• Journal of the Korean Society of Radiology
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• 제14권4호
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• pp.467-475
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• 2020

The purpose of this study is to assess doses to ¹⁸F-FDG, a radioactive drug, during PET examinations, to alleviate anxiety about radiation in patients and carers, to minimize the indiscriminate examination progress caused by medical institution personnel and space clearance problems, and health examination. The dose assessment was measured using a thermo-fluorescent dosimeter (TLD) and an electronic personal dosimeter (EPD) at the location of the cervical (hypothyroid), thorax (heart), and lower abdomen (breeding line) which are the three highest tissue areas of the radiation tissue weighting. In addition, spatial dose rates and radioactivity in urine were measured using GM counters and ion boxes. The results are as follows: First, the personal dosimeter TLD was measured 0.0425±0.0277 mSv in the cervical region, 0.0440±0.0386 mSv in the thorax and 0.0485±0.0436 mSv in the lower abdomen, with little difference in the heart dose depending on radiation sensitivity. The EPD was measured at 0.942±0.141 mSv/h immediately after the cervical position, and 0.192±0.031 mSv/h after 120 minutes. Immediately after the thorax position, 0.516±0.085 mSv/h, 120 minutes later 0.128±0.040 mSv/h. Immediately after the lower abdomen position, 0.468±0.091 mSv/h, and after 120 minutes 0.105±0.021 mSv/h were measured. The spatial dose rate at the GM counter was measured immediately at 0.041±0.005 mSv/h, 120 minutes later at 0.014±0.002 mSv/h. The radioactivity in urine using ion chamber was measured at 0.113±0.24 MBq/cc after 60 minutes and 0.063±0.13 MBq/cc after 120 minutes. As a result, ¹⁸F-FDG should be administered, dose re-evaluated two hours after the PET test is completed, and caregivers should be avoided. In addition, it is deemed necessary to provide patients and carers with sufficient explanations and expected values of exposure dose to avoid reckless testing. It is hoped that the data tested in this study will help patients and families relieve anxiety about radiation, and that the radiation workers' exposure management system and institutional improvements will contribute to the development of medical radiation.

• Journal of vocational education research
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• 제30권4호
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• pp.23-49
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• 2011

Faculty performance evaluation system has been settled down as an uncomfortable but unavoidable system, and it is one of the most important factors to grow the college competitiveness up. In this study, we selected and surveyed faculty evaluation models of several universities and colleges in Korea, and analyzed by comparing each evaluation areas of educational achievement, college-industry collaboration, research, and service. We also identified the properties of the current faculty evaluation models of the junior colleges, and derived several problems from these models such as an imitation of four-year university model, a disorders of job evaluation with respect to the attributes of classified jobs, a large variation of individual item weights, and an insufficient reflection of major characteristics. Based on these surveys and analysis, an improved faculty evaluation model for the junior college is proposed in this study. This model proposed four basic areas-educational achievement, college-industry collaboration, research, and service by considering the importance of the college-industry collaboration in the junior college-as well as the team evaluation area. Weights of the SCI-class paper was selected as a criterion for the arrangement of objective comparison of each evaluation items. We showed the integration method of several different evaluation model with respect to the attributes of classified jobs of each faculties, and evaluation plan of variational characteristics according to the majors of individuals in this model. Finally, we introduced an area fail and rating system to operate efficiently the proposed faculty evaluation model.

• Korean Chemical Engineering Research
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• 제57권1호
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• pp.90-104
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• 2019

In this study, CALPUFF modeling was performed, using a real surface and upper air meterological data to predict trustworthy modeling-results. Pollutant-releases from windscreen chambers of enclosed poultry farms, P1 and P2, and from a open poultry farm, P3, and their diffusing behavior were modeled by CALPUFF modeling with volume sources as well as by finally-adjusted CALPUFF modeling where a linear velocity of upward-exit gas averaged with the weight of each directional-emitting area was applied as a model-linear velocity ($u^M_y$) at a stack, with point sources. In addition, based upon the scenario of poultry farm-releasing odor and particulate matter (PM) removal efficiencies of 0, 20, 50 and 80% or their corresponding emission rates of 100, 80, 50 and 20%, respectively, CALPUFF modeling was performed and concentrations of odor and PM were predicted at the region as a discrete receptor where civil complaints had been frequently filed. The predicted concentrations of ammonia, hydrogen sulfide, $PM_{2.5}$ and $PM_{10}$ were compared with those required to meet according to the offensive odor control law or the atmospheric environmental law. Subsequently their required removal efficiencies at poultry farms of P1, P2 and P3 were estimated. As a result, a priori assumption that pollutant concentrations at their discrete receptors are reduced by the same fraction as pollutant concentrations at P1, P2 and P3 as volume source or point source, were controlled and reduced, was proven applicable in this study. In case of volume source-adopted CALPUFF modeling, its required removal efficiencies of P1 compared with those of point source-adopted CALPUFF modeling, were predicted similar each other. However, In case of volume source-adopted CALPUFF modeling, its required removal efficiencies of both ammonia and $PM_{10}$ at not only P2 but also P3 were predicted higher than those of point source-adopted CALPUFF modeling. Nonetheless, the volume source-adopted CALPUFF modeling was preferred as a safe approach to resolve civil complaints. Accordingly, the required degrees of pollution prevention against ammonia, hydrogen sulfide, $PM_{2.5}$ and $PM_{10}$ at P1 and P2, were estimated in a proper manner.

• Journal of Intelligence and Information Systems
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• 제24권4호
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Nutrition and Health
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• 제55권1호
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• pp.155-173
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• 2022

Purpose: This study was undertaken to update the Nutrition Quotient for Elderly (NQ-E), which reflects dietary quality and behavior among Korean older adults. Methods: The first 29 items of the measurable food behavior checklist were obtained from a previous NQ-E checklist, recent literature reviews, and national nutrition policies and recommendations. One-hundred subjects (50 men and 50 women) aged ≥ 65 years living in the Seoul Metropolitan Area, including Gyeonggi Province, completed a pilot survey from March to April 2021. Based on the results of the pilot study, we conducted factor analysis and frequency analysis to determine whether the items of the survey were properly organized and whether the distribution of answers for each evaluation item was properly distributed. As a result, we reduced the number of items on the food behavior checklist and used 23 items for the national survey. Nationwide, 1,000 subjects (472 men and 528 women) aged > 65 years, completed the checklist survey, which was applied using a face-to-face survey method from May to August 2021. The construct validity of the NQ-E 2021 was assessed using confirmatory factor analysis, LISREL. Results: Seventeen food behavior checklist items were selected for the final NQ-E 2021. Checklist items addressed three factors: balance (8 items), moderation (2 items), and practice (7 items). Standardized path coefficients were used as the weights of items to determine nutrition quotients. NQ-E and three-factor scores were calculated according to the weights of questionnaire items. Conclusion: The updated NQ-E 2021 produced by structural equation modelling provides a suitable tool for assessing the dietary quality and behavior of Korean older adults.

• Journal of Nutrition and Health
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• 제55권2호
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• pp.278-295
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• 2022

Purpose: This study was undertaken to revise and update the Nutrition Quotient (NQ) for Korean adults, a tool used to evaluate dietary quality and behavior. Methods: The first 31 items of the measurable food behavior checklist were adopted based on considerations of the previous NQ checklist, recent literature reviews, national nutrition policies, and recommendations. A pilot survey was conducted on 100 adults aged 19 to 64 residing in Seoul and Gyeonggi Province from March to April 2021 using a provisional 26- item checklist. Pilot survey data were analyzed using factor analysis and frequency analysis to determine whether checklist items were well organized and responses to questions were well distributed, respectively. As a result, the number of items on the food behavior checklist was reduced to 23 for the nationwide survey, which was administered to 1,000 adults (470 men and 530 women) aged 19 to 64 from May to August 2021. The construct validity of the developed NQ (NQ-2021) was assessed using confirmatory factor analysis, linear structural relations. Results: Eighteen items in 3 categories, that is, balance (8 items), moderation (6 items), and practice (4 items), were finally included in NQ-2021 food behavior checklist. 'Balance' items addressed the intake frequencies of essential foods, 'moderation' items the frequencies of unhealthy food intakes or behaviors, and 'practice' items addressed eating behaviors. Items and categories were weighted using standardized path coefficients to calculate NQ-2021 scores. Conclusion: The updated NQ-2021 appears to be suitable for easily and quickly assessing the diet qualities and behaviors of Korean adults.

• Journal of Intelligence and Information Systems
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• 제28권1호
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Nutrition and Health
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• 제56권3호
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• pp.247-263
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• 2023

Purpose: This study was conducted to update the Nutrition Quotient for Adolescents (NQ-A), which is used to assess the overall dietary quality and food behavior among Korean adolescents. Methods: The first 30 candidate items of the measurable eating behavior checklist were obtained based on a previous NQ-A checklist, the results of the seventh Korea National Health and Nutrition Examination Survey data, Korea Youth Risk Behavior Survey data, national nutrition policies and dietary guidelines, and literature reviews. A total of 100 middle and high school students residing in Seoul and Gyeonggi Province participated in a pilot study using the 25-item checklist. Factor analysis and frequency analysis were conducted to determine if the checklist items were organized properly and whether the responses to each item were distributed adequately, respectively. As a result, 22 checklist items were selected for the nationwide survey, which was applied to 1,000 adolescent subjects with stratified sampling from 6 metropolitan cities. The construct validity of the updated NQ-A 2021 was assessed using confirmatory factor analysis. Results: Twenty checklist items were determined for the final NQ-A 2021. The items were composed of three factors: balance (8 items), moderation (9 items), and practice (3 items). The standardized path coefficients were used as the weights of items to determine the nutrition quotients. NQ-A 2021 and 3-factor scores were calculated according to the weights of questionnaire items. The weight for each of the 3 factors was determined as follows: balance, 0.15; moderation, 0.30; and practice, 0.55. Conclusion: The updated NQ-A 2021 is a useful instrument for easily and quickly evaluating the dietary qualities and eating behaviors of Korean adolescents.

• Journal of Intelligence and Information Systems
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• 제19권3호
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• 제24권1호
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.