• Journal of KIISE:Databases
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• v.36 no.4
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• pp.314-320
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• 2009

Skyline queries have recently received considerable attention in the searching services. The skyline contains interesting objects that are not dominated by any other objects on all dimensions. Many related works have processed a skyline on static data or on moving objects in Euclidean space. However, this paper assumes that the point of a skyline query continuously moves in road networks. We propose a new method that efficiently processes continuous skyline queries in road networks through pre-computed shortest range data of objects. Our experiments show that the proposed method is about 100 times faster than previous methods in terms of query processing time.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2600-2608
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• 1998

This paper proposes an algorithm for off line recognition of handwritten characters, especially effective for large-set characters such as Korean and Chinese characters. The algorithm is based on a minimum distance dlassification method which is simple and easy to implement but suffers from low recognition performance. Two strategies have been developed to improve its performance; one is multi-stage pre-classification and the other is candicate reordering. Effectiveness of the algorithm has been proven by and experimet with the samples of 574 classes in a handwritten Korean character catabase named PE02, where 86.0% of recognition accuracy and 15 characters per second of processing speed have been obtained.

• Bulletin of the Korean Chemical Society
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• v.35 no.8
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• pp.2431-2437
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• 2014

In this study, Ni, Ni-Cu and Ni/Cu catalysts were deposited onto C-fiber textiles via the electrophoretic deposition method, and the growth characteristics of carbon nanofibers on the deposited catalyst/C-fiber textiles were investigated. The catalyst deposition onto C-fiber textiles was accomplished by immersing the C-fiber textiles into Ni or Ni-Cu mixed solutions, producing the substrate by post-deposition of Ni onto C-fiber textiles with pre-deposited Cu, and passing it through a gas mixture of $N_2$, $H_2$ and $C_2H_4$ at $700^{\circ}C$ to synthesize carbon nanofibers. For analysis of the characteristics of the synthesized carbon nanofibers and the deposition pattern of catalysts, SEM, EDS, BET, XRD, Raman and XPS analysis were conducted. It was found that the amount of catalyst deposited and the ratio of Ni deposition in the Ni-Cu mixed solution increased with an increasing voltage for electrophoretic deposition. In the case of post-deposition of Ni catalyst onto substrates with pre-deposited Cu, both bimetallic catalyst and carbon nanofibers with a high level of crystallizability were produced. Carbon nanofibers yielded with the catalyst prepared in Ni and Ni-Cu mixed solutions showed a Y-shaped morphology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.416-419
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• 2003

The cellular neural networks have the circuit structure that differs from the form of general neural network. It consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template property. In this paper, time-multiplex image processing technique is applied for processing large images using small size CNN cell block, and we simulate the edge detection of a large image using the simulator implemented with a c program and matlab model. A 5$\times$5 CNN hardware and pre post processor is also implemented and is under test.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.252-256
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• 1998

Accurate mapping of satellite images is one of the most important Parts in many remote sensing applications. Since the position and the attitude of a satellite during image acquisition cannot be determined accurately enough, it is normal to have several hundred meters' ground-mapping errors in the systematically corrected images. The users which require a pixel-level or a sub-pixel level mapping accuracy for high-resolution satellite images must use a number of Ground Control Points (GCPs). In this paper, the performance of two geometric correction algorithms is tested and compared. One is the polynomial warping algorithm which is simple and popular enough to be implemented in most of the commercial satellite image processing software. The other is full camera modelling algorithm using Physical orbit-sensor-Earth geometry which is used in satellite image data receiving, pre-processing and distribution stations. Several criteria were considered for the performance analysis : ultimate correction accuracy, GCP representatibility, number of GCPs required, convergence speed, sensitiveness to inaccurate GCPs, usefulness of the correction results. This paper focuses on the usefulness of the precision correction algorithm for regular image pre-processing operations. This means that not only final correction accuracy but also the number of GCPs and their spatial distribution required for an image correction are important factors. Both correction algorithms were implemented and will be used for the precision correction of KITSAT-3 images.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.38-44
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• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.452-455
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• 2018

Wheel loader performs digging and dumping tasks using boom and bucket. The operation of the wheel loader equipment has a lot of repetitive tasks and the working environment is poor, but only by hand by man. Recently, demands for applying unmanned automated systems are increasing more and more in electrical components. For automated systems, accurate angle detection is indispensable for stable control. This paper proposes a signal processing system for precise angular control with noise robust features. As a result of implementing the proposed system and applying it to the wheel loader boom angle system, it was possible to detect an angle change of about 0.1 degree.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.532-539
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• 2003

The paper proposes a pre-processing method for efficient processing of XML queries in information retrieval systems with a large amount of XML documents. For the pre-processing, we use a signature-based approach. In the conventional (flat document-based) information retrieval systems, user queries consist of keywords and boolean operators, and thus signatures are structured in a flat manner. However, in XML-based information retrieval systems, the user queries have the form of path query. Therefore, the flat signature cannot be effective for XML documents. In the paper, we propose a structured signature for XML documents. Through experiments, we evaluate the performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4866-4888
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• 2020

One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.240-243
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• 2022

Tracking the location of program defects is an essential task for software maintenance and repair. When a bug report is submitted, bug localization is a costly task because of the developer's manual effort. Many researchers have tried to automate the task, but according to the reported results, the performance is still insufficient in practice. Therefore, in this study, we analyzed a large amount of bug report data and the latest research and found that the existing studies used only one preprocessing without considering the characteristics of the bug report. In this paper, to solve the problems mentioned earlier, we propose a pre/post-processing operator selection approach for bug localization.