• 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.

• Nutrition Research and Practice
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• v.9 no.5
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• pp.517-522
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• 2015

BACKGROUND/OBJECTIVES: The objective of this study was to investigate food allergens and prevalence rates of food allergies, followed by comparison of consumer attitudes and preferences regarding food allergy labeling by diagnosis of food allergies. SUBJECTS/METHODS: A total of 543 individuals living in Seoul and Gyeonggi area participated in the survey from October 15 to 22 in 2013. RESULTS: The results show that the prevalence of doctor-diagnosed food allergies was 17.5%, whereas 6.4% of respondents self-reported food allergies. The most common allergens of doctor-diagnosed and self-reported food allergy respondents were peaches (30.3%) and eggs (33.3%), respectively, followed by peanuts, cow's milk, and crab. Regarding consumer attitudes toward food labeling, checking food allergens as an item was only significantly different between allergic and non-allergic respondents among all five items (P < 0.001). All respondents reported that all six items (bold font, font color, box frame, warning statement, front label, and addition of potential allergens) were necessary for an improved food allergen labeling system. PLSR analysis determined that the doctor-diagnosed group and checking of food allergens were positively correlated, whereas the non-allergy group was more concerned with checking product brands. CONCLUSIONS: An effective food labeling system is very important for health protection of allergic consumers. Additionally, government agencies must develop policies regarding prevalence of food allergies in Korea. Based on this information, the food industry and government agencies should provide clear and accurate food labeling practices for consumers.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.545-550
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• 2022

In Deep Learning method, it is well known that it requires large amount of data to train the deep neural network. And it also requires the labeling of each data to fully train the neural network, which means that experts should spend lots of time to provide the labeling. To alleviate the problem of time-consuming labeling process, some methods have been suggested such as weak-supervised method, one-shot learning, self-supervised, suggestive learning, and so on. In this manuscript, those methods are analyzed and its possible future direction of the research is suggested.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.112-122
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• 2007

Training a statistical model for semantic role labeling requires a large amount of manually tagged corpus. However. such corpus does not exist for Korean and constructing one from scratch is a very long and tedious job. This paper suggests a modified algorithm of self-training, an unsupervised algorithm, which trains a semantic role labeling model from any raw corpora. For initial training, a small tagged corpus is automatically constructed iron case frames in Sejong Electronic Dictionary. Using the corpus, a probabilistic model is trained incrementally, which achieves 83.00% of accuracy in 4 selected adverbial cases.

• 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.

• Journal of the Korean Dietetic Association
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• v.23 no.1
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• pp.106-119
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• 2017

The purpose of this study was to examine restaurant customers' use of menu labeling and their perception of menu labeling attributes. Further, the study investigated relations of menu labeling use behavior, and perception of menu labeling attributes with behavioral intentions toward menu labeling. Using a self-administered survey conducted for 2 weeks from the 2nd week of October, 2015, data were collected from restaurant customers who were exposed to menu labeling over 3 months at the time of the survey. A total of 426 respondents completed the survey. Respondents were asked about use of menu labeling, usefulness, ease of understanding, accuracy, and demographic information. There was a difference in menu labeling use behavior according to age, whereas respondents aged 50 years or over showed significantly higher use of menu labeling than those in 20s (P<0.001). Perceptions of menu labeling attributes positively affected behavioral intentions towards menu labeling. While all three menu labeling attributes, 'usefulness', 'ease of understanding', and 'accuracy', were positive factors for behavioral intentions towards menu labeling, usefulness was the biggest attribute explaining behavioral intentions (P<0.001). The study findings offer implications that can be applied to academics, the foodservice industry, and government in an attempt to nurture a healthy eating environment through provision of nutritional information at restaurants.

• Asia Marketing Journal
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• v.20 no.4
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• pp.65-94
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• 2019

The purpose of this study was to analyze the brand loyalty formation by positive labeling. Affecting such factors as involvement, self-image, community engagement, preference, and choice cutback, positive labeling can be seen as one of psychological factors that shapes consumer's behavior and their decision. This study was carried out because little research was done to examine the influence of positive labeling toward brand loyalty, and also to find out the benefits that consumers can get from being labeled in positive terms. Data were collected through survey questionnaire and 151 usable responses were used. Following a series of pretests and confirmatory factor analysis helped to purify measures and verify the psychometric properties of the scale. Structural equation modeling with AMOS was used for testing of research hypotheses. The result of data analysis demonstrated the positive relationship between labeling and brand loyalty, i.e. positive labeling indirectly leads to consumers' loyalty toward a brand. Findings revealed significant relationship between involvement and emotional attachment, as well as the relationship between community engagement and choice cutback. The results gave support for the hypothesis of moderating effect of buzz on the relationship between involvement and emotional attachment, even though the hypothesis of moderating effect of distinction was rejected. Taking Apple's rivalry strategy as initial point, this study highlights the role of labeling in creating social identity. The study attempts to show the positive consequences of labeling strategy for firms that seeks ways of good competition without engaging into conflicts.

• Journal of the Korean Society of Food Culture
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• v.34 no.1
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• pp.34-43
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• 2019

The aim of this study was to develop and evaluate a nutrition education program that was designed to increase the knowledge, attitudes, and skills of Korean adults regarding nutrition labeling. The education program was 45 minutes of short-term training, which was conducted in the form of lectures and exercises. The contents of the program were as follows: in the introduction stage, talking about status and reasons for checking nutrition labels; in the development stage, explanation of nutrition labeling and their content, reading, and identifying sample nutrition labels, as well as comparing nutrition labels and selecting better foods; in the closing stage, summary of nutrition labeling and a pledge to check nutrition labels when purchasing processed food. A total of 53 adults (88.5% female) aged 30 years and over participated in this study. The nutrition labeling awareness of the subjects was increased significantly from 55.8 to 96.2% after the education. After the education, the correct recognition rate of a nutrition label was increased significantly from 26.9 to 78.8% for the amount of food, from 25.0 to 73.1% for the calorie content, from 36.5 to 69.2% for the nutrient contents, and from 30.8 to 82.7% for the percent daily value. The self-efficacy of checking nutrition labels was also increased significantly compared to that before the education. The overall satisfaction score of the nutrition education program was 4.2 out of 5. The outcome showed that the nutrition education program of nutrition labeling improved the participants' awareness and self-efficacy towards checking nutrition labels.

• Journal of the Korean Home Economics Association
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• v.38 no.12
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• pp.29-45
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• 2000

The purposes of this study were to determine what factors influence risk perceptions of females and males for milk produced using food-related biotechnology, to test whether risk perceptions or other factors influence self-protection actions and to estimate milk demand response in light of self-protection actions and other economic and demographic factors. The expected utility model was applied to explain the way consumers would take self-protection actions regarding risk perceptions and to drive milk demand. Telephone interviews were conducted and the data were collected from households(females=1,029, males=437) nationwide in the U.S. And the data were analyzed by Heckman two-step method using the software package LIMDEP. Risk perceptions were found to be influenced not by demographic factors but by outrage factors as well as attitudinal factors in both females and males, although some factors were different. In addition, risk perceptions and labeling availability were found to significantly influence self-protection actions in both groups. Furthermore, as an important concern in this study, self-protection action was found to significantly influence milk demand in only male group, implying a consistent behavior of males. Also milk price and household size were found to significantly influence milk demand in both groups. In fact, the results did demonstrate that labeling availability significantly influenced self-protection actions. That is, in markets where labeled laternatives were present, concerned consumers were more likely to self protect by substituting to these products. A policy implication of this result is that labeling food products produced using biotechnology enhances consumer choice. Hence, consumer could express a more accurate demand response and reduce the perceived food safety risk. Furthermore, education for females might be necessary to have a consistent behavior because self-protection action did not significantly influence female's milk demand, though they have greater risk perceptions than males have.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.205-209
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• 2008

Recently, for development of hardware systems, it has been comercially developed for lane detection system of assistive funtion to drivers. There are so many driving systems that is capable of detecting lane for ideal environment like quite visible lane and sweep curve just like highway, but these kinds of system are hard to apply for self driving system because it is difficult to detect lane in dynamic environment, which have rapid curve or only one sided lane For this paper, we proposed intelligent driving system that is able to detect the lane in case of rapid curve by labeling, or one sided lane by lane prediction. based on experimental results, we prove our lane detection system is able to detect lane not only in ideal environment, but also environment which have rapid curve or one sided lane.