• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1767-1774
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• 2016

In this paper, we propose the adaptation filtering for correcting the movement path of the recognized object by the depth information. When we recognize the object by the depth information, the path error should be occurred because of the noises in the depth information. The path error is corrected by appling the lowpass filtering, but the lowpass filtering is not efficient when the changes of the object's movement are rapid. In this paper, we apply the adaptation filtering that it gives weights adaptively as the difference between the predicted location and the measured location. To apply the adaptation filtering, we can see that the proposed method can correct accurately the path error of the radical change from simulation results.

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.98-104
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• 2024

This study proposes a method to overcome the limited detection range of short-baseline stereo cameras (SBSCs). The proposed method includes two steps: (1) predicting an unscaled initial depth using monocular depth estimation (MDE) and (2) adjusting the unscaled initial depth by a scale factor. The scale factor is computed by triangulating the sparse visual keypoints extracted from the left and right images of the SBSC. The proposed method allows the use of any pre-trained MDE model without the need for additional training or data collection, making it efficient even when considering the computational constraints of small platforms. Using an open dataset, the performance of the proposed method was demonstrated by comparing it with other conventional stereo-based depth estimation methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1002-1006
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• 2006

Temperature monitoring techniques per depth have been recognized as important information in the reservoir environmental issues. However, old measurement method by single temperature sensor and cable type has demerits not only for its limited measuring location but for its inconvenience of users. In this study, multi-channel temperature monitoring system was introduced and executed experiment for actual application feasibility evaluation. Both type of new techniques such as multi-channel addressable built-in temperature sensor and fiber optic multi sensor were tested in Daechung and Imha reservoir. As a result, it was proved that these kinds of temperature monitoring skills had very good performance and availability for a output of spatial, simultaneous thermal distribution focused on the user's convenience. And these measuring method and thermal data will be useful for providing basic information in a water resources investigation like reservoir stratification and environmental problems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.113-121
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• 2007

Commonly, in the Sensor Network that composed with multiple nodes uses Ad-hoc protocol to communicate each other. Each sensed data packets are collected by base node and processed by Host PC. But the Ad-hoc protocol is too vulnerable to Sinkhole attack, where the intruder attracts surrounding nodes with unfaithful routing information, and then performs selective forwarding or changes the data passing through it. The Sinkhole attack increases overhead over the network and boosts energy consumption speed to decrease network's life time. Since the other attacks can be easily adopted through sinkhole attack, the countermeasure must be considered carefully. In this paper, we proposed the Hop-depth algorithm that detects intruder in Sinkhole attack and colluded nodes. First, the proposed algorithm makes list of suspected nodes and identifies the real intruder in the suspected node list through the Hop-depth count value. And recalculates colluder's path information to find the real intruder. We evaluated the performance of the proposed algorithm using NS2. We compared and analyzed the success ratio of finding real intruder, false positive ratio, false negative ratio, and energy consumption.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.10 s.352
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• pp.22-27
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• 2006

In this paper, we propose a method that can generate a computer-generated hologram (CGH) from the depth stream and color image outputs provided by an active sensor add-on camera. Distinguished from an existing holographic display system that uses a computer graphic model to generate CGH, this method utilizes a real camera image including a depth information for each object captured by the camera, as well as color information. This procedure consists of two steps that the acquirement of a depth-annotated image of real object, and generation of CGH according to the 3D information that is extracted from the depth cue. In addition, we display the generated CGH via a holographic display system. In experimental system we reconstruct an image made from CGH with a reflective LCD panel that had a pixel-pitch of 10.4um and resolution of 1408X1050.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.205-213
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• 2018

Development and validation of crop models often require measurements of biomass for the crop of interest. Considerable efforts would be needed to obtain a reasonable amount of biomass data because the destructive sampling of a given crop is usually used. The Kinect sensor, which has a combination of image and depth sensors, can be used for estimating crop biomass without using destructive sampling approach. This approach could provide more data sets for model development and validation. The objective of this study was to examine the applicability of the Kinect sensor for estimation of chinese cabbage fresh weight. The fresh weight of five chinese cabbage was measured and compared with estimates using the Kinect sensor. The estimates were obtained by scanning individual chinese cabbage to create point cloud, removing noise, and building a three dimensional model with a set of free software. It was found that the 3D model created using the Kinect sensor explained about 98.7% of variation in fresh weight of chinese cabbage. Furthermore, the correlation coefficient between estimates and measurements were highly significant, which suggested that the Kinect sensor would be applicable to estimation of fresh weight for chinese cabbage. Our results demonstrated that a depth sensor allows for a non-destructive sampling approach, which enables to collect observation data for crop fresh weight over time. This would help development and validation of a crop model using a large number of reliable data sets, which merits further studies on application of various depth sensors to crop dry weight measurements.

• Journal of Power System Engineering
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• v.15 no.3
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• pp.65-69
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• 2011

This paper introduces the multi-coil eddy current sensor and its characteristic in magnetic material gas turbine rotor. In the past, magnetic particle inspection method was used for qualitative defect evaluation in magnetic material gas turbine rotor. And the ultrasonic inspection method was used for quantitative defect evaluation. Nowadays, eddy current method is used in magnetic gas turbine rotor inspection due to advanced sensor design technology. We developed multi-coil eddy current sensor for the rotor dovetail inspection. At first, rotor stress is analyzed for the determination of sensor position and number. The sensor coils are designed to cover the stress concentration area of rotor dovetail. We select optimum frequency according to material standard penetration data and experiment results. The rotor mock-up and artificial defects were made for the signal detection and resolution analysis of multi-coil eddy current sensor. The results show that signal detection and resolution capabilities are enhanced in comparison to the commercialized sensor enough for the gas turbine rotor inspection. So, this developed multi-coil eddy current sensor substituted for commercialized one and it applied in real gas turbine rotor inspection.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.1
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• pp.83-88
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• 2016

Because of the increasing tool cost for cutting hard-to-cut materials, abrasive water jet (AWJ) milling recently has been regarded as a potential alternative machining method. However, it is difficult to control the depth and width of cut in AWJ milling because they vary depending on many AWJ cutting parameters. On 27 conditions within a limited range of pressure, feed rate, and abrasive flow rate, AWJ cutting was conducted on titanium, and depth profiles were measured with a laser sensor. From the depth profile data, depth and width of cut were acquired at each condition. The relationships between depth and parameters and between width and parameters were derived through regression analysis. The former can provide proper cutting conditions and the latter the proper pick feed necessary to generate a milled surface. It is verified that pressure mostly affects depth, whereas abrasive flow rate mostly affects width.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.195-204
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• 2014

This paper presents the depth evaluation for object detection by automated assembling robots. Pattern distortion analysis from a structured light system identifies an object with the greatest depth from its background. An automated assembling robot should prior select and pick an object with the greatest depth to reduce the physical harm during the picking action of the robot arm. Object detection is then combined with a depth evaluation to provide contour, showing the edges of an object with the greatest depth. The contour provides shape information to an automated assembling robot, which equips the laser based proxy sensor, for picking up and placing an object in the intended place. The depth evaluation process using structured light for an automated electronics assembling robot is accelerated for an image frame to be used for computation using the simplest experimental set, which consists of a single camera and projector. The experiments for the depth evaluation process required 31 ms to 32 ms, which were optimized for the robot vision system that equips a 30-frames-per-second camera.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.8-17
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• 2011

Recently, fusion camera systems that consist of depth sensors and color cameras have been widely developed with the advent of a new type of sensor, time-of-flight (TOF) depth sensor. The physical limitation of depth sensors usually generates low resolution images compared to corresponding color images. Therefore, the pre-processing module, such as camera calibration, three dimensional warping, and hole filling, is necessary to generate the high resolution depth map that is placed in the image plane of the color image. However, the result of the pre-processing step is usually inaccurate due to errors from the camera calibration and the depth measurement. Therefore, in this paper, we present a depth map upsampling method robust these errors. First, the confidence of the measured depth value is estimated by the interrelation between the color image and the pre-upsampled depth map. Then, the detailed depth map can be generated by the modified kernel regression method which exclude depth values having low confidence. Our proposed algorithm guarantees the high quality result in the presence of the camera calibration errors. Experimental comparison with other data fusion techniques shows the superiority of our proposed method.