• Journal of the Korean Society of Food Science and Nutrition
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• v.24 no.6
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• pp.892-900
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• 1995

Dietary data of 538 middle school students have been analysed to identify the contribution of specific foods to absolute intake and between-person-variance in nutrient consumption. The 24-hour-dietary-recall method had been used to collect the data required. Contribution of specific foods, in terms of ranking order for both absolute intake and between-person-variance have been observed. Ranking order of food for absolute intake was given based on the percen of contribution whereas the ranking order of foods for between-person-variance was given based on the percent of contribution whereas the ranking order of foods for between-person-variance was given based on a coefficient fo variation. As a result, for most of the nutrients(except cholesterol), the ranking order of foods for the between-person-variance was quite different from that of absolute intake. The results indicate that to identify between-person-variance of nutrient intake in an epidemiology study, foods with a high ranking in between-person-variance should be included in developing the food frequency questionnaires rather than foods which showed a high ranking in absolute intake.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.6
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• pp.1151-1165
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• 1996

The purpose of this study was to identify the pratical applicability of person-situation benefit segmentations of the female apparel market in Seoul by a prior segmentation method. The specific objectives of this study were 1) to identify the useful demographic variables for person-situation benefit segmentations of the female apparel market, 2) to assess that person- situation benefit segmentations of 1.he female apparel market are accessit)le by developing a profile of each segment based on the interactions of situation and personal characteristics on perceived risk, importance of store attributes and store-type choice, and on brand type prefered by each segment. 3) to assess the validity of person-situation benefit segmentations of the female apparel market in terms of easy accessibility. The data were collected via a questionnaire from 601 housewives of ages 20's to 50's living in Seoul, Korea. The data were analyzed by factor analysis, repeated measure two- way ANOVA and X2-test. The results of this study were as follows. First, the age-by-situation segmention basis and the education-by-situation segmention basis were useful for person-situation benefit segmentations of the female apparel market. Second, there were found three benefit segments (Youth/Fashion oriented users, Brand oriented users and Apathetic users of clothing) using age-by-situation segmention basis. Using education-by-situation segmention basis, five segments (Economic-value, Youth/Fashion, Brand/Self-expression Self-expression, and Apathetic users of clothing) were identified. And beifit segments classified by the age-by-situation segmention and education-by-situation segmention approach were accessible. Third, the pratical applicability of person-situation befeift segmentations of the female apparel market by a prior segmentation method were suggested.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.891-902
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• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• Journal of East-West Nursing Research
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• v.27 no.2
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• pp.166-176
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• 2021

Purpose: This study was conducted to assess the effect of household types on the occurrence of chronic diseases depending on gender and to identify the factors associated with chronic disease occurrence in one-person households. Methods: Multivariate logistic regression analysis was conducted using the data of 15,949 adults with the age of 19 years or older from the sixth Korea National Health and Nutrition Examination Survey (2013~2015). Results: For male, the risk of chronic disease occurrence was higher in one-person households than in multi-person households, and the same held true after adjusting for the confounding factors. For female, no significant relationship was found between household types and chronic disease occurrence. Factors associated with chronic disease occurrence were age, employment state, marital state, smoking, perceived stress, and depression in male, and age, employment state, physical activity, and obesity in female. Conclusion: It is necessary to monitor the disparity in health depending on household types in male. Additionally, providing a gender specific chronic disease prevention and health enhancement program is required.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.31-39
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• 2020

The purpose of this study is to identify the relationship between general fear of crime and specific fear of crime. Also, it is to suggest the way of analyzing fear of crime by summing up the two concepts. This study finds a reason why fear of crime varies from person to person within the same sex and similar age group. In particular, this study intends to analyze the models that affect fear of crime to figure out which is relevant to those in the high-risk group. And with the results, we can devise measurements to effectively reduce fear of crime in a local community. The following facts have been found in this study: positive correlation between general fear of crime and specific fear of crime, method of subdividing group with fear of crime, models that affect fear of crime and sub-items that greatly relate to high-risk groups.

• Journal of Preventive Medicine and Public Health
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• v.54 no.5
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• pp.352-359
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• 2021

Objectives: The aim of this study was to examine the geographic distribution of diabetes mortality in Japan and identify socioeconomic factors affecting differences in municipality-specific diabetes mortality. Methods: Diabetes mortality data by year and municipality from 2013 to 2017 were extracted from Japanese Vital Statistics, and the socioeconomic characteristics of municipalities were obtained from government statistics. We calculated the standardized mortality ratio (SMR) of diabetes for each municipality using the empirical Bayes method and represented geographic differences in SMRs in a map of Japan. Multiple linear regression was conducted to identify the socioeconomic factors affecting differences in SMR. Statistically significant socioeconomic factors were further assessed by calculating the relative risk of mortality of quintiles of municipalities classified according to the degree of each socioeconomic factor using Poisson regression analysis. Results: The geographic distribution of diabetes mortality differed by gender. Of the municipality-specific socioeconomic factors, high rates of single-person households and unemployment and a high number of hospital beds were associated with a high SMR for men. High rates of fatherless households and blue-collar workers were associated with a high SMR for women, while high taxable income per-capita income and total population were associated with low SMR for women. Quintile analysis revealed a complex relationship between taxable income and mortality for women. The mortality risk of quintiles with the highest and lowest taxable per-capita income was significantly lower than that of the middle-income quintile. Conclusions: Socioeconomic factors of municipalities in Japan were found to affect geographic differences in diabetes mortality.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• The Research Journal of the Costume Culture
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• v.28 no.2
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• pp.229-244
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• 2020

The purpose of this study was to develop a lifestyle scale for one-person hair salon users and to identify differences in store choice factors and hairstyle pursuit behaviors according to lifestyle types. Data was collected by survey, with 225 responses being included in the analysis. Data analysis was performed using cross-analysis, factor analysis, Cronbach's α, cluster analysis, ANOVA and the Duncan-test using SPSS 23.0 analysis software. The results of the study were as follows. First, one-person hair salon users were classified according to the following lifestyle groups: The rational appearance management group, the passive appearance management group, and the discriminative appearance management group. Second, store choice factors according to lifestyle group showed significant group-specific differences in relation to store atmosphere, accessibility, and promotion. Conversely, comfort of space in store and word-of-mouth recommendation message were high for all three groups, indicating that these are important factors in relation to store selection. Third, with regard to hairstyle pursuit behaviors according to lifestyle, the discriminative appearance management group showed the same characteristics as high involvement groups that regard all dimensions of hairstyle pursuit behavior as important. The results of this study can be used to suggest efficient operations for one-person hair salons and to suggest differentiated marketing strategies to increase the demand of one-person hair salon users.

• Journal of the Korean Geographical Society
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• v.32 no.4
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• pp.479-490
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• 1997

This study attempted to grasp the etimological meaning of the ancient place name Dumo, and to identify when the ancient place names in Dumo system started to be used by Korean people. The results of analysis of generic toponym and specific toponym of the ancient place names in Dumo system are as follow: Firstly, Chumong, the name of the founder of the Koguryo Dynasty, and his two sons Biryu,the founder of the kingdom Biryu-Paekche, and Oncho, the founder of the kingdom Paekche, are presumed to originate from place name, not from person's name. Particularly, the name of Chumong is considered to be a person's name which comes from Dumo system. Oncho, who claimed to be a son of Chumong, a person of north-Puyo, transterred the capital of his kingdom to the present site of Dumo in Chunggung-dong, Hanam city in present, which is thought to be an early capital of Paekche or a part of it. Secondly, the word of Dumo means a warm space which is surrounded by mountains, protected from wind, endowed with river which provided with water. This kind of spatial cognition gradually evolved as the prototypical locational artifice which was diffused to Manchuria and Japan, and is believed to be introduced to the Korean Peninsula.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.