This paper describes the development of the fast tool servo (FTS) in detail and categorizes existing FTSs according to different principles. The characteristics and differences of these FTSs have been analyzed. A flexurebased long-stroke FTS system for diamond turning is presented with displacement range of 1 mm and bandwidths of 10 Hz. The vertical jump is about 0.045 μm, and the full stroke tracking error is less than 0.15%. A voice coil motor and a piezoelectric actuator are used as the driving elements, and two flexure hinges are developed as the guide mechanisms. The FTS utilizes a linear encoder and a capacitive sensor to measure the displacement of the tool for closed-loop control. The electromechanical design of the FTS and its motion analysis are described. Experimental tests have been carried out to verify the performance of the FTS system. This long-stroke FTS has the advantage of easy machining, high resonance frequency, and error compensation in y-axis direction.
The theories of empirical mode decomposition (EMD) and instantaneous frequency solution which are two parts of Hilbert-Huang Transformation (HHT) are discussed in the paper. We are focus on using the EMD to electrocardiogram (ECG) which can be decomposed into a limited number of intrinsic mode functions. Different thresholds are used to treat intrinsic mode function to achieve de-noising and then compared with the effect of wavelet transform de-noising. Hilbert-Huang Transform is demonstrated to be effective in removing the general noise of ECG.
AbstrAct:As more and more completely sequenced genomes become available, the taxonomic classification of metagenomic data will benefit greatly from supervised classifiers that can be updated instantaneously in response to new genomes. Currently, some supervised classifiers have been developed to assess the organism of metagenomic sequences. We have found that the existing supervised classifiers usually cannot discriminate the training data from different classes accurately when the data contain some outliers. However, the training genomic data (bacterial and archaeal genomes) usually contain a portion of outliers, which come from sequencing errors, phage invasions, and some highly expressed genes, etc. The outliers, treated as noises, prohibit the development of classifiers with better prediction accuracy. To solve the problem, we present a robust supervised classifier, weighted support vector domain description (WSVDD), which can eliminate the interference from some outliers for training genomic data and then generate more accurate data domain descriptions for each taxonomic class. The experimental results demonstrate WSVDD is more robust than other classifiers for simulated Sanger and 454 reads with different outlier rates. In addition, in experiments performed on simulated metagenomes and real gut metagenomes, WSVDD also achieved better prediction accuracy than other classifiers.
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