• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.239-249
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• 2010

The Geostationary Ocean Color Imager (GOCI) data-processing system (GDPS), which is a software system for satellite data processing and analysis of the first geostationary ocean color observation satellite, has been developed concurrently with the development of th satellite. The GDPS has functions to generate level 2 and 3 oceanographic analytical data, from level 1B data that comprise the total radiance information, by programming a specialized atmospheric algorithm and oceanic analytical algorithms to the software module. The GDPS will be a multiversion system not only as a standard Korea Ocean Satellite Center(KOSC) operational system, but also as a basic GOCI data-processing system for researchers and other users. Additionally, the GDPS will be used to make the GOCI images available for distribution by satellite network, to calculate the lookup table for radiometric calibration coefficients, to divide/mosaic several region images, to analyze time-series satellite data. the developed GDPS system has satisfied the user requirement to complete data production within 30 minutes. This system is expected to be able to be an excellent tool for monitoring both long-term and short-term changes of ocean environmental characteristics.

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.376-391
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• 1998

Images from satellites will have 1 to 3 meter ground resolution and will be very useful for analyzing current status of earth surface. An image processing system named GeoWatch with more intelligent image processing algorithms has been designed and implemented to support the detailed analysis of the land surface using high-resolution satellite imagery. The GeoWatch is a valuable tool for satellite image processing such as digitizing, geometric correction using ground control points, interactive enhancement, various transforms, arithmetic operations, calculating vegetation indices. It can be used for investigating various facts such as the change detection, land cover classification, capacity estimation of the industrial complex, urban information extraction, etc. using more intelligent analysis method with a variety of visual techniques. The strong points of this system are flexible algorithm-save-method for efficient handling of large size images (e.g. full scenes), automatic menu generation and powerful visual programming environment. Most of the existing image processing systems use general graphic user interfaces. In this paper we adopted visual program language for remotely sensed image processing for its powerful programmability and ease of use. This system is an integrated raster/vector analysis system and equipped with many useful functions such as vector overlay, flight simulation, 3D display, and object modeling techniques, etc. In addition to the modules for image and digital signal processing, the system provides many other utilities such as a toolbox and an interactive image editor. This paper also presents several cases of image analysis methods with AI (Artificial Intelligent) technique and design concept for visual programming environment.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.121-133
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• 2005

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.331-341
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• 2021

This descriptive study was conducted to examine self rated health and factors influencing self rated health among e-cigarette smoking young and elderly community residents. The data were from the community health survey 2019 and the subjectis were 2,607 participants aged 19-50 years. Data analysis was conducted by descriptive statistics, χ² test, univariate multinominal logistic regression using SPSS 26.0 program and SPSS complex samples statistics. As a result of the study, 90.2% of the young and old e-cigarette smokers had a good subjective health condition and 9.8% had a poor subjective health condition. The factors affecting the subjective health status of e-cigarette smokers are gender(p=.006), age(p=.036), income level(p=.044) in Model I, and physical activity (p=.033) and stress (p<.001) in Model II. As a strategy to improve the subjective health status and quit smoking of e-cigarette smokers in the young and old, nursing intervention strategies are needed to increase physical activity and reduce stress.

• Journal of Conservation Science
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• v.37 no.5
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• pp.440-450
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• 2021

Temperature and humidity data collection using real-time sensors and data loggers was conducted for integrated pest management in the collection storage and exhibition space of the Jeongnimsaji Museum, Buyeo. The real-time temperature and humidity monitoring system collected measurement data every 30 minutes and enabled real-time confirmation of the data through a linked application. If the temperature and humidity data measured in the real-time temperature and humidity monitoring system exceeds the set range, a push notification was sent to the mobile phone of the person in charge to provide status information to establish a continuous management system. Through this, it was possible to immediately recognize and take action when the temperature range exceeded the recommended relic temperature in August. We performed data visualization on the concentration of airborne fungus in the storage area and the inflow path and density of insects. Based on the recommended criteria presented by the National Institute of Cultural Heritage, The data on the spatial and temporal concentration of airborne fungus inside the collection storage were found to be maintained at a value below the standard recommended by the National Institute of Cultural Heritage (80 CFU/m³). Also, as a result of the insect inflow survey, no insects were captured inside the storage area, and in the case of the exhibition space, insects such as Scutigera coleoptrata, Loxoblemmus arietulus, Diestrammena asynamora, Koreoniscus racovitzai were captured. Based on this, as a result of visualization according to the individual density of captured insects by area, it was confirmed that the main inflow paths of insects were the external entrance and the toilet area.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.75-80
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• 2018

The smart city is developing an energy system efficiently through a common management of the city resource for the growth and a low carbon social. However, the smart city doesn't counter a verification effectively about a anomaly pattern detection when existing security technology (authentication, integrity, confidentiality) is used by fixed security key and key deodorization according to generated big data. This paper is proposed the "security nonce generation based on security nonce generation" for anomaly pattern detection of the adversary and a safety of the key is high through the key generation of the KDC (Key Distribution Center; KDC) for improvement. The proposed scheme distributes the generated security nonce and authentication keys to each facilities system by the KDC. This proposed scheme can be enhanced to the security by doing the external pattern detection and changed new security key through distributed security nonce with keys. Therefore, this paper can do improving the security and a responsibility of the smart city platform management data through the anomaly pattern detection and the safety of the keys.

• Tunnel and Underground Space
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• v.31 no.4
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• pp.243-269
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• 2021

Since the early 2010s, the social importance of research and practical projects targeting deep geological disposal of high-level nuclear waste, underground CO₂ storage and characterization of deep subsurface by borehole investigation has been increasing. In this regard, there is also a significant increase in the need for in situ test technology to obtain quantitative and reliable information on the hydraulic characteristics of deep rock mass. Through years of research and development, we have independently set up Deep borehole Hydraulic Test System (DHTS) based on the key apparatuses designed and made with our own technology. Using this system, high precision constant pressure injection tests were successfully completed at the two 1 km boreholes located in Mesozoic granite and sedimentary rock regions, Gyeongju. During the field tests, it was possible to measure very low flow rate below 0.01 l/min with micro flow rate injection/control module. In this paper, the major characteristics of DHTS are introduced and also some results obtained from the high precision field tests under the deep and low permeable rock mass environment are briefly discussed.

• Journal of Apiculture
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• v.34 no.1
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• pp.47-55
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• 2019

Honey bees are a vital part of the food chain as the most important pollinators for a broad palette of crops and wild plants. The climate change and colony collapse disorder (CCD) phenomenon make it challenging to develop ICT solutions to predict changes in beehive and alert about potential threats. In this paper, we report the test results of the bee-counting system which stands out against the previous analogues due to its comprehensive components including an improved dual infrared sensor to detect honey bees entering and leaving the hive, environmental sensors that measure ambient and interior, a wireless network with the bluetooth low energy (BLE) to transmit the sensing data in real time to the gateway, and a cloud which accumulate and analyze data. To assess the system accuracy, 3 persons manually counted the outgoing and incoming honey bees using the video record of 360-minute length. The difference between automatic and manual measurements for outgoing and incoming scores were 3.98% and 4.43% respectively. These differences are relatively lower than previous analogues, which inspires a vision that the tested system is a good candidate to use in precise apicultural industry, scientific research and education.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.