• Title/Summary/Keyword: single-pass

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Secure and Efficient Privacy-Preserving Identity-Based Batch Public Auditing with Proxy Processing

  • Zhao, Jining;Xu, Chunxiang;Chen, Kefei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.2
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    • pp.1043-1063
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    • 2019
  • With delegating proxy to process data before outsourcing, data owners in restricted access could enjoy flexible and powerful cloud storage service for productivity, but still confront with data integrity breach. Identity-based data auditing as a critical technology, could address this security concern efficiently and eliminate complicated owners' public key certificates management issue. Recently, Yu et al. proposed an Identity-Based Public Auditing for Dynamic Outsourced Data with Proxy Processing (https://doi.org/10.3837/tiis.2017.10.019). It aims to offer identity-based, privacy-preserving and batch auditing for multiple owners' data on different clouds, while allowing proxy processing. In this article, we first demonstrate this scheme is insecure in the sense that malicious cloud could pass integrity auditing without original data. Additionally, clouds and owners are able to recover proxy's private key and thus impersonate it to forge tags for any data. Secondly, we propose an improved scheme with provable security in the random oracle model, to achieve desirable secure identity based privacy-preserving batch public auditing with proxy processing. Thirdly, based on theoretical analysis and performance simulation, our scheme shows better efficiency over existing identity-based auditing scheme with proxy processing on single owner and single cloud effort, which will benefit secure big data storage if extrapolating in real application.

A Modified Klobuchar Model Reflecting Characteristics of Ionospheric Delay Error in the Korea Region

  • Dana Park;Young Jae Lee
    • Journal of Positioning, Navigation, and Timing
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    • v.12 no.2
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    • pp.121-128
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    • 2023
  • When calculating the user's position using satellite signals, the signals originating from the satellite pass through the ionosphere and troposphere to the user. In particular, the ionosphere delay error that occurs when passing through the ionosphere delays when the signal is transmitted, generating a pseudorange error and position error at a large rate. Therefore, to improve position accuracy, it is essential to correct the ionosphere layer error. In a receiver capable of receiving dual frequency, the ionosphere error can be eliminated through a double difference, but in a single frequency receiver, an ionosphere correction model transmitted from a Global Navigation Satellite System (GNSS) satellite is used. The popularly used Klobuchar model is designed to improve performance globally. As such, it does not perform perfectly in the Korea region. In this paper, the characteristics of the delay in the ionosphere in the Korean region are identified through an analysis of 10 years of data, and an improved ionosphere correction model for the Korean region is presented using the widely employed Klobuchar model. Through the proposed model, vertical position error can be improved by up to 40% relative to the original Klobuchar model in the Korea region.

A Study on Object Counting by Mixture of Gaussian and Motion Vector (가우시안 혼합 모델과 모션 벡터를 이용한 객체 계수 방법 연구)

  • Kim, Gyu-Jin;An, Tae-Ki;Shin, Jeong-Ryeol
    • Proceedings of the KSR Conference
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    • 2011.05a
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    • pp.1161-1166
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    • 2011
  • A camera is mounted vertically downwards viewing the people heads from the top. This configuration is successful in people counting technique especially when only a few isolated people pass through a counting region in a non-crowded situation. Thus, this paper describes object counting which detects and count moving people using mixture of gaussian and motion vector. This method is intended to estimates the number of people in outdoor environment. This method use single gaussian background modeling which is more robust an noise and has adaptiveness. The experimental results that is based on mixture of gaussian and motion vector is also helpful to design intelligent surveillance.

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Pencil Curve Tracing via Virtual Digitizing (가상 측정을 통한 펜슬곡선 추출)

  • 박정환;김보현;최병규
    • Korean Journal of Computational Design and Engineering
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    • v.2 no.4
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    • pp.253-266
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    • 1997
  • Pencil-curve machining, which is a single-pass ball-end milling along a concave edge on adie surface, is widely employed in die-surface machining. The cutter-path used for pencil-curve machining, which is the trajectory of the “ball-center point” of a ball-endmill sliding along a concave-edge region on the die surface, is called pencil-curve. Presented in the paper is a pencil-curve tracing algorithm in which “concave-type” sharp edges are computed from a “virtually digitized” model of the tool-envelope surface. The resulting “initial” pencil-cures are then refuted by applying a series of fairing operations. illustrative examples and methods for enhancing accuracy are also presented. The proposed pencil-curve tracing algorithm has been successfully implemented in a commercial CAM system specialized in die-machining and in the CAD/CAM system CATIA.

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Line feature extraction in a noisy image

  • Lee, Joon-Woong;Oh, Hak-Seo;Kweon, In-So
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10a
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    • pp.137-140
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    • 1996
  • Finding line segments in an intensity image has been one of the most fundamental issues in computer vision. In complex scenes, it is hard to detect the locations of point features. Line features are more robust in providing greater positional accuracy. In this paper we present a robust "line features extraction" algorithm which extracts line feature in a single pass without using any assumptions and constraints. Our algorithm consists of five steps: (1) edge scanning, (2) edge normalization, (3) line-blob extraction, (4) line-feature computation, and (5) line linking. By using edge scanning, the computational complexity due to too many edge pixels is drastically reduced. Edge normalization improves the local quantization error induced from the gradient space partitioning and minimizes perturbations on edge orientation. We also analyze the effects of edge processing, and the least squares-based method and the principal axis-based method on the computation of line orientation. We show its efficiency with some real images.al images.

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Feedwater Flow Rate Evaluation of Nuclear Power Plants Using Wavelet Analysis and Artificial Neural Networks (웨이블릿 해석과 인공 신경회로망을 이용한 원자력발전소의 급수유량 평가)

  • Yu, Sung-Sik;Seo, Jong-Tae;Park, Jong-Ho
    • 유체기계공업학회:학술대회논문집
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    • 2002.12a
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    • pp.346-353
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    • 2002
  • The steam generator feedwater flow rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow rate in pressurized water reactors, may result in unnecessary plant power derating. The backpropagation network was used to generate models of signals for a pressurized water reactor. Multiple-input single-output heteroassociative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.

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Design Optimization of Axial Flow Compressor Blades with Three-Dimensional N avier-Stokes Solver

  • Lee, Sang-Yun;Kim, Kwang-Yong
    • Journal of Mechanical Science and Technology
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    • v.14 no.9
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    • pp.1005-1012
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    • 2000
  • Numerical optimization techniques combined with a three-dimensional thin-layer Navier-Stokes solver are presented to find an optimum shape of a stator blade in an axial compressor through calculations of single stage rotor-stator flow. Governing differential equations are discretized using an explicit finite difference method and solved by a multi-stage Runge-Kutta scheme. Baldwin-Lomax model is chosen to describe turbulence. A spatially-varying time-step and an implicit residual smoothing are used to accelerate convergence. A steady mixing approach is used to pass information between stator and rotor blades. For numerical optimization, searching direction is found by the steepest decent and conjugate direction methods, and the golden section method is used to determine optimum moving distance along the searching direction. The object of present optimization is to maximize efficiency. An optimum stacking line is found to design a custom-tailored 3-dimensional blade for maximum efficiency with the other parameters fixed.

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A Study on Prediction of Optimized Penetration Using the Neural Network and Empirical models (신경회로망과 수학적 방정식을 이용한 최적의 용입깊이 예측에 관한 연구)

  • 전광석
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.8 no.5
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    • pp.70-75
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    • 1999
  • Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

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Reducing Spectral Signature Confusion of Optical Sensor-based Land Cover Using SAR-Optical Image Fusion Techniques

  • ;Tateishi, Ryutaro;Wikantika, Ketut;M.A., Mohammed Aslam
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.107-109
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    • 2003
  • Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

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Finite Element Analysis of Half Channel Angular Extrusion (HCAE) as a New Severe Plastic Deformation Process (새로운 강소성 가공 공정으로서 Half Channel Angular Extrusion(HCAE)의 유한요소해석)

  • Kim, K.J.
    • Transactions of Materials Processing
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    • v.21 no.3
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    • pp.164-171
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    • 2012
  • This paper focuses on the development of a new SPD (severe plastic deformation) process named HCAE (half channel angular extrusion). HCAE technology is based on principled similar to ECAE, but imposes a larger amount and more effective plastic deformation on materials. The amount of shear deformation can be altered by varying the process parameters. Finite element analyses of HCAE were conducted in order to investigate the characteristics of deformation during HCAE and the simulated results show that the predicted value of imposed plastic strain in a single pass reaches 2.5.