• 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.

• Korean Journal of Hydrosciences
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• 제5권
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• pp.1-16
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• 1994

In this paper distributed models for simulating spatially and temporally varied moving storm in a watershed were developed. The complete simulation in a watershed is achieved through two sequential flow simulations which are overland flow simulation and channel network flow simulation. Two dimensional continuity equation and momentum equation of kinematic approximation were used in the overland flow simulation. On the other hand, in the channel network simulation two types of governing equations which are one dimensional continuity and momentum equations between two adjacent sections in a channel, and continuity and energy equations at a channel junction were applied. The finite difference formulations were used in the channel network model. Macks Creek Experimental Watershed in Idaho, USA was selected as a target watershed and the moving storm on August 23, 1965, which continued from 3:30 P.M. to 5:30 P.M., was utilized. The rainfall intensity fo the moving storm in the watershed was temporally varied and the storm was continuously moved from one place to the other place in a watershed. Furthermore, runoff parameters, which are soil types, vegetation coverages, overland plane slopes, channel bed slopes and so on, are spatially varied. The good agreement between the hydrograph simulated using distributed models and the hydrograph observed by ARS are Shown. Also, the conservations of mass between upstreams and downstreams at channel junctions are well indicated and the wpatial and temporal vaiability in a watershed is well simulated using suggested distributed models.

• 한국전자통신학회논문지
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• 제8권5호
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• pp.791-796
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• 2013

뇌파 건강관리 시스템의 태동은 산업과 연구분야에서 요즘 중요한 쟁점으로 여겨지고 있다. 실시간으로 간질병이나 뇌경색의 환자들의 의료응급서비스를 지원하기 위해서는 EEG신호 감지가 필수적이다. 이러한 시스템을 위하여 효과적인 네트워크를 지원하는 것이 필수적이기 때문에 센서노드 기반의 무선통신 토폴로지를 제안하며 시뮬레이트한다. 마지막으로 이러한 네트워크의 효과적인 토폴로지를 위하여 옵넷 시뮬레이터의 결과를 평가한다.

• 한국정보통신학회논문지
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• 제6권3호
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• pp.487-492
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• 2002

본 논문에서는 동적신경회로망을 이용한 미지의 비선형 시스템 제어 방식을 제안하였다. 제안한 방식은 비선형 시스템의 상태 공간 모델과 유사한 형태의 신경회로망을 구성하여 비선형 시스템을 식별하고, 식별한 정보를 이용하여 제어기를 설계하는 방식이다. 제안한 방식의 유용성을 확인하기 위하여 단일 관절 매니플레이터를 대상으로 시뮬레이션을 수행한 결과 우수한 제어 성능을 확인하였다.

• 전자공학회논문지B
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• 제33B권2호
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• pp.140-148
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• 1996

This paper proposes a combined feedforward/feedback controller which uses jacobians of neural network. The jacobians are calculated form the neural network that identifies the nonlinear plant, which are used for designing a jacobian controller and for training a neural network controller. Normally, it takes much time to train the neural network controller. Combining the neural and the jacobian controller, it can be a stable controller from the beginning of training phase of neural network, and it can be implemented as a learning-while-functioning controller. Simulated resutls for the proposed controller show its effectiveness and better performances.

• 대한전자공학회논문지SD
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• 제45권6호
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• pp.16-21
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• 2008

본 논문은 새롭게 제안된 단일전자 소자(single-electron device) 및 회로를 이용한 새로운 형태의 홉필드 신경회로망(Hopfield neural network)을 소개한다. 홉필드 신경회로망의 전기적 모델 내부에서 가변저항으로 사용되는 단일전자 시냅스(single-electron synapse)와 비선형 활성함수(nonlinear activation function)로 사용되는 두 단의 단일전자 인버터(single-electron inverter)를 몬테-칼로(Monte-Carlo) 방식의 단일전자 회로 시뮬레이터로 동작을 검증한다.

• 산업경영시스템학회지
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• 제30권1호
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• pp.82-88
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• 2007

This paper considers a topological optimization of a computer network design with a cost constraint. The objective is to find the topological layout of links, at maximal reliability, under the constraint that the network cost is less or equal than a given level of budget. This problem is known to be NP-hard. To efficiently solve the problem, a genetic approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a simulated annealing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.379-394
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• 2012

The ever-increasing demand for H.264 scalable video coding (H.264/SVC) distribution motivates researchers to devise ways to enhance the quality of video delivered on the Internet. Furthermore, researchers and practitioners in general depend on computer simulators to analyze or evaluate their designed network architecture or proposed protocols. Therefore, a complete toolset, which is called myEvalSVC, for evaluating the delivered quality of H.264/SVC transmissions in a simulated environment is proposed to help the network and video coding research communities. The toolset is based on the H.264 Scalable Video coding streaming Evaluation Framework (SVEF) and extended to connect to the NS2 simulator. With this combination, people who work on video coding can simulate the effects of a more realistic network on video sequences resulting from their coding schemes, while people who work on network technology can evaluate the impact of real video streams on the proposed network architecture or protocols. To demonstrate the usefulness of the proposed new toolset, examples of H.264/SVC transmissions over 802.11 and 802.11e are provided.

• International Journal of Internet, Broadcasting and Communication
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• 제13권1호
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4483-4501
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• 2020

Recent findings identified the scatter and shape of MFD (macroscopic fundamental diagram) is heavily influenced by the spatial distribution of link density in a road network. This implies that the concept of MFD can be utilized to divide a heterogeneous road network with different degrees of congestion into multiple homogeneous subnetworks. Considering the actual traffic data is usually incomplete and inaccurate while most traffic partition algorithms rely on the completeness of the data, we proposed a three-step partitioned algorithm called Iso-MB (Isoperimetric algorithm - Merging - Boundary adjustment) permitting of incompletely input data in this paper. The proposed algorithm was implemented and verified in a simulated urban transportation network. The existence of well-defined MFD in each subnetwork was revealed and discussed and the selection of stop parameter in the isoperimetric algorithm was explained and dissected. The effectiveness of the approach to the missing input data was also demonstrated and elaborated.