• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.383-386
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• 2002

In this paper, we investigated the pattern recognition performance of the numeric patterns (from 0 to 9) using DNA coding method. The pattern recognition performance of the DNA coding method is compared to the that of the GA(Genetic Algorithm). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by A(Adenine), C(Cytosine), G(Guanine) and T(Thymine), The pattern recognition performance of GA and DNA coding method is evaluated by using the same genetic operators(crossover and mutation) and the crossover probability and mutation probability are set the same value to the both methods. The DNA coding method has better characteristics over genetic algorithms (GA). The reasons for this outstanding performance is multiple possible solution presentation in one string and variable solution string length.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.474-478
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• 1999

Genetic algorithm is an efficient search method using principles of natural selection and population genetics. In conventional genetic algorithms, however, the size of gene pool should be increased to insure a convergency. Therefore, many memory spaces and much computation time were needed. Also, since child chromosomes were generated by chromosome crossover and gene mutation, the algorithms have a complex structure. Thus, in this paper, a compact stereo matching algorithm using a population-based incremental teaming based on probability vector is proposed to reduce these problems. The PBIL method is modified for matching environment. Since the Proposed algorithm uses a probability vector and eliminates gene pool, chromosome crossover, and gene mutation, the matching algorithm is simple and the computation load is considerably reduced. Even if the characteristics of images are changed, stable outputs are obtained without the modification of the matching algorithm.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.2
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• pp.50-58
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• 1998

Genetic Algorithm (GA) consists of selection, reproduction, crossover and mutation processes and many parameters including population size, generation number, the probability of crossover (Pc) and the probability of mutation (Pm). Determining values of the parameters is found critical in the whole optimization process and a sensitivity analysis with them seems mandatory. This paper tries to demonstrate such importance of sensitivity analysis of GA using an example water supply tunnel network of the New York City. For optimization of the network with GA, Pc and Pm vary from 0.5 to 0.9 by an increment of 0.1 and from 0.01 to 0.05 by an increment of 0.01, respectively, while fixing both the population size and the generation number to 100. This sensitivity analysis results in an optimum design of 22.3879 million dollars at the values of 0.8 and 0.01 for Pc and Pm, respectively. In addition, the probability of recombination (Pr) is introduced to check its applicability in the GA optimization of water distribution network. When Pr is 0.05 with the same values of Pc and Pm as above, the optimum design costs 20.9077 million dollars. This is lower than the cost of 22.3879 million dollars for the case of not using Pr by 6.6%. These results indicate that conducting a sensitivity analysis with parameter values and using Pr are useful in the optimization of WDN.

• Journal of Biomedical Engineering Research
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• v.38 no.3
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• pp.123-128
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• 2017

Background and aims: The KCNQ1 S140G mutation involved in $I_{ks}$ channel is a typical gene mutation affecting atrial fibrillation. However, despite the possibility that the S140G gene mutation may affect not only atrial but also ventricular action potential shape and ventricular responses, there is a lack of research on the relationship between this mutation and ventricular fibrillation. Therefore, in this study, we analyzed the correlation and the influence of the KCNQ1 S140G mutant gene on ventricular fibrillation through computer simulation studies. Method: This study simulated a 3-dimensional ventricular model of the wild type(WT) and the S140G mutant conditions. It was performed by dividing into normal sinus rhythm simulation and reentrant wave propagation simulation. For the sinus rhythm, a ventricular model with Purkinje fiber was used. For the reentrant propagation simulation, a ventricular model was used to confirm the occurrence of spiral wave using S1-S2 protocol. Results: The result showed that 41% shortening of action potential duration(APD) was observed due to augmented $I_{ks}$ current in S140G mutation group. The shortened APD contributed to reduce wavelength 39% in sinus rhythm simulation. The shortened wavelength in cardiac tissue allowed re-entrant circuits to form and increased the probability of sustaining ventricular fibrillation, while ventricular electrical propagation with normal wavelength(20.8 cm in wild type) are unlikely to initiate re-entry. Conclusion: In conclusion, KCNQ1 S140G mutation can reduce the threshold of the re-entrant wave substrate in ventricular cells, increasing the spatial vulnerability of tissue and the sensitivity of the fibrillation. That is, S140G mutation can induce ventricular fibrillation easily. It means that S140G mutant can increase the risk of arrhythmias such as cardiac arrest due to heart failure.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.31-37
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• 2013

Background: Throughout Indonesia, thyroid cancer is one of the ten commonest malignancies, with papillary thyroid carcinoma (PTC) in our hospital accounting for about 60% of all thyroid nodules. Although fine needle aspiration biopsy (FNAB) is the most reliable diagnostic tool, some nodules are diagnosed as indeterminate and second surgery is common for PTC. The aim of this study was to establish the diagnostic value and feasibility of testing the BRAF T1799A mutation on FNA specimens for improving PTC diagnosis. Materials and Methods: This prospective study enrolled 95 patients with thyroid nodules and future surgery planned. Results of mutational status were compared with surgical pathology diagnosis. Results: Of the 70 cases included in the final analysis, 62.8% were PTC and the prevalence of BRAF mutation was 38.6%. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for BRAF mutation analysis were 36%, 100%, 100% and 48%, respectively. With other data findings, nodules with "onset less than 5 year" and "hard consistency" were proven as diagnostic determinants for BRAF mutation with a probability of 62.5%. This mutation was also a significant risk factor for extra-capsular extension. Conclusions: Molecular analysis of the BRAF T1799A mutation in FNAB specimens has high specificity and positive predictive value for PTC. It could be used in the selective patients with clinical characteristics to facilitate PTC diagnosis and for guidance regarding extent of thyroidectomy.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.18-21
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• 1995

In this paper we describe a new method for multimodal function optimization using genetic algorithms(GAs). We propose adaptation rules for GA parameters such as population size, crossover probability and mutation probability. In the self organizing genetic algorithm(SOGA), SOGA parameters change according to the adaptation rules. Thus, we do not have to set the parameters manually. We discuss about SOGA and those of other approaches for adapting operator probabilities in GAs. The validity of the proposed algorithm will be verified in a simulation example of system identification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3163-3181
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• 2023

Eavesdropping attacks have seriously threatened network security. Attackers could eavesdrop on target nodes and link to steal confidential data. In the traditional network architecture, the static routing path and the important nodes determined by the nature of network topology provide a great convenience for eavesdropping attacks. To resist monitoring attacks, this paper proposes a random routing mutation defense method based on dynamic path weight (DPW-RRM). It utilizes network centrality indicators to determine important nodes in the network topology and reduces the probability of important nodes in path selection, thereby distributing traffic to multiple communication paths, achieving the purpose of increasing the difficulty and cost of eavesdropping attacks. In addition, it dynamically adjusts the weight of the routing path through network state constraints to avoid link congestion and improve the availability of routing mutation. Experimental data shows that DPW-RRM could not only guarantee the normal algorithmic overhead, communication delay, and CPU load of the network, but also effectively resist eavesdropping attacks.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.54-61
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• 2012

This paper proposes a fusion method of the queen-bee evolution into the rank-based control of mutation probability for improving the performances of genetic algorithms. The rank-based control of mutation probability which showed some performance improvements than the original method was a method that prevented individuals of genetic algorithms from falling into local optimum areas and also made it possible for the individuals to get out of the local optimum areas if they fell into there. This method, however, showed not good performances at the optimization problems that had a global optimum located in a small area regardless of the number of local optimum areas. We think that this is because the method is insufficient in the convergence into the global optimum, so propose a fusion method of the queen-bee evolution into this method in this paper. The queen-bee evolution inspired by reproduction process of queen-bee is a method that can strengthen the convergency of genetic algorithms. From the extensive experiments with four function optimization problems in order to measure the performances of proposed method we could find that the performances of proposed method was considerably good at the optimization problems whose global optimum is located in a small area as we expected. Our method, however, showed not good performances at the problems whose global optima were distributed in broad ranges and even showed bad performances at the problems whose global optima were located far away. These results indicate that our method can be effectively used at the problems whose global optimum is located in a small area.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.121-126
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• 2013

Background: Significance of HPV infection and genic mutation of APC and K-ras in rectal cancer has been investigated but not clarified. The objective of our study was to investigate these parameters in patients with rectal cancer to analyze correlations with biological behaviour, to determine relationships among the three, and also to demonstrate survival prognosis effects. Methods: From December 2007 to September 2008, 75 rectal cancer cases confirmed by histopathology in the Tumor Hospital of Xinjiang Medical University were enrolled. The control group consisted of normal rectal mucous membrane taken simultaneously, a least 10 cm distant from the carcinoma fringe. HPV DNA, the MCR of APC and exon-1 of K-ras were detected by PCR and PCR-SSCP. All results were analyzed in relation to clinical pathological material, using chi-square and correlation analysis via SPSS.13 and Fisher's Exact Probability via STATA. 9.0. All 75 patients were followed up for survival analysis using Kaplan-Meier and Log-rank tests. Results: 55 out of 75 cases demonstrated gene HPV L1 while it was notdetected in normal rectal mucosa tissue. HPV infection was correlated with age and lymphatic metastasis (P<0.05) but not other characteristics, such as ethnicity, tumor size, histological type, tumor type, Duke's stage and infiltration depth. Some 43 cases exhibited APC genic mutation (57.3%) and 34 K-ras genic mutation (45.3%). APC genic mutation was correlated with gender(P<0.05), but not age, histological type, infiltration depth, lymphatic metastasis and Duke's stage. In 55 cases of rectal cancer with HPV infection, there were 31 cases with genic mutation of APC (56.4%) and 24 with genic mutation of K-ras (43.6%). For the 20 cases of rectal cancer with non-HPV infection, the figures were 12 cases (60%) and 10 (50.0%), respectively, with no significant relation. Survival analysis showed no statistical significance for K-ras genic mutation, APC genic mutation or HPV infection (P>0.05). However, the survival time of the patients with HPV infection was a little shorter than in cases without HPV infection. Conclusions: Our results suggest that HPV infection might be an important factor to bring about malignant phenotype of rectal cancer and influence prognosis. Genic mutation of APC and K-ras might be common early molecular events of rectal cancer, but without prognostic effects on medium-term or early stage patients with rectal cancer.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.217-222
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• 2011

Genetic algorithms are one of the most important methods used to solve the Traveling Salesman Problem. Therefore, many researchers have tried to improve the Genetic Algorithm by using different methods and operations in order to find the optimal solution within reasonable time. This paper intends to find a new approach that adopts an improved genetic algorithm to solve the Traveling Salesman Problem, and compare with the well known heuristic method, namely, Kohonen Self-Organizing Map by using different data sets of symmetric TSP from TSPLIB. In order to improve the search process for the optimal solution, the proposed approach consists of three strategies: two separate tour segments sets, the improved crossover operator, and the improved mutation operator. The two separate tour segments sets are construction heuristic which produces tour of the first generation with low cost. The improved crossover operator finds the candidate fine tour segments in parents and preserves them for descendants. The mutation operator is an operator which can optimize a chromosome with mutation successfully by altering the mutation probability dynamically. The two improved operators can be used to avoid the premature convergence. Simulation experiments are executed to investigate the quality of the solution and convergence speed by using a representative set of test problems taken from TSPLIB. The results of a comparison between the new approach using the improved genetic algorithm and the Kohonen Self-Organizing Map show that the new approach yields better results for problems up to 200 cities.