• Title/Summary/Keyword: hybrid detection

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An Improved Hybrid Kalman Filter Design for Aircraft Engine based on a Velocity-Based LPV Framework

  • Liu, Xiaofeng
    • International Journal of Aeronautical and Space Sciences
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    • v.18 no.3
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    • pp.535-544
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    • 2017
  • In-flight aircraft engine performance estimation is one of the key techniques for advanced intelligent engine control and in-flight fault detection, isolation and accommodation. This paper detailed the current performance degradation estimation methods, and an improved hybrid Kalman filter via velocity-based LPV (VLPV) framework for these needs is proposed in this paper. Composed of a nonlinear on-board model (NOBM) and VLPV, the filter shows a hybrid architecture. The outputs of NOBM are used for the baseline of the VLPV Kalman filter, while the system performance degradation factors on-line estimated by the measured real system output deviations are fed back to the NOBM for its updating. In addition, the setting of the process and measurement noise covariance matrices' values are also discussed. By applying it to a commercial turbofan engine, simulation results show the efficiency.

Malware Detection Device Using Hybrid Filter (하이브리드 필터를 이용한 악성코드 탐지 장치)

  • Oh, Dong-Yeob;Park, Jae-Kyung
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2014.07a
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    • pp.67-70
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    • 2014
  • 최근의 다양한 환경에서 악성코드나 의심 코드에 의한 피해가 날로 늘어나고 있는 추세이며 이를 종합적으로 대응할 수 있는 시스템에 대한 연구가 활발히 이루어지고 있는 상황이다. 이러한 악성코드는 사용자의 동의 없이도 PC에 설치되어 사용자가 인지하지 못하는 피해를 지속적으로 영산하고 있으며 그 심각성도 날로 심해지고 있는 실정이다. 또한 다양한 시스템으로부터 수집되는 방대한 양의 데이터를 실시간으로 처리하고 검증하는 기술 및 탐지 기법을 토대로 악성코드를 탐지하고 분석할 수 있는 대응기술로 고도화 되어야만 한다. 이러한 악성코드는 사용자의 PC에 설치되기 이전부터 검사 및 판단하여 사전 대응하는 것이 매우 중요하다. 본 논문에서는 이러한 악성코드가 실제 PC상에 설치되기 이전에 탐지할 수 있는 기법을 제시하며 이를 장치형태로 검증하였다. 본 논문에서 제시하는 기술을 토대로 악상코드 근절에 대한 근본적인 대안을 제시할 것이라 판단한다.

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Double-Dwell Hybrid Acquisition in DS-UWB System

  • Wang YuPeng;Chang Kyung-Hi
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.7A
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    • pp.696-701
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    • 2006
  • In this paper, we analyze the performance of double-dwell hybrid initial acquisition in DS-UWB system via detection, miss, false alarm probabilities and mean acquisition time. In the analysis, we consider the effect of the acquisition sequence, and deployment scenario of the abundant multipath components over the small coverage of the piconet in DS-UWB system. Based on the simulation, we obtain various performance on the mean acquisition time by varying the parameters, such as the total number of hypotheses to be searched, subgroup size, and dwell time. Then, we suggest the optimum parameter set for the initial acquisition in DS-UWB system.

Quantum Coherent Dissociation in a Hybrid Atom-light System with Photon Loss

  • Xiaoyang Yuan;Jialu Yin;Jiahao Xu;Yixiao Huang;Zhengda Hu
    • Current Optics and Photonics
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    • v.8 no.1
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    • pp.105-111
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    • 2024
  • We investigate the effect of photon loss on pair production in a hybrid atom-light system. The loss of light field not only affects the generation of photons, but also prevents the generation of atomic collective excitation, although the atoms are not influenced directly. We propose an unbalanced homodyne detection of the number of atomic collective excitation that overcomes the challenge caused by counting uncertainty in practical measurement. In discussion, we show that the intermode correlations and the number correlation is closely related to the initial input state, while the quadrature correlations are independent of the initial state and always exhibit opposite intermode correlations even in the presence of loss.

Research Trends on Deep Learning for Anomaly Detection of Aviation Safety (딥러닝 기반 항공안전 이상치 탐지 기술 동향)

  • Park, N.S.
    • Electronics and Telecommunications Trends
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    • v.36 no.5
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    • pp.82-91
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    • 2021
  • This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.

A Study on the Hybrid Algorithm for Scene Change Detection (장면전환검출을 위한 Hybrid 알고리즘에 관한 연구)

  • 이문우;박종운;장종환
    • Journal of the Korea Computer Industry Society
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    • v.2 no.4
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    • pp.507-520
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    • 2001
  • In this paper, a hybrid algorithm for well detecting both abrupt and gradual scene changes is proposed. This algorithm examines only the candidate intervals for speedup using the binary tree method and skips the intervals that are not candidate. For accuracy, the temporal difference of variance is used to detect the gradual scene changes while the temporal difference of histogram is used to detect the abrupt scene changes. Experimental results show that the proposed hybrid algorithm using the binary tree method works up about 10 times faster that the sequential method and is effective in detecting abrupt scene change and gradual transitions including dissolving and fading.

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A Hybrid Algorithm for Online Location Update using Feature Point Detection for Portable Devices

  • Kim, Jibum;Kim, Inbin;Kwon, Namgu;Park, Heemin;Chae, Jinseok
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.2
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    • pp.600-619
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    • 2015
  • We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

A Hybrid Active Queue Management for Stability and Fast Adaptation

  • Joo Chang-Hee;Bahk Sae-Woong;Lumetta Steven S.
    • Journal of Communications and Networks
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    • v.8 no.1
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    • pp.93-105
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    • 2006
  • The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

A new method to detect cracks in plate-like structures with though-thickness cracks

  • Xiang, Jiawei;Nackenhorst, Udo;Wang, Yanxue;Jiang, Yongying;Gao, Haifeng;He, Yumin
    • Smart Structures and Systems
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    • v.14 no.3
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    • pp.397-418
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    • 2014
  • In this paper, a simple two-step method for structural vibration-based health monitoring for beam-like structures have been extended to plate-like structures with though-thickness cracks. Crack locations and severities of plate-like structures are detected using a hybrid approach. The interval wavelet transform is employed to extract crack singularity locations from mode shape and support vector regression (SVR) is applied to predict crack serviettes form crack severity detection database (the relationship of natural frequencies and crack serviettes) using several natural frequencies as inputs. Of particular interest is the natural frequencies estimation for cracked plate-like structures using Rayleigh quotient. Only the natural frequencies and mode shapes of intact structures are needed to calculate the natural frequencies of cracked plate-like structures using a simple formula. The crack severity detection database can be easily obtained with this formula. The hybrid method is investigated using numerical simulation and its validity of the usage of interval wavelet transform and SVR are addressed.