• Journal of Nutrition and Health
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• v.38 no.10
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• pp.873-879
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• 2005

Impact and process evaluations were performed in order to verify the effectiveness of a public health nutrition program developed for child-bearing aged women in Korea. Participants included 58 high school girls who were divided into two groups. Each group received four 50 - 60 minute nutrition education lectures regarding healthy eating, osteoporosis, constipation and nutrition labeling in every two weeks. Each session took 50- 60 minutes. Regarding nutrition knowledge, there was a significant increase of degree of perception (p = 0.0004) , but no change in degree of accuracy after implementation (p = 0.9522) . Nutrition education was also effective in attitude change, showing more participants were ready to change their eating behaviors in terms of meal regularity (p = 0.0455) and less processed food intake (p =0.0143) . After implementing nutrition education, effective behavioral changes were observed in milk consumption (p =0.0037) and meal regularity (p = 0.0882) as well as daily activity such as stair use (p = 0.0701) . However, nutrition education had no effect on body mass index and perceived health status. In process evaluation conducted by a 9 item questionnaire, grand mean score was $4.17 \pm$0.72 out of 5. Proportion of items with scores higher than 4 ranged $68-91\%$. These results suggest that the nutrition education program used in this study was effective and useful. For a wider use of this program, more research was recommend for a strategy development of program diffuse. (Korean J Nutrition 38(10): 873$\sim$879,2005)

• Nutrition Research and Practice
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• v.13 no.6
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• pp.521-528
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• 2019

BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. MATERIALS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of $150{\times}150$ and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.

• Knowledge Management Research
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• v.22 no.3
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• pp.91-106
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• 2021

With the recent rapid development of deep learning technology, the demand for analyzing huge text documents in the national R&D field from various perspectives is rapidly increasing. In particular, interest in the application of a BERT(Bidirectional Encoder Representations from Transformers) language model that has pre-trained a large corpus is growing. However, the terminology used frequently in highly specialized fields such as national R&D are often not sufficiently learned in basic BERT. This is pointed out as a limitation of understanding documents in specialized fields through BERT. Therefore, this study proposes a method to build an R&D KoBERT language model that transfers national R&D field knowledge to basic BERT using further pre-training. In addition, in order to evaluate the performance of the proposed model, we performed classification analysis on about 116,000 R&D reports in the health care and information and communication fields. Experimental results showed that our proposed model showed higher performance in terms of accuracy compared to the pure KoBERT model.

• Fashion & Textile Research Journal
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• v.23 no.2
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• pp.247-260
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• 2021

While it is an important element of clothing construction, research has so far been very limited on the similarities between virtual and real clothing in terms of the type of neckline. The purpose of this study is to verify the similarity, accuracy of virtualization, and actuality of neckline, which all play an important role in individual impressions and image formation, and require considerable modification when fitting real samples. A total of 5 neckline models were selected through the analysis of dress composition textbooks. The selected designs were then planned and manufactured in muslin. The specimen clothes were then tested on a female model in her 20s. 2 kinds of virtual bodies were created in order to compare the real and the virtual dressing. The first virtual body was made through an Artec 3D Eva scan of the model, and the other was made by entering the model's measurements in a CLO 3D program. A visual image of the front, side, and back image of both the real and virtual dressing were subsequently collected. The collected images were then evaluated by 20 professional fashion workers who checked the similarity between the real and the virtual versions. The current study found that the similarity between the actual and virtual wearing of the five neckline designs with reality appeared higher with the virtual wearing image using the 3D-scanned body. The results of this study could provide further information on the selection of appropriate avatars to clothing companies that check the fit of clothing by utilizing 3D virtualized programs.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.765-779
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• 2019

Most of the existing evaluation criteria of vibration serviceability rely on the peak acceleration of the structure rather than that of the people keeping their own body unmoved on the structure who is the real receiver of structural vibrations. In order to accurately assess the vibration serviceability, therefore, a full path assessment approach of vibration serviceability based on vibration source, path and receiver is not only tentatively proposed in this paper, taking the peak acceleration of receiver into account, but also introduce a probability procedure to provide more instructive information instead of a single value. In fact, semi-rigid supported on both sides of the structure is more consistent with the actual situation than simply supported or clamped due to the application of the prefabricated footbridge structures. So, the footbridge is regarded as a beam with semi-rigid supported on both sides in this paper. The differential quadrature-integral quadrature coupled method is not only to handle different type of boundary conditions, but also after being further modified via the introduction of an approximation procedure in this work, the time-varying system problem caused by human-structure interaction can be solved well. The analytical results of numerical simulations demonstrate that the modified differential quadrature-integral quadrature coupled method has higher reliability and accuracy compared with the mode superposition method. What's more, both of the two different passive control measures, the tuned mass damper and semi-rigid supported, have good performance for reducing vibrations. Most importantly, semi-rigid supported is easier to achieve the objective of reducing vibration compared with tuned mass damper in design stage of structure.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.4
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• pp.1-10
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• 2019

Unmanned aerial vehicle(UAV) can acquire images with lower cost than conventional manned aircraft and commercial satellites. It has the advantage of acquiring high-resolution aerial images covering in the field area more than 50 ha. The purposes of this study is to develop the rice grain yield distribution using UAV. In order to develop a technology for estimating the rice yield using UAV images, time series UAV aerial images were taken at the paddy fields and the data were compared with the rice yield of the harvesting area for two rice varieties(Singdongjin, Dongjinchal). Correlations between the vegetation indices and rice yield were ranged from 0.8 to 0.95 in booting period. Accordingly, rice yield was estimated using UAV-derived vegetation indices($R^2=0.70$ in Sindongjin, $R^2=0.92$ in Donjinchal). It means that the rice yield estimation using UAV imagery can provide less cost and higher accuracy than other methods using combine with yield monitoring system and satellite imagery. In the future, it will be necessary to study a variety of information convergence and integration systems such as image, weather, and soil for efficient use of these information, along with research on preparing management practice work standards such as pest control and nutrient use based on UAV image information.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.957-966
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• 2018

This study intend to induce citizen's voluntary preliminary disaster prevention activity to reduce damage of typhoon that occurs every year. For this purpose, a survey was conducted to develop Typhoon information contents. The number of samples used in the survey was set to 500 people, and citizens living in Jeju, Busan, and Jeonlanam-do were surveyed for areas with high typhoon disasters in order to develop practical and efficient information. The survey consisted of perception about natural disaster, how to get and use weather information, satisfaction with typhoon information and requirements. The general public perceived the typhoon as the first natural disaster. As a result of responding to the method of obtaining and utilizing weather information, the frequency of collecting weather information at the time of issuance of typhoon special report is higher than usual. The purpose of using weather information is clear and the response rate is high for the purpose of disaster prevention. The medium mainly collecting weather information is Internet portal site and mobile phone besides television. The current satisfaction with typhoon weather information is 34.8%, in addition to the accuracy of prediction, it is necessary to improve the information (that is content) provided. Specific responses to the content were investigated not only for single meteorological factors, but also for possible damage and potential countermeasures in the event of a disaster such as a typhoon. As can be seen from the above results, people are requested to provide information that can be used to detect and cope with disasters. The development of new content using easy accessible media will contribute to the reduction of damages caused by the typhoon that will occur in the future, and also to the disaster prevention activity.

• Steel and Composite Structures
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• v.31 no.5
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• pp.503-516
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• 2019

This work presents a dynamic investigation of functionally graded (FG) plates resting on elastic foundation using a simple quasi-3D higher shear deformation theory (quasi-3D HSDT) in which the stretching effect is considered. The culmination of this theory is that in addition to taking into account the effect of thickness extension (${\varepsilon}_z{\neq}0$), the kinematic is defined with only 4 unknowns, which is even lower than the first order shear deformation theory (FSDT). The elastic foundation is included in the formulation using the Pasternak mathematical model. The governing equations are deduced through the Hamilton's principle. These equations are then solved via closed-type solutions of the Navier type. The fundamental frequencies are predicted by solving the eigenvalue problem. The degree of accuracy of present solutions can be shown by comparing it to the 3D solution and other closed-form solutions available in the literature.

• Journal of the Korean Institute of Educational Facilities
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• v.25 no.6
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• pp.35-44
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• 2018

The purpose of this study is to predict more accurate construction costs and to support efficient decision making in the planning and design stages of smart education facilities. The higher the error in the projected cost, the more risk a project manager takes. If the manager can predict a more accurate construction cost in the early stages of a project, he/she can secure a decision period and support a more rational decision. During the planning and design stages, there is a limited amount of variables that can be selected for the estimating model. Moreover, since the number of completed smart schools is limited, there is little data. In this study, various artificial intelligence models were used to accurately predict the construction cost in the planning and design phase with limited variables and lack of performance data. A theoretical study on an artificial neural network and deep learning was carried out. As the artificial neural network has frequent problems of overfitting, it is found that there is a problem in practical application. In order to overcome the problem, this study suggests that the improved models of Deep Neural Network and Deep Belief Network are more effective in making accurate predictions. Deep Neural Network (DNN) and Deep Belief Network (DBN) models were constructed for the prediction of construction cost. Average Error Rate and Root Mean Square Error (RMSE) were calculated to compare the error and accuracy of those models. This study proposes a cost prediction model that can be used practically in the planning and design stages.