• Journal of Family Relations
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• v.23 no.1
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• pp.3-24
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• 2018

Objective: This study examined the change of informal labeling self-respect, depression/anxiety, and aggression of adolescents over time and relationship between the intercept and the growth of the variables. Method: 4-year longitudinal panel data(n=2,699), Korea Youth Panel Survey (KYPS), were analyzed to verify the influence of informal labeling on self-respect, depression/anxiety, and aggression of adolescents. Through latent growth modeling, temporal change of the variables was examined. Results: Analytic results are as follow. First, the initial status of informal labeling had a negative impact on the initial status of self-respect. The slope of informal labeling also had a negative impact on the slope of self-respect. In contrast, the initial status of informal labeling did not have an significant impact on the slope of self-respect. Second, the initial status of informal labeling had a positive impact on the initial status of aggression. The slope of informal labeling had a negative impact on the slope of aggression. In contrast, the initial status of informal labeling did not have an significant impact on the slope of aggression. Third, the initial status of informal labeling had a positive impact on the initial status of depression/anxiety and a negative impact on the slope of depression/anxiety. The slope of informal labeling had a positive impact on the slope of self-respect. Conclusions: The results suggest the importance of informal labeling on self-respect, depression/anxiety, and aggression of adolescents.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.207-212
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• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• Journal of the Korean Society of Food Culture
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• v.30 no.2
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• pp.190-196
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• 2015

Restaurants implement menu labeling to provide nutritional information to customers in an attempt to help customers select healthy menu items. Considering the increase in food-away-from-home consumption, the purpose of this study was to identify motivators and barriers in restaurant customers regarding use of menu labeling. Data were collected from a survey on restaurant customers in Seoul, Korea. The findings of this study indicate that customers used menu labeling for health reasons. However, barriers to using menu labeling were identified as small font size, difficulty in locating nutritional information display, and difficulty in interpreting nutritional information. In addition, they also suggested expanding the scope of menu labeling for restaurants by including chain restaurants with less than 100 units. The findings of this study offer strategies for the government to improve menu labeling practices for customers.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.175-180
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• 2024

This paper investigates the effect of a deep learning-based system on data labeling task productivity by individuals with developmental disabilities. It was found that interventions, particularly those using AI, significantly improved productivity compared to self-serving task. AI interventions were notably more effective than job coach-led approaches. This research underscores the positive role of AI in enhancing task efficiency for those with developmental disabilities. This study is the first to apply AI technology to the data labeling tasks of individuals with developmental disabilities and highlighting deep learning's potential in vocational training and productivity enhancement for this group.

• The Korean Journal of Food And Nutrition
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• v.24 no.3
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• pp.282-290
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• 2011

This study was performed to investigate the level and recognition and interest in nutrition labeling in restaurants according to consumer interest levels in health and to suggest its application to restaurant lunches. By considering various statistics and data on the frequency of reasons for dining-out, this study examined worker restaurant lunches and investigated the level of recognition of interest in nutrition labeling, the type of nutrition information that is of interest and the preferred format of labeling according to the level of interest in health. According to the results, while the frequency of dining-out by workers was high, their consideration for health and nutrition labeling in restaurants was low. However, a high percentage of consumers responded that nutrition labeling was a customer right and necessary to improve the quality of menu items as well as public health. Therefore, active promotion of nutrition labeling in the dining industry is necessary. Interest levels in additives, product origin and menu ingredients indicated in restaurant menus were higher than for nutritional information such as nutrients and calories. When the preferred format for providing nutrition information was investigated, consumers preferred information written on a menu board, and they wanted to broaden the range of information included in nutrition labeling for menu items beyond calories and nutritional facts. Based on these results, recognition of nutrition labeling in restaurants was found to below and the interest level in health was also lower than expected. However, most consumers responded that nutrition labeling was helpful in choosing menu items can be a tool for nutrition education and can play a role in improving the recognition of nutrition. Therefore, active promotion of nutrition labeling by the dining industry is necessary.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.106-108
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• 2005

XML is situating a standard fur data exchange in the Web. Most applications use database to manage XML documents of high-capacity efficiently. Therefore, most applications create label that expresses structure information of XML data and stores with information of XML document. A number of labeling schemes have been designed to label the element nodes such that the relationships between nodes can be easily determined by comparing their labels. With the increased popularity of XML data on the web, finding a labeling scheme that is able to support order-sensitive queries in the presence of dynamic updates becomes urgent. XML documents that most applications use have many properties as their application. So, in the thesis, we present the most efficient updating methods dependent on properties of XML documents in practical application by choosing a representative labeling method and applying these properties. The result of our test is based on XML data management system, so it expect not only used directly in practical application, but a standard to select the most proper methods for environment of application to develop a new exclusive XML database or use XML.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.14-23
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• 2014

There have been many researches for XML as the international standard to store and exchange data on the internet. Among these research fields, we focus on the techniques labeling the nodes of the XML tree that is required for querying the structural information. A labeling scheme assigns the unique label to the nodes and supports the queries for the structural information such as Ancestor-Descendant and Parent-Child relationships. In this paper, we propose a labeling scheme using vector representation where the assigned labels are not altered although XML documents are changed dynamically. Our labeling scheme reduces the storage requirement for the labels of the XML tree and provides the efficient query by using the fixed-length labels with a short size. Result of performance evaluation shows that our labeling scheme is superior to the previous approaches.

• Journal of the Korean Dietetic Association
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• v.23 no.3
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• pp.274-284
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• 2017

This study examined the awareness, understanding, attitudes, and purchase intention regarding food labeling on bakery products in the context of health consciousness. The purpose of the study was to provide basic data for bakery product labeling, which has been insufficient to date, and to develop measures to expand the labeling system. The results of the study showed that higher subjective understanding and better attitude towards bakery food labeling can positively increase the purchase intention. We believe that the bakery industry needs to promote food labeling proactively, while also developing products addressing health concerns. This study is also valuable to academia because it provides insights into the relationship between the consumer's understanding of and attitudes towards nutritional information and purchase intention. In addition, it is beneficial to the bakery industry because it establishes marketing strategies that increase the purchase intent among both consumers with high health consciousness and those who infrequently purchase baked goods.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.