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A hybrid slantlet denoising least squares support vector regression model for exchange rate prediction

Procedia Computer Science 2010年第1期1卷 2397-2405页

作者： Kaijian He Kin Keung Lai Jerome Yen Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong School of Business Administration North China Electric Power University Beijing 102206 China Department of Finance and Economics Tung Wah College Wylie Road Kowloon Hong Kong

Despite the active exploration of linear and nonlinear modeling of exchange rates, there is no consensus on the optimal forecasting model other than the traditional random walk and ARMA benchmark models in the literature. Given the increasing recognition of heterogeneous market structure, this paper proposes an alternative Slantlet denoising based hybrid methodology that attempts to incorporate the linear and nonlinear data features. The recently emerging Slantlet analysis is introduced to separate the linear data features as it constructs filters with varying lengths at different scales and has more appealing time localization features than the normal wavelet analysis. Meanwhile, the Least Squares Support Vector Regression (LSSVR) is used to model and correct for the empirical errors nonlinear in nature. As empirical studies were conducted in the Euro exchange rate market, the performance of the proposed algorithm was compared with those of benchmark models including random walk, ARMA and LSSVR models. The proposed algorithm outperforms the benchmark models. More importantly the proposed methodology explores and offers deeper insights as to the underlying data generating process.

关键词： Slantlet analysis denoising algorithm ARMA model Random walk model Least squares support vector regression model

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Traffic flow prediction based on combination of support vector machine and data denoising schemes

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 534卷 120642-000页

作者： Tang, Jinjun Chen, Xinqiang Hu, Zheng Zong, Fang Han, Chunyang Li, Leixiao Cent South Univ Sch Traff & Transportat Engn Smart Transport Key Lab Hunan Prov Changsha 410075 Hunan Peoples R China Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China Hunan Commun Engn Polytech Sch Intelligent Transportat Changsha 410132 Hunan Peoples R China Jilin Univ Coll Transportat Jilin 130012 Jilin Peoples R China

Traffic flow prediction with high accuracy is definitely considered as one of most important parts in the Intelligent Transportation Systems. As interfering by some external factors, the raw traffic flow data containing noise may cause decline of prediction performance. This study proposes a prediction method by combining denoising schemes and support vector machine model to improve prediction accuracy. This study comprehensively evaluated the multi-step prediction performance of models with different denoising algorithms using the traffic volume data collected from three loop detectors located on highway in city of Minneapolis. In the prediction performance comparison, five denoising methods including EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition), MA (Moving Average), BW filter (Butterworth) and WL (Wavelet) are considered as candidates, specially, four wavelet types, coif (coiflet), db (daubechies), haar and sym (symlet), are further compared based on accuracy evaluation indicators. The prediction results show that the prediction results of the model combined with denoising algorithm are better that of the model without denoising strategy. Furthermore, the improvement of the EEMD on prediction performance is higher than other denoising algorithms, and WL method with db type achieves higher accuracy than other three types. Through comparing prediction accuracy of different denoising models, this study provides valuable suggestions for selecting the appropriate denoising approach for traffic flow prediction. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Traffic flow prediction denoising algorithm Support vector machine Ensemble empirical mode decomposition

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MedSkim: Denoised Health Risk Prediction via Skimming Medical Claims Data 22

MedSkim: Denoised Health Risk Prediction via Skimming Medica...

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22nd IEEE International Conference on Data Mining (ICDM)

作者： Cui, Suhan Luo, Junyu Ye, Muchao Wang, Jiaqi Wang, Ting Ma, Fenglong Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781665450997

Health risk prediction is a challenge task that aims to predict whether patients would suffer from a certain disease/condition in the near future based on their historical EHR data. Although existing approaches can achieve better performance, none of them can deal with the noise existing in the EHR data explicitly. In this paper, we hypothesize that automatically removing noise from EHR data should help the models further improve the performance. Correspondingly, we propose a novel model named MedSkim, which is able to automatically rule out irrelevant visits and codes by effectively skimming through the EHR data. In particular, the proposed model has a code selection module that can directly make a skipping decision to each individual diagnosis codes and then remove the targetirrelevant ones. A backward probing RNN (BPRNN) is designed to reversely process the EHR data and provide a coarse grained representation learning for visits. Besides, a forward skipping RNN (FSRNN) is proposed to read the EHR in a preceding way and dynamically select important visits and codes based on the results of previous two modules. Finally, the risk prediction module uses the output hidden states from FSRNN for generating the final representation to make predictions. Additionally, we also design an extra regularization term based on the skip rate of the model and combine it with standard cross entropy loss to train the model in an end-to-end setting. Experimental results show that MedSkim achieves the best performance on three real-world datasets compared with the state-of-the-art baselines in terms of PR-AUC, F1 and Cohen's Kappa. Moreover, the ablation study and case study confirm that the proposed MedSkim is reasonable and effective for removing noise from EHR data (1).

关键词： Health Risk Prediction Electronic Health Records denoising algorithm

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\Guided filtering based surface recovery algorithm for white light interferometry 10

\Guided filtering based surface recovery algorithm for white...

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Conference on Optical Metrology and Inspection for Industrial Applications X

作者： Ma, Long Yin, Xutao Yang, Fengyu Pei, Xin Liang, Kun Civil Aviat Univ China Sino European Inst Aviat Engn Tianjin 300300 Peoples R China

ISBN: (纸本)9781510667877;9781510667884

White Light Interferometry (WLI) is a widely used technique for surface recovery. However, it is extremely sensitive to various external disturbances, increasing the uncertainty on measurement results. In this paper, a time-domain guided filtering based surface recovery algorithm is proposed for WLI. The reference signal is firstly simulated according to the spectral map of illuminator employed in the system. The correlation between the actual correlogram and the simulated one is then analyzed through the generalized cubic correlation delay estimation method. The corrected correlogram is obtained as a local linear transformation of the reference one that has been shifted, where the linear coefficients are estimated using least squares analysis. The surface height is then retrieved based on mapping relationship between the phase and frequency. The capability of the proposed method on noise suppression is investigated through simulation under different levels of additive noise. In the experiment, a step height standard (VLSI,181.0nm +/- 2.1nm) is employed, which verifies the performance of the proposed method on measurement accuracy and reliability.

关键词： White light interferometry Guided filtering denoising algorithm Least-squares analysis

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A Weighting Threshold Optimization Method in Speech Recognition

A Weighting Threshold Optimization Method in Speech Recognit...

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Asia-Pacific Conference on Information Processing (APCIP 2009)

作者： Liu, Xuefei Lu, Linlin Environm Management Coll China Dept Informat Engn Qinhuangdao Peoples R China

ISBN: (纸本)9780769536996

Speech recognition accuracy tends to drop down in strong noisy conditions. Accordingly, useful signals may be eliminated if the coefficients are processed with the same wavelet threshold. To solve this problem, a weighting threshold optimization method is proposed. The weighting is optimized according to the relativity of wavelet coefficients in each decomposed layer so that the denoising threshold can be more accurate. The useful speech signals can be reserved completely. The signals anti-interference ability can also be enhanced. an get a better performance especially at adverse conditions.

关键词： speech recognition wavelet transform weighting threshold denoising algorithm correlation principle

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Pre-Processing algorithms on Digital X-ray Mammograms 5

Pre-Processing Algorithms on Digital X-ray Mammograms

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5th IEEE Annual International Smart Cities Conference (ISC2)

作者： Elmoufidi, Abdelali Sultan Moulay Slimane Univ Dept Comp Sci Sci & Technol Fac Beni Mellal Morocco

ISBN: (纸本)9781728108469

Breast cancer is one of the most typical sorts of cancer worldwide, and the most frequent cancer in women. Mammography remains the most effective tool for the early detection of breast cancer, as well as the systems of computer-aided diagnosis (CAD) is typically used as a second opinion by the radiologists. These CAD systems based on mammography images for automatic diagnosis breast cancer. Generally, the mammography images contain some noises and additional unnecessary objects, Such as: artifacts, identification labels, etc. These objects can disrupt the diagnostic process partially or completely. Therefore, a technique of preparing these images before using it is mandatory. So, in this article, we propose a method for automatic preprocessing image. Our method consists of five steps. Firstly suppressed the additional unnecessary objects, next by removing the additional black background, third find the orientation of mammograms, fourth segmented the pectoral muscle, and we end by contrast enhancement. The proposed method is tested on DDSM (Digital database for Screening Mammography) database, consisting of 10480 mammograms, and Mini-MIAS (Mammogram Image Analysis Society, UK) database, consisting of 322 mammograms. The obtained results confirm the efficacy of the proposed method.

关键词： Digital Mammogram Breast Cancer Preprocessing image Computer-Aided Diagnosis (CAD) denoising algorithm

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Non-contact Heart Rate Estimation in Pediatric Intensive Care Units

Non-contact Heart Rate Estimation in Pediatric Intensive Car...

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18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Bal, Ufuk Bal, Alkan MSKU Elekt Elekt Muhendisligi Mugla Turkey Izmir Tepec Egitim & Arastirma Hastanesi Cocuk Klinigi Izmir Turkey

ISBN: (纸本)9781479975723

In this study, we proposed a method for computation of heart rate (HR) from digital color video recordings of the human face. In order to extract photoplethysmographic signals, two orthogonal vectors of RGB (Red Green Blue) color space are used. We used a dual tree complex wavelet transform based denoising algorithm to reduce artifacts (e.g. artificial lighting, movement, etc.). Most of the previous work on skin color based HR estimation performed experiments with healthy volunteers and focused to solve motion artifacts. In addition to healthy volunteers we performed experiments with child patients in pediatric intensive care units. In order to investigate the possible factors that affect the non-contact HR monitoring in a clinical environment, we studied the relation between hemoglobin levels and HR estimation errors. Low hemoglobin causes underestimation of HR. Nevertheless, based on our results, it was concluded that our method could provide acceptable accuracy to estimate mean HR of patients in a clinical environment, especially given that the measurements can be performed remotely.

关键词： cardiology image colour analysis image denoising image motion analysis medical signal processing paediatrics photoplethysmography trees (mathematics) video signal processing wavelet transforms HR estimation errors RGB color space denoising algorithm digital color video recording dual tree complex wavelet transform hemoglobin levels human face motion artifacts noncontact heart rate estimation orthogonal vectors pediatric intensive care units photoplethysmographic signals skin color Color Electrocardiography Estimation Heart rate Monitoring Photoplethysmography Skin Pediatric Intensive Care Units Photoplethysmography orthogonal vector Heart Rate skin color RGB colour space image motion analysis image colour analysis medical signal processing Video signal processing image denoising Pediatrics wavelet transforms Cardiology Electrocardiography

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The Research of Infrared Image denoising Based on Non-local Regularization Method

The Research of Infrared Image Denoising Based on Non-local ...

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第十二届中国Rough集与软计算学术会议、第六届中国Web智能学术研讨会及第六届中国粒计算学术研讨会联合学术会议

作者： SUN Shi-bao SUN Pei shuo Electronic Information Engineering College Henan University of Science and TechnologyLuoyang 47100 Electronic Information Engineering College Henan University of Science and TechnologyLuoyang 47100

Infrared image denoising is the basis of infrared application *** existing denoising methods only consider independent pixel in infrared image,without considering the relationship among *** infrared image structure similarity,non-local regularization method is used for infrared image *** different noise level,the experimental results show that the non-local regularization method has better denoising effect than the total variation regularization model,wavelet transform and the bilateral filter method in infrared image application process.

关键词： Infrared image denoising algorithm Non local regularization method Research

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A Fault Diagnosis Method of Four-Mass Vibration MEMS Gyroscope Based on ResNeXt-50 with Attention Mechanism and Improved EWT algorithm

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MICROMACHINES 2023年第7期14卷 1287-1287页

作者： Gu, Yikuan Wang, Yan Li, Zhong Zhang, Tiantian Li, Yuanhao Wang, Guodong Cao, Huiliang North Univ China Sch Software Taiyuan 030051 Peoples R China Shanxi Software Engn Technol Res Ctr Taiyuan 030051 Peoples R China Beijing Armed Police Corps Beijing 100027 Peoples R China Beijing Inst Aerosp Control Devices Beijing 100039 Peoples R China North Univ China Key Lab Instrumentat Sci & Dynam Measurement Minist Educ Taiyuan 030051 Peoples R China

In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.

关键词： fault diagnosis four-mass vibration MEMS gyroscope denoising algorithm ResNeXt deep neural networks SENet

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Image-type displacement measurement resolution improvement without magnification imaging

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第1期33卷 015103-015103页

作者： Yu, Hai Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China

The imaging-type displacement measurement (IDM) technique is better-suited to high-resolution measurement than the traditional moire fringe technique. In Angular displacement measurement, IDM generally requires lens-magnified imaging processes to reach the proper resolution, which depends on overly large imaging lenses and highly complex, generally unreliable equipment, making the technique not conducive to industrial application. The resolution of an IDM device was improved in this study without the use of an imaging lens. The lensless imaging principle was established, then a new algorithm based on multi-line fusion and denoising was developed to improve the displacement subdivision resolution. The proposed algorithm was verified by both simulation and experimentation. The simulation shows that the proposed algorithm effectively reduces positioning operation noise. In the experiment, an angular measurement resolution of 0.039 '' (25-bit) was achieved on a 70 mm diameter circular grating. The proposed method lays a foundation for further high-resolution displacement measurement techniques.

关键词： displacement measurement high resolution lensless imaging denoising algorithm

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