• Title/Summary/Keyword: 확률 모델

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확장형 히든마코브모델을 이용한 산화막 플라즈마 식각공정의 식각종료점 검출방법

  • Jeon, Seong-Ik;Kim, Seung-Gyun;Hong, Sang-Jin;Han, Seung-Su
    • Proceedings of the Korean Vacuum Society Conference
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    • 2010.02a
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    • pp.407-407
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    • 2010
  • 본 논문에서는 확장된 히든마코브모델을 이용하여 플라즈마 식각공정에서 식각종료검출을 위한 방법을 연구하였다. 플라즈마 식각장비는 유도성 결합플라즈마 시스템을 사용하였으며, 종료점 검출을 위해 식각공정이 진행됨에 따른 플라즈마의 상태를 확인할 수 있는 광학 방사 분광기(Optical Emission Spectroscopy: OES)를 사용하였다. 식각이 진행되는 동안 여기되는 입자들은 특정한 재료에 해당하는 파장에서 빛을 방출한다. 플라즈마상태에서 여기되는 원자와 분자들에 의해서 방출되는 빛은 OES를 통해 식각되는 물질을 확인하기 위해서 특별한 파장의 빛을 선택하여 분석한다. 본 논문에서는 확장된 히든마코브모델을 이용해 산화물이 식각될 때 방출하는 고유한 파장의 빛을 분석하여 식각이 종료되는 시점을 찾는 연구를 하였다. 제안된 확장형 히든마코브 모델은 세미-마코브모델과 분절특징 히든마코브모델을 결합한 것으로, 확률적 통계기법을 통해 종료시점을 찾아내는 방법이다. OES를 통해 얻은 데이터는 식각 종료가 일어나기 전의 파장의 상태와 식각이 종료된 후의 파장의 상태로 구분되어지는데, 식각종료시점에서 파장의 상태가 변화하며 이를 감지하여 식각종료점을 검출한다. 분절특징 히든마코브모델을 이용하여 식각종료시점 전후의 파장의 상태를 모델링 하였으며, 일반적인 마코브 모델의 특정상태가 유지될 시간의 확률을 변형된 세미-마코브 모델을 이용하여 OES를 통해 얻은 데이터 내에서 식각 종료가 일어나기 전의 상태가 유지될 수 있는 확률을 모델링 하였다. 실험을 통해 얻어진 6개의 데이터중 4개를 학습을 위해 사용하여 모델링을 하였고 나머지 2개의 데이터를 검증을 위해 사용한 결과, 확장형 히든마코브모델의 식각종료시점검출에 있어 뛰어난 정확성과 우수성을 증명하였다.

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Stochastic Mobility Model Design in Mobile WSN (WSN 노드 이동 환경에서 stochastic 모델 설계)

  • Yun, Dai Yeol;Yoon, Chang-Pyo;Hwang, Chi-Gon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.8
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    • pp.1082-1087
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    • 2021
  • In MANET(mobile ad hoc network), Mobility models vary according to the application-specific goals. The most widely used Random WayPoint Mobility Model(RWPMM) is advantageous because it is simple and easy to implement, but the random characteristic of nodes' movement is not enough to express the mobile characteristics of the entire sensor nodes' movements. The random mobility model is insufficient to express the inherent movement characteristics of the entire sensor nodes' movements. In the proposed Stochastic mobility model, To express the overall nodes movement characteristics of the network, the moving nodes are treated as random variables having a specific probability distribution characteristic. The proposed Stochastic mobility model is more stable and energy-efficient than the existing random mobility model applies to the routing protocol to ensure improved performances in terms of energy efficiency.

A Practical Study on the New Revenue Estimate Model Of SSM (국내 대형슈퍼의 개량확률모델에 관한 실증연구)

  • Ahn, Sung-Woo;Lee, Sang-Youn;Kim, Pan-Jin;Youn, Myoung-Kil
    • Journal of Distribution Science
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    • v.7 no.3
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    • pp.5-24
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    • 2009
  • In the retail management, store location has an important influence like business skills. The reason for failure to selecting location is that the market analysis model is not popular in business field. It gets worse in supermarket industry. Currently, store developers are relying on simple statistics and the sixth sense as market analysis techniques. lt proves that the market analysis model is not distributed well in the field. This market analysis model can apply to medium and small business market using an existing market analysis model, broad market model. And its study outcome can be theorized as a result. Converse's new retail model can be used as to analyze junction market. Pareto_Huff model can also be used to compute shopping probability. To do so, this study can be divided into walking distance market and driving distance market as a model market. Also it examines industry type such as SM and SSM. By taking consumer survey, condition of consumers to select store will be counted in shopping probability so that it improves the objectivity and reliability. Through this process, derived study outcome can be a new estimated revenue model for practical application of selecting store location in large and medium-sized supermarket.

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Improving Generalization Performance of Neural Networks using Natural Pruning and Bayesian Selection (자연 프루닝과 베이시안 선택에 의한 신경회로망 일반화 성능 향상)

  • 이현진;박혜영;이일병
    • Journal of KIISE:Software and Applications
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    • v.30 no.3_4
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    • pp.326-338
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    • 2003
  • The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

Application of Bayesian network for farmed eel safety inspection in the production stage (양식뱀장어 생산단계 안전성 조사를 위한 베이지안 네트워크 모델의 적용)

  • Seung Yong Cho
    • Food Science and Preservation
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    • v.30 no.3
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    • pp.459-471
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    • 2023
  • The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.

Statistical Probability Analysis of Storage Temperatures of Domestic Refrigerator as a Risk Factor of Foodborne Illness Outbreak (식중독 발생 위해인자로서 가정용 냉장고의 온도에 대한 확률분포 분석)

  • Bahk, Gyung-Jin
    • Korean Journal of Food Science and Technology
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    • v.42 no.3
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    • pp.373-376
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    • 2010
  • The objective of this study was to present the proper probability distribution model based on the data obtained from surveys on domestic refrigerator food storage temperatures in home. Domestic refrigerator temperatures were determined as risk factors in foodborne disease outbreaks for microbial risk assessment (MRA). The temperature was measured by directly visiting 139 homes using a data logger from May to September of 2009. The overall mean temperature for all the refrigerators in the survey was $3.53{\pm}2.96^{\circ}C$, with 23.6% of the refrigerators measuring above $5^{\circ}C$. Probability distributions were also created using @RISK program based on the measured temperature data. Statistical ranking was determined by the goodness of fit (GOF, i.e., the Kolmogorov-Smirnov (KS) or Anderson-Darling (AD) test) to determine the proper probability distribution model. This result showed that the LogLogistic (-10.407, 13.616, 8.6107) distribution was found to be the most appropriate for the MRA model. The results of this study might be directly used as input variables in exposure evaluation for conducting MRA.

Principles for the Development of Mathematics Textbook for Decision-Making based on Storytelling ("의사결정형" 스토리텔링 수학 모델 교과서의 개발 원리: 조건부 확률 단원을 중심으로)

  • Ju, Mi-Kyung;Park, Jung Sook;Oh, Hye Mi;Kim, Young Ki;Park, Yun Gun
    • Communications of Mathematical Education
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    • v.27 no.3
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    • pp.205-220
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    • 2013
  • In this research, in order to investigate the principles for the development of mathematics textbook for decision-making based on storytelling, we conceptualized the educational meaning of decision-making and specified the principles and the methods for the textbook based on decision-making. We illustrated the principles and the methods by the cases from the model textbook for the conditional probability that we have developed. We discussed the implication for the future development and implementation of mathematics textbook for decision-making based on storytelling.

Development and Evaluation of an Ensemble Forecasting System for the Regional Ocean Wave of Korea (앙상블 지역 파랑예측시스템 구축 및 검증)

  • Park, JongSook;Kang, KiRyong;Kang, Hyun-Suk
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.30 no.2
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    • pp.84-94
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    • 2018
  • In order to overcome the limitation of deterministic forecast, an ensemble forecasting system for regional ocean wave is developed. This system predicts ocean wind waves based on the meteorological forcing from the Ensemble Prediction System for Global of the Korea Meteorological Administration, which is consisted of 24 ensemble members. The ensemble wave forecasting system is evaluated by using the moored buoy data around Korea. The root mean squared error (RMSE) of ensemble mean showed the better performance than the deterministic forecast system after 2 days, especially RMSE of ensemble mean is improved by 15% compared with the deterministic forecast for 3-day lead time. It means that the ensemble method could reduce the uncertainty of the deterministic prediction system. The Relative Operating Characteristic as an evaluation scheme of probability prediction was bigger than 0.9 showing high predictability, meaning that the ensemble wave forecast could be usefully applied.

Probabilistic Calibration of Computer Model and Application to Reliability Analysis of Elasto-Plastic Insertion Problem (컴퓨터모델의 확률적 보정 및 탄소성 압착문제의 신뢰도분석 응용)

  • Yoo, Min Young;Choi, Joo Ho
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.37 no.9
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    • pp.1133-1140
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    • 2013
  • A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

Intra-Sentence Segmentation using Maximum Entropy Model for Efficient Parsing of English Sentences (효율적인 영어 구문 분석을 위한 최대 엔트로피 모델에 의한 문장 분할)

  • Kim Sung-Dong
    • Journal of KIISE:Software and Applications
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    • v.32 no.5
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    • pp.385-395
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    • 2005
  • Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.