• 산업기술연구
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• 제30권A호
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• pp.111-117
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• 2010

To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

• Journal of Trauma and Injury
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• 제29권4호
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• pp.139-145
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• 2016

Purpose: The major pelvic trauma results in high mortality with associated fatal other injuries. During early stage of resuscitation, multidisciplinary approach is essential to improve the survival and outcomes. This study aims to report the effect and positive outcome of the trauma team approach on the management of hemodynamically unstable pelvic bone fracture. Methods: This retrospective review included all patients with hemodynamically unstable pelvic bone fracture admitted between March 2007 and December 2015. Patients were divided into group A, which comprised those admitted before the trauma team approach was started, and group B, which comprised those admitted after the approach was started. The advanced trauma life support protocol was followed for all patient. The comparisons between the two groups were based on medical records. Study variables included demographics, initial vital sign, injury severity score, fracture type, and injury mechanism. We analyzed the outcomes in each group with respect to the time interval for doctors' arrival, total length of stay in the emergency department (ED), time interval for computed tomography evaluation, 24-hour mortality, time interval for definitive fixation, and definitive fixation in the time-window of opportunity. Results: Fifty-three patients met the inclusion criteria. No statistically significant differences in demographic data existed between the two groups. The time interval for doctors' arrival (min, $63.09{\pm}50.48$ vs $21.48{\pm}17.75$; p=0.038) and total length of stay in the ED (min, $269.33{\pm}105.96$ vs $115.49{\pm}56.24$; p=0.023) were significantly improved. The 24-hour mortality was not significantly different between the two groups.(%, 14.3 vs 12.0; p=1.000) However, the time interval for definitive fixation and definitive fixation in the time-window of opportunity showed better results. Conclusion: The trauma team approach has positive effects, which include initial resuscitation through multidisciplinary approach and shortening the time interval to definitive fixation, on the management of hemodynamically unstable pelvic bone fracture.

• 대한수학회논문집
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• 제35권2호
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• pp.667-684
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• 2020

The objective of this paper is solving multidimensional backward stochastic differential equations with general time intervals, in L^p (p ≥ 1) sense, where the generator g satisfies a time-varying Osgood condition in y, a time-varying quasi-Hölder continuity condition in z, and its ith component depends on the ith row of z. Our result strengthens some existing works even for the case of finite time intervals.

• 대한수학회보
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• 제50권2호
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• pp.611-626
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• 2013

In the paper we study the finite-time ruin probability in a general risk model with constant interest force, in which the claim sizes are pairwise quasi-asymptotically independent and arrive according to an arbitrary counting process, and the premium process is a general stochastic process. For the case that the claim-size distribution belongs to the consistent variation class, we obtain an asymptotic formula for the finite-time ruin probability, which holds uniformly for all time horizons varying in a relevant infinite interval. The obtained result also includes an asymptotic formula for the infinite-time ruin probability.

• 경영과학
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• 제11권1호
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• pp.145-163
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• 1994

For an Information System to be successful the continual control of the database system(DBS) is very important. In general, such control operations are performed periodically in batches, even in real time systems. We explain DBS related such control operations and describe the decision problem in each of them. Specifically, backup, checkpointing, reorganization, and batch update operations are considered. Then we develop a general model of the batch backup situation to determine the optimal backup timing. Other operations are considered as special cases of the general batch backup operation are derived and compared. We show that, in general, the control limit policy is superior to the fixed time interval policy in terms of the long-run average cost per unit time. Some practical considerations about the implementation of optimal policies are also mentioned.

• 대한의용생체공학회:의공학회지
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• 제31권1호
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• pp.50-55
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• 2010

Gait initiation is a transitional process from the balanced upright standing to the beginning of steady-state walking. Dysbalanced gait initiation often causes stroke patients to fall. The net center of pressure, measured by two triaxial force plates from twenty healthy subjects and two stroke patients, was investigated to assess asymmetry of gait initiation in hemiparetic subjects. The time interval and distance of the net center of pressure(CoP) moved from the initiation point to the toe off(S1) and from the toe off to the initial contact(S2) were calculated during gait initiation of normal and stroke patients. When the patient with right hemiplegia(A) initiated his gait with right foot, the time interval and the distance of the net CoP in S1 and S2 were smaller than that of normal subjects' values. However, he initiated the gait with left foot(unaffected side) the time interval and the distance of net CoP in S1 were larger than normative values. Differently, the patient with left hemiplegia(B) has shown that larger time interval and distance in S1 and smaller time interval and distance in S2 in both sides. His asymmetry(with which side the gait initiated) was not significant. It is too early to conclude that these results could be general characteristics of the stroke patients because the variations were large and moreover, the level of motor recovery of the patients was different. However, it is expected that these trials could help to set up the strategy of the therapy for the rehabilitation or prevention of fall in stroke patients.

• Journal of Positioning, Navigation, and Timing
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• 제9권4호
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• pp.405-409
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• 2020

This paper proposes time management system for unmanned aerial vehicle (UAV) network. The computers of the UAVs need time synchronization that time offset does not exceed the minimum interval of data samples for errorless data blending between the computers. The proposed time management system is composed of time synchronization and general management systems for UAV control. The systems communicate each other for time information and control signals. The synchronization system uses improved version of existing time offset estimation that network time protocol (NTP) uses. The time synchronization is operated when the time offset of any UAV exceeds threshold that preconfigured by the general management system. The demonstration of prototype shows stable time synchronization satisfying preconfigured threshold.

• 대한교통학회지
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• 제18권3호
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• pp.55-76
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• 2000

실시간 통행시간관련자료의 집계시간간격은 보다 신뢰성있는 통행시간정보제공과 교통정보센터의 효율적인 운영을 위해 매우 중요한 요소이다. 그러나 대부분의 기존 VDS 및 TCS교통정보 데이터는 통계학적·공학적 차원에서의 합리적인 연구나 검증없이 경험적 간격으로 집계되고 있다. 본 연구의 목적은 링크 및 교통축(Corridor) 통행시간 산정 및 예측시의 최적 집계 시간간격을 결정할 수 있는 통계학적 모형을 개발하고 실제 도로망에서 수집되는 통행시간자료에 적용하는 것이다 첫째로, 본 연구는 링크 및 교통축 통행시간 산정 및 예측으로 인한 오차를 계량화하는 통계학적 모형을 제시하고, 제시된 모형의 의미를 교통류이론 측면과 통행시간정보 이용자측면에서 살펴보았다. 둘째로, 미국 Texas, Houston의 도시고속도로에서 AVI시스템을 통해 수집된 통행시간자료를 제시된 모형에 적용하였다. 적용결과 링크통행시간 산정을 위한 최적 집계시간간격보다 링크통행시간예측을 위한 최적 집계시간간격이 큰 것으로 나타났으며, 교통축 통행시간 산정 및 예측을 위한 최적 집계시간간격은 교통축을 구성하는 링크간의 상관관계 (Correlation)에 큰 영향을 받는 것으로 분석되었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권1호
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.