• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.5
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• pp.17-30
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• 2001

In this paper, we implemented a scalar multiplier needed at an elliptic curve cryptosystem over standard basis in $GF(2^{163})$. The scalar multiplier consists of a radix-16 finite field serial multiplier and a finite field inverter with some control logics. The main contribution is to develop a new fast finite field inverter, which made it possible to avoid time consuming iterations of finite field multiplication. We used an algorithmic transformation technique to obtain a data-independent computational structure of the Extended Euclid GCD algorithm. The finite field multiplier and inverter shown in this paper have regular structure so that they can be easily extended to larger word size. Moreover they can achieve 100% throughput using the pipelining. Our new scalar multiplier is synthesized using Hyundai Electronics 0.6$\mu\textrm{m}$ CMOS library, and maximum operating frequency is estimated about 140MHz. The resulting data processing performance is 64Kbps, that is it takes 2.53ms to process a 163-bit data frame. We assure that this performance is enough to be used for digital signature, encryption & decryption and key exchange in real time embedded-processor environments.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.579-586
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• 2021

Time series forecasting refers to predicting future time information based on past time information. Accurately predicting future information is crucial because it is used for establishing strategies or making policy decisions in various fields. Recently, a transformer model has been mainly studied for a time series prediction model. However, the existing transformer model has a limitation in that it has an auto-regressive structure in which the output result is input again when the prediction sequence is output. This limitation causes a problem in that accuracy is lowered when predicting a distant time point. This paper proposes a sequential decoding model focusing on the style transformation technique to handle these problems and make more precise time series forecasting. The proposed model has a structure in which the contents of past data are extracted from the transformer-encoder and reflected in the style-based decoder to generate the predictive sequence. Unlike the decoder structure of the conventional auto-regressive transformer, this structure has the advantage of being able to more accurately predict information from a distant view because the prediction sequence is output all at once. As a result of conducting a prediction experiment with various time series datasets with different data characteristics, it was shown that the model presented in this paper has better prediction accuracy than other existing time series prediction models.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.284-293
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• 2015

The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.511-522
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• 2014

During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency - Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.110-119
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• 2010

In this paper, we propose an automatic segmentation of the prostate using intensity distribution and gradient information in MR images. First, active shape model using adaptive intensity profile and multi-resolution technique is used to extract the prostate surface. Second, hole elimination using geometric information is performed to prevent the hole from occurring by converging the surface shape to the local optima. Third, the surface shape with large anatomical variation is corrected by using 2D gradient information. In this case, the corrected surface shape is often represented as rugged shape which is generated by the limited number of vertices. Thus, it is reconstructed by using surface modelling and smoothing. To evaluate our method, we performed the visual inspection, accuracy measures and processing time. For accuracy evaluation, the average distance difference and the overlapping volume ratio between automatic segmentation and manual segmentation by two radiologists are calculated. Experimental results show that the average distance difference was 0.3${\pm}$0.21mm and the overlapping volume ratio was 96.31${\pm}$2.71%. The total processing time of twenty patient data was 16 seconds on average.

• Journal of Korea Multimedia Society
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• v.7 no.7
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• pp.896-904
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• 2004

In this paper, We present an efficient variable rate speech coder for spectral efficiency and packet processing technique for packet loss compensation of a voice codec with 10msec frame in VoIP service. Through disconnecting the users from the spectral resource during silence interval of about 60% period, a variable rate voice coder based on a voice activity detection(VAD) can increase spectral gain by two times. The performance of the method was analyzed by variation of detected voice activity factor and degraded speech frame ratio under various background noise level, and compared those of G.729B of ITU-T 8kbps standard speech codec. A method to compensate lost packets utilized addition of recovery data to a main stream and error concealment scheme for speech quality enhancement, the performance is verified by reconstructed speech quality. The proposed scheme can achieve spectral gain by two times or enhance speech quality by 3dB through reserved bandwidth of VAD. Therefore, the proposed method can enhance a spectral efficiency or speech quality of VoIP.

• The KIPS Transactions:PartD
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• v.14D no.4 s.114
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• pp.381-388
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• 2007

Consumers changed to the subject to produce a digital contents as data transmission technique is advanced and a digital machine is diffused variously. Users are interested greatly in a user creative movie (UCM) production among various online contents. The UCM production method which uses the music and picture is the method that users make the UCM more easily. However, the UCM production service has the problem that any association does not exist in the music and picture and that the picture changes according to fixed time interval without the relation at a music rhythm. To solve this problem, we propose the UCM production method which uses a music analysis and picture analysis in the paper. A music analysis finds a picture change time according to the rhythm and a picture analysis finds the association of the picture. A music analysis finds strong parts of the sound which uses Root-Mean-Square (RMS). And a picture analysis classifies the picture as a scenery picture and people picture which uses structure simplicity of the picture(SSP) and face region detection. A picture analysis got correct result of 86.4% in the experiment and we can finds the association at each picture and arranges the sequence which the picture appears. Therefore, if we use a music and picture analysis at the UCM production, users may make natural and efficient movie.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.4
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• pp.48-54
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• 2020

Cracks in reinforced concrete structures are the most common type of damage and are used as important analytical data to understand the fracture behavior characteristics of structures. Currently, there is a problem that most of the crack investigation relies on visual inspection, therefore many researchers have proposed image analysis techniques to improve the problem. In this study, we proposed a crack evaluation method to be applied at an indoor experimental level using image analysis method. The image analysis technique using color is for distinguishing a boundary surface between objects existing in an image, and is a method for separating similar colors into one region based on a predefined color. In this study, to improve the accuracy of image analysis, blue paint was applied to the concrete surface and bending experiments were performed. The image analysis method was able to measure the crack width with superior accuracy compared to the crack diameter, and at the same time, it was also possible to analyze the deflection of the beam. Both the crack and deformation were able to confirm the accuracy similar to the existing measurement method, and it was found that the image analysis method was very excellent in terms of applicability.

• Progress in Medical Physics
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• v.26 no.2
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• pp.106-111
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• 2015

In Tomotherapy the couch sags during the treatment due to the weight of the patient. In this study, we developed a simple method to obtain the amount of the sag and the pitch angle of the couch using the image processing technique of MVCT images in Tomotherapy. Using the method we evaluated the sag and pitch of couch for 22 head and neck patients and one craniospinal irradiation (CSI) patient. The sag and the average pitch angle of couch were 0.40~1.54 mm and $0.7^{\circ}$ for head and neck patients, respectively. For head and neck patients, the sag increased as the longitudinal length of the irradiation volume increased and the pitch angle showed no relationship with the longitudinal length. For the CSI patient the sag was 4.97 mm. Using the method the amount of the couch sag could be measured easily and the measured data could be useful in determination of margins considering the table sag error.