• Journal of People, Plants, and Environment
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• v.22 no.2
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• pp.167-180
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• 2019

This study was conducted to investigate effective ways to meet social and cultural interest in and needs of gardens and gardening. A total of 191 respondents who answered they were living in Gyeongnam region in the questionnaire were selected: 102 (53.4%) were males and 89 (46.6%) were females. In frequency of garden visits, 45% of the respondents answered they visited gardens once a year. Their preferred companion was family (43.6%), followed by friends/colleagues (24.3%). Their important motives of garden visits included admiration of gardens' scenery and ambience, pleasure in being outdoors, relaxing mentally and physically, and appreciation of plants. Relatively less important motives included understanding or educating about nature and environmental conservation, and interest in garden design and horticulture techniques. In the overall assessment of gardens and gardening, the quality of the establishment, management and operation of botanic gardens and arboreta in Gyeongnam region scored 3.32 scale, which was close to the level of 'fair.' Also, the respondents agreed at 3.91 scale that it was necessary to improve the garden creation, gardening, and garden culture. Meanwhile, many people in Gyeongnam region did not clearly understand differences between garden and public park, also had a very obscure perception of public garden. The results of importance-performance analysis (IPA) indicated that it is necessary to concentrate on directing and developing some programs such as admiration of beautiful and exotic plants, and education on garden culture including garden making and horticultural techniques.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.912-919
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• 2022

The study is an image-based assessment that uses image processing techniques to determine the condition of concrete with surface cracks. The preparations of the dataset include resizing and image filtering to ensure statistical homogeneity and noise reduction. The image dataset is then segmented, making it more suited for extracting important features and easier to evaluate. The image is transformed into grayscale which removes the hue and saturation but retains the luminance. To create a clean edge map, the edge detection process is utilized to extract the major edge features of the image. The Otsu method is used to minimize intraclass variation between black and white pixels. Additionally, the median filter was employed to reduce noise while keeping the borders of the image. Image processing techniques are used to enhance the significant features of the concrete image, especially the defects. In this study, the tonal zones of the histogram and its properties are used to analyze the condition of the concrete. By examining the histogram, the viewer will be able to determine the information on the image through the number of pixels associated and each tonal characteristic on a graph. The features of the five tonal zones of the histogram which implies the qualities of the concrete image may be evaluated based on the quality of the contrast, brightness, highlights, shadow spikes, or the condition of the shadow region that corresponds to the foreground.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.21-30
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• 2020

Bitcoin (BTC) is a type of cryptocurrency that supports transaction/payment of virtual money between BTC users without the presence of a central authority or any third party like bank. It uses some cryptographic techniques namely public- and private-keys, digital signature and cryptographic-hash functions, and they are used for making secure transactions and maintaining distributed public ledger called blockchain. In BTC system, each transaction signed by sender is broadcasted over the P2P (Peer-to-Peer) Bitcoin network and a set of such transactions collected over a period is hashed together with the previous block/other values to form a block known as candidate block, where the first block known as genesis-block was created independently. Before a candidate block to be the part of existing blockchain (chaining of blocks), a computation-intensive hard problem needs to be solved. A number of miners try to solve it and a winner earns some BTCs as inspiration. The miners have high computing and hardware resources, and they play key roles in BTC for blockchain formation. This paper mainly analyses the underlying cryptographic techniques, identifies some weaknesses and proposes their enhancements. For these, two modifications of BTC are suggested ― (i) All BTC users must use digital certificates for their authentication and (ii) Winning miner must give signature on the compressed data of a block for authentication of public blocks/blockchain.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.189-196
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• 2024

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• Journal of Conservation Science
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• v.25 no.3
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• pp.255-271
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• 2009

This study was examined interpretation of making techniques and provenance interpretation of raw materials for the potteries from the Nongseori site in Giheung based on archaeometric characteristics. The potteries are classified into three groups according to the archaeological age. The texture of Neolithic age potteries is sandy soil added a lot of temper such as talc and mica, and Bronze age potteries contain sandy materials which occur naturally include quartz, orthoclase, plagioclase and mica. On the other hand, Proto-three Kingdom Age potteries made of silty soil that sift out coarse minerals from the clay. But all pottery and soil samples in the study were very similar patterns with geochemical evolution trend. This result is sufficient evidence that all pottery samples were produced using the same raw materials from the host rocks around of the site area. The Neolithic age potteries had loose texture and fired probably about 700 to $760^{\circ}C$. The Bronze age potteries had experienced firing about 850 to $900^{\circ}C$. And Proto-three Kingdom Age potteries had compact textured and fired from 900 to $1,050^{\circ}C$. The making techniques of potteries are not represented discontinuation characteristics about the periodic time sequences, and are suggested that revealed a transitional change patterns for production techniques.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.157-164
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• 2015

An efficient retrieval of useful information is a prerequisite of an optimal decision making system. Hence, A research of data mining techniques finding useful patterns from the various forms of data has been progressed with the increase of the application of Big Data for convergence and integration with other industries. Each technique is more likely to have its drawback so that the generalization of retrieving useful information is weak. Another integrated technique is essential for retrieving useful information. In this paper, a uncertainty measure of information is calculated such that algebraic probability is measured by Bayesian theory and then information entropy of the probability is measured. The proposed measure generates the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Hence, the optimal decision rules are induced. Through simulation deciding contact lenses, the proposed approach is compared with the equivalence and value-reduct theories. As the result, the proposed is more general than the previous theories in useful decision-making.

• Journal of the Korean Society of Costume
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• v.17
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• pp.137-148
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• 1991

The use of bark cloth, made of the inner bark of certain trees, was widespread along tropical zones from the Africa to the Hawaii encompassing the globe. They include Malaysia, Indonesia, New Guinea, Polynesian Islands and South America. Among them the Hawaiian bark cloth, named Kapa(pronounced as tapa) was rated as the best quality and most admired. It has variety in designs and colors as well as the most sophistcated production methods. The distinct processes of kapa making are composed of two stages. The first is called first beating and it is a preparatory stage to beat the sea-water soaked bast. It was done with a round beater on a stone anvil. The second beating process was carried out with the squared beater and wooden anvil. The strips from the first beating was soaked again in the water and then beaten lightly to break up fibers. The craftmen laid a bundle of strips over the anvil and beat it into pieces of kapa. The second beater of Hawaii was the most characteristic one among bark cloth producing countries. On their surfaces were the engraved patterns, which were creation of theirs. These distinguished designs enabled them to produce the kapa with the thinner and finer texture and an elaboration of impressed designs known as "watermaks". The Hawaiian culture was self-sufficient one : Everything they used was of their own creation until 19th century. Among their inventions of printing designs on kapa are three most important and distinguished processes. They are the overlaying, the cord snapping and the block printing techniques. Their inventiveness as well as self sufficient environment made it possible to develop their fine art of the kapa making. It is said that the mass producing and cheap western technology of loom forced them to gradually abandon their traditional art and as a result this fine and valuable legacy of Hawaiian traditional kapa making technique is all but disappeared. However it is encouraging and heart warming to find that some of the people as well as specialized researchers pined together to form a group to try to reproduce the old kapa and study the traditional art. They consider the kapa as an expression of the ethnic identity with Hawaii's heritage as well as valuable art of human history.