• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.225-232
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• 2022

Recently, unmanned logistics delivery systems, such as UAV (Unmanned Aerial Vehicle, written as drone below) and autonomous robot delivery systems, have been implemented in many countries due to the rapid development of autonomous driving technology. The development of these new types of advanced unmanned logistics delivery systems is essential not only to become a leading logistics company but also to secure national competitiveness. In this paper, the application of the unmanned logistics delivery system was investigated in terms of market trends, overall technology level of last mile delivery drone and autonomous delivery robot. The direction of response to changes in the last mile delivery service market was checked through a comparison of the technological level between domestic companies that produce last mile devices and advanced foreign companies. As a result of this technology level analysis, the difference between domestic companies and advanced companies was shown using tables and figures to show their relative levels. The results of this analysis reflect the opinions of experts in the field of last-mile delivery technology. In addition, the technology level of unmanned logistics delivery systems for each country was analyzed based on the number of related technology patents. Lastly, insights for the technology level analysis of unmanned last mile delivery systems were proposed as a conclusion.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2927-2934
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• 2023

The influence of the atmospheric radon background on the airborne gamma spectrum can seriously affect researchers' judgement of ground radiation information. However, due to load and endurance, unmanned aerial vehicle (UAV)-borne gamma-ray spectrometry is difficulty installing upward-looking detectors to monitor atmospheric radon background. In this paper, an advanced spectral-ratio method was used to correct the atmospheric radon background for a UAV-borne gamma-ray spectrometry in Inner Mongolia, China. By correcting atmospheric radon background, the ratio of the average count rate of U window in the anomalous radon zone (S5) to that in other survey zone decreased from 1.91 to 1.03, and the average uranium content in S5 decreased from 4.65 mg/kg to 3.37 mg/kg. The results show that the advanced spectral-ratio method efficiently eliminated the influence of the atmospheric radon background on the UAV-borne gamma-ray spectrometry to accurately obtain ground radiation information in uranium exploration. It can also be used for uranium tailings monitoring, and environmental radiation background surveys.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.9
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• pp.81-90
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• 2016

Recently drones have become popular enough to be one of the hobby. The drone refers to an unmanned aerial vehicle which can fly and be steered by a radio wave without a pilot and it has a airplane or helicopter shape. The drone was first started to be used from military purpose, but its usage has been expanded to the private such as construction site, crop-dusting, field discovery, freight shipping and drones to prevent cheating. However the drone that we can see often in the market is expansive, hard to be repaired when it broken down and has a discomfort of the short flight time. In this paper, to solve an uncomfortable talk on the cheap 8-bits microcontrollers ATmega128 Using drone for implementation. Axes gyroscope and accelerometers mcu between posture an attitude control, communications through drone control, pid. Receiver input them into transmitter signals of movements to control drone c the programming was implemented in on the basis of language. drone using ATmega128 microcontroller is possible hovering, By utilizing a pin that are not required for control it can be used as a drone for a variety of uses.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.257-258
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• 2020

기존의 드론 조작은 초보자에게 어려웠다. 초보자의 경우 드론을 조종하다가 드론이 추락하거나 장애물에 걸려 프로펠러 등의 부품들이 손상되는 경우를 빈번하게 마주한다. 본 연구에서는 초보자 또한 드론 파손의 걱정 없이 드론의 조작을 더욱 쉽게 개선시키는 것을 전제로 뇌파와 보조입력인 음성인식을 이용한 드론 컨트롤러 기술을 적용하고자 한다. 현재 대중적으로 출시되어 있는 드론의 경우 호버링 기능을 포함시켜 드론의 추락 위험을 줄여주는 기능을 탑재하고 있다. 하지만 속도가 빠른 드론의 조작에 있어 미숙한 초보자들은 장애물과의 충돌 그리고 드론 착륙 시 기체손상 등의 위험에 대비하기 힘들다. 본 논문은 이러한 문제점들을 개선하기 위해 기존의 드론 컨트롤러 대신 특정한 동작을 상상할 때 발현되는 동작상상뇌파와 음성입력을 적용한 '동작상상뇌파와 음성인식을 이용한 드론 컨트롤러' 기술을 제안한다. 기존의 드론 컨트롤러와는 다르게 빅 데이터 처리기술인 머신러닝을 이용하여 뇌파 데이터를 처리하고 그 데이터들과 입력되는 뇌파 값을 비교하여 드론을 제어한다. 또한 뇌파의 발현이 안정적이지 못하는 상황을 대비한 보조입력인 음성인식을 이용하여 드론의 기체손상을 최소화 시킬 수 있다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.575-580
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• 2023

This study was prevent the decrease in crop output by insect pests and spraying by application uniformity. A flight level 4 m height and 4-5 m/sec. speed are difficult to maintain with a agricultural drone for aerial application, which has been affected by the methods or environmental factors, such as changes in the wind. Therefore, which can allow a controlled application width and spray rate automatically and verified experimentally using drone. The sprayed particles began to decrease from about 3.75 m on the left and right sides of the spray nozzle. According to the number of particles, the effective spraying width was observed to be about 7.5 m, and it was verified that the proposed spraying system was effective in uniform control system.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.445-445
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• 2022

최근 새만금 방조제 건설이 완료됨에 따라 주변 연안지역의 지형에 많은 변화가 감지되었다. 본 연구대상지는 격포해수욕장으로서 새만금 사업 준공 후 연안침식에 따른 모래 유실 등으로 인해 양빈사업 등이 검토되고 있는 상황이다. 본 연구에서는 연안지형 변화 탐지를 위한 UAV (Unmanned Aerial Vehicle) 활용기술을 제시하는 것으로서 총 3회에 걸쳐 UAV 영상을 촬영하였다. 영상촬영은 DJI Inspire 2 UAV를 활용하였으며 VRS(Virtual Reference Service) 측량성과와 연계하여 Pix4D Mapper SW를 통해 정사영상과 수치표면모델(DSM; Digital Surface Model)을 제작하였다. 먼저 2018. 6. 29 ~ 2018. 12. 10 사이의 지형변화 탐지를 수행한 결과 침식과 퇴적의 최대값은 각각 2.56m와 2.24m로 나타났으며 평균적으로는 0.01m의 퇴적이 발생하였다. 그리고 2018. 6. 29 ~ 2019. 6. 14 동안의 침식과 퇴적의 최대값은 각각 2.31m와 2.28m로 나타났으며 평균값은 0.02m의 침식이 발생하였다. 또한 2018. 12. 10 ~ 2019. 6. 14 사이에는 침식과 퇴적의 최대값이 각각 2.28m와 2.55m로 나타났으며 평균값은 0.03m의 침식이 발생하였다. 지형변화를 보다 상세히 모니터링하고자 퇴적과 침식구간을 나누어 분석을 수행한 결과, 2018. 6. 29 ~ 2018. 12. 10 사이에는 0.5m 이내의 침식과 퇴적구간 면적이 각각 13,324.4m²와 14,667.3m²로 퇴적구간의 면적이 1,342.9m² 만큼 높게 나타났으며, 2018. 12. 10 ~ 2019. 6. 14 사이에는 0.5m 이내의 침식과 퇴적구간 면적이 각각 16,176.6m²와 11,723.0m²로 침식구간의 면적이 4,453m² 만큼 높게 나타났다. 또한 2018. 12. 10 ~ 2019. 6. 14 사이에는 0.5m 이내의 침식과 퇴적구간 면적이 각각 16,821.6m²와 11,126.4m²로 침식구간의 면적이 5,695.2m² 만큼 크게 분석되었다. 이와 같이 UAV 영상 기반의 연안지형 모니터링을 수행할 경우 시계열 지형변화를 효과적으로 모니터링할 수 있으며, 이러한 업무는 새만금 방조제 건설에 따른 지형변화의 영향평가 등 다양한 연안업무에 활용될 수 있을 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.594-596
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• 2022

This paper relates to a system for pre-processing high-definition images acquired through a camera mounted on an unmanned aerial vehicle(UAV) and transmitting them to a server through a mobile communication network. In the case of the existing UAV system for image acquisition service, the acquired image was stored in the external storage device of the camera mounted on the UAV, and the image was checked by directly moving the storage device after the flight was completed. In the case of this method, there is a limitation in that it is impossible to check whether image acquisition or pre-processing is properly performed before directly checking image data through an external storage device. In addition, since the data is stored only in an external storage device, there is a disadvantage that data sharing is cumbersome. In this paper, to solve the above problems, we propose a system that can remotely check images in real time. Furthermore, we propose a system and method capable of performing pre-processing such as geo-tagging and transmission through a mobile communication network in addition to image acquisition through shooting in an UAV.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.