• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.5-18
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• 2023

As the development of drones and sensors accelerates, new services and values are created by fusing data acquired from various sensors mounted on drone. However, the construction of spatial information through data fusion is mainly constructed depending on the image, and the quality of data is determined according to the specification and performance of the hardware. In addition, it is difficult to utilize it in the actual field because expensive equipment is required to construct spatial information of high-quality. In this study, super-resolution was performed by applying deep learning to low-resolution images acquired through RGB and THM cameras mounted on a drone, and quantitative evaluation and feature point extraction were performed on the generated high-resolution images. As a result of the experiment, the high-resolution image generated by super-resolution was maintained the characteristics of the original image, and as the resolution was improved, more features could be extracted compared to the original image. Therefore, when generating a high-resolution image by applying a low-resolution image to an super-resolution deep learning model, it is judged to be a new method to construct spatial information of high-quality without being restricted by hardware.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Economic and Environmental Geology
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• v.38 no.2 s.171
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• pp.101-111
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• 2005

The objectives of this study we to assess the extent and degree of environmental contamination and to draw general conclusions on the fate of toxic elements derived from mining activities in Korea. 인t abandoned mines with four base-metal mines and four Au-Ag mines were selected and the results of environmental surveys in those areas were discussed. In the base-metal mining areas, the Sambo Pb-Zn-barite, the Shinyemi Pb-Zn-Fe, the Geodo Cu-Fe and the Shiheung Cu-Pb-Zn mine, significant levels of Cd, Cu, Pb and Zn were found in mine dump soils developed over mine waste materials, tailings and slag. Furthermore, agricultural soils, stream sediments and stream water near the mines were severely contaminated by the metals mainly due to the continuing dispersion downstream and downslope from the sites, which was controlled by the feature of geography, prevailing wind directions and the distance from the mine. In e Au-Ag mining areas, the Kubong, the Samkwang, the Keumwang and the Kilkok mines, elevated levels of As, Cd, Cu, Pb and Zn were found in tailings and mine dump soils. These levels may have caused increased concentrations of those elements in stream sediments and waters due to direct dis-charge downstream from tailings and mine dumps. In the Au-Ag mines, As would be the most characteristic contaminant in the nearby environment. Arsenic and heavy metals were found to be mainly associated with sulfide gangue minerals, and mobility of these metals would be enhanced by the effect of oxidation. According to sequential extraction of metals in soils, most heavy metals were identified as non-residual chemical forms, and those are very susceptible to the change of ambient conditions of a nearby environment. As application of pollution index (PI), giving data on multi-element contamination in soils, over 1.0 value of the PI was found in soils sampled at and around the mining areas.

• 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 the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.63-75
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• 2016

Aquaculture has historically delivered marine products because the country is surrounded by ocean on three sides. Surveys on production have been conducted recently to systematically manage aquaculture facilities. Based on survey results, pricing controls on marine products has been implemented to stabilize local fishery resources and to ensure minimum income for fishermen. Such surveys on aquaculture facilities depend on manual digitization of aerial photographs each year. These surveys that incorporate manual digitization using high-resolution aerial photographs can accurately evaluate aquaculture with the knowledge of experts, who are aware of each aquaculture facility's characteristics and deployment of those facilities. However, using aerial photographs has monetary and time limitations for monitoring aquaculture resources with different life cycles, and also requires a number of experts. Therefore, in this study, we investigated an automatic prototype system for detecting boundary information and monitoring aquaculture facilities based on satellite images. KOMPSAT-3 (13 Scene), a local high-resolution satellite provided the satellite imagery collected between October and April, a time period in which many aquaculture facilities were operating. The ANN classification method was used for automatic detecting such as cage, longline and buoy type. Furthermore, shape files were generated using a digitizing image processing method that incorporates polygon generation techniques. In this study, our newly developed prototype method detected aquaculture facilities at a rate of 93%. The suggested method overcomes the limits of existing monitoring method using aerial photographs, but also assists experts in detecting aquaculture facilities. Aquaculture facility detection systems must be developed in the future through application of image processing techniques and classification of aquaculture facilities. Such systems will assist in related decision-making through aquaculture facility monitoring.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.11-21
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• 2015

As growing interest in Human-Computer Interaction(HCI), research on HCI has been actively conducted. Also with that, research on Natural User Interface/Natural User eXperience(NUI/NUX) that uses user's gesture and voice has been actively conducted. In case of NUI/NUX, it needs recognition algorithm such as gesture recognition or voice recognition. However these recognition algorithms have weakness because their implementation is complex and a lot of time are needed in training because they have to go through steps including preprocessing, normalization, feature extraction. Recently, Kinect is launched by Microsoft as NUI/NUX development tool which attracts people's attention, and studies using Kinect has been conducted. The authors of this paper implemented hand-mouse interface with outstanding intuitiveness using the physical features of a user in a previous study. However, there are weaknesses such as unnatural movement of mouse and low accuracy of mouse functions. In this study, we designed and implemented a hand mouse interface which introduce a new concept called 'Virtual monitor' extracting user's physical features through Kinect in real-time. Virtual monitor means virtual space that can be controlled by hand mouse. It is possible that the coordinate on virtual monitor is accurately mapped onto the coordinate on real monitor. Hand-mouse interface based on virtual monitor concept maintains outstanding intuitiveness that is strength of the previous study and enhance accuracy of mouse functions. Further, we increased accuracy of the interface by recognizing user's unnecessary actions using his concentration indicator from his encephalogram(EEG) data. In order to evaluate intuitiveness and accuracy of the interface, we experimented it for 50 people from 10s to 50s. As the result of intuitiveness experiment, 84% of subjects learned how to use it within 1 minute. Also, as the result of accuracy experiment, accuracy of mouse functions (drag(80.4%), click(80%), double-click(76.7%)) is shown. The intuitiveness and accuracy of the proposed hand-mouse interface is checked through experiment, this is expected to be a good example of the interface for controlling the system by hand in the future.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.

• The Journal of the Petrological Society of Korea
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• v.17 no.1
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• pp.16-35
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• 2008

We study metamorphism of metasedimetary rocks and origin and evolution of leucogranite form Samcheok area, northeastern Yeongnam massif, South Korea. Metamorphic rocks in this area are composed of metasedimentary migmatite, biotite granitic gneiss and leucogranite. Metasedimentary rocks, which refer to major element feature of siliclastic sediment, are divided into two metamorphic zones based on mineral assemblages, garnet and sillimanite zones. According to petrogenetic grid of mineral assemblages, metamorhpic P-T conditions are $740{\sim}800^{\circ}C$ at $4.8{\sim}5.8\;kbar$ in the garnet zone and $640-760^{\circ}C$ at 2.5-4.5kbar in sillimanite zone. The leucogranite (Imwon leucogranite) is peraluminous granite which has high alumina index (A/CNK=1.31-1.93) and positive discriminant factor value (DF > 0). Thus, leucogranite is S-type granite generated from metasedimentary rocks. Major and trace element diagram ($R_1-R_2$ diagram and Rb vs. Y+Nb etc.) show collisional environment such as syn-collisional or volcanic arc granite. Because Rb/sr ratio (1.8-22.9) of leucogranites is higher than Sr/Ba ratio (0.21-0.79), leucogranite would be derived from muscovite dehydrate melting in metasedimentary rocks. Leucogranites have lower concentration of LREE and Eu and similar that of HREE relative to metasedimentary rocks. To examine difference of REEs between leucogranites and metasedimentary rocks, we perform modeling using volume percentage of a leucogranite and a metasedimenatry rock from study area and REE data of minerals from rhyolite (Nash and Crecraft, 1985) and melanosome of migmatite (Bea et al., 1994). Resultants of modeling indicate that LREE and HREE are controlled by monazites and garnet, respectively, although zircon is estimated HREE dominant in some leucogranite without garnet. Because there are many inclusions of accessary phases such as monazite and zircon in biotites from metasedimentary rocks. leucogranitic magma was mainly derived from muscovite-breakdown in metasedimenary rocks. Leucogranites can be subdivided into two types in compliance with Eu anomaly of chondrite nomalized REE pattern; the one of negative Eu anomaly is type I and the other is type II. Leucogranites have lower Eu concetnrations than that of metasedimenary rocks and similar that of both type. REE modeling suggest that this difference of Eu value is due to that of components of feldspars in both leucogranite and metasedimentary rock. The tendency of major ($K_2O$ and $Na_2O$) and face elements (Eu, Rb, Sr and Ba) of leucogranites also indicate that source magma of these two types was developed by anatexis experienced strong fractionation of alkali-feldspar. Conclusionally, leucogranites in this area are products of melts which was generated by muscovite-breakdown of metasedimenary rock in environment of continetal collision during high temperature/pressure metamorphism and then was fractionated and crystallized after extraction from source rock.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.