• Journal of Advanced Marine Engineering and Technology
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• v.25 no.6
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• pp.1237-1243
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• 2001

This paper presents an information about the heat transfer characteristics of impinging jet in eletronic equipment with infrared image processing unit. There have been many experimental investigations and theoretical studies on impinging jet because of application in a wide variety of industrial process including electronic equipment. In this study, we used infrared image processing unit to visualize heat transfer characteristics of impinging jet in eletronic equipment. Infrared image processing unit is one of non-contact temperature measuring methods and it is possible to minimize flow resistance and this measurement is comparatively accurate. The main parameters are distance between nozz1e and heat source. Reynolds number is 6000.

• The Research Journal of the Costume Culture
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• v.14 no.2
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• pp.206-219
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• 2006

The purpose of this study was to design the ideal student uniform for improving the university image which is related to ideal PR (Public Relations). The results of this study were as follows: Firstly, as for the matter of UIP(University Identity Program), the uniform must transfer the university image. Using the UIP symbol color, the uniform is harmonious with university environment, and should be properly expressed visual image of the university. Secondly, the symbol which expressed the tradition and the spirit of the university was the very important factor to transfer its unique university identity and image of the university in the uniform design. The symbol which comes from the character or textile prints of the university symbolic flower or animals should transfer the consistent and unique design of university identity. Thirdly, the uniform has to be designed considering of the fashion trend as well as the essential factors for the uniform, considering such as functional, beautiful, symbolic aspects. In designing the uniform, fashion trend should be considered to combine the students and the uniform users to be one. As mentioned above, the uniform design which is suggested on this study should be helpful for the improvement of the university status and the reinforcement of the university PR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4567-4587
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• 2017

With the growth of the Internet and the extensive applications of image editing software, it has become easier to manipulate digital images without leaving obvious traces. Copy-move is one of the most common techniques for image forgery. Image blind forensics is an effective technique for detecting tampered images. This paper proposes an improved copy-move forgery detection method based on the discrete cosine transform (DCT). The quantized DCT coefficients, which are feature representations of image blocks, are truncated using a truncation factor to reduce the feature dimensions. A method for judging whether two image blocks are similar is proposed to improve the accuracy of similarity judgments. The main transfer vectors whose frequencies exceed a threshold are found to locate the copied and pasted regions in forged images. Several experiments are conducted to test the practicability of the proposed algorithm using images from copy-move databases and to evaluate its robustness against post-processing methods such as additive white Gaussian noise (AWGN), Gaussian blurring, and JPEG compression. The results of experiments show that the proposed scheme effectively detects both copied region and pasted region of forged images and that it is robust to the post-processing methods mentioned above.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.655-657
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• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.413-420
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• 2023

For a long time, researchers have presented excellent results in the field of image retrieval due to many studies on CBIR. However, there is still a semantic gap between these search results for images and human perception. It is still a difficult problem to classify images with a level of human perception using a small number of images. Therefore, this paper proposes an image classification model using deep learning-based transfer learning to minimize the semantic gap between images of people and search systems in image retrieval. As a result of the experiment, the loss rate of the learning model was 0.2451% and the accuracy was 0.8922%. The implementation of the proposed image classification method was able to achieve the desired goal. And in deep learning, it was confirmed that the CNN's transfer learning model method was effective in creating an image database by adding new data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.178-179
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• 2018

Recently, methods of using machine learning in artificial intelligence such as autonomous navigation and speech recognition have been actively studied. Classical image processing methods such as classical boundary detection and pattern recognition have many limitations in order to recognize a specific object or area in a digital image. However, when a machine learning method such as deep-learning is used, Can be obtained. However, basically, a large amount of learning data must be secured for machine learning such as deep-learning. Therefore, it is difficult to apply the machine learning for area classification when the amount of data is very small, such as aerial photographs for environmental analysis. In this study, we apply a transfer-learning technique that can be used when the dataset size of the input image is small and the shape of the input image is not included in the category of the training dataset.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.465-470
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• 2024

The advancement of AI-based image generation technology has resulted in the creation of various images, emphasizing the need for technology capable of accurately discerning them. The amount of generated image data is limited, and to achieve high performance with a limited dataset, this study proposes a model for discriminating generated images using transfer learning. Applying pre-trained models from the ImageNet dataset directly to the CIFAKE input dataset, we reduce training time cost followed by adding three hidden layers and one output layer to fine-tune the model. The modeling results revealed an improvement in the performance of the model when adjusting the final layer. Using transfer learning and then adjusting layers close to the output layer, small image data-related accuracy issues can be reduced and generated images can be classified.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.765-770
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• 2001

The objective of the present study is to investigate the effects of the various inlet boundary layer thickness on convective heat transfer distribution in a turbine cascade endwall and blade suction surface. In addition, the proper height of the boundary layer fences for various inlet boundary layer thickness were applied to turbine cascade endwall in order to reduce the secondary flow, and to verify its influence on the heat transfer process within the turbine cascade. Convective heat transfer distributions on the experimental regions were measured by the image processing system. The results show that heat transfer coefficients on the blade suction surface were increased with an augmentation of inlet boundary layer thickness. However, in a turbine cascade endwall, magnitude of heat transfer coefficients did not change with variation of inlet boundary layer thickness. The results also present that the boundary layer fence is effective in reducing heat transfer on the suction surface. On the other hand, in the endwall region, boundary layer fence brought about the subsidiary heat transfer increment.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.703-708
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• 2009

The present paper describes the application of an improved impression transfer vector method (Sakurai et al., 2007) to transform the three basic dimensions (Evaluation, Activity, and Potency) of higher-order impression. First, a set of shapes and surface textures of faces was represented by multi-dimensional vectors. Second, the variation among faces was coded in reduced parameters derived by applying principal component analysis. Third, a facial attribute along a given impression dimension was analyzed to select discriminative parameters from among principal components with higher sensitivity to impressions, and obtain an impression transfer vector. Finally, the parametric coordinates were changed by adding or subtracting the impression transfer vector and the image was manipulated so that its facial appearance clearly exhibits the transformed impression. A psychological rating experiment confirmed that the impression transfer vector modulated three dimensions of higher-order impression. We discussed the versatility of the impression transfer vector method.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.98-105
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• 2019

Energy autonomy is a system that is sustained by energy from an independent and distributed source such as renewable energy. In this paper, we propose a robotic energy autonomy in which a robot obtains energy from a renewable energy source with a limited storage capacity. As an energy transfer method, wireless power transfer is used to solve the problem of the conventional contact charging method, mechanical complexity, and to obtain high energy transfer efficiency, the image information is used to align the transmitting and receiving coils accurately. A small scale thermoelectric energy source with boost converter, battery charger, and wireless power transfer coil is constructed and an actual charging experiment is conducted to verify the proposed autonomy system.