• Title/Summary/Keyword: data-based model

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Data-Based Model Approach to Predict Internal Air Temperature of Greenhouse (데이터 기반 모델에 의한 온실 내 기온 변화 예측)

  • Hong, Se Woon;Moon, Ae Kyung;Li, Song;Lee, In Bok
    • Journal of The Korean Society of Agricultural Engineers
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    • v.57 no.3
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    • pp.9-19
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    • 2015
  • Internal air temperature of greenhouse is an important variable that can be influenced by the complex interaction between outside weather and greenhouse inside climate. This paper focuses on a data-based model approach to predict internal air temperature of the greenhouse. External air temperature, solar radiation, wind speed and wind direction were measured next to an experimental greenhouse supported by the Electronics and Telecommunications Research Institute and used as input variables for the model. Internal air temperature was measured at the center of three sections of the greenhouse and used as an output variable. The proposed model consisted of a transfer function including the four input variables and tested the prediction accuracy according to the sampling interval of the input variables, the orders of model polynomials and the time delay variable. As a result, a second-order model was suitable to predict the internal air temperature having the predictable time of 20-30 minutes and average errors of less than ${\pm}1K$. Afterwards mechanistic interpretation was conducted based on the energy balance equation, and it was found that the resulting model was considered physically acceptable and satisfied the physical reality of the heat transfer phenomena in a greenhouse. The proposed data-based model approach is applicable to any input variables and is expected to be useful for predicting complex greenhouse microclimate involving environmental control systems.

A Study on Turbulent Flame Propagation Model of S. I. Engines (스파크 점화기관의 난류 화염전파모델에 관한 연구)

  • 유욱재;최인용;전광민
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.18 no.10
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    • pp.2787-2796
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    • 1994
  • The modeling of combustion process is an important part in an engine simulation program. In this study, calculated results using a conventional B-K model and the other model which is called GESIM were compared with experimentally measured data of a three-cylinder spark-ignition engine under wide range of operating conditions. The burn rates calculated from the combustion models were compared with the burn rate calculated from the one-zone heat release analysis that uses measured pressure data as an input data. As a result of the two models' comparison, the GESIM combustion model conformed to be closer to the data acquired from the experiment in wide operating ranges. The GESIM model has been improved by introducing a variable that considers the flame size, the area of flame conacting the piston surface into the model, based on the comparison between the experimental result and the calculated results. The improved combustion model predicts experimental results more precisely than that of GESIM combustion model.

Development of Composite Load Models of Power Systems using On-line Measurement Data

  • Choi Byoung-Kon;Chiang Hsiao Dong;Li Yinhong;Chen Yung Tien;Huang Der Hua;Lauby Mark G.
    • Journal of Electrical Engineering and Technology
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    • v.1 no.2
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    • pp.161-169
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    • 2006
  • Load representation has a significant impact on power system analysis and control results. In this paper, composite load models are developed based on on-line measurement data from a practical power system. Three types of static-dynamic load models are derived: general ZIP-induction motor model, Exponential-induction motor model and Z-induction motor model. For the dynamic induction motor model, two different third-order induction motor models are studied. The performances in modeling real and reactive power behaviors by composite load models are compared with other dynamic load models in terms of relative mismatch error. In addition, numerical consideration of ill-conditioned parameters is addressed based on trajectory sensitivity. Numerical studies indicate that the developed composite load models can accurately capture the dynamic behaviors of loads during disturbance.

Comparison of Performance Measures for Credit-Card Delinquents Classification Models : Measured by Hit Ratio vs. by Utility (신용카드 연체자 분류모형의 성능평가 척도 비교 : 예측률과 유틸리티 중심으로)

  • Chung, Suk-Hoon;Suh, Yong-Moo
    • Journal of Information Technology Applications and Management
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    • v.15 no.4
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    • pp.21-36
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    • 2008
  • As the great disturbance from abusing credit cards in Korea becomes stabilized, credit card companies need to interpret credit-card delinquents classification models from the viewpoint of profit. However, hit ratio which has been used as a measure of goodness of classification models just tells us how much correctly they classified rather than how much profits can be obtained as a result of using classification models. In this research, we tried to develop a new utility-based measure from the viewpoint of profit and then used this new measure to analyze two classification models(Neural Networks and Decision Tree models). We found that the hit ratio of neural model is higher than that of decision tree model, but the utility value of decision tree model is higher than that of neural model. This experiment shows the importance of utility based measure for credit-card delinquents classification models. We expect this new measure will contribute to increasing profits of credit card companies.

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The Determinants of Trust and Participation Intention in Internet Auction : Model Generating Strategy Approach (인터넷 경매사이트에서의 신뢰와 참여의도 결정요인에 관한 연구 : 모델생성전략 접근)

  • Kwahk Kee-Young;Kim Hyo-Jung
    • Journal of the Korean Operations Research and Management Science Society
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    • v.30 no.3
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    • pp.95-117
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    • 2005
  • This research Investigates the determinants of customer Intention to participate In Internet auction. Based on technology acceptance and trust related studies, our research proposes a theoretical model consisting of factors such as perceived usefulness, perceived ease of use, institution based trust, beliefs on sellers, trusting beliefs, and participation Intention. For examining the relationships implied by the research model, a field study using a survey methodology for data collection was conducted. The data were analyzed using AMOS based on the structural equation modeling, a second-generation multivariate technique, which has gained distinct advantages over other technique. After some model modification according to model generating strategy approach, this study shows that trusting beliefs have significant effects on the participating intention in Internet auction site. In conclusion, Implications are discussed along with limitations and further research direction.

Structural Design of Optimized Fuzzy Inference System Based on Particle Swarm Optimization (입자군집 최적화에 기초한 최적 퍼지추론 시스템의 구조설계)

  • Kim, Wook-Dong;Lee, Dong-Jin;Oh, Sung-Kwun
    • Proceedings of the IEEK Conference
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    • 2009.05a
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    • pp.384-386
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    • 2009
  • This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

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Comparison of Performance between MLP and RNN Model to Predict Purchase Timing for Repurchase Product (반복 구매제품의 재구매시기 예측을 위한 다층퍼셉트론(MLP) 모형과 순환신경망(RNN) 모형의 성능비교)

  • Song, Hee Seok
    • Journal of Information Technology Applications and Management
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    • v.24 no.1
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    • pp.111-128
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    • 2017
  • Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

Dynamic Hysteresis Model Based on Fuzzy Clustering Approach

  • Mourad, Mordjaoui;Bouzid, Boudjema
    • Journal of Electrical Engineering and Technology
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    • v.7 no.6
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    • pp.884-890
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    • 2012
  • Hysteretic behavior model of soft magnetic material usually used in electrical machines and electronic devices is necessary for numerical solution of Maxwell equation. In this study, a new dynamic hysteresis model is presented, based on the nonlinear dynamic system identification from measured data capabilities of fuzzy clustering algorithm. The developed model is based on a Gustafson-Kessel (GK) fuzzy approach used on a normalized gathered data from measured dynamic cycles on a C core transformer made of 0.33mm laminations of cold rolled SiFe. The number of fuzzy rules is optimized by some cluster validity measures like 'partition coefficient' and 'classification entropy'. The clustering results from the GK approach show that it is not only very accurate but also provides its effectiveness and potential for dynamic magnetic hysteresis modeling.

Formwork Productivity Analysis Model for Cost-efficient Equipment Operations

  • Hyunsu Lim;Taehoon Kim;Hunhee Cho;Kyung-In Kang
    • International conference on construction engineering and project management
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    • 2013.01a
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    • pp.226-230
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    • 2013
  • In the tall building construction, the slab formwork largely impacts on construction cost. Because productivity of a slab formwork is influenced by a number of and the efficiency of equipment, using the equipment-based construction method, an appropriate equipment input planning is crucial for the productivity. Meanwhile, the general equipment input planning is conducted by intuition based on experience due to the lack of equipment productivity data. Thus, this study develop a simulation model to analyze table formwork productivity and to propose an optimum equipment input plan that reflects the construction process, based on the full consideration of the economic factors. This study developed a simulation model by using CYCLONE and the data for the model was collected by measuring the duration of each unit activity in the tall building where table forms were applied. It is expected that a simulation model helps users to make better decision on the equipment input planning of slab formwork.

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Bayesian Model Selection in the Gamma Populations

  • Kang, Sang-Gil;Kang, Doo-Young
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1329-1341
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    • 2006
  • When X and Y have independent gamma distributions, we consider the testing problem for two gamma means. We propose a solution based on a Bayesian model selection procedure to this problem in which no subjective input is considered. The reference prior is derived. Using the derived reference prior, we compute the fractional Bayes factor and the intrinsic Bayes factors. The posterior probability of each model is used as a model selection tool. Simulation study and a real data example are provided.

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