Application of Wavelet Filter Method in Optical Tweezers Signal Processing

2013 ◽  
Vol 846-847 ◽  
pp. 966-971
Author(s):  
Zi Qiang Wang ◽  
Di Li ◽  
Min Cheng Zhong ◽  
Jin Hua Zhou ◽  
Yin Mei Li ◽  
...  
Keyword(s):  
Signal Processing ◽  
Optical Tweezers ◽  
Threshold Value ◽  
Filter Method ◽  
Wavelet Filter ◽  
Wavelet Base ◽  
Filtering Method ◽  
Filtering Effect ◽  
The Cost ◽  
Better Than

One limitation on the performance of optical tweezers (abbreviated as OT) is the noise inherently present in each setup. Therefore, it is the desire to minimize and possibly eliminate the noise from the OT experiments. In this paper, a filter method based on wavelet analysis is proposed. At first we investigate the properties of OT outputs noise, and introduce the wavelet filtering method in simply. Following, we study on the OTs drift signal using different base: db4 and Haar. And also study on the signal using different filter algorithm: the soft,the hard threshold,and compulsive filter. These main conclusions based on foregoing analysis are reached: more larger the resolving scale is, more perfect the filtering effect is. The soft threshold value filtering effect is better than that of the hard threshold value filtering at the cost of calculation when the threshold value is same. The variance of the compulsive filtering is least when both the wavelet and the resolving scale are same for these filtering methods. For the compulsive filtering with same wavelets, the filtering effect of harr is better than that of db4 and the calculation of the former is fewer. Analysis the dynamic output of OT with different algorithm, it also shows that the effect of filter with the compulsive filtering is better than others. Accordingly, we found that applying the compulsive filtering with the Harr wavelet base and suitable resolving scale to the signal processing of OT outputs signal is helpful for the OT design and construction.

Prediction of construction projects’ costs based on fusion method

2017 ◽  
Vol 34 (7) ◽  
pp. 2396-2408 ◽  
Author(s):  
Fang Shutian ◽  
Zhao Tianyi ◽  
Zhang Ying
Keyword(s):  
Construction Projects ◽  
Design Methodology ◽  
Construction Cost ◽  
Filter Method ◽  
Support Vector ◽  
Content Type ◽  
Filtering Method ◽  
The Cost ◽  
First Time ◽  
Practical Implications

Purpose This study aims to predict the construction cost in China, the authors purposed a fused method. Design/methodology/approach The authors extracted 22 factors which may influence the cost and performed the correlation analysis with cost. They chose the highest 10 factors to predict cost by the fused method. The method fused the Kalman filter with least squares support vector machine and multiple linear regression. Findings Ten factors which affect the cost most were found. The construction cost in China can be predicted by the presented method precisely. The statistical filter method could be used in the field of construction cost prediction. Research limitations/implications The construction cost and construction interior factors are a business secret in China. So, the authors only collected 24 buildings’ data to perform the experiments. Practical implications There is no standard and precise method to predict construction cost in China, so the presented method offers a new way to judge the feasibility of projects and select design schemes of construction. Originality/value The authors purposed a new fused method to predict construction cost. It is the first time that the statistical filtering method was used in this field. The effectiveness was verified by the experiments. Ten factors which have a high relationship with construction cost were found.

Costs and the cost-effectiveness threshold

2017 ◽  
Author(s):  
Jan Abel Olsen
Keyword(s):  
Societal Perspective ◽  
Threshold Value ◽  
Quality Adjusted Life Year ◽  
Healthcare Sector ◽  
The Difference ◽  
The Cost ◽  
Qaly Threshold ◽  
Cost Effectiveness Threshold ◽  
Effectiveness Threshold ◽  
Quality Adjusted Life

Chapter 19 starts by distinguishing between the two contrasting perspectives that an economic evaluation would take: the healthcare sector perspective versus the societal perspective. The former is considered a ‘narrow analysis’ which includes only the costs accruing within the healthcare sector, while the latter represents a ‘broad analysis’ that accounts for all resource implications in all sectors of the economy. After an investigation into various types of costs, a ‘limited societal perspective’ is suggested to be more appropriate than either of the two ‘extreme perspectives’. The chapter continues with a discussion of the cost per quality-adjusted life year (QALY) threshold and explains the difference between a demand side- versus a supply-side approach to determining a threshold value for a QALY.

Structural displacement and strain monitoring based on the edge detection operator

2016 ◽  
Vol 20 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Wei Lu ◽  
Yan Cui ◽  
Jun Teng
Keyword(s):  
Edge Detection ◽  
Image Features ◽  
Image Feature ◽  
Monitoring Method ◽  
Strain Monitoring ◽  
Morphological Operator ◽  
Filtering Effect ◽  
Circular Target ◽  
Plane Displacement ◽  
The Cost

To decrease the cost of instrumentation for the strain and displacement monitoring method that uses sensors as well as considers the structural health monitoring challenges in sensor installation, it is necessary to develop a machine vision-based monitoring method. For this method, the most important step is the accurate extraction of the image feature. In this article, the edge detection operator based on multi-scale structure elements and the compound mathematical morphological operator is proposed to provide improved image feature extraction. The proposed method can not only achieve an improved filtering effect and anti-noise ability but can also detect the edge more accurately. Furthermore, the required image features (vertex of a square calibration board and centroid of a circular target) can be accurately extracted using the extracted image edge information. For validation, the monitoring tests for the structural local mean strain and in-plane displacement were designed accordingly. Through analysis of the error between the measured and calculated values of the structural strain and displacement, the feasibility and effectiveness of the proposed edge detection operator are verified.

scholarly journals An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

2021 ◽  
Vol 13 (12) ◽  
pp. 2259
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Qing Zhao ◽  
Zihan Peng ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Threshold Value ◽  
Deformation Monitoring ◽  
Success Rates ◽  
Multipath Mitigation ◽  
Mode Decomposition ◽  
Observation Noise ◽  
Original Observation ◽  
Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

scholarly journals Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

2021 ◽  
Vol 11 (11) ◽  
pp. 4742
Author(s):  
Tianpei Xu ◽  
Ying Ma ◽  
Kangchul Kim
Keyword(s):  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Prediction System ◽  
Churn Prediction ◽  
Customer Churn ◽  
Feature Grouping ◽  
Recognition Systems ◽  
The Cost ◽  
Better Than

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

The Race for Online Reputation: Implications for Platforms, Firms, and Consumers

2021 ◽  
Author(s):  
Mingwen Yang ◽  
Zhiqiang (Eric) Zheng ◽  
Vijay Mookerjee
Keyword(s):  
Adverse Event ◽  
Marketing Mix ◽  
Digital Economy ◽  
The Other ◽  
The Mean ◽  
The Absolute ◽  
The Cost ◽  
Online Reputation ◽  
Better Than

Online reputation has become a key marketing-mix variable in the digital economy. Our study helps managers decide on the effort they should use to manage online reputation. We consider an online reputation race in which it is important not just to manage the absolute reputation, but also the relative rating. That is, to stay ahead, a firm should try to have ratings that are better than those of its competitors. Our findings are particularly significant for platform owners (such as Expedia or Yelp) to strategically grow their base of participating firms: growing the middle of the market (firms with average ratings) is the best option considering the goals of the platform and the other stakeholders, namely incumbents and consumers. For firms, we find that they should increase their effort when the mean market rating increases. Another key insight for firms is that, sometimes, adversity can come disguised as an opportunity. When an adverse event strikes the industry (such as a reduction in sales margin or an increase in the cost of effort), a firm’s profit can increase if it can manage this event better than its competitors.

scholarly journals An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 35 ◽  
Author(s):  
Hung-Cuong Trinh ◽  
Yung-Keun Kwon
Keyword(s):  
Scoring Function ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Feature Construction ◽  
Training Set ◽  
Robust Solution ◽  
Benchmark Datasets ◽  
Useful Life ◽  
Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

scholarly journals Channel state information–based multi-level fingerprinting for indoor localization with deep learning

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880671 ◽  
Author(s):  
Tao Li ◽  
Hai Wang ◽  
Yuan Shao ◽  
Qiang Niu
Keyword(s):  
Deep Learning ◽  
Channel State Information ◽  
Deep Neural Networks ◽  
Training Phase ◽  
Channel State ◽  
Positioning Accuracy ◽  
State Information ◽  
Filtering Method ◽  
Multi Level ◽  
Better Than

With the rapid growth of indoor positioning requirements without equipment and the convenience of channel state information acquisition, the research on indoor fingerprint positioning based on channel state information is increasingly valued. In this article, a multi-level fingerprinting approach is proposed, which is composed of two-level methods: the first layer is achieved by deep learning and the second layer is implemented by the optimal subcarriers filtering method. This method using channel state information is termed multi-level fingerprinting with deep learning. Deep neural networks are applied in the deep learning of the first layer of multi-level fingerprinting with deep learning, which includes two phases: an offline training phase and an online localization phase. In the offline training phase, deep neural networks are used to train the optimal weights. In the online localization phase, the top five closest positions to the location position are obtained through forward propagation. The second layer optimizes the results of the first layer through the optimal subcarriers filtering method. Under the accuracy of 0.6 m, the positioning accuracy of two common environments has reached, respectively, 96% and 93.9%. The evaluation results show that the positioning accuracy of this method is better than the method based on received signal strength, and it is better than the support vector machine method, which is also slightly improved compared with the deep learning method.

scholarly journals Exploration of a New Location Algorithm for Wireless Sensor Network

2018 ◽  
Vol 14 (06) ◽  
pp. 191
Author(s):  
Chao Huang ◽  
Yuang Mao
Keyword(s):  
Threshold Value ◽  
Location Error ◽  
Original Algorithm ◽  
Node Ratio ◽  
Location Algorithm ◽  
Beacon Node ◽  
New Location ◽  
The Average Range ◽  
Better Than ◽  
Range Calculation

<p class="0abstract"><span lang="EN-US">T</span><span lang="EN-US">o further study the basic principle and localization process of DV-Hop location algorithm, the location error reason of traditional location algorithm caused by the minimum hop number </span><span lang="EN-US">wa</span><span lang="EN-US">s analyzed and demonstrated in detail.</span><span lang="EN-US"> The RSSI ranging technology was introduced to modify the minimum hops stage, and the minimum hop number was improved by the DV-Hop algorithm. </span><span lang="EN-US">For the location error caused by the average hop distance, the hop distance of the original algorithm </span><span lang="EN-US">wa</span><span lang="EN-US">s optimized. The improved location algorithm of DV-Hop average hop distance </span><span lang="EN-US">wa</span><span lang="EN-US">s used to modify the average range calculation by introducing the proportion of beacon nodes and the optimal threshold value. The optimization algorithm of the two different stages </span><span lang="EN-US">wa</span><span lang="EN-US">s combined into an improved location algorithm based on hop distance optimization, and the advantages of the two algorithms </span><span lang="EN-US">we</span><span lang="EN-US">re taken into account.</span><span lang="EN-US">Finally, the traditional DV-Hop location algorithm and the three improved location algorithms </span><span lang="EN-US">we</span><span lang="EN-US">re simulated and analyzed by beacon node ratio and node communication radius with multi angle. The experimental results show</span><span lang="EN-US">ed</span><span lang="EN-US"> that the improved algorithm </span><span lang="EN-US">wa</span><span lang="EN-US">s better than the original algorithm in the positioning stability and positioning accuracy.</span></p>

Switching filtering method for interactive systems

2019 ◽  
Vol 233 (10) ◽  
pp. 1340-1350
Author(s):  
Junyi Hou ◽  
Lei Yu ◽  
Yifan Fang ◽  
Shumin Fei
Keyword(s):  
Real Time ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Interactive Systems ◽  
Interactive System ◽  
The Real ◽  
Noise Interference ◽  
Filtering Method ◽  
Time Performance ◽  
Filtering Effect

Aiming at the problem that the mixed noise interference caused by the mixed projection noise system is not accurate and the real-time performance is poor, this article proposes an adaptive system switching filtering method based on Bayesian estimation switching rules. The method chooses joint bilateral filtering and improved adaptive median filtering as the filtering subsystems and selects the sub-filtering system suitable for the noise by switching rules to achieve the purpose of effectively removing noise. The simulation experiment was carried out by the self-developed human–computer interactive projection image system platform. Through the subjective evaluation, objective evaluation, and running time comparison analysis, a better filtering effect was achieved, and the balance between the filtering precision and the real-time performance of the interactive system was well obtained. Therefore, the proposed method can be widely applied to various human–computer interactive image filtering systems.

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