• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1057-1067
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• 2014

Many genetic variants have been identified to be associated with complex diseases such as hypertension, diabetes and cancers throughout genome-wide association studies (GWAS). However, there still exist a serious missing heritability problem since the proportion explained by genetic variants from GWAS is very weak less than 10~15%. Gene-gene interaction study may be helpful to explain the missing heritability because most of complex disease mechanisms are involved with more than one single SNP, which include multiple SNPs or gene-gene interactions. This paper focuses on gene-gene interactions with the survival phenotype by extending the multifactor dimensionality reduction (MDR) method to the accelerated failure time (AFT) model. The standardized residual from AFT model is used as a residual score for classifying multiple geno-types into high and low risk groups and algorithm of MDR is implemented. We call this method AFT-MDR and compares the power of AFT-MDR with those of Surv-MDR and Cox-MDR in simulation studies. Also a real data for leukemia Korean patients is analyzed. It was found that the power of AFT-MDR is greater than that of Surv-MDR and is comparable with that of Cox-MDR, but is very sensitive to the censoring fraction.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.555-560
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• 2002

Subsequence matching, which consists of index searching and post-processing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a time-series database. This paper discusses optimization of post-processing for subsequence matching. The common problem occurred in post-processing of previous methods is to compare the candidate subsequence with the query sequence for discarding false alarms whenever each candidate subsequence appears during index searching. This makes a sequence containing candidate subsequences to be accessed multiple times from disk, and also have a candidate subsequence to be compared with the query sequence multiple times. These redundancies cause the performance of subsequence matching to degrade seriously. In this paper, we propose a new optimal method for resolving the problem. The proposed method stores ail the candidate subsequences returned by index searching into a binary search tree, and performs post-processing in a batch fashion after finishing the index searching. By this method, we are able to completely eliminate the redundancies mentioned above. For verifying the performance improvement effect of the proposed method, we perform extensive experiments using a real-life stock data set. The results reveal that the proposed method achieves 55 times to 156 times speedup over the previous methods.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.905-916
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• 2019

This study explores the effect of mother wavelet in the bivariate wavelet analysis. A total of four mother wavelets (Bump, Mexican hat, Morlet, and Paul) which are frequently used in the related studies is selected. These mother wavelets are applied to several bivariate time series like white noise and sine curves with different periods, whose results are then compared and evaluated. Additionally, two real time series such as the arctic oscillation index (AOI) and the southern oscillation index (SOI) are analyzed to check if the results in the analysis of generated time series are consistent with those in the analysis of real time series. The results are summarized as follows. First, the Bump and Morlet mother wavelets are found to provide well-matched results with the theoretical predictions. On the other hand, the Mexican hat and Paul mother wavelets show rather short-periodic and long-periodic fluctuations, respectively. Second, the Mexican hat and Paul mother wavelets show rather high scale intervention, but rather small in the application of the Bump and Morlet mother wavelets. The so-called co-movement can be well detected in the application of Morlet and Paul mother wavelets. Especially, the Morlet mother wavelet clearly shows this characteristic. Based on these findings, it can be concluded that the Morlet mother wavelet can be a soft option in the bivariate wavelet analysis. Finally, the bivariate wavelet analysis of AOI and SOI data shows that their periodic components of about 2-4 years co-move regularly every about 20 years.

• Journal of Plant Biotechnology
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• v.47 no.4
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• pp.337-344
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• 2020

Plant growth-promoting microorganisms promote plant growth by supplying nutrients to roots and interacting with the intrinsic factors in plants through volatile organic compounds (VOCs). In this study, we evaluated the effect of UOS, plant growth-promoting fungi (PGPF) isolated from previous study, on the growth of Nicotiana tabacum L. var Xanthi nc. Phylogenetic analysis and GC-MS were used to identify the fungal species and the VOCs emitted by the UOS, respectively. The fresh weight of UOS-treated Nicotiana tabacum L. was 3.8 and 4.2-fold higher than that of the control groups grown in vertical and I-plates, respectively. Moreover, in the UOS-treated plants, the length of the primary root was half and the number of lateral roots were twice compared to those in control plants. The UOS was identified as Phoma sp. by studying spore and mycelial morphology and using phylogenetic analysis. GC-MS revealed that the VOC emitted by the UOS was hexamethylcyclotrisiloxane (D3). These results suggest that the UOS of Phoma sp. influences plant growth and root development through D3. We expect this UOS and its VOC, D3 to be utilized in the future to increase growth and enhance yield for other plants.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.308-317
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• 2022

Researches are underway to efficiently reduce the size of video data transmitted and stored in the image analysis process using deep learning-based machine vision technology. MPEG (Moving Picture Expert Group) has newly established a standardization project called VCM (Video Coding for Machine) and is conducting research on video encoding for machines rather than video encoding for humans. We are researching a multitask that performs various tasks with one image input. The proposed pipeline does not perform all object detection of each task that should precede object detection, but precedes it only once and uses the result as an input for each task. In this paper, we propose a pipeline for efficient multitasking and perform comparative experiments on compression efficiency, execution time, and result accuracy of the input image to check the efficiency. As a result of the experiment, the capacity of the input image decreased by more than 97.5%, while the accuracy of the result decreased slightly, confirming the possibility of efficient multitasking.

• Journal of Platform Technology
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• v.9 no.4
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• pp.10-31
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• 2021

The digital tax, recently referred to as the Google tax was finally agreed at the 31st General Assembly of the OECD (October 8, 2021) with full support by 136 countries and will take effect from 2023. The purpose of this study is to analyze the digital tax prepared by the OECD for global MNEs, and to suggest the impacts on the Korean industry and to present the Korean governmental countermeasures. As the first study, we analyzed the international agreement on digital tax. In results, we found that even if global MNEs do not set up a business operation in overseas countries, if sales and profits are generated, 25% of the excess profit is borne as tax (pillar 1), and when MNEs do business in all the countries, they are liable to at least a 15% tax (pillar 2). We think that countries around the world have prepared a minimum countermeasure to protect their companies in anticipation that global MNEs will easily encroach on their markets in the future. As the second study, in order to discover the reason why the MNEs are so strong, we investigated the trends of Google and B2B SaaS companies in details. In results, we discovered that the global MNEs establishes a digital platform partnership ecosystem that enables them to enter foreign markets easily and expand rapidly. In conclusion, as a countermeasure for the Republic of Korea, governmental policies were proposed at the corporate (startup nurturing), industry, and national level respectively.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.345-360
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• 2022

This study is to explore the awareness of human rights and human rights education of workers in welfare facilities for the disabled from various aspects by using Creswell's mixed methodology. Although 10 years have passed since the statutory compulsory human rights education in welfare facilities for the disabled was implemented, there is still a limit to containing practical contents that can be applied to the welfare field for the disabled. Based on this reality, this study intends to examine in depth what human rights education is perceived by workers in welfare facilities for the disabled. As a result of the study, in the qualitative analysis, human rights in the field of social welfare practice, the perception of human rights education and human rights instructors, the direction of effective human rights education development, and the creation of a human rights-friendly community were presented as major issues. In the quantitative analysis, a survey was conducted targeting the welfare facilities of the disabled in Seongnam City to understand the general status of human rights education and the specific conditions of human rights education including the educational environment. Based on the results, including the results of qualitative analysis, a development direction for statutory compulsory human rights education for workers in welfare facilities for the disabled was proposed.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Journal of Music and Human Behavior
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• v.21 no.2
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• pp.135-157
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• 2024

In this study, the authors developed and implemented an online music-based support program to prevent burnout among university hospital nurses. This study involved 40 nurses from three university hospitals who shared their subjective experiences after participating in 8 music-based non-simultaneous online sessions. The responses were collected as qualitative data and analyzed using the qualitative content analysis method. The analysis identified 66 meaning units, 10 themes, and 3 categories, which included: 'Recovery of physical and psychological stability', 'Self-care and acceptance', and 'Rediscovery of pride and meaning as a nurse'. This study is significant for exploring the experiences of university hospital nurses who participated in a remotely implemented music-based psycho-emotional support program, with respect to burnout prevention. For future directions, we suggest a more in-depth exploration of specific burnout factors and an expansion of research through the diversification of research methods to refine programs aimed at alleviating nurse burnout.