In this paper, we propose an odor sampling strategy with flicking motion for chemical plume tracing(CPT). CPT consists of locating a chemical source according to the chemical particles in the air. The capability of pe...
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ISBN:
(数字)9781728166674
ISBN:
(纸本)9781728166681
In this paper, we propose an odor sampling strategy with flicking motion for chemical plume tracing(CPT). CPT consists of locating a chemical source according to the chemical particles in the air. The capability of performing CPT by an autonomous robot is highly demanded, yet difficult due to the complex behavior of airflow. From the viewpoint of bio-inspired systems, we focused on the flicking of an insect, by which it swings its antennae (chemical sensors) variably. We expected the flicking provide variable sampling resolutions according to searching phases. We first designed and implemented a variable air sampling mechanism that gave flicking ability to the robot. Then, we introduced the flicking to a common CPT algorithm inspired by the programmed behavior of a silkworm moth, which was composed of three motion phases. For the motion phases, we tested all combinations of the wide or narrow range of the sampling system. Through CPT experiments, results indicated that the flicking could improve CPT performance.
Optimal entrainment of a quantum nonlinear oscillator to a periodically modulated weak harmonic drive is studied in the semiclassical regime. By using the semiclassical phase reduction theory recently developed for qu...
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In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important [1] . The application of renew...
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ISBN:
(数字)9781665404945
ISBN:
(纸本)9781665404952
In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important [1] . The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV) [2][3] . In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series.
This paper studies the finite-time control issue of semi-Markov jump systems with time-varying delays based under passivity framework. Firstly, sufficient criterion is derived to ensure that finite-time boundedness co...
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This paper studies the finite-time control issue of semi-Markov jump systems with time-varying delays based under passivity framework. Firstly, sufficient criterion is derived to ensure that finite-time boundedness condition can be satisfied and the desired passivity performance can be achieved simultaneously. Then, state-feedback controller procedure is presented with help of linear matrix inequalities(LMIs). Finally, a simulation example is provided for showing the applicability of the developed method.
In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities t...
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This paper studies algorithmic strategies to effectively reduce the number of infections in susceptible-infected-recovered (SIR) epidemic models. We consider a Markov chain SIR model and its two instantiations in the ...
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As the most widely used non-contact sealing structure in aero engines and gas turbines, labyrinth seal is often used to reduce the leakage of compressor and turbine regions. During the working process of engines, the ...
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In this paper, a cascade control framework is proposed a class of remote operated vehicles (ROVs) to enhance the control performance and capacity of interference resistance. Specifically, the control framework is comp...
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ISBN:
(数字)9781728164168
ISBN:
(纸本)9781728164175
In this paper, a cascade control framework is proposed a class of remote operated vehicles (ROVs) to enhance the control performance and capacity of interference resistance. Specifically, the control framework is composed of a fuzzy-based controller in primary loop and a PID neural network based controller in secondary loop. In the primary loop, fuzzy controller is implemented to avoid precise establishment of ROV model for calculating the expected velocity of the vehicle. Furthermore, in the secondary loop, a PID neural network with adaptation is adopted to accomplish the capacity of resisting disturbance. Eventually, the control framework is verified by theoretical and simulation analyses. The obtained results indicate that the proposed cascade control framework endows the ROV with the ability to guarantee the control performance and attenuate external disturbance effectively.
This paper studies the drag-tracking guidance problem of uncertain entry vehicles. In contrast to the existing results whose envelope of uncertainty merely depends on the drag error, the considered uncertainty is allo...
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This paper studies the drag-tracking guidance problem of uncertain entry vehicles. In contrast to the existing results whose envelope of uncertainty merely depends on the drag error, the considered uncertainty is allowed to be not bigger than a function of drag error and integral term of drag error, which inevitably occurs in practice since the uncertainty term is relative to velocity of vehicle. An output feedback guidance law(bank angle magnitude) without drag rate measurement is constructed that makes the drag-tracking error converge near zero in the presence of uncertainties. The Monte Carlo simulation is done to illustrate the advantage of the developed method.
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