• 융합정보논문지
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• 제9권8호
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• pp.1-8
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• 2019

대화시스템은 인간과 컴퓨터의 상호작용에 새로운 패러다임이 되고 있다. 자연어로써 상호작용함으로써 인간은 보다 자연스럽고 편리하게 각종 서비스를 누릴 수 있게 되었다. 대화시스템의 구조는 일반적으로 음성 인식, 자연어 이해, 문맥 파악 등의 여러 모듈의 파이프라인으로 이뤄지는데, 본 연구에서는 자연어 이해 모듈의 도메인 분류 문제를 풀기 위해 convolutional neural network, random forest 등의 기계학습 모델을 비교하였다. 사람이 직접 태깅한 총 7개 서비스 도메인 데이터에 대하여 각 문장의 도메인을 분류하는 실험을 수행하였고 random forest 모델이 F1 score 0.97 이상으로 가장 높은 성능을 달성한 것을 보였다. 향후 다른 기계학습 모델들을 추가 실험함으로써 도메인 분류 성능 개선을 지속할 계획이다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제61차 동계학술대회논문집 28권1호
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• pp.11-14
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• 2020

The importance of data in the development of deep learning is very high. Data with high morphological features are usually utilized in the domains where careful lens calibrations are needed by a human to capture those data. Synthesis of high morphological data for that domain can be a great asset to improve the classification accuracy of systems in the field. Unsupervised learning can be employed for this task. Generating photo-realistic objects of interest has been massively studied after Generative Adversarial Network (GAN) was introduced. In this paper, we propose Morpho-GAN, a method that unifies several GAN techniques to generate quality data of high morphology. Our method introduces a new suitable training objective in the discriminator of GAN to synthesize images that follow the distribution of the original dataset. The results demonstrate that the proposed method can generate plausible data as good as other modern baseline models while taking a less complex during training.

• 무역상무연구
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• 제73권
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• pp.111-128
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• 2017

The main objective of this study is to examine the required aptitudes for undergraduate students who want to join export-oriented Small & Medium Enterprises(SMEs). 178 Dataset from a survey of exporting firms in Gwangju, Korea, were used to analyze the study. The results of the study are as follows ; First, the most required aptitude is 'the capability related to build up human relationship'. So students should learn negotiation skills in the college. In addition to this, student also try to join informal club and cultivate teamwork capabilities. Second, finding out a job in export-oriented SMEs is needed to equip with problem-solving capabilities. To do it, students should learn various subjects related to trade theory. Additionally, having some certificates like 'international trade master' can be better. Third, communication capabilities including foreign language and international business skills will be also required for students who are preparing for joining export-oriented SMEs. However, capabilities related to information technology and basic statistic skills does not have statistically significant correlation to recruitment intention. As a result, students who have such above-mentioned four aptitudes may have better position to find out jobs in export-oriented SMEs.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.868-871
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• 2006

Mortality of domestic people from cardiovascular disease ranked second, which followed that of from cancer last year. Therefore, it is very important and urgent to enhance the reliability of medical examination and treatment for cardiovascular disease. Heart Rate Variability (HRV) is the most commonly used noninvasive methods to evaluate autonomic regulation of heart rate and conditions of a human heart. In this paper, our aim is to extract a quantitative measure for HRV to enhance the reliability of medical examination for cardiovascular disease, and then develop a prediction method for extracting multi-parametric features by analyzing HRV from ECG. In this study, we propose a hybrid Bayesian classifier called FP-based Bayesian. The proposed classifier use frequent patterns for building Bayesian model. Since the volume of patterns produced can be large, we offer a rule cohesion measure that allows a strong push of pruning patterns in the pattern-generating process. We conduct an experiment for the FP-based Bayesian classifier, which utilizes multiple rules and pruning, and biased confidence (or cohesion measure) and dataset consisting of 670 participants distributed into two groups, namely normal and patients with coronary artery disease.

• Journal of Microbiology and Biotechnology
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• 제29권8호
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• pp.1221-1229
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• 2019

Mycobacterium tuberculosis, a causative pathogen of tuberculosis (TB), still threatens human health worldwide. To find a novel drug to eradicate this pathogen, we tested taurine-5-bromosalicylaldehyde Schiff base (TBSSB) as an innovative anti-mycobacterial drug using Mycobacterium smegmatis as a surrogate model for M. tuberculosis. We investigated the antimicrobial activity of TBSSB against M. smegmatis by plotting growth curves, examined the effect of TBSSB on biofilm formation, observed morphological changes by scanning electron microscopy and transmission electron microscopy, and detected differentially expressed proteins using two-dimensional gel electrophoresis coupled with mass spectrometry. TBSSB inhibited mycobacterial growth and biofilm formation, altered cell ultrastructure and intracellular content, and inhibited cell division. Furthermore, M. smegmatis adapted itself to TBSSB inhibition by regulating the metabolic pathways and enzymatic activities of the identified proteins. NDMA-dependent methanol dehydrogenase, NAD(P)H nitroreductase, and amidohydrolase AmiB1 appear to be pivotal factors to regulate the M. smegmatis survival under TBSSB. Our dataset reinforced the idea that Schiff base-taurine compounds have the potential to be developed as novel anti-mycobacterial drugs.

• 스마트미디어저널
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• 제8권2호
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.

• 아태비즈니스연구
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• 제13권3호
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• pp.59-71
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• 2022

Purpose - We investigate how firms transfer financial risks to employees in a form of flexible employment contracts and layoffs. Design/methodology/approach - Based on the literature on the prevalence of shareholder value ideology and the associated 'risk shift', we examined how stock price volatility is associated with a firm's use and hiring of nonstandard employees, and the number of employees lay-offed. We test our hypotheses using a longitudinal, multi-source, dataset of Korean firms from 2003 to 2011. Findings - We found support for the relationship between stock price volatility and flexible employment contracts and layoffs after controlling for actual risks such as increased debt or decreased sales. However, we found that the relationship is moderated by the power of professional CEOs relative to that of shareholders, in that powerful CEOs are more likely to transfer the external risks, i.e. stock price volatility, to employees. Research implications or Originality - This study contributes the emerging stream of literature that explore the effect of stock market pressures and governance structures on human resource management.

• Mycobiology
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• 제49권6호
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• pp.559-581
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• 2021

The coastal marine ecosystems of Vietnam are one of the global biodiversity hotspots, but the biodiversity of marine fungi is not well known. To fill this major gap of knowledge, we assessed the genetic diversity (ITS sequence) of 75 fungal strains isolated from 11 surface coastal marine and deeper waters in Nha Trang Bay and Van Phong Bay using a culture-dependent approach and 5 OTUs (Operational Taxonomic Units) of fungi in three representative sampling sites using next-generation sequencing. The results from both approaches shared similar fungal taxonomy to the most abundant phylum (Ascomycota), genera (Candida and Aspergillus) and species (Candida blankii) but were different at less common taxa. Culturable fungal strains in this study belong to 3 phyla, 5 subdivisions, 7 classes, 12 orders, 17 families, 22 genera and at least 40 species, of which 29 species have been identified and several species are likely novel. Among identified species, 12 and 28 are new records in global and Vietnamese marine areas, respectively. The analysis of enzyme activity and the checklist of trophic mode and guild assignment provided valuable additional biological information and suggested the ecological function of planktonic fungi in the marine food web. This is the largest dataset of marine fungal biodiversity on morphology, phylogeny and enzyme activity in the tropical coastal ecosystems of Vietnam and Southeast Asia. Biogeographic aspects, ecological factors and human impact may structure mycoplankton communities in such aquatic habitats.

• ETRI Journal
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• 제44권4호
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.207-209
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• 2022

IoT환경에서 스마트 디바이스로부터 사람의 신체 활동을 인식하여 생활 패턴 데이터를 수집할 수 있게 되었다. 본 논문에서는 제안된 모델은 예측단계와 추천단계로 구성한다. 예측 단계는 생활 패턴 데이터로부터 수집된 데이터셋을 기계학습을 통해 로지스틱 회귀와 k-최근접 이웃 알고리즘을 활용하여 불안과 우울의 척도를 예측한다. 추천 단계는 불안과 우울 증상으로 분류된 경우 이를 호전시킬 수 있는 음식과 가벼운 운동을 추천하기 위해 주성분 분석 알고리즘을 적용한다. 제안한 불안·우울 예측과 음식·운동 추천은 개인의 삶의 품질 개선에 파급효과가 있을 것으로 기대한다.