Column Generation (CG) technique is popularly applied in solving the crew scheduling problem of large size, which is generally modeled as an Integer Linear Programming (ILP) problem. The traditional CG algorithms for ...
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Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is design...
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Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is designed to estimate the real-time posture of user's lower limb. This system can be used both for monitoring the user's walking status and for realizing the relative localization. A belt of multiple sonar sensors worn by the user can detect the obstacle of unknown environment. Simultaneously, this sonar belt can also be used as the absolute localization device in a similar way as the mobile robot. An EKF is applied to realize the data fusion of different sensor systems. The abilities of obstacle avoidance and indoor localization of the proposed ETA are both verified through experiments.
A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges a...
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A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges associated with simultaneous power and thermal management under current-based control.
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide...
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.
To validate the robust stability of the flight control system of hypersonic flight vehicle, which suffers from a large number of parametrical uncertainties, a new clearance framework based on structural singular value...
This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with...
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This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.
The terminal guidance problem of a hypervelocity gliding vehicle to intercept a stationary target in the planar scenario is considered. In addition to impact position accuracy, the guidance law must meet the impact an...
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ISBN:
(纸本)9781467355322
The terminal guidance problem of a hypervelocity gliding vehicle to intercept a stationary target in the planar scenario is considered. In addition to impact position accuracy, the guidance law must meet the impact angle and speed demand. This problem is formulated as an infinite-time horizon nonlinear regulator problem, and solved with the state-dependent Riccati equation (SDRE) control technique. We convert the system to a linear-like structure with state-dependent coefficient (SDC) matrices and derive a closed-loop state-feedback control law using the SDRE method. A new state is introduced concerning the impact speed constraint. By rotating the coordinate system, the guidance scheme is extended to satisfy arbitrary impact angle. The state weighting matrix is chosen as the function of time-to-go to include the distance information between the vehicle and target. The numerical simulations are carried out for different impact angles and speeds, the results of which verify the effectiveness of the proposed guidance approach.
This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed ev...
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ISBN:
(纸本)9781479900305
This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed event-triggered consensus control is designed by taking into account the effect of the nonlinear interconnections. Then, based on the Lyapunov functional method and the Kronecker product technique, sufficient conditions are obtained to guarantee the consensus in the form of linear matrix inequality (LMI). Finally, a simulation example is proposed to illustrate the effectiveness of the developed theory.
Point matching is an important component of image registration. Recent years, Coherent Point Drift (CPD) method becomes a very popular point matching approach. CPD treats point matching as a probability estimation pro...
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Point matching is an important component of image registration. Recent years, Coherent Point Drift (CPD) method becomes a very popular point matching approach. CPD treats point matching as a probability estimation problem and speeds up the process of matching a lot. In this method, one set of points are thought to be sampled from a Gaussian Mixture Model (GMM), which is centered by the other set of points. However, CPD is sensitive to outliers and noises, especially when the noise ratio increased or the number of outliers gets much high. To deal with this problem, we introduce shape context into the step of searching for matching points and then improve the form of prior probabilities of GMM in this paper. The main idea of our method is that if the most points in a data set are likely to be matched to a particular centroid, this Gaussian component should be have a more influence to GMM. Therefore, we set prior probability of GMM with the similarity between GMM components and the data set. And the computation of similarity is based on shape context. The experiments on 2D and 3D images show that when noise ratio is low, our method performs as well as CPD does, but as the ratio increased, our method is more robust and satisfactory than CPD.
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