• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.137-140
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• 2011

시설의 안전성 평가에 대한 연구는 안전성에 영향을 주는 데이터를 정량화하여 획일적인 자동 수행하는 안전관리가 주를 이루고 있다. 이와 달리 자율수행은 수집 된 상황 정보나 상태 데이터를 이용하여 안전성을 예측하고 사고 위험성을 경보하여 사고를 예방 할 수 있다. 본 연구에서는 다양한 시설물 중에서 가스배관의 부식에 대한 판단을 위해서 신경망의 대표적 비지도학습인 자기조직화지도를 적용한다. SOM의 적용에서는 주변효과를 보완하기 위해서 관계적관점지도로 맵을 구성한다. 학습 할 데이터는 가스배관의 방식전위이다. 배관의 부식상태를 확인하기 위하여 수집 된 데이터인 방식전위에는 부식에 대한 위험요인이 내재되어 있다. 학습 후 새로운 데이터가 입력되면 각 상태 군집의 중심뉴런과 맵핑된 뉴런의 유사도를 측정하여 배관의 부식상태를 결정한다. 제안 된 방법으로 판단 된 결과를 기존에 사람이 판단한 결과와 비교하여 검증한다. 이를 통해 배관의 부식상태를 자율적이고 신속하게 판단하여 지능화 된 가스배관 관리로 활용한다.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.45-49
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• 2022

The fourth industrial revolution, internet of things, and the expansion of online web services have increased an exponential growth and deployment in the number of cloud data centers (CDC). The cloud is emerging as new paradigm for delivering the Internet-based computing services. Due to the dynamic and non-linear workload and availability of the resources is a critical problem for efficient workload and resource management. In this paper, we propose the particle swarm optimization (PSO) based gated recurrent unit (GRU) neural network for efficient prediction the future value of the CPU and memory usage in the cloud data centers. We investigate the hyper-parameters of the GRU for better model to effectively predict the cloud resources. We use the Google Cluster traces to evaluate the aforementioned PSO-GRU prediction. The experimental shows the effectiveness of the proposed algorithm.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.105-115
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• 2013

Increasing the river cross-section by barrage construction causes rises in the average river water levels and discharge rates in the rainy season. The time series patterns for groundwater levels measured at 23 riverside monitoring wells along the lower Nakdong River are compared for two cases: before and after water-filling at the Changnyeong-Haman Barrage. Monthly average groundwater levels indicate a distinct increase in groundwater levels in the upstream riverside close to the barrage. River-water level management by barrage gate control in August, during the rainy season, resulted in a 0.1 m decrease in groundwater levels, while water-filling at the barrage in December caused a 1.3 m increase in groundwater levels. The results of hierarchical cluster analysis indicate that seven groundwater monitoring wells and river water levels were in the same group before barrage construction, but that this number increased to 14 after barrage construction. Principal component analysis revealed that the explanation power of two principal components corresponding to river fluctuation, PC1 and PC2, was approximately 82% before barrage construction but decreased to 45% after construction. This finding indicates that the effect of the river level component that contributes to change in groundwater level, decreases after barrage construction; consequently, other factors, including groundwater pumping, become more important. Continuous surveying and monitoring is essential for understanding change in the hydrological environment. Water policy that takes groundwater-surface water interaction into consideration should be established for riverside areas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.415-424
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• 2009

As one of the unsupervised learning neural network methods, self-organizing maps(SOM) are applied to various fields. It reduces the dimension of multidimensional data by representing observations on the low dimensional manifold. On the other hand, the minimal spanning tree(MST) of a graph that achieves the most economic subset of edges connecting all components by a single open loop. In this study, we apply the MST technique to SOM with subnodes. We propose SOM's with embedded MST and a distance measure for optimum choice of the size and shape of the map. We demonstrate the method with Fisher's Iris data and a real gene expression data. Simulated data sets are also analyzed to check the validity of the proposed method.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.471-476
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• 2004

Recently, provides information which is most necessary to the user the research against the web information recommendation system for the Internet shopping mall is actively being advanced. the back which it will drive in the object. In that Dynamic Web Recommendation Method Using SOM (Self-Organizing Feature Maps) has the advantages of speedy execution and simplicity but has the weak points such as the lack of explanation on models and fired weight values for each node of the output layer on the established model. The method proposed in this study solves the lack of explanation using the Bayesian reasoning method. It does not give fixed weight values for each node of the output layer. Instead, the distribution includes weight using Hybrid SOM. This study designs and implements Dynamic Web Recommendation Method Using Hybrid SOM. The result of the existing Web Information recommendation methods has proved that this study's method is an excellent solution.

• Journal of KIISE
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• v.44 no.1
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• pp.51-56
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• 2017

The research area for solving question answering (QA) problems using artificial intelligence models is in a methodological transition period, and one such architecture, the dynamic memory network (DMN), is drawing attention for two key attributes: its attention mechanism defined by neural network operations and its modular architecture imitating cognition processes during QA of human. In this paper, we increased accuracy of the inferred answers, by adapting an automatic data augmentation method for lacking amount of training data, and by improving the ability of time perception. The experimental results showed that in the 1K-bAbI tasks, the modified DMN achieves 89.21% accuracy and passes twelve tasks which is 13.58% higher with passing four more tasks, as compared with one implementation of DMN. Additionally, DMN's word embedding vectors form strong clusters after training. Moreover, the number of episodic passes and that of supporting facts shows direct correlation, which affects the performance significantly.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.382-382
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• 2011

최근의 극심한 기상이변으로 인하여 발생되는 유출량의 예측에 관한 사항은 치수 이수는 물론 방재의 측면에서도 역시 매우 중요한 관심사로 부각되고 있다. 강우-유출 관계는 유역의 수많은 시 공간적 변수들에 의해 영향을 받기 때문에 매우 복잡하여 예측하기 힘든 요소이며, 과거에는 추계학적 예측모형이나 확정론적 예측모형 혹은 경험적 모형 등을 사용하여 유출량을 예측하였으나 최근에는 인공신경망과 퍼지모형 그리고 유전자 알고리즘과 같은 인공지능기반의 모형들이 많이 사용되고 있다. 하지만 유출량을 예측하고자 할 때 학습자료 및 검정자료로써 사용되는 유출량은 수위-유량 관계곡선식으로부터 구하는 경우가 대부분으로 이는 이렇게 유도된 유출량의 경우 오차가 크기 때문에 그 신뢰성에 문제가 있을 것으로 판단된다. 따라서 본 논문에서는 수위를 직접 예측함으로써 이러한 오차의 문제점을 극복 하고자 한다. Neuro-Fuzzy 모형은 과거자료의 입 출력 패턴에서 정보를 추출하여 지식으로 보유하고, 이를 근거로 새로운 상황에 대한 해답을 제시하도록 하는 인공지능분야의 학습기법으로 인간이 과거의 경험과 훈련으로 지식을 축적하듯이 시스템의 입 출력에 의하여 소속함수를 최적화함으로서 모형의 구조를 스스로 조직화한다. 따라서 수학적 알고리즘의 적용이 어려운 강우와 유출관계를 하천유역이라는 시스템에서 발생된 신호체계의 입 출력패턴으로 간주하고 인간의 사고과정을 근거로 추론과정을 거쳐 수문계의 예측에 적용할 수 있을 것이다. 유전자 알고리즘은 적자생존의 생물학 원리에 바탕을 둔 최적화 기법중의 하나로 자연계의 생명체 중 환경에 잘 적응한 개체가 좀 더 많은 자손을 남길 수 있다는 자연선택 과정과 유전자의 변화를 통해서 좋은 방향으로 발전해 나간다는 자연 진화의 과정인 자연계의 유전자 메커니즘에 바탕을 둔 탐색 알고리즘이다. 즉, 자연계의 유전과 진화 메커니즘을 공학적으로 모델화함으로써 잠재적인 해의 후보들을 모아 군집을 형성한 뒤 서로간의 교배 혹은 변이를 통해서 최적 해를 찾는 계산 모델이다. 이러한 유전자 알고리즘은 전역 샘플링을 중심으로 한 수법으로 해 공간상에서 유전자의 개수만큼 복수의 탐색점을 설정할 뿐만 아니라 교배와 돌연변이 등으로 좁아지는 탐색점 바깥의 영역으로 탐색을 확장할 수 있기 때문에 지역해에 빠질 위험성이 크게 줄어든다. 따라서 예측과 패턴인식에 강한 뉴로퍼지 모형의 해 탐색방법을 유전자 알고리즘을 사용한다면 보다 정확한 해를 찾는 것이 가능할 것으로 판단된다. 따라서 본 논문에서는 선행우량 및 상류의 수위자료로부터 하류의 단시간 수위예측에 관해 연구하였으며, 이를 위해 유전자 알고리즘을 이용항여 소속함수를 최적화 시키는 형태의 Neuro-Fuzzy모형에 대하여 연구하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.36-41
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• 2010

An advanced form of Multistage Feature-based Classification Model(AMFCM), called AMFCM, is proposed in this paper. AMFCM like MFCM does not use the concatenated form of available feature vectors extracted from original data to classify each data, but uses only groups related to each feature vector to classify separately. The prpposed AMFCM improves the contribution rate used in MFCM and proposes a confusion table for each local classifier using a specific feature vector group. The confusion table for each local classifier contains accuracy information of each local classifier on each class of data. The proposed AMFCM is applied to the problem of music genre classification on a set of music data. The results demonstrate that the proposed AMFCM outperforms MFCM by 8% - 15% on average in terms of classification accuracy depending on the grouping algorithms used for local classifiers and the number of clusters.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.671-678
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• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.