• Fire Science and Engineering
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• v.16 no.3
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• pp.77-83
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• 2002

We measured flame extinguishing concentration and flue gas composition in the n-heptane fuel cup-burner system using inert gas agents such as nitrogen, argon, carbon dioxide and their mixtures. The flame extinguishing concentration of binary gaseous mixture was well predicted by model which contains the flame extinguishing concentration and composition of pure components. The higher average specific gravity of the mixed inert gas agents, the more excellent flame extinguishing performance. And the structure of enclosed space also affects the fire extinguishing. The composition of carbon dioxide in the flue gas was decreased with increasing extinguishing agent used. Nitrogen monoxide production is not related with increasing nitrogen, but increased at rapid mass flow rate of air in the cup-burner.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.10
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• pp.735-745
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• 2001

Processors require both intensive and extensive functional verification in their design phase due to their general purpose. The proposed random vector verification method for embedded control RISC cores meets this goal by contributing assistance for conventional methods. The proposed method proved its effectiveness during the design of CalmRISCTM-32 developed by Yonsei Univ. and Samsung. It adopts a cycle-accurate instruction level simulator as a reference model, runs simulation in both the reference and the target HDL and reports errors if any difference is found between them. Consequently, it successfully covers errors designers easily pass over and establishes other new error check points.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.127-132
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• 2017

In this paper, we propose a controller gain based on model based design and implement the two-wheel inverted pendulum type robot using NXT Lego and RobotC language. Two-wheel inverted pendulum robot consists of NXT mindstorm, servo DC motor with encoder, gyro sensor, and accelerometer sensor. We measurement wheel angle using bulit-in encoder and calculate wheel angle speed using moving average method. Gyro measures body angular velocity and accelerometer measures body pitch angle. We calculate body angle with complementary filter using gyro and accelerometer sensor. The control gain is a weighted value for wheel angle, wheel angular velocity, body pitch angle, and body pich angular velocity, respectively. We experiment and observe the effect of two-wheel inverted pendulum with respect to change of control gains.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.407-414
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• 2007

A sensor fault detection scheme(SFDS) for a class of nonlinear systems that can be represented by Takagi-Sugeno fuzzy model is proposed. Basically, the SFDS may be considered as a multiple observer scheme(MOS) in which the bank of state observers and the detection & isolation logic are included. However, the proposed scheme has two great differences from the conventional MOSs. First, the proposed scheme includes fuzzy fault detection observers(FFDO) that are constructed based on the T-S fuzzy model that provides very good approximation to nonlinear dynamic systems. Secondly, unlike the conventional MOS, the FFDOS are driven not parallelly but sequentially according to the predetermined sequence to avoid the massive computational burden, which is known to be the biggest obstacle to the practical application of the multiple observer based FDI schemes. During the operating time, each FFDO generates the residuals carrying the information of a specified fault, and the corresponding fault detection logic unit performs the logical operations to detect and isolate the fault of interest. The proposed scheme is applied to an inverted pendulum control system for sensor fault detection/isolation. Simulation study shows the practical feasibility of the proposed scheme.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.523-529
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• 2002

The present study describes neural networks for stereoscopic vision, which are applied to identifying human speech. In speech recognition based on stereoscopic vision neural networks (SVNN), the similarities are first obtained by comparing input vocal signals with standard models. They are then given to a dynamic process in which both competitive and cooperative processes are conducted among neighboring similarities. Through the dynamic processes, only one winner neuron is finally detected. In a comparative study, the two-layered SVNN was 7.7% higher in recognition accuracies than the hidden Markov model (HMM). From the evaluation results, it was noticed that SVNN outperformed the existing HMM recognizer.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1729-1740
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• 2017

This paper describes a vision-based method that effectively recognize a traffic light. The method consists of two steps of traffic light detection and discrimination. Many related studies have used color information to detect traffic light, but color information is not robust to the varying illumination environment. This paper proposes a new method of traffic light detection based on intensity and saturation. When a traffic light is turned on, the light region usually shows values with high saturation and high intensity. However, when the light region is oversaturated, the region shows values of low saturation and high intensity. So this study proposes a method to be able to detect a traffic light under these conditions. After detecting a traffic light, it estimates the size of the body region including the traffic light and extracts the body region. The body region is compared with five models which represent specific traffic signals, then the region is discriminated as one of the five models or rejected as none of them. Experimental results show the performance of traffic light detection reporting the precision of 97.2%, the recall of 95.8%, and correct recognition rate of 94.3%. These results shows that the proposed method is effective.

• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.400-405
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• 2006

In this paper, we propose a moving object tracking algorithm by using motion estimation in UAV(Unmanned Aerial Vehicle) video. Proposed algorithm is based on generation of initial image from detected reference image, and tracking of moving object under the time-varying image. With a series of this procedure, tracking process is stable even when the UAV camera sways by correcting position of moving object, and tracking time is relatively reduced. A block matching algorithm is also utilized to determine the similarity between reference image and moving object. An experimental result shows that our proposed algorithm is better than the existing full search algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.416-419
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• 2003

The cellular neural networks have the circuit structure that differs from the form of general neural network. It consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template property. In this paper, time-multiplex image processing technique is applied for processing large images using small size CNN cell block, and we simulate the edge detection of a large image using the simulator implemented with a c program and matlab model. A 5$\times$5 CNN hardware and pre post processor is also implemented and is under test.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1130-1139
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• 1998

This paper presents an integrated solution for the content-based news video indexing and the retrieval. Currently, it is impossible to automatically index a general video, but we can index a specific structural video such as news videos. Our proposed model extracts automatically the key frames by using the structured knowledge of news and consists of the news item segmentation, caption recognition and search browser modules. We present above three modules in the following: the news event segmentation module recognizes an anchor-person shot based on face recognition, and then its news event are divided by the anchor-person's frame information. The caption recognition module detects the caption-frames with the caption characteristics, extracts their character region by the using split-merge method, and then recognizes characters with OCR software. Finally, the search browser module could make a various of searching mechanism possible.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.709-712
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• 2009

대부분의 고속도로 안개경고시스템은 시정측정을 위해 고가의 광학센서를 사용하고 있으나 운전자의 시정감각과 유사하면서도 비교적 저가인 CCTV를 이용한 시정측정에 관한 연구가 활발히 이루어지고 있다. 그러나 대부분의 CCTV를 이용한 시정측정 방법은 ROI를 기반으로 하고 있어 설치가 까다롭고 기존 CCTV를 활용하기 어렵다는 문제점을 가지고 있다. 본 논문에서는 고속도로상의 안개경고는 약 1~2Km이내의 시정일 때 발생되며, 눈으로 물체를 식별할 수 있는 최대거리가 시정이라는 기초적인 개념에 근거하여 고속도로 안개경고시스템에 사용될 수 있는 Non-ROI 기반의 실시간 CCTV 시정측정 방법을 제안한다. 이를 위해 본 논문에서는 고속도로상에 주행중인 차량의 실시간 이동영역과 가시선을 검출하고 카메라와 도로간의 상관관계를 나타내는 도로모델을 이용하여 시정측정을 수행하는 방법을 제시한다. 제안된 방법은 1~2Km 이내의 시정측정을 위한 방법으로 ROI가 필요없고 직관적이고 현실적인 주야간 시정측정이 가능하며 기존의 고속도로 CCTV에 바로 적용할 수 있다는 장점이 있다.