• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.12
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• pp.1217-1222
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• 2012

A wall flow sensor has been used for feedback flow control and wall shear stress measurement. In this study, we developed a new wall flow sensor by combining the PIV algorithm and the micro image sensor used in an optical mouse. The feasibility of the wall flow sensor was investigated by using simulated fluid flow experiments. Compared with the quadrature signal from imaging devices, the accuracy of the wall flow velocity measurement was improved and the dynamic range increased. In addition, the depth information of particles was also measured by using the defocusing imaging technique.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.232-237
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• 1998

This paper describes a new method for estimating time-to-collision which exhibits high tolerance to noise contained in camera images. Time to collision (TTC) is one of the most important parameters available from a camera attached to a mobile machine. TTC indirectly stands far the translation speed of the camera and is usually calculated either from successive images or optical flow by using intimate relationship between TTC and flow divergence. In most cases, however, it is not easy to get accurate optical flow, which makes it difficult to calculate TTC. In this study it is proved that if the target has a smooth surface, the average of divergence over any point-symmetric region on the image is equal to the divergence of the center of the region. It means that required divergence can be calculated by integrating optical flow vectors over a symmetric region. It is expected that in the process of the integration, accidental noise is canceled if they are independent of optical flow and the motion of the camera. Experimental results show that TTC can be estimated regardless of the surface condition. It is also shown that influence of noise is eliminated as the area of integration increases.

• Journal of Biosystems Engineering
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• v.23 no.5
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• pp.481-490
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• 1998

A computer vision system to measure low flow rate of solid particles was developed and tested to examine its performance with various sized 7 kinds of seeds, perilla, mung bean, paddy, small red bean, black soybean, Cuba bean and small potato tuber. The test was performed for two types of particle flow, continuous and discontinuous. For the continuous flow tested with perilla, mung bean and paddy, the tests resulted correlation coefficients for the flow rates measured by the computer vision and direct method about 0.98. Average errors of the computer vision measurement were in a range of 6∼9%. For the discontinuous flow tested with small red bean, black soybean, Cuba bean and small potato tuber, the tests resulted correlation coefficients for the flow rates measured by the computer vision and direct method 0.98∼0.99. Average errors of the computer vision measurement were in a range of 5∼10%. Performance of the computer vision system was compared with that of the conventional optical sensor to count particles in discontinuous flow. The comparison was done with black soybean, Cuba bean and small potato tuber, and resulted that the computer vision has much better performance than the optical sensor in a sense of precision of the measurement.

• Journal of Sensor Science and Technology
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• v.8 no.2
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• pp.133-138
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• 1999

In this pager, the authors have implemented a blood flow simulator and a LDF(laser Doppler flowmeter) probe using self-mixing effect of the laser diode. The purpose of the blood flow simulator is to simulate microvascular blood flow in tissue. It consists of melinex film (thickness = $123\;{\mu}m$) which has similar optical characteristics to epidermis and porous polyethylene filter (Vyon, porosity 35%, mean pore size $50\;{\mu}m$, thickness=1 mm) which has similar optical characteristics to dermis. The blood flowmeter probe consists of laser diode(5 mW, 780 nm wavelength), CD lens(focal length 12 mm). current-to-voltage converter, highpass filter, and premplifier. It doesn't need optical fiber, therefore, implementation of the probe is simpler than conventional probe using optical fiber.

• Journal of ILASS-Korea
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• v.6 no.1
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• pp.18-24
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• 2001

Liquid fossil fuel contaminated by water can cause trouble in the combustion processes and affect the endurance of a combustion system. Using an optical sensor to monitor the water content instantaneously in a fuel pipeline is an effective means of controlling the fuel quality in a combustion system. In two component liquid flows of oil and water, the flow pattern and characteristics of water droplets are changed with various flow conditions. Additionally, the light scattering of the optical sensor measuring the water content is also dependent on the flow patterns and droplet characteristics. Therefore, it is important to investigate the detailed behavior of water droplets in the pipeline of the fuel transportation system. In this study, the flow patterns and characteristics of water droplets in the turbulent pipe flow of two component liquids of gasoline and water were investigated using optical measurements. The dispersion of water droplets in the gasoline flow was visualized, and the size and velocity distributions of water droplets were simultaneously measured by the phase Doppler technique. The Reynolds number of the gasoline pipe flow varied in the range of $4{\times}10^{4}\;to\;1{\times}10^{3}$, and the water content varied in the range of 50 ppm to 300 ppm. The water droplets were spherical and dispersed homogeneously in all variables of this experiment. The velocity of water droplets was not dependent on the droplet size and the mean velocity of droplets was equal to that of the gasoline flow. The mean diameter of water droplets decreased and the number density increased with the Reynolds number of the gasoline flow.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1543-1567
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• 2015

In this study, a wireless crack sensor is developed for monitoring cracks propagating in two dimensions. This sensor is developed by incorporating a laser-based optical navigation sensor board (ADNS-9500) into a smart wireless platform (Imote2). To measure crack propagation, the Imote2 sends a signal to the ADNS-9500 to collect a sequence of images reflected from the concrete surface. These acquired images can be processed in the ADNS-9500 directly (the navigation mode) or sent to Imote2 for processing (the frame capture mode). The computed crack displacement can then be transmitted wirelessly to a base station. The design and the construction of this sensor are reported herein followed by some calibration tests on one prototype sensor. Test results show that the sensor can provide sub-millimeter accuracy under sinusoidal and step movement. Also, the two modes of operation offer complementary performance as the navigation mode is more accurate in tracking large amplitude and fast crack movement while the frame capture mode is more accurate for small and slow crack movement. These results illustrate the feasibility of developing such a crack sensor as well as point out directions of further research before its actual implementation.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.430-433
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• 2018

This paper proposes the Kalman filter (KF) with optical flow method to estimate the position and the velocity of unmanned aerial vehicles (UAVs) in the absence of global positioning system (GPS). A downward-looking camera, a gyroscope and an ultrasonic sensor are fused to compensate the measurement from optical-flow method. To overcome the problem of dealing with noise in onboard sensors, the KF is incorporated to efficiently predict the velocity and estimate the position. Basic mechanisms of optical flow and the KF are introduced and experiments are conducted to show how the techniques involved improve the estimations.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.178-186
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• 2007

This study is attempted to correct an error of electronic blood pressure meter with an optical sensor. In general, for a hospitalized patient, ECG, blood pressure, oxygen saturation, and respiration are basically measured to monitor the patient's condition. Opening of a blood vessel after it is occluded by pressurizing the cuff influences the blood flow of peripheral blood vessels as well as oscillation changes in the cuff. Blood vessels are occluded and peripheral blood flow disappears at cuff pressure above the examinee's blood pressure, while blood vessels are opened and peripheral blood flow appears again at cuff pressure under the examinee's blood pressure. Then Disappear-Appear Point Length(DAPL) of peripheral blood flow can be judged with the signal of peripheral blood flow, thus is available as a factor of error correction for electronic blood pressure meter. Also, systolic or diastolic blood pressure can be corrected with Appear-Point-Pressure(APP) of cuff pressure at a point where blood flow occurs and Appear-Maximum Pressure(AMP) of cuff pressure at the maximum amplitude point of peripheral blood flow after peripheral blood flow appears again. For verification, 27 examinees were selected, and their blood value was obtained through experimental procedure of 4 stages including induction of blood pressure change. The examinees were divided into two groups of experimental group and control group, regression analysis was conducted for experimental group, and correction of a blood pressure error was verified with optical signal by applying the regression equation calculated in experimental group to control group. As an experimental result, mean of the whole measurement errors was 5mmHg or more, which did not meet the standard fur blood pressure meter. As a result of correcting blood pressure measurements with data of DAPL, APP, and AMP as drawn out of PPG signal, systolic blood pressure, mean blood pressure, and diastolic blood pressure were $-0.6{\pm}4.4mmHg,\;-1.0{\pm}3.9mmHg$ and $-1.3{\pm}5.4mmHg$, respectively, indicating that mean of the whole measurement errors was greatly improved, and standard deviation was decreased.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.133-139
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• 2013

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.19-22
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• 2003

This paper proposes an algorithm for navigation of an autonomous mobile robot with vision sensor. For obstacle avoidance, we used a curvature trajectory method. Using this method, translational and rotational speeds are controlled independently and the mobile robot traces a smooth curvature trajectory that consists of circle trajectories to a target point. While trying to avoid obstacles, the robot fan be goal-directed using curvature trajectory.