• Journal of KIISE:Computing Practices and Letters
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• v.5 no.4
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• pp.416-425
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• 1999

복잡한 무결성 제약 조건을 효율적으로 확인하기 위해 제약 조건들을 룰 베이스(rule base)에 저장하고 별도의 룰 관리 시스템과 제약 조건 관리 시스템을 통해 제약 조건을 확인하는 기법이 많은 연구자들에 의해 연구되고 발표되었다. 그러나 제약 조건 관리 시스템이 실행시간에 응용 프로그램을 항상 모니터링하고 있다가 데이타의 수정이 요청될 때마다 개입하여 프로세스를 중단시키고 관련 제약 조건을 확인하는 기존의 방법들은 처리 시간의 지연을 피할 수 없다. 본 논문은 컴파일 시간에 제약 조건 확인 코드를 응용 프로그램에 미리 삽입할 것을 제안한다. 응용 프로그램 자체 내에 제약 조건 확인 코드가 삽입되기 때문에 실행 시간에 다른 시스템의 제어를 받지 않고 직접 제약 조건의 확인 및 데이타베이스의 접근이 가능해져서 처리 시간의 지연을 피할 수 있을 것이다. 이를 위해 어떤 구문이 제약 조건의 확인을 유발하는 지를 추적하였고, 컴파일러가 그러한 구문을 어떻게 전처리 과정에서 검색하는지 그리고 그러한 구문마다 어떻게 해당 제약 조건 확인 코드를 삽입할 수 있는 지를 객체지향1) 데이타베이스 언어인 Objectivity/C++에 대해 gcc의 YACC 코드를 변경함으로써 보여 주었다.Abstract To cope with the complexity of handling integrity constraints, numerous researchers have suggested to use a rule-based system, where integrity constraints are expressed as rules and stored in a rule base. A rule manager and an integrity constraint manager cooperate to check the integrity constraints efficiently. In this approach, however, the integrity constraint manager has to monitor the activity of an application program constantly to catch any database operation. For each database operation, it has to check relevant rules with the help of the rule manager, resulting in considerable delays in database access. We propose to insert the constraints checking code in the application program directly at compile time. With constraints checking code inserted, the application program can check integrity constraints by itself without the intervention of the integrity constraint manager. We investigate what kind of statements require the checking of constraints, show how the compiler can detect those statements, and show how constraints checking code can be inserted into the program, by modifying the GCC YACC file for Objectivity/C++, an object-oriented database programming language.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.9
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• pp.815-820
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• 2016

An elastic polymer (e.g., PDMS) blended with EFG particles is a promising conductive composite for fabricating soft sensors that can detect an object's deformation up to or more than 50%. Here, we develop large-strain, sprayable soft sensors using a mixture of PDMS and EFG particles, which are used as a host elastomer and electrically conductive particles, respectively. A solution for a conductive composite mixture is prepared by the microwave-assisted graphite exfoliation, followed by ultrasonication-induced fragmentation of the exfoliated graphite and ultrasonic blending of PDMS and EFG. Using the prepared solutions for composite and pure PDMS, 1-, 2-, and 3-axis soft sensors are fabricated by airbrush stencil technique where composite mixture and pure PDMS are materials for sensing and insulating layers, respectively. We characterize the soft strain sensors after investigating the effect of PDMS/EFG wt% on mechanical compliance and electrical conductance of the conductive composite.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.575-579
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• 2006

The land use in suburban area has been changed rapidly due to the urban expansion in Korea during the last few decades. And such land use changes result in various environmental problems such as biodiversity decrease, habitat fragmentation, air pollution and urban heat island. Remote Sensing (RS) and Geographical Information Systems (GIS) can be used for land cover change detection to understand the impact and trend of the land use change. Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times and it can provide quantitative and comparative information for the land use/cover change. RS is less expansive than field survey for producing land use maps, and can be accessed quickly and repetitively for large area. Also it can be used for change detection using multi-temporal land use/cover by accumulated data. Therefore, the purpose of this study is to detect and quantitatively evaluate urban land cover change in urban stream watershed area for the last few decades and ultimately to provide the basic data for urban land use planning and management.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.568-573
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• 2018

UAV(Unmanned Aerial Vehicle) is widely used in space information construction, agriculture, fisheries, weather observation, communication, and entertainment fields because they are cheaper and easier to operate than manned aircraft. In particular, UAV have attracted much attention due to the speed and cost of data acquisition in the field of spatial information construction. However, ortho image images produced using UAVs are distorted in buildings and forests. It is necessary to solve these problems in order to utilize the geospatial information field. In this study, fixed wing, rotary wing, vertical take off and landing type UAV were used to detect distortions of ortho image of UAV under various conditions, and various object areas such as construction site, urban area, and forest area were captured and analysed. Through the research, it was found that the redundancy of the unmanned aerial vehicle image is the biggest factor of the distortion phenomenon, and the higher the flight altitude, the less the distortion phenomenon. We also proposed a method to reduce distortion of orthoimage by lowering the resolution of original image using DTM (Digital Terrain Model) to improve distortion. Future high-quality unmanned aerial vehicles without distortions will contribute greatly to the application of UAV in the field of precision surveying.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.469-481
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• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.41-47
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• 2019

In this study, a smart assistive device is designed to recognize pedestrian signal and to provide audio instructions for visually impaired people in crossing streets safely. Walking alone is one of the biggest challenges to the visually impaired and it deteriorates their life quality. The proposed device has a camera attached on a pair of glasses which can detect traffic lights, recognize pedestrian signals in real-time using a machine learning algorithm on GPU board and provide audio instructions to the user. For the portability, the dimension of the device is designed to be compact and light but with sufficient battery life. The embedded processor of device is wired to the small camera which is attached on a pair of glasses. Also, on inner part of the leg of the glasses, a bone-conduction speaker is installed which can give audio instructions without blocking external sounds for safety reason. The performance of the proposed device was validated with experiments and it showed 87.0% recall and 100% precision for detecting pedestrian green light, and 94.4% recall and 97.1% precision for detecting pedestrian red light.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.661-666
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• 2019

Diverging collimators is used to obtain reduced images of an object, or to detect a wide filed-of-view (FOV) using a small gamma camera. In the gamma camera using the diverging collimators, the block scintillator, and the pixel scintillator array, gamma rays are obliquely incident on the scintillator surface when the source is located the periphery of the FOV. Therefore, the spatial resolution is reduced because it is obliquely detected in depth direction. In this study, we designed a novel system to improve the spatial resolution in the periphery of the FOV. Using a tapered crystal array to configure the scintillation pixels to coincide with the angle of the collimator's hole allows imaging to one scintillation pixel location, even if events occur to different depths. That is, even if is detected at various points in the diagonal direction, the gamma rays interact with one crystal pixel, so resolution does not degrade. The resolution of the block scintillator and the tapered crystal array was compared and evaluated through Geant4 Application for Tomographic Emission (GATE) simulation. The spatial resolution of the obtained image was 4.05 mm in the block scintillator and 2.97 mm in the tapered crystal array. There was a 26.67% spatial resolution improvement in the tapered crystal array compared to the block scintillation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.77-83
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• 2020

The heat treatment facility is in a situation where the scope of application of the remote IOT system is expanding due to the harsh environment caused by high heat and long working hours among the root industries. In this heat treatment process environment, the IOT middleware is required to play a pivotal role in interpreting, managing and controlling data information of IoT devices (sensors, etc.). Until now, the system controlled by the heat treatment remotely was operated with the command of the operator's batch system without overall monitoring of the site situation. However, for the safety and precise control of the heat treatment facility, it is necessary to control various sensors and recognize the surrounding work environment. As a solution to this, the heat treatment safety support system presented in this paper proposes a support system that can detect the access of the work manpower to the heat treatment furnace through thermal image detection and operate safely when ordering work from a remote location. In addition, an OPEN CV-based deterioration analysis system using DNN deep learning network was constructed for faster and more accurate recognition than general fixed hot spot monitoring-based thermal image analysis. Through this, we would like to propose a system that can be used universally in the heat treatment environment and support the safety management specialized in the heat treatment industry.