• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.1
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• pp.53-62
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• 2016

In recent years, UAV Photogrammetry based on an ultra-light UAS(Unmanned Aerial System) installed with a low-cost compact navigation device and a camera has attracted great attention through fast and accurate acquirement of geo-spatial data. In particular, UAV Photogrammetry do gradually replace the traditional aerial photogrammetry because it is able to produce DEMs(Digital Elevation Models) and Orthophotos rapidly owing to large amounts of high resolution image collection by a low-cost camera and image processing software combined with computer vision technique. With these advantages, UAV-Photogrammetry has therefore been applying to a large scale mapping and cadastral surveying that require accurate position information. This paper presents experimental results of an accuracy performance test with images of 4cm GSD from a fixed wing UAS to demarcate parcel boundaries in agricultural area. Consequently, the accuracy of boundary point extracted from UAS orthoimage has shown less than 8cm compared with that of terrestrial cadastral surveying. This means that UAV images satisfy the tolerance limit of distance error in cadastral surveying for the scale of 1: 500. And also, the area deviation is negligible small, about 0.2%(3.3m²), against true area of 1,969m² by cadastral surveying. UAV-Photogrammetry is therefore as a promising technology to demarcate parcel boundaries.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.323-331
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• 2017

In this paper, four types of deposition control processes such as temperature, pressure, input/output(I/O), and gas were replaced by the Internet of Things(IoT) to analyze the data load and sequence procedure before and after the application of it. Through this analysis, we designed the load balancing in the sensing area of the deposition process by creating the sequence diagram of the deposition process. In order to do this, we were modeling of the sensor I/O according to the arrival process and derived the result of measuring the load of CPU and memory. As a result, it was confirmed that the reliability on the deposition processes were improved through performing some functions of the equipment controllers by the IoT. As confirmed through this paper, by applying the IoT to the deposition process, it is expected that the stability of the equipment will be improved by minimizing the load on the equipment controller even when the equipment is expanded.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.535-544
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• 2016

Even though existing methods for orthophoto production using expensive aircraft are effective in large areas, they are drawbacks when dealing with renew quickly according to geographic features. But, as UAV(Unmanned Aerial Vehicle) technology has advanced rapidly, and also by loading sensors such as GPS and IMU, they are evaluates that these UAV and sensor technology can substitute expensive traditional aerial photogrammetry. Orthophoto production by using UAV has advantages that spatial information of small area can be updated quickly. But in the case of existing researches, images of same altitude are used in orthophoto generation, they are drawbacks about repetition of data and renewal of data. In this study, we targeted about small slope area, and by using low-end UAV, generated orthophoto and DEM(Digital Elevation Model) through different altitudinal images. The RMSE of the check points is σ_h = 0.023m on a horizontal plane and σ_v = 0.049m on a vertical plane. This maximum value and mean RMSE are in accordance with the working rule agreement for the aerial photogrammetry of the National Geographic Information Institute(NGII) on a 1/500 scale digital map. This paper suggests that generate orthophoto of high accuracy using a different altitude images. Reducing the repetition of data through images of different altitude and provide the informations about the spatial information quickly.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.90-94
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• 2007

Location tracking automation of resources in construction industry is one of the most important procedures to improve construction project performance and reduce the period of construction. Recently, location tracking technologies have proven to be effective in tracking construction materials and equipment in real time through the instrumentality of RFID (Radio Frequency Identification). By using wireless communication and inter-working system between RFID and USN, it is possible that construction engineers receive the location information of construction resources without additional efforts that move the RFID reader to read tags periodically. In the inter-working system, RFID reader delivers the acquired materials information to sensor node which is connected by serial interface. Then sensor node transmits the received data to the data aggregation terminal that is a sink node. The data aggregation terminal can transmit collected data to construction manager who is out of construction site using infrastructure such as CDMA(Code Division Multiple Access) network. The combination model of the two system and field test scenarios are presented in this paper.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.177-184
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• 2010

In these day, many data including sensor, delivery, credit and stock data are generated continuously in massive quantity. It is difficult to learn from these data because they are large in volume and changing fast in their concepts. To handle these problems, learning methods based in sliding window methods over time have been used. But these approaches have a problem of rebuilding models every time new data arrive, which requires a lot of time and cost. Therefore we need very simple incremental learning methods. Bayesian method is an example of these methods but it has a disadvantage which it requries the prior knowledge(probabiltiy) of data. In this study, we propose a learning method based on attribute values. In the proposed method, even though we don't know the prior knowledge(probability) of data, we can apply our new method to data. The main concept of this method is that each attribute value is regarded as an expert learner, summing up the expert learners lead to better results. Experimental results show our learning method learns from data very fast and performs well when compared to current learning methods(decision tree and bayesian).

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.333-338
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• 2019

In the field of radiotherapy, the Quality Assurance(QA) procedure to verify the safety of treatment is considered to be very important. However, due to various problems of the conventional dosimeters used for the QA, researches on these dosimeters have been actively carried out to replace them. In this study, to maximize the sensitivity by visible light(VL) emitted from phosphors, blended hybrid sensors were fabricated by blending various weight percent(wt%) of $Gd_2O_2S:Tb$ which is a phosphor with excellent fluorescence efficiency into $PbI_2$. Then, the electrical properties to high energy radiation from the blended sensors and the pure $PbI_2$ sensor were compared and evaluated. As a result of the sensitivity evaluation, the sensor of 3wt% showed the highest value with more than 40% difference from the other sensors, and gradual decreasing in sensitivity was observed with increasing wt% except for the sensor of 3wt%. Also, in the reproducibility evaluation, the pure $PbI_2$ sensor exhibited a large variation in coefficient of variation(CV)>0.015, while all the blended sensors showed CV<0.015.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.85-101
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• 2019

Recently, thank to the popularization of light-weight drone through the significant developments in computer technologies as well as the advanced automated procedures in photogrammetry, Unmanned Aircraft Systems have led to a growing interest in industry as a whole. Documentation, maintenance, and restoration projects of large scaled cultural property would required accurate 3D phenomenon modeling and efficient visual inspection methods. The object of this study verify on the accuracies achieved of 3D phenomenon reconstruction as well as on the validity of the preservation, maintenance and restoration of large scaled cultural property by UAS photogrammetry. The test object is cltural heritage(treasure 1324) that is the rock-carved standing Bodhisattva in Soraesan Mountain, Siheung, documented in Goryeo Period(918-1392). This standing Bodhisattva has of particular interests since it's size is largest stone Buddha carved in a rock wall and is wearing a lotus shaped crown that is decorated with arabesque patterns. The positioning accuracy of UAS photogrammetry were compared with non-target total station survey results on the check points after creating 3D phenomenal models in real world coordinates system from photos, and also the quantified informations documented by Culture Heritage Administration were compared with UAS on the bodhisattva image of thin lines. Especially, tests the validity of UAS photogrammetry as a alternative method of visual inspection methods. In particular, we examined the effectiveness of the two techniques as well as the relative fluctuation of rock surface for about 2 years through superposition analysis of 3D points cloud models produced by both UAS image analysis and ground laser scanning techniques. Comparison studies and experimental results prove the accuracy and efficient of UAS photogrammetry in 3D phenomenon modeling, maintenance and restoration for various large-sized Cultural Heritage.

• Korean Journal of Optics and Photonics
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• v.33 no.6
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• pp.331-337
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• 2022

Raman spectroscopy is an optical technique that can identify molecules in a label-free manner, and is therefore heavily investigated in various areas ranging from biomedical engineering to materials science. Probe-based Raman spectroscopy can perform minimally invasive chemical analysis, and thus has potential as a real-time diagnostic tool during surgery. In this study, Raman experimentation was calibrated by examining the Raman shifts with respect to the concentrations of chemical substances. Raman signal characteristics, targeted for normal mice and cerebral tissues of the 5xFAD dementia mutant model with accumulated amyloid beta plaques, were measured and analyzed to explore the possibility of diagnosis of Alzheimer's disease. The application to the diagnosis of dementia was cross-validated by measuring Raman signals of amyloid beta. The results suggest the potential of Raman spectroscopy as a diagnostic tool that may be useful in various areas of application.

• Journal of Platform Technology
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• v.10 no.3
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• pp.60-68
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• 2022

As AI-technology develops, interest in the safety of autonomous driving is increasing. Recently, autonomous vehicles have been increasing, but efforts to solve side effects have been sluggish. In particular, night autonomous vehicles have more problems. This is because the probability of accidents is higher in the night driving environment than in the day environment. There are more factors to consider for self-driving at night. Among these factors, reflection of light or reflected light of lighting may be a fundamental cause of night accidents. Therefore, this study proposes method to reduce accidents and improve safety by reducing reflected light generated by the headlights of opposite vehicles or various surrounding light that appear as an important problem in night autonomous vehicles. Therefore, first, in an image obtained by a sensor of a night autonomous vehicle, illumination reflected light is extracted using reflected light characteristic information, and a color of each pixel using a reflection coefficient is found to reduce a special area generated by geometric characteristics. In addition, we find a new area using only the brightness component of the specular area, define it as Illuminated Reflection Light (IRL), and finally present a method to reduce it. Although the illumination reflection light could not be completely reduce, generally satisfactory results could be obtained. Therefore, it is believed that the proposed study can reduce casualties by solving the problems of night autonomous driving and improving safety.

• Journal of Platform Technology
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• v.11 no.3
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• pp.24-31
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• 2023

Recently, interest in UAM (Urban Air Mobility, UAM), which can take off and land vertically in the operation of urban air transportation systems, has been increasing. Therefore, various start-up companies are developing related technologies as eco-friendly future transportation with advanced technology. However, studies on ways to increase safety in the operation of UAM are still insignificant. In particular, efforts are more urgent to improve the safety of risks generated in the process of attempting to land in the city center by UAM equipped with autonomous driving. Accordingly, this study proposes a plan to safely land by avoiding dangerous region that interfere when autonomous UAM attempts to land in the city center. To this end, first, the latitude and longitude coordinate values of dangerous objects observed by the sense of the UAM are calculated. Based on this, we proposed to convert the coordinates of the distorted planar image from the 3D image to latitude and longitude and then use the calculated latitude and longitude to compare the pre-learned feature descriptor with the HOG (Histogram of Oriented Gradients, HOG) feature descriptor to extract the dangerous Region. Although the dangerous region could not be completely extracted, generally satisfactory results were obtained. Accordingly, the proposed research method reduces the enormous cost of selecting a take-off and landing site for UAM equipped with autonomous driving technology and contribute to basic measures to reduce risk increase safety when attempting to land in complex environments such as urban areas.