• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.374-377
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• 2004

Existing Clustering Methods for spatial data mining process only point objects, not objects with polygonometry such as lines and areas. It is because that distance computation between objects with polygonomery for clustering is more complex than point objects. To solve this problem, we design a clustering method based on regular grid cell structures. In details, it refutes cost and time for distance computation using cell relationships in grid cell structures.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.53.2-53
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• 2001

The Non-Contact Optical Sensor using the Hologram Laser for CD Pickup was developed to measure a shape of transparent objects and shown a good performance. Therefore the problems caused by the contact sensor are solved by using the Non-Contact Sensor. The Non-Contact Sensor has to move toward the objects and obtain the Focus Error Signal to measure a position of transparent objects. However, if the distance between the sensor and the object is shorter than the working distance of the objective lens, the sensor will be collided against the objects. In this paper we proposed a new algorithm to estimate the start position of the Focus Error Signal to solve the problems of collision between the sensor and the objects. In addition, we verified that the algorithm is free from the collision in the real time measurement.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.23-30
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• 2020

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.9-16
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• 1997

In this paper, a newly radial electronic shearography system was developed in order to study vibration characteristics of symmetrical objects. We utilized the electro-optic holography theory for quantificational analysis and a porror prism for shearing two inages radially in this study. These image data obtained by this shearography give us various distributions of the gradient of vibration amplitude, and they are useful informations to study vibrational characteristics of symmetrical objects. Finally this developed system with phase stepping and modulation was applied to fans and disks to inspect characteristics of the vibration and the blance of symmetrical objects and obtained good results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.65-71
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• 2024

An indirect visual SLAM takes raw image data and exploits geometric information such as key-points and line edges. Due to various environmental changes, SLAM performance may decrease. The main problem is caused by dynamic objects especially in highly crowded environments. In this paper, we propose a robust feature-based visual SLAM, building on ORB-SLAM, via multi-channel dynamic objects estimation. An optical flow and deep learning-based object detection algorithm each estimate different types of dynamic object information. Proposed method incorporates two dynamic object information and creates multi-channel dynamic masks. In this method, information on actually moving dynamic objects and potential dynamic objects can be obtained. Finally, dynamic objects included in the masks are removed in feature extraction part. As a results, proposed method can obtain more precise camera poses. The superiority of our ORB-SLAM was verified to compared with conventional ORB-SLAM by the experiment using KITTI odometry dataset.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.119-126
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• 2003

The problem of tracking moving objects in a video stream is discussed in this pa-per. We discussed the popular technique of optical flow for moving object detection. Optical flow finds the velocity vectors at each pixel in the entire video scene. However, optical flow based methods require complex computations and are sensitive to noise. In this paper, we proposed a new method based on the Hough transform and on voting accumulation for improving the accuracy and reducing the computation time. Further, we applied the Boo-lean based edge detector for edge detection. Edge detection and segmentation are used to extract the moving objects in the image sequences and reduce the computation time of the CHT. The Boolean based edge detector provides accurate and very thin edges. The difference of the two edge maps with thin edges gives better localization of moving objects. The simulation results show that the proposed method improves the accuracy of finding the optical flow vectors and more accurately extracts moving objects' information. The process of edge detection and segmentation accurately find the location and areas of the real moving objects, and hence extracting moving information is very easy and accurate. The Combinatorial Hough Transform and voting accumulation based optical flow measures optical flow vectors accurately. The direction of moving objects is also accurately measured.

• Journal of The Korean Astronomical Society
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• v.30 no.1
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• pp.83-94
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• 1997

We have identified the candidates for the primordial galaxies in the process of formation in the Hubble Deep Field (hereafter HDF). In order to select these objects we have removed objects brighter than 29-th magnitude in the HDF images and smoothed the maps with the Gaussian filters with the FWHM of 0.8' and 4' to obtain the difference maps. This has enabled us to find. very faint diffuse structures close to the sky level. Peaks are identified in the difference map for each of three HDF chips with three filters (F450W, F606W, and F814W). They have the apparent AB magnitudes typically between 29 and 31. The objects identified in different wavelengths filters have a strong cross-correlations. The correlation lengths are about 0.8'. This means that an object found in one filter can be also found as a peak within 0.8' separation in another filter, thus telling the reality of the identified objects. This angular scale is also the size of the primordial galaxies which have strong color fluctuations on their surfaces. Their large-scale distribution quite resembles that of nearby galaxies, supporting the idea that these objects are ancestors of the present bright galaxies forming at statistically high density regions. Inspections on individual objects show that these primordial galaxy candidates have tiny multiple glares embedded in diffuse backgrounds. Their radial light distributions are quite different from that of nearby bright galaxies. We may be now looking at the epoch of galaxy formation.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.3
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• pp.237-241
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• 1999

It was investigated that the condition of fishing ground gave what kind of effect school type in relation to the property for the target school after the vertical temperature profile and school type were observed when tuna purse seiner for tuna purse seine was operated in the western pacific Ocean around the equator between December 12, 1993 and January 26, 1994. The results are as follows;1. This study divided fish schools associated with drift objects, a one targeted-operation, and the pelagic migrating fish schools that are another one for the property. The total of 23 operations were complected including 6 that targeted fish schools associated with drift objects and 17 that targeted pelagic migrating fish schools. It was known that the main operation was conducted on pelagic migrating fish school.2. Average and standard deviation of the depth of the surface mixing layer was 86.0m and 13.8m for fish schools associated with drift objects. 61.6m and 13.0m for pelagic migrating fish schools, respectively. The average depth of the surface mixing layer of the pelagic migrating fish schools was lower when compared to the fish schools associated with drift objects however the concentration of standard deviation was higher than those fish schools associated with drifted objects. Accordingly, it was assumed that the effect of vertical temperature profile for pelagic migrating fish school was higher than the fish schools associated with drift objects.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.663-666
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• 2005

The Geography Markup Language (GML) is an XML encoding for the transport and storage of geographic information, including both the geometry and properties of geographic features. This paper uses the GML to provide extendibility and interoperability of spatial data in moving objects management system. Since the purpose of the system is to provide locations of the moving objects in the web and mobile environments, we used the GML both for presenting map data and trajectories of the moving objects. The proposed system is composed of Location Data Interface, Moving Objects Engine, and Web/Mobile Presentation Interface. We utilized the concept of Web Map Server, that is web mapping technology of OGC (Open Geospatial Consortium), to integrate map data and the location information of the moving objects. In the process of the integration, we used the standard data model and interfaces while defining new application schema. Since our suggested system uses open spatial data encoding and interfaces, both extendibility and interoperability are guaranteed.