• International Journal of Control, Automation, and Systems
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• 제1권3호
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Journal of Microbiology and Biotechnology
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• 제3권2호
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• pp.65-72
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• 1993

A heterologous gene expression and secretion system using a methylotrophic yeast, Hansenula polymorpha was developed for the production of anticoagulant hirudin. Hirudin gene was expressed under the control of a strong and inducible methanol oxidase (MOX or AOX) promoter. The mating factor a pre-pro leader sequence of Saccharomyces cerevisiae was employed for hirudin to be secreted into the extracellular medium. Hirudin expression cassette was introduced into three strains of H. polymorpha, A16, HPBl and DLl which have different genetic backgrounds. This expression cassette was stably integrated into the host chromosomal DNA. Biologically active and mature hirudin was efficiently expressed and secreted into the extracellular medium. About 19 mg/L of hirudin was found in the culture supernatant in the case of a two-copy integrant of the strain HPBl under suboptimal culture conditions.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 1999년도 봄 학술발표회 논문집
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• pp.331-338
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• 1999

In the present study, a searching technique for critical slip surface in two dimensional slope stability analysis is proposed. The failure surface generation and analysis has been usually limited to simple geometric shapes. However, more random surfaces need to be examined for some particular ground conditions. For this purpose, random searching technique is developed using genetic algorithm. The generalized limit equilibrium method is employed as the method of stability analysis. Using this technique, the factor of safety is compared with the result by using simplified Bishop's method. In addition, the convergent trend of fitness value is analyzed.

• Asian-Australasian Journal of Animal Sciences
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• 제27권4호
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• pp.574-579
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• 2014

A lysozyme gene from breast of Tibetan sheep was successfully expressed by secretion using a-factor signal sequence in the methylotrophic yeast, Pichia pastoris GS115. An expression yield and specific activity greater than 500 mg/L and 4,000 U/mg was obtained. Results at optimal pH and temperature showed recombinant lysozyme has higher lytic activity at pH 6.5 and $45^{\circ}C$. This study demonstrates the successful expression of recombinant lysozyme using the eukaryotic host organism P. pastoris paving the way for protein engineering. Additionally, this study shows the feasibility of subsequent industrial manufacture of the enzyme with this expression system together with a high purity scheme for easy high-yield purification.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1305-1308
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• 2003

A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to as a GA-BPNN. The GA played a role of controlling training factors simultaneously. The training factors to be optimized are the hidden neuron, training tolerance, initial weight magnitude, and two gradients of bipolar sigmoid and linear functions. Each etch response was optimized separately. The proposed scheme was evaluated with a set of experimental plasma etch data. The etch process was characterized by a $2^3$ full factorial experiment. The etch responses modeled are aluminum (A1) etch rate, silica profile angle, A1 selectivity, and dc bias. Additional test data were prepared to evaluate model appropriateness. The GA-BPNN was compared to a conventional BPNN. Compared to the BPNN, the GA-BPNN demonstrated an improvement of more than 20% for all etch responses. The improvement was significant in the case of A1 etch rate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.263-268
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• 2010

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of in dividuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become to real offspring. From this multiple offspring competition, our GA rarel falls into the premature convergence and easily gets out of the local optimum areas without negative effects. This makes our GA fast evolve to the global optimum. Experimental results with four function optimization problems showed that our method was superior to the original GA and had similar performances to the best ones of queen-bee GA with best parameters.

• 한국작물학회지
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• 제52권2호
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• pp.123-128
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• 2007

Demand for high quality rice has always been a major factor in the international rice marketing. In the present study, doubled haploid (DH) population derived from anther culture of a Tongil/japonica hybrid was used for genetic analysis of rice grain quality. The average values of DH lines for grain weight, grain length and the ratio of grain length to width were near the mid-parent value. More than 40% DH lines showed transgressive segregation for grain weight, length, amylose and lipid content, but less than 10% DH lines observed on ratio of length to width and grain thickness were transgressive segregation. Correlation analysis between appearance qualities and physicochemical characters indicated that grain width and grain thickness both significantly and negatively correlated to protein and lipid content. A highly significant negative correlation between protein content and amylose content was observed.

• Structural Engineering and Mechanics
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• 제19권1호
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• pp.21-42
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• 2005

A method is presented to detect and assess the structural damage from changes in natural frequencies using Genetic Algorithm (GA). Using the natural frequencies of the structure, it is possible to formulate the inverse problem in optimization terms and then to utilize a solution procedure employing GA to assess the damages. The technique has been applied to a cantilever beam and a plane frame, each one with different damage scenario to study the efficiency of the developed algorithm. A laboratory tested data has been used to verify the proposed algorithm. The study indicates the potentiality of the developed code to solve a wide range of inverse identification problems in a systematic way. The outcomes show that this method can detect and estimate the amount of damages with satisfactory precision.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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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.