project. In a comprehensive conclusion, the applicability of ENERWATER was confirmed based on sewage statistics data and labeling can be used to set priorities for the energy diagnosis and improvement project.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.645-651
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• 2005

The construction noise Is one of the main causes of environmental impact. The complaints about it have been increasing every year. In order to reduce construction machinery noise, the product noise labeling for construction machinery has been recommended since 1 February 1996 and In the near future the government will convert the recommended product noise labeling to the mandatory product noise labeling for high noise construction machinery. Therefore, we investigated the noise and vibration level of 302 construction machines in construction work sites. This paper presents the basic data that will be used for setting the standard of construction machinery noise and predicting the noise level near construction work sites in environmental Impact assessment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.548-553
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• 2004

The construction noise has been considered as one of environment pollution and complaints about it have been increasing every year. In order to reduce construction machinery noise, the product noise labeling for construction machinery has been recommended since 1 February 1996 and in the near future the government will convert the recommended product noise labeling to the mandatory product noise labeling fir high noise construction machinery. Therefore, we investigated the noise and vibration level of 302 construction machines and the effect of noise reduction for the soundproof facilities in construction work sites to support the policy of the government. Additionally, we present the basic data that will be used for setting the standard of construction machinery noise and predicting the noise level near construction work sites in environmental impact assessment.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.164-169
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• 2010

본 논문에서는 계층적 데이터를 관계형 데이터베이스 시스템에 저장하기 위한 요구사항을 만족하는 XML 레이블링 기법으로서 Farey Sequence 를 응용한 Faray 레이블링 기법을 제안한다. 이는 일반적인 동적 삽입연산 외에 형제간 노드 사이에 새 노드를 삽입할 경우 추가적인 레이블의 사이즈 증가 없이 기존노드의 리레이블링이 없고, 말단 노드의 삭제시 레이블의 재사용이 원할하며, 부모와 자식 사이에 새 노드를 삽입하는 경우가 빈번한 계층적 데이터 관리 시에 리레이블링을 최소화 할 수 있는 방법이다. 기존 XML 레이블링 기법이 부모 자식사이에 삽입 연산을 하는 경우 하위의 모든 노드를 리레이블링 해야 하는것에 비하여 본 기법은 오직 1개의 하위 노드만을 리레이블링 하기 때문에 해당 경우의 계층적 데이터의 동적 삽입 시 하위 노드의 개수에 상관없이 일정하게 연산시간을 유지할 수 있다.

• Korean Journal of Culture and Social Issue
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• v.22 no.4
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• pp.567-593
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• 2016

This study examined longitudinal mediated effects of informal labeling on the relationship between adolescent abuse and academic achievement using autoregressive cross-lagged modeling. Data were obtained from the second, third, and fourth waves of the middle school student cohort (N=3,168) of the Korean Youth Panel Survey. The major longitudinal findings of this study are as follows: First, adolescent abuse was found to have a positive association with future informal labeling. Second, informal labeling was found to have a negative association with future academic achievement. Finally, the longitudinal relationship between adolescent abuse and academic achievement was partially mediated by informal labeling. Based on these results, this study suggests directions for adolescent abuse prevention. The need for education and prevention of informal labeling was discussed, as well as the direction of intervention programs for adolescents with experience of informal labeling. Furthermore, this study may provide empirical evidence for labeling theory and contribute to increasing awareness on the longitudinal influence of adolescent abuse and informal labeling.

• Journal of KIISE
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• v.44 no.7
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• pp.686-691
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• 2017

Telemarketing has become the center of marketing action of the industry in the information society. Recently, machine learning has emerged in many areas, especially, financial prediction. Financial data consists of lots of unlabeled data in most parts, and therefore, it is difficult for humans to perform their labeling. In this paper, we propose a fusion method of semi-supervised learning for automatic labeling of unlabeled data to predict telemarketing. Specifically, we integrate labeling results of label propagation and co-training with a decision tree. The data with lower reliabilities are removed, and the data are extracted that have consistent label from two labeling methods. After adding them to the training set, a decision tree is learned with all of them. To confirm the usefulness of the proposed method, we conduct the experiments with a real telemarketing dataset in a Portugal bank. Accuracy of the proposed method is 83.39%, which is 1.82% higher than that of the conventional method, and precision of the proposed method is 19.37%, which is 2.67% higher than that of the conventional method. As a result, we have shown that the proposed method has a better performance as assessed by the t-test.