• Archives of Plastic Surgery
• /
• v.46 no.3
• /
• pp.204-213
• /
• 2019

Background In implant-based breast reconstruction, acellular dermal matrix (ADM) is essential for supporting the inferolateral pole. Recent studies have compared non-sterilized freeze-dried ADM and sterilized pre-hydrated ADM, but have not assessed whether differences were attributable to factors related to sterile processing or packaging. This study was conducted to compare the clinical outcomes of breast reconstruction using two types of sterile-processed ADMs. Methods Through a retrospective chart review, we analyzed 77 consecutive patients (85 breasts) who underwent tissue expander/implant breast reconstruction with either freeze-dried ADM (35 breasts) or pre-hydrated ADM (50 breasts) from March 2016 to February 2018. Demographic variables, postoperative outcomes, and operative parameters were compared between freeze-dried and pre-hydrated ADM. Biopsy specimens were obtained for histologic analysis. Results We obtained results after adjusting for variables found to be significant in univariate analyses. The total complication rate for freeze-dried and pre-hydrated ADMs was 25.7% and 22.0%, respectively. Skin necrosis was significantly more frequent in the freeze-dried group than in the pre-hydrated group (8.6% vs. 4.0%, P=0.038). All other complications and operative parameters showed no significant differences. In the histologic analysis, collagen density, inflammation, and vascularity were higher in the pre-hydrated ADM group (P=0.042, P=0.006, P=0.005, respectively). Conclusions There are limited data comparing the outcomes of tissue expander/implant breast reconstruction using two types of sterile-processed ADMs. In this study, we found that using pre-hydrated ADM resulted in less skin necrosis and better integration into host tissue. Pre-hydrated ADM may therefore be preferable to freeze-dried ADM in terms of convenience and safety.

• Korean Journal of Child Studies
• /
• v.20 no.3
• /
• pp.231-244
• /
• 1999

This study examined the effects of short and long term playing of video gamer on children's visual parallel processing. All of the 64 fourth grade subjects were above average in IQ. They were classified into high and low video game users. Instruments were a visual parallel processing task consisting of imagery integration items, computers, and the arcade video game, Pac-Man. Subjects were pre-tested with a visual parallel processing task. After one week, the experimental group played video games for 15 minutes, but the control group didn't play. Immediately following this, all children were post-tested by the same task used on the pretest. The data was analyzed by ANCOVA and repeated measures ANOVA. The results showed that relaying short-term video games improved visual parallel processing and that long term experience with video games also affected visual parallel processing. there were no differences between high and low users in visual parallel processing after playing short term video games.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.582-585
• /
• 2005

The Geostationary Operational Environmental Satellite (GOES) 9, which is currently located at 155°E geostationary orbits, has transmitted earth observation data acquired by imager to CDA at NOAA. After the acquisition on ground, observation data are corrected on ground and re-transmitted to GOES-9 for the dissemination to users. In this paper, the procedure and result from raw data acquisition and pre-processing for earth observation imagery retrieval from GOES-9 Raw data acquired in Korea at May 2005 are introduced.

• Journal of Navigation and Port Research
• /
• v.35 no.10
• /
• pp.847-853
• /
• 2011

The purpose of this study is to develope a series of business models for promotion of value added logistics activities of lumber hinterland in Incheon Northport. A set of policies to create value added developed are an import market diversification model using Incheon Northport, a model by building integration processing center by pallet facility, a model by joint logistics center, an export-oriented Pre-cut material development model and an export processing value added model.

• Journal of the Korean Society of Industry Convergence
• /
• v.2 no.2
• /
• pp.41-49
• /
• 1999

In general, FORTRAN is used for numerical analysis and OPS5 or LISP is used for expert systems, This causes problems at the interface because the various applications require different computing languages or environments. This paper describes the approach used to take AutoCAD as a user-interface for an existing finite element structural analysis package. Some principles concerning database management related to data-exchange interface of pre- and post-processing system for FORTRAN structural analysis program are discussed, and numerical examples demonstrate the power of the combination of these programs.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.6 no.2
• /
• pp.39-50
• /
• 2002

Several pre-processing techniques for earthquake data from earthquake monitoring institutes in Korea including Korea Electric Power Research Institute are thoroughly reviewed. Among these techniques for removing an instrumental response, removing the non-causal ringing distortion by FIR filter, checking calibration status of seismic stations, and minimizing the window effect are introduced and applied to real data. It is also recommended that analysts evaluate S/N ratio in the frequency domain and consider the possibility of using the saturated earthquake data.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.71-73
• /
• 2022

최근 인프라를 관리할 필요가 없고 폭발적으로 늘어나는 요청을 유연하게 대처할 수 있는 장점 때문에 서버리스 컴퓨팅 사용이 늘어나고 있다. 하지만 서버리스 컴퓨팅은 사용자 코드의 실행 환경을 준비하기 위한 콜드 스타트 과정이 필요하고, 서비스가 복잡해짐에 따라 전체 실행 시간 중 콜드 스타트로 인한 지연시간이 늘어나는 문제가 발생한다. 본 논문에서는 서버리스 컴퓨팅 기반의 워크플로우에 대해 콜드 스타트로 인한 지연 시간을 완화하는 아키텍처 및 기법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.557-560
• /
• 2023

In this paper, we introduce a pre-training method leveraging the capabilities of the Vision Transformer (ViT) for disease diagnosis in conventional Fundus images. Recognizing the need for effective representation learning in medical images, our method combines the Vision Transformer with a Masked Autoencoder to generate meaningful and pertinent image augmentations. During pre-training, the Masked Autoencoder produces an altered version of the original image, which serves as a positive pair. The Vision Transformer then employs contrastive learning techniques with this image pair to refine its weight parameters. Our experiments demonstrate that this dual-model approach harnesses the strengths of both the ViT and the Masked Autoencoder, resulting in robust and clinically relevant feature embeddings. Preliminary results suggest significant improvements in diagnostic accuracy, underscoring the potential of our methodology in enhancing automated disease diagnosis in fundus imaging.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.59-68
• /
• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.12
• /
• pp.4866-4888
• /
• 2020

One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.