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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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Adaptive iterative guided filtering for suppressing background noise in ptychographical imaging

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OPTICS AND LASERS IN ENGINEERING 2023年 160卷

作者： Qiao, Ziling Wen, Xiu Zhou, Xuyang Qin, Feng Liu, Shutian Gao, Bin Liu, Wei Chi, Dazhao Liu, Zhengjun Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin 150001 Peoples R China Harbin Inst Technol Sch Phys Harbin 150001 Peoples R China Heilongjiang Univ Sch Data Sci & Technol Harbin 150080 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Peoples R China Harbin Inst Technol State Key Lab Adv Welding & Joining Harbin 150001 Peoples R China

Ptychography is a lens-free imaging method which can outstrip the resolution limitations of optical apparatuses, even so the algorithm automatically totes background noise during the imaging process. In order to rehabilitate a more exquisite image, an adaptive iterative guided filtering method is designed. The guided filter is incorporated into the computation process of ptychography. The self-adaptive functionality is achieved by controlling the constraint factors of iteration times. The high edge-preserving performance of guided filtering could rub out background noise without messing up resolution. Furthermore, compared with other spatial filtering algorithms, it is validated that the proposed algorithm has the best refactoring and the highest computing efficiency.

关键词： Ptychography Guided filter denoising algorithm

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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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Using the Redundant Convolutional Encoder-Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device

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SENSORS 2020年第16期20卷 4611.-4611.页

作者： Reljin, Natasa Lazaro, Jesus Hossain, Md Billal Noh, Yeon Sik Cho, Chae Ho Chon, Ki H. Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA Univ Zaragoza IIS Aragon Aragon Inst Engn Res I3A Biomed Signal Interpretat & Computat Simulat BSIC Zaragoza 50009 Spain CIBER Bioengn Biomat & Nanomed CIBER BBN Madrid 28029 Spain Univ Massachusetts Coll Nursing Amherst MA 01003 USA Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01002 USA

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. denoising was performed using the redundant convolutional encoder-decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70-100% vs. 34-97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7-19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 +/- 0.25 dB (mean +/- standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm imp

关键词： denoising algorithm redundant convolutional encoder-decoder (R-CED) wearable devices electrocardiogram (ECG) motion artifacts signal-to-noise ratio (SNR) ratio of power cross-correlation

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denoising GRAVITATIONAL WAVES WITH ENHANCED DEEP RECURRENT denoising AUTO-ENCODERS 44

DENOISING GRAVITATIONAL WAVES WITH ENHANCED DEEP RECURRENT D...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Shen, Hongyu George, Daniel Huerta, Eliu. A. Zhao, Zhizhen Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Dept Astron Urbana IL USA Univ Illinois NCSA Urbana IL USA

ISBN: (纸本)9781479981311

denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc. For this task, a combination of a recurrent neural net (RNNs) with a denoising Auto-Encoder (DAEs) has shown promising results. However, this combined model is challenged when operating with low signal-to-noise ratio (SNR) data embedded in non-Gaussian and non-stationary noise. To address this issue, we design a novel model, referred to as 'Enhanced Deep Recurrent denoising Auto-Encoder' (EDRDAE), that incorporates a signal amplifier layer, and applies curriculum learning by first denoising high SNR signals, before gradually decreasing the SNR until the signals become noise dominated. We showcase the performance of EDRDAE using time-series data that describes gravitational waves embedded in very noisy backgrounds. In addition, we show that EDRDAE can accurately denoise signals whose topology is significantly more complex than those used for training, demonstrating that our model generalizes to new classes of gravitational waves that are beyond the scope of established denoising algorithms.

关键词： RNN denoising algorithm time series gravitational waves

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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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A method for editing motor unit potential trains obtained by decomposition of surface electromyographic signals

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JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY 2020年 50卷 102383-102383页

作者： Kumar, Robert I. Mallette, Matt M. Cheung, Stephen S. Stashuk, Daniel W. Gabriel, David A. Brock Univ Dept Kinesiol St Catharines ON Canada Univ Waterloo Dept Syst Design Engn Waterloo ON Canada

Rather than discarding motor unit potential trains (MUPTs) because they do not meet 100% validity criteria, we describe and evaluate a novel editing routine that preserves valid discharge times, based on decreasing shape variability (variance ratio, VR) within a MUPT. The error filtered estimation (EFE) algorithm is then applied to the remaining 'high confidence' discharge times to estimate inter-discharge interval (IDI) statistics. Decomposed surface EMG data from the flexor carpi radialis recorded from 20 participants during 60% MVC wrist flexion was used. There were two levels of denoising criteria (relaxed and strict) criteria for removing MUPs to decrease the VR and increase the signal-to-noise ratio (SNR) of a MUPT. In total, VR decreased 24.88% and SNR increased 6.0% (p's < 0.05). The MUP template peak-to-peak (P-P) amplitude and P-P duration were dependent on the level of denoising (p's < 0.05). The standard error of the estimate (SEE) of the mean IDI before and after editing using the relaxed criteria (3.2% versus 3.69%), was very similar (p > 0.05). The same was true for the SEE between denoising criteria, which increased only to 5.14% for the strict criteria (p > 0.05). Editing the MUPTs resulted in a significant decrease in MUP shape variability and in the measures extracted from the MUP templates, with trivial differences between the SEE of the mean IDI between the edited and unedited MUPTs.

关键词： Surface decomposition Validity denoising algorithm Electromyography Error-filtered estimation algorithm Motor units

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Effectiveness of Non-Local Means algorithm with an Industrial 3 MeV LINAC High-Energy X-ray System for Non-Destructive Testing

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SENSORS 2020年第9期20卷 2634-2634页

作者： Kim, Kyuseok Choi, Jaegu Lee, Youngjin Korea Electrotechnol Res Inst KERI Electromed Device Res Ctr 111 Hanggaul Ro Ansan 15588 Gyeonggi Do South Korea Gachon Univ Dept Radiol Sci 191 Hambakmoero Incheon 21936 South Korea

Industrial high-energy X-ray imaging systems are widely used for non-destructive testing (NDT) to detect defects in the internal structure of objects. Research on X-ray image noise reduction techniques using image processing has been widely conducted with the aim of improving the detection of defects in objects. In this paper, we propose a non-local means (NLM) denoising algorithm to improve the quality of images obtained using an industrial 3 MeV high-energy X-ray imaging system. We acquired X-ray images using various castings and assessed the performance visually and by obtaining the intensity profile, contrast-to-noise ratio, coefficient of variation, and normalized noise power spectrum. Overall, the quality of images processed by the proposed NLM algorithm is superior to those processed by existing algorithms for the acquired casting images. In conclusion, the NLM denoising algorithm offers an efficient and competitive approach to overcome the noise problem in high-energy X-ray imaging systems, and we expect the accompanying image processing software to facilitate and improve image restoration.

关键词： non-destructive testing industrial high-energy X-ray imaging system denoising algorithm non-local means approach quantitative evaluation of image performance

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Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

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JOURNAL OF VIBROENGINEERING 2018年第5期20卷 2045-2059页

作者： Ren, Gang Jia, Jide Mei, Jianmin Jia, Xiangyu Han, Jiajia Mil Transportat Univ Postgrad Training Brigade Tianjin Peoples R China Mil Transportat Univ Mil Vehicle Dept Tianjin Peoples R China

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

关键词： variational mode decomposition Euclidean distance diesel engine vibration signal denoising algorithm

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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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