• Journal of IKEEE
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• v.25 no.2
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• pp.362-370
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• 2021

In this paper, we propose the Autonomous Stair-climbing system based on data from ToF sensors and IMU in developing stair-climbing robots to passive wheelchair users. Autonomous stair-climbing system are controlled by separating the timing of landing gear operation by location and utilizing state machines. To prove the theory, we construct and experiment with standard model stairs. Through an experiment to get the Attack angle, the average error of operating landing gear was 2.19% and the average error of the Attack angle was 2.78%, and the step division and status transition of the autonomous stair-climbing system were verified. As a result, the performance of the proposed techniques will reduce constraints of transportation handicapped.

• Design & Manufacturing
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• v.14 no.2
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• pp.14-20
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• 2020

In OLED panel production process, it is necessary to cut a part of protective film as a preprocess for lighting inspection. The current method is to recognize only the fiducial mark of the cut-out panel. Bare Glass Cutting does not compensate for machining cumulative tolerances. Even though process defects still occur, it is necessary to develop technology to solve this problem because only the Align Mark of the panel that has already been cut is used as the reference point for alignment. There is a lot of defective lighting during panel lighting test because the correct protective film is not cut on the panel power and signal application pad position. In laser cutting process to remove the polarizing film / protective film / TSP film of OLED panel, laser processing is not performed immediately after the panel alignment based on the alignment mark only. Therefore, in this paper, we performed real time inspection which minimizes the mechanism tolerance by correcting the laser cutting path of the protective film in real time using Machine Vision. We have studied calibration algorithm of Vision Software coordinate system and real image coordinate system to minimize inspection resolution and position detection error and edge detection algorithm to accurately measure edge of panel.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.545-557
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• 2022

This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images. Land cover map was calculated based on the SVM supervised classification technique. In order to find the optimal combination to classify the DMZ wildfire damage area, supervised classification according to various kernel and band combinations in the SVM was performed and the accuracy was evaluated through the error matrix. Verification was performed by comparing the results of the wildfire detection based on satellite image and data by the wildfire statistical annual report in 2020 and 2021. Also, wildfire damage areas was detected for which there is no current data in 2022. This is to quickly determine reliable results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.910-919
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• 2012

In this paper, we described development of on-board image processing system and its process and verified its performance through flight experiment. The image processing board has single ARM(Advanced Risk Machine) processor. We performed Embedded Linux Porting. Algorithm to be applied for object tracking is color-based image processing algorithm, it can be designed to track the object that has specific color on ground in real-time. To verify performance of the on-board image processing system, we performed flight test using the PNUAV, UAV developed by LAB. Also, we performed optimization of the image processing algorithm and kernel to improve real-time performance. Finally we confirmed that proposed system can track the blue-color object within four pixels error range consistently in the experiment.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1175-1182
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• 2017

Recent trend in the manufacturing industry is focused on the convergence with IoT and Big Data, by emergence of the 4th Industrial Revolution. To realize a smart factory, the proposed system based on MTConnect technology collects and integrates various status information of machines from many production facilities including heterogeneous devices. Also it can distribute the acquisited status of heterogeneous manufacturing machines to the remote devices. As a key technology of a flexible automated production line, the proposed system can provide much possibility to manage important information such as error detection and processing state management in the unmanned automation line.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1737-1742
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• 2015

In the industrial field for product inspection needs not only on the surface of the product but also the internal components defect inspection. Generally, optical inspection is mainly used for item inspection from production process. However, this is only to check defect of surface it is difficult to perform inspection of goods internal. To overcome these limitations, Instead of optical device by using the portable X- ray DR image acquisition device system developed to obtain an image in real time at the same time and determine product defects. After obtaining the X- ray image, the inspection product within error range is passed after machine image processing. Also, the results and numbers are stored by users.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.395-397
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• 2019

Despite technical advance, human error is the main reason for maritime accidents. To ensure a safety of maritime transporting environment, technical and methodological improvement to react to various types of maritime accidents should be developed instead of ambiguously anticipating maritime accidents due to human errors. Survey, questionnaires, and interview have been routinely applied to understand objective human lookout pattern differences in various navigational situations. Although the descriptive methodology helps systematically categorizing different patterns of human behavior to avoid accidents, the subjective methods limit to objectively recognize physical behavior patterns during navigation. The purpose of the study is to develop an objective lookout pattern detection system using wearable sensors in the simulated navigation environment. In the simulated maritime navigation environment, each participant performed a given navigational situation by wearing the wearable sensors on the wrist, trunk, and head. Activity classification algorithm that was developed in the previous navigation activity classification research was applied. The physical lookout behavior patterns before and after situation-aware showed distinctive patterns, and the results are expected to reduce human errors of navigators.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.