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Ghiseok Kim     Institute, Department or Faculty Head 
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Ghiseok Kim published an article in April 2019.
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Kye-Sung Lee

14 shared publications

Division of Scientific Instrumentation, Korea Basic Science Institute, Daejeon 34133, South Korea

Hwan Hur

10 shared publications

Division of Scientific Instrumentation, Korea Basic Science Institute, Daejeon 34133, South Korea

Ah-Yeong Lee

1 shared publications

Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

Sang-Yeon Kim

1 shared publications

Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

Suk-Ju Hong

1 shared publications

Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

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Publications
Article 0 Reads 0 Citations Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery Suk-Ju Hong, Yunhyeok Han, Sang-Yeon Kim, Ah-Yeong Lee, Ghis... Published: 06 April 2019
Sensors, doi: 10.3390/s19071651
DOI See at publisher website ABS Show/hide abstract
Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.
Article 0 Reads 0 Citations Moisture Content Measurement of Broadleaf Litters Using Near-Infrared Spectroscopy Technique Ghiseok Kim, Suk-Ju Hong, Ah-Yeong Lee, Ye-Eun Lee, Sangjun ... Published: 24 November 2017
Remote Sensing, doi: 10.3390/rs9121212
DOI See at publisher website ABS Show/hide abstract
Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. The maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all of the candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture.
Article 1 Read 0 Citations 3D Defect Localization on Exothermic Faults within Multi-Layered Structures Using Lock-In Thermography: An Experimental ... Ji Yong Bae, Kye-Sung Lee, Hwan Hur, Ki-Hwan Nam, Suk-Ju Hon... Published: 13 October 2017
Sensors, doi: 10.3390/s17102331
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Micro-electronic devices are increasingly incorporating miniature multi-layered integrated architectures. However, the localization of faults in three-dimensional structure remains challenging. This study involved the experimental and numerical estimation of the depth of a thermally active heating source buried in multi-layered silicon wafer architecture by using both phase information from an infrared microscopy and finite element simulation. Infrared images were acquired and real-time processed by a lock-in method. It is well known that the lock-in method can increasingly improve detection performance by enhancing the spatial and thermal resolution of measurements. Operational principle of the lock-in method is discussed, and it is represented that phase shift of the thermal emission from a silicon wafer stacked heat source chip (SSHSC) specimen can provide good metrics for the depth of the heat source buried in SSHSCs. Depth was also estimated by analyzing the transient thermal responses using the coupled electro-thermal simulations. Furthermore, the effects of the volumetric heat source configuration mimicking the 3D through silicon via integration package were investigated. Both the infrared microscopic imaging with the lock-in method and FE simulation were potentially useful for 3D isolation of exothermic faults and their depth estimation for multi-layered structures, especially in packaged semiconductors.
Article 1 Read 1 Citation Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques Suk-Ju Hong, Shin-Joung Rho, Ah-Yeong Lee, Heesoo Park, Jins... Published: 01 January 2017
Journal of Spectroscopy, doi: 10.1155/2017/1082612
DOI See at publisher website ABS Show/hide abstract
Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.
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