In recent years, Internet data has grown exponentially, but due to the lack of labels, the data that can be used is still relatively small. To solve this problem, research on weak supervision has emerged. However, common weakly supervised research often focuses on either single-modal data or multi-modal data research, which cannot be compatible with both types of data at the same time. Motivated by this observation, we propose a unified cross-modal weakly supervised classification ensemble framework (UCWSC) to tackle this issue. Especially, our proposed framework is based on high-order feature information of different modes. First, We introduce a feature fusion method based on high-order features to increase the amount of acquired information. Then we propose a modified Feature MixMatch algorithm with learning from feature representations. We propose feature fusion and decision fusion methods for weakly supervised classification of multi-modal data with voting and weighting mechanisms as discriminators to obtain the final classification results, respectively. We demonstrate the compatibility of these techniques, our classification accuracy can reach around 99% on the Wikipedia dataset and 78% on the MVSA-Multiple dataset.

A power electronics transformer (PET) based on dual active bridge (DAB) with input series and output parallel (ISOP) cascaded topology is studied in this paper. Fe-based nanocrystalline alloys transformer cores and Silicon Carbide (SiC) power MOSFETs are adopted to improve the power density and efficiency. Mathematical derivation of the input voltage distribution is deduced with the conclusion that the voltage equalization cannot be achieved autonomously. A feedback control method is proposed to balance the input voltage. The measurement of the dynamic B-H loops of the nanocrystalline core under the excitation of square wave and sinusoidal wave is conducted. The high frequency isolation transformer (HFIT) is designed based on the loss performance calculated using the dynamic B-H loop and Ohm's law. The optimized operating flux density is around ±0.68 T at 20 kHz, which is much higher than the empirical value around ±0.3 T to take full advantage of the nanocrystalline core. Finally, a 13.2 kVA prototype is built to verify the above analysis and design.

The multiplex 3-phase winding motors suffer from the problem of the magnetic coupling between the normal winding and the fault winding. To investigate the behaviors of the dual-winding PMSMs on the short-circuited fault conditions, a fault analysis model is developed that includes a dual-winding PMSM circuit model with a short-circuited fault coil within some phase coil and an inverter model with a vector control. The 1/10 and 1/4 turn-to-turn short-circuited faults are simulated with a customized dual-winding PMSM of 4.7 kW. The results show that even with a short-circuited current up to three times of the rated value, the damping torque produced by the fault current can be compensated if the motor windings and their driving inverters have a back-up capacity of about 30% the rated value. An experimental platform with the 4.7kW dual-winding PMSM is built for verifying the analysis model. The simulated waveforms are very close to the measured ones not only for the turn-to-turn short-circuited but also for phase-to-phase short-circuited faults. With the short-circuited current being limited to the rated value, the damping torque produced by the phase-to-phase short-circuited current is too large to be compensated.

SUMMARY: Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in the last decades. Here, we present CPSS 2.0, an updated version of CPSS 1.0 for small RNA sequencing data analysis, with the following improvements: (i) a substantial increase of supported species from 10 to 48; (ii) improved strategies applied to detect ncRNAs; (iii) more ncRNAs can be detected and profiled, such as lncRNA and circRNA; (iv) identification of differentially expressed ncRNAs among multiple samples; (v) enhanced visualization interface containing graphs and charts in detailed analysis results. The new version of CPSS is an efficient bioinformatics tool for users in non-coding RNA research. AVAILABILITY AND IMPLEMENTATION: CPSS 2.0 is implemented in PHP + Perl + R and can be freely accessed at http://114.214.166.79/cpss2.0/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias. RESULTS: To provide an alternative option, here, we report Anaconda, a comprehensive pipeline that allows flexible integration of multiple CNV-calling methods and systematic annotation of CNVs in analyzing WES data. Just by one command, Anaconda can generate CNV detection result by up to four CNV detecting tools. Associated with comprehensive annotation analysis of genes involved in shared CNV regions, Anaconda is able to deliver a more reliable and useful report in assistance with CNV-associate cancer researches. CONCLUSION: Anaconda package and manual can be freely accessed at http://mcg.ustc.edu.cn/bsc/ANACONDA/ .

This paper presents a method for evaluations and measurements of a high frequency nanocrystalline core transformer for power converters. An experimental platform is set up to measure the B-H characteristics of the core and the performances of the transformer with a frequency range up to 100 kHz. Using the measured B-H characteristics, a 3D FEM model of the transformer is built to simulate the performances and calculate the equivalent circuit parameters of the transformer that will be verified by the experimental data. The results show that the B-H characteristics of the nanocrystalline core are non-linear with frequency but well linear before it starts to saturation at a magnetic field of 0.6~0.8 T depending on the frequency. The linear relative permeabilities of the core are as high as around 20000 at room temperature, and the iron losses are so low that the efficiencies of the transformer are kept over 99% in the frequency range of under 100 kHz and with a magnetic field within 0.66 T.

This paper presents a method to analyze the dynamic physical limits of a phase-shifted full bridge (PSFB) circuit used for the power supply of Magnetic Resonance Imaging (MRI) gradient amplifiers. Based on the time-optimal control theory, the detailed optimal transient response process under a sudden load current change is analyzed considering the sample time delay, the minimum voltage undershoot and the minimum recovery time are calculated. The analytic results are validated by simulations and experiments on a 120 V/30 A PSFB converter.

In order to estimate the speed fluctuation band of interior permanent magnet synchronous motor (IPMSM) under speed sensorless control with cyclic fluctuating load accurately, an improved rotor speed estimation method is proposed. A rotor flux oriented vector control with conventional model reference adaptive system (MRAS) based rotor position/speed estimation is employed as the basic control strategy for IPMSM drive. The finite element method (FEM)-based model of IPMSM is built by magnetic field analysis in JMAG software to obtain motor parameter variations caused by different phase currents and rotor position. These motor parameter variations are used in MRAS for precise speed fluctuation band estimation. Furthermore several motor output torque patterns matching the cyclic fluctuating load with feed-forward compensation strategy are developed to lower the speed ripple. Simulation and experimental results demonstrate the feasibility and effectiveness of the proposed control methods, which shows that the error between actual speed fluctuation band and estimated speed fluctuation band is limited within a very low level.

UNLABELLED: Next generation sequencing (NGS) techniques have been widely used to document the small ribonucleic acids (RNAs) implicated in a variety of biological, physiological and pathological processes. An integrated computational tool is needed for handling and analysing the enormous datasets from small RNA deep sequencing approach. Herein, we present a novel web server, CPSS (a computational platform for the analysis of small RNA deep sequencing data), designed to completely annotate and functionally analyse microRNAs (miRNAs) from NGS data on one platform with a single data submission. Small RNA NGS data can be submitted to this server with analysis results being returned in two parts: (i) annotation analysis, which provides the most comprehensive analysis for small RNA transcriptome, including length distribution and genome mapping of sequencing reads, small RNA quantification, prediction of novel miRNAs, identification of differentially expressed miRNAs, piwi-interacting RNAs and other non-coding small RNAs between paired samples and detection of miRNA editing and modifications and (ii) functional analysis, including prediction of miRNA targeted genes by multiple tools, enrichment of gene ontology terms, signalling pathway involvement and protein-protein interaction analysis for the predicted genes. CPSS, a ready-to-use web server that integrates most functions of currently available bioinformatics tools, provides all the information wanted by the majority of users from small RNA deep sequencing datasets. AVAILABILITY: CPSS is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/db/cpss/index.html or http://mcg.ustc.edu.cn/sdap1/cpss/index.html.

UNLABELLED: High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species. AVAILABILITY: miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.