61 research outputs found
Stabilisation of descriptor Markovian jump systems with partially unknown transition probabilities
This paper is concerned with the stability and stabilisation problems for continuous-time descriptor Markovian jump systems with partially unknown transition probabilities. In terms of a set of coupled linear matrix inequalities (LMIs), a necessary and sufficient condition is firstly proposed, which ensures the systems to be regular, impulse-free and stochastically stable. Moreover, the corresponding necessary and sufficient condition on the existence of a mode-dependent state-feedback controller, which guarantees the closed-loop systems stochastically admissible by employing the LMI technique, is derived; the stabilizing state-feedback gain can also be expressed via solutions of the LMIs. Finally, numerical examples are given to demonstrate the validity of the proposed methods
Effect of P2G on Flexibility in Integrated Power-Natural Gas-Heating Energy Systems with Gas Storage
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition
Most existing compound facial expression recognition (FER) methods rely on large-scale labeled compound expression data for training. However, collecting such data is labor-intensive and time-consuming. In this paper, we address the compound FER task in the cross-domain few-shot learning (FSL) setting, which requires only a few samples of compound expressions in the target domain. Specifically, we propose a novel cascaded decomposition network (CDNet), which cascades several learn-to-decompose modules with shared parameters based on a sequential decomposition mechanism, to obtain a transferable feature space. To alleviate the overfitting problem caused by limited base classes in our task, a partial regularization strategy is designed to effectively exploit the best of both episodic training and batch training. By training across similar tasks on multiple basic expression datasets, CDNet learns the ability of learn-to-decompose that can be easily adapted to identify unseen compound expressions. Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed CDNet against several state-of-the-art FSL methods
Decoupling and Interacting Multi-Task Learning Network for Joint Speech and Accent Recognition
Accents, as variations from standard pronunciation, pose significant
challenges for speech recognition systems. Although joint automatic speech
recognition (ASR) and accent recognition (AR) training has been proven
effective in handling multi-accent scenarios, current multi-task ASR-AR
approaches overlook the granularity differences between tasks. Fine-grained
units capture pronunciation-related accent characteristics, while
coarse-grained units are better for learning linguistic information. Moreover,
an explicit interaction of two tasks can also provide complementary information
and improve the performance of each other, but it is rarely used by existing
approaches. In this paper, we propose a novel Decoupling and Interacting
Multi-task Network (DIMNet) for joint speech and accent recognition, which is
comprised of a connectionist temporal classification (CTC) branch, an AR
branch, an ASR branch, and a bottom feature encoder. Specifically, AR and ASR
are first decoupled by separated branches and two-granular modeling units to
learn task-specific representations. The AR branch is from our previously
proposed linguistic-acoustic bimodal AR model and the ASR branch is an
encoder-decoder based Conformer model. Then, for the task interaction, the CTC
branch provides aligned text for the AR task, while accent embeddings extracted
from our AR model are incorporated into the ASR branch's encoder and decoder.
Finally, during ASR inference, a cross-granular rescoring method is introduced
to fuse the complementary information from the CTC and attention decoder after
the decoupling. Our experiments on English and Chinese datasets demonstrate the
effectiveness of the proposed model, which achieves 21.45%/28.53% AR accuracy
relative improvement and 32.33%/14.55% ASR error rate relative reduction over a
published standard baseline, respectively.Comment: Accepted by IEEE Transactions on Audio, Speech and Language
Processing (TASLP
CPT: Competence-progressive Training Strategy for Few-shot Node Classification
Graph Neural Networks (GNNs) have made significant advancements in node
classification, but their success relies on sufficient labeled nodes per class
in the training data. Real-world graph data often exhibits a long-tail
distribution with sparse labels, emphasizing the importance of GNNs' ability in
few-shot node classification, which entails categorizing nodes with limited
data. Traditional episodic meta-learning approaches have shown promise in this
domain, but they face an inherent limitation: it might lead the model to
converge to suboptimal solutions because of random and uniform task assignment,
ignoring task difficulty levels. This could lead the meta-learner to face
complex tasks too soon, hindering proper learning. Ideally, the meta-learner
should start with simple concepts and advance to more complex ones, like human
learning. So, we introduce CPT, a novel two-stage curriculum learning method
that aligns task difficulty with the meta-learner's progressive competence,
enhancing overall performance. Specifically, in CPT's initial stage, the focus
is on simpler tasks, fostering foundational skills for engaging with complex
tasks later. Importantly, the second stage dynamically adjusts task difficulty
based on the meta-learner's growing competence, aiming for optimal knowledge
acquisition. Extensive experiments on popular node classification datasets
demonstrate significant improvements of our strategy over existing methods.Comment: arXiv admin note: substantial text overlap with arXiv:2206.11972 by
other author
Comprehensive analysis of hypoxia-related genes for prognosis value, immune status, and therapy in osteosarcoma patients
Osteosarcoma is a common malignant bone tumor in children and adolescents. The overall survival of osteosarcoma patients is remarkably poor. Herein, we sought to establish a reliable risk prognostic model to predict the prognosis of osteosarcoma patients. Patients ’ RNA expression and corresponding clinical data were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus databases. A consensus clustering was conducted to uncover novel molecular subgroups based on 200 hypoxia-linked genes. A hypoxia-risk models were established by Cox regression analysis coupled with LASSO regression. Functional enrichment analysis, including Gene Ontology annotation and KEGG pathway analysis, were conducted to determine the associated mechanisms. Moreover, we explored relationships between the risk scores and age, gender, tumor microenvironment, and drug sensitivity by correlation analysis. We identified two molecular subgroups with significantly different survival rates and developed a risk model based on 12 genes. Survival analysis indicated that the high-risk osteosarcoma patients likely have a poor prognosis. The area under the curve (AUC) value showed the validity of our risk scoring model, and the nomogram indicates the model’s reliability. High-risk patients had lower Tfh cell infiltration and a lower stromal score. We determined the abnormal expression of three prognostic genes in osteosarcoma cells. Sunitinib can promote osteosarcoma cell apoptosis with down-regulation of KCNJ3 expression. In summary, the constructed hypoxia-related risk score model can assist clinicians during clinical practice for osteosarcoma prognosis management. Immune and drug sensitivity analysis can provide essential insights into subsequent mechanisms. KCNJ3 may be a valuable prognostic marker for osteosarcoma development
Observer-Based Fuzzy Integral Sliding Mode Control For Nonlinear Descriptor Systems
This paper investigates observer-based stabilization for nonlinear descriptor systems using a fuzzy integral sliding mode control approach. Observer-based integral sliding mode control strategies for the T-S fuzzy descriptor systems are developed. A two step design is ?rst developed to obtain the observer gains and coef?cients in the switching function using linear matrix inequalities, and the results are used to facilitate the development of a single step design approach, which is seen to be convenient but introduces some conservatism in the design. The potential application to a class of mechanical systems is also considered. Since the descriptor system representation of mechanical systems is adopted, it is shown that in contrast to the existing fuzzy sliding mode control methods based on the normal system representation, the resulting T-S fuzzy system does not contain different input matrices for each local subsystem and the required number of fuzzy rules is consequently markedly reduced. Finally, the balancing problem of a pendulum on a car is numerically simulated to demonstrate the effectiveness of the proposed method
Robust Stabilisation of T-S Fuzzy Stochastic Descriptor Systems via Integral Sliding Modes
This paper addresses the robust stabilisation problem for T-S fuzzy stochastic descriptor systems using an integral sliding mode control paradigm. A classical integral sliding mode control scheme and a non-parallel distributed compensation (Non-PDC) integral sliding mode control scheme are presented. It is shown that two restrictive assumptions previously adopted developing sliding mode controllers for T-S fuzzy stochastic systems are not required with the proposed framework. A unified framework for sliding mode control of T-S fuzzy systems is formulated. The proposed Non-PDC integral sliding mode control scheme encompasses existing schemes when the previously imposed assumptions hold. Stability of the sliding motion is analysed and the sliding mode controller is parameterised in terms of the solutions of a set of linear matrix inequalities (LMIs) which facilitates design. The methodology is applied to an inverted pendulum model to validate the effectiveness of the results presented
Effects of surfactants/stabilizing agents on the microstructure and properties of porous geopolymers by direct foaming
Metakaolin-based porous geopolymers were synthesized by direct foaming using various surfactants/stabilizing agents with or without chemical pore-forming agent (hydrogen peroxide). The effects of surfactants/stabilizing agents and solid loading on their pore morphology, density, porosity, and some properties, such as thermal conductivity and compression strength, were investigated. Experimental data and different theoretical models were successfully applied to evaluate both compression strength (σ) and effective thermal conductivity (λ) as a function of porosity (ε). Porous geopolymers with higher ε presented both a lower value of mechanical strength and improved thermal conductivity performance. The variation of σ with ε could be well described by the minimum solid area (MSA) model, and the variation of λ with ε was found to be more accurately described using a universal model derived from the five basic models
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