• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.35-45
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• 2018

Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.

• Journal of radiological science and technology
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• v.27 no.3
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• pp.31-41
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• 2004

Attention deficit hyperactivity disorder(ADHD)is one of the most common psychiatric disorders in childhood, especially school age children and persisting into adult. ADHD is affected 7.6% in our children, Korea. and persisting into $15{\sim}20%$ in adult. And it is characterized by hyperactivity, inattention and impulsivity. Brain imaging is one of way to diagnosis for ADHD. Brain imaging studies may be provide information two types - structural and functional imaging. Structural and functional images of the brain play an important role in management of neurologic and psyciatric disorders. Brain SPECT, with perfusion imaging radiopharmaceuticals is one of the appropriate test to diagnosis of neurologic and psychiatric diseases. Ther are a few studies about separated analysis between boys and girls ADHD SPECT brain images. Selection of Probability level(P-value) is very important to determind the abnormalities when analysis a data by SPM. SPM is a statistical method used for image analysis and determine statistical different between two groups-normal and ADHD. Commonly used P-value is P<0.05 in statistical analysis. The purpose of this study is to evaluation of blood flow clusters distribution, between boys and girls ADHD. The number of normal boys are 8(6-7y, average : $9.6{\pm}3.9y$) and 51(4-11y, average : $9.0{\pm}2.4$) ADHD patients, and normal girls are 4(6-12y, average : $9{\pm}2.4y$) and 13(2-13y, average $10{\pm}3.5y$) ADHD patiens. Blood flow tracer $^{99m}Tc-ethylcysteinate$ dimer(ECD) injected as rCBF agent and take blood flow images after 30 min. during sleeping by SPECT camera. The anatomical region of hyperperfusion of rCBF in boys ADHD group is posterior cingulate gyrus and hyperperfusion rate is 15.39-15.77% according to p-value. And girls ADHD group appears at posterior cerebellum, Lt. cerbral limbic lobe and Lt. Rt. cerebral temporal lobe. These areas hyperperfusion rate are 24.68-31.25%. Hypoperfusion areas in boys ADHD,s brain are Lt. cerebral insular gyrus, Lt. Rt. frontal lobe and mid-prefrontal lobe, these areas decresed blood flow as 15.21-15.64%. Girls ADHD decreased blood flow regions are Lt. cerebral insular gyrus, Lt. cerebral frontal and temporal lobe, Lt. Rt. lentiform nucleus and Lt. parietal lobe. And hypoperfusion rate is 30.57-30.85% in girls ADHD. The girls ADHD group's perfusion rate is more variable than boys. The studies about rCBF in ADHD, should be separate with boys and girls.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.1
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• pp.1-16
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• 2007

The use of information on natural resources is indispensable to most agricultural activities to avoid disasters, to improve input efficiency, and to increase lam income. Most information is prepared and managed at a spatial scale called the "Hydrologic Unit" (HU), which means watershed or small river basin, because virtually every environmental problem can be handled best within a single HU. South Korea consists of 840 such watersheds and, while other watershed-specific information is routinely managed by government organizations, there are none responsible for agricultural weather and climate. A joint research team of Kyung Hee University and the Agriculture, forestry and Fisheries Information Service has begun a 4-year project funded by the Ministry of Agriculture and forestry to establish a watershed-specific agricultural weather information service based on "high definition" digital climate maps (HD-DCMs) utilizing the state of the art geospatial climatological technology. For example, a daily minimum temperature model simulating the thermodynamic nature of cold air with the aid of raster GIS and microwave temperature profiling will quantify effects of cold air drainage on local temperature. By using these techniques and 30-year (1971-2000) synoptic observations, gridded climate data including temperature, solar irradiance, and precipitation will be prepared for each watershed at a 30m spacing. Together with the climatological normals, there will be 3-hourly near-real time meterological mapping using the Korea Meteorological Administration's digital forecasting products which are prepared at a 5 km by 5 km resolution. Resulting HD-DCM database and operational technology will be transferred to local governments, and they will be responsible for routine operations and applications in their region. This paper describes the project in detail and demonstrates some of the interim results.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.537-546
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• 2004

Adiponectin is adipocyte complement-related protein which is highly specialized to play important roles in metabolic and honnonal processes. This protein, called GBP-28, AdipoQ, and Acrp30, is encoded by the adipose most abundant gene transcript 1 (APM1) which locates on human chromosome 3q27 and mouse chromosome 16. In order to determine chromosomal localization of the porcine APM1, we carried out PCR analysis using somatic cell hybrid panel as well as porcine whole genome radiation hybrid (RH) panel. The result showed that the porcine APM1 located on chromosome 13q41 or 13q46-49. These locations were further investigated with the two point analysis of RH panel, revealed the most significant linked marker (LOD score 20.29) being SIAT1 (8 cRs away), where the fat-related QTL located. From the SSCP analysis of APM1 using 8 pig breeds, two distinct SSCP types were detected from K~ native and Korean wild pigs. The determined sequences in Korean native and Korean wild pigs showed that two nucleotide positions (T672C and C705G) were substituted. The primary sequence of the porcine APM1 has 79 to 87% identity with those of human, mouse, and bovine APM1. The domain structures of the porcine APM1 such as signal sequence, hypervariable region, collagenous region. and globular domain are also similar to those of mammalian genes.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.1
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• pp.62-66
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• 2015

Purpose Various database comparison methods(DCM) are used for analyzing functional neuro-imaging. It is possible to statistically evaluate decreased or increased metabolism of abnormal patient's brain by comparing with asymptomatic controls in DCM. And results of DCM are additionally used for easily explaining defect region. The aim of this study was to evaluate usefulness of statistical parametric mapping(SPM) and scenium. Materials and Methods Data of 15 patients($62.02{\pm}15.03year$) underwent $^{18}F-FDG$ brain PET/CT were collected and analyzed. Biograph TruePoint 40 with TrueV, (Siemens) was used as a PET/CT scanner. Scenium(version 4.0) in Syngo.via(version VA30A) and SPM99 were applied for statistical evaluation. Consistency between PET reading and result of DCM were evaluated by 5 nuclear medicine physicians through a questionnaire survey. SUV and SD changes were evaluated by changing iteration, gaussian filter and matrix size in scenium. And average required time for generating result of SPM99 and scenium was compared by 3 medical technologists. Results Consistency from the result of SPM99 and scenium showed 84% and 92.4% compare to PET reading. When iteration 4, FWHM 8 and matrix size 168, SUV and SD were decreased by 0.59%, 8.73%, 4.69%, 20.38% and 0.88%, 8.25% respectively compare to routine parameter(iteration 8, FWHM 2 and matrix size 336) of scenium. Average required time of SPM99 and Scenium took 282 seconds and 116 seconds to generate result. Conclusion Results of SPM99 and Scenium showed high consistency compare to PET reading. Various parameters can be controled by user when using SPM. However, normal database needs to be acquired. And it takes significant amount of time and effort for the first set up. On the other hand, Scenium provides normal database even though modifiable parameters are limited. Therefore, more informations could be provided for brain PET/CT if properly understanding and selecting each DCM.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.41-52
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• 2022

As a cultivar of Korean wheat, 'Keumgang' wheat variety has a fast growth period and can be grown stably. Hexaploid wheat (Triticum aestivum) has moderately high salt tolerance compared to tetraploid wheat (Triticum turgidum L.). However, the molecular mechanisms related to salt tolerance of hexaploid wheat have not been elucidated yet. In this study, the candidate genes related to salt tolerance were identified by investigating the genes that are differently expressed in Keumgang variety and examining salt tolerant mutation '2020-s1340.'. A total of 85,771,537 reads were obtained after quality filtering using NextSeq 500 Illumina sequencing technology. A total of 23,634,438 reads were aligned with the NCBI Campala Lr22a pseudomolecule v5 reference genome (Triticum aestivum). A total of 282 differentially expressed genes (DEGs) were identified in the two Triticum aestivum materials. These DEGs have functions, including salt tolerance related traits such as 'wall-associated receptor kinase-like 8', 'cytochrome P450', '6-phosphofructokinase 2'. In addition, the identified DEGs were classified into three categories, including biological process, molecular function, cellular component using gene ontology analysis. These DEGs were enriched significantly for terms such as the 'copper ion transport', 'oxidation-reduction process', 'alternative oxidase activity'. These results, which were obtained using RNA-seq analysis, will improve our understanding of salt tolerance of wheat. Moreover, this study will be a useful resource for breeding wheat varieties with improved salt tolerance using molecular breeding technology.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.609-617
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• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.

• Journal of Plant Biotechnology
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• v.39 no.1
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• pp.33-48
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• 2012

As a scientific curiosity to understand the structure and the function of crops and experimental efforts to apply it to plant breeding, genetic maps have been constructed in various crops. Especially, in the case of Brassica crop, genetic mapping has been accelerated since genetic information of model plant $Arabidopsis$ was available. As a result, the whole $B.$ $rapa$ genome (A genome) sequencing has recently been done. The genome sequences offer opportunities to develop molecular markers for genetic analysis in $Brassica$ crops. RFLP markers are widely used as the basis for genetic map construction, but detection system is inefficiency. The technical efficiency and analysis speed of the PCR-based markers become more preferable for many form of $Brassica$ genome study. The massive sequence informative markers such as SSR, SNP and InDels are also available to increase the density of markers for high-resolution genetic analysis. The high density maps are invaluable resources for QTLs analysis, marker assisted selection (MAS), map-based cloning and comparative analysis within $Brassica$ as well as related crop species. Additionally, the advents of new technology, next-generation technique, have served as a momentum for molecular breeding. Here we summarize genetic and genomic resources and suggest their applications for the molecular breeding in $Brassica$ crop.