• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.39-47
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• 2024

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman's rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.

• Journal of Preventive Medicine and Public Health
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• v.46 no.5
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• pp.226-236
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• 2013

Objectives: The aim of this study was to examine the levels of serum 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and evaluate their association with age, body mass index, smoking, military record-based variables, and estimated exposure to Agent Orange in Korean Vietnam veterans. Methods: Serum levels of TCDD were analyzed in 102 Vietnam veterans. Information on age, body mass index, and smoking status were obtained from a self-reported questionnaire. The perceived exposure was assessed by a 6-item questionnaire. Two proximitybased exposures were constructed by division/brigade level and battalion/company level unit information using the Stellman exposure opportunity index model. Results: The mean and median of serum TCDD levels was 1.2 parts per trillion (ppt) and 0.9 ppt, respectively. Only 2 Vietnam veterans had elevated levels of TCDD (>10 ppt). The levels of TCDD did not tend to increase with the likelihood of exposure to Agent Orange, as estimated from either proximity-based exposure or perceived self-reported exposure. The serum TCDD levels were not significantly different according to military unit, year of first deployment, duration of deployment, military rank, age, body mass index, and smoking status. Conclusions: The average serum TCDD levels in the Korean Vietnam veterans were lower than those reported for other occupationally or environmentally exposed groups and US Vietnam veterans, and their use as an objective marker of Agent Orange exposure may have some limitations. The unit of deployment, duration of deployment, year of first deployment, military rank, perceived self-reported exposure, and proximity-based exposure to Agent Orange were not associated with TCDD levels in Korean Vietnam veterans. Age, body mass index and smoking also were not associated with TCDD levels.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.8
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• pp.372-377
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• 2008

Multiclass cancer classification has been actively investigated based on gene expression profiles, where it determines the type of cancer by analyzing the large amount of gene expression data collected by the DNA microarray technology. Since gene expression data include many genes not related to a target cancer, it is required to select informative genes in order to obtain highly accurate classification. Conventional rank-based gene selection methods often use ideal marker genes basically devised for binary classification, so it is difficult to directly apply them to multiclass classification. In this paper, we propose a novel method for multiclass gene selection, which does not use ideal marker genes but directly analyzes the distribution of gene expression. It measures the class-discriminability by discretizing gene expression levels into several regions and analyzing the frequency of training samples for each region, and then classifies samples by using the naive Bayes classifier. We have demonstrated the usefulness of the proposed method for various representative benchmark datasets of multiclass cancer classification.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.461-473
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• 2020

This study examined the effect of using web-based distance program in home health education for nursing college students in COVID-19 special disaster area. The study was carried out according a nonequivalent control group pretest-posttest design. The study subjects were 49 nursing college students from K City, Gyeongsangbuk-do. The web-based distance program was conducted for 2 weeks. The data collection period was from June 1, 2020 to June 12, 2020. Collected data were analyzed using SPSS PC+ 19.0 with the Fisher' exact test, Wilcoxon rank sum test, ANCOVA with pretest value as covariate. The experimental group had significantly different in knowledge related home health nursing, perceived motivation, and learning commitment in comparison to the control group. This suggests that the web-based distance program in the COVID-19 special disaster area can be applied as a way to increase nursing students' knowledge related home health nursing, perceived motivation, and learning commitment.

• Nutrition Research and Practice
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• v.6 no.4
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• pp.349-356
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• 2012

Several studies have been conducted on dietary patterns based on carbohydrate nutrition in Asian populations. We examined the cross-sectional associations in dietary patterns based on carbohydrate nutrition, including the glycemic index (GI) with dyslipidemia and diabetes among the Korean adult population. We analyzed 9,725 subjects (3,795 men and 5,930 women, ${\geq}$ 20 years) from the Fourth Korea National Health and Nutrition Examination Survey. Dietary information was collected using single 24-hour recall. Reduced rank regression was used to derive dietary patterns from 22 food groups as predictor variables and four dietary factors related to the quantity and quality of carbohydrates as response variables. Two dietary patterns were identified: 1) the balanced pattern was characterized by high intake of various kinds of foods including white rice, and 2) the rice-oriented pattern was characterized by a high intake of white rice but low intake of vegetables, fruits, meat, and dairy products. Both patterns had considerable amounts of total carbohydrate, but GI values differed. The rice-oriented pattern was positively associated with hypertriglyceridemia in men and low high density lipoprotein-cholesterol in both men and women. The balanced pattern had no overall significant association with the prevalence of dyslipidemia or diabetes, however, men with energy intake above the median showed a reduced prevalence of diabetes across quintiles of balanced pattern scores. The results show that dietary patterns based on carbohydrate nutrition are associated with prevalence of dyslipidemia and diabetes in the Korean adult population.

• Therapeutic Science for Rehabilitation
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• v.4 no.1
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• pp.53-62
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• 2015

Objective : This study aimed to evaluate the effects of multisensory and Motor-Based group activity program on the problem behaviors and burden of care in persons with dementia. Methods : Five persons with moderate to severe dementia and significant problem behaviors, received 4 weeks of multisensory and Motor-based group activity program. The Neuropsychiatric Inventory was employed in the pretest and posttest. Analysis was performed using SPSS and including Wilcoxon signed rank test. Results : Participants' average problem behavior scores decreased from 70 in the pretest to 38 after program. Mean scores of burden of care decreased from 47 to 29. Conclusion : Multisensory and Motor-Based group activity program can be an effective method to decrease problem behaviors and burden of care for older people with dementia.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.145-155
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• 2015

Noise reduction has been actively studied in the digital image processing and recently, block-based denoising algorithms are widely used. In particular, a low rank approximation employing WNNM(Weighted Nuclear Norm Minimization) and block-based approaches demonstrated the potential for effective noise reduction. However, the algorithm based on low rank a approximation generates the artifacts in the image restoration step. In this paper, we analyzes the image content using the STFT(Short Time Fourier Transform) and proposes an effective method of minimizing the artifacts generated from the conventional algorithm. To evaluate the performance of the proposed scheme, we use the test images containing a wide range of noise levels and compare the results with the state-of-art algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.141-150
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• 2023

Background: This study was conducted to verify the effectiveness of geriatric dental hygiene education by developing and operating an industrial demand-based curriculum for geriatric dental hygiene. Methods: Wilcoxon signed rank test was performed to verify the before-and-after differences in major competency achievement, geriatric dental hygiene awareness, and class satisfaction according to industrial demandbased field-oriented practical education, and Spearman's correlation analysis was performed to confirm the association between each factor(p<0.05). Results: In the case of major competency achievement, 'communication competence with the older adults' was significantly improved(p=0.031) after conducting industrial demand-based field-oriented practical training. Conclusion: It is believed that the understanding of the older adults and the practical skills for oral care of the older adults can be further developed when the learners are provided with a practical curriculum that can be used in the geriatric industrial field.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.