• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.315-327
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• 2004

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.383-392
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• 2005

Vegetation classes, especially grass and tree classes, are often confused in classification when conventional spectral pattern recognition techniques are used to classify urban areas. This paper reports on a study to improve the classification results by using an automated process of considering height information in separating urban vegetation classes, specifically tree and grass, using three-band, high-spatial resolution, digital aerial imagery. Height information was derived photogrammetrically from stereo pair imagery using cross correlation image matching to estimate differential parallax for vegetation pixels. A threshold value of differential parallax was used to assess whether the original class was correct. The average increase in overall accuracy for three test stereo pairs was $7.8\%$, and detailed examination showed that pixels reclassified as grass improved the overall accuracy more than pixels reclassified as tree. Visual examination and statistical accuracy assessment of four test areas showed improvement in vegetation classification with the increase in accuracy ranging from $3.7\%\;to\;18.1\%$. Vegetation classification can, in fact, be improved by adding height information to the classification procedure.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.487-499
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• 2009

Atmospheric correction of satellite measurements is a major step to estimate accurate surface reflectance of solar spectrum channels. In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model used to retrieve surface reflectance from MODIS (MODerate resolution Imaging Spectrometer) top of atmosphere (TOA) reflectance. It is fast and simple atmospheric correction method, so it uses for work site operation in various satellite. This study attempts a test of accuracy of SMAC through a sensitivity test to detected error sources and to improve accuracy of surface reflectance using SMAC. The results of SMAC as compared with MODIS surface reflectance (MOD09) was represented that low accuracy ($R^2\;=\;0.6196$, Root Means Square Error (RMSE) = 0.00031, bias = - 0.0859). Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Among the input parameters, Aerosol Optical Depth (AOD) is the most influence input parameter. In order to modify AOD term in SMAC code, Stepwise multiple regression was performed with testing and remove variable in three stages with independent variables of AOD at 550nm, solar zenith angle, viewing zenith angle. Surface reflectance estimation by using Newly proposed AOD term in the study showed that improve accuracy ($R^2\;=\;0.827$, RMSE = 0.00672, bias = - 0.000762).

• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.583-599
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• 2022

Although the object detection accuracy with a single image has been significantly improved with the advance of deep learning techniques, the detection accuracy for pig monitoring is challenged by occlusion problems due to a complex structure of a pig room such as food facility. These detection difficulties with a single image can be mitigated by using a video data. In this research, we propose a method in pig detection for video monitoring environment with a static camera. That is, by using both image processing and deep learning techniques, we can recognize a complex structure of a pig room and this information of the pig room can be utilized for improving the detection accuracy of pigs in the monitored pig room. Furthermore, we reduce the execution time overhead by applying a pruning technique for real-time video monitoring on an embedded board. Based on the experiment results with a video data set obtained from a commercial pig farm, we confirmed that the pigs could be detected more accurately in real-time, even on an embedded board.

• Design & Manufacturing
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• v.16 no.2
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• pp.53-59
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• 2022

Unlike other press forming processes, ISF (Incremental sheet forming) doesn't require a punch and die set. However, during the ISF processes unwanted bending deformation occurred around the target geometry. This paper is aimed to analyze the effect of the die structure, which is supported by bolts, on the geometric accuracy of the ISF processes. In this research, the ISF processes with Al5052 sheet of 0.5 mm, the tool diameter of 6 mm and the stepdown of 0.4 mm was employed. L-shaped, step-shaped, relief-shaped geometry were employed in experiments. Sectional view and the plastic strain were compared. From this research we find out that the bolt supported ISF processes increases the geometric accuracy of products very effectively.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.167-174
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• 2023

This paper attempted to improve the accuracy of the colorectal cancer diagnosis model using image data augmentation in convolutional neural network. Image data augmentation was performed by flipping, rotation, translation, shearing and zooming with basic image manipulation method. This study split 4000 training data and 1000 test data for 5000 image data held, the model is learned by adding 4000 and 8000 images by image data augmentation technique to 4000 training data. The evaluation results showed that the clasification accuracy for 4000, 8000, and 12,000 training data were 85.1%, 87.0%, and 90.2%, respectively, and the improvement effect depending on the increase of image data was confirmed.

• Journal of Microbiology and Biotechnology
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• v.34 no.4
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• pp.891-901
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• 2024

This study focuses on improving the 3D printability of pea protein with the help of food inks designed for jet-type 3D printers. Initially, the food ink base was formulated using nanocellulose-alginate with a gradient of native potato starch and its 3D printability was evaluated. The 3D-printed structures using only candidates for the food ink base formulated with or without potato starch exhibited dimensional accuracy exceeding 95% on both the X and Y axes. However, the accuracy of stacking on the Z-axis was significantly affected by the ink composition. Food ink with 1% potato starch closely matched the CAD design, with an accuracy of approximately 99% on the Z-axis. Potato starch enhanced the stacking of 3D-printed structures by improving the electrostatic repulsion, viscoelasticity, and thixotropic behavior of the food ink base. The 3D printability of pea protein was evaluated using the selected food ink base, showing a 46% improvement in dimensional accuracy on the Z-axis compared to the control group printed with a food ink base lacking potato starch. These findings suggest that starch can serve as an additive support for high-resolution 3D jet-type printing of food ink material.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.383-388
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• 2011

This paper presents to study the accuracy improvement of the laser navigation using FIS(fuzzy inference system) and the reliability. As wireless guidance system, the top-mounted laser with the laser navigation can rotate $360^{\circ}$ with phototransistor or other optical sensors that read the return signal from reflectors mounted at the perimeter of the workspace. The type of major existing guidance systems is a wire guidance system. Because they have high accuracy and fast response time, they are used to most industries. However, their installation cost is very expensive and maintenance is very difficult because their sensors are placed approximately 1 inch below the ground or embedded in the floor. To solve those problems, the laser navigation was developed as a wire guidance system. It does not need to reconstruct a floor or ground. And it can reduce costs of installation and maintenance because changing the layout is easy. However, it is difficult to apply to an industrial field because it is easily affected by disturbances which cause loss and damage of data, and has slow respond time. Therefore, we study the accuracy improvement of the laser navigation. The proposed method is a correction method using reliability of the laser navigation. here, reliability is calculated by FIS which is designed with the analyzed characteristics of the laser navigation. For performance comparison, we use original position data form the laser navigation and position data corrected by original reliability from the laser navigation. In experimental result, we verified that the performance of the proposed method compared the others is improved by about 50% or more.