• Title/Summary/Keyword: 다층모델

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Short-Term Electrical Load Forecasting using Structure Identification of Neuro-Fuzzy Models (뉴로-퍼지 모델의 구조 학습을 이용한 단기 전력 수요 예측 시스템)

  • Park, Young-Jin;Shim, Hyun-Jeong;Wang, Bo-Hyeun
    • Proceedings of the KIEE Conference
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    • 2000.07a
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    • pp.102-106
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    • 2000
  • 본 논문은 뉴로-퍼지 모델의 구조학습을 이용하여 한 시간 앞의 전력 수요를 예측하는 체계적인 방법을 제안한다. 제안된 예측시스템은 시간 단위로 뉴로-퍼지 모델을 재학습하기 위해서 필요한 초기 구조를 요일 유형과 시간 별로 미리 생성하고, 이를 초기 구조 뱅크에 저장한다. 예측이 수행되는 시점의 요일 유형에 따라 선택된 초기 구조를 이용하여 뉴로-퍼지 모델을 초기화하고, 학습하고, 예측을 수행한다. 제안된 방법의 실효성을 검증하기 위해 1996년과 1997년의 실제 전력 수요 데이터를 이용하여 모의 실험을 수행한다. 실험결과 제안된 방법은 기존의 다층 퍼셉트론을 이용한 방법과 비교하여 예측의 정확도 측면과 신뢰도 측면에서 모두 향상된 결과를 얻는다.

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특성 예측 수식모델과 이를 이용한 박막의 특성 제어

  • Jeong, Jae-In;Yang, Ji-Hun;Jang, Seung-Hyeon;Park, Hye-Seon
    • Proceedings of the Korean Vacuum Society Conference
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    • 2010.08a
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    • pp.146-146
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    • 2010
  • 진공이나 특정 가스 분위기 또는 플라즈마를 이용하여 박막을 제조하는 방법은 공정 조건에 따라 그 특성이 현저히 달라지며 대부분의 경우 제조된 박막에 대한 성분 및 조직의 분석과 박막이 구현하는 특성을 파악한 후 공정 조건을 최적화하게 되는 번거로움이 있다. 특히, 박막 제조 시스템에 따라 제조되는 박막이 특성이 달라지거나 원하는 공정조건에서 원하는 특성의 박막을 얻지 못하는 경우가 종종 발생하고 있다. 한편, 최근의 박막 제조 기술은 결정립 미세화 및 나노화, 다층화, 다성분계 박막 등을 통해 다기능을 구현하는 연구가 활발히 진행되고 있다, 이러한 다기능성 박막을 제조하기 위해서는 박막의 조직제어 기술과 함께 특성을 예측하고 제어하는 기술이 필요하게 된다. 본 연구에서는 상기의 문제점을 근본적으로 해결하고 다기능성 박막의 특성을 예측하고 제어하기 위한 코팅 수식모델을 개발하고 이를 응용하는데 필요한 시스템 구성에 대한 연구를 진행하였다. 코팅 수식 모델은 정해진 물질계의 각 공정별 특성 데이터를 이용하여 내삽 또는 외삽을 통해 수식화하였으며 이를 바탕으로 특성을 예측하는 프로그램을 개발하였고, 시스템에 따른 차이를 줄이기 위해 플라즈마 진단장치를 이용하여 시스템을 동기화시키는 작업을 진행하였다. 이러한 수식 모델을 바탕으로 TiN 피막의 특성예측 및 제어에 대한 기초연구를 소개한다.

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An Attribute Ordering Optimization in Bayesian Networks for Prognostic Modeling of the Metabolic Syndrome (대사증후군의 예측 모델링을 위한 베이지안 네트워크의 속성 순서 최적화)

  • Park Han-Saem;Cho Sung-Bae
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06a
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    • pp.1-3
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    • 2006
  • 대사증후군은 당뇨병, 고혈압, 복부 비만, 고지혈증 등의 질병이 한 개인에게 동시에 발현하는 것을 말하며, 최근 경제여건의 향상 및 식생활 습관의 변화와 함께 우리나라에서도 심각한 문제가 되고 있다. 한편 불확실성의 처리를 위해 많이 사용되는 베이지안 네트워크는 사람이 분석 가능한 확률 기반의 모델로 최근 의학분야에서 질병의 진단이나 예측모델을 구성하기 위한 방법으로 유용하게 사용되고 있다. 베이지안 네트워크의 구조를 학습하는 대표적인 알고리즘인 K2 알고리즘은 속성이 입력되는 순서의 영향을 받으며, 따라서 이 또한 하나의 주제로써 연구되어 왔다. 본 논문에서는 유전자 알고리즘을 이용하여 베이지안 네트워크에 입력되는 속성 순서를 최적화하며 이 과정에서 의학지식을 적용해 효율적인 최적화가 가능하도록 하였다. 제안하는 모델을 통해 1993년의 데이터를 가지고 1995년의 상태를 예측하는 분류 실험을 수행한 결과 속성 순서 최적화 후에 이전보다 향상된 예측율을 보였으며 또한 다층 신경망, k-최근접 이웃 등을 이용한 다른 모델보다 더 높은 예측율을 보였다.

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Study on the Multilevel Effects of Integrated Crisis Intervention Model for the Prevention of Elderly Suicide: Focusing on Suicidal Ideation and Depression (노인자살예방을 위한 통합적 위기개입모델 다층효과 연구: 자살생각·우울을 중심으로)

  • Kim, Eun Joo;Yook, Sung Pil
    • 한국노년학
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    • v.37 no.1
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    • pp.173-200
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    • 2017
  • This study is designed to verify the actual effect on the prevention of the elderly suicide of the integrated crisis intervention service which has been widely provided across all local communities in Gyeonggi-province focusing on the integrated crisis intervention model developed for the prevention of elderly suicide. The integrated crisis intervention model for the local communities and its manual were developed for the prevention of elderly suicide by integrating the crisis intervention theory which contains local community's integrated system approach and the stress vulnerability theory. For the analysis of the effect, the geriatric depression and suicidal ideation scale was adopted and the data was collected as follows; The data was collected from 258 people in the first preliminary test. Then, it was collected from the secondary test of 184 people after the integrated crisis intervention service was performed for 6 months. The third collection of data was made from 124 people after 2 or 3 years later using the backward tracing method. As for the analysis, the researcher used the R Statistics computing to conduct the test equating, and the vertical scaling between measuring points. Then, the researcher conducted descriptive statistics analysis and univariate analysis of variance, and performed multi-level modeling analysis using Bayesian estimation. As a result of the study, it was found out that the integrated crisis intervention model which has been developed for the elderly suicide prevention has a statistically significant effect on the reduction of elderly suicide in terms of elderly depression and suicide ideation in the follow-up measurement after the implementation of crisis intervention rather than in the first preliminary scores. The integrated crisis intervention model for the prevention of elderly suicide was found to be effective to the extent of 0.56 for the reduction of depression and 0.39 for the reduction of suicidal ideation. However, it was found out in the backward tracing test conducted 2-3 years after the first crisis intervention that the improved values returned to its original state, thus showing that the effect of the intervention is not maintained for long. Multilevel analysis was conducted to find out the factors such as the service type(professional counseling, medication, peer counseling), characteristics of the client (sex, age), the characteristics of the counselor(age, career, major) and the interaction between the characteristics of the counselor and intervention which affect depression and suicidal ideation. It was found that only medication can significantly reduce suicidal ideation and that if the counselor's major is counseling, it significantly further reduces suicidal ideation by interacting with professional counseling. Furthermore, as the characteristics of the suicide prevention experts are found to regulate the intervention effect on elderly suicide prevention in applying integrated crisis intervention model, the primary consideration should be given to the counseling ability of these experts.

Bond Strength of Wafer Stack Including Inorganic and Organic Thin Films (무기 및 유기 박막을 포함하는 웨이퍼 적층 구조의 본딩 결합력)

  • Kwon, Yongchai;Seok, Jongwon
    • Korean Chemical Engineering Research
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    • v.46 no.3
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    • pp.619-625
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    • 2008
  • The effects of thermal cycling on residual stresses in both inorganic passivation/insulating layer that is deposited by plasma enhanced chemical vapor deposition (PECVD) and organic thin film that is used as a bonding adhesive are evaluated by 4 point bending method and wafer curvature method. $SiO_2/SiN_x$ and BCB (Benzocyclobutene) are used as inorganic and organic layers, respectively. A model about the effect of thermal cycling on residual stress and bond strength (Strain energy release rate), $G_c$, at the interface between inorganic thin film and organic adhesive is developed. In thermal cycling experiments conducted between $25^{\circ}C$ and either $350^{\circ}C$ or $400^{\circ}C$, $G_c$ at the interface between BCB and PECVD $ SiN_x $ decreases after the first cycle. This trend in $G_c$ agreed well with the prediction based on our model that the increase in residual tensile stress within the $SiN_x$ layer after thermal cycling leads to the decrease in $G_c$. This result is compared with that obtained for the interface between BCB and PECVD $SiO_2$, where the relaxation in residual compressive stress within the $SiO_2$ induces an increase in $G_c$. These opposite trends in $G_cs$ of the structures including either PECVD $ SiN_x $ or PECVD $SiO_2$ are caused by reactions in the hydrogen-bonded chemical structure of the PECVD layers, followed by desorption of water.

Improvement of precipitation forecasting skill of ECMWF data using multi-layer perceptron technique (다층퍼셉트론 기법을 이용한 ECMWF 예측자료의 강수예측 정확도 향상)

  • Lee, Seungsoo;Kim, Gayoung;Yoon, Soonjo;An, Hyunuk
    • Journal of Korea Water Resources Association
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    • v.52 no.7
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    • pp.475-482
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    • 2019
  • Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

A Study on Evaluation of Moduli of 3 Layered Flexible Pavement Structures using Deflection Basins (처짐곡선을 이용한 3층 아스팔트 포장 구조체의 물성 추정에 관한 연구)

  • Kim, Soo Il;Kim, Moon Kyum;Yoo, Ji Hyeung
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.9 no.1
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    • pp.97-107
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    • 1989
  • An inverse self-iterative procedure is developed to estimate layer moduli of 3 layered flexible pavement structures from FWD deflection basins. The theoretical deflection basins of pavement structures obtained by full factorial design are used for the parametric study on the characteristics of deflection basins and the regression analysis. The factorial design is performed for asphalt pavement structures with stabilized base layer and granular base layer, respectively. The initially assumed layer moduli by regression equations and relations between the rate of change of moduli and deflections are used in the procedure to ensure efficiency and accuracy of self-iterative model. The SINELA computer program is used for inverse self-iterative applications to determine theoretical responses. The computer program of this procedure is coded for personal computers and is verified through numerical model tests.

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Modeling of Shear-mode Rotary MR Damper Using Multi-layer Neural Network (다층신경망을 이용한 전단모드 회전형 MR 댐퍼의 모델링)

  • Cho, Jeong-Mok;Huh, Nam;Joh, Joong-Seon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.7
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    • pp.875-880
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    • 2007
  • Scientific challenges in the field of MR(magnetorheological) fluids and devices consist in the development of MR devices, the mathematical modeling and simulation of MR devices, and the development of (optimal) control algorithm for MR device systems. To take a maximum advantage of MR fluids in control applications a reliable mathematical model, which predicts their nonlinear characteristics, is needed. A inverse model of the MR device is required to calculate current(or voltage) input of MR damper, which generates required damping force. In this paper, we implemented test a bench for shear mode rotary MR damper and laboratory tests were performed to study the characteristics of the prototype shear-mode rotary MR damper. The direct identification and inverse dynamics modeling for shear mode rotary MR dampers using multi-layer neural networks are studied.

Analyzing Characteristics of the Smart City Governance (스마트시티 거버넌스 특성 분석)

  • LEE, Sang-Ho;LEEM, Youn-Taik
    • Journal of the Korean Association of Geographic Information Studies
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    • v.19 no.2
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    • pp.86-97
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    • 2016
  • This study aims to analyze the characteristics of the smart city governance through the multi-layer governance model, which includes administrative governance(AG), technological governance(TG), and global governance(GG). The results of the smart city governance are as follows. Multi-layered governance was modeled to enable cross-checking of each element of the propelling process and types of governance. AG has transitioned from a public partnership to a public-private people partnership(pppp) through a public-private partnership(ppp). TG has the characteristics of information communication technologies(ICTs) - eco technologies(EcoTs) - Spatial technology convergence including physical center, information software platforms such as the CCTV convergence center, and virtualization such as the cloud data center. GG aims at developing killer applications and ICTs-embedded space with intelligent buildings such as a smart city special zone to enable overseas exports. The smart city roadshow and forum have been developed as a platform for overseas exports with competition as well as cooperation.

A Multilayer Perceptron-Based Electric Load Forecasting Scheme via Effective Recovering Missing Data (효과적인 결측치 보완을 통한 다층 퍼셉트론 기반의 전력수요 예측 기법)

  • Moon, Jihoon;Park, Sungwoo;Hwang, Eenjun
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.2
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    • pp.67-78
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    • 2019
  • Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.