Noise Sensitivity Problem Research Articles

Remote Sensing Image Segmentation Based on Probabilistic Fuzzy Local Information Clustering

Abstract. In this paper, a remote sensing image segmentation based on probabilistic fuzzy local information clustering algorithm is proposed. First, assuming that the spectral measure within the same ground object follows the same probability distribution. The dissimilarity between pixel and object is modeled by the negative logarithm of the Gaussian probability density function. It can improve the noise sensitive problem caused by the Euclidean distance which can only describe the data with isotropic distribution. Then, in order to consider the effect of local spatial constraint, on the one hand, the probability measure is used to modify the local fuzzy factor to establish the dissimilarity measure with spatial constraints. On the other hand, the hidden Markov random field is used to model the prior probability model of pixel membership. Next, the entropy regularization term of the objective function is built by combining the Kullback-Leibler(KL) maximum entropy model to further improve the robustness and noise resistance. The qualitative and quantitative analysis of simulated image and different types of real remote sensing images show that the proposed algorithm can effectively overcome the above problems and further improve the accuracy of image segmentation to over 95%.

Classification of high-dimensional imbalanced biomedical data based on spectral clustering SMOTE and marine predators algorithm

The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm’s optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods.

Design of Lumped Disturbance Observer in Current Loop of IPMSM Based on Recursive Integral Sliding Mode Surface

To overcome the problem of current control effect being reduced by unideal factors in a motor control system, such as motor parameter variation, inverter dead time, nonlinearity of the system, etc., a sliding mode disturbance observer for an interior permanent magnet synchronous motor is proposed in this paper. The model of an interior permanent magnet synchronous motor with unideal factors is designed, and the unideal factors are unified into lumped disturbances of motor stator voltage. Then, the observer for lumped disturbance is designed. A recursive integral sliding surface is used to replace the terminal sliding surface to avoid the noise sensitivity and singularity problem of the traditional terminal sliding mode observer. The observer can estimate the lumped disturbance of the current loop without relying on the accurate system model in finite time. Moreover, the structure of the current loop does not need to be adjusted while using the observer to observe and compensate for disturbances. Experiments are carried out to verify the effectiveness of the proposed observer.

Mechanical Fault Diagnosis With Noisy Multisource Signals via Unified Pinball Loss Intuitionistic Fuzzy Support Tensor Machine

In this paper, a challenging and significant intelligent fault diagnosis task is investigated, in which multisource sensor signals with intense noise and outlier disturbances are jointly analyzed. Such a scenario has hardly been considered in industrial research. To this end, we develop a novel tensor-based nonlinear classifier called unified pinball loss intuitionistic fuzzy support tensor machine (UPIFSTM), which can successfully solve the above tasks and improve the performance of fault diagnosis in practical applications. First, the noisy multisource signals are converted into time-frequency images and reconstructed into tensor samples to mine the time domain, frequency domain features and coupled structure information in the spatial domain. Next, we design two nonlinear forms of nonmembership functions in the tensor space, and obtain an intuitionistic fuzzy score for each training sample to enhance the robustness of the model. Subsequently, pinball loss function is introduced to better handle noise sensitivity and resampling instability problems. Note that, we employ the tensor robust principal component analysis method to accurately recover the low-rank tensors corrupted by sparse noise from the original tensors. Finally, two numerical examples are presented to verify the feasibility and validity of the proposed method.

Possibilistic c-means clustering based on the nearest-neighbour isolation similarity

Clustering is widely used in data mining and machine learning. The possibilistic c-means clustering (PCM) method loosens the constraint of the fuzzy c-means clustering (FCM) method to solve the problem of noise sensitivity of FCM. But there is also a new problem: overlapping cluster centers are not suitable for clustering non-cluster distribution data. We propose a novel possibilistic c-means clustering method based on the nearest-neighbour isolation similarity in this paper. All samples are taken as the initial cluster centers in the proposed approach to obtain k sub-clusters iteratively. Then the first b samples farthest from the center of each sub-cluster are chosen to represent the sub-cluster. Afterward, sub-clusters are mapped to the distinguishable space by using these selected samples to calculate the nearest-neighbour isolation similarity of the sub-clusters. Then, adjacent sub-clusters can be merged according to the presented connecting strategy, and finally, C clusters are obtained. Our method proposed in this paper has been tested on 15 UCI benchmark datasets and a synthetic dataset. Experimental results show that our proposed method is suitable for clustering non-cluster distribution data, and the clustering results are better than those of the comparison methods with solid robustness.

A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm

Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves the noise sensitivity problem of Fuzzy c-means (FCM) and coincident clusters problem of possibilistic c-means (PCM). Though PFCM is a highly reliable clustering algorithm but the efficiency of the algorithm can be further improved by introducing the concept of suppression. Suppression-based algorithms employ the winner and non-winner based suppression technique on the datasets, helping in performing better classification of real-world datasets into clusters. In this paper, we propose a suppression-based possibilistic fuzzy c-means clustering algorithm (SPFCM) for the process of clustering. The paper explores the performance of the proposed methodology based on number of misclassifications for various real datasets and synthetic datasets and it is found to perform better than other clustering techniques in the sequel, i.e., normal as well as suppression-based algorithms. The SPFCM is found to perform more efficiently and converges faster as compared to other clustering techniques.

Image Processing Based on Fuzzy Mathematics Theory

In order to further improve the problems of poor rationality and weak antinoise ability of existing image processing algorithms and technical algorithms, an image processing research method based on fuzzy mathematical theory is proposed. First, aiming at the ill-posed problem of the PFCM algorithm, the neutrality and rejection degree are used to construct a regular term and embed the algorithm objective function to enhance the correlation between the attribute parameters of the fuzzy set of the sample graph, so as to solve the ill-posed problem of the PFCM algorithm. Secondly, in view of the same noise sensitivity problem of PFCM algorithm as a traditional fuzzy clustering algorithm, combined with the robust ideas of FCM_S1 and FCM_S2 algorithms, the objective function of robust segmentation algorithm for graph fuzzy clustering (RPFCM_s) is constructed. The misclassification rate of the clustering algorithm proposed in this study in image segmentation is reduced by 38%–76%, and the misclassification rate of the corresponding segmentation result of the ATPFCA algorithm is reduced by 5%–77%. Therefore, the algorithm not only improves the effective segmentation efficiency of the fuzzy mathematical theory algorithm for the processing of uneven grayscale images but also enhances the anti-noise robustness of the algorithm.

Hadamard-Haar (2D) dual domain ghost imaging

We propose a Hadamard-2D Haar transform computational ghost imaging scheme, which uses the rearranged Hadamard illumination pattern for sampling, and obtains the 2D-Haar wavelet coefficients directly by performing a low-order Hadamard transform on the acquired signal, and the information of the target object is obtained by the inverse transform. This scheme improves the problem of noise sensitivity of the computational ghost imaging based on Hadamard and the problem of weak illumination intensity in the high frequency region of 2D-Haar wavelet transform-based computational ghost imaging. Theoretical and experimental proofs show that the method is more robust to external noise and has more advantages in reconstructing images at low sampling rates thanks to the extraction property of 2D-Haar wavelet for edge information.

Image segmentation based on super-pixel with neighborhood constrained and multi-level FCM clustering

In order to solve the problem of noise sensitivity and low precision in image segmentation, a new image segmentation method based on super-pixel with neighborhood constrained and multi-level FCM clustering is proposed. Firstly, an improved SLIC algorithm of FCM is designed based on the local pixel region of the image to get the refined super-pixel. Then, the improved FCM clustering algorithm is combined with the spatial relationship of neighborhood super-pixel to mark the super-pixel categories. In order to verify the effectiveness of the algorithm, the artificial synthetic image with noise and natural image without noise are used for comparison experiments. The experimental result show the algorithm proposed in this paper can not only suppress the noise, but also improve the processing accuracy.

A possibilistic fuzzy Gath-Geva clustering algorithm using the exponential distance

As a famous clustering algorithm, Gath-Geva (GG) clustering has been widely applied in many fields. Unlike fuzzy c-means (FCM) clustering, GG clustering uses the exponential distance with the fuzzy covariance matrix instead of Euclidean distance to cluster hyperellipsoidal data accurately. Due to the existence of probabilistic constraint, GG clustering is sensitive to noise data. For possibilistic fuzzy c-means (PFCM), the type of clustering data limits its application. To solve the noise sensitivity problem of GG clustering and extend PFCM for clustering hyperellipsoidal data, a possibilistic fuzzy Gath-Geva (PFGG) clustering algorithm is proposed. The PFGG clustering is derived from GG clustering in combination with PFCM clustering. FCM, GG, PFCM and PFGG are run on the several datasets to compare their clustering results. The experimental results show that the performance of PFGG is significantly better than the other clustering algorithms.

On the use of low-pass filters in high-gain observers

To address the well-known noise sensitivity problems associated with high-gain observers, we insert a low-pass filter on the measurement channel. Considering nonlinear plants in observability canonical form, we first motivate an architecture where the output error is filtered by a linear system parametrized by its arbitrary order and a scalar positive gain. Our main result establishes an exponential finite gain bound for the estimation error, from the measurement noise, this gain being dependent on the high-gain and filter parameters. We also prove bounds depending on the filter parameters characterizing improved high-frequency gains from the measurement noise to the estimation error. The proposed construction is shown to behave desirably in numerical simulations.

Non-stationary Gaussian process regression applied in validation of vehicle dynamics models

This work compares methods to compute confidence bands in a validation task of a vehicle single-track model. The confidence bands are computed from time series by naïve method, Gaussian process regression and heteroscedastic and non-stationary Gaussian process regression. The simulation model considers the epistemic uncertainty of the vehicle mass parameter by Latin hypercube sampling. The validation procedure compares all stochastically simulated time series of the vehicle yaw rate with the confidence band of the reference data. The model is marked as valid if the yaw rate for each time step is within the confidence band of the reference data. The data was challenging due to noise and time-varying variance and smoothness. Due to required data pre-processing and the high sensitivity to noise in the reference data, the naïve method has generated unusable confidence bands and cannot be recommended for similar validation tasks. Gaussian process regression solved the problem of noise sensitivity, but was not able to model the time-varying length scale of the reference data. Therefore, heteroscedastic and non-stationary Gaussian process regression is proposed to calculate accurate confidence bands of time-varying and noisy reference data for the validation of dynamic models by a confidence band approach.

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Ellipse center extraction is the important basis of camera calibration with circle arrays calibration board, which directly affects the measurement accuracy of machine vision system. Aiming at the problem of high noise sensitivity of the most ellipse center extraction methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of multi-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jumping and transformation of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center extraction method are proposed based on the dynamic granularity matrix space model. Experimental results show that the proposed method has higher accuracy, robustness and lower camera calibration error.

Centered kernel alignment inspired fuzzy support vector machine

Support vector machine (SVM) is a theoretically well motivated algorithm developed from statistical learning theory which has shown impressive performance in many fields. In spite of its success, it still suffers from the noise sensitivity problem originating from the assumption that each training point has equal importance or weight in the training process. To relax this problem, the SVM was extended to the fuzzy SVM (FSVM) by applying a fuzzy membership to each training point such that different training points can make different contributions to the learning of the decision surface. Although well-determined fuzzy memberships can improve classification performance, there are no general guidelines for their construction. In this paper, inspired by the centered kernel alignment (CKA), which measures the degree of similarity between two kernels (or kernel matrices), we propose a new fuzzy membership function calculation method in which a heuristic function derived from the CKA is used to calculate the dependence between a data point and its associated label. Although the CKA induced FSVM is similar to the kernel target alignment (KTA) induced FSVM, there is actually a critical difference. Without that centering, the definition of alignment does not correlate well with the performance of learning machines. Extensive experiments are performed on real-world data sets from the UCI benchmark repository and the application domain of computational biology which validate the superiority of the proposed FSVM model in terms of several classification performance measures.

An Improved Neural Network Classifier Using Fuzzy Nearest Neighbor Partitioning Method

In this study, a novel method, fuzzy nearest neighbor partitioning (FNNP), is proposed for improving the classification capability of the neural network. For the original nearest neighbor partitioning method, the “short-sightedness defect” problem restrains the nearest neighbor partitioning method to learn valuable experience from the distribution information outside the samples’ nearest neighbors. Moreover, the noise sensitive problem that exists in the process of classifying the unknown samples further influences the performance of the nearest neighbor partitioning method. To overcome the short-sightedness defect problem, fuzzy logic theory is integrated into the FNNP, and promotes the ability that the FNNP learns experience from extensive distribution information. In addition, an improved classification strategy, which adopts the concept of the fuzzy nearest neighbor, is employed to increase the immunity of FNNP to noise when the unknown samples are classified. The proposed FNNP is compared with other classification methods for some famous datasets. The results of the experiments indicate that the proposed method achieves promising classification performance for various indicators.

Machine Learning Approach-Based Gamma Distribution for Brain Tumor Detection and Data Sample Imbalance Analysis

Recently, artificial intelligence applications in magnetic resonance imaging have been applied in several clinical studies. The analysis of brain tumors without human intervention is considered a significant area of research because the extracted brain images need to be optimized using a segmentation algorithm that is highly resilient to noise and cluster size sensitivity problems and automatically detects the region of interest (ROI). In this paper, an improved orthogonal gamma distribution-based machine-learning approach is used to analyze the under- segments and over-segments of brain tumor regions to automatically detect abnormalities in the ROI. Further data imbalances due to improper edge matching in the abnormal region is sampled by matching the edge coordinates and sensitivity, and the selectivity parameters are measured using the machine learning algorithm. The benchmark medical image database was collected and analyzed to validate the efficiency and accuracy of the optimal automatic detection in tumor and non-tumor regions. The mean error rate of the algorithm was determined using a mathematical formulation. The system is evaluated based on experimental results that showed the method of orthogonal gamma distribution with the machine learning approach attained an accuracy of 99.55% in detecting brain tumors. This research contributes to the field of brain abnormality detection and analysis without human intervention in the health care sector.

An Improved Forward-Looking Sonar 3D Visualization Scheme of Underwater Objects

This paper presents an improved scheme for forward-looking sonar (FLS) visualization, including an echo acquisition method, a global threshold based on local outlier factor and a reconstruction method. Based on the model of line target in the shallow water, the paper presents an echo acquisition method with impulse response convolution and ray tracing, and considering the reverberation. Then, a transform matrix is presented for the coordinate transformation to obtain the point cloud. After that, the paper presents the global threshold based on local outlier factor (LOF) method to solving the problem of noise sensitivity in the global thresholding. Finally, the paper presents a novel reconstruction method, including the coarse step via random sample consensus (RANSC) and the refined step via Power crust. Both simulation and experimental results show that the scheme in this paper produced superior result compared with the state-of the art.

A Fault Diagnosis Method for One‐Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one‐dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.

Phase Noise Resistant Rotation Iteration-Based Carrier Interferometry Code Design for High Speed Optical OFDM Systems

The spreading codes have been applied to the orthogonal frequency-division multiplexing (OFDM) system to suppress the peak-to-average power ratio. However, in the spreading code aided high data rate optical OFDM (OOFDM) systems, the subcarrier number is large, which induces the problem of phase noise sensitivity due to the narrow intervals between the adjacent subcarriers. In this letter, we present phase noise resistant rotation iteration-based carrier interferometry (RICI) spreading codes to resist the phase noise interference. With the aid of rotation iteration, the long RICI codes are constructed from the short carrier interferometry codes and the intervals are enlarged. Notably, we prove that the RICI codes are orthogonal with enlarged interval, thus they can combat the phase noises and provide more reliable performances for the OOFDM systems. Theoretical and simulation results are provided to demonstrate the benefits achieved by the proposed RICI codes, which can provide more stable communication in the presence of phase noises.

Negative impact of noise and noise sensitivity on mental health in childhood.

Introduction:Noise and noise sensitivity have negative effects on mental health and are not well-studied in children and adolescents. In this study, we investigated these effects in the aforementioned population with respect to sociodemographic variables and environmental factors.Materials and Methods:In this population-based study conducted in two large cities in South Korea, 918 elementary and middle-school students were included. After direct measurements at the selected sites, a noise map was created using an interpolation method. The road traffic noise of the participants’ residential areas was calculated based on this noise map. Noise sensitivity was assessed on an 11-point Likert scale. Using multivariate logistic regression, we investigated the relationship among noise, noise sensitivity, and the Child Behavior Checklist. Further analyses were performed subdividing the data according to household income levels.Results:Noise sensitivity was significantly associated with internalizing, externalizing, and total behavioral problems. Noise was positively associated with total behavioral problems. In the low-income group, the degree of association with problem behaviors was higher, whereas the relationship between noise sensitivity and externalization problems disappeared in the high-income group.Conclusion:Noise and noise sensitivity are negatively associated with the mental health of children and adolescents, particularly in low-income groups. The findings of this study suggest that noise sensitivity and socioeconomic status should be considered in coping with negative effects of noise in children and adolescents.