Aiming at the problems such as high altitude flight difficulty and poor wind resistance of rotary-wing UAV in disaster rescue, this paper proposes a vertical take-off and landing turbojet powered aircraft. The vehicle...
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ISBN:
(数字)9798350363173
ISBN:
(纸本)9798350363180
Aiming at the problems such as high altitude flight difficulty and poor wind resistance of rotary-wing UAV in disaster rescue, this paper proposes a vertical take-off and landing turbojet powered aircraft. The vehicle uses fuel as its power source, and four turbojet engines with vector propulsion systems provide thrust. Aiming at the difficult problem of controller design caused by complex nonlinear characteristics of aircraft, a T-S modeling method of aircraft is presented. Firstly, a dynamic model of the aircraft is established by analyzing the relationship between the force and rotation of the aircraft. On this basis, the T-S fuzzy model of aircraft is established by using sector nonlinear method. The model can decompose the complex nonlinear system into several simple linear subsystems, which is more convenient for controller design and analysis. To verify the validity of the T-S model, a fuzzy LQR controller is designed. Simulation results show that the designed controller can realize fast stabilization of the system. The structure design of the aircraft and T-S fuzzy control are of great practical significance for disaster relief in the complex plateau environment.
Remarkable successes in deep learning have spurred significant growth in the field of neural architecture search (NAS), which is rapidly advancing as a promising technique for automating the design of network architec...
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Population initialization is a crucial and essential step in evolutionary multi-objective optimization (EMO) algorithms. The quality of the generated initial population can significantly affect the performance of an E...
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Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** th...
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Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.
With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which ...
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Abnormal accumulation of hyperphosphorylated tau protein plays a pivotal role in a collection of neurodegenerative diseases named tauopathies,including Alzheimer's disease(AD).We have recently conceptualized the d...
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Abnormal accumulation of hyperphosphorylated tau protein plays a pivotal role in a collection of neurodegenerative diseases named tauopathies,including Alzheimer's disease(AD).We have recently conceptualized the design of hetero-bifunctional chimeras for selectively promoting the proximity between tau and phosphatase,thus specifically facilitating tau dephosphorylation and ***,we sought to optimize the construction of tau dephosphorylating-targeting chimera(DEPTAC)and obtained a new chimera D14,which had high efficiency in reducing tau phosphorylation both in cell and tauopathy mouse models,while showing limited ***,D14 ameliorated neurodegeneration in primary cultured hippocampal neurons treated with toxic tau-K1 8 fragments,and improved cognitive functions of tauopathy *** results suggested D14 as a cost-effective drug candidate for the treatment of tauopathies.
Activating the stimulator of interferon genes(STING)signaling pathway is critical for enhancing antitumor immunity and remodeling the immunosuppressive tumor microenvironment(TME).Herein,we report the preparation of S...
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Activating the stimulator of interferon genes(STING)signaling pathway is critical for enhancing antitumor immunity and remodeling the immunosuppressive tumor microenvironment(TME).Herein,we report the preparation of STING-activating nanoparticles via metal coordination-driven assembly of a synthetic STING agonist(i.e.,SR717)and a chemotherapeutic drug(i.e.,curcumin).After intravenous administration,the assembled nanoparticles could efficiently accumulate in tumors to improve the bioavailability of SR717 and trigger potent STING pathway activation for effective immune ***,the released curcumin evokes immunogenic cell death in tumors and regulates amino acid metabolism by inhibiting the activation of indoleamine 2,3-dioxygenase 1,leading to the reversal of the immunosuppressive *** antitumor immunity induced by nanoparticles significantly inhibits the growth of primary,recurrent,and metastatic *** assembled nanoparticles are promising for the co-delivery of STING agonists and drugs in improved tumor chemo-immunotherapy.
Augmented reality-based brain-computerinterface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world ...
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Stochastic sampling methods for open-ended neural text generation severely affect the diversity and novelty of generated texts. Traditional sampling methods only prune the low-likelihood part of the predicted distribu...
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In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. T...
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In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.
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