• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1518-1526
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• 2004

In this paper, an algorithm using wavelet transform for detecting a cut that is a radical scene transition point, and fade and dissolve that are gradual scene transition points is proposed. The conventional methods using wavelet transform for this purpose is using features in both spatial and frequency domain. But in the proposed algorithm, the color space of an input image is converted to YUV and then luminance component Y is transformed in frequency domain using 2-level lifting. Then, the histogram of only low frequency subband that may contain some spatial domain features is compared with the previous one. Edges obtained from other higher bands can be divided into global, semi-global and local regions and the histogram of each edge region is compared. The experimental results show the performance improvement of about 17% in recall and 18% in precision and also show a good performance in fade and dissolve detection.

• Korean Journal of Community Nutrition
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• v.14 no.6
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• pp.831-845
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• 2009

Hypertension and obesity are important modifiable risk factors for cardiovascular disease, the leading cause of death in Korea. Therefore, we assessed the association between dietary pattern and obesity in hypertensive patients to formulate health promotion strategies for the older population. Dietary information was collected from hypertensive patients visiting community health education and information center by using 24 hour recall method. The 2005 DRIs for Koreans was used to evaluate the dietary adequacy. When subjects were categorized by body mass index (BMI) as normal, overweight and obese, no significant difference in energy intake was found among groups. Dietary intakes of folate, and vitamin C in obese hypertensive patients were significantly lower than in normal weight patients (p < 0.05). When we compare the nutritional status by waist circumferences, dietary intakes of zinc, vitamin A, thiamin, vitamin C and folate were significantly lower in the obese group. Vegetable intake was significantly lower in the obese group according to BMI as well as waist circumference. Energy intake from carbohydrate was significantly higher in obese hypertensive patients (p < 0.05). Obese hypertensive patients had a higher risk of nutritional inadequacy compared to normal weight patients. Our results indicated the need for developing interventions that encourage greater consumption of vegetables while cutting down salt intake with wise selection of staple foods, for obese hypertensive patients.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.151-160
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• 2002

Cow image processing technique would be useful not only for recognizing an individual but also for establishing the image database and analyzing the shape of cows. A cow (Holstein) has usually the unique speckle pattern. In this study, the individual recognition of cow was carried out using the speckle pattern and the content-based image retrieval technique. Sixty cow images of 16 heads were captured under outdoor illumination, which were complicated images due to shadow, obstacles and walking posture of cow. Sixteen images were selected as the reference image for each cow and 44 query images were used for evaluating the efficiency of individual recognition by matching to each reference image. Run-lengths and positions of runs across speckle area were calculated from 40 horizontal line profiles for ROI (region of interest) in a cow body image after 3 passes of 5$\times$5 median filtering. A similarity measure for recognizing cow individuals was calculated using Euclidean distance of normalized G-frame histogram (GH). normalized speckle run-length (BRL), normalized x and y positions (BRX, BRY) of speckle runs. This study evaluated the efficiency of individual recognition of cow using Recall(Success rate) and AVRR(Average rank of relevant images). Success rate of individual recognition was 100% when GH, BRL, BRX and BRY were used as image query indices. It was concluded that the histogram as global property and the information of speckle runs as local properties were good image features for individual recognition and the developed system of individual recognition was reliable.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.57-66
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• 2003

We propose a technique to extract a relevant passage from text collection based on field-associated terms since they tries to concentrate relevant text to users query. Documents are supposed to be managed as a whole without any segmentation into small pieces, but the method presented is independent upon any text-embedded auxiliary information, and is based on topic continuity and transition. For users needs-relative sentences or passages, we present a passage retrieval techniques by using occurrence frequency of a field-associated term to delimit text, that is likely to be relevant to a particular topic, considering continuity and transition within topic flowing in text. We evaluate 50 Japanese documents and verify the usefulness with 82% for average precision and 63% for recall.

• Nutrition Research and Practice
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• v.11 no.4
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• pp.307-318
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• 2017

BACKGROUND/OBJECTIVES: Relocation to new environments can have a negative impact on health by altering body weight and dietary patterns. This study attempted to elucidate changes in body weight, food security, and their current food and nutrient consumption in adult North Korean refugees (NKR) living in South Korea (SK). SUBJECTS/METHODS: This study analyzed data on 149 adult NKR from a North Korean refugee health in SK cohort at four time points (leaving North Korea, entering SK, first examination, and second examination). Body weight was self-reported at the two earlier time points and directly measured at the two later time points. Food security, diet-related behaviors (dietary habits and food consumption), and sociodemographic information were obtained using a self-administered questionnaire. Nutrient intake information was obtained by one-day 24-hour recall. Statistical analyses were performed with SPSS ver 23.0. RESULTS: Body weight increased during relocation by an average of 4 kg, although diversified patterns were observed during the settlement period in SK. Approximately 39.6% of subjects maintained their body weight between the first and second examinations, whereas 38.6% gained and 22.1% lost at least 3% of their body weight at the first examination by the second examination. Food security status improved from 12.1% food secure proportion to 61.7%. NKR showed generally good food and nutrient consumption (index of nutrient quality: 0.77-1.93). The body weight loss group showed the most irregular meal consumption pattern (P < 0.05), and eating-out was infrequent in all three groups. Consumption frequencies of food groups did not differ by group, except in the fish group (P = 0.036). CONCLUSION: This study observed considerable body weight adjustment during the settlement period in SK after initial weight gain, whereas food security consistently improved. More detailed understanding of this process is needed to assist healthy settlement for NKR in SK.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.369-372
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• 2009

Recently, newspapers as well as web documents include many newly coined words such as "mid"(meaning "American drama" since "mi" means "America" in Korean and "d" refers to the "d" of drama) and "anseup"(meaning "pathetic" since "an" and "seup" literally mean eyeballs and moist respectively). However, these words cause a Korean analyzing system's performance to decrease. In order to recognize these unknown word automatically, this paper propose a step-by-step approach consisting of an unknown noun recognition phase based on full text analysis, an unknown verb recognition phase based on web document frequency, and an unknown noun recognition phase based on web document frequency. The proposed approach includes the phase based on full text analysis to recognize accurately the unknown words occurred once and again in a document. Also, the proposed approach includes two phases based on web document frequency to recognize broadly the unknown words occurred once in the document. Besides, the proposed model divides between an unknown noun recognition phase and an unknown verb recognition phase to recognize various unknown words. Experimental results shows that the proposed approach improves precision 1.01% and recall 8.50% as compared with a previous approach.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.87-97
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• 2010

A set of definitions for traceability and quick recall service function were urgently needed to facilitate handling of events threatening food safety caused by livestock diseases or germ contaminations. However, the research on the unified management system for both international and domestic livestock products distribution was scarce. The livestock products traceability evaluation model proposed in the paper was composed of four modules: the Forward Transaction Trace (FTT) module, the Backward Record Trace (BRT) module, the Forward Product Trace (FPT) module, and the Origin Pedigree (OP) module. The evaluation indexes for each module was included and finally a pilot system evaluating the proposed management model was suggested and displayed. The result of the paper was expected to be a solution to the distributed traceability system and the proposed traceability management model could be expected to any food traceability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.4
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• pp.1-8
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• 2019

Plant phenomics is a technique for observing and analyzing morphological features in order to select plant varieties of excellent traits. The conventional methods is difficult to apply to the phenomics system. because the color threshold value must be manually changed according to the detection target. In this paper, we propose the convolution neural network (CNN) structure that can automatically segment plants from the background for the phenomics system. The LeafNet consists of nine convolution layers and a sigmoid activation function for determining the presence of plants. As a result of the learning using the LeafNet, we obtained a precision of 98.0% and a recall rate of 90.3% for the plant seedlings images. This confirms the applicability of the phenomics system.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.