• 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.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.1007-1014
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• 2015

An optimally designed six-phase induction motor (6PIM) is compared with an initial design induction motor having the same ratings. The Genetic Algorithm (GA) method is used for optimization and multi objective function is considered. Comparison of the optimum design with the initial design reveals that better performance can be obtained by a simple optimization method. Also in this paper each design of 6PIM, is simulated by MAXWELL_2D. The obtained simulation results are compared in order to find the most suitable solution for the specified application, considering the influence of each design upon the motor performance. Construction a 6PIM based on the information obtained from GA method has been done. Quality parameters of the designed motors, such as: efficiency, power losses and power factor measured and optimal design has been evaluated. Laboratory tests have proven the correctness of optimal design.

• 경영과학
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• 제25권3호
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• pp.45-57
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• 2008

This paper considers a genetic algorithm for sequencing activities and allocating resources to reduce the over all completion time of workflow in the presence resource constraints. The algorithm provides an integrated solution for two sub-problems. The first is to decide the priority for the activities which require the same resource. The other problem is to select one among available resources for each activity by considering the incurred setup time and the performance factor of each resource. We evaluate the algorithm performance for three different kinds of workflows including parallel structures. Computational results show that the proposed algorithm is more effective than a previous work.