Cancellation of nonlinear distortion based on integration of FCM clustering algorithm and adaptive-two-stage linear approximation

A hybrid system of the fuzzy c-means （FCM） clustering algorithm and adaptive-two-stage linear approximation was presented for nonlinear distortion cancellation of radio frequency （RF） power amplifier （PA）. This mechanism can effectively eliminate noise, adaptively model PA＇s instantaneous change, and efficiently correct nonlinear distortion. This article puts forward the FCM clustering algorithm for clustering received signals to eliminate white noise, and then uses the adaptive-two-stage linear approximation to fit the inverse function of the amplitude＇s and phase＇s nonlinear mapping during the training phase. Parameters of the linear function and similarity function are trained using the gradient-descent and minimum mean-square error criteria. The proposed approach＇s training results is directly employed to eliminate sampling signal＇s nonlinear distortion. This hybrid method is realized easier than the multi-segment linear approximation and could reduce the received signal＇s bit error rate （BER） more efficiently.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%.

Characterization of particulate products for aging of ethylbenzene secondary organic aerosol in the presence of ammonium sulfate seed aerosol

Aging of secondary organic aerosol（SOA） particles formed from OH– initiated oxidation of ethylbenzene in the presence of high mass（100–300 μg/m^3） concentrations of（NH_4）_2SO_4seed aerosol was investigated in a home-made smog chamber in this study.The chemical composition of aged ethylbenzene SOA particles was measured using an aerosol laser time-of-flight mass spectrometer（ALTOFMS） coupled with a Fuzzy C-Means（FCM） clustering algorithm.Experimental results showed that nitrophenol,ethyl-nitrophenol,2,4-dinitrophenol,methyl glyoxylic acid,5-ethyl-6-oxo-2,4-hexadienoic acid,2-ethyl-2,4-hexadiendioic acid,2,3-dihydroxy-5-ethyl-6-oxo-4-hexenoic acid,1H-imidazole,hydrated N-glyoxal substituted1H-imidazole,hydrated glyoxal dimer substituted imidazole,1H-imidazole-2-carbaldehyde,N-glyoxal substituted hydrated 1H-imidazole-2-carbaldehyde and high-molecular-weight（HMW） components were the predominant products in the aged particles.Compared to the previous aromatic SOA aging studies,imidazole compounds,which can absorb solar radiation effectively,were newly detected in aged ethylbenzene SOA in the presence of high concentrations of（NH_4）_2SO_4seed aerosol.These findings provide new information for discussing aromatic SOA aging mechanisms.