A novel frequency and polarization reconfigurable water patch antenna is proposed for radio communication in the UHF band. Based on theoretical analysis and simulation results, water is an ideal material for designing...
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The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning *** traditional scheduling methods cannot...
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The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning *** traditional scheduling methods cannot meet these needs due to their ***-learning is an inherent ability of reinforcement learning(RL) algorithm inhered from its continuous learning and trial-and-error ***-regulation of scheduling could be enabled by the emerging digital twin(DT) technology because of its virtual-real mapping and mutual control *** paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm,which was called explicit exploration and asynchronous update proximal policy optimization algorithm(E2APPO).Firstly,the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops,strengthening the self-regulation of the scheduling ***,an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling ***,the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms,such as wellknown scheduling rules and genetic algorithms,as well as other existing scheduling methods based on reinforcement *** comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy.
The definition of motion priority enables robot groups to handle competition and cooperation better when performing physical tasks. In this paper, we propose a priority-based step reward mechanism, which is a new rewa...
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Experimental records in the cerebral cortex indicate that ammonia injection can lead to seizures. Considering that astrocytes play a vital role in mediating the uptake and absorption of ammonium ions in the extracellu...
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Experimental records in the cerebral cortex indicate that ammonia injection can lead to seizures. Considering that astrocytes play a vital role in mediating the uptake and absorption of ammonium ions in the extracellular space of the cortical circuit, we constructed a new astrocyte neuron coupling model, which is composed of a neuron and its astrocytes connected through the extracellular space, taking into account of the influence of extracellular ammonium ions. The numerical results verified the previous experimental observation that obtained epileptic firing modes of neurons and postsynaptic GABA reversal potential depolarization triggered by ammonia injection. In addition, we also determined the concentration–response relationship between the ammonium ion concentration and the time of entering epilepsy and predicted the threshold of the ammonium ion concentration for the onset of epilepsy.
Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that ma...
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Mainstream partial nitritation-anammox(PNA)process easily suffers from performance instability and even reactor collapse in ***,it is of great significance to unveil the characteristic of performance recovery,understa...
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Mainstream partial nitritation-anammox(PNA)process easily suffers from performance instability and even reactor collapse in ***,it is of great significance to unveil the characteristic of performance recovery,understand the intrinsic mechanism and then propose operational *** this study,we combined long-term reactor operation,batch tests,and metagenomics to reveal the succession of microbial community and functional metabolism variation from system collapse to *** aeration control(0.10-0.25mg O_(2)/L)was critical for performance *** was also found that Candidatus Brocadia became the dominant flora and its abundance increased from 3.5%to 11.0%.Significant enhancements in carbon metabolism and phospholipid biosynthesis were observed during system recovery,and the genes abundance related to signal transduction was dramatically *** up-regulation of sdh and suc genes showed the processes of succinate dehydrogenation and succinyl-CoA synthesis might stimulate the production of amino acids and the synthesis of proteins,thereby possibly improving the activity and abundance of AnAOB,which was conducive to the performance ***,the increase in abundance of hzs and hdh genes suggested the enhancement of the anammox *** in the abundance of key genes involved in nitrogen metabolism indicated that nitrogen removal pathway was more diverse after system *** achievement of performance recovery was driven by anammox,nitrification and denitrification coupled with dissimilatory nitrate reduction to *** results provide deeper insights into the recovery mechanism of PNA system and also provide a potential regulation strategy for the stable operation of the mainstream PNA process.
Personalized learner modeling uses learners’ historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD). This is essential for web-based learning services such as lear...
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A 2-dimension linguistic lattice implication algebra(2DL-LIA)can build a bridge between logical algebra and 2-dimension fuzzy linguistic *** this paper,the notion of a Boolean element is proposed in a 2DL-LIA and some...
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A 2-dimension linguistic lattice implication algebra(2DL-LIA)can build a bridge between logical algebra and 2-dimension fuzzy linguistic *** this paper,the notion of a Boolean element is proposed in a 2DL-LIA and some properties of Boolean elements are *** derivations on 2DL-LIAs are introduced and the related properties of derivations are ***,it proves that the derivations on 2DL-LIAs can be constructed by Boolean elements.
A metalized through-hole perturbation structure is proposed to effectively control multiple modes of substrate-integrated waveguide (SIW) filters. The method manipulates six modes (TE101, TE201, TE102, TE202, TE301, a...
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Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing meth...
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Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.
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