• 한국농공학회지
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• 제35권2호
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• pp.23-32
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• 1993

This paper describes the background and the development of a hydrologic network flow model. The model was development to simulate daily water demand and supply for selected stream reaches within a watershed, and used as a tool for evaluating, simulating, and planning a water resources system. The proposed network flow model considers daily runoff from subareas, various water demands, and diversion structures within each subarea. Daily streamflow at a reach is simulated after balancing the water demands from subareas. The lateral inflow from subareas is simulated using a modified tank model. Total water demands consist of the daily demands for agricultural, domestic, industrial, livestock, fishery, and environmental uses within a rural district. The return flow, diversions from sources and storage components such as reservoirs were also incorporated into the mode l . The developed model is a generalized version that may be applied to different combinations of river reaches for a given system. This may help potential users identify areas where water supply does not suffice the demands for different time horizons.

• 대한교통학회지
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• 제20권1호
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• pp.101-110
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• 2002

본 연구는 통행배분 모형과 네트워크 로딩의 일반적인 원리 및 그 관계를 규명하고. 시간을 고려한 네트워크 로딩방법 즉, 동적네트워크 로딩 방법을 소개한다. 우선 본 연구에서는, 동적 네트워크 로딩을 올바로 구현하기 위해 인과성 (causality), FIFO(First-In-First-Out), 교통량전파(flow propagation), 교통류보존(flow conservation) 등의 조건이 만족되어야 함을 제기한다. 그리고, 구체적인 동적 네트워크 로딩 방법을 알고리즘 형식으로 설명하였으며, 이후 링크 비용함수로써 결정적 대기행렬모형을 도입하여 가상 네트워크 속에서 동적 네트워크 로딩이 어떻게 이루어지는지를 수치적으로 보여준다.

• 자원환경지질
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• 제28권5호
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• pp.503-512
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• 1995

2차원 불연속 절리 모델 (Discrete Fracture Model)을 사용하여 절리망 내에서 지하수 흐름을 시뮬레이션하였다. 불연속 절리 모델에서는 지하수가 오직 절리망을 통해서 흐른다고 가정한다. 이와 같은 분석은 결정질암 같이 지질 매체의 투수율이 매우 낮은 경우에 유용하다. 하지만 불연속 절리망을 완벽하게 구현하는 것이 불가능하므로, 이에 접근하는 방법으로 확률 통계적 모델이 제안되었다. 확률-통계적 모텔은 특성인자(밀도, 방향, 길이, 틈새두께 등)가 특별한 분포 유형을 갖는다고 가정한다. 확률-통계적 모델은 가정된 분포를 따르도록 특성인자를 생성한다. 이 후 본 모델을 통해 분석된 몇몇 특성인자를 가지고 절리망을 생성한다. 절리망을 생성한 이 후 지하수의 유통을 계산하기 위해 유한요소법을 적용하였다. 이 때 일차원 선요소가 유한요소망의 주요 요소이다. 시뮬레이션 결과는 절리망 내의 주요 흐름 경로를 통해 보여진다. 절리망 내의 지하수 속도를 비교하여 주요 흐름 경로를 찾아낸다. 본 연구에서 개발된 모델은 절리망 내의 지하수 흐름에 특성인자들이 미치는 영향을 평가할 수 있는 방법을 제공한다. 이를 위하여 30번의 생성을 하는 몬테카를로 시뮬레이션을 통해서 여러 특성 인자들이 지하수 흐름에 미치는 영향을 평가하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.2039-2043
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• 2007

Like other major river basin systems in the West of the United States the Platte River Basin are faced with the challenges of allocating more water for plant and animal species. A part of the Central Platte River was designated as critical habitat for the whooping crane in 1978. The water allocation system in the Platte River Basin is dominated by the Prior Appropriation Doctrine, which allocates water according to the priorities based on the date of water use. The Platte River Basin segregated into five subregions for purpose of analysis. 24 years of historic records of monthly flow and all the demands were complied. The simulation of river basin modeling includes physical operation of the system including water allocation by water rights and interstate compact agreements, reservoir operations, and diversion with consumptive use and return flow. MODSIM, a generalized river basin network model, was used for estimating the timing and magnitude of impacts on river flows and diversions associated with water transfers from each region. A total of 20 alternatives were considered, covering transfers from each of the five regions of basin with several options. The result shows that the timing and availability of augmented water at the critical habitat is not only a function of use by junior appropriators, but also of river losses, and timing of return flows.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Asia pacific journal of information systems
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• 제26권3호
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• Journal of Communications and Networks
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• 제14권3호
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4060-4066
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• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.382-382
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• 2020

과거 많은 연구에서 다수의 모형의 결과를 이용한 앙상블 방법론은 인공지능 모형 (artificial neural network)의 예측 능력에 향상을 갖고 온다 논하였다. 본 연구에서는 미계측유역의 저수량(low flow)의 예측을 위하여 Jittering을 기반으로 한 인공지능 모형을 제시하고자 한다. 기본적인 방법론은 설명변수들에게 백색 잡음(white noise)를 삽입하여 훈련되는 자료를 증가시키는 것이다. Jittering을 기반으로 한 인공지능 모형에 대한 효과를 검증하기 위하여 본 연구에서는 Multi-output neural network model을 기반으로 모형을 구축하였다. 다음으로 Jittering을 기반으로 한 앙상블 모형을 variable importance measuring algorithm과 결합시켜서 유역특성치와 예측되는 저수량의 특성치들의 관계를 추론하였다. 본 연구에서 사용되는 방법론들의 효용성을 평가하기 위해서 미동북부에 위치하고 있는 총 207개의 유역을 사용하였다. 결과적으로 본 연구에서 제시한 Jittering을 기반으로 한 인공지능 앙상블 모형은 단일예측모형 (single modeling approach)을 정확도 측면에서 우수한 것으로 확인되었다. 또한, 적은 숫자의 앙상블 모형에서도 그 정확성이 단일예측모형보다 우수한 것을 확인하였다. 마지막으로 본 연구에서는 유역특성치들의 효과가 살펴보고자 하는 저수량의 특성치들에 따라서 일관적으로 영향을 미치거나 그 중요도가 변화하는 것을 확인하였다.