• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.161-167
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• 2016

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.

• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.23-33
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• 2000

In this paper, we describe an algorithm which can extract human faces with natural pose from complex backgrounds. This method basically adopts the concept that facial region has the nearly same gray level for all pixels within appropriately scaled blocks. Based on the idea, we develop a hierarchial process that first, a block image data with pyramid structure of input image is generated, and some candidate regions for facial regions in the block image are Quickly determined, then finally the detailed facial features; organs are decided. To find the features easily, we introduce a local gray level transform which emphasizes dark and small regions, and estimate the geometrical triangle constraints among the facial features. The merit of our method is that we can be freed from the parameter assignment problem since the algorithm utilize a simple brightness computation, consequently robust systems not being depended on specific parameter values can be easily constructed.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.358-363
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• 2007

Reinforcing steel work plays an important role in terms of its structural performance or weight of construction cost for reinforced concrete structures. Precise estimation of re-bar quantity gives a basis for managing the reinforcing steel work effectively. However, the estimation process is still performed ineffectively based upon the expert's experience or manpower in spite of the advanced technology or improvement efforts. Therefore, the purpose of this research is to develop a prototype system for taking-off the quantity of reinforcing steel bars quickly and accurately in an order consistent with the specific members identified on the drawings. An estimate algorithm considering the connection, settlement and coating thickness of re-bars was suggested regarding to their replacement conditions which places more emphasis on constructibility. Also, this system produces the shop drawings automatically with the calculation results.

• Journal of Navigation and Port Research
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• v.37 no.4
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• pp.337-343
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• 2013

To ensure the safety for vessels anchored in stormy weather, duty officer and VTS operator have to frequently check whether their anchors are dragging. To judge dragging of the anchored vessel, it is important for VTS operator to recognize the turning circle and its center of the anchored vessel. The judgement for the anchored vessel dragging can be made by using Radar and AIS. If it is available, CCTV or eye-sighting can be used to know the center of turing circle. However, the VTS system collects individual ship's dynamic information from AIS and ARPA radar and monitors of the anchored vessels, it is difficult for VTS operator not only to get the detailed status information of the vessels, but also to know the center of turning circle. In this study, we propose an efficient algorithm to estimate the center of turning circle of anchored vessel by using the ship's heading and position data, which were from AIS. To verify the effectiveness of the proposed algorithm, the experimental study was made for the anchored vessel under real environments.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.936-946
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• 2009

A graph cuts method has recently attracted a lot of attentions for image segmentation, as it can globally minimize energy functions composed of data term that reflects how each pixel fits into prior information for each class and smoothness term that penalizes discontinuities between neighboring pixels. In previous approaches to graph cuts-based automatic image segmentation, GMM(Gaussian mixture models) is generally used, and means and covariance matrixes calculated by EM algorithm were used as prior information for each cluster. However, it is practicable only for clusters with a hyper-spherical or hyper-ellipsoidal shape, as the cluster was represented based on the covariance matrix centered on the mean. For arbitrary-shaped clusters, this paper proposes graph cuts-based image segmentation using mean shift analysis. As a prior information to estimate the data term, we use the set of mean trajectories toward each mode from initial means randomly selected in $L^*u^*{\upsilon}^*$ color space. Since the mean shift procedure requires many computational times, we transform features in continuous feature space into 3D discrete grid, and use 3D kernel based on the first moment in the grid, which are needed to move the means to modes. In the experiments, we investigate the problems of mean shift-based and normalized cuts-based image segmentation methods that are recently popular methods, and the proposed method showed better performance than previous two methods and graph cuts-based automatic image segmentation using GMM on Berkeley segmentation dataset.

• Journal of Korean Society of Forest Science
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• v.96 no.3
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• pp.251-258
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• 2007

Airborne Lidar (light detection and ranging) can be an effective alternative in forest inventory to overcome the limitations of conventional field survey and aerial photo interpretation. In this study, we attempt to develop methodologies to identify individual trees and to estimate tree height from airborne Lidar data. Initially, digital elevation model (DEM) data representing the exact ground surface were generated by removing non-ground returns from the multiple-return laser point clouds, obtained over the coniferous forest site of rugged terrain. Based on the canopy height model (CHM) data representing non-ground layer, individual tree heights are extracted through pseudo-grid method and moving window filtering algorithm. Comparing with field survey data and aerial photo interpretation on sample plots, the number of trees extracted from Lidar data show over 90% accuracy and tree heights were underestimated within 1.1m in average at two plantation stands of pine (Pinus koraiensis) and larch (Larix leptolepis).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.3
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• pp.205-211
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• 2017

To achieve an advanced control of automobiles, it is necessary to acquire the values of the parameters of a vehicle in real time to conduct precise vehicle control practices such as automatic platooning control. Vehicle control is especially required in controlling trucks, as the mass and inertia change widely according to the loading conditions. Thereafter, we propose to estimate the yaw moment of inertia of the truck in real-time during travelling, by applying the dual Kalman filter algorithm, which estimates the state variables and values of the parameters simultaneously in real-time. The simulation results show that the proposed method is effective for the estimation, which uses commercial software for simulating and analyzing the vehicle dynamics.

• Journal of Biomedical Engineering Research
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• v.18 no.1
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• pp.25-36
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• 1997

To regulate cardiac output of the Total Artificial Heart(TAH) physiologically, the hemodynamic information must be toed back to the controller. So far, our group has developed an automatic cardiac output control algorithm using the motor current waveform, It is, however difficult to detect the preload level such as a filling status of ventricular inflow and the variation of atrial pressures within normal physiologic range(0-15 mmHg) by analyzing the motor current which simultaneously reflects the afterload effect. On the other hin4 the interventricular volume pressure(IVP) which is not influenced by arterload but by preload is a good information source for the estimation of preload states. In order to find the relationship between preload and IVP waveform, we set up the artificial heart system on the Donovan type mock circulatory system and measured the IVP waveform, right and left atrial pressures, inflow and outflow waveforms and the signals represented the information of moving actuator's position. We shows the feasibility of estimating the hemodynamic changes of inflow by using IVP waveform. fife found that the negative peak value of IVP waveform is linearly related to atrial pressures. And we also found that we could use the time to reach the negative peak in IVP waveform, the time to open outflow valve, the area enclosed IVP waveform as unfu parameters to estimate blood filling volume of diastole ventricle. The suggested method has advantages of avoiding thrombogenesis, bacterial niche formation and increasing longterm reliability of sensor by avoiding direct contact to blood.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.125-132
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• 2008

Omnidirectional camera system with a wide view angle is widely used in surveillance and robotics areas. In general, most of previous studies on estimating a projection model and the extrinsic parameters from the omnidirectional images assume corresponding points previously established among views. This paper presents a novel omnidirectional camera calibration based on automatic contour matching. In the first place, we estimate the initial parameters including translation and rotations by using the epipolar constraint from the matched feature points. After choosing the interested points adjacent to more than two contours, we establish a precise correspondence among the connected contours by using the initial parameters and the active matching windows. The extrinsic parameters of the omnidirectional camera are estimated minimizing the angular errors of the epipolar plane of endpoints and the inverse projected 3D vectors. Experimental results on synthetic and real images demonstrate that the proposed algorithm obtains more precise camera parameters than the previous method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.69-76
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• 2008

This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.