• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 추계학술대회
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• pp.592-601
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• 2007

In the conventional double auction approaches, two basic assumptions are usually applied - (1) each trader has a linear or quasi-linear utility function of price and quantity, (2) buyers as well as sellers have identical utility functions. However, in practice, these assumptions are unrealisitc. Therefore, a flexible and integrated double auction mechanism that can integrate all traders' diverse utility functions is necessary. We propose a double auction mechanism with resource allocation based on nonlinear utility functions, namely a flexible synchronous double auction system where each participant can express a diverse utility function on the price and quantity. In order to optimize the total market utility consists of multiple complex utility functions of traders, our study proposes a genetic algorithm (GA) We show the viability of the proposed mechanism through several simulation experiments.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.219-226
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• 2018

Collaborative filtering has been most popular approach to recommend items in online recommender systems. However, collaborative filtering is known to suffer from data sparsity problem. As a simple way to overcome this problem in literature, Jaccard index has been adopted to combine with the existing similarity measures. We analyze performance of such combination in various data environments. We also find optimal weights of factors in the combination using a genetic algorithm to formulate a similarity measure. Furthermore, optimal weights are searched for each user independently, in order to reflect each user's different rating behavior. Performance of the resulting personalized similarity measure is examined using two datasets with different data characteristics. It presents overall superiority to previous measures in terms of recommendation and prediction qualities regardless of the characteristics of the data environment.

• 대한안전경영과학회지
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• 제22권4호
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• pp.45-52
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• 2020

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

• ETRI Journal
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• 제44권2호
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• pp.327-338
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• 2022

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.

• 한국정밀공학회지
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• 제13권11호
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• pp.82-91
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• 1996

In this study, we put our object to carry out adaptive modeling of cutting process in turning system, and to find out the optimal cutting conditions to maximize material removal rate under some constraints. We used a back-propagation neural network to model the cutting process adaptively and a genetic algorithm to find out optimal cutting conditions. The experimental results show that a back-propagation neural network could model the cutting process effciently, and optimized cutting conditions for maximizing the material removal rate were obtained through the adaptive process model and genetic algorithms. Therefore, the proposed approach can be applied to the real machining system.

• 한국정밀공학회지
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• 제13권4호
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• pp.143-151
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• 1996

The concept of cellular manufacturing is to decompose a manufacturing system into subsystems, which are easier to manage than the entire manufacturing system. The objective of cellular manufacturing is to group parts with similar processing requirements into part families and machines into cells which meet the processing needs of part families assigned to them. This paper presents a methodology for cell formation based on genetic algorithm which produces improved cell formation in terms of total moves, which is a weighted sum of both intercell moves and intracell moves. A sample problem is solved for two, three and four cells with an approach based on genetic algorithms.

• Journal of Genetic Medicine
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• 제20권1호
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• pp.15-24
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• 2023

Monogenic disorders are traditionally attributed to the presence of mutations in a single gene. However, recent advancements in genomics have revealed instances where the phenotypic expression of apparently monogenic disorders cannot be fully explained by mutations in a single gene alone. This review article aims to explore the emerging concept of digenic or oligogenic inheritance in seemingly monogenic disorders. We discuss the underlying mechanisms, clinical implications, and the challenges associated with deciphering the contribution of multiple genes in the development and manifestation of such disorders. We present relevant studies and highlight the importance of adopting a broader genetic approach in understanding the complex genetic architecture of these conditions.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.111-118
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• 2023

In the ever-changing landscape of finance, the fusion of artificial intelligence (AI)and pair trading strategies has captured the interest of investors and institutions alike. In the context of supervised machine learning, crafting precise and accurate labels is crucial, as it remains a top priority to empower AI models to surpass traditional pair trading methods. However, prevailing labeling techniques in the financial sector predominantly concentrate on individual assets, posing a challenge in aligning with pair trading strategies. To address this issue, we propose an inventive approach that melds the Triple Barrier Labeling technique with pair trading, optimizing the resultant labels through genetic algorithms. Rigorous backtesting on cryptocurrency datasets illustrates that our proposed labeling method excels over traditional pair trading methods and corresponding buy-and-hold strategies in both profitability and risk control. This pioneering method offers a novel perspective on trading strategies and risk management within the financial domain, laying a robust groundwork for further enhancing the precision and reliability of pair trading strategies utilizing AI models.

• Journal of Genetic Medicine
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• 제20권2호
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• pp.46-51
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• 2023

Exonic copy number variation (CNV), involving deletions and duplications at the gene's exon level, presents challenges in detection due to their variable impact on gene function. The study delves into the complexities of identifying large CNVs and investigates less familiar but recurrent exonic CNVs, notably enriched in East Asian populations. Examining specific cases like DRC1, STX16, LAMA2, and CFTR highlights the clinical implications and prevalence of exonic CNVs in diverse populations. The review addresses diagnostic challenges, particularly for single exon alterations, advocating for a strategic, multi-method approach. Diagnostic methods, including multiplex ligation-dependent probe amplification, droplet digital PCR, and CNV screening using next-generation sequencing data, are discussed, with whole genome sequencing emerging as a powerful tool. The study underscores the crucial role of ethnic considerations in understanding specific CNV prevalence and ongoing efforts to unravel subtle variations. The ultimate goal is to advance rare disease diagnosis and treatment through ethnically-specific therapeutic interventions.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.12-12
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• 2017

Genetic improvement in potato can be carried out through several approaches, as sexual crosses, somatic hybridization, mutation and genetic engineering. Although the approach is different, but the goal is the same, to get a superior cultivar. Mutation and genetic engineering are very interesting methods for genetic improvement of potato plants. Mutation by gamma-ray irradiation have been performed to get some new potato cultivars which are more resistant to disease and have higher productivity. We have carried out a mutation of some potato cultivars and obtained some excellent clones to be potentially released as new superior cultivars. By the mutation method, we have released one potato cultivar for the French fries industry, and we registered one cultivar of potato for chips, and two cultivar for vegetable potatoes. Actually we are doing multi-location trial for three clones to be released as new cultivars. Through genetic engineering, several genes have been introduced into the potato plant, and we obtained several clones of transgenic potato plants. Transgenic potato plants containing FBPase gene encoding for fructose bisphosphatase, have a higher rate of photosynthesis and higher tuber productivity than non-transgenic plants. This result suggests that FBPase plays an important role in increasing the rate of photosynthesis and potato tuber productivity. Some transgenic potatoes containing the Hd3a gene are currently being evaluated for their productivity. Over expression of the Hd3a gene is expected to increase tuber productivity and induce flowering in potatoes. Transgenic potato plants containing MmPMA gene encoding for plasma membrane ATPse are more tolerant to low pH than non-transgenic plants, indicating that plasma membrane ATPase plays an important role in the potato plant tolerance to low pH stress. Transgenic potato plants containing c-lysozyme genes, are highly tolerant of bacterial wilt diseases caused by Ralstonia solanacearum and bacterial soft rot disease caused by Pectobacterium carotovorum. Expression of c-lyzozyme gene plays an important role in increasing the resistance of potato plants to bacterial diseases.