• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.783-789
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• 2001

The purpose of this study is to build and to manage environment models with line segments from the sonar range data on obstacles in unknown and varied environments. The proposed method subsequently employs a two-stage data-transform process in order to extract environmental line segments from the range data on obstacles. In the first stage, the occupancy grid extracted from the range data is accumulated to a two-dimensional local histogram grid. In the second stage, a line histogram extracted from an local histogram gird is based on a Hough transform, and matching is a process of comparing each of the segments in the global line segments map against the line segments to detect similarity in overlap, orientation, and arrangement. Each of these tests is made by comparing one of the parameters in the segment representation. After the tests, new line segments are composed to the global line segments map. The proposed technique is illustrated by experiments in an indoor environment.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.971-971
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• 2009

• ETRI Journal
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• v.40 no.5
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• pp.677-686
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• 2018

A novel data hiding technique based on modified histogram shifting that incorporates multi-bit secret data hiding is proposed. The proposed technique divides the image pixel values into embeddable and nonembeddable pixel values. Embeddable pixel values are those that are within a specified limit interval surrounding the peak value of an image. The limit interval is calculated from the number of secret bits to be embedded into each embeddable pixel value. The embedded secret bits can be perfectly extracted from the stego image at the receiver side without any overhead bits. From the simulation, it is found that the proposed technique produces a better quality stego image compared to other data hiding techniques, for the same embedding rate. Since the proposed technique only embeds the secret bits in a limited number of pixel values, the change in the visual quality of the stego image is negligible when compared to other data hiding techniques.

• Journal of the Korean Society for Precision Engineering
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• v.2 no.1
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• pp.21-27
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• 1985

차량, 항공, 교량 및 기계구조물 등 랜덤한 실동하중을 받는 구조물의 응력 및 변위, 진동등의 피로 Data 또는 TRAFFIC 등에 의한 건축물의 변위 및 응력발생빈도를 집록하기 위한 Histogram Recorder System에 대한 내용과 측정예를 소개하고자 한다. 본 System은 8(4)챈 널의 스트레인 게이지 또는 스트레인 게이지식 각종변환기와 각종 Sensor로부터의 출력전압 등 Analog 입력을 수록하여, Digital 처리하여 micro computer를 사용, 미리 프로그램된 방식에 따라 측정과 동시에 실동시간으로 해석처리하여 빈도수로서 내부에 기억저장 시키는 것이다. 따라서 본 Histogtram Recorder 본체는 소형으로 견고하며 조금도 제어부분을 갖지 않고 소요의 해석방법의 프로그램백만을 셋트한 개별의 제어기만을 통해가지고 프로그램을 기입만 하며는 그다음은 손하나 안대고도, 그리고도 또 측정중에 제3자에 의한 제어조작 잘못이 발생할 위험도 없고 1 년이상에 걸친 장기간의 .+-. 32 Slices의 각 레벨당 각각 40 억을 넘는 대량의 빈도수를 자동적으로 집록 할 수가 있다. 집록된 Data는 제어장치에 의해 정리된 Datam는 제어장치에 의해 정리된 Histogram의 형태로 읽어나갈 수가 있어 관찰이 가능할 뿐만 아니라 프린터기록 또는 기록장치에 이송시켜서 Data 만 따로 가져올 수가 있어 필요에 따라서는 one line으로 Host computer에 접속시킬 수가 있어 더욱 고도의 처리를 할 수가 있다. 빈도해석프로그램으로서는 극대, 극소, 최대, 최소, 진폭, 시간 등을 pack으로서 준비되어 있어 이에 대한 시스템의 고성 동작 및 성능등을 소개하고자 한다.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.87-93
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• 2022

Since 99% of PCs operating in the defense domain use the Windows operating system, detection and response of Window-based malware is very important to keep the defense cyberspace safe. This paper proposes a model capable of detecting malware in a Windows PE (Portable Executable) format. The detection model was designed with an emphasis on rapid update of the training model to efficiently cope with rapidly increasing malware rather than the detection accuracy. Therefore, in order to improve the training speed, the detection model was designed based on a Bidirectional LSTM (Long Short Term Memory) network that can detect malware with minimal sequence data without complicated pre-processing. The experiment was conducted using the EMBER2018 dataset, As a result of training the model with feature sets consisting of three type of sequence data(Byte-Entropy Histogram, Byte Histogram, and String Distribution), accuracy of 90.79% was achieved. Meanwhile, it was confirmed that the training time was shortened to 1/4 compared to the existing detection model, enabling rapid update of the detection model to respond to new types of malware on the surge.

• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.79-91
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• 2013

This paper proposes a systematic methodology that could be used to decide which one shows the best performance among space filling curves (SFCs) in applying lower-dimensional transformations to histogram sequences. A histogram sequence represents a time-series converted from an image by the given SFC. Due to the high-dimensionality nature, histogram sequences are very difficult to be stored and searched in their original form. To solve this problem, we generally use lower-dimensional transformations, which produce lower bounds among high dimensional sequences, but the tightness of those lower-bounds is highly affected by the types of SFC. In this paper, we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality, which comes from an intuition of "if the entries are adjacent in a histogram sequence, their corresponding cells should also be adjacent in its original image." We also propose spatial locality preservation metric (slpm in short) that quantitatively evaluates spatial locality and present its formal computation method. We then evaluate five SFCs from the perspective of slpm and verify that this evaluation result concurs with the performance evaluation of lower-dimensional transformations in real image matching. Finally, we perform k-NN (k-nearest neighbors) search based on lower-dimensional transformations and validate accuracy of the proposed slpm by providing that the Hilbert-order with the highest slpm also shows the best performance in k-NN search.

• Journal of Veterinary Clinics
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• v.24 no.4
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• pp.568-572
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• 2007

This study was carried out to show at the fractal analysis complements the practical disadvantage of gray level histogram which is designed to measure the quantitative classification of echo patterns in ultrasonographic image of parenchymal organs such as spleen and kidney and it is a practical method of measurement for quantitative classification. By using ultrasonographs, kidney and spleen of 21 healthy Beagles were fixed under different gain settings to be scanned for echo patterns and results were analyzed with body gray level histogram and fractal analysis. Then it was compared based on the statistical data obtained. Although there was a proportionate increase in histogram along with gain settings, there were consistencies in the fractal dimension. In terms of quantitative analysis in ultrasonographic images, fractal analysis is concluded to complement the practical disadvantage of gray level histogram.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.927-934
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• 2003

Database query optimates the selectivety of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show adventages of the proposed approach.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.281-292
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• 2004

Selectivity estimation for spatial query is very important process used in finding the most efficient execution plan. Many works have been performed to estimate accurate selectivity. Although they deal with some problems such as false-count, multi-count, they can not get such effects in little memory space. Therefore, we propose a new technique called MW Histogram which is able to compress summary data and get reasonable results and has a flexible structure to react dynamic update. Our method is based on two techniques : (a) MinSkew partitioning algorithm which deal with skewed spatial datasets efficiently (b) Wavelet transformation which compression effect is proven. The experimental results showed that the MW Histogram which the buckets and wavelet coefficients ratio is 0.3 is lower relative error than MinSkew Histogram about 5%-20% queries, demonstrates that MW histogram gets a good selectivity in little memory.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1239-1247
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• 2015

In this study, 64 administrative regions with high frequencies of apartment trade in Seoul, Korea are classified by the apartment sale price. To consider distributions of apartment price for each region as well as the mean of the price, the symbolic histogram-valued data approach is employed. Symbolic data include all types of data which have internal variation in themselves such as intervals, lists, histograms, distributions, and models, etc. As a result of the cluster analysis using symbolic histogram data, it is found that Gangnam, Seocho, and Songpa districts and regions near by those districts have relatively higher prices and larger dispersions. This result makes sense because those regions have good accessibility to downtown and educational environment.