• Journal of the Korea Computer Graphics Society
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• v.25 no.1
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• pp.23-32
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• 2019

Cybersickness is a symptom of dizziness that occurs while experiencing Virtual Reality (VR) technology and it is presumed to occur mainly by crosstalk between the sensory and cognitive systems. However, since the sensory and cognitive systems cannot be measured objectively, it is difficult to measure cybersickness. Therefore, methodologies for measuring cybersickness have been studied in various ways. Traditional studies have collected answers to questionnaires or analyzed EEG data using machine learning algorithms. However, the system relying on the questionnaires lacks objectivity, and it is difficult to obtain highly accurate measurements with the machine learning algorithms. In this work, we apply Deep Neural Network (DNN) deep learning algorithm for objective cybersickness measurement from EEG data. We also propose a data preprocessing for learning and network structures allowing us to achieve high performance when learning EEG data with the deep learning algorithms. Our approach provides cybersickness measurement with an accuracy up to 98.88%. Besides, we analyze video characteristics where cybersickness occurs by examining the video segments causing cybersickness in the experiments. We discover that cybersickness happens even in unusually persistent changes in the darkness such as the light in a room keeps switching on and off.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1196-1203
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• 2004

Autonomous agents need considerable computational resources to perform rational decision-making. The complexity of decision-making becomes prohibitive when large number of agents are present and when decisions have to be made under time pressure. One of approaches in time-critical domains is to respond to an observed condition with a predefined action. Although such a system may be able to react very quickly to environmental conditions, predefined plans are of less value if a situation changes and re-planning is needed. In this paper we investigate strategies intended to tame the computational burden by using off-line computation in conjunction with on-line reasoning. We use performance profiles computed off-line and the notion of urgency (i.e., the value of time) computed on-line to choose the amount of information to be included during on-line deliberation. This method can adjust to various levels of real-time demands, but incurs some overhead associated with iterative deepening. We test our framework with experiments in a simulated anti-air defense domain. The experiments show that the off-line performance profiles and the on-line computation of urgency are effective in time-critical situations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7A
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• pp.646-653
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• 2010

This paper provides a unified framework for the joint optimal subchannel and power allocation in multi-hop relay network, where each node in the network has multiple parallel subchannels such as in OFDM or MIMO system. When there are multiple parallel subchannels between nodes, the relay node decides how to match the subchannel at the first hop with the one at the second hop aside from determining the power allocation. Joint optimal design of subchannel matching and power allocation is, in general, known to be very difficult to solve due to the combinatorial nature involved in subchannel matching. Despite this difficulty, we use a simple rearrangement inequality and show that seemingly difficult problems can be efficiently solved. This includes several existing solution methods as special cases. We also provide various design examples to show the effectiveness of the proposed framework.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.145-154
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• 2007

Determining stop spacing is a very important process in transit system planning. This study is involved in an analytical approach to decide the transit stop spacing. Transit stop spacing should be longer as 1) user access speed, 2) user travel time, and 3) dwell time increase, and shorter as 1) passengers (boardings and alightings) and 2) headway increase. In this study, a methodology is proposed to determine transit stop spacing to minimize total cost (user cost plus operator cost) with irregular passenger distribution (boardings and alightings) Without considering in-vehicle passengers, the transit stop spacing should be shorter in the concentrated sections of the passenger distribution than in others to minimize total cost. Through the conceptual analysis, it is verified that the transit stop spacing could be longer as the in-vehicle passengers increase in certain sections. This study proposes a simple practical method to determine transit stop spacing and locations instead of a dynamic programming method which generally includes a complex and difficult calculation. If the space axis is changed to a time axis. the methodology of this study could be expanded to analyze a solution for the transit service (or headway) schedule problem.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.505-517
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• 2008

This paper proposes a rewriting algorithm of OWL-DL ontology query in SPARQL. Currently, to obtain inference results of given SPARQL queries, Web ontology repositories construct inference ontology models and match the SPARQL queries with the models. However, an inference model requires much larger space than its original base model, and reusability of the model is not available for other inferrable SPARQL queries. Therefore, the aforementioned approach is not suitable for large scale SPARQL query processing. To resolve tills issue, this paper proposes a novel SPARQL query rewriting algorithm that can obtain results by rewriting SPARQL queries and accomplishing query operations against the base ontology model. To achieve this goal, we first define OWL-DL inference rules and apply them on rewriting graph pattern in queries. The paper categorizes the inference rules and discusses on how these rules affect the query rewriting. To show the advantages of our proposal, a prototype system based on lena is implemented. For comparative evaluation, we conduct an experiment with a set of test queries and compare of our proposal with the previous approach. The evaluation result showed the proposed algorithm supports an improved performance in efficiency of the inferrable SPARQL query processing without loss of completeness and soundness.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.55-55
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• 2017

가뭄은 국민생활 및 경제 등에 막대한 손실을 초래하며, 지역사회 공동체나 사회기능에 심각한 영향을 끼칠 수 있는 재해이다. 가뭄피해 최소화를 위해서는 단기대응, 복구지원 등의 사후대책에서 사전대비 및 예방으로의 정책 전환이 필요하며, 이러한 정책 수립을 뒷받침하기 위해서는 가뭄에 따른 정량적인 피해영향 평가가 우선적으로 필요하다. 하지만 가뭄 피해의 범위 및 형태는 워낙 광범위하기 때문에, 피해추정을 위한 잣대라 할 수 있는 영향평가 기법조차 제대로 정립되지 못하고 있는 실정이다. 국내에서는 분야별(기상, 농업, 수문)로 지수화 된 지표를 이용한 가뭄 평가가 주로 수행되고 있으며, 경제적 영향평가는 방법론에 대한 시범 연구 수준이다. 가뭄기록조사 등 과거 가뭄피해 자료에서도 피해액의 금액환산이 되지 않은 사례가 대부분이며 급수차지원, 관정개발 등 사후복구비 위주의 일부 자료만이 피해금액으로 제시되어 있을 뿐이다. 댐, 저수지 등에 의한 용수공급 안정성으로 인해, 기상학적인 가뭄이 즉시 물부족으로 인한 피해로 이어지지는 않지만, 물부족이 발생하거나 부족량이 예측되는 상황에서 피해규모를 시스템적으로 추정 및 비교할 수 있는 기법 개발의 필요성에 의해 잠재피해액 개념의 공급지장비용 추정기법을 개발하였다. 공급지장비용 또는 편익 도출을 위한 이론적 배경으로, 경제적 가치 또는 파급효과를 분석하기 위한 방법은 경제학적 접근법과 비경제학적 접근법으로 구분된다. 경제학적 접근법에서 사용하는 진술선호 기법의 경우 전국을 대상으로 설문 등의 과정을 거쳐 지불의사액을 도출하는 과정이 필요하기 때문에 많은 조사비용이 소요된다. 비경제학적 또는 공학적 접근법으로 분류되는 대체비용법은 이론적 배경이 약하고 대체항목의 선택에 주의가 필요하다는 단점이 있으나, 물가자료, 산업통계, 수자원통계 등 기초자료의 주기적 업데이트가 유리하며, 정신적 피해를 제외할 경우 피해비용 추정결과의 편차가 진술선호기법 보다는 작은 장점이 있다. 본 연구에서는 피해비용의 과대추정에 유의하여 대체비용법에 기반한 일본 후생노동성의 감 단수피해추정기법을 우리나라 자료에 맞게 수정하여 공급지장비용을 추정하였으며, 경제학적 접근법에 의한 용수의 한계가치비용 등과 비교를 통해 적용성을 검토하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.68-78
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• 2016

Safety is the most important factor for train control systems. Model-based design and safety activities for way-side equipment in train control systems are important factors. Model-based architecture verification was carried out to develop an effective control system, which is represented by model-based failure mode and effects analysis (FMEA). An architecture verification method was created based on FMEA to take advantage of a design model and improve the train safety control system. Case studies were applied to architecture verification scenarios, and the results demonstrate the usability of the method. The improved method is expected to reduce the cost and time in the conceptual design for future development of model-based verification train control systems.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.486-492
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• 2019

The system-based approach is needed to select an optimal mix of weapon systems and ship platform among a variety of design alternatives with the uncertainties of the initial required operational capability. In the early-phase design, which included a feasibility study and concept design, it is possible to cause problems when a review of the operational concept, database development, and systematic design are not done, thereby producing uncertain and unstable requirements. To select the best solution without trial-and-error, the U.S. navy has applied the set-based method for the early-phase design of a new ship-to-shore connector. The ship synthesis model plays an important role in applying the set-based method, but only a few countries possess this model and have prohibited this model from being transferred to other countries. This paper suggests a set-based method using a genetic algorithm and decision-making theory through benchmarking existing ship data. The algorithm was verified using the DDG-51 class ship synthesis model to optimize the weapon system design, which has been released for research purposes.