Robust Adaptive Repetitive and Iterative Learning Control for Rotary Systems Subject to Spatially Periodic Uncertainties

This book chapter reviews and summarizes the recent progress in the design of spatial‐based robust adaptive repetitive and iterative learning control. In particular, the collection of methods aims at rotary systems that are subject to spatially periodic uncertainties and based on nonlinear control paradigm, e.g., adaptive feedback linearization and adaptive backstepping. We will elaborate on the design procedure (applicable to generic nth‐order systems) of each method and the corresponding stability and convergence theorems

Robust Adaptive Fuzzy Control for a Class of Switching Power Converters

This chapter provides the reader with a control-centric modeling and analysis approach along with a nonlinear control design for a class of switching power converters. A comprehensive model combining the respective state variable models of the interval subsystems is established. Comparison with PSpice simulation justifies the credibility of the model. Based on this model, internal/BIBO stability can be studied for each interval subsystem. Moreover, controllability and observability can also be analyzed to help determine subsequent control configuration. The established model is further investigated for advanced control design, i.e., robust adaptive fuzzy control

Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach

Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based rate-compatible LDPC ecoding network, which utilizes the structure of raptor-like LDPC codes and can deal with multiple code rates. In the proposed network, different portions of parameters are activated to deal with distinct code rates, which leads to parameter sharing among tasks. Numerical experiments demonstrate the effectiveness of the proposed method. Training the specially designed network under multiple code rates makes the decoder compatible with multiple code rates without sacrificing frame error rate performance

Fisher information analysis on quantum-enhanced parameter estimation in electromagnetically-induced-transparency spectrum with single photons

Electromagnetically-induced-transparency (EIT) spectroscopy has been used as a sensitive sensor in quantum metrology applications. The sensitivity of a sensor strongly depends on the measurement precision of EIT spectrum. In this work, we present a theoretical study of the spectral lineshape measurement on a three-level $\Lambda$-type EIT media based on Fisher information (FI) analysis. Using two kinds of probing source: the single-photon Fock state and the coherent state, we calculate the FI in an EIT medium and quantify the quantum advantage and limitations of the single-photon probe. The analysis of FI structure also provides a clear picture to classify the spectral lineshape into two different regimes, the EIT and Aulter-Townes splitting (ATS). This work provides a systematic analysis of the single-photon EIT spectrum, which provides essential knowledge of quantum sensing based on EIT and deepens our understanding of spectral characteristics of $\Lambda$-type media.Comment: 15 pages, 15 figure

Preparation and Characterization of Hyperbranched Oligomer Modified LiMn2O4 thin Film Cathodes

AbstractThe LiMn2O4 thin film electrode modified by hyperbranched oligomer has been prepared and studied. The hyperbranched oligomer can be self-polymerized for reducing the thermal-runaway hazard by the internal short protection mechanism in lithium ion batteries. The influence of the oligomer coating on cathode has been studied with the binder/carbon-free thin film. In the various C-rate charged-discharged tests, there are no significant differences between the cells with/without the oligomer coating. In this research, the hyperbranched oligomer additive acts as a protection to improve the thermal stability of LiMn2O4. Therefore, the property of thermal stability and its function in safety may benefit the future development of electric vehicles

Effect of end-stage renal disease on long-term survival after a first-ever mechanical ventilation: a population-based study

The 30-day, 6-month, and 1-, 2-, 5-, and 10-year survival rate differences in the ESRD Pos and ESRD Neg groups from the beginning. (DOCX 17 kb

Learning with Free Object Segments for Long-Tailed Instance Segmentation

One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects. In this paper, we explore the possibility to increase the training examples without laborious data collection and annotation. We find that an abundance of instance segments can potentially be obtained freely from object-centric images, according to two insights: (i) an object-centric image usually contains one salient object in a simple background; (ii) objects from the same class often share similar appearances or similar contrasts to the background. Motivated by these insights, we propose a simple and scalable framework FreeSeg for extracting and leveraging these "free" object foreground segments to facilitate model training in long-tailed instance segmentation. Concretely, we investigate the similarity among object-centric images of the same class to propose candidate segments of foreground instances, followed by a novel ranking of segment quality. The resulting high-quality object segments can then be used to augment the existing long-tailed datasets, e.g., by copying and pasting the segments onto the original training images. Extensive experiments show that FreeSeg yields substantial improvements on top of strong baselines and achieves state-of-the-art accuracy for segmenting rare object categories