Trend Prediction Method for Aircraft Electrical Device Contact Resistance

Depth profiling has been performed by using Auger electron spectrometry (AES) and X-ray photoelectron spectrometry (XPS) in combination with Ar-ion sputtering. The data obtained by both surface-analytical methods have been evaluated by means of factor analysis and afterwards by applying an artificial neural network or fuzzy clustering in order to determine the compositional layering of different samples such as a Cr2O3/CrN sandwich layer, tarnish layers on a nickel based alloy and on steel, and the coating of a Si3N4 ceramic powder. The applied artificial neural network was a Kohonen network. It turned out that the method of fuzzy c-means clustering was more successful than Kohonen network due to the fact that fuzzy c-means clustering starts with more input information which can be obtained from factor analysis.

In fuzzy C-means (FCM) clustering, each data point belongs to a cluster to a degree specified by a membership grade. FCM partitions a collection of vectors in c fuzzy groups and finds a cluster center in each group such that the dissimilarity measure is minimized. This paper presents a training algorithm for the radial basis function (RBF) network using symmetry-based Fuzzy C-means (SFCM) clustering method which is the modified version of FCM clustering method based on point symmetry distance measure. The training algorithm which uses SFCM clustering method to train the network has a number of advantages such as faster training time, more accurate predictions and reduced network architecture compared to the standard RBF networks. The proposed training algorithm has been implemented in the RBF networks created by the newrb function of MATLAB which uses gradient based iterative method as learning strategy, therefore the new network will undergo a hybrid learning process. The networks called Symmetry-based Fuzzy C-means Clustering–Radial Basis Function Network (SFCM/RBF) has been tested against the standard RBF network and the networks called standard Fuzzy C-means Clustering (FCM)-RBF network (FCM/RBF) in forecasting. The experimental models has been tested on three real world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, and Phytoplankton problem. Keywords: Fuzzy c-means clustering; SFCM; Radial basis function network; point symmetry distance; forecasting.

The purpose of medical image segmentation is to divides the lesions image with a special meaning and background regional. The characteristics of medical images are generally more complex. There is often overlap between different regions, the edge of the area is vague. Research based on fuzzy clustering method for medical image segmentation, fuzzy C-means clustering and fuzzy kernel clustering methods were studied and discussed fully considered the spatial information, the single objective problem conversion into multi-objective problem. The experimental results show that the method use in medical image segmentation achieved good results.

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

A method derived from fuzzy and ISODATA clustering algorithm is proposed to classify face contours. An improved ASM method is used to get face contours as one of face shape features. By using the modified ISODATA method based on Hausdorff distance, which is more suitable to classify "shape" features, face contours are clustering into 7 classes. Then the fuzzy c-mean clustering method is used to fuzzy categorize the face contours into different classes. Experiment shows that this clustering approach could automatic classify the human face contours fast and reasonably. Moreover, it could help to accelerate the speed of face recognition and improve the accuracy of face recognition.

Magnetotelluric (MT) inversion algorithms are an essential tool in exploration geophysics because they provide us with resistivity models of the subsurface. Inverse problems are ill-posed and non-unique. Resistivity models produced by geologically unconstrained MT inversions are typically smooth, which means that the geological interpretations obtained based on the resistivity models will be ambiguous and uncertain. To solve the above problems, geophysicists have made many efforts over the years to introduce any prior information (such as geological structure information and physical property data) into models, which could help to better constrain the inverted model with the inversion algorithms. In the last few years, there has been renewed interest in machine learning techniques. Various machine learning methods have been applied to inversions. Using the fuzzy c-means (FCM) clustering method, geophysicists proposed new inversion algorithms that are capable of building petrophysical information into the inversion. Although the FCM has been used and advanced in previous work, geophysicists concluded that a priori information of the correct number and value of cluster centers is very important for a successful FCM inversion. In fact, it is difficult to obtain the appropriate clustering information in some geophysical survey areas. In this study, we present an effective way to build the petrophysical information for the MT inversion based on FCM clustering algorithm. When the actual petrophysical information is insufficient, considering the characteristics of MT data, we perform a one-dimensional blocky inversion that can clearly identify the interface with distinct electrical property contrast; then, we obtain the number and value of cluster centers from 1D blocky inversion for the further MT inversion with FCM clustering. The algorithm uses guided FCM clustering to improve the model within the iterative minimization during MT inversion. In our MT inversion method, we integrate the geophysical inversion and geological differentiation into a unified scheme, which interact and enhance each other. The resistivity models obtained from the inversion respect the geophysical and petrophysical data and are easy to geologically interpret. We tested the algorithm using two synthetic examples and a field data example.

Lithium batteries are increasingly used in electric vehicle applications. However, different manufacturing processes and technical constraints lead to battery inconsistency, even for batteries in the same production batch. High-rate discharging negatively affects battery consistency and results in service life reduction. A multi-parameter sorting method at high-rate operation was proposed in this study. The method was applied to sort batteries for cars. The sorted datasets were compared and analyzed by the fuzzy C-mean clustering method, the K-means clustering method, and the simulated annealing genetic algorithm. The comparisons proved that the genetic annealing algorithm was more suitable for battery classification. The clustered batteries were assembled into modules in series and parallel for experimental validation. The test results showed that the battery module cycle life was improved.

The study introduces a concept of mappings realized in presence of information granules and offers a design framework supporting the formation of such mappings. Information granules are conceptually meaningful entities formed on a basis of a large number of experimental input–output numeric data available for the construction of the model. We develop a conceptually and algorithmically sound way of forming information granules. Considering the directional nature of the mapping to be formed, this directionality aspect needs to be taken into account when developing information granules. The property of directionality implies that while the information granules in the input space could be constructed with a great deal of flexibility, the information granules formed in the output space have to inherently relate to those built in the input space. The input space is granulated by running a clustering algorithm; for illustrative purposes, the focus here is on fuzzy clustering realized with the aid of the fuzzy C-means algorithm. The information granules in the output space are constructed with the aid of the principle of justifiable granularity (being one of the underlying fundamental conceptual pursuits of Granular Computing). The construct exhibits two important features. First, the constructed information granules are formed in the presence of information granules already constructed in the input space (and this realization is reflective of the direction of the mapping from the input to the output space). Second, the principle of justifiable granularity does not confine the realization of information granules to a single formalism such as fuzzy sets but helps form the granules expressed any required formalism of information granulation. The quality of the granular mapping (viz. the mapping realized for the information granules formed in the input and output spaces) is expressed in terms of the coverage criterion (articulating how well the experimental data are “covered” by information granules produced by the granular mapping for any input experimental data). Some parametric studies are reported by quantifying the performance of the granular mapping (expressed in terms of the coverage and specificity criteria) versus the values of a certain parameters utilized in the construction of output information granules through the principle of justifiable granularity. The plots of coverage–specificity dependency help determine a knee point and reach a sound compromise between these two conflicting requirements imposed on the quality of the granular mapping. Furthermore, quantified is the quality of the mapping with regard to the number of information granules (implying a certain granularity of the mapping). A series of experiments is reported as well.

In this paper we apply fuzzy subtractive and fuzzy c-means clustering (FSC and FCM) methods for polymer data mining for making MEMS (microelectromechanical system) sensor array-based electronic nose (E-nose). A MEMS cantilever coated with a selective polymer responds to the target chemical by its preferential sorption and ensuing modification of the cantilever parameters (mass, stiffness, and surface stress). An E-nose system operates by using an array of broadly selective sensors (polymer coatings) and application of pattern recognition. The selection of polymer coatings is crucial for desired performance features of an E-nose. A minimal set of polymers from a large list of prospective polymers were selected by the FSC and FCM methods. The detection and monitoring of fish spoilage was taken to be the case study. Both the static and the dynamic mode MEMS sensing were employed for data generation and principal component analysis. A comparative analysis of polymer selection method vis-a-vis sensing mode is carried out.

As one of the most important components in rotating machinery, gearbox is fragile due to its harsh working environment. A new feature extraction method for gearbox fault diagnosis integrating singular value decomposition (SVD) with multidimensional scaling (MDS) is proposed in this article. SVD is used to extract the singular components (SCs), which reflect the main energy of the vibration signal, and the kurtosis values of SCs are calculated to detect the fault sensitivity of each singular value. The kurtosis-weighted singular values are applied to classify different conditions of gearbox supplemented by the MDS and fuzzy C-means clustering (FCM) method. Five gearbox operating conditions including tooth breakage, tooth pitting, gear eccentricity and normal are simulated to test the performance of proposed feature extraction method in an experiment rig. Partition coefficient (PC) and partition entropy (PE) are used to evaluate the classification effect of weighted singular values. The result suggests that the proposed kurtosis-weighted singular values perform better in distinguishing the different conditions of gearbox than original singular values.

Region segmentation is one of the most common methods of medical image segmentation. However, it still has some disadvantages in practice: (1) Threshold choice will result in a poor performance in the image segmentation if there is little difference in the gray of an image. (2) Region-based segmentation algorithm is usually uncertain in defining the edge between the object and the background. This paper implements an improved algorithm to overcome these problems. In the algorithm, the FCM (fuzzy C-means) clustering method is used to improve the accuracy of image segmentation according to its stability. And Roberts operator is also added to compensate the deficiency in edge detection. This medical image segmentation method is a combination of region segmentation and edge segmentation, which is based on OTSU threshold segmentation, fuzzy C-means clustering and Roberts operator. Experiments show that the improved segmentation algorithm has a better performance than those traditional algorithms in the effect.

Aiming at a large number of ambiguous, imprecise and incomplete data in the real world, fuzzy time series has come into being and developed into an effective forecasting approach. In the process of modeling and forecasting of fuzzy time series, the prediction performance of fuzzy time series can be effectively improved by partitioning the universe of discourse into different lengths. In this paper, a forecasting approach for fuzzy time series, which introduces the granularity mechanism into interval division and employs differential data for incremental forecasting, is proposed to solve the problem of time series forecasting with high forecasting precision. In the proposed approach, in order to describe the fuzzy logic relationship and fuzzy trend of historical data, we first do differential processing on the historical samples. Then, Fuzzy C-means (FCM) clustering algorithm is used to generate several partition intervals tentatively. In the sequel, we use the principle of justifiable granularity to constantly adjust the width of all the intervals, so that these information granules associated with corresponding intervals become the most "informative" information granules. Finally, the boundary of information granules is used as the basis of interval division to complete the forecasting task. An illustrative example is provided to demonstrate the essence of the proposed approach. The comparative experiment with other representative approaches shows that the proposed approach can significantly improve the prediction accuracy of time series.

Fuzzy Clustering is applied to the problem of detecting landmines in Ground Penetrating Radar (GPR). The DARPA Backgrounds Data provides a rich source of signatures derived from a cluttered environment with a variety of sensors. One sensor used in the Backgrounds collection was the GPR developed and fielded by Geo-Centers, Inc. This GPR provides a three-dimensional array of intensity returns corresponding to a volume underneath the ground. In this paper, a novel approach to processing that GPR is described. The approach relies on computing edge direction and magnitude features in the volume and comparing them to prototypes generated using fuzzy c-means clustering. A confidence map is generated corresponding to the surface traversed by the system. The confidence map is thresholded to produce detections. Experimental results show a reduction in false alarm rates from about 40% using the standard processing method to about 4% using the three-dimensional, fuzzy clustering method.

The segmentation of the human brain magnetic resonance imaging (MRI) plays a highly decisive role in diagnosing numerous diseases like tumors, Alzheimer's disease, edema, dementia etc. But it is a very challenging task because of presence of noise in the MRI images and also because the boundaries between different tissues of the brain cannot be easily distinguished. Standard fuzzy c-means clustering (FCM) method is proposed to segment the brain MRI accurately and to handle the noise. There are many variants of FCM and one such variant is the Intuitionistic fuzzy c-means clustering algorithm (IFCM). It incorporates the advantages of intuitionistic fuzzy set theory. The IFCM handles the uncertainty, but is not robust to noise as it does not consider any local spatial information. Hence, in this paper a novel approach, namely the Robust and improved intuitionistic fuzzy c-means clustering algorithm (RIIFCM) is proposed. This algorithm is robust to noise as it considers local spatial information. We have demonstrated the efficiency of the RIIFCM algorithm compared to six other algorithms used for the brain image segmentation. The segmentation is carried out on a simulated MRI brain image and we demonstrate that the RIIFCM algorithm outperforms the other existing algorithms by calculating the similarity indices, false positive ratio (FPR) and false negative ratio (FNR).

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can reflect signals and transmissive signals simultaneously and can extend the coverage of signals. A conventional RIS mainly focuses on the case where the signal source and the target are on the same side. In this paper, a STAR-RIS-assisted non-orthogonal multiple access (NOMA) downlink communication system is considered to maximize the achievable rate for users by jointly optimizing the power-allocation coefficients, active beamforming, and STAR-RIS beamforming under the mode-switching (MS) protocol. The critical information of the channel is first extracted using the Uniform Manifold Approximation and Projection (UMAP) method. Based on the key extracted channel features, STAR-RIS elements and users are clustered individually using the fuzzy C-mean clustering (FCM) method. The alternating optimization method decomposes the original optimization problem into three sub-optimization problems. Finally, the sub-problems are converted to unconstrained optimization methods using penalty functions for the solution. Simulation results show that when the number of elements of RIS is 60, the achievable rate of the STAR-RIS-NOMA system is about 18% higher than that of the RIS-NOMA system.