• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.60.4-60
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• 2001

Electrical impedance tomograpy (EIT) determines the resistivity distribution inside an inhomogeneous target by means of voltage and current measurements conducted at the target boundary. In this paper, a genetic algorithm (GA) approach is proposed for the solution of the EIT image reconstruction. Results of numerical experiments of EIT solved by the GA approach are presented and compared to that obtained by the modified Newton-Raphson method. The GA approach is relatively expensive in terms of computing time and resources, and at present this limits the applicability of GA to the field of static imaging. However, the continuous and rapid growth of computing resources makes the development of real-time dynamic imaging applications based on GA´s conceivable in the near future.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.87-97
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• 2019

Data mining technology is applied to various fields because it is a technique for analyzing vast amount of data and finding useful information. In this paper, we propose a big data analysis method that uses Apriori algorithm, which is a data mining technique, to find the related factors that have negative and positive influences on school adjustment. Among Korea Child and Youth Panel Survey(KCYPS), data related to adjustment to school life and data showing parental inclinations were extracted from the data of fourth grade elementary school students, first year middle school students, and high school freshman students, respectively and we have mapped the useful association rules among them. As a result, the factors affecting school adjustment were different according to the timing of the growth process, we were able to find interesting rules by looking for connections between rules. On the other hand, the factors that positively influenced school adjustment were not significantly different from each other, and overall, they were associated with positive variables.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.73-80
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• 2023

The recent growth in the use of mobile devices has contributed to increased computing and storage requirements. Cloud computing has been used over the past decade to cater to computational and storage needs over the internet. However, the use of various mobile applications like Augmented Reality (AR), M2M Communications, V2X Communications, and the Internet of Things (IoT) led to the emergence of mobile cloud computing (MCC). All data from mobile devices is offloaded and computed on the cloud, removing all limitations incorporated with mobile devices. However, delays induced by the location of data centers led to the birth of edge computing technologies. In this paper, we discuss one of the edge computing technologies, i.e., cloudlet. Cloudlet brings the cloud close to the end-user leading to reduced delay and response time. An algorithm is proposed for scheduling tasks on cloudlet by considering VM's load. Simulation results indicate that the proposed algorithm provides 12% and 29% improvement over EMACS and QRR while balancing the load.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.2
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• pp.444-454
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• 2014

The range of optimization problem in aquaculture is very wide, resulting from the range of species, mode of operation. Quite a few studies focus marine net-cages, but studies on land based culture farm are few or no. This paper considers a allocation problem to meet production planning in land based aquaculture system. A water pool allocation model in land based aquaculture system was developed. The solution finds the value of decision variable to minimize yearly production costs that sums up the water pool usage cost and sorting cost. The model inputs were (1) the fish growth rate (2) critical standing corp (3) number of water pool (4) number of fish. The model outputs were (5) number of water pool in growing phase (6) cost of cultivation (6) optimal facility allocation(number of water pool for each growing phase). To solve the problem, an efficient heuristic algorithm based on a greedy manner is developed. Branch and bound and heuristic is evaluated through numerical examples.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.439-448
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• 2001

본 연구는 시간자료(Longitudinal data)의 분석을 위하여 Fuzzy k-means 군집분석 방법을 확장한 알고리즘을 제안한다. 이 논문에서 제안하는 군집분석방법은 각각의 개체에 대응하는 성장곡선에 Fuzzy k-means 군집분석의 알고리즘을 결합하는 것을 핵심아이디어로한다. 분석결과는 생성된 군집을 성장곡선모형으로 표현할 수 있고 또한 추정된 모형의 식을 활용하여 새로운 개체를 분류도 할수 있음을 보인다. 그리고 이 군집분석방법은 아직 자라지 않은 나이 어린 개체가 미래에 어느 군집에 속할 것인가 하는 분류와 함께 이 개체의 향후 성장상태를 예측을 하는 데에도 적용이 가능하다. 제안된 알고리즘을 원숭이(macaque)의 상악동(maxillary sinus)의 자료에 적용한 실례로 보인다.

• Transactions of Materials Processing
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• v.13 no.3
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• pp.248-252
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• 2004

The mechanical properties of polycrystalline thin-films, critical for Micro-Electro-Mechanical Systems (MEMS) components, are known to have the size effect and the scatter in the length scale of microns by the numbers of intensive investigation by experiments and simulations. So, the consideration of the microstructure is essential to cover these length scale effects. The lattice based stochastic model for the microstructure evolution is used to simulate the actual microstructure, and the fast and reliable algorithm is described in this paper. The kinetics parameters, which are the key parameters for the microstructure evolution based on the nucleation and growth mechanism, are extracted from the given micrograph of a polycrystalline material by an inverse method. And the method is verified by the comparison of the quantitative measures, the number of grains and the grain size distribution, for the actual and simulated microstructures. Finite element mesh is then generated on this lattice based microstructure by the developed code. And the statistical finite element analysis is accomplished for selected microstructure.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1188-1195
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• 2001

A numerical analysis has been carried out to investigate the influences of thermosolutal convection on the heat and mass transfer and solute segregation in crystals grown by the vertical Bridgman technique. The governing equations are solved by a finite-volume method using the power law scheme and the SIMPLE algorithm in which body-fitted coordinate system has been used. A primary convective cell driven by thermal gradients forms in the bulk of the domain, while a secondary convective cell driven by solutal gradients forms near interface. As the solutal Rayleigh number increases, secondary cell becomes to be stronger and has a great influence on the radial concentration along the interface.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.16-22
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• 2009

The inflection S-shaped software reliability growth model (SRGM) proposed by Ohba(1984) is one of the most commonly used models and has been discussed by many authors. The main purpose of this paper is to estimate the parameters of Ohba's SRGM within the Bayesian framework by applying the Markov chain Monte Carlo techniques. While the maximum likelihood estimates for these parameters are well known, the Bayesian method for the inflection S-shaped SRGM have not been discussed in the literature. The proposed methods can be quite flexible depending on the choice of prior distributions for the parameters of interests. We also compare the Bayesian methods with the maximum likelihood method numerically based on the real data.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.259-261
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• 2002

데이터마이닝 분야에서 빈발항목집합 탐사에 관한 연구는 활발히 진행되어 왔지만 여전히 많은 메모리 공간과 시간을 필요로 한다. 특히 apriori 알고리즘에 기반한 방법들은 긴 패턴이 생성될수록 지수적으로 시간과 공간이 증가한다. 최근에 발표된 fp-growth는 일반적인 데이터 집합에서 우수한 성능을 보이나 희소 데이터 집합에서 효율적인 성능을 보여주지 못한다. 본 논문에서는 길이가 2인 빈발항목집합 L2에 기반한 L2-tree 구조를 제안한다. 또한 L2-tree에서 빈발항목집합을 탐사하는 L2-traverse 알고리즘을 제안한다. L2-tree는 L2를 기반으로 하기 때문에 L2가 상대적으로 적은 희소 데이터 집합 환경에서 적은 메모리 공간을 사용하게 된다. L2-traverse 알고리즘은 별도의 추출 데이터베이스를 생성하는 FP-growth와 달리 단순히 L2-tree를 오직 한번의 깊이 우선 탐사를 통해 빈발항목집합을 찾는다. 최적화 기법으로써 길이가 3인 빈발항목집합 L3가 되지 않는 L2 패턴들을 미리 제거하는 방법으로 C3-traverse 알고리즘을 제안하며 실험을 통해 기존 알고리즘과 비교 검증한다.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.675-689
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• 2021

Nowadays, cloud computing is being adopted for more organizations. However, since cloud computing has a virtualized, volatile, scalable and multi-tenancy distributed nature, it is challenging task to perform attack detection in the cloud following conventional processes. This work proposes a solution which aims to collect web server logs by using Flume and filter them through Spark Streaming in order to only consider suspicious data or data related to denial-of-service attacks and reduce the data that will be stored in Hadoop Distributed File System for posterior analysis with the frequent pattern (FP)-Growth algorithm. With the proposed system, we can address some of the difficulties in security for cloud environment, facilitating the data collection, reducing detection time and consequently enabling an almost real-time attack detection.