• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.363-368
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• 2007

Most surgery illumination systems have been developed as passive systems. However, sometimes it is inconvenient to relocate the position of the illumination system whenever the surgeon changes his pose. To cope with such a problem, this study develops an auto-illumination system that is autonomously tracking the surgeon's movement. A 5-DOF serial type manipulator system that can control (X, Y, Z, Yaw, Pitch) position and secure enough workspace is developed. Using 3 ultrasonic sensors, the surgeon's position and orientation could be located. The measured data aresent to the main control system so that the robot can be auto-tracking the target. Finally, performance of the developed auto-illuminating system was verified through a preliminary experiment in the operating room environment.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.456-458
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• 2004

Recent medical robot systems perform surgery operations, by following the preplanned trajectory and surgical procedures. Depending on the complexity of surgery operations, they are operated in manual, semi-automatic or full automatic mode. To improve the performance of those medical robot systems, development of the simulator and more advanced auto-illumination system, in which intensity of light, direction and focal point can be controlled automatically according to the varied environments during surgical operations. are required. Therefore, in this paper, the simulator for SPINEBOT system which is a computer-intergrated robotic surgery system are developed. And further, an auto-illumination system which will be integrated to the SPINEBOT system is investigated and its preliminary design is described.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.432-435
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• 2005

The Mega-pixel camera phones become main trends in mobile phone market. The lens modules used in mesa-pixel camera phones need high resolution. One of the main factors of resolution drop is the defects of bare lens. Though there are many advantages in auto-inspection of defects of bare lens, high technical problems take the defect inspections to be done with manual process. In this paper, the type and the source of defects were described and bare lens defect auto-inspection system design was explained. The designed auto-inspection system is composed of illumination optics part, focusing optics part and auto-moving system. With the proposed auto-inspection system, fast and uniform inspection of bare lens can be achieved.

• Electrical & Electronic Materials
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• v.9 no.10
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• pp.1027-1032
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• 1996

The auto-illumination controller for office, residence, and so on was studied. The system consists of parts of a power supply, a signal oscillator, a lamp controller and two kinds of sensor. The lamp controller has two thyristors triggered by the IR sensor(SCRI) and CdS sensor(SCR2) respectively, When the illuminance around this system is higher than operating value of its sensor, lamp is turned off automatically. Otherwise, the light of lamp gets dim by CdS sensor. In case IR sensor senses the body heat of people around itself, the illuminance of the lamp gets maximum. The illuminance of the lamp can be changed dimmly by control of the variable resistor (RV) connected with SCR2 in series. The turning - on time of the lamp can be also controlled using a variable resistor(Rt) connected with a signal oscillator in parallel. Changing resistance Rt changes the time constant(.tau.), which triggers the gate of SCR2. Though people left the surrounding of lamp, the lamp keeps light for a while.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.7
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• pp.313-318
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• 2006

An automatic sample centering system is underway at the protein crystallography beam line of the Pohang Light Source to improve the efficiency of the crystal screening process. A sample pin which contains a protein crystal is mounted on a goniometer head. Then the crystal should be moved to the center of X-ray beam by controlling the motorized goniometer to obtain diffraction data. Since the X-ray beam is located at the center of the image obtained from the CCD camera when the image of the sample pin is in focus, an auto-focusing algorithm is a very important part in the auto-sample-centering system. However the results of applying several well-known auto focusing algorithms directly to the images are not satisfactory owing to the following factors: misalignment of CCD camera, non-uniform cryo-stream in the background of the image and the supporter of the loop. The performance of an auto-focusing algorithm can be increased if the algorithm is applied to only the loop region identified. Non-uniform cryo-stream and a various illumination condition and a stain, which is shown in the image, are main obstacles to loop region identification. In this paper, a simple loop region identification algorithm, which can solve these problems, is proposed and the effective ness of the proposed scheme is shown by applying the auto-focusing algorithm to the loop region identified.

• Journal of ILASS-Korea
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• v.10 no.3
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• pp.17-28
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• 2005

The Holographic Particle Velocimetry system can be a promising optical tool for the measurements of three dimensional particle velocities. In this study, diffused illumination holographic system to measure the sizes and 3D velocities of moving particles based on automatic image processing was developed. First of all basic optical systems for pulse laser recording, continuous laser reconstruction, and image acquisition, were constructed. To determine the position of particles in the optical axis, new three auto-focusing parameters(AEP), namely, Correlation Coefficient, Sharpness Index, and Depth Intensity were introduced and verified. The developed system was applied to spray droplets to validate the capability of the system. Three dimensional positions of particles viewed from two sides were decided using AFP and then 3D velocities of Particles were extracted by particle tracking algorithm. Comparison of measurement results of sizes and 3D velocities of particles with those obtained by laser instrument, PDPA, showed good consistency of the developed holographic system.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.4 s.247
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• pp.287-297
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• 2006

The Holographic Particle Velocimetry system can be a promising optical tool for the measurements of three dimensional particle velocities. One of inherent limitations of particle holography is the very long depth of field of particle images, which causes considerable difficulty in the determination of particle positions in the optical axis. In this study, we introduced three auto-focusing parameters corresponding to the size of particles, namely, Correlation Coefficient, Sharpness Index, and Depth Intensity to determine the focal plane of a particle along the optical axis. To investigate the suitability of the above parameters, the plane image of dot array screens containing different size of dots was recorded by diffused illumination holography and the positions of each dot in the optical axis were evaluated. In addition, the effect of particle position from the holographic film was examined by changing the distance of the screen from the holographic film. All measurement results verified that the evaluated positions using suggested auto-focusing parameters remain within acceptable range of errors. These research results may provide fundamental information for the development of the holographic velocimetry system based on the automatic image processing.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1109-1114
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• 2015

This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.3
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• pp.36-40
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• 2013

Recently, electronic equipments become smaller, more functional, and more complex than before. As these trends, MLC(multi-layer ceramic) circuit has been emerged to a promising technology in semiconductor inspection industry. Especially, multi-layer ceramic which is consisted of many fine-pitch multi-hole is used to produce a semiconductor inspection unit. The hole is processed by UV laser. But, working conditions are changed all the time. Therefore real time measurement of fine-pitch multi-hole is very important method for ensuring performance. In this paper we found the best method for illuminating and auto focusing. And, we verified our equipment.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.