• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5244-5259
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• 2019

With the continuous development of LBS (Location Based Service) applications, privacy protection has become an urgent problem to be solved. Differential privacy technology is based on strict mathematical theory that provides strong privacy guarantees where it supposes that the attacker has the worst-case background knowledge and that knowledge has been applied to different research directions such as data query, release, and mining. The difficulty of this research is how to ensure data availability while protecting privacy. Spatial multidimensional data are usually released by partitioning the domain into disjointed subsets, then generating a hierarchical index. The traditional data-dependent partition methods need to allocate a part of the privacy budgets for the partitioning process and split the budget among all the steps, which is inefficient. To address such issues, a novel two-step partition algorithm is proposed. First, we partition the original dataset into fixed grids, inject noise and synthesize a dataset according to the noisy count. Second, we perform IH-Tree (Improved H-Tree) partition on the synthetic dataset and use the resulting partition keys to split the original dataset. The algorithm can save the privacy budget allocated to the partitioning process and obtain a more accurate release. The algorithm has been tested on three real-world datasets and compares the accuracy with the state-of-the-art algorithms. The experimental results show that the relative errors of the range query are considerably reduced, especially on the large scale dataset.

• 정보보호학회논문지
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• 제32권4호
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• pp.691-708
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• 2022

머신러닝과 딥러닝의 기술이 보편화되면서 산업제어시스템의 이상(비정상) 탐지 연구에도 적용이 되기 시작하였다. 국내에서는 산업제어시스템의 이상 탐지를 위한 인공지능 연구를 활성화시키기 위하여 HAI 데이터셋을 개발하여 공개하였고, 산업제어시스템 보안위협 탐지 AI 경진대회를 시행하고 있다. 이상 탐지 연구들은 대개 기존의 딥러닝 학습 알고리즘을 변형하거나 다른 알고리즘과 함께 적용하는 앙상블 학습 모델의 방법을 통해 향상된 성능의 학습 모델을 만드는 연구가 대부분 이었다. 본 연구에서는 학습 모델과 데이터 전처리(pre-processing)의 개선을 통한 방법이 아니라, 비정상 데이터를 탐지하여 라벨링 한 결과를 보정하는 후처리(post-processing) 방법으로 이상 탐지의 성능을 개선시키는 연구를 진행하였고, 그 결과 기존 모델의 이상 탐지 성능 대비 약 10%이상의 향상된 결과를 확인하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.493-495
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• 2023

Deep learning methods and attention mechanisms have been incorporated to improve facial emotion recognition, which has recently attracted much attention. The fusion approaches have improved accuracy by combining various types of information. This research proposes a fusion network with self-attention and local attention mechanisms. It uses a multi-layer perceptron network. The network extracts distinguishing characteristics from facial images using pre-trained models on RAF-DB dataset. We outperform the other fusion methods on RAD-DB dataset with impressive results.

• 정보보호학회논문지
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• 제31권3호
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• pp.401-410
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• 2021

최근 산업제어시스템은 인터넷에 연결하지 않은 폐쇄적 상태로 운영하는 과거와 달리 원격지에서 데이터를 확인하고 시스템 유지보수를 위해서 개방적 통합적인 스마트한 환경으로 변화하고 있다. 반면에 상호연결성이 증가하는 만큼 산업제어시스템을 대상으로 사이버 공격이 증가함에 따라 산업 공정의 비정상 탐지를 위한 다양한 연구가 진행되고 있다. 산업 공정의 결정적 규칙적인 점을 고려하여 정상데이터만을 학습시킨 탐지 모델의 결과 값과 실제 값을 비교해서 비정상 여부를 판별하는 것이 적절하다고 할 수 있다. 본 논문에서는 HAI 데이터셋 20.07과 21.03을 이용하며, 순환신경망에 게이트 구조가 적용된 GRU 알고리즘으로 서로 다른 타임 스텝을 적용한 모델을 결합하여 앙상블 모델을 생성한다. 그리고 다양한 성능평가 분석을 통해 단일 모델과 앙상블 순환신경망 모델의 탐지 성능을 비교하였으며 제안하는 모델이 산업제어시스템에서 비정상 탐지하는데 더욱 적합한 것으로 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권7호
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• pp.1252-1273
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• 2011

In Web Service Composition (WSC) area, services selection aims at selecting an appropriate candidate from a set of functionally-equivalent services to execute the function of each task in an abstract WSC according to their different QoS values. In despite of many related works, few of previous studies consider transactional constraints in QoS-aware WSC, which guarantee reliable execution of Composite Web Service (CWS) that is composed by a number of unpredictable web services. In this paper, we propose a novel global selection-optimal approach in WSC by considering both transactional constraints and end-to-end QoS constraints. With this approach, we firstly identify building rules and the reduction method to build layer-based Directed Acyclic Graph (DAG) model which can model transactional relationships among candidate services. As such, the problem of solving global optimal QoS utility with transactional constraints in WSC can be regarded as a problem of solving single-source shortest path in DAG. After that, we present Graph-building algorithms and an optimal selection algorithm to explain the specific execution procedures. Finally, comprehensive experiments are conducted based on a real-world web service QoS dataset. The experimental results show that our approach has better performance over other competing selection approaches on success ratio and efficiency.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.873-880
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• 2021

The purpose of this study is to investigate the impact of internal control on credit risk of joint stock commercial banks in Vietnam from 2007 to 2018. Furthermore, we specify bank-specific characteristics and macroeconomic conditions, and analyze how these factors affect credit risk of banks: the number of board members, the number of board members with banking or finance background as ratio of total board members, loans to total assets ratio, loans to deposit ratio, the number of days between the year-end and the publication of the financial statements, and the use of top four auditing firms proxy for five elements of internal control. By using the dataset of 30 Vietnamese joint stock commercial banks and Bayesian linear regression via Random-walk Metropolis Hastings algorithm, the results of this study show that five elements of internal control have a impact on bank credit risk, namely, control environment, risk assessment, control activities, information and communication, and monitoring activities. For factors of banks' characteristics, bank size and financial leverage have a negative impact on banks' credit risk, and bank age has a positive effect. For macroeconomic factors, inflation has a positive impact and economic growth has a negative impact on banks' credit risk.

• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.1237-1246
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• 2021

The purpose of the research is to evaluate how education influences the income of household heads, who are young adult in rural Vietnam. In order to examine the impact of education on the households where their heads are young adults, in this paper, the authors employ two research methods. First, ordinary least squares (OLS) regression is used to study the impact of education on different groups of income; second, quantile regression is applied to find out how education influences the income of households. The dataset includes a survey of 800 young households aged between18 and 35 who are the head of agricultural farms in rural areas. The findings indicate that education has a positive impact on income of young households. Furthermore, the results prove that the longer schooling years, the higher income youth can attain. The results showed that, at the survey time (Sep 2019), the average monthly income of rural young adults who are joining the production process shows a big gap between low and high incomes. Moreover, the study has revealed that other factors positively affect the incomes, namely, joining job-related associations, land resource, hired labour, hi-tech application as well as extension of producing unit.

• 산업경영시스템학회지
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• 제46권2호
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• pp.22-31
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• 2023

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• 인터넷정보학회논문지
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• 제24권5호
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• pp.51-66
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• 2023

본 논문에서는 비즈니스 프로세스 모델의 생명주기관리를 지원하는 대표적인 지식발견기술인 프로세스 마이닝과 지식개선기술인 프로세스 리엔지니어링 접근방법을 기반으로 하는 새로운 유형의 프로세스 발견 프레임워크를 제안한다. 또한, 제안된 프레임워크를 기반으로 하는 프로세스 마이닝 시스템을 개발하고, 이를 통한 실험적 검증을 수행한다. 실험적 효과검증에 적용된 프로세스 실행 이벤트 로그를 특별히 프로세스 빅-로그(Process BIG-Logs)라고 정의하고, 분산 비즈니스 프로세스 관리 시스템의 로깅메커니즘과 연계된 조각-실행로그이력들을 클러스터링하는 전처리과정을 거친 마이닝의 입력데이터세트로 활용한다. 결과적으로, 본 논문에서는 구조적 정보제어넷기반 프로세스 마이닝 알고리즘인 ρ-알고리즘을 개선한 제어경로기반 프로세스 그룹 발견 알고리즘과 프레임워크를 설계 및 구현하고, 구현된 시스템을 이용하여 제안한 알고리즘과 프레임워크의 정확성을 실험적으로 검증한다.