• 제목/요약/키워드: and Pre-Processing

검색결과 1,916건 처리시간 0.035초

A comparative study between sterile freeze-dried and sterile pre-hydrated acellular dermal matrix in tissue expander/implant breast reconstruction

  • Cheon, Jeong Hyun;Yoon, Eul Sik;Kim, Jin Woo;Park, Seung Ha;Lee, Byung Il
    • Archives of Plastic Surgery
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    • 제46권3호
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    • pp.204-213
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    • 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.

아동의 전자게임 활동이 시각적 병행처리에 미치는 영향 (The Effects of Playing Video Games on Children's Visual Parallel Processing)

  • 김숙현;최경숙
    • 아동학회지
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    • 제20권3호
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    • pp.231-244
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    • 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.

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GOES-9 Raw Data Acquisition & Image Extraction

  • Kang C. H.;Park D. J.;Koo I. H.;Ahn S. I.;Kim E. K.
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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    • pp.582-585
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    • 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.

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인천북항 목재배후단지 부가가치물류 활성화방안 (A Study on Promotion of Value Added Logistics(VAL) Activities of Lumber Hinterland in Incheon Northport)

  • 정태원;한종길
    • 한국항해항만학회지
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    • 제35권10호
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    • pp.847-853
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    • 2011
  • 본 연구는 수입된 목재를 가공하여 내수로 전환하는 활동뿐만 아니라 인천항에서의 부가가치 활동을 통해 완제품, 반제품의 형태로 타국가로의 재수출이 가능한 비즈니스 모델을 제안하고자 하였다. 구체적으로 부가가치를 창출하기 위한 세부방안을 살펴보면 인천북항을 활용하는 수입다변화 모델, 통합가공센터 팰릿시설 조성 모델, 목재 공동물류센터 모델, 수출형 Pre-Cut 자재개발모델 그리고 수출가공형 부가가치 창출모델을 제시하였다.

유한요소 구조해석 프로그램의 전후처리 접속장치의 설계 (Data-Exchange Interface Design of Pre-& Post-Processing System for Finite Element Structural Analysis Program)

  • 신영식;서진국
    • 한국산업융합학회 논문집
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    • 제2권2호
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    • pp.41-49
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    • 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.

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전력연구원 지진관측자료의 사전자료처리 기법 및 효과적인 활용에 관한 고찰 (Review on Pre-processing of Earthquake Data from KEPRI Seismic Monitoring System)

  • 연관희;박동희;최원학;장천중
    • 한국지진공학회논문집
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    • 제6권2호
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    • pp.39-50
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    • 2002
  • 본 논문에서는 국내의 지진자료를 이용하여 지진특성을 추정하기 전에 적용할 수 있는 사전자료처리기법을 종합적으로 검토하였다. 사전처리 기법으로는 계기보정, 센서검교정상태 확인, 윈도우에 의한 스펙트럼 왜곡 최소화, non-causal ringing에 의한 초동 왜곡 보정 기법을 분석하였으며, 자료 선택시 주파수 영역의 S/N비 확인 및 포화된 자료의 사용가능성 여부를 제시하였다.

서버리스 플랫폼에서 연속된 콜드 스타트 완화를 위한 Pre-Warming 기법 (Mitigating Cold Start Chain by Pre-Warming Containers in Serverless Platform)

  • 김세진;유문상;유헌창
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2022년도 춘계학술발표대회
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    • pp.71-73
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    • 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.

마스크된 복원에서 질병 진단까지: 안저 영상을 위한 비전 트랜스포머 접근법 (From Masked Reconstructions to Disease Diagnostics: A Vision Transformer Approach for Fundus Images)

  • ;변규린;추현승
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2023년도 추계학술발표대회
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    • pp.557-560
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    • 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.

고속 해상 객체 분류를 위한 양자화 적용 기반 CNN 딥러닝 모델 성능 비교 분석 (Comparative Analysis of CNN Deep Learning Model Performance Based on Quantification Application for High-Speed Marine Object Classification)

  • 이성주;이효찬;송현학;전호석;임태호
    • 인터넷정보학회논문지
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    • 제22권2호
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    • pp.59-68
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    • 2021
  • 최근 급속도로 성장하고 있는 인공지능 기술이 자율운항선박과 같은 해상 환경에서도 적용되기 시작하면서 디지털 영상에 특화된 CNN 기반의 모델을 적용하는 관련 연구가 활발히 진행되고 있다. 이러한 해상 서비스의 경우 인적 과실을 줄이기 위해 충돌 위험이 있는 부유물을 감지하거나 선박 내부의 화재 등 여러 가지 기술이 접목되기에 실시간 처리가 매우 중요하다. 그러나 기능이 추가될수록 프로세서의 제품 가격이 증가하는 문제가 존재해 소형 선박의 선주들에게는 비용적인 측면에서 부담이 된다. 또한 대형 선박의 경우 자율운항선박의 시스템을 감안할 때, 연산 속도의 성능 향상을 위해 복잡도가 높은 딥러닝 모델의 성능을 개선하는 방법이 필요하다. 따라서 본 논문에서는 딥러닝 모델에 경량화 기법을 적용해 정확도를 유지하면서 고속으로 처리할 수 있는 방법에 대해 제안한다. 먼저 해상 부유물 검출에 적합한 영상 전처리를 진행하여 효율적으로 CNN 기반 신경망 모델 입력에 영상 데이터가 전달될 수 있도록 하였다. 또한, 신경망 모델의 알고리즘 경량화 기법 중 하나인 학습 후 파라미터 양자화 기법을 적용하여 모델의 메모리 용량을 줄이면서 추론 부분의 처리 속도를 증가시켰다. 양자화 기법이 적용된 모델을 저전력 임베디드 보드에 적용시켜 정확도와 처리 속도를 사용하는 임베디드 성능을 고려하여 설계하는 방법을 제안한다. 제안하는 방법 중 정확도 손실이 제일 최소화되는 모델을 활용해 저전력 임베디드 보드에 비교하여 기존보다 최대 4~5배 처리 속도를 개선할 수 있었다.

Incorporating Recognition in Catfish Counting Algorithm Using Artificial Neural Network and Geometry

  • Aliyu, Ibrahim;Gana, Kolo Jonathan;Musa, Aibinu Abiodun;Adegboye, Mutiu Adesina;Lim, Chang Gyoon
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제14권12호
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    • pp.4866-4888
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    • 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.