• 한국컴퓨터정보학회논문지
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• 제26권6호
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• pp.37-46
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• 2021

본 논문에서는 Azure Kinect를 사용하여 요가 자세의 정확도를 측정하고 판단하는 프로그램을 설계하고 구현하였다. 이 프로그램은 Azure Kinect Camera와 센서를 통해 사용자의 모든 관절 위치를 측정한다. 측정한 관절의 값은 두 가지 방법으로 정확도를 판단하는 데이터로 사용된다. 측정된 관절 데이터는 삼각법과 피타고라스의 정리를 통하여 관절의 각도를 구한다. 또한, 측정된 관절 값은 상대적인 위치 값으로 변경한다. 각각 계산하여 구한 값은 목표하고자 하는 자세의 관절 값 및 상대적 위치 값과 비교하여 정확도를 판단한다. Azure Kinect Camera를 통해 사용자가 본인의 자세를 확인할 수 있도록 화면을 구성하고 사용자의 자세 정확도를 피드백으로 전달해 사용자의 자세 향상을 유도한다.

• 소성∙가공
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• 제31권4호
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.17-22
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• 2021

Recently, image super-resolution techniques used in convolutional neural networks (CNN) have led to remarkable performance in the research area of digital image processing applications and computer vision tasks. Convolutional layers stacked on top of each other can design a more complex network architecture, but they also use more memory in terms of the number of parameters and introduce the vanishing gradient problem during training. Furthermore, earlier approaches of single image super-resolution used interpolation technique as a pre-processing stage to upscale the low-resolution image into HR image. The design of these approaches is simple, but not effective and insert the newer unwanted pixels (noises) in the reconstructed HR image. In this paper, authors are propose a novel single image super-resolution architecture based on synchronized depthwise separable convolution with Dense Skip Connection Block (DSCB). In addition, unlike existing SR methods that only rely on single path, but our proposed method used the synchronizes path for generating the SISR image. Extensive quantitative and qualitative experiments show that our method (SDCN) achieves promising improvements than other state-of-the-art methods.

• Journal of the Korean Wood Science and Technology
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• 제50권6호
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• pp.490-498
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• 2022

The current study aimed to verify the image segmentation ability of rays in tangential thin sections of conifers using artificial intelligence technology. The applied model was Mask region-based convolutional neural network (Mask R-CNN) and softwoods (viz. Picea jezoensis, Larix gmelinii, Abies nephrolepis, Abies koreana, Ginkgo biloba, Taxus cuspidata, Cryptomeria japonica, Cedrus deodara, Pinus koraiensis) were selected for the study. To take digital pictures, thin sections of thickness 10-15 ㎛ were cut using a microtome, and then stained using a 1:1 mixture of 0.5% astra blue and 1% safranin. In the digital images, rays were selected as detection objects, and Computer Vision Annotation Tool was used to annotate the rays in the training images taken from the tangential sections of the woods. The performance of the Mask R-CNN applied to select rays was as high as 0.837 mean average precision and saving the time more than half of that required for Ground Truth. During the image analysis process, however, division of the rays into two or more rays occurred. This caused some errors in the measurement of the ray height. To improve the image processing algorithms, further work on combining the fragments of a ray into one ray segment, and increasing the precision of the boundary between rays and the neighboring tissues is required.

• International Journal of Contents
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• 제18권3호
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• pp.11-20
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• 2022

Human Emotion Recognition is an exciting topic that has been attracting many researchers for a lengthy time. In recent years, there has been an increasing interest in exploiting contextual information on emotion recognition. Some previous explorations in psychology show that emotional perception is impacted by facial expressions, as well as contextual information from the scene, such as human activities, interactions, and body poses. Those explorations initialize a trend in computer vision in exploring the critical role of contexts, by considering them as modalities to infer predicted emotion along with facial expressions. However, the contextual information has not been fully exploited. The scene emotion created by the surrounding environment, can shape how people perceive emotion. Besides, additive fusion in multimodal training fashion is not practical, because the contributions of each modality are not equal to the final prediction. The purpose of this paper was to contribute to this growing area of research, by exploring the effectiveness of the emotional scene gist in the input image, to infer the emotional state of the primary target. The emotional scene gist includes emotion, emotional feelings, and actions or events that directly trigger emotional reactions in the input image. We also present an attention-based fusion network, to combine multimodal features based on their impacts on the target emotional state. We demonstrate the effectiveness of the method, through a significant improvement on the EMOTIC dataset.

• Geomechanics and Engineering
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• 제32권6호
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• pp.627-637
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• 2023

Volleyball is an international sport with many fans. This sport has made significant progress in schools and clubs. Volleyball is suitable for all age groups and can be used in different environments. It has many social and physical benefits. During the game provides special physical training for the players and is considered one of the most exciting games. Another type of volleyball is beach volleyball, a beach sport and one of the Olympic sports held on the sand with the same rules as volleyball. This sport is usually played in coastal areas, especially with wide sandy beaches. Because this sport is played in open spaces, the players stay in this space for a long time and are exposed to dangerous ultraviolet radiation. It is a wavelength of light in the range of electromagnetic waves with a wavelength between 10 and 400 nm. This wavelength is shorter than visible light and more protracted than X-ray. Ultraviolet (UV) rays are naturally present in sunlight and include about 10% of all waves emitted from the sun's surface. Prolonged exposure to ultraviolet light causes acute and chronic damage to the skin and vision and even destroys the entire immune system. Different covers of the earth's surface reflect different amounts of UV rays. For example, snow cover, sand, and seawater surface reflect this radiation. Therefore, the health of volleyball players is in danger due to this harmful radiation. This work aims to introduce a type of clothing made of nanoparticles that can repel ultraviolet rays and protect beach volleyball players whose health is at risk from this radiation.

• 전기전자학회논문지
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• 제27권3호
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• pp.280-287
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• 2023

딥러닝의 발전은 컴퓨터 비전 문제를 해결할 수 있지만, 높은 정확도를 위해서는 대규모 데이터셋이 필요하다. 본 논문에서는 객체 바운딩 박스와 이미지 엣지 성분을 이용한 이미지 생성 기법을 제안한다. 객체 탐지를 통해 이미지 내의 객체 바운딩 박스를 추출하고 이미지 엣지 성분을 함께 이미지 생성모델의 입력값으로 사용하여 새로운 이미지 데이터를 생성한다. 실험 결과, 제안 기법으로 생성된 이미지는 이미지 품질 평가에서 소스 이미지와 유사한 품질을 보였고, 딥러닝 훈련과정에서도 좋은 성능을 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.818-819
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• 2010

고층빌딩, 지하역사와 같은 대형 복합건물은 건물 자체의 복합도 증가와 더불어 이에 따른 위험요소(hazard)의 증가를 가져오고 위험상황에 대처해야 할 소방관들의 대응기술 및 훈련도 고도화되어야 한다. 실제 화재현장, 특히 대형 복합건물에 대한 훈련은 비용, 시간뿐만 아니라 소방관 안전 확보라는 차원에서 불가능하며 반복적인 훈련이나 그 평가가 어렵다는 점에서 가상환경을 이용한 훈련 시스템이 필요하다. 본 논문에서는 복합건물에 대한 소방관 팀 훈련 시스템 구현을 위한 가상환경의 설계 및 구현을 소개하고자 한다. 가상환경은 소방관의 훈련 및 평가, 팀 단위훈련을 제공하고 현실감 있는 훈련을 위하여 가상현실, 증강현실, 물리체험 모듈을 이용한 사용자 인터페이스를 제공한다. 가상환경 구축을 위하여 대상 건축물에 대한 3 차원 모델을 구축하고 도출된 발생 가능한 화재 시나리오에 대한 수치적 모델링을 수행하고 그 결과를 가상환경 및 훈련 시나리오에 반영하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.447-450
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• 2011

This paper shows the application of P-N Learning [4] method in the soccer ball detection and improvement for increasing the speed of processing. In the P-N learning, the learning process is guided by positive (P) and negative (N) constraints which restrict the labeling of the unlabeled data, identify examples that have been classified in contradiction with structural constraints and augment the training set with the corrected samples in an iterative process. But for the long-view in the soccer game, P-N learning will produce so many ferns that more time is spent than other methods. We propose that color histogram of each frame is constructed to delete the unnecessary details in order to decreasing the number of feature points. We use the mask to eliminate the gallery region and Line Hough Transform to remove the line and adjust the P-N learning's parameters to optimize accurate and speed.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.345-354
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• 2022

The importance of the public sector's role in fostering a positive business climate has prompted public sector organizations to consistently enhance their performance. The study aims to develop service orientation dimensions for public sectors and examine the relationship between service orientation and organizational performance. A field survey was employed in this study. Six hundred questionnaires were distributed, and four hundred and eighty-eight were returned and analyzed. Factor analysis and multiple regression analysis were used in the dataset. This study identifies five dimensions of organizational service orientation in public sector service organizations: technology-service standard-communication, service vision, service delivery, service training and powering, and servant leadership. The result also concludes that service orientation influences organizational performance, such as corporate growth, service quality image, IT effectiveness, service innovation, and public complaint. This study's findings imply that public sector organizations should rectify service orientation factors to increase corporate growth, service quality image, IT effectiveness, service innovation, and public complaint reduction. Managerial guidelines are presented for developing a service orientation.