• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.457-460
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• 2023

본 연구는 시설작물의 병충해 진단을 위해 딥러닝 모델을 응용한 인공지능 서비스 앱, Dr. Vegetable을 제안하고자 한다. 농업 현장에서 숙련된 농부는 한눈에 농작물의 병충해를 판단할 수 있지만 미숙련된 농부는 병충해 피해를 발견하더라도 그 종류와 해결 방법을 찾아내기가 매우 어렵다. 또한 아무리 숙련된 농부라고 할지라도 육안검사만으로 병충해를 조기에 발견하는 것은 쉽지 않다. 한편 시설작물의 경우 병충해에 의한 연쇄피해가 발생할 우려가 있으므로 병충해의 조기 발견 및 방제가 매우 중요하다. 즉, 농부의 경험에 따른 농작물 병해충 진단은 정확성을 장담할 수 없으며 비용과 시간적인 측면에서 위험성이 높다고 할 수 있다. 본 논문에서는 YOLOv5를 활용하여 상추, 고추, 토마토 등 농작물의 병충해를 진단하는 인공지능 서비스를 제안한다. 특히 한국지능정보사회진흥원이 운영하고 있는 AI 통합 플랫폼인 AI 허브에서 제공하는 노지 작물 질병 및 해충 진단 이미지를 사용하여 딥러닝 모델을 학습하였다. 본 연구를 통해 개발된 모바일 어플리케이션을 이용하여 실제 시설농장에서 병충해 진단 서비스를 적용한 결과 약 86%의 정확도, F1 Score 0.84, 그리고 0.98의 mAP 값을 얻을 수 있었다. 본 연구에서 개발한 병충해 진단 딥러닝 모델을 다양한 조도에서 강인하게 동작하도록 개선한다면 농업 현장에서 널리 활용될 수 있을 것으로 기대한다.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.525-534
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• 2023

Gas leak detection system is a key to minimize the loss of life due to the explosiveness and toxicity of gas. Most of the leak detection systems detect by gas sensors or thermal imaging cameras. To improve the performance of gas leak detection system using single-modal methods, the paper propose multimodal approach to gas sensor data and thermal camera data in developing a gas type identification model. MultimodalGasData, a multimodal open-dataset, is used to compare the performance of the four models developed through multimodal approach to gas sensors and thermal cameras with existing models. As a result, 1D CNN and GasNet models show the highest performance of 96.3% and 96.4%. The performance of the combined early fusion model of 1D CNN and GasNet reached 99.3%, 3.3% higher than the existing model. We hoped that further damage caused by gas leaks can be minimized through the gas leak detection system proposed in the study.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.453-466
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• 2024

To improve the monitoring of Coastal Debris in the South Korea, which is difficult to estimate due to limited resources and vertex-based surveys, an approach based on UAV(Unmanned Aerial Vehicle) images and the RT-DETR(Realtime DEtection TRansformer) model was proposed for detecting Coastal Debris. By comparing to field investigation, the study suggested the possibility of quantitatively detecting coastal garbage and estimating the total capacity of garbage deposited on the natural coastline of the South Korea. The RT-DETR model achieved an accuracy of 0.894 for mAP@0.5 and 0.693 for mAP@0.5:0.95 in training. When applied to unmanaged coasts, the accuracy for the total number of coastal debris items was 72.9%. It is anticipated that if guidelines for defining monitoring of unmanaged coasts are established alongside this research, it should be possible to estimate the total capacity of the deposited coastal debris in the South Korea.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Journal of Digital Policy
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• v.3 no.1
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• pp.27-32
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• 2024

In this paper, proposes a super resolution method that enhances the quality of results by refining texture features, contrasting each, and utilizing the results as weights. For the improvement of quality, a precise and clear restoration result in details such as boundary areas is crucial in super resolution, along with minimizing unnecessary artifacts like noise. The proposed method constructs a residual block structure with multiple paths and skip-connections for feature estimation in conventional Convolutional Neural Network (CNN)-based super resolution methods to enhance quality. Additional learning is performed for sharpened and blurred image results for further texture analysis. By contrasting each super resolution result and allocating weights through this process, the proposed method achieves improved quality in detailed and smoothed areas of the image. The experimental results of the proposed method, evaluated using the PSNR and SSIM values as quality metrics, show higher results compared to existing algorithms, confirming the enhancement in quality.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.19-25
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• 2024

The worldwide aging population trend is causing an increase in the incidence of major retinal diseases that can lead to blindness, including glaucoma, cataract, and macular degeneration. In the field of ophthalmology, there is a focused interest in diagnosing diseases that are difficult to prevent in order to reduce the rate of blindness. This study proposes a deep learning approach to accurately diagnose ocular diseases in fundus photographs using less data than traditional methods. For this, Convolutional Neural Network (CNN) models capable of effective learning with limited data were selected to classify Conventional Fundus Images (CFI) from various ocular disease patients. The chosen CNN models demonstrated exceptional performance, achieving high Accuracy, Precision, Recall, and F1-score values. This approach reduces manual analysis by ophthalmologists, shortens consultation times, and provides consistent diagnostic results, making it an efficient and accurate diagnostic tool in the medical field.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.23-32
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• 2024

NVS (Novel View Synthesis) is a field in computer vision that reconstructs new views of a scene from a set of input views. Real-time rendering and high performance are essential for NVS technology to be effectively utilized in various applications. Recently, 3D-GS (3D Gaussian Splatting) has gained popularity due to its faster training and inference times compared to those of NeRF (Neural Radiance Fields)-based methodologies. However, since 3D-GS reconstructs a 3D (Three-Dimensional) scene by splitting and cloning (Density Control) Gaussian points, the number of Gaussian points continuously increases, causing the model to become heavier as training progresses. To address this issue, we propose two methodologies: 1) Gaussian blending, an improved density control methodology that removes unnecessary Gaussian points, and 2) a performance enhancement methodology using a depth estimation model to minimize the loss in representation caused by the blending of Gaussian points. Experiments on the Tanks and Temples Dataset show that the proposed methodologies reduce the number of Gaussian points by up to 4% while maintaining performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.229-235
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• 2024

Recent years have seen active research on methods for efficiently processing and interpreting large volumes of data in the fields of artificial intelligence and machine learning. One of these data processing technologies, Vector Symbolic Architecture (VSA), offers an innovative approach to representing complex symbols and data using high-dimensional vectors. VSA has garnered particular attention in various applications such as natural language processing, image recognition, and robotics. This study quantitatively evaluates the characteristics and performance of VSA methodologies by applying five VSA methodologies to the MNIST dataset and measuring key performance indicators such as encoding speed, decoding speed, memory usage, and recovery accuracy across different vector lengths. BSC and VT demonstrated relatively fast performance in encoding and decoding speeds, while MAP and HRR were relatively slow. In terms of memory usage, BSC was the most efficient, whereas MAP used the most memory. The recovery accuracy was highest for MAP and lowest for BSC. The results of this study provide a basis for selecting appropriate VSA methodologies depending on the application area.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.45-54
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• 2021

This study was written to create educational content in non-destructive fields based on Mixed Reality. Currently, in the field of radiation, there is almost no content for educational Mixed Reality-based educational content. And in the field of non-destructive inspection, the working environment is poor, the number of employees is often 10 or less for each manufacturer, and the educational infrastructure is not built. There is no practical training, only practical training and safety education to convey information. To solve this, it was decided to develop non-destructive worker education content based on Mixed Reality. This content was developed based on Microsoft's HoloLens 2 HMD device. It is manufactured based on the resolution of 1280 ⁎ 720, and the resolution is different for each device, and the Side is created by aligning the Left, Right, Bottom, and TOP positions of Anchor, and the large image affects the size of Atlas. The large volume like the wallpaper and the upper part was made by replacing it with UITexture. For UI Widget Wizard, I made Label, Buttom, ScrollView, and Sprite. In this study, it is possible to provide workers with realistic educational content, enable self-directed education, and educate with 3D stereoscopic images based on reality to provide interesting and immersive education. Through the images provided in Mixed Reality, the learner can directly operate things through the interaction between the real world and the Virtual Reality, and the learner's learning efficiency can be improved. In addition, mixed reality education can play a major role in non-face-to-face learning content in the corona era, where time and place are not disturbed.