• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.821-830
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• 2017

The internet is an important infra resource that it controls the economy and society of our country. Also, it is providing convenience and efficiency of the everyday life. But, a case of various are occurred through an using vulnerability of an internet infra resource. Recently various attacks of unknown to the user are an increasing trend. Also, currently system of security control is focussing on patterns for detecting attacks. However, internet threats are consistently increasing by intelligent and advanced various attacks. In recent, the darknet is received attention to research for detecting unknown attacks. Since the darknet means a set of unused IP addresses, no real systems connected to the darknet. In this paper, we proposed an algorithm for finding black IPs through collected the darknet traffic based on a statistics data of port information. The proposed method prepared 8,192 darknet space and collected the darknet traffic during 3 months. It collected total 827,254,121 during 3 months of 2016. Applied results of the proposed algorithm, black IPs are June 19, July 21, and August 17. In this paper, results by analysis identify to detect frequency of black IPs and find new black IPs of caused potential cyber threats.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.209-220
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• 2014

Compressive Sensing (CS) is a new signal acquisition paradigm which enables sampling under Nyquist rate for a special kind of signal called sparse signal. There are plenty of CS recovery methods but their performance are still challenging, especially at a low sub-rate. For CS recovery of natural images, regularizations exploiting some prior information can be used in order to enhance CS performance. In this context, this paper addresses improving quality of reconstructed natural images based on Dantzig selector and smooth filters (i.e., Gaussian filter and nonlocal means filter) to generate a new regularization called smooth residual error regularization. Moreover, total variation has been proved for its success in preserving edge objects and boundary of reconstructed images. Therefore, effectiveness of the proposed regularization is verified by experimenting it using augmented Lagrangian total variation minimization. This framework is considered as a new CS recovery seeking smoothness in residual images. Experimental results demonstrate significant improvement of the proposed framework over some other CS recoveries both in subjective and objective qualities. In the best case, our algorithm gains up to 9.14 dB compared with the CS recovery using Bayesian framework.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.643-651
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• 2017

Steganography is one of information hiding technologies and discriminated from cryptography in that it focuses on avoiding the existence the hidden information from being detected by third parties, rather than protecting it from being decoded. In this paper, as an image steganography method which uses images as media, we propose a new block matching method that embeds information into the discrete wavelet transform (DWT) domain. The proposed method, based on a statistical analysis, reduces loss of embedding capacity due to inequable use of candidate blocks. It works in such a way that computes the variance of each candidate block, preserves candidate blocks with high frequency components while reducing candidate blocks with low frequency components by compressing them exploiting the k-means clustering algorithm. Compared with the previous block matching method, the proposed method can reconstruct secret images with similar PSNRs while embedding higher-capacity information.

• Geophysics and Geophysical Exploration
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• v.10 no.1
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• pp.44-49
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• 2007

It is difficult to obtain high-resolution images by 3D gravity inversion, because the problem is extremely underdetermined - there are too many model parameters. In order to reduce the number of model parameters we propose a 3D gravity inversion scheme utilising Euler deconvolution as a priori information. The essential point of this scheme is the reduction of the nonuniqueness of solutions by restricting the inversion space with the help of Euler deconvolution. We carry out a systematic exploration of the growing body process, but only in the restricted space within a certain radius of the Euler solutions. We have tested our method with synthetic gravity data, and also applied it to a real dataset, to delineate underground cavities in a limestone area. We found that we obtained a more reasonable subsurface density image by means of this combination between the Euler solution and the inversion process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1926-1938
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• 2009

In this study, detection system of the blood flow variation rate was implemented using the variation effect of bio electric impedance at time of the blood pressure measurement by means of impedance method. The blood pressure measurement was performed by the oscillometric method. The mean arterial pressure was calculated using maximum amplitude algorithm. The systolic and diastolic pressure were estimated by establishment of the various characteristic ratio according to mean arterial pressure range. Alternative static current source and lock_in amplifier were introduced to impedance measurement. The variation of blood volume was measured using variation bio impedance according to induced cuff pressure at measuring area.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.325-330
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• 2006

Integrating processes are frequently encountered in process industries. Generally they are most commonly associated with level control problems. In this paper, tuning formulae of PID controllers for set-point tracking and load disturbance rejection are presented on integrating processes with time delay. In particular, the controller parameters are determined such that performance criteria(IAE, ISE and ITSE) are minimized. Optimal PID parameter sets are obtained by means of a real-coded genetic algorithm(RCGA) and then tuning rules are addressed using obtained PID parameter sets and another RCGA. The performances of the proposed tuning rules are tested on two processes.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.511-511
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• 2023

기후변화로 동절기 기온 저하에 따른 수도계량기의 동파는 지속적으로 심화되고 있으며, 이는 계량기 교체 비용, 누수, 누수량 동결에 의한 2차 피해, 단수 등 사회적 문제를 야기한다. 이와같은 문제를 해결하고자 구조적 대책으로 개별 가정에서 동파 방지형 계량기를 설치할 수 있으나 이를 위한 비용발생이 상당하고, 비구조적 대책으로는 기상청의 동파 지도 알림 서비스를 활용하여 사전적으로 대응하고자 하나, 기상청자료는 대기 온도를 중심으로 제공하고 있기 때문에 해당서비스만으로는 계량기의 동파를 예측하는데 필요한 추가적인 다양한 변수를 활용하는데 한계가 있다. 최근 정부와 공공부문에서 22개 지역, 110개소 이상의 수도계량기함내 IoT 온도센서를 시범 설치하여 계량기 함내의 상태 등을 확인할 수 있는 사업을 수행했다. 전국적인 계량기 상태의 예측과 진단을 위해서는 추가적인 센서 설치가 필요할 것이나, IoT센서 설치 비용 등의 문제로 추가 설치가 더딘 실정이다. 본 연구에서는 겨울 동파 예방을 위해 실제 온도센서를 기반으로 가상센서를 구축하고, 이를 혼합한 하이브리드 방식으로 동파위험 기준에 따라 전국 동파위험 지도를 구축하였다. 가상센서 개발을 위해 독립변수로 위경도, 고도, 음·양지, 보온재 여부 및 기상정보(기온, 강수량, 풍속, 습도)를 활용하고, 종속변수로 실제 센서의 온도를 사용하여 기계학습 모델을 개발하였다. 지역 특성에 따라 정확한 모델을 구축하기 위해 위치정보 및 보온재여부 등의 변수를 활용하여 K-means 방법으로 군집화 하였으며, 각 군집별로 3가지의 기계학습 회귀모델을 적용하였다. 최적의 군집 수를 검토한 결과 4개가 적정한 것으로 판단되었다. 군집의 특성은 지역별 구분과 유사한 패턴을 보이며, 모든 군집에서 Gradient Boosting 회귀모델을 적용하는 것이 적합한 것으로 나타났다. 본 연구에서 개발한 모델을 바탕으로 조건에 따라 동파 예측 알람서비스에 실무적으로 활용할 수 있도록 양호·주의·위험·매우위험 총 4개의 기준을 설정하였다. 실제 본 연구에서 개발된 알고리즘을 국가상수도정보 시스템에 반영하여 테스트 수행중에 있으며, 향후 지속 검증을 할 예정에 있다. 이를 통해 동파 예방 및 피해 최소화, 물절약 등 직간접적 편익이 기대된다.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.