1,256 publications from this institution
This paper investigates the problem of passive controller design for a class of nonlinear systems under variable sampling. The Takagi-Sugeno (T-S) fuzzy modeling method is utilized to represent the nonlinear systems. Attention is focused on the design of passive controller for the T-S fuzzy systems via sampled-data control approach. Under the concept of very-strict passivity, a novel time-dependent Lyapunov functional is constructed to develop passive analysis criteria and passive controller synthesis conditions. A new sampled-data controller is designed to guarantee that the resulting closed-loop system is very-strictly passive. These conditions are formulated in the form of linear matrix inequalities (LMIs), which can be solved by convex optimization approach. Finally, an application example is given to demonstrate the feasibility and effectiveness of the proposed results.
Compared to the single-source domain adaptation fault diagnosis methods, the multi-source domain adaptation methods can not only take advantage of the rich and diverse diagnostic information of multiple source domains but also draw on the feature alignment of single-source setting to reduce the domain discrepancy. However, forcing the alignment of feature distributions is challenging and may lead to negative transfer. Meanwhile, labeled data are often scarce and difficult to collect in actual production, which can be mitigated by multi-source information, but the diagnostic performance of the model is degraded by large domain differences. To tackle the above issues, a domain attribute and feature transfer network is proposed to model multi-source information domains in a unified deep network and achieve cross-domain fault diagnosis. In the transferable attributes learning section, we adopt an attention mechanism and a domain attribute loss function to extract transferable latent attributes from multi-source information. In the transferable features learning section, we apply the local maximum mean discrepancy metric to adjust the category distribution of single-source information and target domains. Then, intra-class compactness learning and pseudo-labeling learning strategies are utilized to further obtain richer feature representations. Finally, we propose the knowledge fusion module to fuse the results of multi-source information classifiers to yield a more reliable diagnosis result. Extensive experiments on three different multi-source information datasets show the superiority of our method compared to the state-of-the-art (SOTA) methods by comparing indicators from various aspects.
Vehicle crashworthiness can be assessed by the variety of methods - the most common and direct one is a vehicle crash test. Visual inspection and obtained measurements, such as car acceleration, are used to examine impact severity of an occupant and overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using a feedforward neural network to estimate the system by use of nonlinear autoregressive (NAR) models. Specifically, feasibility of applying neural networks with an NAR model to the analysis of experimental data is explored by application to measurements of a vehicle 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.
No abstract is provided for this article.
With the integration of artificial intelligence and traffic systems, intelligent traffic systems are utilizing enhanced perception coverage and computational capabilities to provide data-intensive solutions, achieving higher levels of performance than traditional systems. This paper combines the D3QN algorithm from deep reinforcement learning with practical issues and proposes an intelligent emergency traffic signal control system based on Deep Reinforcement Learning (DRL). The system takes into account pedestrian movement and utilizes real-time traffic data and environmental information to model traffic flow and road conditions within a novel state space. It employs the Dueling Double Deep Q-Network (D3QN) to optimize signal control strategies. The system dynamically adjusts signal timings to enhance operational efficiency at intersections. By using the Weibull distribution to simulate realistic traffic congestion and actual traffic data from Shanyin Road in Hangzhou for validation, the results demonstrate that this method converges faster and is more stable compared to other methods, significantly reducing traffic congestion. Furthermore, by incorporating pedestrian movement, this method reduces pedestrian waiting times by 44.736% during peak periods and 22.95% during off-peak periods, while maintaining comparable vehicle queue lengths, delay times, and carbon dioxide emissions. This approach shows the potential improvement of smart urban mobility and resolving intersection congestion challenges.
Summary This article proposes a novel fuzzy sliding‐mode control scheme under an adaptive control strategy for energy management mechanism in electric vehicles that are subject to a regenerative braking system. The effectiveness of the fuzzy logic controller is to adjust the sliding mode parameters according to the slip ratio tracking error between the optimal slip ratio and the actual slip ratio. Specifically, the proposed torque distribution strategy can integrate the best battery condition and energy recovery efficiency under the practical constraints through this control method by fixing the pneumatic braking torque and motor torque. Finally, an electric vehicle model is established in the Simulink environment to verify the applicability of the proposed control algorithm.
This paper deals with designing the controller of LTI system based on data-driven techniques. We propose a scheme embedding a residual generator into control loop based on realization of the Youla parameterization for advanced controller design. Basic idea of the proposed scheme is constructing the residual generator by using the solution of the Luenberger equations as well as the well-established relationship between diagnosis observer (DO) and the parity vector. Besides, the core of the above idea is straightly using the process measurements to obtain the parity space based on the Subspace Identification Method (SIM), rather than establishing the system model. At last, a simulation based on the numerical model demonstrates the performance and effectiveness of the proposed scheme.
This paper deals with the fault tolerant control (FTC) design for a Vertical Takeoff and Landing (VTOL) aircraft subject to external disturbances and actuator faults. The aim is to synthesize a fault tolerant controller ensuring trajectory tracking for the nonlinear uncertain system represented by a Takagi–Sugeno (T–S) model. In order to design the FTC law, a proportional integral observer (PIO) is adopted which estimate both of the faults and the faulty system states. Based on the Lyapunov theory and ℒ2 optimization, the trajectory tracking performance and the stability of the closed loop system are analyzed. Sufficient conditions are obtained in terms of linear matrix inequalities (LMI). Simulation results show that the proposed controller is robust with respect to uncertainties on the mechanical parameters that characterize the model and secures global convergence.
In this paper we address the problem of vibration reduction of buildings with delayed measurements, where the delays are time-varying and bounded. We focus on a convex optimization approach to the problem of state-feedback H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control design. An appropriate Lyapunov-Krasovskii functional and some free weighting matrices are used to establish some delay-range-dependent sufficient conditions for the design of desired controllers in terms of linear matrix inequalities (LMIs). The controller, which guarantees asymptotic stability and an H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance, simultaneously, for the closed-loop system of the structure, is then developed. The performance of the controller is evaluated through the simulation of an n-story base-isolated building.
The idea of a completely independent, self-healing, self-deploying, and self-optimising communication network is an appealing one. This paper outlines why financial pressures will drive wireless communication networks towards the adoption of autonomic systems with the above characteristics, that will eventually become cognisant, exhibiting some degree of self- awareness. As a step in this direction, we explore the concept of robotic wireless base stations and discuss their behaviour subject to principles inspired by Asimov's Laws of Robotics. Finally, a discussion of how such laws would be applicable in current and future scenarios is presented.
Many linguistic aggregation methods have been proposed and applied in the linguistic decision‐making problems. In practice, experts need to assess a number of values in a side of reference domain higher than in the other one; that is, experts use unbalanced linguistic values to express their evaluation for problems. In this paper, we propose a new linguistic aggregation operator to deal with unbalanced linguistic values in group decision making, we adopt 2‐tuple representation model of linguistic values and linguistic hierarchies to express unbalanced linguistic values, and moreover, we present the unbalanced linguistic ordered weighted geometric operator to aggregate unbalanced linguistic evaluation values; a comparison example is given to show the advantage of our method.
In this article, the sliding mode control (SMC) design problem is investigated for a class of discrete-time interval type-2 fuzzy systems, in which the scheduling of sensors is ruled by the round-Robin communication protocol. This means that at any time, only one sensor node can transmit its value to the controller side. To deal with this phenomenon, a compensation scheme is proposed for other sensor nodes, based on which the past measured signal stored in the corresponding buffers may be utilized by the controller. And then, a token-dependent sliding mode controller is synthesized. Sufficient conditions are derived such that the system states can reach a neighborhood of the sliding surface and the resultant closed-loop fuzzy system is input-to-state stable. Finally, simulation results verify the effectiveness of the proposed SMC method.
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No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure. However, most existing network alignment methods have added assumptions of additional constraints to guide the alignment, such as having a set of seed node-node correspondences across the networks or the existence of side-information. Instead, we seek to develop a general unsupervised network alignment algorithm that makes no additional assumptions. Recently, network embedding has proven effective in many network analysis tasks, but embeddings of different networks are not aligned. Thus, we present our Deep Adversarial Network Alignment (DANA) framework that first uses deep adversarial learning to discover complex mappings for aligning the embedding distributions of the two networks. Then, using our learned mapping functions, DANA performs an efficient nearest neighbor node alignment. Furthermore, we present an unsupervised heuristic to perform model selection for DANA. We perform experiments on real world datasets to show the effectiveness of our framework for first aligning the graph embedding distributions and then discovering node alignments that outperform existing methods.
There are various real-world applications such as video ads, airport screenings, courtroom trials, and job interviews where deception detection can play a crucial role. Hence, there are immense demands on deception detection in videos. However, videos are inherently complex; moreover, they lack detective labels in many real-world applications, which poses tremendous challenges to traditional deception detection methods. In this paper, we study the problem of deception detection in videos. In particular, we provide a principled way to capture rich information into a coherent model and propose an end-to-end framework DEV to detect DEceptive Videos automatically, which is robust to the small number of training data. Experimental results on real-world videos demonstrate the effectiveness of the proposed framework and further experiments are conducted to understand important factors of deception detection in videos.
