• Journal of Korea Water Resources Association
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• v.46 no.3
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• pp.267-276
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• 2013

An Improvement of channel structure for sound fish habitat suitability enhancement is investigated. The restoration target species of Zacco Koreanus which is represented a sound aquatic ecosystem is selected by investigating results of the fish fauna and the river environments. The changes of lower channel width for enhancing suitability indices for water velocity and depth result in habitat suitability enhancement in general by PHABSIM simulation. The habitat suitability enhancement is not guaranteed consistently by the changes of lower channel is confirmed. The optimal lower channel widths at each reach are presented by genetic algorithm of optimization which is considering the enhancement of suitability indices for water velocity and depth with given instream flows. The suggested plan of the lower channel modification will contribute to the various projects for the environmental improvement of aquatic system.

• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.303-316
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• 2001

In this study, Radial Basis Function(RBF) Neural Networks Model, a kind of Hybrid Neural Networks was applied to hydrological forecasting in a small watershed. RBF Neural Networks Model has four kinds of parameters in it and consists of unsupervised and supervised training patterns. And Gaussian Kernel Function(GKF) was used among many kinds of Radial Basis Functions(RBFs). K-Means clustering algorithm was applied to optimize centers and widths which ate the parameters of GKF. The parameters of RBF Neural Networks Model such as centers, widths weights and biases were determined by the training procedures of RBF Neural Networks Model. And, with these parameters the validation procedures of RBF Neural Networks Model were carried out. RBF Neural Networks Model was applied to Wi-Stream basin which is one of the IHP Representative basins in South Korea. 10 rainfall events were selected for training and validation of RBF Neural Networks Model. The results of RBF Neural Networks Model were compared with those of Elman Neural Networks(ENN) Model. ENN Model is composed of One Step Secant BackPropagation(OSSBP) and Resilient BackPropagation(RBP) algorithms. RBF Neural Networks shows better results than ENN Model. RBF Neural Networks Model spent less time for the training of model and can be easily used by the hydrologists with little background knowledge of RBF Neural Networks Model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.163-168
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• 2009

Most image segmentation methods are to represent observed feature vectors at each pixel, which are assumed as appropriated probability models. These models can be used by statistical estimating or likelihood clustering algorithms of feature vectors. EM algorithms have some calculation problems of maximum likelihood for unknown parameters from incomplete data and maximum value in post probability distribution. First, the performance is dependent upon starting positions and likelihood functions are converged on local maximum values. To solve these problems, we mixed the Gausian function and histogram at all the level values at the image, which are proposed most suitable image segmentation methods. This proposed algoritms are confirmed to classify most edges clearly and variously, which are implemented to MFC programs.

• 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.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.23-32
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• 2006

This paper describes the optimization of a language model and an acoustic model to improve speech recognition using Korean unit digits. Since the model is composed of a finite state network (FSN) with a disyllable, recognition errors of the language model were reduced by analyzing the grammatical features of Korean unit digits. Acoustic models utilize a demisyllable pair to decrease recognition errors caused by inaccurate division of a phone or monosyllable due to short pronunciation time and articulation. We have used the K-means clustering algorithm with the transformed successive state splitting in the feature level for the efficient modelling of feature of the recognition unit. As a result of experiments, 10.5% recognition rate is raised in the case of the proposed language model. The demi-syllable fair with an acoustic model increased 12.5% recognition rate and 1.5% recognition rate is improved in transformed successive state splitting.

• Atmosphere
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• v.24 no.1
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• pp.123-130
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• 2014

The automated prediction system for seasonal tropical cyclone (TC) activity is established at the National Typhoon Center of the Korea Meteorological Administration (KMA) to provide effective operation and control of the system for user who lacks knowledge of the system. For automation of the system, two procedures which include subjective decisions by user are performed in advance, and their output data are provided as input data. To provide the capability to understand the operational processes for operational user, the input and output data are summarized with each process, and the directory structure is reconstructed following KMA's standard. We introduce a user interface using namelist input parameters to effectively control operational conditions which is fixed or should be manually set in the previous version of the prediction system. To operationally use early prediction which become available through the automation, its performances are evaluated according to initial condition dates. As a result, high correlations between the observed and predicted TC counts are kept for all track clusters even though advancing the initial condition date from May to January.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.41-54
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• 2021

It is very essential to estimate the water body area using remote exploration for water resource management, analysis and prediction of water disaster damage. Hydrophysical detection using satellites has been mainly performed on large satellites equipped with optical and SAR sensors. However, due to the long repeat cycle, there is a limitation that timely utilization is impossible in the event of a disaster/disaster. With the recent active development of Micro satellites, it has served as an opportunity to overcome the limitations of time resolution centered on existing large satellites. The Micro satellites currently in active operation are ICEYE in Finland and Capella satellites in the United States, and are operated in the form of clusters for earth observation purposes. Due to clustering operation, it has a short revisit cycle and high resolution and has the advantage of being able to observe regardless of weather or day and night with the SAR sensor mounted. In this study, the operation status and characteristics of micro satellites were described, and the water area estimation technology optimized for micro SAR satellite images was applied to the Daecheong Dam basin on the Korean Peninsula. In addition, accuracy verification was performed based on the reference value of the water generated from the optical satellite Sentinel-2 satellite as a reference. In the case of the Capella satellite, the smallest difference in area was shown, and it was confirmed that all three images showed high correlation. Through the results of this study, it was confirmed that despite the low NESZ of Micro satellites, it is possible to estimate the water area, and it is believed that the limitations of water resource/water disaster monitoring using existing large SAR satellites can be overcome.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.147-160
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• 2010

The system virtualization shows interest in the consolidation of servers for the efficient utilization of system resources. There are many various researches to utilize a server machine more efficiently through the system virtualization technique, and improve performance of the virtualization software. These researches have studied with the activity to control the resource allocation of virtual machines dynamically focused on CPU, or to manage resources in the cross-machine using the migration. However, the researches of the memory management have been wholly lacking. In this respect, the use of memory is limited to allocate the memory statically to virtual machine in server consolidation. Unfortunately, the static allocation of the memory causes a great quantity of the idle memory and decreases the memory utilization. The underutilization of the memory makes other side effects such as the load of other system resources or the performance degradation of services in virtual machines. In this paper, we suggest the dynamic allocation of the memory in Xen to control the memory allocation of virtual machines for the utilization without the performance degradation. Using AR model for the prediction of the memory usage and ACO (Ant Colony Optimization) algorithm for optimizing the memory utilization, the system operates more virtual machines without the performance degradation of servers. Accordingly, we have obtained 1.4 times better utilization than the static allocation.

• Communications of Mathematical Education
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• v.12
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• pp.463-477
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• 2001

국가의 국가경쟁력은 학창시절 학생의 학력만큼 중요하다. 성인의 경우, 학력을 위한 노하우는 체험을 통해 터득할 수 있었다. 그러나 국가경쟁력에 관해서는 우물 안 내부관점을 벗어나기 힘들어 추진 방향과 제도 운영에 자칫 시행착오를 범하기 쉽다. 이는 사안에 대해 본질적인 접근보다 껍데기만을 쫓기 때문이었다. 이 현상을 분석하려면 관점과 보이지 않는 영역의 것들을 다룰 수 있는 수학적 사고법이 필요하며, 이 능력은 현 지식정보화 사회에서 매우 긴요하다. 그러나 현실은 여러 가지 이유로 수학적 사고법을 비롯한 기초학문을 위기로 몰아가고 있고, 안타깝지만 그 중심에 수학교육이 자리잡고 있다. 수학교육의 위기를 유발하는 요인으로 제도 운영에 관한 건이 있다. 제도 운영에서 한 변수의 변화, 예로 대입의 계열교차지원 허용 건, 교원임용고시에서 교과교육학 영역의 출제 건과 복수전공, 부전공 자격소지자에 대한 가산점 부여 건은 수학교육과 교육과정에 직, 간접적으로 영향을 미친다. 이 관계를 사범대학 수학교육과 현장의 사례를 통하여 조명하고, 그 문제점을 지적하고자 한다. 현 사범교육은 졸업이수학점 140학점 체제하에서 제 7차 교육과정에 따른 복수전공, 부전공 우대 정책을 펴고 있다. 수학교육과의 경우, 부전공 열풍이 불어 전공선택 과목이 3학년 1학기부터 폐강될 위기에 처해 있다. 교양교육의 고사 또는 전광교육이 예전보다 반으로 줄어들게 된 사범대학 실상에 비애감을 느끼게 된다. 이는 전문화된 교사 양성, 나아가 미래 국가경쟁력 향상에 심각한 저해 요소로 작용할 것이다. 복잡다단한 세상에서 최적화를 향한 개선 노력이 멈춰서는 안 된다. 현행 교원임용고시 운영상의 문제점을 공론화하고, 수학교육인의 중지를 모아야 할 긴박한 시점이다. 이를 계기로 교원임용고시의 운영개선과 수학교육과 교육과정을 한층 더 견실하게 하는 데에 이바지하고자 한다. 것이라면 후속연구로 이러한 가능성을 실험연구로 검증하고자 한다.toceros resting spores/Chaetoceroe vegetative cells도 80 cm 보다 상층에서는 높게 나타나 규조온도지수 분포와도 일치하는 경향을 보인다. 이상의 규조군집 분석 결과에 의하면, 홀로세의 후빙기동안 본 연구 지역인 동해 북동부에는 대마 난류의 유입이후 현재와 유사한 환경이 우세하게 발달했으나, 난류종 P. doliolus의 변화는 동해내에서 대마난류의 세기가 반복되었음을 지시하고 있다./3 수준으로 높다. 결론적으로 풍부한 화학물질들을 함유한 제주해류는 남해 및 동해의 생지화학적 과정들에 있어 상당히 중요함을 시사한다.다. 수조 상층수 중 Cu, Cd, As 농도는 모든 FW, SW수조에서 시간이 지남에 따라 일관성 있게 감소하였고, 제거속도는 Cu가 다른 원소에 비해 빨랐다. 제거속도는 FW 3개 수조 중 FW5&6에서 세 원소 모두 가장 느렸고, SW 3개 수조 중에서는 SW1&2에서 가장 빨랐다. SW와 FW간 제거속도 차이는 세 원소 모두 명확치 않았다 Cr은 FW에서 전반적으로 감소하는 경향을 보였지만 SW에서는 실험 초기에 감소하다 24시간 이후에는 증가 후 일정한 양상을 보였다. Pb은 FW에서 전반적으로 감소했지만 SW에서는 초기에 급격히 증가 후 다시 급격히 감소하는 양상을 보였다 Pb 또한 Cu, Cd, As와 마찬가지로 SW1&2에서 제거속도가 가장 빠르게 나타났다. FW 상층수 중 Hg는 시간에 따라 급격히 감소했고, 제거속도는 Fw5&6에서 가장 느렸다. 이러한 결과에 근거할 때 벼가 자라고 있고 이분해성 유기물이 풍부한 FW1&2, FW3&4 토양과 상층수에서는 유기물의 분해 활동이 활발하였지만, 벼가 경작되지 않는 FW5&6과 SW 에서는 유기물이 상대적으로 결핍되어 유기물의 분해활동이 적었을 것으로 판단된다

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.49-60
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• 2019

With the appearance of self-driving cars and electric cars, the automobile industry is rapidly changing. In the midst of these changes, HMI studies are becoming more important as to how the driver obtains safety and convenience with controlling the vehicle. This study sought to understand how automobile manufacturers understand the driving situation, and how they define and limit driver interaction. For this, prior studies about HMI were reviewed and 15 participants performed an on-road study to drive vehicles from five manufacturers with using their interfaces. The results of the study confirmed that buttons and switches that are easily controlled by the user while driving were different from manufacturer to manufacturer. And there are some buttons that are more intensively controlled and others that are difficult to control while driving. It was able to derive 'selection and concentration' from Audi's vehicle, 'optimization of the driving ' from BMW's, 'simple and minimize' from Benz's vehicle, 'remove the manual distraction' from the vehicle of Lexus, and 'visual stability' from KIA's vehicle as the distinctive keywords for the HMI. This shows that each manufacturer has a different definition and interpretation of the driver's driving control area. This study has a distinct value in that it has identified the characteristics of vehicle-specific HMI in actual driving conditions, which is not apparent in appearance. It is expected that this research approach can be useful to see differences in interaction through actual driving despite changes in driving environment such as vehicle platooning and self-driving technology.