Stabilization of Mode-Dependent Impulsive Hybrid Systems Driven by DFA With Mixed-Mode Effects

Zhang, Junhui; Li, Anni; Lü, Wei

doi:10.1109/tnnls.2019.2921020

Cited by 15 publications

(3 citation statements)

References 39 publications

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“…In the future, we will apply other state-of-the-art deep learning models, their ensembles, data augmentation techniques, and methods for reducing noisy label problems (e.g., label transition matrix method, MentorMix, and DivideMix), and increase the number of datasets to enhance severity classification performance, effectiveness, and reliability [22,[42][43][44]. Moreover, we also plan to develop an efficient and lightweight model that can be used on mobile devices.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Ha,

Go,

Choi

2024

Applied Sciences

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Various scalp and hair disorders distress numerous people. Severe scalp hair disorders have an adverse effect on appearance, self-confidence, and quality of life. Therefore, early and exact diagnosis of various scalp hair disorders is important for timely treatment. However, conventional manual examination method is time-consuming, objective, and labor-intensive. The presented study proposes an intelligent healthcare platform for identifying severity levels of six common scalp hair disorders such as dryness, oiliness, erythema, folliculitis, dandruff, and hair loss. To establish a suitable scalp image classification model, we tested three deep learning models (ResNet-152, EfficientNet-B6, and ViT-B/16). Among the three tested deep learning models, the ViT-B/16 model exhibited the best classification performance with an average accuracy of 78.31%. In addition, the attention rollout method was applied to explain the decision of the trained ViT-B/16 model and highlight approximate lesion areas with no additional annotation procedure. Finally, Scalp checker software was developed based on the trained ViT-B/16 model and the attention rollout method. Accordingly, this proposed platform facilitates objective monitoring states of the scalp and early diagnosis of hairy scalp problems.

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Section: Discussionmentioning

confidence: 99%

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Ha,

Go,

Choi

2024

Applied Sciences

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“…The rapid evolution of deep learning [23][24][25][26] has rendered it a more potent tool than traditional machine learning for extracting abstract information through multi-layer neural networks, significantly improving classification performance [27]. Deep learning techniques are the forefront methods of HSIC.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learnable Spectral–Spatial Fusion Transformer for Hyperspectral Image Classification

Wang,

Sun,

Xiang

et al. 2024

Remote Sensing

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In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning the relevance of deep semantic features and grappling with escalating computational costs as network depth increases. In contrast, the transformer framework is adept at capturing the relevance of high-level semantic features, presenting an effective solution to address the limitations encountered by CNN-based approaches. This article introduces a novel adaptive learnable spectral–spatial fusion transformer (ALSST) to enhance HSI classification. The model incorporates a dual-branch adaptive spectral–spatial fusion gating mechanism (ASSF), which captures spectral–spatial fusion features effectively from images. The ASSF comprises two key components: the point depthwise attention module (PDWA) for spectral feature extraction and the asymmetric depthwise attention module (ADWA) for spatial feature extraction. The model efficiently obtains spectral–spatial fusion features by multiplying the outputs of these two branches. Furthermore, we integrate the layer scale and DropKey into the traditional transformer encoder and multi-head self-attention (MHSA) to form a new transformer with a layer scale and DropKey (LD-Former). This innovation enhances data dynamics and mitigates performance degradation in deeper encoder layers. The experiments detailed in this article are executed on four renowned datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and the University of Pavia (UP). The findings demonstrate that the ALSST model secures optimal performance, surpassing some existing models. The overall accuracy (OA) is 99.70%, 89.72%, 97.84%, and 99.78% on four famous datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and University of Pavia (UP), respectively.

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“…For the robust distributed MPC (DMPC), it is motivated by dynamically decoupled agents, which need to achieve some coordination and perform a cooperation task through a distributed manner in actual industry [4][5][6]. For such networked control systems, the constraints include not only the conventional time-triggered constraints, such as state constraints at sampling times but also communication constraints, such as the limited bandwidth when transmitting data by using wireless networks [7][8][9][10][11]. This wireless transmission can reduce the cabling and communication infrastructure cost to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Robust event‐triggered distributed min–max model predictive control of continuous‐time non‐linear systems

2020

IET Control Theory & Appl

Self Cite

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Due to the features of event-triggered control in exploiting and saving system resources, they have been widely applied in sensor networks, multi-agent systems, networked control systems and so on. In this study, the authors focused on robust event-triggered distributed model predictive control (RETDMPC). Subject to disturbances and parametric uncertainties, they first applied the min-max model to RETDMPC. The min-max RETDMPC methodology is used to guarantee the robustness of the system state by taking the worst possible case of unknown uncertainties into consideration. Furthermore, in this framework, a new cost function is developed in which unknown uncertainties are considered. Next, sufficient conditions are provided to ensure the feasibility and stability of their developed min-max RETDMPC. Finally, a practical example is given to illustrate the advantages of their algorithm by comparing to the conventional model predictive control.

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Stabilization of Mode-Dependent Impulsive Hybrid Systems Driven by DFA With Mixed-Mode Effects

Cited by 15 publications

References 39 publications

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Adaptive Learnable Spectral–Spatial Fusion Transformer for Hyperspectral Image Classification

Robust event‐triggered distributed min–max model predictive control of continuous‐time non‐linear systems

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