• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.259-271
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• 2020

In Korea, snow damage has happened in the region with little snowfalls in history. Also, accidental damage was caused by heavy snow leads and the public interest on heavy snow has been increased. Therefore, policy about the Natural Disaster Reduction Comprehensive Plan has been changed to include the mitigation measures of snow damage. However, since heavy snow damage was not frequent, studies on snowfall have not been conducted on different points. The characteristics of snow data commonly are not the same as the rainfall data. Some southern coastal areas in Korea are snowless during the year. Therefore, a joint probability distribution was suggested to analyze the snow data with many 0s in a previous research and fitness from the joint probability distribution was higher than the conventional methods. In this study, snow frequency analysis was implemented using the joint probability distribution and compared to the design codes. The results were compared to the design codes. The results of this study can be used as the basic data to develop a procedure for the snow frequency analysis in the future.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.184-188
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• 2016

The cleaning robot is popularly used as a home appliance. The state-of-the-art cleaning robot can clean more efficiently by using information gathered from its sensor, which is difficult for low-price cleaning robots due to limitation in this aspect. In this paper, we suggested a method for the moving pattern of cleaning robot based on grammatical evolution. Optimized program is generated by using moving pattern grammar, which is defined by Backus-Naur form. In addition, conditional probability is used between each of the grammar elements during the program creation process. The proposed method is evaluated by robot simulation in order to verify its performance and further compare it with existing algorithms. The experiment results showed that the proposed method is better than the compared algorithms.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.139-147
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• 2011

There is a growing tendency toward a vehicle demand and a utilization of traffic information systems. Due to various kinds of traffic information systems and increasing of communication data, the traffic information service requires a very high IT infrastructure. A cloud computing environment is an essential approach for reducing a IT infrastructure cost. And the traffic information service needs a provisioning scheduling method for managing a resource. So we propose a provisioning scheduling with conditional probability inference (PSCPI) for the traffic information service on cloud environment. PSCPI uses a naive bayse inference technique based on a status of a virtual machine. And PSCPI allocates a job to the virtual machines on the basis of an availability of each virtual machine. Naive bayse based PSCPI provides a high throughput and an high availability of virtual machines for real-time traffic information services.

• The Mathematical Education
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• v.58 no.3
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• pp.347-365
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• 2019

It is important to pay attention to how teachers recognize and use curriculum materials in order to link written curriculum and enacted curriculum. In this study, 90 preservice mathematics teachers were surveyed to identify their perspective and reading of curriculum materials. Especially, we focused on the curriculum documents, textbooks, and teachers' guidebooks containing the concept of conditional probability which is addressed in highschool mathematics curriculum. The various misconceptions of conditional probability were reported in the many researches, and there are multiple methods to introduce conditional probability in mathematics classes. As a result, curriculum materials have some limits to be used as they are and considered to be reconstructable by participants, but their curriculum reading were mainly classified to be descriptive and evaluative, not to be interpretive. However, unlike curriculum documents, textbooks and teachers' guidebooks were partially interpreted by participants using their knowledge of conditional probability. The purpose of this study is to investigate the profession of mathematics teachers in terms of curriculum implementation. We expect that this study will provide a basic framework for analyzing mathematics teachers' works and suggest some implications for the professional development of mathematics teachers.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.357-361
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• 2010

미래의 연별 최대 강수량 예측의 정확성을 향상시키는데 역사적 자료가 도움이 된다는 많은 연구 결과가 있었다. 관측의 오차와 자료의 손실로 역사자료를 이용한 강수 예측 방법은 절단자료의 분석을 중심으로 연구되었다. 대표적인 역사자료의 이용방법으로 조건부 적률을 이용한 B17B [Interagency Committee in Water Data, 1982], 조건부적률과적률 관계식을 이용한 Expected Moment Algorithm(EMA) [Cohn et al.;1997], 조건부 확률가중적률을 이용한 Partial Probability Weighted Moment (PPWM)[Wang ; 1991] 방법이 있다. 본 연구에서는 역사적 자료를 반영하는 방법에 있어 B17B와 EMA의 관계를 밝히고 그러한 관계가 PPWM에 동일하게 적용할 수 있음을 보였다. 우리는 B17B와 EMA의 관계를 적률방정식으로 표현하였고 PPWM에서 확률가중 적률 방정식을 정의함으로써 PPWM을 확장하였다. 본 연구에서 제안한 새로운 역사 자료를 이용한 강수예측 방법론을 Expected Probability Weighted Momemt (EPWM) 방법이라고 부르고 그 예측 방법의 성능을 다른 예측방법과 시뮬레이션 결과를 통해 비교하였다. 역사 자료 방법론의 비교는 Generalized Extreme Value (GEV) 분포를 이용하여 이루어졌으며, 각 방법론은 GEV분포의 형태모수(shape parameter)따라 다른 특성을 나타난다는 것을 보였다. 뿐만 아니라 여기서 제안한 EPWM 방법은 대부분의 경우에 좋은 추정량을 준다는 것을 보였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.847-851
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• 2004

In the original mixture density network(MDN), which was introduced by Bishop and Nabney, the parameters of the conditional probability density function are represented by the output vector of a single multi-layer perceptron. Among the recent modification of the MDNs, there is the so-called modified mixture density network, in which each of the priors, conditional means, and covariances is represented via an independent multi-layer perceptron. In this paper, we consider a further simplification of the modified MDN, in which the conditional means are linear with respect to the input variable together with the development of the MATLAB program for the simplification. In this paper, we first briefly review the original mixture density network, then we also review the modified mixture density network in which independent multi-layer perceptrons play an important role in the learning for the parameters of the conditional probability, and finally present a further modification so that the conditional means are linear in the input. The applicability of the presented method is shown via an illustrative simulation example.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.10
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• pp.141-150
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• 1997

분산은 확률모델을 표현하는 유용한 변수중 하나이다. 입력변수에 대한 함수로 표현되는 조건부 분산을 학습하는 신경회로망에 대한 많은 연구가 있어왔다. VALEAN이라는 신경회로망 역시 이러한 많은 연구중 하나인데 이것은 기본적으로 feedforward 다층 퍼셉트론 구조를 가지며 새롭게 제시된 에너지 함수를 사용하고 있다. 이 논문에서는 이 에너지 모델에 의해 결정되는 피드백에러(델타)가 신경망의 transient, steady state에서 미치는 영향을 다루었다. 과도 상태 분석에서는 델타와 수렴성, 안정성에 관한 내용을 다루고 모의 실험을 하였으며 정상 상태 분석에서는 신경회로망의 정상상태 에러의 크기와 델타의 크기사이의 상관관계에 대하여 다루었다. 학습 알고 리듬이 확률적이므로 정상상태 역시 확률적인 상태를 나타낸다. 따라서 델타의 크기에 따른 정상 상태 에러의 최대치는 확률적인 모델을 가지게 된다. 여기서는 이 확률 관계를 분석적으로 규명하고 이에 따라 원하는 신뢰도로 정상 상태 에러를 제어하기 위해 필요한 델타의 크기를 예측할 수 있는 이론적 배경을 마련하게 된다.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.437-446
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• 2000

THe drought analysis is performed by applications of truncation level method and conditional probability concept for hydrologic time series in Han river basin. The distributed trend of conditional probability is determined using kriging method for the time series. This study uses daily flowrate, monthly rainfall, and daily high temperature data sets. The daily flowrate data of 12 years(1986~1997) is used for the analysis. Also, the 14 years' data sets(1986~1999) for monthly rainfall and daily high temperature obtained from the National Weather Service of Korea are used in this study. In the cases of flowrate and rainfall data sets, the estimated value corresponding to the truncation level is decreased as the truncation level is increased but in the high temperature data, the value is increased as the truncation level is increased. The conditional probability varies according to the observations and sites. However, the distributed trend of drought is similar over the basin. As a result, the possibility of the drought is high in the middle and lower parts of Han river basin and thus it is recommended the distributed trend of drought be considered when the plan or measures for drought are established.