Magnetic flux leakage detection will generate a huge amount of data on pipeline surface defects for ***,in order to improve the accuracy and efficiency of defect detection,it is significant to find out the appropriate...
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ISBN:
(纸本)9781538629185
Magnetic flux leakage detection will generate a huge amount of data on pipeline surface defects for ***,in order to improve the accuracy and efficiency of defect detection,it is significant to find out the appropriate method of extracting and analyzing the defect *** thesis mainly studies and analyzes the magnetic flux leakage data detected from the pipelines,and mainly realize the analysis of defect signal *** selection of defect signal features is completed by finding the feature quantity related to the length,width and depth of the defect through control variate *** the selection of defect signal features,a sample library for defect feature selection is *** algorithm is simulated based on principal component analysis,which is used to optimize the defect features.
simulation of ultrasound pressure fields is representative computationally demanding algorithm. In order to get results back in finite time, the number of source points and observation points are ofte
ISBN:
(纸本)9781467389808
simulation of ultrasound pressure fields is representative computationally demanding algorithm. In order to get results back in finite time, the number of source points and observation points are ofte
The efficiency of network virtualization depends on the appropriate assignment of resources. The underlying problem, called virtual network embedding, has been much discussed in the literature, and many algorithms hav...
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The efficiency of network virtualization depends on the appropriate assignment of resources. The underlying problem, called virtual network embedding, has been much discussed in the literature, and many algorithms have been proposed, attempting to optimize the resource assignment in various respects. Evaluation of those algorithms requires a large number of randomly generated embedding scenarios. This paper presents a novel scenario generation approach and demonstrates how to produce scenarios with a guaranteed exact solution, thereby, facilitating better evaluation of embedding algorithms.
In the sensor network blind calibration problem, the gains and offsets of sensors are estimated from noisy observations of unknown underlying signals. This is in general a non-identifiable problem, unless restrictive ...
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ISBN:
(纸本)9781509041183
In the sensor network blind calibration problem, the gains and offsets of sensors are estimated from noisy observations of unknown underlying signals. This is in general a non-identifiable problem, unless restrictive assumptions on the signal subspace or sensor observations are imposed. To overcome these assumptions, we propose a dynamic Bayesian nonparametric model. We show that if the unknown underlying signals follow the first-order auto-regressive process, then the sensor gains and offsets are identifiable. Furthermore, our model allows sensors to form clusters, where each cluster observes the same underlying signal. The clusters are however not known a priori, and are learned through the sensor data. We present a block Gibbs sampling inference method based on the forward filtering backward sampling algorithm. simulation results suggest that our approach can estimate the sensor gains and offsets with good accuracy, and performs better than methods that first perform clustering and then blind calibration.
This paper presents a real-time performance evaluation of Neyman-Pearson (NP) criteria based energy detection algorithm for spectrum sensing in cognitive radio. We have implemented the energy detection algorithm on a ...
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ISBN:
(纸本)9781509020300
This paper presents a real-time performance evaluation of Neyman-Pearson (NP) criteria based energy detection algorithm for spectrum sensing in cognitive radio. We have implemented the energy detection algorithm on a Wireless Open-Access Research Platform (WARP). The algorithm is validated using bursts of QPSK signal. Each burst has four frames with 1024 samples. For validation, in each burst, only one frame is occupied with the signal. We have compared the implementation results with algorithm simulation results. The experimental results reveal that the algorithm can detect signal up to SNR of -4dB, and -7dB in real time and simulation respectively with the probability of detection and probability of false alarm as 0.9 and 0.1 respectively. The detection time for performing the sensing operation in WARP board is evaluated as 4.7μSec.
This paper studies how to determine an optimal order of recovering interdependent Cyber Physical Systems (CPS) after a large scale failure. In such a CPS, some failed devices must be repaired first before others can. ...
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ISBN:
(纸本)9781509013296
This paper studies how to determine an optimal order of recovering interdependent Cyber Physical Systems (CPS) after a large scale failure. In such a CPS, some failed devices must be repaired first before others can. In addition, such failed devices require a certain amount of repair resources and may take multiple stages to repair. We consider two scenarios: 1) reserved model where all the required repair resources should be prepared at the beginning of repairing a device;and 2) opportunistic model where we can partially repair a device with only part of the required resources. For each scenario, we model it using an Integer Linear Programming (ILP) and use a relaxation and rounding method to design an ILP based algorithm. In addition, we also design a Dynamic Programming (DP) based algorithm. simulation results show that ILP based algorithm outperforms DP based algorithm by 10%-20% in systems with less than 200 failed devices, but DP based algorithm can support extreme large size systems with more than 5000 failed devices.
Learning activities are increasingly performed in online learning environments. Automatic assessment is used in many systems to give students immediate and personalized feedback allowing them to solve exercises regard...
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ISBN:
(纸本)9781479999675
Learning activities are increasingly performed in online learning environments. Automatic assessment is used in many systems to give students immediate and personalized feedback allowing them to solve exercises regardless of time and place. However, this creates new challenges for students because they are required to independently regulate their own learning. Because of the lack of direct guidance, it is easy for students to resort to bad study habits. In this paper, we investigate what kinds of harmful study habits can be observed in online learning environments that utilize automatic assessment. We study data from two computer science courses with programming exercises and algorithm simulation exercises. The results are in line with earlier findings that starting to study near the deadline is linked to inferior performance. This is also observed in a within-subject comparison which suggests that the relationship is causal. Furthermore, signs of trial-and-error problem solving are observed in some students and they are also correlated with inferior performance in the exercises and the exam.
This work describes how feed-forward Artificial Neural Networks (ANNs) can perform Analog-to-Digital (A/D) conversion with a linear and non-linear relationship between the analog input and the digital output in order ...
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This work describes how feed-forward Artificial Neural Networks (ANNs) can perform Analog-to-Digital (A/D) conversion with a linear and non-linear relationship between the analog input and the digital output in order to eliminate the linearization stage without modifying the analog-to-digital converter's elements and architecture. Adding to that, the speed of this A/D converter will not be reduced due to the unchanged conversion algorithm. simulation for two types of non-linear input has been performed. The results are discussed and a future work is presented.
In order to apply kaleidoscope patterns into textiles or garments, several methods are studied in this paper. Based on Java language, fractal algorithms, M set algorithm and Julia set algorithm are used to simulate ka...
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In order to apply kaleidoscope patterns into textiles or garments, several methods are studied in this paper. Based on Java language, fractal algorithms, M set algorithm and Julia set algorithm are used to simulate kaleidoscope patterns. The simulation results show that the kaleidoscope patterns programed can be used in textile and clothing. It provides users with more choice.
In this paper, we derive a new improved proportionate normalized least mean square (IPNLMS) algorithm with unconventional minimization criterion that minimizes the summation of each squared Euclidean norm of differenc...
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ISBN:
(纸本)9781479989218
In this paper, we derive a new improved proportionate normalized least mean square (IPNLMS) algorithm with unconventional minimization criterion that minimizes the summation of each squared Euclidean norm of difference between the currently updated coefficient vector and past coefficient vectors, which is called the improved IPNLMS (I-IPNLMS) algorithm. simulation results demonstrate that the proposed I-IPNLMS algorithm has the superiority of the lower misalignment than the conventional IPNLMS algorithm in the context of sparse system identification with a low signal-noise-ratio (SNR).
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