1,256 publications from this institution
Due to the significant growth in transportation industries, the importance of interactions between vehicle technologies and humans from the safety aspect plays an important role in the new developments of complex vehicle systems. Therefore, from academic and industrial viewpoints, increasing attention has been paid to complex vehicle correlative technologies with full consideration of issues like safety, robust performance, impact analysis, optimization, automation, motion control, etc. All aforementioned issues provide a basis for the design and operation of practical vehicle systems in order to achieve desired complex tasks.
Vehicle crash test is the most direct and common method to assess vehicle crashworthiness. Visual inspection and obtained measurements, such as car acceleration, are used, e.g. to examine impact severity of an occupant or to assess overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using nonlinear autoregressive (NAR) model which parameters are estimated by the use of feedforward neural network. NAR model presented in this study is derived from the more general one – nonlinear autoregressive with moving average (NARMA). Suitability of autoregressive systems for data-based modeling was confirmed by application of neural networks with a NAR model to experimental data – measurements of vehicle acceleration during a crash test. This model allows us to predict the kinematic responses (acceleration, velocity, and displacement) of a given car during a collision. The major advantage of this approach is that those plots can be obtained without additional teaching of a network.
This paper is devoted to tackling the control problem for a class of discrete-time stochastic systems with randomly occurring sensor saturations. The considered sensor saturation phenomenon is assumed to occur in a random way based on the time-varying Bernoulli distribution with measurable probability in real time. The aim of the paper is to design a nonfragile gain-scheduled controller with probability-dependent gains which can be achieved by solving a convex optimization problem via semidefinite programming method. Subsequently, a new kind of probability-dependent Lyapunov functional is proposed in order to derive the controller with less conservatism. Finally, an illustrative example will demonstrate the effectiveness of our designed procedures.
Earthquakes and ambient vibrations can cause serious problems for cultural heritage objects; consequently, preserving these objects against mentioned sources of vibration has received more attention in recent years. To address this problem, in this paper, inerter is used to overcome the deficiency of a vibration isolator in the lower frequency range and performance of this passive device is evaluated experimentally. Specifically, first, the scaled model of an actual isolator and statue is presented. This structure has been designed and manufactured based on the results of a performance test, which has been performed on a famous statue of Michelangelo Buonarroti: Pieta Rondanini. In order to improve the performance of the isolator, a ball-screw type inerter has been designed and manufactured in this research. This device is introduced to the scaled structure and its effect on the dynamic behavior of the isolator is checked using a sine sweep vibration test. The experimental tests were performed on a shaking table in the horizontal direction. Then, the effectiveness of inerters on the dynamic behavior of the isolation system is demonstrated. It is shown that the isolator equipped with the manufactured inerters has better performance in the lower frequency range.
No abstract is provided for this article.
No abstract is provided for this article.
It is important to account for the gap between the dynamics of a plant to be controlled and its mathematical model to improve the control performance. Hence, a controller is designed based on this. In this study, the gap is termed as a lumped disturbance, and is considered with respect to the polynomial fuzzy model that is more effective to represent the plant dynamics. More specifically, a design of state observer is first proposed under the existence of the lumped disturbance, and this is then followed by the proposal of a disturbance observer to estimate the lumped disturbance for further use in the controller design. Finally, a controller is developed for the case in which the control state as well as the lumped disturbance are unavailable. Additionally, computer simulations are provided to illustrate the effectiveness of the proposed approach.
This paper addresses the output feedback sliding-mode control (SMC) problem for discrete-time uncertain nonlinear systems through Takagi-Sugeno fuzzy dynamic models. Combining with the sliding surface, a descriptor system is constructed to characterize the sliding motion dynamics. Sufficient conditions for asymptotic stability analysis of the sliding motion are attained by the piecewise quadratic Lyapunov functions within a convex optimization setup. Two SMC design approaches are proposed to ensure the finite-time convergence of the sliding surface. Two simulation examples are presented to show the effectiveness of the proposed approaches.
This report considers attempts to develop dummy motorcyclists with breakable legs. Material characteristics are discussed. The variation in the scatter fracture load of different materials is compared using the Weibull modulus. The materials used in the different dummy legs have been calibrated statically and uni-axially whereas in crash tests multi-axial dynamic loads are sustained. The Independent Action criterion is used to show that: (1) compressive and torsion loads have only a small effect on bending; and (2) differences in results from different laboratories is the result of scatter in the material characteristics. The effect that leg fracture has on dummy trajectory is described using previously published experimental pedestrian impacts, motorcycle crash tests and pedestrian and car occupant computer simulation studies. Head trajectory is shown to be largely unaffected by leg fracture. For the covering abstract of the conference see IRRD 864606.
No abstract is provided for this article.
The practical implementation of deep convolutional networks faces challenges such as feature redundancy and diminished learning efficiency. To address these issues, this paper focuses on Deep Convolutional Neural Networks (DCNNs), exploring two key aspects: feature extraction and training efficiency. The aim is to enhance the prediction accuracy, robustness, and generalization of deep convolutional networks. The proposed algorithms are applied to image classification. In response to the problem of feature redundancy in DCNN, an improved DCNN algorithm based on residual Dropout convolution (RD Conv) is proposed. The proposed algorithm includes residual Dropout paths and convolutional paths, which are randomly switched during training, resulting in random changes in network depth and enhancing the diversity of feature extraction. The residual path randomly selects input features, compresses and amplifies them, increasing the diversity of feature inputs in the downstream parameter layer. Only convolutional paths are used in prediction to ensure stable prediction results. To address the issue of decreased saturation training efficiency in DCNN filters caused by the fixed positions of the activation and saturation regions of the activation function, an improved DCNN algorithm based on a random activation function is proposed. By combining the different primary and secondary activation functions in the active and saturated regions, the filters in the saturated region can participate in the weight update process, increasing feature generation. Our RD Conv proposes a new dropout module that simultaneously utilizes the dropout layer, convolution layer and batch norm layer, while eliminating variance offset. Additionally, our random activation mechanism integrates two different activation functions into a new activation function, improving training efficiency. By using two linear functions as sub-activation functions, the random depth network is expanded beyond the constraints of a residual structure. The experimental results on the image classification datasets Cifar-10, Cifar-100 and Caltech-256 demonstrate that the proposed algorithm accelerates the convergence speed of the loss function and effectively improves the network's prediction accuracy. Additionally, it improves the training efficiency, alleviates overfitting, and achieves better generalization and accuracy. In most experiments, the T O P 1 accuracy of the model is increased by at least 2%, while the T O P 5 accuracy improves by at least 1%. The best result achieved by the proposed algorithm enhances the T O P 1 accuracy by 6.24%, and the T O P 5 accuracy by 3.62%. On the TGS Salt Identification and THUCNews datasets, the proposed algorithm still improved the performance of the model in image segmentation and text classification tasks, indicating that the proposed algorithm has good generalization ability.
This paper is dealing with the problem of observer-based event-triggered sliding mode control for fractional-order uncertain switched systems with a positive order less than one. Firstly, a fractional-order state observer is designed, based on which a fractional-order integral sliding surface function is proposed. Then, utilizing the estimated observer error and sliding mode error vectors, an event-triggered condition is constructed to decide whether the current control signal should be updated or not. Besides, sufficient conditions are derived in the forms of linear matrix inequalities (LMIs) to ensure finite-time stability of the augmented closed-loop system by adopting an average dwell time approach. Thereafter, to avoid the occurrence of infinite triggers within finite time, this paper also discusses the Zeno behavior and refines the results in the previous literature. Finally, to illustrate the effectiveness and superiority of the proposed method, three numerical simulations are provided.
This paper is concerned with the output-feedback controller design for consensus of a class of heterogeneous linear multiagent systems with the aperiodic sampled-data measurement. Under mild assumptions that the sampling periods are taken from a given set and the agent systems are time synchronized, an equivalent switched system model is first proposed for the heterogeneous agent system with nonuniform sampling. The overall leader-following tracking control problem (LFTCP) is then formulated as the output regulation of a discrete-time switched system. By using some algebraic manipulations, the control problem under consideration is further decoupled into two control subproblems, i.e., a static output-feedback (SOF) control problem plus a simple feedback control problem related to the communication topology. Based on the Lyapunov stability theory, some sufficient conditions are obtained for the solvability of LFTCP. In our results, the SOF controller gains are determined by solving some strict linear matrix inequalities. Finally, a simulation study on the modified Caltech multivehicle wireless testbed is presented to show the effectiveness of the proposed design method.
This paper develops one model to explore the relationship between the subsidy policy and the agricultural total factor productivity (TFP). It indicates that the agricultural TFP will be lower after the subsidy policy is implemented and there exists a negative relation between the subsidy and TFP, if subsidies are associated with the acreage. Using Malmquist index, this paper measures the changes of TFP in China's cotton production before and after the subsidy policy is implemented. The results verify that the subsidy policy could not increase but decrease the TFP of China's cotton production, not only in the whole country but also in major provinces of China. Based on the positive study, some policy implications are provided in the end of this paper.
The state-of-energy (SOE) and state-of-health (SOH) are two crucial quotas in the battery management systems, whose accurate estimation is facing challenges by electric vehicles' (EVs) complexity and changeable external environment. Although the machine learning algorithm can significantly improve the accuracy of battery estimation, it cannot be performed on the vehicle control unit as it requires a large amount of data and computing power. This paper proposes a joint SOE and SOH prediction algorithm, which combines long short-term memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural networks (CNNs) for EVs based on vehicle-cloud collaboration. Firstly, the indicator of battery performance degradation is extracted for SOH prediction according to the historical data; the Bayesian optimization approach is applied to the SOH prediction combined with Bi-LSTM. Then, the CNN-LSTM is implemented to provide direct and nonlinear mapping models for SOE. These direct mapping models avoid parameter identification and updating, which are applicable in cases with complex operating conditions. Finally, the SOH correction in SOE estimation achieves the joint estimation with different time scales. With the validation of the National Aeronautics and Space Administration battery data set, as well as the established battery platform, the error of the proposed method is kept within 3%. The proposed vehicle-cloud approach performs high-precision joint estimation of battery SOE and SOH. It can not only use the battery historical data of the cloud platform to predict the SOH but also correct the SOE according to the predicted value of the SOH. The feasibility of vehicle-cloud collaboration is promising in future battery management systems.
Pole changing line‐start permanent magnet synchronous motor (LSPMSM) can significantly improve the starting capability of LSPMSM by decreasing the braking torque and pulsating torque during starting process. The fundamental reason for the elimination of braking torque is that there is no generating effect during minor‐pole starting process. That is to say, induced electromotive force (EMF) does not exist when the magnetic field generated by multi‐pole rotor PMs cut the minor‐pole stator winding. Therefore, the induced EMF of pole changing LSPMSM is analysed theoretically and calculated analytically. Considering that the magnetic flux per pole of the coils under the adjacent pole are different, the induced EMFs of a conductor, a full‐pitch coil, a coil group in minor‐pole stator winding are, respectively, calculated, and then the general analytical expression of the induced EMF of a phase winding with different rotor/stator pole ratio is derived. Then, the motors with different rotor/stator pole ratios are simulated based on finite element method to obtain the corresponding EMFs. Meanwhile, a 6/8 pole changing LSPMSM is taken as prototype. The consistency of the simulation, experiment, and the analytical calculation results effectively verify the validity of the derived general analytical expression and theoretical analysis.
