• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1949-1952
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• 2004

GPS is widely used for outdoor positioning in many applications. But it is not suitable for positioning in an obstacle environment such as urban area, tunnels and so on, due to variable signal level. So new technology of the positioning is required to provide the consistent error level regardless of any changes in any environment. Abrupt changes of GPS signal can be detected by various fault detection and isolation methods. Conventional FDI (Fault Detection and Isolation) methods are categorized into two approaches. One approach is the snapshot method that uses measurements only at present step. The other approach is the filtering method that uses measurements stacked from previous step to present step. The FDI result of the snapshot method can be considered reliable independently with previous results and the FDI result of the filtering method is more reliable and detection time is a little longer. Therefore combining approach of two methods is proposed for increasing FDI performance in this paper. Three approaches that are the snapshot method, the filtering method and the combining method are compared to show the probability of correct FDI in simulations. The combining approach presents best result of FDI among them and shows the consistent accuracy irrespective of any changes in outdoor environment.

• Structural Engineering and Mechanics
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• v.75 no.5
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• pp.585-594
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• 2020

Detecting the damage of indeterminate trusses is of major importance in the literature. This paper proposes a quick approach in this regard, utilizing a precise mathematical approach based on Finite Element Method. Different to a general two-step method defined in the literature essentially based on optimization approach, this method consists of three steps including Damage-Suspected Element Identification step, Imminent Damaged Element Identification step, and finally, Damage Severity Detection step and does not need any optimizing algorithm. The first step focuses on the identification of damage-suspected elements using an index based on modal residual force vector. In the second step, imminent damage elements are identified among the damage-suspected elements detected in the previous step using a specific technique. Ultimately, in the third step, a novel relation is derived to calculate the damage severity of each imminent damaged element. To show the efficiency and quick function of the proposed method, three examples including a 25-bar planar truss, a 31-bar planar truss, and a 52-bar space truss are studied; results of which indicate that the method is innovatively capable of suitably detecting, for indeterminate trusses, not only damaged elements but also their individual damage severity by carrying out solely one analysis.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.9-14
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• 2015

Outlier detection and removal is a crucial step needed for various image processing applications such as image registration. Random Sample Consensus (RANSAC) is known to be the best algorithm so far for the outlier detection and removal. However RANSAC requires a cosiderable computation time. To drastically reduce the computation time while preserving the comparable quality, a outlier detection and removal method based on modified K-means is proposed. The original K-means was conducted first for matching point pairs and then cluster merging and member exclusion step are performed in the modification step. We applied the methods to various images with highly repetitive patterns under several geometric distortions and obtained successful results. We compared the proposed method with RANSAC and showed that the proposed method runs 3~10 times faster than RANSAC.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1244-1253
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• 2018

The color of traffic light is sensitive to various illumination conditions. Especially it loses the hue information when oversaturation happens on the lighting area. This paper proposes a traffic light recognition method robust to these illumination variations. The method consists of two steps of traffic light detection and recognition. It just uses the intensity and saturation in the first step of traffic light detection. It delays the use of hue information until it reaches to the second step of recognizing the signal of traffic light. We utilized a deep learning technique in the second step. We designed a deep convolutional neural network(DCNN) which is composed of three convolutional networks and two fully connected networks. 12 video clips were used to evaluate the performance of the proposed method. Experimental results show the performance of traffic light detection reporting the precision of 93.9%, the recall of 91.6%, and the recognition accuracy of 89.4%. Considering that the maximum distance between the camera and traffic lights is 70m, the results shows that the proposed method is effective.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.15-22
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• 2019

There are many ways to perform face recognition. The first step of face recognition is the face detection step. If the face is not found in the first step, the face recognition fails. Face detection research has many difficulties because it can be varied according to face size change, left and right rotation and up and down rotation, side face and front face, facial expression, and light condition. In this study, facial features are extracted and the extracted features are geometrically reconstructed in order to improve face recognition rate in extracted face region. Also, it is aimed to adjust face angle using reconstructed facial feature vector, and to improve recognition rate for each face angle. In the recognition attempt using the result after the geometric reconstruction, both the up and down and the left and right facial angles have improved recognition performance.

• Journal of Power System Engineering
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• v.2 no.3
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• pp.60-67
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• 1998

Edge detection is the first step and very important step in image analysis. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for comparing the performances of different detectors. This cost function is made of desirable characteristics of edges such as thickness, continuity, length, region dissimilarity. And we use a simulated annealing algorithm for minimum of cost function. Simulated annealing are a class of adaptive search techniques that have been intensively studied in recent years. We present five strategies for generating candidate states. Experimental results(building image and test image) which verify the usefulness of our simulated annealing approach to edge detection are better than other operator.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.566-569
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• 1987

The primary difficulties encountered in the use of step motors are underdamped response when stopping at a specified position and dynamic instability during high-speed slewing. This paper proposes a speed and position detection scheme using the back EMF generated by the rotating permanent magnet field of a two-phase 1.8.deg. hybrid step motor, and presents its application to the state-variable feedback control of the hybrid step motor. All simulation results in a single step response show that the hybrid step motor performances such as peak overshoot and settling time are greatly improved.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.257-264
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• 2013

In this study, we have designed the variable amplitude threshold algorithm that can enhance the accuracy of step count using variable amplitude. This algorithm converts the x, y, z sensor values into a single energy value($E_t$) by using SVM(Signal Vector Magnitude) algorithm and can pick step count out over 99% of accuracy through the peak data detection algorithm and fixed peak threshold. To prove the results, We made the noise filtering with the fixed amplitude threshold from the amplitude of energy value that found out the detection error was increasing, and it's the key idea of the variable amplitude threshold that can be adapted on the continuous data evaluation. The experiment results shows that the variable amplitude threshold algorithm can improve the average step count accuracy up to 98.9% at 10 Hz sampling rate and 99.6% at 20Hz sampling rate.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.63-73
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• 2009

This paper presents a vehicle detection method that uses convolution matching method based on a simple color information. An input image is expressed as 8 oriented color expression(Red, Green, Blue, White, Black, Cyan, Yellow, Magenta) considering an orientation of a pixel color vector. It makes the image very reliable and strong against changes of illumination condition or environment. This paper divides the vehicle detection into a hypothesis generation step and a hypothesis verification step. In the hypothesis generation step, the vehicle candidate region is found by vertical edge and shadow. In the hypothesis verification step, the convolution matching and the complexity of image edge are used to detect real vehicles. It is proved that the proposed method has the fast and high detection rate on various experiments where the illumination source and environment are changed.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.361-368
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• 2007

In this paper, we propose a smoke detection method which is based on block-wise difference of image frames in video. Our proposed method is composed of three steps which are (a) the detection step of the changed regions against the background, (b) the background update step, and (c) the smoke determination step from the changed regions. We first construct the block mean Image of frames in video. And to extract the changed regions against the background, we use a block-wise difference between background's block mean image and a current input frame's block mean image. After applying projections in block-based difference images, we can determine the changed regions as rectangles using projections of difference images. we propose a update scheme of background's block mean image using the projections. We decide the smoke region using the femoral statistics of the central position and YUV color in the changed region.