Dynamic background noise removal from overlapping GC-MS peaks via an entropy minimization algorithm

2017 ◽  
Vol 9 (18) ◽  
pp. 2667-2672 ◽  
Author(s):  
Chun Kiang Chua ◽  
Yunbo Lv ◽  
Hua Jun Zhang ◽  
Xiao Yu Gu
Keyword(s):  
Background Noise ◽  
Mass Spectra ◽  
Chemical Compounds ◽  
Noise Removal ◽  
Minimization Algorithm ◽  
Dynamic Background ◽  
Accurate Identification ◽  
Entropy Minimization ◽  
Minimization Approach ◽  
Removal System

An entropy minimization approach is applied as a dynamic background noise removal system. Clean and pure mass spectra were extracted from overlapping GC-MS peaks and led to the accurate identification of chemical compounds.

scholarly journals Corrections to “Flow-Based Clustering on Directed Graphs: A Structural Entropy Minimization Approach”

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 41676-41677
Author(s):  
Yicheng Pan ◽  
Wei Luo ◽  
Feng Zheng ◽  
Shaojiang Wang ◽  
Yuan Yao ◽  
...  
Keyword(s):  
Directed Graphs ◽  
Entropy Minimization ◽  
Structural Entropy ◽  
Minimization Approach

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

scholarly journals Accurate identification of mass peaks for tandem mass spectra using MCMC model

2015 ◽  
Vol 20 (5) ◽  
pp. 453-459
Author(s):  
Hui Li ◽  
Chunmei Liu ◽  
Mugizi Robert Rwebangira ◽  
Legand Burge
Keyword(s):  
Mass Spectra ◽  
Tandem Mass ◽  
Accurate Identification ◽  
Tandem Mass Spectra

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map

2021 ◽  
Vol 263 (2) ◽  
pp. 4441-4445
Author(s):  
Hyunsuk Huh ◽  
Seungchul Lee
Keyword(s):  
Deep Learning ◽  
Background Noise ◽  
Ground Truth ◽  
Noise Removal ◽  
Industrial Manufacturing ◽  
Proposed Model ◽  
Audio Data ◽  
Passive Noise ◽  
Removal Technique ◽  
Noise Canceling

Audio data acquired at industrial manufacturing sites often include unexpected background noise. Since the performance of data-driven models can be worse by background noise. Therefore, it is important to get rid of unwanted background noise. There are two main techniques for noise canceling in a traditional manner. One is Active Noise Canceling (ANC), which generates an inverted phase of the sound that we want to remove. The other is Passive Noise Canceling (PNC), which physically blocks the noise. However, these methods require large device size and expensive cost. Thus, we propose a deep learning-based noise canceling method. This technique was developed using audio imaging technique and deep learning segmentation network. However, the proposed model only needs the information on whether the audio contains noise or not. In other words, unlike the general segmentation technique, a pixel-wise ground truth segmentation map is not required for this method. We demonstrate to evaluate the separation using pump sound of MIMII dataset, which is open-source dataset.

scholarly journals MMHub, a database for the mulberry metabolome

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Dong Li ◽  
Bi Ma ◽  
Xiaofei Xu ◽  
Guo Chen ◽  
Tian Li ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Chemical Compounds ◽  
Bioactive Metabolites ◽  
Molecular Formula ◽  
Experimental Conditions ◽  
Chemical Structures ◽  
Pubchem Compound ◽  
Specific Distribution ◽  
Small Chemical ◽  
Metabolome Data

Abstract Mulberry is an important economic crop plant and traditional medicine. It contains a huge array of bioactive metabolites such as flavonoids, amino acids, alkaloids and vitamins. Consequently, mulberry has received increasing attention in recent years. MMHub (version 1.0) is the first open public repository of mass spectra of small chemical compounds (<1000 Da) in mulberry leaves. The database contains 936 electrospray ionization tandem mass spectrometry (ESI-MS2) data and lists the specific distribution of compounds in 91 mulberry resources with two biological duplicates. ESI-MS2 data were obtained under non-standardized and independent experimental conditions. In total, 124 metabolites were identified or tentatively annotated and details of 90 metabolites with associated chemical structures have been deposited in the database. Supporting information such as PubChem compound information, molecular formula and metabolite classification are also provided in the MS2 spectral tag library. The MMHub provides important and comprehensive metabolome data for scientists working with mulberry. This information will be useful for the screening of quality resources and specific metabolites of mulberry. Database URL: https://biodb.swu.edu.cn/mmdb/

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