• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1709-1722
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• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.176-183
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• 2021

Purpose: The introduction of smart factories that reflect the 4th industrial revolution technologies such as AI, IoT, and VR, has been actively promoted in Korea. However, in order to solve various problems arising from existing file-based operating systems, this research will focus on identifying and verifying non-file system-based data protection technology. Method: The research will measure security storage that cannot be identified or controlled by the operating system. How to activate secure storage based on the input of digital key values. Establish a control unit that provides input and output information based on BIOS activation. Observe non-file-type structure so that mapping behavior using second meta-data can be performed according to the activation of the secure storage. Result: First, the creation of non-file system-based secure storage's data input/output were found to match the hash function value of the sample data with the hash function value of the normal storage and data. Second, the data protection performance experiments in secure storage were compared to the hash function value of the original file with the hash function value of the secure storage after ransomware activity to verify data protection performance against malicious ransomware. Conclusion: Smart factory technology is a nationally promoted technology that is being introduced to the public and this research implemented and experimented on a new concept of data protection technology to protect crucial data within the information system. In order to protect sensitive data, implementation of non-file-type secure storage technology that is non-dependent on file system is highly recommended. This research has proven the security and safety of such technology and verified its purpose.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.93-98
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• 2023

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.157-171
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• 2022

Electrical resistivity tomography (ERT) is a traditional and representative geophysical method for determining the resistivity distributions of surrounding soil and rock volumes. Depth resolution profiles and sensitivity distribution sections of the resistivities with respect to various electrode configurations are calculated and investigated using numerical model data. Shallow vertical resolution decreases in the order of Wenner, Schlumberger, and dipole-dipole arrays. A high investigable depth in homogeneous medium is calculated to be 0.11-0.19 times the active electrode spacing, but is counterbalanced by a low vertical resolution. For the application of ERT depth resolution profiles and sensitivity distributions, we provide subsurface structure models for two types of land-creping failure (planar and curved), subvertical fracture, and weathered layer over felsic and mafic igneous rocks. The dipole-dipole configuration appears to be most effective for mapping land-creeping failure planes (especially for curved planes), whereas the Wenner array gives the best resolution of soil horizons and shallow structures in the weathered zone.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.541-554
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• 2019

The Taebaeksan Zone of the Okcheon Belt is a prominent fold-thrust belt, preserving evidence for overlapped polyphase and diachronous orogenic events during crustal evolution of the Korean Peninsula. The Pyeongchang-Jeongseon area of the northwestern Taebaeksan Zone is fault-bounded on the western Jucheon and southern Yeongwol areas, showing lateral variations in stratigraphy and structural geometries. For better understanding these geological characteristics of the northwestern Taebaeksan Zone, we have studied the structural geometry of the Pyeongchang-Jeongseon area. For this, we have firstly carried out the SHRIMP U-Pb age analysis of the age-unknown sedimentary rock to clarify stratigraphy for structural interpretation. The results show the late Carboniferous to middle Permian dates, indicating that it is correlated to the Upper Paleozoic Pyeongan Supergroup. In addition to this, we interpreted the geometric relationships between structural elements from the detailed field investigation of the study area. The major structure of the northwestern Taebaeksan Zone is the regional-scale Jeongseon Great syncline, having NE-trending hinge with second-order folds such as the Jidongri and Imhari anticlines and the Nambyeongsan syncline. Based on the stereographic and down-plunge projections of the structureal elements, the structural geometry of the Jeongseon Great syncline can be interpreted as a synformal culmination, plunging slightly to the south at its southern area, and north at the northern area. The different map patterns of the northern and southern parts of the study area should be resulted in different erosion levels caused by the plunging hinges. Considering the Jeongseon Great syncline is the major structure that constrains the distribution of the Paleozoic strata of the Pyeongchang and Jeongseon areas, the symmetric repetition of the lower Paleozoic Joseon Supergroup in both limbs should be re-examined by structural mapping of the Hangmae and Hoedongri formations in the Pyeongchang and Jeongseon areas.

• Economic and Environmental Geology
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• v.45 no.1
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• pp.31-40
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• 2012

This paper examined the characteristics of ductile and brittle structural elements with detailed mapping by lithofacies classification to clarify the relationship between the geological structure and the geochemical high-grade uranium anormal zone and to provide the basic information on the flow of groundwater in the eastern area of Shinbo mine, Jinan-gun, Jeollabuk-do, Korea. It indicates that this area is mainly composed of Precambrian quartzite, metapelite, metapsammite, which show a zonal distribution of mainly ENE-WSW trend, and age unknown pegmatite and Cretaceous porphyry which intrude them. But the Cretaceous Jinan Group which unconformably covers them, contrary to assumption, could not be observed. The main ductile deformation structures of Precambrian metasedimentary rocks were formed at least through three phases of deformation [ENE striking regional foliation (D1) -> ENE or EW striking crenulation foliation (D2) -> WNW or EW trending open, tight, kink folds (D3)]. The predominant orientation of S1 regional foliation strikes ENE and dips south, being similar to the zonal distribution of Precambrian metasedimentary rocks. Most predominant orientation of high-angled brittle fracture (dip angle ${\geq}45^{\circ}$) [ENE (frequency: 24.3%) > NS (23.9%) > (N)NW (18.8%) > WNW (16.9%) > NE (16.1%) fracture sets in descending frequency order], which is closely related to the flow of groundwater, strikes ENE and dips south. It also agrees with the zonal distribution of metasedimentary rocks and the predominant orientation of S1 regional foliation. The next one strikes NS and dips east or west. Considering the controlling factor of the geochemical uranium anormal zone in the Shinbo mine and its eastern areas from the above structural data. the uranium source rock in these areas might be pegmatite and the geochemical uranium anormal zone in the Sinbo mine area could be formed by an secondary enrichment through the flow of pegmatite aquifer's groundwater into the Sinbo mine area like the previous research's result.

• The Journal of the Petrological Society of Korea
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• v.25 no.3
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• pp.211-230
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• 2016

Detailed mapping along the Keumwang fault reveals a complex history of multiple brittle reactivations following late Jurassic and early Cretaceous ductile shearing. The fault core consists of a 10~50 m thick fault gouge layer bounded by a 30~100 m thick damaged zone. The Pre-cambrian gneiss and Jurassic granite underwent at least six distinct stages of fault movements based on deformation environment, time and mechanism. Each stage characterized by fault kinematics and dynamics at different deformation environment. Stage 1 generated mylonite series along the Keumwang shear zone by sinistral ductile shearing during late Jurassic and early Cretaceous. Stage 2 was a mostly brittle event generating cataclasite series superimposed on the mylonite series of the Keumwang shear zone. The roundness of pophyroclastes and the amount of matrix increase from host rocks to ultracataclasite indicating stronger cataclastic flow toward the fault core. At stage 3, fault gouge layer superimposed on the cataclasite generated during stage 2 and the sedimentary basins (Umsung and Pungam) formed along the fault by sinistral strike-slip movement. Fragments of older cataclasite suspended in the fault gouge suggest extensive reworking of fault rocks at brittle deformation environments. At stage 4, systematic en-echelon folds, joints and faults were formed in the sedimentary basins by sinistral strike-slip reactivation of the Keumwang fault. Most of the shearing is accommodated by slip along foliations and on discrete shear surfaces, while shear deformation tends to be relatively uniformly distributed within the fault damage zone developed in the mudrocks in the sedimentary basins. Fine-grained andesitic rocks intruded during stage 4. Stage 5 dextral strike-slip activity produced shear planes and bands in the andesitic rocks. ESR(Electron Spin Resonance) dates of fault gouge show temporal clustering within active period and migrating along the strike of the Keumwang fault during the stage 6 at the Quaternary period.

• 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.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.80-92
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• 2007

Forest fire is the dominant large-scale disturbance mechanism in the Korean temperate forest, and it strongly influences forest structure and function. Moreover burn severity incorporates both short- and long-term post-fire effects on the local and regional environment. Burn severity is defined by the degree to which an ecosystem has changed owing to the fire. Vegetation rehabilitation may specifically vary according to burn severity after fire. To understand burn severity and process of vegetation rehabilitation at the damaged area after large-fire is required a lot of man powers and budgets. However the analysis of burn severity in the forest area using satellite imagery can acquire rapidly information and more objective results remotely in the large-fire area. Space and airbone sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. For classifying fire damaged area and analyzing burn severity of Samcheok fire area occurred in 2000, Cheongyang fire in 2002, and Yangyang fire in 2005 we utilized Normalized Burn Ratio(NBR) technique. The NBR is temporally differenced between pre- and post-fire datasets to determine the extent and degree of change detected from burning. In this paper we use pre- and post-fire imagery from the Landsat TM and ETM+ imagery to compute the NBR and evaluate large-scale patterns of burn severity at 30m spatial resolution. 65% in the Samcheok fire area, 91% in the Cheongyang fire area and 65% in the Yangyang fire area were corresponded to burn severity class above 'High'. Therefore the use of a remotely sensed Differenced Normalized Burn Ratio(${\Delta}NBR$) by RS and GIS allows for the burn severity to be quantified spatially by mapping damaged domain and burn severity across large-fire area.