• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.83-86
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• 1998

In this paper, a fuzzy model based on the Polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. the new algorithm uses PNN algorithm based on Group Mehtod of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3004-3006
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• 2000

In this paper, we propose an optimal design method of Fuzzy-Neural Networks model with parallel structure for complex and nonlinear systems. The proposed model is consists of a multiple number of FNN connected in parallel. The proposed FNNs with parallel structure is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. We use a HCM clustering and GAs to identify the structure and the parameters of the proposed model. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model. we use the time series data for gas furnace and the numerical data of nonlinear function.

• 대한공업교육학회지
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• v.33 no.1
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• pp.249-264
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• 2008

In this dissertation, shallow $p^+-n$ junctions were formed by ion implantation and dual-step annealing processes and a new simulator is designed to model boron diffusion in silicon. This simulator predicts the boron distribution after ion implantation and annealing. The dopant implantation was performed into the crystalline substrates using $BF_2$ ions. The annealing was performed with a RTA(Rapid Thermal Annealing) and a FA(Furnace Annealing) process. The model which is used in this simulator takes into account nonequilibrium diffusion, reactions of point defects, and defect-dopant pairs considering their charge states, and the dopant inactivation by introducing a boron clustering reaction. FA+RTA annealing sequence exhibited better junction characteristics than RTA+FA thermal cycle from the viewpoint of sheet resistance and the simulator reproduced experimental data successfully. Therefore, proposed diffusion simulator and FA+RTA annealing method was able to applied to shallow junction formation for thermal budget. process.

• Computers and Concrete
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• v.5 no.6
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• pp.559-572
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• 2008

The effects of fly ash and superplasticizer (SP) on workability of concrete are quite difficult to predict because they are dependent on other concrete ingredients. Because of high complexity of the relations between workability and concrete compositions, conventional regression analysis could be not sufficient to build an accurate model. In this study, a workability model has been built using artificial neural networks (ANN). In this model, the workability is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, coarse aggregate, and fine aggregate. The effects of water/binder ratio (w/b), fly ash-binder ratio (fa/b), superplasticizer-binder ratio (SP/b), and water content on slump were explored by the trained ANN. This study led to the following conclusions: (1) ANN can build a more accurate workability model than polynomial regression. (2) Although the water content and SP/b were kept constant, a change in w/b and fa/b had a distinct effect on the workability properties. (3) An increasing content of fly ash decreased the workability, while raised the slump upper limit that can be obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.259-262
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• 1997

In this paper, a fuzzy model based on the polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. The new algorithm uses PNN algorithm based on Group Method of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy anhd feasibility than other works achieved previously.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.138-143
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• 2006

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our model's output performance using the Box and Jenkins's gas furnace data and Sugeno's non-linear process data.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.119-127
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• 2006

Granulated Blast Furnace Slag (GBFS) is produced in the manufacture process of pig-iron and shows a similar particle formation to that of natural sea sand and also shows light weight, high shear strength, well permeability, and especially has a latent hydraulic property by which GBFS is solidified with time. Therefore, when GBFS is used as a backfill material of quay or retaining walls, the increase of shear strength induced by the hardening is presumed to reduce the earth pressure and consequently the construction cost of harbor structures decreases. In this study, using the model sand box (50 cm$\times$50 cm$\times$100 cm), the model wall tests were carried out on GBFS and Toyoura standard sand, in which the resultant earth pressure, a wall friction and the earth pressure distribution at the movable wall surface were measured. In the tests, the relative density was set as Dr=25, 55 and 70% and the wall was rotated at the bottom to the active earth pressure side and followed by the passive side. The maximum horizontal displacement at the top of the wall was set as ${\pm}2mm$. By these model test results, it is clarified that the resultant earth pressure obtained by using GBFS is smaller than that of Toyoura sand, especially in the active-earth pressure.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Electronics and Telecommunications Trends
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• v.13 no.3 s.51
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• pp.71-83
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• 1998

반도체 제조용 웨이퍼의 온도를 측정하고 제어하는 기술의 진보로 열처리 장비 시장에서 점점 더 각광을 받고 있는 급속 열처리(rapid thermal process: RTP) 장치의 최근 기술 동향을 전반적으로 조사 분석하였다. RTP의 장점, 온도 제어 모델링 기술(model-based control), 최근에 개발된 여러 종류의 RTP 시스템 설계 및 이들 각각의 기술적인 문제들이 기술된다. 새롭게 개발된 단일 wafer furnace와 광자 효과를 이용한 rapid photothermal process (RPP)에 관해서도 기술하였다. 아울러 최근 열처리 장비 업체들의 현황과 열처리 장비 시장의 향후 전망에 관해서도 검토하였다.