• Journal of Welding and Joining
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• v.20 no.1
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• pp.69-75
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• 2002

This study investigated the effect of the system parameters on penetration depth measurement using infrared temperature sensing system. The distance from focusing lens to detector was varied to diminish the error in measuring weld bead width. The effect of bead surface shape on measured surface temperature profile was evaluated using specimen heated by electric resistance. The measuring distance from laser beam was changed to optimize the measuring point. The results indicated that the monitoring device of surface temperature using infrared detector array was applicable to real time penetration depth control.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1147-1167
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• 2015

Wireless smart sensors, which have become popular for monitoring applications, are an attractive option for implementing structural control systems, due to their onboard sensing, processing, and communication capabilities. However, wireless smart sensors pose inherent challenges for control, including delays from communication, acquisition hardware, and processing time. Previous research in wireless control, which focused on semi-active systems, has found that sampling rate along with time delays can significantly impact control performance. However, because semi-active systems are guaranteed stable, these issues are typically neglected in the control design. This work achieves active control with smart sensors in an experimental setting. Because active systems are not inherently stable, all the elements of the control loop must be addressed, including data acquisition hardware, processing performance, and control design at slow sampling rates. The sensing hardware is shown to have a significant impact on the control design and performance. Ultimately, the smart sensor active control system achieves comparable performance to the traditional tethered system.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.31-37
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• 2015

Data generated from sensors are exploding with recent development of IoT. This paradigm shift requires various industry fields that demand instant actions to analyze the arising data on a real-time basis, along with the real-time visualization analysis. As the existing visualization systems, however, perform visualization after storing data, the response time of the server cannot guarantee the ms-level processing that is close to real-time. They also have a problem of destroying data that can be major resources as they do not possess the process resources. Therefore, a smart gardening system that applies a real-time visualization algorithm using IoT sensing data under a gardening environment was designed and implement in this study. The response time of the server was measured to evaluate the performance of the suggested system. As a result, the response speed of the suggested real-time visualization algorithm was guaranteeing the ms-level processing close to real-time.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.259-276
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• 2011

Short-term land cover changes, such as forest fire scar and crop harvesting, can be detected by high temporal resolution satellite imagery like MODIS and AVHRR. Because these optical satellite images are often obscured by clouds, the static cloud-free composite methods (maximum NDVI, minblue, minVZA, etc.) has been used based on non-overlapping composite period (8-day, 16-day, or a month). Due to relatively long time lag between successive images, these methods are not suitable for observing short-term land cover changes in near-real time. In this study, we suggested a new dynamic cloud-free composite algorithm that uses cut-and-patch method of cloud-masked daily MODIS data using MOD35 products. Because this dynamic composite algorithm generates daily cloud-free MODIS images with the most recent information, it can be used to monitor short-term land cover changes in near-real time. The dynamic composite algorithm also provides information on the date of each pixel used in compositing, thereby makes accurately identify the date of short-term event.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.747-761
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• 2021

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.

• Korean Journal of Optics and Photonics
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• v.19 no.1
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• pp.1-8
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• 2008

Shack-Hartmann sensors are widely employed as a wavefront measuring device in various applications. Adaptive optics is one of the major applications. Since an adaptive optics system should be operated in real-time, high-speed wavefront sensing is essential. In high-speed operation, integration time of an image detector is very short. In this case, noises such as readout noise and photon noise greatly influence the accuracy of wavefront sensing. Therefore a fast and noise-insensitive centroid finding algorithm is required for the real-time wavefront sensing. In this paper, the multi-resolution correlation method is proposed. By employing multi-resolution images, this method greatly reduces the computation time when compared to the fast Fourier transform (FFT) correlation method. The verification is performed through the computational simulation. In this paper, the center of mass method, correlation method and multi-resolution correlation method are employed to compare the measurement accuracy of the centroid finding algorithms. The accuracy of a Shack-Hartmann wavefront sensor using the proposed algorithm is proved to be comparable to that of the conventional correlation method.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.323-330
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• 2016

Communication, Ocean and Meteorological Satellite (COMS) was launched in 2010 with three payloads that include Ka-band communication payload developed by Ministry of Science, ICT and Future Planning (MSIP) and Electronics and Telecommunications Research Institute (ETRI). This study introduces a real-time remote vessel monitoring system built in the Socheongcho Ocean Research Station using the Ka-band communication satellite. The system is composed of three steps; real-time data collection, transmission, and processing/visualization. We describe hardware (H/W) and software systems (S/W) installed to perform each step and the whole procedure that made the raw data become vessel information for a real-time ocean surveillance. In addition, we address functional requirements of H/W and S/W and the important considerations for successful operation of the system. The system is now successfully providing, in near real-time, ship information over a VHF range using AIS data collected in the station. The system is expected to support a rapid and effective surveillance over a huge oceanic area. We hope that the concept of the system can be fully used for real-time maritime surveillance using communication satellite in future.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.628-632
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• 2005

Remote sensing through atmospheric turbulence had been hard works for a long time, because wavefront distortion due to the Earth's atmospheric turbulence deteriorates image quality. But due to the appearance of adaptive optics, it is no longer difficult things. Adaptive optics is the technology to correct random optical wavefront distortions in real time. For past three decades, research on adaptive optics has been performed actively. Currently, most of newly built telescopes have adaptive optical systems. Adaptive optical system is typically composed of three parts, wavefront sensing, wavefront correction and control. In this work, the wavefront sensing technology for adaptive optical system is treated. More specifically, shearing interferometers and Shack-Hartmann wavefront sensors are considered. Both of them are zonal wavefront sensors and measure the slope of a wavefront. . In this study, the shearing interferometer is made up of four right-angle prisms, whose relative sliding motions provide the lateral shearing and phase shifts necessary for wavefront measurement. Further, a special phase-measuring least-squares algorithm is adopted to compensate for the phase-shifting error caused by the variation in the thickness of the index-matching oil between the prisms. Shack-Hartmann wavefront sensors are widely used in adaptive optics for wavefront sensing. It uses an array of identical positive lenslets. And each lenslet acts as a subaperture and produces spot image. Distortion of an input wavefront changes the location of spot image. And the slope of a wavefront is obtained by measuring this relative deviation of spot image. Structures and measuring algorithms of each sensor will be presented. Also, the results of wavefront measurement will be given. Using these wavefront sensing technology, an adaptive optical system will be built in the future.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.705-708
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• 2005

Mobile Mapping Systems using the vehicle equipped the GPS, IMU, CCD Cameras is the effective system for the management of the road facilities, update of the digital map, and etc. They must provide users with the sensor data which is acquired by Mobile Mapping Systems in real-time so that users can process what they want by using the latest data. But it' s not an easy process because the amount of sensor data is very large, particularly image data to be transmitted. So it is necessary to reduce the amount of image data so that it is transmitted effectively. In this study, the effective method was suggested for the compression/decompression image data using the Wavelet Transformation and Huffman Coding. This technique will be possible to transmit of the geographic information effectively such as position data, attitude data, and image data acquired by Mobile Mapping Systems in the wireless internet environment when data is transmitted in real-time.