article considers the H-infinity static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invariant parametric uncertainties. By applying polynomial c...
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article considers the H-infinity static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invariant parametric uncertainties. By applying polynomial chaos theory, the control synthesis problem is solved using a high-dimensional expanded system, which characterizes stochastic state uncertainty propagation. A closed-loop polynomial chaos transformation is proposed to derive the closed-loop expanded system. The approach explicitly accounts for the closed-loop dynamics and preserves the L-2 induced gain, which results in smaller transformation errors compared to existing polynomial chaos transformations. The effect of using finite-degree polynomial chaos expansions is first captured by a norm-bounded linear differential inclusion, and then addressed by formulating a robust polynomial chaos based control synthesis problem. This proposed approach avoids the use of high-degree polynomial chaos expansions to alleviate the destabilizing effect of truncation errors, which significantly reduces computational complexity. A numerical example illustrates the effectiveness of the proposed approach.
A brain-computer interface (BCI) enables a user to communicate directly with a computer using only the central nervous system. An affective BCI (aBCI) monitors and/or regulates the emotional state of the brain, which ...
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A brain-computer interface (BCI) enables a user to communicate directly with a computer using only the central nervous system. An affective BCI (aBCI) monitors and/or regulates the emotional state of the brain, which could facilitate human cognition, communication, decision-making, and health. The last decade has witnessed rapid progress in aBCI research and applications, but there does not exist a comprehensive and up-to-date tutorial on aBCIs. This tutorial fills the gap. It introduces first the basic concepts of BCIs and then, in detail, the individual components in a closed-loop aBCI system, including signal acquisition, signal processing, feature extraction, emotion recognition, and brain stimulation. Next, it describes three representative applications of aBCIs, i.e., cognitive workload recognition, fatigue estimation, and depression diagnosis and treatment. Several challenges and opportunities in aBCI research and applications, including brain signal acquisition, emotion labeling, diversity and size of aBCI datasets, algorithm comparison, negative transfer in emotion recognition, and privacy protection and security of aBCIs, are also explained.
This article proposes a flexible formation tracking control protocol (FFTC) for multiple unmanned surface vessels (USVs) to pass through narrow water channels with unknown curvatures. An observer is developed to estim...
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This article proposes a flexible formation tracking control protocol (FFTC) for multiple unmanned surface vessels (USVs) to pass through narrow water channels with unknown curvatures. An observer is developed to estimate the curvatures of channels. A flexible formation tracking control protocol is, thereby designed to steer USV fleets to pass through narrow channels in a flexible serial formation. Furthermore, the asymptotically stable conditions of the closed-loop multi-USV systems are theoretically derived. Finally, both numerical simulation and experimental results with a fleet of three HUSTER-0.3 USVs are reported to substantiate the effectiveness of the proposed FFTC protocol for navigating through narrow channel in a laboratory context.
Delineating and removing brackets on 3D dental models and then reconstructing the tooth surface can enable orthodontists to premake retainers for patients. It eliminates the waiting time and avoids the change of tooth...
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ISBN:
(纸本)9783031439865;9783031439872
Delineating and removing brackets on 3D dental models and then reconstructing the tooth surface can enable orthodontists to premake retainers for patients. It eliminates the waiting time and avoids the change of tooth position. However, it is time-consuming and laborintensive to process 3D dental models manually. To automate the entire process, accurate bracket segmentation and tooth surface reconstruction algorithms are of high need. In this paper, we propose a graph-based network named BSegNet for bracket segmentation on 3D dental models. The dynamic dilated neighborhood construction and residual connection in the graph network promote the bracket segmentation performance. Then, we propose a simple yet effective projection-based method to reconstruct the tooth surface. We project the vertices of the hole boundary on the tooth surface onto a 2D plane and then triangulate the projected polygon. We evaluate the performance of BSegNet on the bracket segmentation dataset and the results show the superiority of our method. The framework integrating the segmentation and reconstruction achieves a low reconstruction error and can be used as an effective tool to assist orthodontists in orthodontic treatment.
This work proposes a memristor-based quantized convolutional auto-encoder (MQCAE) and applies it in an image denoising application. The pulse width or amplitude is gradually tuned by incremental steps in current memri...
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This work proposes a memristor-based quantized convolutional auto-encoder (MQCAE) and applies it in an image denoising application. The pulse width or amplitude is gradually tuned by incremental steps in current memristive programming methods, which can be extremely time-consuming. In this work, a multi-level programming method without incremental steps is proposed. MQCAE is composed of five kinds of network layers: convolution layers, deconvolution layers, activation function layers, batch normalization layers and max-pooling layers. We design a memristive circuit for realizing convolution and deconvolution with different kernel parameters. The proposed circuit generates one output feature row every cycle. Analog data buffers are designed to store the intermediate data among network layers. In addition, a reconfigurable analog circuit for realizing activation functions and batch normalization is presented. By using analog temp modules, convolution layers and max-pooling layers are able to compute simultaneously. We construct the MQCAE and introduce the pipeline technique among network layers based on the circuit modules. As a result, MQCAE is able to process one FashionMNIST image in 36 cycles with clock frequency 20 kHz. Finally, we verify the effectiveness of MQCAE in an image denoising task. The results show that the denoising performance of proposed scheme is close to software model while the processing speed is faster.
MotivationCategorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a popula...
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MotivationCategorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization *** achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.
This paper studies the voltage restoration problem of the DC Microgrid (MG) cluster with a two-layer communication structure under Denial-of-Service (DoS) attacks. Based on the open multiagent framework, an asynchrono...
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This article unifies the stability criteria of asymptotic, exponential, and finite-time control within a single framework for fuzzy neural networks (FNNs) with infinite time delays. First, the boundedness and differen...
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This article unifies the stability criteria of asymptotic, exponential, and finite-time control within a single framework for fuzzy neural networks (FNNs) with infinite time delays. First, the boundedness and differentiability for time delays are removed. Then, Lipschitz condition for activation function is relaxed, which is allowed to have jumping discontinuous points. To stabilize FNNs, the analytical method is established by comparison principle, contradiction method and inequality techniques. Moreover, different from the traditional Lyapunov method and finite-time stability theorem, several sufficient conditions are deduced and the suppression functions are designed to guarantee asymptotic, exponential, and finite-time stabilization for FNNs by adjusting the parameters of the same controller. There is not necessary to construct the complex integral-type Lyapunov functional to deal with infinite time delays and to design power function in controller for finite-time stabilization. In addition, the designed event-triggered mechanism has the inherent advantages of saving communication resources and indirectly eliminates the chattering caused by signum function. Finally, simulations are presented to illustrate the feasibility and effectiveness of the theoretical results.
We present the first framework capable of synthesizing the all-in-focus neural radiance field (NeRF) from inputs without manual refocusing. Without refocusing, the camera will automatically focus on the fixed object f...
This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animation. Specifically, to preserve the ro...
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