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Deep neural networks can learn deep feature representation for hyperspectral image (HSI) interpretation and achieve high classification accuracy in different datasets. However, counterintuitively, the classification performance of deep learning models degrades as their depth increases. Therefore, we add identity mappings to convolutional neural networks for every two convolutional layers to build deep residual networks (ResNets). To study the influence of deep learning model size on HSI classification accuracy, this paper applied ResNets and CNNs with different depth and width using two challenging datasets. Moreover, we tested the effectiveness of batch normalization as a regularization method with different model settings. The experimental results demonstrate that ResNets mitigate the declining-accuracy effect and achieved promising classification performance with 10% and 5% training sample percentages for the University of Pavia and Indian Pines datasets, respectively. In addition, t-Distributed Stochastic Neighbor Embedding (t-SNE) provides a direct view of the extracted features through dimensionality reduction.

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A human-robot interactive control is proposed to govern the assistance provided by a lower limb exoskeleton robot to patients in the gait rehabilitation training. The rehabilitation training robot with two lower limb exoskeletons is driven by the pneumatic proportional servo system and has two rotational degrees of freedom of each lower limb. An adaptive admittance model is adopted considering its suitability for human-robot interaction. The adaptive law of the admittance parameters is designed with Sigmoid function and the reinforcement learning algorithm. Individualized admittance parameters suitable for patients are obtained by reinforcement learning. Experiments in passive and active rehabilitation training modes were carried out to verify the proposed control method. The passive rehabilitation training experimental results verify the effectiveness of the inner-loop position control strategy, which can meet the demands of gait tracking accuracy in rehabilitation training. The active rehabilitation training experimental results demonstrate that the personal adaption and active compliance are provided by the interactive controller in the robot-assistance for patients. The combined effects of flexibility of pneumatic actuators and compliance provided by the controller contribute to the training comfort, safety, and therapeutic outcome in the gait rehabilitation.

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Travel time is an important indicator of network performance used in traffic operations and management. Commonly deployed inductive loop detectors (ILDs) measure time-mean-speed (TMS), whereas space-mean-speed (SMS) is required to calculate the travel time. A well-known relationship between the TMS and the SMS was derived by Wardrop. However, this relationship cannot be used in practice to estimate travel times as it requires knowledge of the variance of the SMS. The variance of the SMS is not measured by the ILDs and is normally not available in practice. A novel formulation is presented in this paper to estimate the SMS using TMS obtained from ILDs. In addition, two additional models based on the formulation are developed to improve the estimation performance by taking traffic states into account. The initial results show that the proposed formulation can used to estimate the SMS, and hence the travel time, accurately using real-world data. IntroductionTravel time estimation (TTE) has been a high-interest topic in highway operation and management for years. Inductive loop detector (ILD) is the most widely deployed type of traffic sensor that provides data for TTE. ILDs provide a number of point-based measurements of traffic variables such as spot speed or time-mean-speed (TMS), flow and occupancy. These output measurements are used to evaluate traffic performance for traffic operation and management. Besides such point-based measurements, travel time is also an important indicator of traffic performance. However, ILDs cannot directly provide link travel times. Estimating travel time using data from widely deployed ILDs would be attractive to traffic management agencies, as models that use data from existing devices can minimise additional costs for obtaining more information.For example, every link in England's highway network is equipped with ILDs to monitor and record the traffic flow, occupancy and TMS (National Traffic Control Centre; NTCC 2009). Some roadway sections consisting of multiple links are installed with automatic number plate recognition (ANPR) cameras to measure travel time. Due to the high cost of procuring and installing ANPR cameras, relatively few road sections have ANPR cameras in the highway network. Hence, there is no travel time information available for most of the links. Therefore, models that estimate travel time using data from ILDs are of particular interest for those links without ANPR cameras. Motivated by this practical problem, a simple and efficient method using ILD data to estimate the space-mean-speed (SMS), which can be used to calculate link travel times, is presented in this paper. BackgroundResearchers and engineers have studied TTE since the late 1920s to evaluate transport performance and planning improvement. After decades of development, TTE is being used in the area of road network performance, traveller information systems and dynamic route guidance (Krishnan 2008). TTE models vary according to the input data used, such as ILD, probe vehicle technologies, license p...