• Culinary science and hospitality research
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• v.20 no.5
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• pp.119-123
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• 2014

This study was intended to investigate food habits, dietary variety and the effects of nutrient intake of female college students. Food habits such as regularity of meal time, repast of breakfast, lunch, and supper, and duration of meal were assessed via a self reporting questionnaire, 24 hrs recall method for nutrient intake was obtained from 155 female college students in Yangju-si. Dietary variety was assessed by dietary diversity score(DDS). The mean height and weight of the subjects were $161.25{\pm}3.52cm$ and $52.26{\pm}4.52kg$, respectively. The dietary habits of female college students was generally inadequate. In the survey of dietary habits, the ratio of skipping breakfast 3~6 days a week in total subjects was 87.09%, showing a rather higher ratio of skipping breakfast. In the evaluation of nutrient intakes by DDS, subjects who had higher DDS had significantly higher nutrient intakes of energy, protein, carbohydrate, fiber, calcium, phosphate, iron, vitamin A, vitamin C, and cholesterol(p<0.05). There was a great difference in nutrient intakes, suggesting the risk of nutritional imbalance. These findings suggest that nutritional education based on female college students' eating variety and dietary habits(regularity of meal time, skipping breakfast and night snacks per week, and duration of meal) may be required to improve dietary variety. it is considered that the improvement in dietary habits will contribute to the improvement of nutrition.

• Proceedings of the Korean Association for Survey Research Conference
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• 2002.11a
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• pp.255-274
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• 2002

We classify rotation sampling designs into two classes. The first class replaces sample units within the same rotation group while the second class replaces sample units between different rotation groups. The first class is specified by the three-way balanced design which is a multi-level version of previous balanced designs. We introduce an extended generalized composite estimator (EGCE) and derive its variance and mean squared error for each of the two classes of design, cooperating two types of correlations and three types of biases. Unbiased estimators are derived for difference between interview time biases, between recall time biases, and between rotation group biases. Using the variance and mean squared error, since any rotation design belongs to one of the two classes and the EGCE is a most general estimator for rotation design, we evaluate the efficiency of EGCE to simple weighted estimator and the effects of levels, design gaps, and rotation patterns on variance and mean squared error.

• The korean journal of orthodontics
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• v.42 no.5
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• pp.255-262
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• 2012

Objective: We aimed to compare the retention characteristics of Essix and Hawley retainers. Methods: Adolescents undergoing fixed appliance treatment at 2 centers were recruited for this study. Twenty-two patients (16 women and 6 men) wore Essix retainers (Essix group) while 20 (14 women and 6 men) wore Hawley retainers (Hawley group). The mean retention time was 1 year, and the mean follow-up recall time for both groups was 2 years. Two qualified dental examiners evaluated the blind patient data. Maxillary and mandibular dental casts and lateral cephalograms were analyzed at 4 stages: pretreatment (T1), post-treatment (T2), post-retention (T3), and follow-up (T4). Results: The results revealed that Essix appliances were more efficient in retaining the anterior teeth in the mandible during a 1-year retention period. The irregularity index increased in both arches in both groups after a 2-year post-retention period. The mandibular arch lengths increased during treatment and tended to return to their original value after retention in both groups; however, these changes were statistically significant only in the Hawley group. Cephalometric variables did not show any significant differences. Conclusions: The retention characteristics of both Essix and Hawley retainers are similar.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.57-65
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• 2020

At present, the existing virus recognition systems usually use signature approach to detect malicious executable files, but these methods often fail to detect new and invisible malware. At the same time, some methods try to use more general features to detect malware, and achieve some success. Moreover, machine learning-based approaches are applied to detect malware, which depend on features extracted from malicious codes. However, the different distribution of features oftraining and testing datasets also impacts the effectiveness of the detection models. And the generation oflabeled datasets need to spend a significant amount time, which degrades the performance of the learning method. In this paper, we use transfer learning to detect new and previously unseen malware. We first extract the features of Portable Executable (PE) files, then combine transfer learning training model with KNN approachto detect the new and unseen malware. We also evaluate the detection performance of a classifier in terms of precision, recall, F1, and so on. The experimental results demonstrate that proposed method with high detection rates andcan be anticipated to carry out as well in the real-world environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.197-200
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• 2006

A scene changing detection using histogram and header information of H.264 video stream is presented in this paper. The method using histogram is normal to be detect the changing scene. But this technique results in a lot of processing time because video data is compressed and decompressed to video stream and calculated the difference of histogram between scenes on the each frame. The method using H.264 header information is available to detect the scene change at real time without the process of calculation. Histogram and header information is more rapid for scene change detection with being the same performance in precision and recall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.20-34
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• 2021

Today, IoT devices are flooding, and traffic is increasing rapidly. The Internet of Things creates a variety of added value through connections between devices, while many devices are easily targeted by attackers due to security vulnerabilities. In the IoT environment, security diagnosis has problems such as having to provide different solutions for different types of devices in network situations where various types of devices are interlocked, personal leakage of security solutions themselves, and high cost, etc. To avoid such problems, a TCP-based active scan was presented. However, the TCP-based active scan has limitations that it is difficult to be applied to real-time systems due to long detection times. To complement this, this study uses UDP-based approaches. Specifically, a lightweight active scan algorithm that effectively identifies devices using UPnP protocols (SSDP, MDNS, and MBNS) that are most commonly used by manufacturers is proposed. The experimental results of this study have shown that devices can be distinguished by more than twice the true positive and recall at an average time of 1524 times faster than Nmap, which has a firm position in the field.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.967-970
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• 2020

In a previous work we have done, we presented a monitoring system to automatically detect some dogs' behaviors from videos. However, the input video data used by that system was pre-trimmed to ensure it contained a dog only. In a real-life situation, the monitoring system would continuously receive video data, including frames that are empty and ones that contain people. In this paper, we propose a YOLOv4-based system for automatic object detection and trimming of dog videos. Sequences of frames trimmed from the video data received from the camera are analyzed to detect dogs and people frame by frame using a YOLOv4 model, and then records of the occurrences of dogs and people are generated. The records of each sequence are then analyzed through a rule-based decision tree to classify the sequence, forward it if it contains a dog only or ignore it otherwise. The results of the experiments on long untrimmed videos show that our proposed method manages an excellent detection performance reaching 0.97 in average of precision, recall and f-1 score at a detection rate of approximately 30 fps, guaranteeing with that real-time processing.

• Korean Journal of Community Nutrition
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• v.20 no.2
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• pp.120-128
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• 2015

Objectives: The purpose of this study was to examine the relative impact of seasonal differences and age on dietary vitamin D intakes, outdoor activity time and serum vitamin D status among Korean postmenopausal women. Methods: A cross-sectional study was conducted with 164 free-living postmenopausal women (mean age=55.4 years) in Daegu, Korea. Dietary intake, daily physical activity patterns and fasting blood samples were collected during summer and winter. We compared the results from 75 women during summer and 89 women during winter. Dietary intake of vitamin D was assessed by 24-hour recall method. Daily outdoor activity time was derived from physical activity diary. Results: The average dietary intake of vitamin D of the participants was $3.7{\mu}g$ during summer, $3.3{\mu}g$ during winter, showing no significant difference between the two seasons. The average time spent on outdoor activities was not significantly different between summer ($=35.1{\pm}75.0min/d$) and winter ($=48.5{\pm}76.8min/d$). The average serum 25-(OH) vitamin D concentrations of participants was $17.5{\pm}7.5ng/mL$ in the summer and $13.4{\pm}4.3ng/mL$ in the winter, showing no significant differences by season. Dietary intake of vitamin D was not related to age. When total subjects were divided into two groups by age, the average serum 25-(OH) vitamin D concentration was significantly higher in older group (p=0.047) and time spent on outdoor activities was also (p=0.018) significantly higher in the older group. Conclusions: In order to improve the current vitamin D status of Korean postmenopausal women, nutrition education programs should focus more on adequate intake of vitamin D, while maintaining enough outdoor activities over the season.

• Journal of The Korean Society of Clinical Toxicology
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• v.14 no.2
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• pp.115-121
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• 2016

Purpose: Because carbon monoxide (CO)-intoxicated patients with an alert mental status and only mild cognitive dysfunction may be inadequately assessed by traditional bedside neurologic examination in the emergency department (ED), they may not receive appropriate treatment. Methods: We retrospectively investigated the incidence and features of cognitive dysfunction using the Korean version of the Mini-Mental State Examination (MMSE-K) in ED patients with CO poisoning with alert mental status. We conducted a retrospective review of 43 consecutive mild CO poisoned patients with a Glasgow Coma Scale score of 15 based on documentation by the treating emergency physician in the ED between July 2014 and August 2015. Results: Cognitive dysfunction, defined as a score of less than 24 in the MMSE-K, was diagnosed in six patients (14%) in the ED. In the MMSE-K, orientation to time, memory recall, and concentration/calculation showed greater impairments. The mean age was significantly older in the cognitive dysfunction group than the non-cognitive dysfunction group (45.3 yrs vs. 66.5 yrs, p<0.001). Among the initial symptoms, experience of a transient change in mental status before ED arrival was significantly more common in the cognitive dysfunction group (32.4% vs. 100%, p=0.003). Conclusion: Patients with CO poisoning and an alert mental status may experience cognitive dysfunction as assessed using the MMSE-K during the early stages of evaluation in the ED. In the MMSE-K, orientation to time, memory recall, and concentration/calculation showed the greatest impairment.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.141-166
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• 2018

Information technology improves the efficiency of humanities research. In humanities research, information technology can be used to analyze a given topic or document automatically, facilitate connections to other ideas, and increase our understanding of intellectual history. We suggest a method to identify and automatically analyze the relationships between arguments contained in unstructured data collected from humanities writings such as books, papers, and articles. Our method, which is called history mining, reveals influential relationships between arguments and the philosophers who present them. We utilize several classification algorithms, including a deep learning method. To verify the performance of the methodology proposed in this paper, empiricists and rationalism - related philosophers were collected from among the philosophical specimens and collected related writings or articles accessible on the internet. The performance of the classification algorithm was measured by Recall, Precision, F-Score and Elapsed Time. DNN, Random Forest, and Ensemble showed better performance than other algorithms. Using the selected classification algorithm, we classified rationalism or empiricism into the writings of specific philosophers, and generated the history map considering the philosopher's year of activity.