• BMB Reports
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• 제46권2호
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• pp.124-129
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• 2013

FK506 binding protein 12 (FK506BP) belongs to a family of immunophilins, and is involved in multiple biological processes. However, the function of FK506BP in corneal disease remains unclear. In this study, we examined the protective effects on dry eye disease in a Botulinum toxin A (BTX-A) induced mouse model, using a cell-permeable PEP-1-FK506BP protein. PEP-1-FK506BP efficiently transduced into human corneal epithelial cells in a time- and dose-dependent manner, and remained stable in the cells for 48 h. In addition, we demonstrated that topical application of PEP-1-FK506BP was transduced into mouse cornea and conjunctiva by immunohistochemistry. Furthermore, topical application of PEP-1-FK506BP to BTX-A-induced mouse model markedly inhibited expression levels of pro-inflammatory cytokines such as interleukin-$1{\beta}$ (IL-$1{\beta}$), tumor necrosis factor-${\alpha}$ (TNF-${\alpha}$) and macrophage inhibitory factor (MIF) in corneal and conjunctival epithelium. These results suggest PEP-1-FK506BP as a potential therapeutic agent for dry eye diseases.

• KIEE International Transactions on Electrophysics and Applications
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• 제5C권3호
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• pp.138-142
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• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.323-336
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• 2024

The accurate forecasting of insurance claims is a critical component for insurers' risk management decisions. Hierarchical Bayesian parametric (BP) models can be used for health insurance claims forecasting, but they are unsatisfactory to describe the claims distribution. Therefore, Bayesian nonparametric (BNP) models can be a more suitable alternative to deal with the complex characteristics of the health insurance claims distribution, including heavy tails, skewness, and multimodality. In this study, we apply both a BP model and a BNP model to predict group health claims using simulated and real-world data for a private life insurer in Indonesia. The findings show that the BNP model outperforms the BP model in terms of claims prediction accuracy. Furthermore, our analysis highlights the flexibility and robustness of BNP models in handling diverse data structures in health insurance claims.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
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• pp.120.1-120.1
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• 2015

We investigate the degradation pattern of Black phosphorus (BP) field effect transistor (FETs) investigated by using an mechanically exfoliated BP that react O2 and water vapor in ambient condition, degradation. The BP FETs was electrically measured every 20 minutes (1cycle) in the air, the total cycle is 100. We show electrical changes with Mobility, On/off ratio, Current and a significant positive shift in the threshold voltage. We extracted the current level at Vgs-Vth = 0, -10, -20 and fitting with Swiss-cheese model. This model suggested that Swiss-cheese model is well fitted with degradation pattern of BP FETs.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
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• pp.167.1-167.1
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• 2015

We investigate the degradation pattern of Black phosphorus (BP) field effect transistor (FETs) was investigated by using an mechanically exfoliated BP that react O2 and water vapor in ambient condition, degradation. The BP FETs was electrically measured every 20 minutes (1cycle) in the air, the total cycle is 100. We show electrical changes with Mobility, On/off ratio, Current and a significant positive shift in the threshold voltage. We extracted the current level at Vgs-Vth = 0, -10, -20 and fitting with Swiss-cheese model. This model suggested that Swiss-cheese model is well fitted with degradation pattern of BP FETs.

• Structural Monitoring and Maintenance
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• 제10권2호
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• Industrial Engineering and Management Systems
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• 제11권3호
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• pp.233-240
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• 2012

The implementation of business process management (BPM) systems in large number of business organizations transforms BPM system into such a level of maturity and tends to collect large repositories of business process (BP) models. This issue encourages BP flexibility that leads to a large number of process variants derived from the same model, but differing in structure, to be stored in the large repositories of BP models. Therefore, the repositories may include thousands of activities and related business objects with variation of requirements and quality of service. It is a common practice to customize processes from reference processes or templates in order to reduce the time and effort required to design and deploy processes on all levels. In order to address redundancy and underutilization problems, a generic process model, called as reference BP, is absolutely necessary to cover the best of process variants. This study aims to develop multiple-objective business process genetic algorithm (MOBPGA) to find a set of non-dominated (Pareto) solutions of business reference model to enhance conventional approach which considered only a single objective on creating BP reference model by using proximity score measurement. A mixed-integer linear program is constructed to evaluate performance of the proposed MOBPGA on small-scale problems by using standard measures for multiple-objective techniques. The results will show the viability of applying MOBPGA in terms of simultaneously maximizing proximity score measurement, minimizing total duration, and total costs of the selected reference model.

• 한국통신학회논문지
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• 제19권10호
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• pp.2014-2027
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• 1994

이 논문에서는 서비스 우선순위가 다른 2개의 독자적인 입력 스트림을 가지는 큐잉 모델을 연구하였다. 구체적으로 head-of-line 우선 순위가 적용되는 IBP+BP/D/I시스템을 분석하였는데, 여기에는 IBP는 Interrupted Bernoulli Process를 BP는 Bernoulli Process를 표시한다. BP 스트립은 IBP 스트립에 대하여 서비스 우선권을 가진다. 본 논문은 이 우선 순위 큐에 대하여 시스템 상태의 안정상태분포와 각 클래스 별 고객의 대기 시간분포 및 출발시간 간격분포를 구할 수 잇는 정확한 분석방법을 제공하고 있다. 우선 순위가 다른 두 입력스트림의 다양한 파라미터들이 시스템의 성능에 어떠한 영향을 미치는지 보여줄 수 있는 수치적 예들도 제시하였다.

• 대한전자공학회논문지SP
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• 제44권5호
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• pp.54-63
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• 2007

본 논문은 영상 완성(image completion)을 위해 계층적으로 적용되는 새로운 에너지 최적화 방식을 제안한다. 영상 완성의 목적은 영상의 특정 영역이 지워진 상태에서, 그 지워진 부분을 나머지 부분과 시각적으로 어울리도록 완성시키는 기법을 말한다. 본 논문에서는 전역적 특징의 탐지, 주변 환경 변화에 대한 유연성, 계산비용의 감소, 영상 인페인팅과 같은 관련기법들로의 확장성 문제들을 다룰 수 있도록 마르코프 랜덤 필드(Markov Random Field)로 모델링 된 예제 기반 방식(exampler-based mehtod) 접근법을 택한다. 그리고 MRF에서의 에너지 최적화를 위해 BP 알고리즘(Belief Propagation Algorithm)의 변형인 우선순위 BP 알고리즘(Priority-Belief Propagation Algorithm)을 적용하였다. 본 논문에서 제안한 계층적 우선순위 BP 알고리즘(Hierarchical Priority-Belief Propagation Algorithm)은 MRF의 정점의 수를 줄이고 메시지를 계층적으로 전파한다. 이렇게 계층적 우선순위 BP 알고리즘을 영상 완성에 적용하여 여러 영상들에서 바람직한 결과를 얻었다.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).