• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.187-192
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• 2021

This study proposes a system for take-off in a forest or similar complex environment using an object detector. In the simulator, a raspberry pi is mounted on a quadcopter with a length of 550mm between motors on a diagonal line, and the experiment is conducted based on edge computing. As for the images to be used for learning, about 150 images of 640⁎480 size were obtained by selecting three points inside Kunsan University, and then converting them to black and white, and pre-processing the binarization by placing a boundary value of 127. After that, we trained the SSD_Inception model. In the simulation, as a result of the experiment of taking off the drone through the model trained with the verification image as an input, a trajectory similar to the takeoff was drawn using the label.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.99-104
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• 2022

Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect, age and gender classification has been one of the hot topics among computer vision researchers in recent decades. Deployment of deep Convolutional Neural Network(: CNN) models achieved state-of-the-art performance. However, the most of CNN based architectures are very complex with several dozens of training parameters so they require much computation time and resources. For this reason, we propose a new CNN-based classification algorithm with significantly fewer training parameters and training time compared to the existing methods. Despite its less complexity, our model shows better accuracy of age and gender classification on the UTKFace dataset.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

• Journal of IKEEE
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• v.27 no.3
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• pp.251-257
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• 2023

In this paper, we propose a dual-structured self-attention method that improves the lack of regional features of the vision transformer's self-attention. Vision Transformers, which are more computationally efficient than convolutional neural networks in object classification, object segmentation, and video image recognition, lack the ability to extract regional features relatively. To solve this problem, many studies are conducted based on Windows or Shift Windows, but these methods weaken the advantages of self-attention-based transformers by increasing computational complexity using multiple levels of encoders. This paper proposes a dual-structure self-attention using self-attention and neighborhood network to improve locality inductive bias compared to the existing method. The neighborhood network for extracting local context information provides a much simpler computational complexity than the window structure. CIFAR-10 and CIFAR-100 were used to compare the performance of the proposed dual-structure self-attention transformer and the existing transformer, and the experiment showed improvements of 0.63% and 1.57% in Top-1 accuracy, respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.145-147
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• 2020

As deep learning technology advances, there have been many attempts to improve video codecs such as High-Efficiency-Video-Coding (HEVC) using deep learning technology. One of the most researched approaches is improving filters inside codecs through image restoration researches. In this paper, we propose a method 01 replacing the sample adaptive offset (SAO) filtering with a deep neural network. The proposed method uses the deep neural network to find the optimal offset value. The proposed network consists of two subnetworks to find the offset value and its type of the signal, which can restore nonlinear and complex type of error. Experimental results show that the performance is better than the conventional HEVC in low delay P and random access mode.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.349-349
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• 2020

기후변화로 인한 돌발홍수와 같은 집중적인 강우현상은 노후화된 제방의 안정성 저하 및 붕괴 등을 야기시킨다. 향후 홍수량이 증가함에 따라 하천의 통수면적이 부족하여 침수 및 범람의 위험성이 증가할 것으로 생각된다. 계획규모 이상의 홍수가 발생하여 홍수위가 제방고보다 높을 때 월류에 의한 제방붕괴로 이어지며, 이러한 월류에 의한 제방붕괴는 가장 전형적인 것이다. 지금까지 월류에 의한 제방붕괴에 관한 연구는 연구자의 다양한 관점 및 방법을 통해 진행되고 있다. 실제 제방붕괴를 관측하는 것은 불가능하므로 기존의 소규모 수리실험 및 모델링을 통한 제방붕괴 메커니즘 분석에는 사실상 한계가 있다. 이러한 점에서 실규모 수리실험을 통한 월류에 의한 제방붕괴 메커니즘을 3차원으로 분석할 필요가 있다. 본 연구에서는 드론 영상을 이용하여 제방붕괴 메커니즘 분석 연구를 수행하였다. 제방은 시간의 흐름에 따라 붕괴양상이 발전한다는 점 등에서 매우 복잡한 물리적 특성이 있다. 드론의 오토촬영 기법을 통한 제방이 붕괴되는 순간을 촬영하기는 쉽지 않기 때문에 셔터스피드촬영 기법을 적용하였다. 특히, 짧은 시간에 변화되는 제방의 붕괴양상을 구체적으로 표현하기 위해 두 대의 드론을 횡·종 방향으로 동시에 비행하여 분석 시 3차원 입체감을 최대화하였다. 이후 횡·종 방향에서 동 시간대 수집된 드론 이미지를 분류하여 PIX4D 매핑 기법을 활용한 최소 정합을 통하여 드론을 활용한 제방붕괴 메커니즘 분석의 활용 가능성을 제시하였다. 향후 스마트 시대의 물산업 경쟁력을 제고함에 있어, 폭이 좁은 하천에 효율적이며 고해상도 시공간 자료를 확보할 수 있는 드론을 활용한 스마트 하천재해 예측 및 관리기술 개발을 통한 하천 원격탐사의 경쟁력을 확보하는 것이 중요하다고 사료된다.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.105-115
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• 2023

In this paper, we describe the process and method of converting tens of TB of time-varying AMR (adaptive mesh refinement) volume data generated as a result of numerical model simulation into optimized data that can be used for various XR devices. AMR volume data is a useful data format for complex modeling and simulation, and it can efficiently express materials such as star clusters and gases that exist in the very wide outer space used in this study. we analyzes the metadata of AMR data, samples it at low resolution, optimizes information in important areas, and converts it into a data set that can be used even on relatively low performance XR devices. Finally, we introduces how the optimized data was utilized and visualized through the development of immersive XR content using the data set.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.423-430
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• 2023

This dissertation discusses the formulation of a nonlinear storytelling board that preserves the contextual perspective of characters. Storytelling encompasses the director's creative intention by leveraging the interaction of various elements to construct a logical narrative that explores cause and effect. Its primary objective is to enhance viewers' empathy. Consequently, there is a pressing need for comprehensive research on differentiating storytelling from storyboarding. Moreover, the integrated approach to storytelling and storyboarding holds scholarly value in understanding the process of narrative composition and visualization. Thus, a study proposes a method for constructing nonlinear storytelling boards considering the discrete camera perspective and contextual scene continuity, ultimately contributing to visual complexity and correlation comprehension. This approach enables a careful and simultaneous consideration of the correlations that deepen cognition, including the physical, emotional, and event rhythms mentioned in Karen Perlman's theory.

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1015-1026
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• 2023

The confluence of rivers, where rivers meet, is a place known for complex water mixing dynamics. Sometimes, these rivers flow downstream without mixing. While this non-mixing can pose challenges for water quality management, it also offers the potential for improved water extraction in nearby water intakes (Chilseo). In this study, we analyzed the mixing dynamics at the confluence of the Nakdong River and the Nam River using drone imagery, water quality indicators like Electrical Conductivity, and hydraulic factor Secondary Flow. We found that meandering effects hindered mixing, as shown by the comparison of Secondary Flow and Electrical Conductivity distributions. Additionally, the Chilseo Water Purification Plant downstream of the Nakdong River-Nam River confluence extracted unmixed Nam River water during certain periods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.448-453
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• 2023

As the production of small quadcopter drones has diversified and multi-sensors have been installed in FC due to the spread of MCU capable of high-speed processing, small drones that can perform special-purpose operations rather than simple operations have been realized. Hovering, attitude control, and position movement control were possible through the IMU in the FC mounted on the drone, but control is not easy when GPS connection and video communication are not possible in a closed building with a complex structure. In this study, when encountering an obstacle with a change in altitude in such a space, we proposed a method to overcome the obstacle and perform autonomous flight using optical flow and IR sensors using the Lucas-Kanade method. Through experiments, the drone's altitude flight on stairs that replace the complex structure of a closed space with stable hovering motion has a success rate of 98% within the tolerance of 10 [cm] due to external influences, and reliable autonomous flight up and down is achieved.