• Water for future
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• v.27 no.1
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• pp.89-99
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• 1994

Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of ø-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.165-178
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• 2011

A gigantic ship is constructed by assembling various types of ship blocks, each block being made by cutting and piecing the steel-plates together. The steel-plate piling process as the initial stage of ship construction sorts and manages the steel-plates according to the ship blocks that the steel-plates are used to make. The steel-plate piling process poses some problems such as process delay due to piling errors, safety vulnerability due to the handling of extra heavy-weight objects, and the uncertainty of work plan due to lack of information management in the pile spaces. We constructed a steel-plate piling process system based on the context-aware computing to resolve such problems. We built simulation system that can simulate the piling process and then established a smart space within the system by using tags, sensors and a real-time location system in order to collect context information. Workers receive an appropriate or intelligent service from the system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.28-34
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• 2012

Various feature extraction algorithms are widely applied to real-time image processing applications for extracting significant features from images. Feature extraction algorithms are mostly combined with image processing algorithms mostly for image tracking and recognition. Feature extraction function is used to supply feature information to the other image processing algorithms and it is mainly implemented in a preprocessing stage. Nowadays, image processing applications are faced with embedded system implementation for a real-time processing. In order to satisfy this requirement, it is necessary to reduce execution time so as to improve the performance. Reducing the time for executing a feature extraction function dose not only extend the execution time for the other image processing algorithms, but it also helps satisfy a real-time requirement. This paper explains FAST (Feature from Accelerated Segment Test algorithm) of E. Rosten and presents FPGA-based embedded hardware accelerator architecture. The proposed acceleration scheme can be implemented by using approximately 2,217 Flip Flops, 5,034 LUTs, 2,833 Slices, and 18 Block RAMs in the Xilinx Vertex IV FPGA. In the Modelsim - based simulation result, the proposed hardware accelerator takes 3.06 ms to extract 954 features from a image with $640{\times}480$ pixels and this result shows the cost effectiveness of the propose scheme.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.465-479
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• 2020

Regardless of the domestic and foreign governments/companies, SOC (Security Operation Center) has operated 24 hours a day for the entire year to ensure the security for their IT infrastructures. However, almost all SOCs have a critical limitation by nature, caused from heavily depending on the manual analysis of human agents with the text-based monitoring architecture. Even though, in order to overcome the drawback, technologies for a comprehensive visualization against complex cyber threats have been studying, most of them are inappropriate for the security monitoring in large-scale networks. In this paper, to solve the problem, we propose a novel visual approach for intuitive threats monitoring b detecting suspicious IP address, which is an ultimate challenge in cyber security monitoring. The approach particularly makes it possible to detect, trace and analysis of suspicious IPs statistically in real-time manner. As a result, the system implemented by the proposed method is suitably applied and utilized to the real-would environment. Moreover, the usability of the approach is verified by successful detecting and analyzing various attack IPs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6274-6281
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• 2015

Radio-based train control system has driving headway shortening effect by real-time train interval control using two-way radio communication between onboard and wayside systems, and reduces facility investment because it does not require any track-circuit. Automatic train protection(ATP), the most significant part of the radio-based train control system, makes sure a safe distance between preceding and following trains, based on real-time train location tracing. In this paper, we propose the overall ATP train interval control algorithm to control the safe interval between trains, and preprocessing-based speed profile calculation algorithm to improve the processing speed of the ATP. The proposed speed profile calculation algorithm calculates the permanent speed limit for track and train in advance and uses as the most restrictive speed profile. If the temporary speed limit is generated for a particular track section, it reflects the temporary speed limit to pre-calculated speed profile and improves calculation performance by updating the speed profile for the corresponding track section. To evaluate the performance of the proposed speed profile calculation algorithm, we analyze the proposed algorithm with O-notation and we can find that it is possible to improve the time complexity than the existing one. To verify the proposed ATP train interval control algorithm, we build the train interval control simulator. The experimental results show the safe train interval control is carried out in a variety of operating conditions.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2461-2469
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• 1997

Target detection and recognition problems, in which neural networks are widely used, require translation invariant and real-time processing in addition to the requirements that general pattern recognition problems need. This paper presents a novel architecture that meets the requirements and explains effective methodology to train the network. The proposed neural network is an architectural extension of the shared-weight neural network that is composed of the feature extraction stage followed by the pattern recognition stage. Its feature extraction stage performs correlational operation on the input with a weight kernel, and the entire neural network can be considered a nonlinear correlation filter. Therefore, the output of the proposed neural network is correlational plane with peak values at the location of the target. The architecture of this neural network is suitable for implementing with parallel or distributed computers, and this fact allows the application to the problems which require realtime processing. Net training methodology to overcome the problem caused by unbalance of the number of targets and non-targets is also introduced. To verify the performance, the proposed network is applied to detection and recognition problem of a specific automobile driving around in a parking lot. The results show no false alarms and fast processing enough to track a target that moves as fast as about 190 km per hour.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.642-642
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• 2012

최근에 들어 지구온난화에 따른 기후변화의 영향으로 국지성 집중호우와 돌발성 호우가 한반도 뿐 아니라 전 세계적으로도 많이 나타나고 있고, 그로 인한 이상홍수의 발생이 우리나라의 인명 및 재산피해를 날로 증가시키고 있는 추세이다. 그러나 현재 국내의 홍수방어시스템은 정확도 및 선행시간 확보 등의 측면에서 국민들의 요구수준까지는 그 역할을 수행하지 못하고 있는 실정이다. 또한 최근 4대강 살리기 사업을 통해 수행된 보 설치 및 하도 준설로 인해 하천환경의 변화가 크게 발생하여, 보다 정확하고 신속한 홍수위 예측기법이 요구되고 있는 실정이다. 이에 따라 현재 4대강 홍수통제소에서는 정확한 홍수위예측을 위해 4대강 본류 및 주요 지류에 대해 수리모형을 구축하고 있고, 기존의 저류함수모형에 의한 강우-유출 해석기법을 적용하여 주요 지류에 대한 유입량을 산정하기 위한 모형을 구축중에 있다. 국내 홍수방어 시스템에 현재까지 사용되어 오고 있는 저류함수모형 및 수위-유량 관계식을 이용한 방법은 물리적 기반의 홍수예측모형으로 유역의 지형학적 인자와 그에 따른 여러 변수를 포함하기 때문에 하천환경의 변화로 인해 각각의 추적과정에서 오차들이 발생하여 해석결과에 영향을 미치는 단점이 있다. 이에 반해 데이터 기반 모형은 강우-유출 모형에서 사용되는 많은 수문학적 자료 및 매개변수들의 사용 없이 오직 수위 및 강우측정 자료만을 이용하여 홍수를 예측하는 모형이다. 본 연구에서는 낙동강 유역에 대해 보다 정확한 홍수위 예측을 위해 현재 낙동강홍수통제소에서 구축중인 낙동강 본류의 수리모형의 주요 지류의 유입량 산정을 위해 기존의 물리적 기반 모형이 아닌 뉴로-퍼지(Neuro-Fuzzy) 모형을 이용한 data 기반 모형을 적용해 기존 물리적 기반 모형과 비교 분석 하고자 하였다. 낙동강의 주요지류인 감천, 금호강, 남강, 내성천, 밀양강, 반변천, 위천, 황강을 적용유역으로 선정하여 유역별로 티센망을 구축하였고, 각 지류별로 수위관측소를 선정하여 최근 10년동안 낙동강유역의 홍수예 경보가 발령되었거나 많은 비가 온 사상을 선정해 모형을 검증하였다. 모형은 실시간 수위측정 자료와 강우자료 및 해당유역 댐의 방류량 자료를 이용해 유역별 최적 입력자료 조합을 선정하여 간단하게 구축할 수 있었다. 또한 10분 단위 및 30분 단위의 입출력 자료로 모형을 구축하여 비교하였다. 이번 연구에서 수행한 낙동강유역에서의 뉴로-퍼지(Neuro-Fuzzy) 모형을 이용한 홍수예측기법을 통해 몇가지 data만으로 유역의 주요지점에 대한 홍수위와 홍수량을 예측할 수 있음을 확인할 수 있었다. 모의 결과는 실측치와 비교해 정확도 면에서 우수함을 보여 주었으나 예측시간이 길어질수록 실측치의 경향을 벗어나는 결과를 보였다. 그러나 실시간 홍수예 경보에 있어서는 만족할만한 선행시간을 확보할 수 있었다. 구축된 Data 기반 모형이 물리적 기반 모형과 더불어 낙동강 홍수예 경보를 위한 모형으로 사용될 수 있다면 보다 효율적인 예 경보 체계 구축에 도움을 줄 수 있을 것으로 판단된다.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.494-503
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• 2011

In this paper, we propose a recognition application of facial expression for laughter theraphy on smartphone. It detects face region by using AdaBoost face detection algorithm from the front camera image of a smartphone. After detecting the face image, it detects the lip region from the detected face image. From the next frame, it doesn't detect the face image but tracks the lip region which were detected in the previous frame by using the three step block matching algorithm. The size of the detected lip image varies according to the distance between camera and user. So, it scales the detected lip image with a fixed size. After that, it minimizes the effect of illumination variation by applying the bilateral symmetry and histogram matching illumination normalization. After that, it computes lip eigen vector by using PCA(Principal Component Analysis) and recognizes laughter expression by using a multilayer perceptron artificial network. The experiment results show that the proposed method could deal with 16.7 frame/s and the proposed illumination normalization method could reduce the variations of illumination better than the existing methods for better recognition performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.523-533
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• 2019

The convergence of engineering and IT technology has brought many changes to the industry as well as academic research. In particular, computer simulation technology has evolved to a level that can accurately simulate actual physical phenomena and analyze them in real time. In this paper, we describe the CFD technology, which is mainly used in industry, and the post processor that uses the augmented reality which is emerging as the post-processing. Research on the visualization of fluid simulation results using AR technology is actively being carried out. However, due to the large size of the result data, it is limited to researches that are published in a desktop environment. Therefore, it is limitation that needs to be reviewed in actual space. In this paper, we discuss how to solve these problems. We analyze the fluid analysis results in the post-processing, and then perform optimizing data (more than 70%)to support operation in the mobile environment. In the visualization, lightweight data is used to perform real-time tracking using cloud computing, The analysis result is matched to the screen and visualized. This allows the user to review and analyze the fluid analysis results in an efficient and immersive manner in the various spaces where the simulation is performed.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.711-719
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• 2021

EBS has been producing numerous educational contents with traditional virtual studio production systems since the early 2000s and applied AR video production system in October 2020, twenty-years after. Although the basic concept of synthesizing graphic elements and actual image in real time by tracking camera movement and lens information is similar to the previous one but the newly applied AR video production system contains some of advanced technologies that are improved over the previous ones. Marker tracking technology that enables camera movement free and position tracking has been applied that can track the location stably, and the operating software has been applied with Unreal Engine, one of the representative graphic engines used in computer game production, therefore the system's rendering burden has been reduced, enabling high-quality and real-time graphic effects. This system is installed on a crane camera that is mainly used in a crane shot at the live broadcasting studio and applied for live broadcasting programs for children and some of the videos such as program introductions and quiz events that used to be expressed in 2D graphics were converted to 3D AR videos which has been enhanced. This paper covers the effect of introduction and application of the AR video production system on EBS content production and the future development direction and possibility.