• Journal of the Korean Magnetic Resonance Society
• /
• v.25 no.3
• /
• pp.39-44
• /
• 2021

The CRISPR-Cas system provides adaptive immunity for bacteria and archaea against invading phages and foreign plasmids. In the Class 1 CRISPR-Cas system, multi-subunit Cas proteins assemble with crRNA to bind to DNA targets. To disarm the bacterial defense system, bacteriophages evolved anti-CRISPR (Acr) proteins that actively inhibit the host CRISPR-Cas function. Here we report the backbone resonance assignments of AcrIF7 protein that inhibits the type I-F CRISPR-Cas system of Pseudomonas aeruginosa using triple-resonance nuclear magnetic resonance spectroscopy. We employed various computational methods to predict the structure and binding interface of AcrIF7, and assessed the model with experimental data. AcrIF7 binds to Cas8f protein via flexible loop regions to inhibit target DNA binding, suggesting that conformational heterogeneity is important for the Cas-Acr interaction.

• Korean Journal of Remote Sensing
• /
• v.26 no.3
• /
• pp.317-324
• /
• 2010

In recent years, many automatic classification approaches have been employed. An automatic classification method can be effective, time-saving and can produce objective results due to the exclusion of operator intervention. This paper proposes a classification method based on automated training for high resolution multispectral images using ancillary data. Generally, it is problematic to automatically classify high resolution images using ancillary data, because of the scale difference between the high resolution image and the ancillary data. In order to overcome this problem, the proposed method utilizes the classification results of a Landsat image as a medium for automatic classification. For the classification of a Landsat image, a maximum likelihood classification is applied to the image, and the attributes of ancillary data are entered as the training data. In the case of a high resolution image, a K-means clustering algorithm, an unsupervised classification, was conducted and the result was compared to the classification results of the Landsat image. Subsequently, the training data of the high resolution image was automatically extracted using regular rules based on a RELATIONAL matrix that shows the relation between the two results. Finally, a high resolution image was classified and updated using the extracted training data. The proposed method was applied to QuickBird and SPOT-5 images of non-accessible areas. The result showed good performance in accuracy assessments. Therefore, we expect that the method can be effectively used to automatically construct thematic maps for non-accessible areas and update areas that do not have any attributes in geographic information system.

• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.1176-1179
• /
• 2020

Vessel path prediction methods generally predict the latitude and longitude of a future location directly. However, in the case of direct prediction, errors could be large since the possible output range is too broad. In addition, error accumulation could occur since recurrent neural networks-based methods employ previous predicted data to forecast future data. In this paper, we propose a vessel path prediction method that does not directly predict the longitude and latitude. Instead, the proposed method predicts the acceleration of the vessel. Then the acceleration is employed to generate the velocity and direction, and the values decide the longitude and latitude of the future location. In the experiment, we show that the proposed method makes smaller errors than the direct prediction method, while both methods employ the same model.

• Korean Journal of Remote Sensing
• /
• v.38 no.1
• /
• pp.103-110
• /
• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.597-598
• /
• 2015

In order to improve the reliability of cost estimation results using CBR, there has been a continuous issue on similarity measurement to accurately compute the distance among attributes and cases to retrieve the most similar singular or plural cases. However, these existing similarity measures have limitations in taking the covariance among attributes into consideration and reflecting the effects of covariance in computation of distances among attributes. To deal with this challenging issue, this research examines the weighted Mahalanobis distance based similarity measure applied to CBR cost estimation and carries out the comparative study on the existing distance measurement methods of CBR. To validate the suggest CBR cost model, leave-one-out cross validation (LOOCV) using two different sets of simulation data are carried out. Consequently, this research is expected to provide an analysis of covariance effects in similarity measurement and a basis for further research on the fundamentals of case retrieval.

• International Journal of Advanced Culture Technology
• /
• v.11 no.4
• /
• pp.34-41
• /
• 2023

The purpose of this paper is to consider the expansion of non-traditional security threats and the national-level response to the emergence of emerging security threats in ultra-uncertain VUCA situations. As a major research method for better analysis, the theoretical approach was referred to papers published in books and academic journals, and technical and current affairs data were studied through the Internet and literature research. The instability and uncertainty of the international order and security environment in the 21st century brought about a change in the security paradigm. Human security emerged as the protection target of security was expanded to individual humans, and emerging security was emerging as the security area expanded. Emerging security threatsthat have different characteristicsfrom traditionalsecurity threats are expressed in various ways, such as cyber threats, new infectious disease threats, terrorist threats, and abnormal climate threats. First, the policy and strategic response to respond to emerging security threats is integrated national crisis management based on artificial intelligence applying the concept of Foresight. Second, it is to establish network-based national crisis management smart governance. Third, it is to maintain the agile resilience of the concept of Agilience. Fourth, an integrated response system that integrates national power elements and national defense elements should be established.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.4
• /
• pp.1-11
• /
• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.6
• /
• pp.201-208
• /
• 2014

In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.18 no.6
• /
• pp.779-788
• /
• 2015

The SONAR waterfall image(SWI) is used typically to target detection in SONAR operation and is managed with additional data linked in the naval combat system. The SWI and additional data are very important to classify a kind of target. Although additional data of the SWI is essential and must be kept together with the SWI, it was stored separately in the current system. In this paper, we propose an improved information management method in the naval combat system, where additional data can be contained in the SWI together by using information hiding techniques. The experimental results show that the effectiveness of information hiding techniques in the naval combat system. It is demonstrated that the information hiding techniques can be applied to the SWI that can make the naval combat system to be robust and secure.

• Journal of Information Technology and Architecture
• /
• v.10 no.1
• /
• pp.71-77
• /
• 2013

XML schema matching is the task of finding semantic correspondences between elements of two schemas. XML schema matching plays an important role in many application, such as schema integration, data integration, data warehousing, data transformation, peer-to-peer data management, semantic web etc. In this paper, we propose an XML element matching algorithm based on rules and structural properties. The proposed algorithm involves classifying elements as unique or non-unique elements according to the structural properties of XML documents and deciding on element matching in accordance with rules. We present experimental results that demonstrate the effectiveness of the proposed approach.