• Title/Summary/Keyword: Network Convergence

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No-Reference Sports Video-Quality Assessment Using 3D Shearlet Transform and Deep Residual Neural Network (3차원 쉐어렛 변환과 심층 잔류 신경망을 이용한 무참조 스포츠 비디오 화질 평가)

  • Lee, Gi Yong;Shin, Seung-Su;Kim, Hyoung-Gook
    • Journal of Korea Multimedia Society
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    • v.23 no.12
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    • pp.1447-1453
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    • 2020
  • In this paper, we propose a method for no-reference quality assessment of sports videos using 3D shearlet transform and deep residual neural networks. In the proposed method, 3D shearlet transform-based spatiotemporal features are extracted from the overlapped video blocks and applied to logistic regression concatenated with a deep residual neural network based on a conditional video block-wise constraint to learn the spatiotemporal correlation and predict the quality score. Our evaluation reveals that the proposed method predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

Diagnosis of Calcification of Lung Nodules on the Chest X-ray Images using Gray-Level based Analysis (흉부 X-ray 영상 내 폐 결절의 석회화 여부 진단을 위한 화소 밝기 분석 기법)

  • Hyeon-Jin Choi;Dong-Yeon Yoo;Joo-Sung Sun;Jung-Won Lee
    • Annual Conference of KIPS
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    • 2023.05a
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    • pp.681-683
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    • 2023
  • 폐암은 전 세계적으로 사망률이 가장 높은 암 질환으로, 조기 발견 및 신속한 치료를 위해서는 흉부 X-ray 영상 내 악성 결절을 놓치지 않는 것이 중요하다. 그러나 흉부 X-ray 영상은 정밀도의 한계로 진단 결과에 대한 신뢰도가 낮아, 이를 보조하는 도구의 개발이 요구된다. 기존의 폐암 진단 보조 도구는 학습 기반의 기법으로, 진단 결과에 대한 설명성(explainability)이 없다는 위험성을 갖는다. 이에 본 논문에서는 통계 분석에 기반한 결절의 석회화 여부 진단 기법을 제안한다. 제안하는 기법은 결절과 해부학적 구조물의 밝기 차 분포로부터 석회화 여부를 판단하며, 그 결과 민감도 65.22%, 특이도 88.48%, 정확도 83.41%의 성능을 보였다.

Development of Travelling Control Algorithm Based Fuzzy Perception and Neural Network for Two Wheel Driving Robot (퍼지추론 및 뉴럴네트워크 기반 2휠구동 로봇의 주행제어알고리즘 개발)

  • Kang, Eon-Uck;Yang, Jun-Seok;Cha, Bo-Nam;Park, In-Soo
    • Journal of the Korean Society of Industry Convergence
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    • v.17 no.2
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    • pp.69-76
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    • 2014
  • This paper proposes a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

Performance Analysis of SyncML Server System Using Stochastic Petri Nets

  • Lee, Byung-Yun;Lee, Gil-Haeng;Choi, Hoon
    • ETRI Journal
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    • v.26 no.4
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    • pp.360-366
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    • 2004
  • Synchronization Markup Language (SyncML) is a specification of a common data synchronization framework for synchronizing data on networked devices. SyncML is designed for use between mobile devices that are intermittently connected to a network and network services that are continuously available on the network. We have designed and developed a data synchronization system based on the SyncML protocol and evaluated the throughput of the system using the stochastic Petri nets package (SPNP) and analyzed the relationship between the arrival rate and the system resources. Using this model, we evaluate various performance measures in different situations, and we estimate the relationship between the arrival rate and the system resources. From the results, we can estimate the optimal amount of resources due to the arrival rate before deploying the developed system.

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A Study on the Network Centric Warfare Information Security for Ubiquitous Network Computing (유비쿼터스 환경에서의 NCW 정보보호 대책)

  • Kwon, Moon-Taek
    • Convergence Security Journal
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    • v.10 no.3
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    • pp.15-22
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    • 2010
  • Information security is a critical issue for network centric warfare(NCW). This paper provides defense information security guidelines for NCW, especially for ubiquitous network computing environment. For this purpose, this paper identified changes of battle aspect of tactical level and characteristics of information threats, and finally, the research suggested several information security guidelines for NCW. This paper is to intended to help military organization's planners determine practical and implemental plans in the near future.

Temporal matching prior network for vehicle license plate detection and recognition in videos

  • Yoo, Seok Bong;Han, Mikyong
    • ETRI Journal
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    • v.42 no.3
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    • pp.411-419
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    • 2020
  • In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

A MODIFIED EXTENDED KALMAN FILTER METHOD FOR MULTI-LAYERED NEURAL NETWORK TRAINING

  • KIM, KYUNGSUP;WON, YOOJAE
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.22 no.2
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    • pp.115-123
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    • 2018
  • This paper discusses extended Kalman filter method for solving learning problems of multilayered neural networks. A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We consider an efficient learning algorithm for deep neural network. Extended Kalman filter method is applied to parameter estimation of neural network to improve convergence and computation complexity. We discuss how an efficient algorithm should be developed for neural network learning by using Extended Kalman filter.

A Study on the Digital Implementation of Multi-layered Neural Networks for Pattern Recognition (패턴인식을 위한 다층 신경망의 디지털 구현에 관한 연구)

  • 박영석
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.2
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    • pp.111-118
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    • 2001
  • In this paper, in order to implement the multi-layered perceptron neural network using pure digital logic circuit model, we propose the new logic neuron structure, the digital canonical multi-layered logic neural network structure, and the multi-stage multi-layered logic neural network structure for pattern recognition applications. And we show that the proposed approach provides an incremental additive learning algorithm, which is very simple and effective.

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An Log Visualization Method of Network Security Equipment for Private Information Security (개인정보 보호를 위한 네트워크 보안장비의 로그 가시화 방법 연구)

  • Sim, Hee-Youn;Kim, Hyung-Jong
    • Convergence Security Journal
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    • v.8 no.4
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    • pp.31-40
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    • 2008
  • Recently, network forensic research which analyzes intrusion-related information for tracing of attackers, has been becoming more popular than disk forensic which analyzes remaining evidences in a system. Analysis and correlation of logs from firewall, IDS(Intrusion Detect System) and web server are important part in network forensic procedures. This work suggests integrated graphical user interface of network forensic for private information leakage detection. This paper shows the necessity of various log information for network forensic and a design of graphical user interface for security managers who need to monitor the leakage of private information.

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