332 research outputs found
Independent modal variable structure fuzzy active vibration control of thin plates laminated with photostrictive actuators
AbstractPhotostrictive actuators can produce photodeformation strains under illumination of ultraviolet lights. They can realize non-contact micro-actuation and vibration control for elastic plate structures. Considering the switching actuation and nonlinear dynamic characteristics of photostrictive actuators, a variable structure fuzzy active control scheme is presented to control the light intensity applied to the actuators. Firstly, independent modal vibration control equations of photoelectric laminated plates are established based on modal analysis techniques. Then, the optimal light switching function is derived to increase the range of sliding modal area, and the light intensity self-adjusting fuzzy active controller is designed. Meanwhile, a continuous function is applied to replace a sign function to reduce the variable structure control (VSC) chattering. Finally, numerical simulation is carried out, and simulation results indicate that the proposed control strategy provides better performance and control effect to plate actuation and control than velocity feedback control, and suppresses vibration effectively
Recommended from our members
Dynamic Network Characteristics of Power-electronics-based Power Systems
Power flow studies in traditional power systems aim to uncover the stationary relationship between voltage amplitude and phase and active and reactive powers; they are important for both stationary and dynamic power system analysis. With the increasing penetration of large-scale power electronics devices including renewable generations interfaced with converters, the power systems become gradually power-electronics-dominant and correspondingly their dynamical behavior changes substantially. Due to the fast dynamics of converters, such as AC current controller, the quasi-stationary state approximation, which has been widely used in power systems, is no longer appropriate and should be reexamined. In this paper, for a better description of network characteristics, we develop a novel concept of dynamic power flow and uncover an explicit dynamic relation between the instantaneous powers and the voltage vectors. This mathematical relation has been well verified by simulations on transient analysis of a small power-electronics-based power system, and a small-signal frequency-domain stability analysis of a voltage source converter connected to an infinitely strong bus. These results demonstrate the applicability of the proposed method and shed an improved light on our understanding of power-electronics-dominant power systems, whose dynamical nature remains obscure
Optimal Demand Shut-offs of AC Microgrid using AO-SBQP Method
Microgrids are increasingly being utilized to improve the resilience and
operational flexibility of power grids, and act as a backup power source during
grid outages. However, it necessitates that the microgrid itself could provide
power to the critical loads. This paper presents an algorithm named alternating
optimization based sequential boolean quadratic programming tailored for
solving optimal demand shut-offs problems arising in microgrids. Moreover, we
establish local superlinear convergence of the proposed approximate Boolean
quadratic programming method over nonconvex problems. In the end, the
performance of the proposed method is illustrated on the modified IEEE 30-bus
case study
Adversarial Directed Graph Embedding
Node representation learning for directed graphs is critically important to
facilitate many graph mining tasks. To capture the directed edges between
nodes, existing methods mostly learn two embedding vectors for each node,
source vector and target vector. However, these methods learn the source and
target vectors separately. For the node with very low indegree or outdegree,
the corresponding target vector or source vector cannot be effectively learned.
In this paper, we propose a novel Directed Graph embedding framework based on
Generative Adversarial Network, called DGGAN. The main idea is to use
adversarial mechanisms to deploy a discriminator and two generators that
jointly learn each node's source and target vectors. For a given node, the two
generators are trained to generate its fake target and source neighbor nodes
from the same underlying distribution, and the discriminator aims to
distinguish whether a neighbor node is real or fake. The two generators are
formulated into a unified framework and could mutually reinforce each other to
learn more robust source and target vectors. Extensive experiments show that
DGGAN consistently and significantly outperforms existing state-of-the-art
methods across multiple graph mining tasks on directed graphs.Comment: 8 pages, 5 figure
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes
Accurate tissue segmentation of thick-slice fetal brain magnetic resonance
(MR) scans is crucial for both reconstruction of isotropic brain MR volumes and
the quantification of fetal brain development. However, this task is
challenging due to the use of thick-slice scans in clinically-acquired fetal
brain data. To address this issue, we propose to leverage high-quality
isotropic fetal brain MR volumes (and also their corresponding annotations) as
guidance for segmentation of thick-slice scans. Due to existence of significant
domain gap between high-quality isotropic volume (i.e., source data) and
thick-slice scans (i.e., target data), we employ a domain adaptation technique
to achieve the associated knowledge transfer (from high-quality
volumes to thick-slice scans). Specifically, we first register the
available high-quality isotropic fetal brain MR volumes across different
gestational weeks to construct longitudinally-complete source data. To capture
domain-invariant information, we then perform Fourier decomposition to extract
image content and style codes. Finally, we propose a novel Cycle-Consistent
Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge
learned from high-quality isotropic volumes for accurate tissue segmentation of
thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated
isotropic volumes to guide tissue segmentation on unannotated thick-slice
scans. Extensive experiments on a large-scale dataset of 372 clinically
acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much
better performance than cutting-edge methods quantitatively and qualitatively.Comment: 10 pages, 9 figures, 5 tables, Fetal MRI, Brain tissue segmentation,
Unsupervised domain adaptation, Cycle-consistenc
Needle δ13C and mobile carbohydrates in Pinus koraiensis in relation to decreased temperature and increased moisture along an elevational gradient in NE China
A tree's crown interacts with atmospheric variables such as CO2, temperature, and humidity. Physioecology of leaves/needles (e.g. δ13C, mobile carbohydrates, and nitrogen) is, therefore, strongly affected by microclimate in and surrounding a tree crown. To understand the physiological responses of leaves to changes in air temperature and moisture, we measured δ13C, soluble sugars, starch, and total nitrogen (N) concentrations in current year and 1-yr-old needles of Pinus koraiensis trees, and compared the growing season air temperature and relative humidity within and outside P. koraiensis crowns along an elevational gradient from 760 to 1,420ma.s.l. on Changbai Mountain, NE China. Our results indicated that needle N and mobile carbohydrates concentrations, as well as needle δ13C values changed continuously with increasing elevation, corresponding to a continuous decrease in air temperature and an increase in relative humidity. Needle carbon and nitrogen status is highly significantly negatively correlated with temperature, but positively correlated with relative humidity. These results indicate that increases in air temperature in combination with decreases in relative humidity may result in lower levels of N and mobile carbohydrates in P. koraiensis trees, suggesting that future climate changes such as global warming and changes in precipitation patterns will directly influence the N and carbon physiology at P. koraiensis individual level, and indirectly affect the competitive ability, species composition, productivity and functioning at the stand and ecosystem level in NE China. Due to the relatively limited range of the transect (760-1,420m) studied, further research is needed to explain whether the present results are applicable to scales across large elevational gradient
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
How to efficiently transform large language models (LLMs) into instruction
followers is recently a popular research direction, while training LLM for
multi-modal reasoning remains less explored. Although the recent LLaMA-Adapter
demonstrates the potential to handle visual inputs with LLMs, it still cannot
generalize well to open-ended visual instructions and lags behind GPT-4. In
this paper, we present LLaMA-Adapter V2, a parameter-efficient visual
instruction model. Specifically, we first augment LLaMA-Adapter by unlocking
more learnable parameters (e.g., norm, bias and scale), which distribute the
instruction-following ability across the entire LLaMA model besides adapters.
Secondly, we propose an early fusion strategy to feed visual tokens only into
the early LLM layers, contributing to better visual knowledge incorporation.
Thirdly, a joint training paradigm of image-text pairs and
instruction-following data is introduced by optimizing disjoint groups of
learnable parameters. This strategy effectively alleviates the interference
between the two tasks of image-text alignment and instruction following and
achieves strong multi-modal reasoning with only a small-scale image-text and
instruction dataset. During inference, we incorporate additional expert models
(e.g. captioning/OCR systems) into LLaMA-Adapter to further enhance its image
understanding capability without incurring training costs. Compared to the
original LLaMA-Adapter, our LLaMA-Adapter V2 can perform open-ended multi-modal
instructions by merely introducing 14M parameters over LLaMA. The newly
designed framework also exhibits stronger language-only instruction-following
capabilities and even excels in chat interactions. Our code and models are
available at https://github.com/ZrrSkywalker/LLaMA-Adapter.Comment: Code and models are available at
https://github.com/ZrrSkywalker/LLaMA-Adapte
- …