• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.17-22
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• 2023

This study analyzed the genres of popular character web dramas and studied the development of story recommendations through the language model GPT. As a result of the study, it was confirmed that similar cliches are repeated in web dramas. In this study, a common story structure (cliché) was analyzed and a typical story structure was standardized and presented so that even unskilled video producers can easily produce character web dramas. For analysis, clichés of web dramas in the school romance genre, which is the most popular genre among teenagers, were listed in order of success. In addition, this study studied the story recommendation mechanism for users by learning the clichés that were analyzed and cataloged in GPT. Through this study, it is expected to accelerate the production of various contents as well as popular popularity through the acceptance of various databases from the standpoint of database consumption theory of web contents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.465-468
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• 2022

The incidence of traffic accidents is increasing every year, and Korea is among the top OECD countries. In order to improve this, various road traffic laws are being implemented, and various traffic control methods using equipment such as unmanned speed cameras and traffic control cameras are being applied. However, as drivers avoid crackdowns by detecting the location of traffic control cameras in advance through navigation, a mobile crackdown system that can be cracked down is needed, and research is needed to increase the recognition rate of vehicle license plates on the side of the road for accurate crackdown. This paper proposes a method to improve the vehicle number recognition rate on the road side by applying a gradient correction algorithm using image processing. In addition, custom data learning was conducted using a CNN-based YOLO algorithm to improve character recognition accuracy. It is expected that the algorithm can be used for mobile traffic control cameras without restrictions on the installation location.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.839-849
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• 2017

Recently, the role of the kitchen has evolved from the space for previous survival to the space that shows the present life and culture. Along with these changes, the use of IoT technology is spreading. As a result, the development and diffusion of new smart devices in the kitchen is being achieved. The user experience for using these smart devices is also becoming important. For a natural interaction between a user and a computer, better interactions can be expected based on context awareness. This paper examines the Natural User Interface (NUI) that does not touch the device based on the user interface (UI) of the smart device used in the kitchen. In this method, we use the image processing technology to recognize the user's hand gesture using the camera attached to the device and apply the recognized hand shape to the interface. The gestures used in this study are proposed to gesture according to the user's context and situation, and 5 kinds of gestures are classified and used in the interface.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.303-306
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• 2021

이미지 초해상도는 영상 취득 과정에서 센서와 렌즈의 물리적인 한계 등으로 인하여 의해 화질이 저하된 이미지를 더 높은 배율로 복원하는 문제이다. 이미지 초해상도는 딥러닝을 통해 놀라운 성능향상을 이루었지만, 카메라로 촬영된 실제 이미지에서는 좋은 성능을 내지 못하였다. 이는 딥러닝에서는 'bicubic' 커널로 down-sampling된 합성 이미지 데이터를 사용하였던 것과 달리 실제 이미지에서는 'bicubic' 커널을 통한 화질 저하와는 다른 화질 저하, 즉 다른 커널을 통한 화질 저하가 발생하기 때문이다. 따라서 실제 이미지에 대한 성능을 높이기 위해서는 이에 대한 정확한 커널 예측이 필요하다. 최근 주목받기 시작한 이미지 초해상도를 위한 커널 예측은 초해상도를 잘 시켜주는 커널을 직접 찾는 방법[10, 13]과 이미지의 분포와 커널을 통해 다운샘플된 이미지에 대한 분포를 일치시켜주면서 커널을 예측하는 방법[14]으로 나누어져 있다. 그러나 두 방법 모두 ill-posed problem 인 커널 예측 문제를 한 장의 이미지만으로 해결하려는 것이기 때문에 정확한 예측에는 어려움이 발생한다. 따라서 본 논문에서는 두 장의 이미지를 활용한 이미지 화질 저하 커널 예측 방법을 제안한다. 제안된 방법은 두 장의 이미지가 같은 카메라를 통해 촬영되었으며 이때 이미지 화질 저하는 카메라에 의해서만 영향을 받는다는 가정을 기반으로 한다. 즉, 두 장의 이미지는 같은 커널을 통해 저하된 이미지라는 가정을 한다. 제안된 방법은 [14]에서처럼 이미지 분포를 기반으로 한 커널 예측을 진행하며, 이미지 초해상도를 진행하고자 하는 이미지 외에 참고 이미지 또한 같은 커널에서 화질 저하를 시켰을 때 본래의 이미지와 같은 분포에 있도록 학습을 진행한다. 결과적으로 본 논문에서는 두 장의 이미지를 사용하였을 때 더욱 정확하게 커널을 찾을 수 있음을 보여준다. 두 장의 이미지를 활용하는 방식이 한 장의 이미지만을 활용하는 기존의 최고 수준의 방법에 비해 합성된 다양한 커널 데이터셋[14]에서 약 0.17dB 성능 향상이 있었다.

• Trans-
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• v.14
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• pp.123-155
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• 2023

This study analyzes the characteristics of the social value creation process through the collective appreciation of film. It focuses on the historical development of community cinema in Japan. In modern-day Japan, where digital video is easily accessible and the use of private, personalized media spaces widespread, a sub-culture of collective film appreciation is spreading, as more and more Japanese begin to attend movie screenings in non-commercial theaters. In addition, Japanese community cinema center has begun to integrate and support this viewing experience, which has come to be known as community cinema. A literature review revealed the following characteristics of community cinema. First, local theater screening groups or appreciation groups cooperate with residents to establish and operate movie theaters. Second, these spaces create theoretical and practical participatory learning opportunities that foster understanding of and participation in film culture, through large-scale associations with organizations or institutions that offer viewings. Third, based on collective appreciation, the film culture created through repeated joint viewings produces a social arena in which community can be realized. In these communities film can be put to socially productive uses, such as problem solving.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.329-332
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• 2022

This study aims to identify problems with existing e-learning content and non-face-to-face class methods, improve students' concentration, improve class achievement and educational effectiveness, and propose an artificial intelligence class system design using a web server. By using the function of face and eye tracking using OpenCV to identify attendance and concentration, and by inducing feedback through voice or message to questions asked by the instructor in the middle of class, learners relieve boredom caused by online classes and test by runner If the score is not reached, we propose an artificial intelligence education program system design that can bridge the academic gap and improve academic achievement by providing educational materials and videos for the wrong problem.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.520-523
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• 2022

일본 우주항공연구개발기구(Japan Aerospace Exploration Agency, JAXA)는 2007년부터 2017년까지 달 탐사선 셀레네(Selenological and Engineering Explorer, SelEnE)가 관측한 데이터를 수집하고, 연구했다. JAXA는 지구 상층 대기에 존재하는 산소가 자기장의 꼬리 부분에 실려 달로 이동한다는 사실을 발견했다. 하지만 이 연구는 아직 진행 중이며 달의 산화 과정 규명에 추가 연구가 필요하다. 본 논문에서는 생성적 적대 신경망(Generative Adversarial Networks, GAN)으로 달 분화구의 영구 그림자 영역을 제거하고, 물과 얼음을 발견하여 선행 연구의 완성도를 향상하고자 한다. 실험에 사용할 모델은 CIPS(Conditionally Independent Pixel Synthesis)다. CIPS는 실제 같은 영상을 고해상도로 합성한다. 합성할 데이터의 최적인 가중치 초기화 및 파라미터 갱신 방법, 활성 함수 조합은 실험을 통해 확인한다. 필요에 따라 앙상블 학습을 할 수도 있다. 성능평가는 FID(Frechet Inception Distance), 정밀도, 재현율을 사용한다. 제안한 방법은 진행 중인 연구의 시간과 비용을 절약하고, 인과관계를 더욱 명확히 밝히는 데 도움 될 수 있다고 사료된다.