• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.12
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• pp.97-102
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• 2011

We propose a novel displacement measurement method in the LVDT (Linear Variable Differential Transformer) structure. This proposed algorithm is independent of coil pattern, which may be implemented to PCB, or transformer component, because it is based on the signal-mapping method. we have manufactured several boards which have different coil patterns and our algorithm is ported into TMS320F2812 of TI DSP chipset. The output signal has high accuracy and high stability although PCB coil pattern are coarse.

• Journal of Ocean Engineering and Technology
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• v.35 no.3
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• pp.183-190
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• 2021

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera's mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2-3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• Bulletin of the Korean Mathematical Society
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• v.48 no.5
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• pp.991-1002
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• 2011

An iterative algorithm is provided to find a common element of the set of fixed points of a nonexpansive mapping and the set of solutions of some variational inequality in a Hilbert space. Using this result, we consider a strong convergence result for finding a common fixed point of a nonexpansive mapping and a strictly pseudocontractive mapping. Our results include the previous results as special cases and can be viewed as an improvement and refinement of the previously known results.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.488-493
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• 1989

In this paper, the structure of CMAC address mapping is first revisited, and the address hashing function and the random mapping is discussed in the conventional CMAC implementation. Then the effective size of CMAC memory is derived from the modulus property of the CMAC address vector, and a new hashing function for the effective memory mapping is proposed for a CMAC implementation with feasible memory size and no troublesome random mapping. Finally, the performance of the conventional CMAC learning algorithm and that of the proposed new CMAC scheme arc compared via simulations.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.15-17
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• 2006

This paper presents a new global intensity compensation method for extracting moving object in a visual surveillance system by compensating time variant intensity changes of background region. The method that compensates a little changes of intensity due to time variant illumination change and automatic gain control of camera is called global intensity compensation. The proposed method expresses global intensity change with a mapping table to describe complex form of intensity change while the previous method models this global intensity change with a simple function as a straight line. The proposed method builds the mapping table by calculating the cross histogram between two images and then by selecting an initial point for generating the mapping table by using Hough transform applied to the cross histogram image. Then starting from the initial point, the mapping table is generated according to the proposed algorithm based on the assumption that reflects the characteristic of global intensity change. Experimental results show that the proposed method makes the compensation error much smaller than the previous GIC method

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.5_6
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• pp.213-222
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• 2003

Most existing algorithms adopt the forward mapping method that is based on vector representation. Problem of existing algorithms Is the exponential increase of memory usage with number of layers. This degrades the accuracy of the boundary pattern representation. Our method uses bitmap representation and does not require any additional post-processing for conversion of vector-form results to bitmap-form. A new and efficient algorithm is presented in this paper for the generation of hyperbolic patterns by means of backward mapping methods.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.33-48
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• 2006

The Semantic Web and its related technologies have been opening the era of information sharing via the Web. There are, however, several huddles still to overcome in the new era, and one of the major huddles is the issue of information integration, unless a single unified and huge ontology could be built and used which could address everything in the world. Particularly in the e-business area, the problem of information integration is of a great concern for product search and comparison at various Internet shopping sites and e-marketplaces. To overcome this problem, we proposed an ontology-driven mapping algorithm between heterogeneous product classification and description frameworks. We also peformed a comparative evaluation of the proposed mapping algorithm against a well-Down ontology mapping tool, PROMPT.

• Journal of Korea Multimedia Society
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• v.13 no.11
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• pp.1621-1629
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• 2010

This paper presents an improved fast MSRCR algorithm that MSRs are commonly adopted at tone mapping in color vision. Conventional MSRs consist of three SSRs, which use three Gaussian functions with different scales as those surround ones. This convolution processes require much computation load. Therefore, the proposed algorithm adopts a hierarchical discrete correlation which is equivalent to Gaussian function and the Retinex process is only applied to the luminance channel in order to get a fast processing. A simple color preservation scheme is applied to the Retinex output from the luminance channel in the proposed MSRCR algorithm. Experimental results show that the proposed algorithm required less number of oprations and computation time about 1/9.5 and 1/3.5 times, respectively, than those of the simplest MSR and was equivalent to conventional MSRs.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.339-346
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• 2006

Increased usage of autonomous underwater vehicle (AUV) has led to the development of alternative navigational methods that do not employ the acoustic beacons and dead reckoning sensors. This paper describes a simultaneous localization and mapping (SLAM) scheme that uses range sonars mounted on a small AUV. The SLAM is one of such alternative navigation methods for measuring the environment that the vehicle is passing through and providing relative position of AUV by processing the data from sonar measurements. A technique for SLAM algorithm which uses several ranging sonars is presented. This technique utilizes an unscented Kalman filter to estimate the locations of the AUV and objects. In order for the algorithm to work efficiently, the nearest neighbor standard filter is introduced as the algorithm of data association in the SLAM for associating the stored targets the sonar returns at each time step. The proposed SLAM algorithm is tested by simulations under various conditions. The results of the simulation show that the proposed SLAM algorithm is capable of estimating the position of the AUV and the object and demonstrates that the algorithm will perform well in various environments.