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Journal articles on the topic 'Weighted adaptive average'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Capizzi, Giovanna, and Guido Masarotto. "An Adaptive Exponentially Weighted Moving Average Control Chart." Technometrics 45, no. 3 (August 2003): 199–207. http://dx.doi.org/10.1198/004017003000000023.

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2

Cao, Liqin, Lei Jiao, Zhijiang Li, Tingting Liu, and Yanfei Zhong. "Grayscale Image Colorization Using an Adaptive Weighted Average Method." Journal of Imaging Science and Technology 61, no. 6 (November 1, 2017): 605021–6050210. http://dx.doi.org/10.2352/j.imagingsci.technol.2017.61.6.060502.

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3

Mahmoud, Mahmoud A., and Alyaa R. Zahran. "A Multivariate Adaptive Exponentially Weighted Moving Average Control Chart." Communications in Statistics - Theory and Methods 39, no. 4 (February 10, 2010): 606–25. http://dx.doi.org/10.1080/03610920902755813.

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4

Wang, Youqing, Xiangwei Wu, and Xue Mo. "A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction." Diabetes Technology & Therapeutics 15, no. 10 (October 2013): 792–801. http://dx.doi.org/10.1089/dia.2013.0104.

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5

HUBELE, NORMA FARIS, and SHING I. CHANG. "Adaptive Exponentially Weighted Moving Average Schemes Using a Kalrnan Filter." IIE Transactions 22, no. 4 (December 1990): 361–69. http://dx.doi.org/10.1080/07408179008964190.

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6

Huang, Wenpo, Lianjie Shu, and Yan Su. "An accurate evaluation of adaptive exponentially weighted moving average schemes." IIE Transactions 46, no. 5 (February 5, 2014): 457–69. http://dx.doi.org/10.1080/0740817x.2013.803642.

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7

HUANG, Kui. "TCP-Friendly Congestion Control Mechanism Based on Adaptive Weighted Average." Journal of Software 16, no. 12 (2005): 2124. http://dx.doi.org/10.1360/jos162124.

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8

Arshad, Asma, Muhammad Noor‐ul‐Amin, and Muhammad Hanif. "Function‐based adaptive exponentially weighted moving average dispersion control chart." Quality and Reliability Engineering International 37, no. 6 (April 20, 2021): 2685–98. http://dx.doi.org/10.1002/qre.2883.

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9

Galetto, Fernando J., Guang Deng, Mukhalad Al-Nasrawi, and Waseem Waheed. "Edge-Aware Filter Based on Adaptive Patch Variance Weighted Average." IEEE Access 9 (2021): 118291–306. http://dx.doi.org/10.1109/access.2021.3106907.

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10

Zheng, R., and S. Chakraborti. "A Phase II nonparametric adaptive exponentially weighted moving average control chart." Quality Engineering 28, no. 4 (July 14, 2016): 476–90. http://dx.doi.org/10.1080/08982112.2016.1183255.

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11

Mei Kang, 梅康, 刘小勤 Liu Xiaoqin, 沐超 Mu Chao, and 秦晓琪 Qin Xiaoqi. "Fast Defogging Algorithm Based on Adaptive Exponentially Weighted Moving Average Filtering." Chinese Journal of Lasers 47, no. 1 (2020): 0109001. http://dx.doi.org/10.3788/cjl202047.0109001.

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12

Zhao, Yong-qiang, Quan Pan, and Hong-cai Zhang. "Adaptive polarization image fusion based on regional energy dynamic weighted average." Optoelectronics Letters 1, no. 3 (November 2005): 224–27. http://dx.doi.org/10.1007/bf03033849.

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13

Shu, Lianjie. "An adaptive exponentially weighted moving average control chart for monitoring process variances." Journal of Statistical Computation and Simulation 78, no. 4 (March 28, 2008): 367–84. http://dx.doi.org/10.1080/00949650601108000.

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14

Ting, Yung, Tho Van Nguyen, and Jia-Ci Chen. "Design and performance evaluation of an exponentially weighted moving average–based adaptive control for piezo-driven motion platform." Advances in Mechanical Engineering 10, no. 6 (June 2018): 168781401876719. http://dx.doi.org/10.1177/1687814018767194.

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In this article, building a controlled system with velocity feedback in the inner loop for a platform driven by piezoelectric motors is investigated. Such a motion control system is subject to disturbance such as friction, preload, and temperature rise in operation. Especially, temperature rise is an essential problem of using piezoelectric motor, but very few research works address this topic in depth. Exponentially weighted moving average method has been widely used in process control to deal with systematic change and drift disturbance. It is attempted to map the exponentially weighted moving average method and the predictor corrector control with two exponentially weighted moving average formulas into a run-to-run model reference adaptive system for velocity control. Using a predictive friction model, a dead-zone compensator is built that can reduce the friction effect and provide an approximately linear relation of the input voltage and the output velocity for the subsequent exponentially weighted moving average or predictor corrector control control design. Comparison of the exponentially weighted moving average, predictor corrector control, and proportional–integral–derivative controllers is carried out in experiment with different speed patterns on a single-axis and a bi-axial platform. The results indicate that the proposed run-to-run-model reference adaptive system predictor corrector control is superior to the other methods.

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15

Benyahmed, Yahyia Mohamed, Azuraliza Abu Bakar, and Abdul Razak Hamdan. "Adaptive Piecewise Approximation Based on Weighted Average Approach for Symbolic Time Series Representation." International Review on Computers and Software (IRECOS) 10, no. 8 (August 31, 2015): 862. http://dx.doi.org/10.15866/irecos.v10i8.7151.

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16

Nazir, Hafiz Zafar, Tahir Hussain, Noureen Akhtar, Muhammad Abid, and Muhammad Riaz. "Robust adaptive exponentially weighted moving average control charts with applications of manufacturing processes." International Journal of Advanced Manufacturing Technology 105, no. 1-4 (August 14, 2019): 733–48. http://dx.doi.org/10.1007/s00170-019-04206-y.

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17

Saleh, Nesma A., Mahmoud A. Mahmoud, and Abdel-Salam G. Abdel-Salam. "The Performance of the Adaptive Exponentially Weighted Moving Average Control Chart with Estimated Parameters." Quality and Reliability Engineering International 29, no. 4 (April 17, 2012): 595–606. http://dx.doi.org/10.1002/qre.1408.

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18

Tang, Anan, Philippe Castagliola, Jinsheng Sun, and XueLong Hu. "An adaptive exponentially weighted moving average chart for the mean with variable sampling intervals." Quality and Reliability Engineering International 33, no. 8 (May 11, 2017): 2023–34. http://dx.doi.org/10.1002/qre.2164.

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19

Mitra, Amitava, Kang Bok Lee, and Subhabrata Chakraborti. "An adaptive exponentially weighted moving average-type control chart to monitor the process mean." European Journal of Operational Research 279, no. 3 (December 2019): 902–11. http://dx.doi.org/10.1016/j.ejor.2019.07.002.

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20

Vezeris, Dimitrios, Themistoklis Kyrgos, Ioannis Karkanis, and Vasiliki Bizergianidou. "Automated trading systems’ evaluation using d-Backtest PS method and WM ranking in financial markets." Investment Management and Financial Innovations 17, no. 2 (June 17, 2020): 198–215. http://dx.doi.org/10.21511/imfi.17(2).2020.16.

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Given the popularity and propagation of automated trading systems in financial markets among institutional and individual traders in recent decades, this work attempts to compare and evaluate such ten systems based on different popular technical indicators in combination – for the first time – with the d-Backtest PS method for parameter selection. The systems use the technical indicators of Moving Averages (MA), Average Directional Index (ADX), Ichimoku Kinko Hyo, Moving Average Convergence/Divergence (MACD), Parabolic Stop and Reverse (SAR), Pivot, Turtle and Bollinger Bands (BB), and are enhanced by Stop Loss Strategies based on the Average True Range (ATR) indicator. Improvements in the speed of the back-testing computations used by the d-Backtest PS method over weekly intervals allowed examining all systems on a 3.5 years trading period for 7 assets in financial markets, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, XAU/USD, WTI, and BTC/USD. To evaluate the systems more holistically, a weighted metric is introduced and examined, which, apart from profit, takes into account more factors after normalization like the Sharpe Ratio, the Maximum Drawdown and the Expected Payoff, as well as a newly introduced Extended Profit Margin factor. Among the automated systems examined and evaluated using the weighted metric, the Adaptive Double Moving Average (Ad2MA) system stands out, followed by the Adaptive Pivot (AdPivot), and the Adaptive Average Directional Index (AdADX) systems. AcknowledgmentsWe would like to thank Dr. Christos Schinas for his time and invaluable guidance towards the methodology of the weighted metric. We would also like to thank Michalis Foulos for the hardware setup and support and Nektarios Mitakidis for his contribution to the representation of the results.This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-02342).

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21

Yamaguchi, Tadashi, Yoshihiro Kawase, and Shota Ishimura. "3-D adaptive FEA with weighted node density technique." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 39, no. 5 (July 15, 2020): 1201–13. http://dx.doi.org/10.1108/compel-01-2020-0005.

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Purpose This paper aims to propose a method to create 3-D finite element meshes automatically using the Delaunay tetrahedralization with the weighted node density technique. Using this method, the adaptive finite element analysis (FEA) was carried out for the calculation of the magnetic field of an eddy current verification model to clarify the usefulness of the method. Moreover, the error evaluation function for the adaptive FEA was also discussed. Design/methodology/approach The method to create the 3-D finite element meshes using the Delaunay tetrahedralization is realized by the weighted node density technique, and Zienkiewicz-Zhu’s error estimator is used as the error evaluation function of the adaptive FEA. Findings The magnetic flux density vectors on the node in the error evaluation function for the adaptive FEA should be calculated with the weighted average by the reciprocal of the volume of elements. Originality/value This paper describes the method to create 3-D finite element meshes and the comparison among calculation methods of the magnetic flux density vectors on the node for the error estimator.

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22

Wei, Jianjun, Zhenyuan Wang, and Xinpeng Xing. "A Wireless High-Sensitivity Fetal Heart Sound Monitoring System." Sensors 21, no. 1 (December 30, 2020): 193. http://dx.doi.org/10.3390/s21010193.

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In certain cases, the condition of the fetus can be revealed by the fetal heart sound. However, when the sound is detected, it is mixed with noise from the external environment as well as internal disturbances. Our exclusive sensor, which was constructed of copper with an enclosed cavity, was designed to prevent external noise. In the sensor, a polyvinylidene fluoride (PVDF) piezoelectric film, with a frequency range covering that of the fetal heart sound, was adopted to convert the sound into an electrical signal. The adaptive support vector regression (SVR) algorithm was proposed to reduce internal disturbance. The weighted-index average algorithm with deviation correction was proposed to calculate the fetal heart rate. The fetal heart sound data were weighted automatically in the window and the weight was modified with an exponent between windows. The experiments show that the adaptive SVR algorithm was superior to empirical mode decomposition (EMD), the self-adaptive least square method (LSM), and wavelet transform. The weighted-index average algorithm weakens fetal heart rate jumps and the results are consistent with reality.

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23

Aly, Aya A., Nesma A. Saleh, Mahmoud A. Mahmoud, and William H. Woodall. "A Reevaluation of the Adaptive Exponentially Weighted Moving Average Control Chart When Parameters are Estimated." Quality and Reliability Engineering International 31, no. 8 (September 8, 2014): 1611–22. http://dx.doi.org/10.1002/qre.1695.

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24

Aly, Aya A., Mahmoud A. Mahmoud, and Ramadan Hamed. "The Performance of the Multivariate Adaptive Exponentially Weighted Moving Average Control Chart with Estimated Parameters." Quality and Reliability Engineering International 32, no. 3 (April 17, 2015): 957–67. http://dx.doi.org/10.1002/qre.1806.

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25

Zhang, Yibo, Zhili Sun, Yutao Yan, Zhenliang Yu, and Jian Wang. "An Efficient Adaptive Reliability Analysis Method Based on Kriging and Weighted Average Misclassification Rate Improvement." IEEE Access 7 (2019): 94954–65. http://dx.doi.org/10.1109/access.2019.2928332.

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26

Haq, Abdul. "A maximum adaptive exponentially weighted moving average control chart for monitoring process mean and variability." Quality Technology & Quantitative Management 17, no. 1 (October 9, 2018): 16–31. http://dx.doi.org/10.1080/16843703.2018.1530181.

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27

Noor-ul-Amin, Muhammad, and Afshan Riaz. "Performance of adaptive exponentially weighted moving average control chart in the presence of measurement error." Journal of Statistical Computation and Simulation 91, no. 11 (March 7, 2021): 2328–43. http://dx.doi.org/10.1080/00949655.2021.1891540.

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28

Xu, Shiwu, Chih-Cheng Chen, Yi Wu, Xufang Wang, and Fen Wei. "Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication." Sensors 20, no. 16 (August 8, 2020): 4432. http://dx.doi.org/10.3390/s20164432.

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The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.

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29

Di, Hong Wei, Kai Han Zhang, and Hui Gao. "Adaptive Video Denoising Based on Spatio-Temporal Combination." Applied Mechanics and Materials 321-324 (June 2013): 1230–33. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1230.

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An algorithm of adaptive video denoising base on spatio-temporal combination is demonstrated. The adaptive threshold function is obtained through unary linear regression analysis combining interval estimation and hypothesis test. By motion detection to multi-frame images, still regions and motion regions of video image are distinguished through the adaptive threshold. Temporal weighted average filter to the still regions and spatial ANL filter to the motion regions are used separately. Experimental results show that the proposed algorithm works well.

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30

Viriri, Serestina, and Brett Lagerwall. "Increasing Face Recognition Rates Using Novel Classification Algorithms." International Journal of Computers Communications & Control 11, no. 3 (March 24, 2016): 381. http://dx.doi.org/10.15837/ijccc.2016.3.571.

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This paper describes and discusses a set of algorithms which can improve ace recognition rates. These algorithms include adaptive K-Nearest Neighbour, daptive weighted average, reverse weighted average and exponential weighted average. ssentially, the algorithms are extensions to the basic classification algorithm sed in most face recognition research. Whereas the basic classification algorithm elects the subject with the shortest associated distance, the algorithms presented in his paper manipulate and extract information from the set of distances between a est image and the training image set in order to obtain more accurate classifications. he base system to which the algorithms are applied uses the eigenfaces technique or recognition with an adapted Viola and Jones algorithm for face extraction. Most f the algorithms proposed show a consistent improvement over the baseline test.

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31

Varghese, Justin, Mohamed Samiulla Khan, Madappa Siddappa, Saudia Subash, Mohamed Ghouse, and Omer Bin Hussain. "Efficient adaptive fuzzy-based switching weighted average filter for the restoration of impulse corrupted digital images." IET Image Processing 8, no. 4 (April 1, 2014): 199–206. http://dx.doi.org/10.1049/iet-ipr.2013.0297.

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32

Noor-ul-Amin, Muhammad, Afshan Riaz, and Amer Ibrahim Al-Omari. "Adaptive Exponentially Weighted Moving Average Control Chart for Monitoring Process Mean Under Ranked Set Sampling Schemes." Iranian Journal of Science and Technology, Transactions A: Science 45, no. 5 (June 25, 2021): 1777–87. http://dx.doi.org/10.1007/s40995-021-01159-4.

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33

Zeng, Rongda, Zihao Wu, Shengbang Deng, Jian Zhu, and Xiaoyu Chi. "Adaptive smoothing length method based on weighted average of neighboring particle density for SPH fluid simulation." Virtual Reality & Intelligent Hardware 3, no. 2 (April 2021): 129–41. http://dx.doi.org/10.1016/j.vrih.2018.12.001.

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34

He, Ping, Hong Jian Zhang, Chao Liu, and Yuan Guo. "An Improved Method of Adaptive Median Filter Based on Noise Density." Applied Mechanics and Materials 530-531 (February 2014): 403–6. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.403.

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To filter salt and pepper noise and protect the texture details of images effectively, an improved method of adaptive median filter is proposed. It can detect the suspicious noise by adjusting the filter window size and adopting the filter algorithm of adaptive texture direction in low density noise area and the filter algorithm of euclidean distance weighted average in high density noise area. Experimental results show that this method has better de-noising and detail-preserving performance.

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35

Aly, Aya A., Ramadan M. Hamed, and Mahmoud A. Mahmoud. "Optimal design of the adaptive exponentially weighted moving average control chart over a range of mean shifts." Communications in Statistics - Simulation and Computation 46, no. 2 (October 27, 2016): 890–902. http://dx.doi.org/10.1080/03610918.2014.983650.

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36

Han, Qinkai, Zhentang Wang, and Tao Hu. "Novel Condition Monitoring Method for Wind Turbines Based on the Adaptive Multivariate Control Charts and SCADA Data." Shock and Vibration 2020 (September 15, 2020): 1–16. http://dx.doi.org/10.1155/2020/8865776.

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A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.

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37

Wu, Yunfeng, Xin Luo, Fang Zheng, Shanshan Yang, Suxian Cai, and Sin Chun Ng. "Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/913897.

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This paper presents a novel adaptive linear and normalized combination (ALNC) method that can be used to combine the component radial basis function networks (RBFNs) to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error) and the better fidelity (characterized by normalized correlation coefficient) of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

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38

Yang, Shan, Xinyue Lei, Zhenfeng Liu, and Guorong Sui. "An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space." IET Image Processing 15, no. 8 (February 8, 2021): 1722–32. http://dx.doi.org/10.1049/ipr2.12140.

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39

Bi, Jingxue, Yunjia Wang, Xin Li, Hongji Cao, Hongxia Qi, and Yongkang Wang. "A novel method of adaptive weighted K-nearest neighbor fingerprint indoor positioning considering user’s orientation." International Journal of Distributed Sensor Networks 14, no. 6 (June 2018): 155014771878588. http://dx.doi.org/10.1177/1550147718785885.

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There are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward an adaptive weighted K-nearest neighbor fingerprint positioning method considering the user’s orientation. First, the orientation fingerprint database model is proposed, which includes the position, orientation, and the sequence of mean received signal strength indicator at each reference point. Second, the fuzzy c-means algorithm is used to cluster orientation fingerprint database taking the hybrid distance of the signal domain and position domain as the clustering feature. Finally, the proposed adaptive algorithm is developed to select K-reference points by matching operation, to remove the reference points with larger signal-domain distances, minimum and maximum coordinate values, and calculate the weighted mean coordinates of the remaining reference points for positioning results. The experimental results show that the average error decreases by 0.7 m, and the root mean square error decreases to about 1.3 m by the proposed technique. And, we conclude that the proposed adaptive weighted K-nearest neighbor fingerprint positioning method can improve positioning accuracy.

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40

Li, Zhixing, and Boqiang Shi. "Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance." Shock and Vibration 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2841249.

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A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.

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41

Zhang, Zhou, and Wang. "A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems." Symmetry 11, no. 8 (August 3, 2019): 997. http://dx.doi.org/10.3390/sym11080997.

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Orthogonal frequency division multiplexing (OFDM) systems have inherent symmetric properties, such as coding and decoding, constellation mapping and demapping, inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) operations corresponding to multi-carrier modulation and demodulation, and channel estimation is a necessary module to resist channel fading in the OFDM system. However, the noise in the channel will significantly affect the accuracy of channel estimation, which further affects the recovery quality of the final received signals. Therefore, this paper proposes an efficient noise suppression channel estimation method for OFDM systems based on adaptive weighted averaging. The basic idea of the proposed method is averaging the last few channel coefficients obtained from coarse estimation to suppress the noise effect, while the average frame number is adaptively adjusted by combining Doppler spread and signal-to-noise ratio (SNR) information. Meanwhile, to better combat the negative effect brought by Doppler spread and inter-carrier interference (ICI), the proposed method introduces a weighting factor to correct the weighted value of each frame in the averaging process. Simulation results show that the proposed channel estimation method is effective and provides better performance compared with other conventional channel estimation methods.

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42

Yang, Yan Zhu, Wei Liang Liu, Neng Jie Chen, and Wei Zhu. "A Mixed Noise Filtering Algorithm for High-Speed Sequence Image Processing." Applied Mechanics and Materials 415 (September 2013): 318–24. http://dx.doi.org/10.4028/www.scientific.net/amm.415.318.

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t is a effective means by using high-speed vision to locate mobile targets. Under the circumstance of high frame rate and high sensitivity (300Hz), in addition to the Gaussian noise and impulse noise, the image quality is also influenced by atmospheric instability, and it is mainly expressed as Gaussian noise. An improved adaptive threshold weighted mean (IATWM) de-noising algorithm is proposed in this paper. According to the characteristics of impulse noise, the algorithm is able to obtain the threshold adaptively and separate the impulse noise. Then, the weighted median filtering algorithm is used to remove the impulse noise. And the improved weighted average filter algorithm is adopted to remove the Gaussian noise for graphics with Gaussian noise. The algorithm could deal with the Gaussian noise and impulse noise separately, avoiding the weaken handling for the parts not subject to pixels pollution of the impulse noise. The experimental results show that the processing result of the algorithm is able to retain the image details, superior to the traditional filtering algorithms and MTM algorithm. In addition, the algorithm provides an effective way to eliminate the mixed noise, along with a good effect on the high-speed sequence image processing.

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43

Cheng, Huan Xin, Fei Yi, and Li Cheng. "Fault Detection and Diagnosis and Fault Tolerance Compensation for Electric Actuator." Applied Mechanics and Materials 328 (June 2013): 144–48. http://dx.doi.org/10.4028/www.scientific.net/amm.328.144.

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In the paperhe faults of loops for the process control system, electric actuators (valves) faults for example,established a historical fault library to diagnose the type of faults.Through system modeling method, a mathematical model analyzed the residuals robustness, which proposed a weighted moving average residuals and adaptive threshold envelope trajectory-based fault detection methods.Finally, the faults were adjusted by the active fault tolerant compensation module reasonably.

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44

UTARI, NI KADEK ENDAH YANITA, I. GUSTI AYU MADE SRINADI, and MADE SUSILAWATI. "MODEL GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) FAKTOR-FAKTOR YANG MEMENGARUHI KECELAKAAN LALU LINTAS DI PROVINSI BALI." E-Jurnal Matematika 8, no. 2 (June 6, 2019): 140. http://dx.doi.org/10.24843/mtk.2019.v08.i02.p245.

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The number of traffic accidents in Bali kept increasing since 2015 until 2017. The factors that affected the traffic accidents in every region were suspected to be varied according to geographic position. This geographic effect was known as spatial heterogeneity. Spatial heterogeneity was analized by using Geographically Weighted Regression (GWR). This study aim to model the factors which affected the traffic accidents in every subdistrict in Bali by using fixed and adaptive gaussian kernel. The result showed that GWR with adaptive gaussian kernel was better at estimated the models because it had higher value of which was at . The factors which significantly affected the number of traffic accident in 57 subdistrict in Bali were the average rainfall and the number of population within age of 15 to 29 years old.

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Hu, Kai, Aiguo Song, Min Xia, XiaoLing Ye, and YanYan Dou. "An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/573941.

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A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA) to decide weights in a back propagation neural network (BPN). It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

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Bi, Jingxue, Yunjia Wang, Xin Li, Hongxia Qi, Hongji Cao, and Shenglei Xu. "An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering." Sensors 18, no. 8 (August 1, 2018): 2502. http://dx.doi.org/10.3390/s18082502.

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The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.

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JIN, JIANGANG. "AN ADAPTIVE IMAGE SCALING ALGORITHM BASED ON CONTINUOUS FRACTION INTERPOLATION AND MULTI-RESOLUTION HIERARCHY PROCESSING." Fractals 28, no. 08 (July 10, 2020): 2040016. http://dx.doi.org/10.1142/s0218348x20400162.

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Traditional interpolation algorithms often blur the edges of the target image due to low-pass filtering effects, making it difficult to obtain satisfactory visual effects. Especially when the reduction ratio becomes small, the phenomenon of jagged edges and partial information loss will occur. In order to obtain better image scaling quality, an adaptive image scaling algorithm based on continuous fraction interpolation and multi-resolution hierarchical processing is proposed. In order to overcome the noise problem of the original image, this paper first performs wavelet decomposition on the original image to obtain multiple images with different resolutions. Secondly, in order to eliminate the influence of local area variance on the overall image, weighted average is performed on images of different resolutions. Then, in order to overcome the blurring effect of the weighted average image, by calculating the variance of the three groups of pixels around the target pixel, selecting a group of pixels with the smallest variance and using the Salzer continuous fraction interpolation equation to obtain the gray value of the target pixel. Finally, the multiple corrected images are stitched together into a scaled image. The algorithm in this paper achieves a high-order smooth transition between pixels in the same feature area, and can also adaptively modify the pixels of the image. The experimental results show that the edge of the target image obtained by the algorithm in this paper is clear, and the algorithm complexity is low, which is convenient for hardware implementation and can realize real-time image scaling.

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Surender, Vellore P., and Ranjan Ganguli. "Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data." Journal of Engineering for Gas Turbines and Power 127, no. 2 (April 1, 2005): 329–39. http://dx.doi.org/10.1115/1.1850491.

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The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter that is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal that simulates a single fault in the gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their nonrepeatability values that were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and a FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring, as they can adapt to changes in quality of incoming data.

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Liu, Chao, Jing Liu, and Lu Lu Zhang. "A New Image Segmentation Model." Applied Mechanics and Materials 519-520 (February 2014): 541–47. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.541.

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To build a new image segmentation model based on level set theory : Add edge detection operator to edgeless active contour model to detect local information; introduce adaptive coefficient of area item to let the model autonomously adjust and evolve curve position according to image information; adopt weighted average gray value to replace traditional absolute mean value to reduce error and improve segmentation result. Experimental result comparison shows that the new model can detect global information and local information at the same time, adaptively adjust curve evolution direction, and has a fast segmentation speed. Compared to edgeless active contour model, the new model is a more effective image segmentation method as it has greater advantages in image segmentation accuracy and computational complexity.

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Albanwan, H., and R. Qin. "ADAPTIVE AND NON-ADAPTIVE FUSION ALGORITHMS ANALYSIS FOR DIGITAL SURFACE MODEL GENERATED USING CENSUS AND CONVOLUTIONAL NEURAL NETWORKS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 283–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-283-2021.

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Abstract. The digital surface models (DSM) fusion algorithms are one of the ongoing challenging problems to enhance the quality of 3D models, especially for complex regions with variable radiometric and geometric distortions like satellite datasets. DSM generation using Multiview stereo analysis (MVS) is the most common cost-efficient approach to recover elevations. Algorithms like Census-semi global matching (SGM) and Convolutional Neural Networks (MC-CNN) have been successfully implemented to generate the disparity and recover DSMs; however, their performances are limited when matching stereo pair images with ill-posed regions, low texture, dense texture, occluded, or noisy, which can yield missing or incorrect elevation values, in additions to fuzzy boundaries. DSM fusion algorithms have proven to tackle such problems, but their performance may vary based on the quality of the input and the type of fusion which can be classified into adaptive and non-adaptive. In this paper, we evaluate the performance of the adaptive and nonadaptive fusion methods using median filter, adaptive median filter, K-median clustering fusion, weighted average fusion, and adaptive spatiotemporal fusion for DSM generated using Census and MC-CNN. We perform our evaluation on 9 testing regions using stereo pair images from Worldview-3 satellite to generate DSMs using Census and MC-CNN. Our results show that adaptive fusion algorithms are more accurate than non-adaptive algorithms in predicting elevations due to their ability to learn from temporal and contextual information. Our results also show that MC-CNN produces better fusion results with a lower overall average RMSE than Census.

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