Constrained Dynamic Systems Estimation Based on Adaptive Particle Filter

For the state estimation problem, Bayesian approach provides the most general formulation. However, most existing Bayesian estimators for dynamic systems do not take constraints into account, or rely on specific approximations. Such approximations and ignorance of constraints may reduce the accuracy of estimation. In this paper, a new methodology for the states estimation of constrained systems with nonlinear model and non-Gaussian uncertainty which are commonly encountered in practice is proposed in the framework of particles filter. The main feature of this method is that constrained problems are handled well by a sample size test and two particles handling strategies. Simulation results show that the proposed method can outperform particles filter and other two existing algorithms in terms of accuracy and computational time

Salient Object Detection via Integrity Learning

Albeit current salient object detection (SOD) works have achieved fantastic progress, they are cast into the shade when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both the micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object, while at the macro level, the model needs to discover all salient objects from the given image scene. To facilitate integrity learning for salient object detection, we design a novel Integrity Cognition Network (ICON), which explores three important components to learn strong integrity features. 1) Unlike the existing models that focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e.,, kernel shape and context) and increase the feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce the integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects at the macro level, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of ICON, comprehensive experiments are conducted on seven challenging benchmarks, where promising results are achieved

Sequence-level Semantic Representation Fusion for Recommender Systems

With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the utilization of the associated \emph{textual data} of items (eg product title) and study how text features can be effectively fused with ID features in sequential recommendation. However, there exists distinct data characteristics for the two kinds of item features, making a direct fusion method (eg adding text and ID embeddings as item representation) become less effective. To address this issue, we propose a novel {\ul \emph{Te}}xt-I{\ul \emph{D}} semantic fusion approach for sequential {\ul \emph{Rec}}ommendation, namely \textbf{\our}. The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts. The key strategy lies in that we transform the text embeddings and ID embeddings by Fourier Transform from \emph{time domain} to \emph{frequency domain}. In the frequency domain, the global sequential characteristics of the original sequences are inherently aggregated into the transformed representations, so that we can employ simple multiplicative operations to effectively fuse the two kinds of item features. Our fusion approach can be proved to have the same effects of contextual convolution, so as to achieving sequence-level semantic fusion. In order to further improve the fusion performance, we propose to enhance the discriminability of the text embeddings from the text encoder, by adaptively injecting positional information via a mixture-of-experts~(MoE) modulation method. Our implementation is available at this repository: \textcolor{magenta}{\url{https://github.com/RUCAIBox/TedRec}}.Comment: 8 pages, 5 figure

Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans

Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation classes (along with new training datasets or not) are required to be added. In real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually-trained then frozen encoder coupled with incrementally-added decoders can extract and preserve sufficiently representative image features for new classes to be subsequently and validly segmented. To maintain a single network model complexity, we trim each decoder progressively using neural architecture search and teacher-student based knowledge distillation. To incorporate with both healthy and pathological organs appearing in different datasets, a novel anomaly-aware and confidence learning module is proposed to merge the overlapped organ predictions, originated from different decoders. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment total 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task)

INNOVATIVE METHOD FOR BOILER PERFORMANCE ENHANCEMENT AND NOX REDUCTION FOR SMALL-SCALE (0.58 MW) AND LARGE-SCALE (660 MW) BOILERS

Ph.DDOCTOR OF PHILOSOPHY (FOE

Numerical investigation and thermodynamic analysis of syngas production through chemical looping gasification using biomass as fuel.

10.1016/j.fuel.2019.03.007Fuel24615466-47

CFD simulation of a fluidized bed reactor for biomass chemical looping gasification with continuous feedstock

10.1016/j.enconman.2019.112143Energy Conversion and Management20111214

A Rotor Position Detection Method for Permanent Magnet Synchronous Motors Based on Variable Gain Discrete Sliding Mode Observer

The purpose of this paper is to study the sensor-less rotor position estimation method for permanent magnet synchronous motors, and to achieve accurate estimation of rotor position in different conditions. Firstly, the traditional super-twisting observer algorithm is analyzed, and a new discrete variable gain sliding mode observer is designed to solve the buffeting problem in discrete systems, taking the reaction force as the disturbance signal. By estimating the back potential of the observer, the buffeting problem in the sliding mode algorithm can be effectively improved as shown by the simulation results. Then, to solve the problem of phase delay in rotor position estimation, an adaptive orthogonal phase-locked loop method is used to compensate the estimation error caused by the change in motor speed and increase the estimation accuracy of rotor position. The stability of the method can be proven by Lyapunov’s second method. Simulation experiments verify the accuracy of the proposed PMSM rotor position estimation method