• Title/Summary/Keyword: FAST software

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Performance analysis of Various Embedding Models Based on Hyper Parameters (다양한 임베딩 모델들의 하이퍼 파라미터 변화에 따른 성능 분석)

  • Lee, Sanga;Park, Jaeseong;Kang, Sangwoo;Lee, Jeong-Eom;Kim, Seona
    • Annual Conference on Human and Language Technology
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    • 2018.10a
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    • pp.510-513
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    • 2018
  • 본 논문은 다양한 워드 임베딩 모델(word embedding model)들과 하이퍼 파라미터(hyper parameter)들을 조합하였을 때 특정 영역에 어떠한 성능을 보여주는지에 대한 연구이다. 3 가지의 워드 임베딩 모델인 Word2Vec, FastText, Glove의 차원(dimension)과 윈도우 사이즈(window size), 최소 횟수(min count)를 각기 달리하여 총 36개의 임베딩 벡터(embedding vector)를 만들었다. 각 임베딩 벡터를 Fast and Accurate Dependency Parser 모델에 적용하여 각 모들의 성능을 측정하였다. 모든 모델에서 차원이 높을수록 성능이 개선되었으며, FastText가 대부분의 경우에서 높은 성능을 내는 것을 알 수 있었다.

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Keywords-based Video Summary System using FastText Algorithm (FastText 알고리즘을 이용한 사용자 지정 키워드 기반 동영상 요약 시스템)

  • Kyungmin Kim;Seungmin Park
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.07a
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    • pp.693-694
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    • 2023
  • 본 논문에서는 FastText 알고리즘을 기반으로 한 사용자 지정 키워드 기반 동영상 요약 시스템을 제안한다. 사용자가 키워드를 입력하면 시스템은 해당 키워드와 관련된 단어들을 FastText를 통해 추출하며, 이를 STT (Speech-to-Text)로 변환된 동영상에서 타임 스탬프 기반으로 인식한다. 인식된 키워드와 관련된 내용은 클립 형식으로 요약되어 사용자에게 제공된다. 본 연구의 목적은 숏폼 콘텐츠 환경에서 효과적인 콘텐츠 추출 및 제공을 통해 사용자 경험과 정보 제공의 효율성을 향상시키기 위함이다. 제안된 시스템은 사용자 지정 키워드에 맞춰 다양한 동영상 플랫폼에서 효율적인 영상 요약을 제공함으로써 온라인 동영상 환경에서 큰 혁신을 이끌어낼 것으로 기대된다.

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Separation of Occluding Pigs using Deep Learning-based Image Processing Techniques (딥 러닝 기반의 영상처리 기법을 이용한 겹침 돼지 분리)

  • Lee, Hanhaesol;Sa, Jaewon;Shin, Hyunjun;Chung, Youngwha;Park, Daihee;Kim, Hakjae
    • Journal of Korea Multimedia Society
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    • v.22 no.2
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    • pp.136-145
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    • 2019
  • The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

DEVELOPMENT OF HIGH-RESOLUTION SATELLITE IMAGE PROCESSING SYSTEM BY USING CBD

  • Yoon, Chang-Pak;Seo, Ji-Hoon;Kim, Kyung-Ok
    • Proceedings of the KSRS Conference
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    • 2002.10a
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    • pp.49-52
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    • 2002
  • High-resolution satellite image processing software should be able to ensure accurate, fast, compact data processing in offline or online environment. In this paper, component software for high-resolution satellite image processing is developed using OpenGIS components and real-time data processing architecture. The developed component software is composed of three major packages, which are data provide package, user interface package, and fast data processing package. The data provider package encodes and decodes diverse image/vector data formats and give identical data access methods to developers. The user interface package supports menus, toolbars, dialogs, and events to use easier. The fast data processing package follows the OpenGIS's data processing standards, which can deal with several processors as components with standard procedural functionalities.

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Fast Extraction of Pedestrian Candidate Windows Based on BING Algorithm

  • Zeng, Jiexian;Fang, Qi;Wu, Zhe;Fu, Xiang;Leng, Lu
    • Journal of Multimedia Information System
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    • v.6 no.1
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    • pp.1-6
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    • 2019
  • In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

Fast Recovery Routing Algorithm for Software Defined Network based Operationally Responsive Space Satellite Networks

  • Jiang, Lei;Feng, Jing;Shen, Ye;Xiong, Xinli
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.7
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    • pp.2936-2951
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    • 2016
  • An emerging satellite technology, Operationally Responsive Space (ORS) is expected to provide a fast and flexible solution for emergency response, such as target tracking, dense earth observation, communicate relaying and so on. To realize large distance transmission, we propose the use of available relay satellites as relay nodes. Accordingly, we apply software defined network (SDN) technology to ORS networks. We additionally propose a satellite network architecture refered to as the SDN-based ORS-Satellite (Sat) networking scheme (SDOS). To overcome the issures of node failures and dynamic topology changes of satellite networks, we combine centralized and distributed routing mechanisms and propose a fast recovery routing algorithm (FRA) for SDOS. In this routing method, we use centralized routing as the base mode.The distributed opportunistic routing starts when node failures or congestion occur. The performance of the proposed routing method was validated through extensive computer simulations.The results demonstrate that the method is effective in terms of resoving low end-to-end delay, jitter and packet drops.

A Study on Tools for Creater's Subtitle using fastText and OpenCV (fastText와 OpenCV를 이용하여 크리에이터 맞춤 영상자막 수정 방법 연구)

  • Choi, Wonchil;Jo, Sehyeon;Yoon, DongWoo;Woo, Hojin;Kim, Youngjong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2019.05a
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    • pp.566-567
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    • 2019
  • 영상으로 콘텐츠를 개발하는 사람들을 '크리에이터'라 칭한다. 이들이 사람들에게 재미를 주고 이목을 끌기 위해 표준어 이외에 다양한 유행어와 신조어들을 만들어내며 이들을 영상뿐만 아니라 자막으로 활용하게 된다. 이러한 자막이 있는 영상 제작시 대본을 제작하는데 있어 자유도가 높은 크리에이터들의 특징상 맞춤법 오류 및 오타의 문제가 생긴다. 하지만 영상제작 도구에는 맞춤법 검사 기능이 없어 검사를 미리 하기에는 어려운 점이 있다. 우리는 이 문제점을 해결하기 위해 영상을 완성 하고 최종 검토를 할 때 맞춤법 검사를 하기 쉽도록 프로그램을 개발한다. OpenCV를 통해 영상의 자막을 글자로써 인식을 하고, fastText 모델을 통해 인식된 글자가 맞춤법에 맞는지 크리에이터에게 제안해주는 맞춤형 프로그램을 개발하고자 한다.

Fast Acquisition Method for GPS L2C Software Receiver (GPS L2C 소프트웨어 수신기의 빠른 신호 획득 기법)

  • Kwon, Keum-Cheol;Shim, Duk-Sun
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.1754-1755
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    • 2011
  • GPS modernization program offers a new civil signal on L2 band and there are currently 9 GPS satellites transmitting L2C signal. The acquisition of L2C takes much time comparing with that of L1 signal. This paper suggests a fast acquisition method for the L2C GPS signals for software receivers.

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Individual Pig Detection using Fast Region-based Convolution Neural Network (고속 영역기반 컨볼루션 신경망을 이용한 개별 돼지의 탐지)

  • Choi, Jangmin;Lee, Jonguk;Chung, Yongwha;Park, Daihee
    • Journal of Korea Multimedia Society
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    • v.20 no.2
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    • pp.216-224
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    • 2017
  • Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

Software Implementation of Lightweight Block Cipher CHAM for Fast Encryption

  • Kim, Taeung;Hong, Deukjo
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.10
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    • pp.111-117
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    • 2018
  • CHAM is a lightweight block cipher, proposed in ICISC 2017. CHAM-n/k has the n-bit block and the k-bit key, and designers recommend CHAM-64/128, CHAM-128/128, and CHAM-128/256. In this paper, we study how to make optimal software implementation of CHAM such that it has high encryption speed on CPUs with high computing power. The best performances of our CHAM implementations are 1.6 cycles/byte for CHAM-64/128, 2.3 cycles/byte for CHAM-128/128, and 3.8 cycles/byte for CHAM-128/256. The comparison with existing software implementation results for well-known block ciphers shows that our results are competitive.