• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.675-682
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• 2021

Fire accidents on land and at sea can cause serious casualties; specifically, owing to the nature of marine plants and ships, the mortality rate at sea from suffocation in confined spaces is significantly higher than that on land. To prevent such cases of asphyxiation, it is essential to install ventilation fans that can outwardly direct these toxic gases from fires; however, considering the scale of marine fires, the installation of large ventilation fans is not easy owing to the nature of marine structures. Therefore, in this study, we developed a new concept for fire safety technology to control toxic gases generated by fires from applied direct current (DC) electric fields. In the event of a fire, most flames contain large numbers of positive and negative charges from chemi-ionization, which generates an "ionic wind" by Lorentz forces through the applied electric fields. Using these ionic winds, an experimental study was performed to artificially control the fire smoke caused by burning paper and styrofoam, which are commonly used as insulation materials in general buildings and ships. The experiments showed that a fire smoke could be artificially controlled by applying a DC voltage in excess of ±5 kV and that relatively effective control was possible by applying a negative voltage rather than a positive voltage.

• Journal of the Korean Wood Science and Technology
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• v.32 no.5
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• pp.75-85
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• 2004

The natural dyes from Gardenia jasminoides, Carthamus tinctorius L., Rhus javanica, Lithospermum erythrorhizon, Caesalpinia sappan L., and Castanea crenata were extracted under different pH in distilled water, As the pH in distilled water went from acid to alkali, the much deeper colors in the same color tone were generated from the individual plant species. Before dyeing, wood species were treated by different mordants including AI, Cu, Cr and Fe for color-fixing between wood and the natural dyes. Each mordant could develop independent color on the surface of the woods. The wood species dyed by the natural dyes created deep-tone colors according to higher pH and temperature of the dyeing solution, leading to deeper penetration of the dyes into the wood tissues. Finally through the computer modelling of natural-dyed wood furniture, it was confirmed that the colored furniture can adequately be compatible with the current interior spaces of diverse colors.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.967-971
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• 2023

Recently, a variety of AI-based platform services are available, and one of them is ChatGPT that processes a large quantity of data in the natural language and generates an answer after self-learning. ChatGPT can perform various tasks including software programming in the IT sector. Particularly, it may help generate a simple program and correct errors using C Language, which is a major programming language. Accordingly, it is expected that ChatGPT is capable of effectively using Verilog HDL, which is a hardware language created in C Language. Verilog HDL synthesis, however, is to generate imperative sentences in a logical circuit form and thus it needs to be verified whether the products are executed properly. In this paper, we aim to select small-scale logical circuits for ease of experimentation and to verify the results of circuits generated by ChatGPT and human-designed circuits. As to experimental environments, Xilinx ISE 14.7 was used for module modeling, and the xc3s1000 FPGA chip was used for module embodiment. Comparative analysis was performed on the use area and processing time of FPGA to compare the performance of ChatGPT products and Verilog HDL products.

• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.13-26
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• 2024

The CBRN(Chemical, Biological, Radiological, and Nuclear) hazard prediction model is a system that supports commanders in making better decisions by creating contamination distribution and damage prediction areas based on the weapons used, terrain, and weather information in the events of biochemical and radiological accidents. NBC_RAMS(Nuclear, Biological and Chemical Reporting And Modeling S/W System) developed by ADD (Agency for Defense Development) is used not only supporting for decision making plan for various military operations and exercises but also for post analyzing CBRN related events. With the NBC_RAMS's core engine, we introduced a CBR hazard assessment scenario analysis system that can generate contaminant distribution prediction results reflecting various CBR scenarios, and described how to apply it in specific purposes in terms of input information, meteorological data, land data with land coverage and DEM, and building data with pologon form. As a practical use case, a technology development case is addressed that tracks the origin location of contaminant source with artificial intelligence and a technology that selects the optimal location of a CBR detection sensor with score data by analyzing large amounts of data generated using the CBRN scenario analysis system. Through this system, it is possible to generate AI-specialized CBRN related to training and analysis data and support planning of operation and exercise by predicting battle field.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.3
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• pp.740-746
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• 2004

In order to identify the anti-inflammatory and analgesic properties of natural herbal extracts, widely used in the Korean traditional medicine, an in vitro screening system was designed using pGL3, a luciferase reporter vector, and the tumor necrosis factor (TNF)-α and cyclooxygenase (COX)-II as target genes. The promoter regions of each gene was generated by PCR using the human chromosome as template DNA, and inserted into pGL3 vector with Kpnl and Hindlll. The final construct was transfected into human myleomonocytic leukemia cells (U937) that could be differentiated and activated by phorbol 12-myristate 13-acetate (PMA) or lipopolysaccharide (LPS). Using this system, we tested the anti-inflammatory and analgesic effects of several herbal extracts being regarded to have the medicinal effects of diminishing the body heat and complementing Qi. The well-known chemicals of PD98059 and berberine chloride were used as controls of the transcriptional inhibitors of TNF-α and COX-II, respectively. Among them, Salvia miltiorrhiza (Dan-Sam) was found to exhibit the significant medicinal properties of anti-inflammatory and analgesic effects.

• Research in Plant Disease
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• v.11 no.2
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• pp.140-145
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• 2005

A collection of 12 Erwinia carotovora strains from blackleg diseased potato in Jeju island was characterized genetic diversity by 5. cayotovora subsp. atposeptica (Eca)-specific PCR, PCR-RFLP of the two genes (16S rRNA and pel) and repetitive sequence PCR (ERIC-PCR). The results were compared with those of the other E. carotovora representative strains. None of the blackleg strains produced PCR amplicons with Eca-specific primers in contrast to the single 690 bp amplicon obtained with Eca strains. In addition, on the basis of pel gene RFLP with Sau3AI, the blackleg strains belonged to the pattern 2 whereas Eca strains belonged to the other one (pattern 3). By analysis of 16S rDNA RELP generated with HinfI, the most strains including the E. carotovera subsp. carotovora (Ecc) representative strains used in this study belonged to the pattern 1 whereas the blackleg strains belonged to the pattern 2 except for one strain. Moreover, ERIC-PCR analysis showed that the blackleg strains were closely related to each other and had an unique DNA band. Based on these molecular approaches, we have confirmed that the blackleg disease of potato is caused by a different E. carotovora from Eca and Ecc in Jeju island.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.95-106
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• 2021

The purpose of this study is to derive major topics in content R&D and provide directions for academic development by exploring research trends over the past 20 years using topic modeling targeting 9,858 papers published in the Journal of the Korean Contents Association. To secure the reliability and validity of the extracted topics, not only the quantitative evaluation technique but also the qualitative technique were applied step-by-step and repeated until a corpus of the level agreed upon by the researchers was generated, and detailed analysis procedures were presented accordingly. As a result of the analysis, 8 core topics were extracted. This shows that the Korean Contents Association is publishing convergence and complex research papers in various fields without limiting to a specific academic field. Also, before 2012, the proportion of topics in the field of engineering and technology appeared relatively high, while after 2012, the proportion of topics in the field of social sciences appeared relatively high. Specifically, the topic of 'social welfare' showed a fourfold increase in the second half compared to the first half. Through topic-specific trend analysis, we focused on the turning point in time at which the inflection point of the trend line appeared, explored the external variables that affected the research trend of the topic, and identified the relationship between the topic and the external variable. It is hoped that the results of this study can provide implications for active discussions in domestic content-related R&D and industrial fields.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.