• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1401-1411
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• 2016

The Multi-Prime RSA and the Prime Power RSA are the variants of the RSA cryptosystem, where the Multi-Prime RSA uses the modulus $N=p_1p_2{\cdots}p_r$ for distinct primes $p_1,p_2,{\cdots},p_r$ (r>2) and the Prime Power RSA uses the modulus $N=p^rq$ for two distinct primes p, q and a positive integer r(>1). This paper analyzes the security of these systems by using the technique given by Heninger and Shacham. More specifically, this paper shows that if the $2-2^{1/r}$ random portion of bits of $p_1,p_2,{\cdots},p_r$ is given, then $N=p_1p_2{\cdots}p_r$ can be factorized in the expected polynomial time and if the $2-{\sqrt{2}}$ random fraction of bits of p, q is given, then $N=p^rq$ can be factorized in the expected polynomial time. The analysis is then validated with experimental results for $N=p_1p_2p_3$, $N=p^2q$ and $N=p^3q$.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.917-926
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• 2021

Lodging rice is one of critical agro-meteorological disasters. In this study, the UAV-based multispectral imageries before and after rice lodging in rice paddy field of Jeollanamdo agricultural research and extension servicesin 2020 was analyzed. The UAV imagery on 14th Aug. includesthe paddy rice without any damage. However, 4th and 19th Sep. showed the area of rice lodging. Multispectral camera of 10 bands from 444 nm to 842 nm was used. At the area of restoration work against lodging rice, the reflectance from 531 nm to 842 nm were decreased in comparison to un-lodging rice. At the area of lodging rice, the reflectance of around 668 nm had small increases. Further, the blue and NIR (Near-Infrared) wavelength had larger. However, according to the types of lodging, the change of reflectance was different. The NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge) shows dome sensitivities to lodging rice, but they were different to types of lodging. These results will be useful to make algorithm to detect the area of lodging rice using a UAV.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1235-1249
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• 2022

This study applied the web crawling technique for extracting big data news on water quality accidents in the water supply system and presented the algorithm in a procedural way to obtain accurate water quality accident news. In addition, in the case of a large-scale water quality accident, development patterns such as accident recognition, accident spread, accident response, and accident resolution appear according to the occurrence of an accident. That is, the analysis of the development of water quality accidents through key keywords and sentiment analysis for each stage was carried out in detail based on case studies, and the meanings were analyzed and derived. The proposed methodology was applied to the larval accident period of Incheon Metropolitan City in 2020 and analyzed. As a result, in a situation where the disclosure of information that directly affects consumers, such as water quality accidents, is restricted, the tone of news articles and media reports about water quality accidents with long-term damage in the event of an accident and the degree of consumer pride clearly change over time. could check This suggests the need to prepare consumer-centered policies to increase consumer positivity, although rapid restoration of facilities is very important for the development of water quality accidents from the supplier's point of view.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.883-896
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• 2023

As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.153-163
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• 2017

The objective of this study is to enhance the model's speed of estimating weather variables (e.g., minimum/maximum temperature, sunshine hour, PRISM (Parameter-elevation Regression on Independent Slopes Model) based precipitation), which are applied to the Agrometeorological Early Warning System (http://www.agmet.kr). The current process of weather estimation is operated on high-performance multi-core CPUs that have 8 physical cores and 16 logical threads. Nonetheless, the server is not even dedicated to the handling of a single county, indicating that very high overhead is involved in calculating the 10 counties of the Seomjin River Basin. In order to reduce such overhead, several cache and parallelization techniques were used to measure the performance and to check the applicability. Results are as follows: (1) for simple calculations such as Growing Degree Days accumulation, the time required for Input and Output (I/O) is significantly greater than that for calculation, suggesting the need of a technique which reduces disk I/O bottlenecks; (2) when there are many I/O, it is advantageous to distribute them on several servers. However, each server must have a cache for input data so that it does not compete for the same resource; and (3) GPU-based parallel processing method is most suitable for models such as PRISM with large computation loads.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.