• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.258-269
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• 2002

There have been many studies of automatic term recognition (ATR) and they have achieved good results. However, there are scopes to improve the performance of extracting terms still further by using the additional technical dictionaries. This paper focuses on the method for extracting terms using the hierarchy among technical dictionaries. Moreover, a statistical method based on frequencies, foreign words, and nested relations assists extracting terms which do not appear in dictionaries. Our method produces relatively good results for this task.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.332-338
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• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.777-784
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• 2008

Recently in the field of model checking, to overcome the state explosion problem, the method of using a SAT-solver is mainly researched. To use a SAT-solver, the system to be verified is translated into CNF and the Boolean cardinality constraint is widely used in translating the system into CNF. In BCC it is dealt with set of boolean variables, but there is no translating method of the sequence among Boolean variables. In this paper, we propose methods for translating the problem, which is extracting a subsequence with length k from a sequence of Boolean variables, into CNF formulas. Through experimental results, we show that our method is more efficient than using only BCC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2287-2312
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• 2018

Extracting key visual information from images containing natural scene is a challenging task and an important step for the visually impaired to recognize information based on tactile graphics. In this study, a novel method is proposed for extracting salient regions based on global contrast enhancement and saliency cuts in order to improve the process of recognizing images for the visually impaired. To accomplish this, an image enhancement technique is applied to natural scene images, and a saliency map is acquired to measure the color contrast of homogeneous regions against other areas of the image. The saliency maps also help automatic salient region extraction, referred to as saliency cuts, and assist in obtaining a binary mask of high quality. Finally, outer boundaries and inner edges are detected in images with natural scene to identify edges that are visually significant. Experimental results indicate that the method we propose in this paper extracts salient objects effectively and achieves remarkable performance compared to conventional methods. Our method offers benefits in extracting salient objects and generating simple but important edges from images containing natural scene and for providing information to the visually impaired.

• Smart Media Journal
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• v.10 no.1
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• pp.39-46
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• 2021

This study is about a method of extracting a summary from a news article in consideration of the importance of each sentence constituting the article. We propose a method of calculating sentence importance by extracting the probabilities of topic sentence, similarity with article title and other sentences, and sentence position as characteristics that affect sentence importance. At this time, a hypothesis is established that the Topic Sentence will have a characteristic distinct from the general sentence, and a deep learning-based classification model is trained to obtain a topic sentence probability value for the input sentence. Also, using the pre-learned ELMo language model, the similarity between sentences is calculated based on the sentence vector value reflecting the context information and extracted as sentence characteristics. The topic sentence classification performance of the LSTM and BERT models was 93% accurate, 96.22% recall, and 89.5% precision, resulting in high analysis results. As a result of calculating the importance of each sentence by combining the extracted sentence characteristics, it was confirmed that the performance of extracting the topic sentence was improved by about 10% compared to the existing TextRank algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1711-1717
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• 2017

In this paper, we propose a fast and accurate method of extracting lens array lattice in integral imaging by using an appropriate calibration pattern image and fast median filtering. In order to extract the lattice of a lens array, vertical and horizontal edge images are required. To extract edge images, the well-known previous method used separable median filters. However, this method is slow and difficult to determine the median filter size. In order to overcome this problem, we try to improve speed by calculating median value through binary counting method. In addition, we propose a calibration pattern image that detects edges well and improves the accuracy. Experimental results indicate that the proposed method is superior to the existing method in extracting the lattice of a lens array in integral imaging.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.10
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• pp.1-8
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• 2002

C- V method is a means to determine the effective channel length for miniaturized MOSFET's. This method achieves L$_{eff}$ by extracting a unique channel length independent extrinsic overlap length($\Delta$L) at a critical gate bias point. In this paper, we conducted an experiment on two different C-V methods. L$_{eff}$ extracted from experiment is compared with L$_{eff}$ simulated from a two-dimensional (2-D) device simulator, and the accuracy of C-V method for L$_{eff}$ extraction is analyzed.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.77-80
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• 2001

In general, the informations of the inner image that user interested in are limited to a special domain. In this paper, as using Wavelet Transform for dividing image into high frequency and low frequency, We can separate foreground including many data. After calculating object boundary of separated part, We extract special features using Color Coherence Vector. According to results of this experiment, the method of comparing data extracting foreground features is more effective than comparing data extracting features of entire image when we extract the image user interested in.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.301-304
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• 1999

In this paper, we proposed the new method of extracting the position and the length of the input-line by using only two parameters ($\theta$, $\rho$) from the HT(Hough Transform) data. The computer simulations and the optical experiments by using the HT CGH(Computer Generated Hologram) filter is perfermed. The results are very similar to those of the computer simulation results.

• Journal of the Korean Institute of Navigation
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• v.22 no.4
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• pp.37-44
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• 1998

This paper presents a procedure for automated extraction of ridge shadows in noisy gray images. This procedure mainly consists of 1) a deformable model which is designed basing upon the knowledge about the shape of shadows and is expected to be useful in extracting ridge shadows especially located in low signal to noise ratio background, and 2) the scale space scheme which is also useful even if there is less information about the size and the positions of ridge shadows in advance. This procedure is applied to artificial images and its performance is evaluated experimentally.