474 research outputs found
Distributed H
This paper considers a distributed H∞ sampled-data filtering problem in sensor networks with stochastically switching topologies. It is assumed that the topology switching is triggered by a Markov chain. The output measurement at each sensor is first sampled and then transmitted to the corresponding filters via a communication network. Considering the effect of a transmission delay, a distributed filter structure for each sensor is given based on the sampled data from itself and its neighbor sensor nodes. As a consequence, the distributed H∞ sampled-data filtering in sensor networks under Markovian switching topologies is transformed into H∞ mean-square stability problem of a Markovian jump error system with an interval time-varying delay. By using Lyapunov Krasovskii functional and reciprocally convex approach, a new bounded real lemma (BRL) is derived, which guarantees the mean-square stability of the error system with a desired H∞ performance. Based on this BRL, the topology-dependent H∞ sampled-data filters are obtained. An illustrative example is given to demonstrate the effectiveness of the proposed method
Development of a SMA-fishing-line-McKibben bending actuator
High power-to-weight ratio soft artificial muscles are of overarching importance to enable inherently safer solutions to human-robot interactions. Traditional air driven soft McKibben artificial muscles are linear actuators. It is impossible for them to realize bending motions through a single McKibben muscle. Over two McKibben muscles should normally be used to achieve bending or rotational motions, leading to heavier and larger systems. In addition, air driven McKibben muscles are highly nonlinear in nature, making them difficult to be controlled precisely. A SMA(shape memory alloy)–fishing–line–McKibben (SFLM) bending actuator has been developed. This novel artificial actuator, made of a SMA-fishing-line muscle and a McKibben muscle, was able to produce the maximum output force of 3.0 N and the maximum bending angle (the rotation of the end face) of 61°. This may promote the application of individual McKibben muscles or SMA-fishing-line muscles alone. An output force control method for SFLM is proposed, and based on MATLAB/Simulink software the experiment platform is set up, the effectiveness of control system is verified through output force experiments. A three-fingered SFLM gripper driven by three SFLMs has been designed for a case study, which the maximum carrying capacity is 650.4 ± 0.2 g
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
This paper presents Scalable Semantic Transfer (SST), a novel training
paradigm, to explore how to leverage the mutual benefits of the data from
different label domains (i.e. various levels of label granularity) to train a
powerful human parsing network. In practice, two common application scenarios
are addressed, termed universal parsing and dedicated parsing, where the former
aims to learn homogeneous human representations from multiple label domains and
switch predictions by only using different segmentation heads, and the latter
aims to learn a specific domain prediction while distilling the semantic
knowledge from other domains. The proposed SST has the following appealing
benefits: (1) it can capably serve as an effective training scheme to embed
semantic associations of human body parts from multiple label domains into the
human representation learning process; (2) it is an extensible semantic
transfer framework without predetermining the overall relations of multiple
label domains, which allows continuously adding human parsing datasets to
promote the training. (3) the relevant modules are only used for auxiliary
training and can be removed during inference, eliminating the extra reasoning
cost. Experimental results demonstrate SST can effectively achieve promising
universal human parsing performance as well as impressive improvements compared
to its counterparts on three human parsing benchmarks (i.e.,
PASCAL-Person-Part, ATR, and CIHP). Code is available at
https://github.com/yangjie-cv/SST.Comment: Accepted to CVPR2
OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment
Cross-lingual and cross-domain knowledge alignment without sufficient
external resources is a fundamental and crucial task for fusing irregular data.
As the element-wise fusion process aiming to discover equivalent objects from
different knowledge graphs (KGs), entity alignment (EA) has been attracting
great interest from industry and academic research recent years. Most of
existing EA methods usually explore the correlation between entities and
relations through neighbor nodes, structural information and external
resources. However, the complex intrinsic interactions among triple elements
and role information are rarely modeled in these methods, which may lead to the
inadequate illustration for triple. In addition, external resources are usually
unavailable in some scenarios especially cross-lingual and cross-domain
applications, which reflects the little scalability of these methods. To tackle
the above insufficiency, a novel universal EA framework (OTIEA) based on
ontology pair and role enhancement mechanism via triple-aware attention is
proposed in this paper without introducing external resources. Specifically, an
ontology-enhanced triple encoder is designed via mining intrinsic correlations
and ontology pair information instead of independent elements. In addition, the
EA-oriented representations can be obtained in triple-aware entity decoder by
fusing role diversity. Finally, a bidirectional iterative alignment strategy is
deployed to expand seed entity pairs. The experimental results on three
real-world datasets show that our framework achieves a competitive performance
compared with baselines
Mechanical Properties of Nanostructured CoCrFeNiMn High-Entropy Alloy (HEA) Coating
An equiatomic CoCrFeMnNi high-entropy alloy (HEA) thin film coating has been successfully developed by high-vacuum Radio Frequency (RF) magnetron sputtering. The deposition of a smooth and homogenous thin film with uniformly distributed equiaxed nanograins (grain size ~ 10 nm) was achieved through this technique. The thin film coating exhibits a high hardness of 6.8 ± 0.6 GPa, which is superior compared to its bulk counterpart owing to its nanocrystalline structure. Furthermore, it also shows good ductility through nanoindentation, which demonstrates its potential to serve as an alternative to traditional transition metal nitride or carbide coatings for applications in micro-fabrication and advanced coating technologies
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
Federated Learning (FL) can be used in mobile edge networks to train machine
learning models in a distributed manner. Recently, FL has been interpreted
within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL
significant advantages in fast adaptation and convergence over heterogeneous
datasets. However, existing research simply combines MAML and FL without
explicitly addressing how much benefit MAML brings to FL and how to maximize
such benefit over mobile edge networks. In this paper, we quantify the benefit
from two aspects: optimizing FL hyperparameters (i.e., sampled data size and
the number of communication rounds) and resource allocation (i.e., transmit
power) in mobile edge networks. Specifically, we formulate the MAML-based FL
design as an overall learning time minimization problem, under the constraints
of model accuracy and energy consumption. Facilitated by the convergence
analysis of MAML-based FL, we decompose the formulated problem and then solve
it using analytical solutions and the coordinate descent method. With the
obtained FL hyperparameters and resource allocation, we design a MAML-based FL
algorithm, called Automated Federated Learning (AutoFL), that is able to
conduct fast adaptation and convergence. Extensive experimental results verify
that AutoFL outperforms other benchmark algorithms regarding the learning time
and convergence performance
Endogenous hormone and nutritional ingredient of pendulous characteristics of Cunninghamia lanceolata var. Luotian
Cunninghamia lanceolata var. Luotian (herein, Luotian), a natural variety of Cunninghamia lanceolata (Chinese fir, herein Lanceolata), shows great potential for agricultural protection short-period and high-density plantations because of its narrow, pointed, tower-shaped crowns, thin braches and older drooping branches that die naturally at 6-7 years old. The physiological mechanisms of the pendulousness and the natural death of the branches are still unclear. To investigate these mechanisms, phytohormone levels and nutritional ingredients of different positions of branches in Luotian and Lanceolata were determined. Phytohormone analyses showed that IAA and ZR played critical functions on the branch initial growth, and that GA3 was related to bending of branches. Meanwhile, ABA may enhance abscission of the branches in Luotian. Nutritional ingredient analyses showed that the accumulation of nutritional ingredients in Luotian and Lanceolata were consistent with the morphology of their branches. An integrated analysis implied that phytohormones had a strong influence on pendulous characteristics by interacting with other factors. This study helps to elucidate the mechanism governing the pendulous trait and provides theoretical basis and technical support for its cultivation, introduction, plantation management and genetic improvement
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Convolutional neural networks excel in histopathological image
classification, yet their pixel-level focus hampers explainability. Conversely,
emerging graph convolutional networks spotlight cell-level features and medical
implications. However, limited by their shallowness and suboptimal use of
high-dimensional pixel data, GCNs underperform in multi-class histopathological
image classification. To make full use of pixel-level and cell-level features
dynamically, we propose an asymmetric co-training framework combining a deep
graph convolutional network and a convolutional neural network for multi-class
histopathological image classification. To improve the explainability of the
entire framework by embedding morphological and topological distribution of
cells, we build a 14-layer deep graph convolutional network to handle cell
graph data. For the further utilization and dynamic interactions between
pixel-level and cell-level information, we also design a co-training strategy
to integrate the two asymmetric branches. Notably, we collect a private
clinically acquired dataset termed LUAD7C, including seven subtypes of lung
adenocarcinoma, which is rare and more challenging. We evaluated our approach
on the private LUAD7C and public colorectal cancer datasets, showcasing its
superior performance, explainability, and generalizability in multi-class
histopathological image classification
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