• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.248-248
• /
• 2020

토양수분은 증발산, 유출, 침투 등 물수지 요소들과 밀접한 연관이 있는 주요한 변수 중에 하나이다. 토양수분의 정도는 토양의 특성, 토지이용 형태, 기상 상태 등에 따라 공간적으로 상이하며, 특히 기상 상태에 따라 시간적 변동성을 보이고 있다. 기존 토양수분 측정은 토양시료 채취를 통한 실내 실험 측정과 측정 장비를 통한 현장 조사 방법이 있으나 시간적, 경제적 한계점이 있으며, 원격탐사 기법은 공간적으로 넓은 범위를 포함하지만 시간 해상도가 낮은 단점이 있다. 또한, 모델링을 통한 토양수분 예측 기술은 전문적인 지식이 요구되며, 복잡한 입력자료의 구축이 요구된다. 최근 머신러닝 기법은 수많은 자료 학습을 통해 사용자가 원하는 출력값을 도출하는데 널리 활용되고 있다. 이에 본 연구에서는 토양수분과 연관된 다양한 기상 인자들(강수량, 풍속, 습도 등)을 활용하여 머신러닝기법의 반복학습을 통한 토양수분의 예측 가능성을 분석하고자 한다. 이를 위해 시공간적으로 토양수분 실측 자료가 잘 구축되어 있는 청미천과 설마천 유역을 대상으로 머신러닝 기법을 적용하였다. 두 대상지에서 2008년~2012년 수문자료를 확보하였으며, 기상자료는 기상자료개방포털과 WAMIS를 통해 자료를 확보하였다. 토양수분 자료와 기상자료를 머신러닝 알고리즘을 통해 학습하고 2012년 기상 자료를 바탕으로 토양수분을 예측하였다. 사용되는 머신러닝 기법은 의사결정 나무(Decision Tree), 신경망(Multi Layer Perceptron, MLP), K-최근접 이웃(K-Nearest Neighbors, KNN), 서포트 벡터 머신(Support Vector Machine, SVM), 랜덤 포레스트(Random Forest), 그래디언트 부스팅 (Gradient Boosting)이다. 토양수분과 기상인자 간의 상관관계를 분석하기 위해 히트맵(Heat Map)을 이용하였다. 히트맵 분석 결과 토양수분의 시간적 변동은 다양한 기상 자료 중 강수량과 상대습도가 가장 큰 영향력을 보여주었다. 또한 다양한 기상 인자 기반 머신러닝 기법 적용 결과에서는 두 지역 모두 신경망(MLP) 기법을 제외한 모든 기법이 전반적으로 실측값과 유사한 형태를 보였으며 비교 그래프에서도 실측값과 예측 값이 유사한 추세를 나타냈다. 따라서 상관관계있는 과거 기상자료를 통해 머신러닝 기법 기반 토양수분의 시간적 변동 예측이 가능할 것으로 판단된다.

• Journal of Astronomy and Space Sciences
• /
• v.40 no.4
• /
• pp.199-215
• /
• 2023

The Korea Pathfinder Lunar Orbiter (KPLO), the first South Korea lunar exploration probe, successfully arrived at the Moon on December, 2022 (UTC), following a 4.5-month ballistic lunar transfer (BLT) trajectory. Since the launch (4 August, 2022), the KPLO magnetometer (KMAG) has carried out various observations during the trans-lunar cruise phase and a 100 km altitude lunar polar orbit. KMAG consists of three fluxgate magnetometers capable of measuring magnetic fields within a ± 1,000 nT range with a resolution of 0.2 nT. The sampling rate is 10 Hz. During the originally planned lifetime of one year, KMAG has been operating successfully while performing observations of lunar crustal magnetic fields, magnetic fields induced in the lunar interior, and various solar wind events. The calibration and offset processes were performed during the TLC phase. In addition, reliabilities of the KMAG lunar magnetic field observations have been verified by comparing them with the surface vector mapping (SVM) data. If the KPLO's mission orbit during the extended mission phase is close enough to the lunar surface, KMAG will contribute to updating the lunar surface magnetic field map and will provide insights into the lunar interior structure and lunar space environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.456-477
• /
• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Korean Journal of Environment and Ecology
• /
• v.32 no.6
• /
• pp.686-697
• /
• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of the Korea Computer Graphics Society
• /
• v.16 no.1
• /
• pp.1-7
• /
• 2010

With the widespread use of vision-based surveillance systems, the capability for person identification is now an essential component. However, the CCTV cameras used in surveillance systems tend to produce relatively low-resolution images, making it difficult to use face recognition techniques for person identification. Therefore, an algorithm is proposed for person identification in CCTV camera images based on the clothing. Whenever a person is authenticated at the main entrance of a building, the clothing feature of that person is extracted and added to the database. Using a given image, the clothing area is detected using background subtraction and skin color detection techniques. The clothing feature vector is then composed of textural and color features of the clothing region, where the textural feature is extracted based on a local edge histogram, while the color feature is extracted using octree-based quantization of a color map. When given a query image, the person can then be identified by finding the most similar clothing feature from the database, where the Euclidean distance is used as the similarity measure. Experimental results show an 80% success rate for person identification with the proposed algorithm, and only a 43% success rate when using face recognition.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.8
• /
• pp.708-718
• /
• 2002

In this paper, the problems in identifying the noise sources by using the sound intensity technique are dealt with for the general radiated near-field from vibro-acoustic sources. For this purpose, a three-dimensional model structure resembling the engine room of a car or heavy equipment is considered. Similar to the practical situations, the model contains many mutually coherent and incoherent noise sources distributed on the complicated surfaces. The sources are located on the narrow, connected, reflecting planes constructed with rigid boxes, of which a small clearance exists between the whole box structure and the reflecting bottom. The acoustic boundary element method is employed to calculate the acoustic intensity at the near-field surfaces and interior spaces. The effects of relative source phases, frequencies, and locations are investigated, from which the results are illustrated by the contour map, vector plot, and energy streamlines. It is clearly observed that the application of sound intensity technique to the reactive or reverberant field, e.g., scanning over the upper engine room as is usually practiced, can yield the detection of fake sources. For the precise result for such a field, the field reactivity should be checked a priori and the proper effort should be directed to reduce or improve the reactivity of sound field.

• Spatial Information Research
• /
• v.21 no.3
• /
• pp.89-101
• /
• 2013

Recently, importance of indoor space is on the rise, as larger and more complex buildings are taking place due to development of building technology. Accordingly, range of the target area of spatial information service is rapidly expanding from outdoor space to indoor space. Various demands for indoor spatial information are expected to be created in the future through development of high technologies such as IT Mobile and convergence with various area. Thus this research takes a look at available methods for building indoor spatial information and then builds high accuracy three-dimensional indoor spatial information using indoor high accuracy laser survey and 3D vector process technique. The accuracy of built 3D indoor model is evaluated by overlap analysis method refer to a digital map, and the result showed that it could guarantee its positional accuracy within 0.04m on the x-axis, 0.06m on the y-axis. This result could be used as a fundamental data for building indoor spatial data and for integrated use of indoor and outdoor spatial information.

• The Journal of the Korean Society for Microbiology
• /
• v.35 no.3
• /
• pp.263-271
• /
• 2000

A clinical isolate of Klebsiella pneumoniae K7746 produced the extended-spectrum ${\beta}$-lactamase (ESBL) SHV-12. A 6.6 kb BamHI fragment containing the $bla_{SHV-12}$ gene of K7746 strain was cloned into pCRScriptCAM vector resulting in the recombinant plasmid p7746-Cl. The restriction map of 3.6 kb inserted DNA and sequences immediately surrounding $bla_{SHV-12}$ of p7746-C1 were homologous to plasmid pMPA2a carrying $bla_{SHV-2a}$. In addition, both $bla_{SHV-12}$ and $bla_{SHV-2a}$ were expressed from a common hybrid promoter made of the -35 region derived from the left inverted repeat of IS26 and the -10 region from the $bla_{SHV}$ promoter itself. The results indicate that $bla_{SHV-12}$ and $bla_{SHV-2a}$ may have evolved from a common ancestor in the sequential order of $bla_{SHV-2a}$ first, followed by $bla_{SHV-12}$. Furthermore, by the PCR mapping method using primers corresponding to the IS26 and $bla_{SHV}$, the association between IS26 and $bla_{SHV}$ was studied in 12 clinical isolates carrying $bla_{SHV-2a}$, 27 clinical isolates carrying $bla_{SHV-12}$, and 5 reference strains carrying $bla_{SHV-1}$ to $bla_{SHV-5}$. All 39 strains carrying $bla_{SHV-2a}$ or $bla_{SHV-12}$ were positive by the PCR, providing confirmative evidence that IS26 has been involved in the evolution and dissemination of $bla_{SHV-2a}$ and $bla_{SHV-12}$. But 5 reference strains carrying $bla_{SHV-1}$ to $bla_{SHV-5}$ were negative by the PCR. Therefore, we concluded that the molecular evolutionary pathway of $bla_{SHV-2a}$ and $bla_{SHV-12}$ may be different from that of other $bla_{SHV-ESBL}$, e.g., $bla_{SHV-2}$, $bla_{SHV-3}$, $bla_{SHV-4}$, and $bla_{SHV-5}$.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.4
• /
• pp.362-370
• /
• 2001

In this paper, we proposed multispectral image compression method using CIP (classified inter-channel prediction) and SVQ (selective vector quantization) in wavelet domain. First, multispectral image is wavelet transformed and classified into one of three classes considering reflection characteristics of the subband with the lowest resolution. Then, for a reference channel which has the highest correlation and the same resolution with other channels, the variable VQ is performed in the classified intra-channel to remove spatial redundancy. For other channels, the CIP is performed to remove spectral redundancy. Finally, the prediction error is reduced by performing SVQ. Experiments are carried out on a multispectral image. The results show that the proposed method reduce the bit rate at higher reconstructed image quality and improve the compression efficiency compared to conventional methods. Index Terms-Multispectral image compression, wavelet transform, classfied inter-channel prediction, selective vetor quantization, subband with lowest resolution.