• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.387-388
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• 2020

As the utilization of microservice architectural style in diverse applications are increasing, the microservice deployment cost became a concern for many companies. We propose an approach to reduce the deployment cost by generating an algorithm which minimizes the cost of basic operation of a physical machine and the cost of resources assigned to a physical machine. This algorithm will produce optimal resource allocation and deployment location based on genetic algorithm process.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.05a
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• pp.106-110
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• 2004

In any large heavy industry like that of ship building, there exist a lot of complications for the arrangement of building blocks optimally for the minimal space consumption. The major problem arises at yard because of laxity in space for arranging the building blocks of ship under construction. A standardized erection sequence diagram is generally available to provide the prioritised erection sequence. This erection sequence diagram serves as the frame work. In order to make a timely erection of the blocks a post plan has to be developed so that the blocks lie in the nearest possible vicinity of the material handling devices while keeping the priority of erection. Therefore, the blocks are arranged in the pre-erection area. This kind of readiness of blocks leads to a very complex problem of space. This arises due to the least available space leading to an urgent need of an availability of intelligent spatial schedule without compromising the rate of production. There exists two critical problems ahead namely, the spatial occupation layout of pre-erection area and the emptying pattern in the spatial vicinity. The block shape is assumed be rectangular. The related input data's are the dates of erection (earliest as well as the latest), geometrical parameters of block available on pre-erection area, slack time and the like.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.2 s.109
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• pp.48-59
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• 2005

The purpose of this study was to examine the ecological characteristics of herbaceous communities by systematic and scientific analysis of their structure and diversity in the Midongsan Arboretum and to offer raw data for a long-tenn monitoring study. The importance value and diversity index of species appearing in twenty plots from July to September, 2004 were analyzed and a management plan for these communities is presented. Vascular plants were represented by 60 taxa of 23 families, 51 genera, 50 species and 10 varieties. Based on the results of importance value analysis, the most dominant species was Artemisia princeps var. orientalis, followed by Setaria viridis and Erigeron canadensis. The diversity index analysis showed that plot no. 5 had the highest H' and H'_{max}$(2.0135 and 2.6391). It's species composition was comparatively more diverse and it's structure more stable than other plots. Artemisia montana and Dactylis glomerata showed the highest correlation between species. Because herbaceous communities are important biological habitats and provide important function in environmental conservation, it is important to properly preserve these communities. At the same time, in order to preserve genetic resources and improve spatial function, it may be necessary to consider removing herbaceous communities in certain areas. In the future, the relations between physicochemical soil properties and herbaceous communities should be examined and community movement should be studied.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.596-604
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• 2020

Previous studies on pipe auto-routing algorithms generally used such algorithms as A*, Dijkstra, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization, to satisfy the relevant constraints of its own field and improve the output quality. On the other hand, this study aimed to significantly improve path-finding speed by applying the Jump Point Search (JPS) algorithm, which requires lower search cost than the abovementioned algorithms, for pipe routing. The existing JPS, however, is limited to two-dimensional spaces and can only find the shortest path. Thus, it requires several improvements to be applied to pipe routing. Pipe routing is performed in a three-dimensional space, and the path of piping must be parallel to the axis to minimize its interference with other facilities. In addition, the number of elbows must be reduced to the maximum from an economic perspective, and preferred spaces in the path must also be included. The existing JPS was improved for the pipe routing problem such that it can consider the above-mentioned problem. The fast path-finding speed of the proposed algorithm was verified by comparing it with the conventional A* algorithm in terms of resolution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Korean Journal of Plant Resources
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• v.21 no.1
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• pp.41-46
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• 2008

Molecular relationship and genetic diversity of 21 wild tea collections which grown natural region in Korea were investigated based on PCR-RFLP analysis using DFR genes. Approximately 1.4kb fragment of the DFR gene from wild tea samples were successfully amplified use DFR 4+5 primer pair. On the bases of restriction fragment length polymorphism(RFLP) analysis using Hpa II and Mse I enzymes, three different band patterns shown from Hpa II enzyme and showed genetic diversity between same region wild tea group. Six kind of restriction enzyme profiles obtained from digested with restriction endonuclease Mse I and shown two kind of restriction enzyme profiles collected from same region wild tea at Ungpo. The results of RFLP analysis indicated that wild tea showed genetic diversity among different regions of tea groups, but also between same region wild tea.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.9-21
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• 2004

The current paper proposes a genetic algorithm (GA)-based segmentation method that can automatically extract and track moving objects. The proposed method mainly consists of spatial and temporal segmentation; the spatial segmentation divides each frame into regions with accurate boundaries, and the temporal segmentation divides each frame into background and foreground areas. The spatial segmentation is performed using chromosomes that evolve distributed genetic algorithms (DGAs). However, unlike standard DGAs, the chromosomes are initiated from the segmentation result of the previous frame, then only unstable chromosomes corresponding to actual moving object parts are evolved by mating operators. For the temporal segmentation, adaptive thresholding is performed based on the intensity difference between two consecutive frames. The spatial and temporal segmentation results are then combined for object extraction, and tracking is performed using the natural correspondence established by the proposed spatial segmentation method. The main advantages of the proposed method are twofold: First, proposed video segmentation method does not require any a priori information second, the proposed GA-based segmentation method enhances the search efficiency and incorporates a tracking algorithm within its own architecture. These advantages were confirmed by experiments where the proposed method was success fully applied to well-known and natural video sequences.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.429-436
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• 2016

In this study, the genetic algorithm based optimal structural design method is proposed. The objective functions are to minimize the cost and $CO_2$ emissions, simultaneously. The cost and $CO_2$ emissions are calculated based on the cross-sectional dimensions, length, material strength, and reinforcement ratio of beam and column members. Thus, the cost and $CO_2$ emissions are evaluated by using the amounts of concrete and reinforcement used to construct a building. In this study, the cost and $CO_2$ emissions calculated at the phases of material transportation, construction, and building operation are excluded. The constraint conditions on the strength of beam and column members and the inter-story drift ratio are considered. The linear static analysis by using OpenSees is automatically conducted in the proposed method. The genetic algorithm is employed to solve the formulated problem. The proposed method is validated by applying it to the 4-story reinforced concrete moment frame example.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.45-51
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• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).