• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.295-298
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• 1996

In distillation column control, secondary measurements such as temperatures and flows are widely used in order to infer product composition. This paper addresses the design of static estimators using the secondary measurements for estimating the product compositions of the multicomponent distillation columns. Based on the unified framework for the estimator problems, the relationships among several typical static estimators are discussed including the effect of the measured inputs. Design guidelines for the composition estimator using PLS regression are also presented. The estimator based on the guidelines is robust to sensor noise and has a good predictive power.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.70-75
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• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.28-38
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• 1995

The flank wear at the minor cutting edge significantly affects the geometric accuracy and surface roughness in finish machining. A fuzzy estimator based on a fuzzy inference algorithm with a max-min composition rule is introduced to evaluate the minor flank wear length. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration signal. These features, dispersions, are used for constructing linguistic rules, and then the fuzzy inferences are carried out with test data sets collected under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length.

• Animal Bioscience
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• v.34 no.4
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• pp.558-566
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• 2021

Objective: Objectives were to estimate energy and protein requirements of dairy crossbred steers, as well as to evaluate equations previously described in the literature (HH46 and CS16) to predict the carcass and empty body chemical composition of crossbred dairy cattle. Methods: Thirty-three Holstein×Zebu steers, aged 19±1 months old, with an initial shrunk body weight (BW) of 324±7.7 kg, were randomly divided into three groups: reference group (n = 5), maintenance level (1.17% BW; n = 4), and the remaining 24 steers were randomly allocated to 1 of 4 treatments. Treatments were: intake restricted to 85% of ad libitum feed intake for either 0, 28, 42, or 84 d of an 84-d finishing period. Results: The net energy and the metabolizable protein requirements for maintenance were 0.083 Mcal/EBW0.75/d and 4.40 g/EBW0.75, respectively. The net energy (NEG) and protein (NPG) requirements for growth can be estimated with the following equations: NEG (Mcal/kg EBG) = $0.2973_{({\pm}0.1212)}{\times}EBW^{0.4336_{({\pm}0.1002)}$ and NPG (g/d) = 183.6(±22.5333)×EBG-2.0693(±4.7254)×RE, where EBW, empty BW; EBG, empty body gain; and RE, retained energy. Crude protein (CP) and ether extract (EE) chemical contents in carcass, and all the chemical components in the empty body were precisely and accurately estimated by CS16 equations. However, water content in carcass was better predicted by HH46 equation. Conclusion: The equations proposed in this study can be used for estimating the energy and protein requirements of crossbred dairy steers. The CS16 equations were the best estimator for CP and EE chemical contents in carcass, and all chemical components in the empty body of crossbred dairy steers, whereas water in carcass was better estimated using the HH46 equations.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1553-1556
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• 1997

In the paper, we porpose a new mehtod of extending PLS(Partial Least Squares) regressiion method to nonlinear framework and apply it to the estimation of product compositions in high-purity distillation column. There have veen similar efforets to overcome drawbacks of PLS by using nonlinear-mapping ability of meural networks, however, they failed to show great improvement over PLS since they focused only in capturing nonlinear functional relationship between input data, not on nonlinear correlation inthe data set. By incorporating the structure of Robust Auto Associative Networks(RAAN) into that of previous nonlinear PLS, we can handle nonlinear correlation as well as nonlinear functional relationship. The application result shows that the proposed method performs better than previous ones even for nonlinearities caused by changing operating conditions, limited observations, and existence of meas-unrement noises.

• Journal of Korean Society of Forest Science
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• v.97 no.2
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• pp.171-180
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• 2008

The characterization of tree species and forest stand conditions is useful in the planning of activities aimed to conserve biodiversity. The main objective of this study was to describe tree species diversity, species composition and stand structure of tropical deciduous forests distributed in three regions in Myanmar. Forest inventory was conducted in the Oktwin teak bearing forest, the Letpanpin community forest and Alaungdaw Kathapa National Park. According to the Jackknife estimator of species richness, 85 species (${\pm}18.16$), 70 species (${\pm}5.88$) and 186 species (${\pm}17.10$) belonging to 31 families were found in the Oktwin teak bearing forest, 33 families in Letpanpin community forest and 53 families in Alaungdaw Kathapa national park, respectively. Shannon's diversity indices were significantly different among the forests (p<0.05). It ranged from 3.36 to 4.36. Mean tree density (n/ha) of the Oktwin teak bearing forest, Letpanpin community forest and Alaungdaw Kathapa National Park were 488 (${\pm}18.6$), 535 (${\pm}15.6$) and 412 (${\pm}14.1$), while basal areas per hectare were $46.96m^2({\pm}3.23),\;49.01m^2({\pm}5.08)\;and\;60.03m^2({\pm}3.88)$, respectively. At the family level, Verbenaceae, Myrtaceae and Combretaceae occupied the highest importance value index, while at the species level it was Tectona grandis, Lagerstoremia speciosa and Xylia xylocarpa.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.