• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.5
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• pp.410-416
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• 2016

After the Sewol Ferry accident, the importance of maritime safety has been emphasized in Korea. In particular, educational and experience training are not only being conducted for maritime personnel but also in schools and at maritime-related organizations in order to broadly instill maritime safety awareness. Based on SOLAS regulations, safety education for sailors conducted every 10 days passenger boats, and fire-fighting drills and abandon-ship training should be conducted once a month on merchant ships. After the Sewol Ferry accident, the maximum number of trainees was reduced from 40 to 20 in order to improve the effectiveness of these training sessions by requiring all trainees to participate in the actual training. The current training process consists of two steps: textbook-based theoretical training and actual practice. Current training environment provides limited capability from human and facility recourses which limit the numbers of trainee participated and system operation time. By introducing the simulation training, it will improve the trainee skill and performance prior to the on-site training and allow the more effective and rapid progress on actual practice. Therefore, it will be proposed the three-step training method in order to improve the effectiveness on fire-fighting drill in Maritime Safety Education on this study. This study suggests a three step training method that would increase the efficiency of maritime safety education. An image-training step to enhance individual task awareness and equipment usage via simulation techniques after theoretical training has been added. To implement this simulation, a virtual training session will be conducted before actual training, based on knowledge obtained from theoretical training, which is expected to increase the speed with which trainees can adapt during the practical training session. In addition, due to the characteristics of the simulation, repeated training is possible for reaction drills in emergency circumstances and other various scenarios that are difficult to replicate in actual training. The efficiency of training is expected to improve because trainees will have practiced before practical training takes place, which will decrease the time needed for practical training and increase the number of training sessions that can be executed, increasing the efficiency of training overall. This study considers development methods for fire-fighting drill simulations using virtual reality techniques.

• Journal of Nutrition and Health
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• v.56 no.2
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• pp.203-216
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• 2023

Purpose: This study examines the weight control behavior depending on university students' motives of using social media. Methods: The participants were 447 university students in the Jeonbuk area. Collected data were analyzed using factor analysis, cluster analysis, analysis of variance, and χ² tests with SPSS v. 26.0. Considering the motives of using social media, we investigated the usage of social media, dietary behavior related to social media, and weight control behavior. Results: Using the K-clustering method, the motives to use social media were categorized into three clusters: cluster 1 was the interest-centered group, cluster 2 was the multipurpose information-seeking group, and cluster 3 was the relationship-centered group. Among the various social media sites, YouTube (86.8%), Instagram (76.1%), and Facebook (61.1%) were the most visited by the subjects. The dietary behavior related to social media in cluster 2 was significantly higher than clusters 1 and 3 (p < 0.001). Clusters 1 and 2 showed a significantly higher dissatisfaction with one's weight (p < 0.05) and consequent interest in weight control than cluster 3 (p < 0.001). Cluster 2 used weight control-related information from social media significantly more than other clusters (p < 0.05). Weight control experiences in cluster 1 and 2 were significantly higher than in cluster 3 (p < 0.001). Conclusion: Differences in dietary behavior related to social media and weight control behavior were observed between cluster types of motivation to use social media. Based on the usage motives of university students and their behaviors, we propose that educational programs should be conducted for weight control using social media.

• Journal of the Korean Applied Science and Technology
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• v.37 no.1
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• pp.91-101
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• 2020

The purpose of this study was to investigate the influence of obesity on the expression of autophagy-related proteins in cardiac muscle. To this end, obesity was induced in rats through 20 weeks of high-fat diet, and the animals were then subjected to 8 weeks of treadmill exercise. Subsequently, the expression of proteins that regulate the induction of autophagy, formation of autophagosome, and fusion of autophagosome and lysosome was confirmed. Obesity was induced in the experimental animals (SD rats) through 20 weeks of high-fat diet (carbohydrate: 20%, fat: 60%, and protein: 20%), and they were subsequently subjected to 8 weeks of treadmill exercise (5 days/week, 30 min/day, 5 minutes; 8m/min, 5 minutes; 11m/min, 20 minutes; 14m/min). The experimental groups comprised the normal diet control group (ND-CON, n=10), high-fat diet comparison group (HFD-CON, n=10), and high-fat exercise group (HFD-TE, n=10). Oral glucose tolerance test was conducted before and after 8 weeks of treadmill exercise, and the area under the curve (AUC) was calculated. Through fasting insulin and fasting glucose levels, HOMA-IR, which is an index of insulin resistance, and abdominal visceral fat/body weight (AVF/BW) were calculated for comparison. Moreover, autophagy-related proteins were analyzed from cardiac tissue to investigate the effects of exercise training. Obesity was successfully induced in the HFD-CON group through long-term high-fat diet, and the HFD-CON group had higher body weight, AUC, HOMA-IR, and AVF/BW compared to the ND-CON group. The HFD-TE group, which underwent 8 weeks of treadmill exercise, showed improvements in AUC, HOMA-IR, and AVF/BW. Although the body weight tended to decrease as well, there was no statistically significant difference. mTOR and AMPK, which are involved in the induction of autophagy, both decreased in obesity but increased upon exercise. Beclin-1, BNIP3, ATG-7, p62, and LC3, which are related to the formation of autophagosomes, all increased in obesity and decreased after exercise. Cathepsin L and LAMP2, which regulate the fusion of autophagosome and lysosome, both decreased in obesity and increased upon exercise. Physical activity, including treadmill exercise, was found to induce normal autophagy and improve pathological phenomena observed in metabolic diseases. Therefore, the findings suggest the need to consider treadmill exercise as a primary means to achieve effective prevention and treatment of cardiac diseases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.

• 한국노년학
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• v.31 no.2
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• pp.355-369
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• 2011

The purpose of this study was to find the effects of long-term combined exercise on regional bone mineral density(BMD) and cardiovascular disease(CVD) risk factors in the elderly with osteoporosis(OP). For the purpose, the subjects of this study were separated by two groups with thirty-one elderly women, who the first group was combined exercise group(CEG, n=16) and second group was non exercise group(CON, n=15). The combined exercise program was made up of warm-up (10min), work-out (aerobic; 30~45min/HRR 40~60%, resistance; 1RM * 50-70%, 8-10 * 2set ~ 10-15 * 1set), and cool-down (10min). Exercise group of the inspection have been trained 5 times a week for 1years. The results : At first, the variables of regional BMD were significantly different to pelvis, spine, trunk and T-score in two groups. At second, the variables of CVD risk factors were significantly different to SBP and DBP as well as TC, TG, LDL-C and HDL-C in two groups. As results of these conclusion, this study have positively effect shown that CEG was superior to CON in regional BMD(pelvis, spine, trunk and T-score), blood pressure(SBP, DBP) and plasma lipids(TC, TG, and LDL-C). Especially, the long-term combined exercise was provides a striking overall health quality of life with improving BMD and reduced CVD risk factors in the elderly with OP. In the future, other researches should deal with specific measures that reduction in mortality due to chronic disease and improvement quality of life for the development of programs in multiple researches of osteoporosis and chronic diseases.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used