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adaptive vertex-centered finite volume methods for general second-order linear elliptic partial differential equations

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IMA JOURNAL OF NUMERICAL ANALYSIS 2019年第2期39卷 983-1008页

作者： Erath, Christoph Praetorius, Dirk Tech Univ Darmstadt Dept Math Dolivostr 15 D-64293 Darmstadt Germany TU Wien Inst Anal & Sci Comp Wiedner Hauptstr 8-10 A-1040 Vienna Austria

We prove optimal convergence rates for the discretization of a general second-order linear elliptic partial differential equation with an adaptive vertex-centered finite volume scheme. While our prior work Erath & Praetorius (2016, adaptive vertex-centered finite volume methods with convergence rates. SIAM J. Numer. Anal., 54, 2228-2255) was restricted to symmetric problems, the present analysis also covers nonsymmetric problems and hence the important case of present convection.

关键词： finite volume method Cea-type quasi-optimality a posteriori error estimators adaptive algorithm local mesh refinement optimal convergence rates nonsymmetric problems

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adaptive QM/MM coupling for crystalline defects

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2019年第0期354卷 351-368页

作者： Chen, Huajie Liao, Mingjie Wang, Hao Wang, Yangshuai Zhang, Lei Beijing Normal Univ Sch Math Sci Beijing Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Inst Nat Sci Shanghai Peoples R China Shanghai Jiao Tong Univ Minist Educ Key Lab Sci & Engn Comp MOE LSC Shanghai Peoples R China Sichuan Univ Sch Math Chengdu Sichuan Peoples R China

QM (quantum mechanics) and MM (molecular mechanics) coupling methods are widely used in simulations of crystalline defects. In this paper, we construct a residual based a posteriori error indicator for QM/MM coupling approximations. We show that this error indicator is reliable and can be computed efficiently by certain sampling techniques. Based on the error indicator and Dorfler marking strategy, we design an adaptive QM/MM algorithm for crystalline defects and demonstrate the efficiency of this algorithm with some numerical experiments. (C) 2019 Elsevier B.V. All rights reserved.

关键词： QM/MM coupling A posteriori error estimate adaptive algorithm Crystal defects

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algorithm Based adaptive Parametric Testing for Outlier Detection and Test Time Reduction

Algorithm Based Adaptive Parametric Testing for Outlier Dete...

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31st IEEE International Conference on Microelectronic Test Structures (ICMTS)

作者： Katragadda, Veenadhar Muthee, Martin Gasasira, Arthur Seelmann, Frank Liao, Jiun-Hsin GLOBALFOUNDRIES 400 Stone Break Rd Extens Malta NY 12065 USA

ISBN: (纸本)9781538650714

Parallel test capability, enabled by numerous independent measurement channels has significantly increased throughput in parametric testing. It involves testing of numerous devices simultaneously synchronously or asynchronously. The number of devices tested for a given pad layout is increased by using higher dimensional arrays, the hallmark of which is pad sharing. Parallel testing of multiple devices with shared pads is vulnerable to device fails, where a failing device adversely affects measurement of all other devices. Information about this failing device or compromised measurement would only be evident at post analysis where a retest with a recipe change can then be ordered. In some cases retest is impossible as wafers would have already moved on to subsequent processing steps, thereby losing valuable learning opportunity. On the other hand, having to wait for post analysis requires time. Ideally failure detection and subsequent re-measure is done dynamically while the device is under test. This would require that decision making capability to be implemented in an automated tester equipment. In this work, we will discuss an algorithm based approach to adaptively change the test program allowing testing or skipping devices based on data collected real time while device is under test. The adaptive algorithm is also extended to aid in test time efficiency by eliminating tests based on measurement results of preceding tests.

关键词： adaptive algorithm Parallel testing Parametric testing Outlier Detection Automated Test Equipment (ATE) Keysight P9000 MPPT

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A Novel algorithm for Network Anomaly Detection Using adaptive Machine Learning

A Novel Algorithm for Network Anomaly Detection Using Adapti...

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International Conference on Advanced Computing and Intelligent Engineering (ICACIE)

作者： Kumar, D. Ashok Venugopalan, S. R. Govt Arts Coll Dept Comp Sci Tiruchirappalli Tamil Nadu India GoI Minist Def Aeronaut Dev Agcy Bengaluru 560017 Karnataka India

ISBN: (纸本)9789811068751;9789811068744

Threats on the Internet are posting high risk to information security and network anomaly detection has become an important issue/area in information security. Data mining algorithms are used to find patterns and characteristic rules in huge data and this is very much used in Network Anomaly Detection System (NADS). Network traffic has several attributes of qualitative and quantitative nature, which needs to be treated/normalized differently. In general, a model is built with the existing data and the system is trained with the model and then used to detect intrusions. The major and important issue with such NADS is that the network traffic changes over time;in such cases, the system should get trained automatically or retrained. This paper presents an adaptive algorithm that gets trained according to the network traffic. The presented algorithm is tested with Kyoto University's 2006+ Benchmark dataset. It can be observed that the results of the proposed algorithm outperform all the known/commonly used classifiers and are very much suitable for network anomaly detection.

关键词： Intrusion Anomaly Network traffic Normalization Performance metrics adaptive algorithm Kyoto 2006+ Naive Bayes classification

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AK-MSS: An adaptation of the AK-MCS method for small failure probabilities

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STRUCTURAL SAFETY 2020年 86卷 101971-101971页

作者： Xu, Chunlong Chen, Weidong Ma, Jingxin Shi, Yaqin Lu, Shengzhuo Harbin Engn Univ Coll Aerosp & Civil Engn Harbin 150001 Heilongjiang Peoples R China

Structural reliability analysis aims to estimate the failure probability of a structure with respect to the performance function. This estimation may be a difficult task when the computation of a structural response requires large computational efforts. Although simulation-based methods can be used directly, they may require a large number of calls to the performance function for small failure probabilities. Metamodels (such as Kriging) that can replace the original performance function can be applied to address the computational costs. Among these methods, the active learning reliability method combining Kriging and Monte Carlo simulation (AK-MCS) is efficient, except for small failure probabilities and system reliability analysis. In this paper, a modified algorithm that combines the AK-MCS and the modified subset simulation (MSS) is proposed to estimate small failure probabilities. The strategy replaces the initial population with a large population that is generated by the MSS. No prior knowledge about the probability level is needed, and the sample size will adaptively change according to the estimation that is obtained in the last iteration and the target coefficient of variation. Therefore, the limit state can be covered by the new population, which is important for refining the Kriging model. The efficiency and accuracy of the proposed algorithm are illustrated using several examples.

关键词： Reliability analysis Small failure probabilities Kriging model adaptive algorithm Modified subset simulation

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Application of adaptive Wavelet Transform for Gear Fault Diagnosis Using Modified-LLMS Based Filtered Vibration Signal

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2019年第3期12卷 257-262页

作者： Sahoo, Sudarsan Das, Jitendra K. KIIT Univ Sch Elect Engn Bhubaneswar India

Background: Vibration signature acquired from a gear mesh can be used to identify the defect present in a gear mesh hence can be used to diagnose the condition of a gear mesh. But the signal acquired from the subject may not be noise free and may be non stationary. Methods: Before going for the analysis of the acquired signal a preprocessing on the acquired signal is required to make it noise free. In the present work in first phase, the acquired vibration signal is filtered to reduce the noise and to improve the SNR (signal to noise ratio). The filtering is done by an adaptive Noise Cancellation (ANC) technique. A modified Leaky Least Mean Square (LLMS) based adaptive algorithm along with a digital filter is used to achieve the ANC. The signal acquired from a healthy gear is used as the reference signal for the adaptive filter based dc-noising process. In the second phase of the present work adaptive Wavelet Transform (AWT) is used to detect the fault by extracting the features from the filtered vibration signal. From the signal pattern the adaptive wavelet is designed. The adaptive wavelet scalogram is compared with the standard wavelet scalogram. Results: The result shows that the adaptive wavelet scalogram is better in analyzing the vibration signal. In this work a gear drive experimental set-up is made. Two different types of defective gears are used for the experiment. In type-1 defective gear one tooth is broken and in type-2 defective gear two teeth are broken. Initially, the vibration signal is acquired from a healthy gear which is used as the reference signal. Then the vibration signal from type-1 defective gear and type-2 defective gear is acquired and processed for the analysis and to identify the defects. Conclusion: The present work shows that with the application of modified-LLMS algorithm and AWT the proposed technique of signal processing is more suitable for the fault identification and hence for the condition monitoring of the gear.

关键词： adaptive wavelet adaptive Noise Cancellation (ANC) gear fault modified-LLMS adaptive algorithm leaky least mean square

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Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

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COMPUTATIONAL ECONOMICS 2019年第3期53卷 991-1017页

作者： Khorunzhina, Natalia Richard, Jean-Francois Copenhagen Business Sch Dept Econ Porcelaenshaven 16A DK-2000 Frederiksberg Denmark Univ Pittsburgh Dept Econ 4917 Wesley W Posvar Hall Pittsburgh PA 15260 USA

The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approximates the corresponding importance sampling variance. All functions of interest are evaluated under Gaussian product rules. Since product rules suffer from an obvious curse of dimensionality, the proposed algorithm as presented is only applicable to models whose non-linear and/or non-Gaussian subspace is of dimension up to three. Extensions to higher-dimensional applications would require the use of sparse grids, as discussed in the paper. Examples include a sequential (filtering) evaluation of the likelihood function of a stochastic volatility model where all relevant densities (filtering, predictive and likelihood) are closely approximated by mixtures.

关键词： Finite mixture Distance measure Gaussian quadrature Importance sampling adaptive algorithm Stochastic volatility Density kernel

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An adaptive Semi-local algorithm for Node Ranking in Large Complex Networks 16th

An Adaptive Semi-local Algorithm for Node Ranking in Large C...

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16th International Conference on Service-Oriented Computing (ICSOC)

作者： Ye, Fanghua Chen, Chuan Zhang, Jie Wu, Jiajing Zheng, Zibin Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Sun Yat Sen Univ Natl Engn Res Ctr Digital Life Guangzhou Peoples R China

ISBN: (纸本)9783030035969;9783030035952

The issue of node ranking in complex networks is a classical problem that has obtained much attention over the past few decades, and a great variety of methods have consequently been developed. These proposed methods can be roughly categorized into the global and local methods. The global methods are usually time-consuming and the local methods may be inaccurate. In this paper, we propose a novel semi-local algorithm ASLA (adaptive Semi-Local algorithm) that seeks a tradeoff between the time efficiency and the ranking accuracy to overcome the limitations of the global and local methods. ASLA is able to adaptively determine the potential influence scope for each node. Then, the influence value of each node is calculated based on such a personalized influence scope. Finally, all the nodes are ranked according to their influence values. To evaluate the performance of ASLA, we have conducted extensive experiments on both synthetic networks and real-world networks, with the results demonstrating that ASLA is not only more efficient than the global methods but also more accurate than the local methods.

关键词： Node ranking adaptive algorithm Complex networks

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A Framework of Cubature- Hi/H∞ -Fault Detection and Robust H-Infinity Kalman Filter of Ship SINS/GNSS Integrated System

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IEEE ACCESS 2020年 8卷 196963-196974页

作者： Guo, Muzhuang Guo, Chen Zhang, Chuang Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China Dalian Maritime Univ Sch Nav Dalian 116026 Peoples R China

This article deals with the problem of considering both robust fault detection and accurate state estimation for a strapdown inertial navigation system and global navigation satellite system (SINS/GNSS)-integrated navigation system for ships within a unified framework. Under the assumption that the process disturbance and measurement noise being l(2)-norm bounded, a cubature H-i/H-infinity fault detection filter (FDF) considering both sensitivity and robustness to disturbance is proposed to detect faults with a postfilter and an observer gain. However, the estimated state value of the above algorithm is theoretically a suboptimal solution. To solve this limitation, an adaptive algorithm based on the measurement residual and a scaling factor is proposed to improve the accuracy of state estimation in the proposed cubature H-i/H-infinity FDF. To guarantee stability when faults are extremely large, the framework uses a robust H-infinity cubature Kalman filter. Finally, a simulation is implemented to demonstrate the effectiveness of the proposed method, considering the threshold selection for different cases.

关键词： SINS/GNSS-integrated navigation on ships cubature Kalman filter (CKF) H-i/H-infinity fault detection filter (FDF) adaptive algorithm H-infinity robust filter

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On FxLMS Scheme for Active Noise Control at Remote Location

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IEEE ACCESS 2020年 8卷 214071-214086页

作者： Munir, Muhammad Waqas Abdulla, Waleed H. Univ Auckland Dept Elect Comp & Software Engn Auckland 1010 New Zealand

The conventional adaptive noise control algorithms create a zone of quiet at the location of an error microphone. Several virtual active noise control (ANC) algorithms have been suggested in the literature to introduce a more flexible positioning of the zone of quiet. The main objective of the proposed ANC system in our research is to minimize the noise disturbance on human hearing by considering the auditory system characteristics. We have introduced the human perception of acoustic disturbances in our ANC modeling. This paper presents a novel approach based on a psychoacoustically enabled remote ANC algorithm to minimize the acoustic noise at a zone away from the error microphone location. This approach depends on modeling the transfer function between the error microphone and the remote zone of quiet. The developed model has been implemented using an FPGA real-time module, and the performance is validated at four different remote locations. Noise-weighting filters are incorporated to observe the psychoacoustic characteristics of the proposed model. In the experimental setup, the acoustic noise at the physical and remote locations are monitored to quantify the noise reduction characteristics of the proposed algorithm. The simulation and experimental results show a noise reduction of 15-18 dBs has been achieved, which is an average improvement of 5-8 dBs over the standard ANC model.

关键词： Microphones Psychoacoustic models Psychoacoustics Acoustics Signal processing algorithms adaptive systems Mathematical model Acoustics acoustic signal processing active noise reduction adaptive algorithm adaptive filters least mean square method mathematical model noise cancellation psychoacoustic

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