• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.36-43
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• 1999

This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

• Genomics & Informatics
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• v.8 no.2
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• pp.90-93
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• 2010

A-kinase-anchoring proteins (AKAPs) are scaffold proteins which compartmentalize protein kinase A (PKA, cAMP-dependent protein kinase) and other enzymes to specific subcellular sites. The spatiotemporal control of these enzymes by AKAPs is important for cellular function like cell growth and development etc. Hence, it is important to understand the basic function of AKAPs and their functional domains. However, diverse names, function, cellular localizations and many members of AKAPs increase difficulties when researchers search appropriate AKAPs for their experimental purpose. Nevertheless, there was no previous AKAPs-related database regardless of their important cellular functions and difficulty of finding appropriate AKAPs. So, we developed AKAPs database (AKAPDB), which contains their sequence information, functions and other information derived from prediction programs and other databases. Therefore, we propose that AKAPDB can be an important tool to researchers in the related fields. AKAPDB is available via the internet at http://plaza3.snu.ac.kr/akapdb/.

• Smart Structures and Systems
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• v.18 no.3
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• pp.425-448
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• 2016

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

• Journal of the Korean Society for information Management
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• v.37 no.4
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• pp.61-80
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• 2020

This study attempted to propose various application and function improvement plans by analyzing robots operated in the libraries. Thus, the types and functions of robots operated by 16 domestic and foreign libraries were examined. Most of them were used for Librarian Assistance (Book Inventory, Book Delivery, Etc.) and User Service (Facility Guide, Search Aids, Etc.). Besides, the introduction of robots in the National Library of Korea (NLK) and their functional limitations were analyzed. As a result, this study presented the need to develop additional functions for the robot, develop quarantine and security robots, the need for a national-level policy for robot diffusion, and build a robot ecosystem.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.133-137
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• 2023

In this paper, we developed a 'comprehensive pet management application' that combines pet AI diagnosis, animal hospital search, smart household accounts, and community functions. The application can solve the inconvenience of users who have to use multiple functions as separate applications, and can easily use pet AI diagnosis services through photos, provides animal hospital information using crawling, finds nearby animal hospitals, and supports smart households that can scan receipts using OCR text extraction techniques. By using this application, information necessary for raising pets such as health and consumption details of pets can be managed in one system.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.45-51
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• 2011

This paper proposes a method to accelerate the evolution speed of individuals through hybrid reproduction of monogenesis and gamogenesis. Monogenesis as a reproduction method that bacteria or monad without sexual distinction divide into two individuals has an advantage for local search and gamogenesis as a reproduction method that individuals with sexual distinction mate and breed the offsprings has an advantages for keeping the diversity of individuals. These properties can be properly used for improvement of evolution speed of individuals in genetic algorithms. In this paper, we made relatively good individuals among selected parents to do monogenesis for local search and forced relatively bad individuals among selected parents to do gamogenesis for global search by increasing the diversity of chromosomes. The mutation probability for monogenesis was set to a lower value than that of original genetic algorithm for local search and the mutation probability for gamogenesis was set to a higher value than that of original genetic algorithm for global search. Experimental results with four function optimization problems showed that the performances of three functions were very good, but the performances of fourth function with distributed global optima were not good. This was because distributed global optima prevented individuals from steady evolution.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.225-230
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• 2017

The Sijo provides dynamic rated therapeutic activities in our life. This study aims to search for the literary therapeutic function secreted from the Sijo that ends with a noun. As a result, the noun used at the final sentence secretes a predicative function. This kind of Sijo functions as twelve sound steps, even though it is condensed of just eleven sound steps with one sound step omitted. This functional secretion of Sijo is therapeutic predicate concerned with encoding of literary therapy. Thus it become possible to activate the therapeutic encoding in Sijo or a language by uttering only noun, instead of the predicate. That's because the noun in the last sentence of Sijo permeated in the human body and is done subject, and neuron of the body becomes a predicate, so that the Sijo's subject and the neuron's predicate are fused into a sentence. During the course the human body seems to recognize that the neuron's nucleus analyzes the information of the noun and makes a new sentence. This recognition might also be regarded as a process of encoding that has therapeutic functions secreted from the human body.

• Journal of the Korean Society for information Management
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• v.16 no.2
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• pp.105-134
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• 1999

For efficient and precise searches in the Web environment, resources should be coded in a structured way. HTML does not cover semantic structure because of its fixed tagging. XML, which has emerged as an alternative standard markuplanguage, uses custom tags that allow structural searching. Therefore, this study aims to compare XML with HTML in terms of their characteristics and retrieval functions. In order to test retrieval functions of XML- and HTML-based systems, we constructed an experimental XML-based system. The XML-based system has several advantages over the HTML system. However, some improvements are needed to make the XML system more comprehensive and effective. First, XML document search engines with user-friendly interfaces are needed. Second, popular Web browsers such as Explorer and Communicator need to support XML 1.0 specification completely. Third, Open DTD format, which will allow information retrieval systems to retrieve documents and compress them into one single format, is also needed to control Web documents more efficiently.