• Applied Chemistry for Engineering
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• v.32 no.5
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• pp.509-515
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• 2021

In this study, poly lactic-co-glycolic acid (PLGA) nanoparticles (PNP) were prepared through double (w/o/w) emlusion and emulsifying solvent-evaporation technique using PLGA, which has biocompatibility and biodegradability. To maximize stability and bioavailability of the particles, chitosan-coated PLGA nanoparticles (CPNP) were prepared by charge interaction between PNP and chitosan. We demonstrated that CPNP can be utilized as a drug carrier of oral administration. The chemical structure of CPNP was analyzed by ¹H-NMR and FT-IR, and all characteristic peaks appeared, confirming that it was successfully prepared. In addition, particle size and zeta potential of CPNP were analyzed using dynamic light scattering (DLS) while morphological images were obtained using transmission electron microscope (TEM). Thermal decomposition behavior of CPNP was observed through thermogravimetric analysis (TGA). In addition, the cytotoxicity of CPNP was confirmed by MTT assay at HEK293 and L929 cell lines, and it was proved that there is no toxicity confirmed by the cell viability of above 70% at all concentrations. These results suggest that the CPNP developed in this study may be used as an oral drug delivery carrier.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.495-495
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• 2022

충적하천에 댐이 건설되면 하류 하천의 식생환경에 변화를 주게 된다. 이입, 활착 및 성장과정을 거치는 제외지 식생은 하천의 유량이 증가하면 소류력과 세굴로 인해 쓸려 내려가기 때문에, 일반적으로 홍수기를 거치면 식생 활착 및 성장 이전의 사주 모습으로 복귀하게 된다. 그러나 댐의 홍수조절 기능은 홍수기에도 하류 하천에 식생을 정착시킬 만큼 큰 소류력을 발생시키지 않아 결국 식생의 성장에 기여하게 된다. 또한 댐은 상류로부터 공급되었던 세립토를 차단시켜 하류 하천의 하상 표면을 조립화 시키게 되는데, 이는 하상의 고정력을 증대시켜 식생의 안정적인 성장을 돕게 된다. 하천에 식생이 자리잡게 되면 홍수 시 흐름에 대한 저항력의 증가로 수위가 높아질 수 있고 제방에 자리 잡은 식생의 이탈은 제방의 붕괴 위험을 증가시킬 수 있고, 식생 사주의 발달이 지속되면 이동사주에서 정지사주로 변화되어 기존의 충적하천 자체가 사라지게 될 수도 있고, 하류 하천의 수리 및 유사 거동 특성도 변화시켜, 기존과 다른 하천 경관 및 수생태계 출현할 수 있다. 국내에서는 2000년대부터 이러한 하천 제외지 사주의 식생 활착이 가속화되어 왔으며, 특히 내성천, 황강 등 댐이 건설된 낙동강 지류 하천에서 이런 현상이 두드러지게 나타났다. 이에 따라 관련된 많은 조사·연구들이 수행되었으며, 변화 원인으로 홍수조절, 유사공급 변화 및 기후변화가 제시되었다. 그러나 댐이 건설되지 않은 하천(비조절 하천)에 대한 조사와 같은 비교군 지역의 정량적 분석은 상대적으로 부족하여, 정확한 제외지 식생 증가에 대한 원인을 규명하지 못하였다고 판단된다. 이에 비조절 하천이라 판단되는 감천을 대상으로 식생의 변화 양상을 분석하고, 유사 및 기후변화 등과의 관계를 본 연구에서 제시하였다. 감천은 상류에 김천부항댐이 건설되었으나, 댐의 영향범위가 전체 유역면적의 약 6%로 비조절 하천에 가깝고 사주가 발달된 충적하천이므로, 하천 본류에 댐이 건설된 다른 지역과의 비교군으로 적합하다고 판단하였다. 대상 지점은 상, 중, 하류의 사주가 발달된 지역으로 선정하였고, 본 연구에서 개발된 이미지 처리기법을 적용하여 사주 면적을 계산하였다. 분석결과 식생역으로 전환된 모래 사주의 면적은 봄~초여름 강우량과 밀접한 관계가 있는 것으로 파악되었다. 특히 초여름까지 가뭄이 지속될 때, 식생역은 증가하였다. 그러나 식생 활착 이전에 큰 유량 발생 시 식생이 쓸려 내려가고 유사가 그 자리를 대체하여 다시 사주가 발달하는 과정을 보여 주어, 감천의 사주 식생역의 증가 추세는 없는 것으로 파악되었다. 향후 댐이 본류에 건설된 조절 하천과의 비교를 통해 사주의 식생역 증가에 대한 댐 건설의 영향을 파악할 것이다.

• Korean Journal of Optics and Photonics
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• v.35 no.1
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• pp.9-17
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• 2024

A plenoptic optical system for microscopy comprises an objective lens, tube lens, microlens array (MLA), and an image sensor. Numerical aperture (NA) matching between the tube lens and MLA is used for optimal performance. This paper extends performance predictions from NA matching to unmatching cases and introduces a computational technique for plenoptic configurations using optical analysis software. Validation by fabricating and experimenting with two sample systems at 10× and 20× magnifications resulted in predicted spatial resolutions of 12.5 ㎛ and 6.2 ㎛ and depth of field (DOF) values of 530 ㎛ and 88 ㎛, respectively. The simulation showed resolutions of 11.5 ㎛ and 5.8 ㎛, with DOF values of 510 ㎛ and 70 ㎛, while experiments confirmed predictions with resolutions of 11.1 ㎛ and 5.8 ㎛ and DOF values of 470 ㎛ and 70 ㎛. Both formula-based prediction and simulations yielded similar results to experiments that were suitable for system design. However, regarding DOF values, simulations were closer to experimental values in accuracy, recommending reliance on simulation-based predictions before fabrication.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

• Cartoon and Animation Studies
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• s.50
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• pp.327-347
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• 2018

From the end of the 18th century to the end of the 19th century, the late 19th century was a genre of a new art that was in contrast to the distribution between social class and low class, and it was also a popular culture that attempted to transform the late Joseon Dynasty's social class. It is no exaggeration to say that it is the origin of the Korean folk art, started as popular art concepts, use colorful techniques and decorations which doesn't yield to ordinary iconography. But, because of the attempt of this technique was used by lower class, the meaning of the idea was lowered from iconography to secular picture. Ethics character picture, passed on to the present from going through the upheaval cultural time, was started from secular picture and transformed into hyukpil time illustration, and it represented popular arts until now. This thesis aims to reflect the meaning, various visual expressions and the lifestyle of Ethics Character Picture of filial piety and brotherly love, which is a unique genre of popular arts. Also, propose to suggest about the kinetic typography using video media, and how the traditional ethics character picture, which are combined with video technology, effects to the advertisements. These kind of attempts will show the world about the korea's traditional contents, and through the various media information it can be recreated as national symbolic key words. Furthermore, its meaningful to pass down the noble and cultural Ethics Character Picture of filial piety and brotherly love to younger generations. And by realigning to modern expression, it is predicted that it will be significantly meaningful to pass down and make the younger generations to understand to spirit of the ancestors. This will allow various attempts to reconstruct various items of contents from Korea's traditional contents to new media content that merged with video media.