Image super-resolution using supervised multi-scale feature extraction network

Multimedia Tools and Applications - Image super-resolution using deep convolutional networks have recently achieved great successes. However, previous studies have failed to consider the spatial...     

Full-scaled deep metric learning for pedestrian re-identification

Multimedia Tools and Applications - The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that...     

A novel 1D chaotic system for image encryption,authentication and compression in cloud

Multimedia Tools and Applications - To solve the privacy image problem stored in cloud, we propose a compressive sensing (CS) based image compression, authentication, encryption algorithm in cloud,...     

LogGAN: a Log-level Generative Adversarial Network for Anomaly Detection using Permutation Event Modeling

Information Systems Frontiers - System logs that trace system states and record valuable events comprise a significant component of any computer system in our daily life. Each log contains...     

A hybrid approach to recognize generic sections in scholarly documents

International Journal on Document Analysis and Recognition (IJDAR) - Discourse parsing of scholarly documents is the premise and basis for standardizing the writing of scholarly documents,...     

Finite-time Containment Control of Nonlinear Delayed Fractional Multi-agent Systems

This article deals with finite-time containment control (FTCC) of nonlinear delayed fractional multi-agent systems (FMASs). Addressing fractional Razumikhin approach, properties of fractional calculus and analytical techniques, a quadratic Lyapunov function is constructed to derive sufficient conditions on FTCC by adopting a discontinuous control protocol, the employed method may overcome well the troubles arising from time delays and fractional derivatives. Numerical simulations further clarify the reliability and validity of our theoretical conclusions.

     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Modeling and Control of Hybrid-driven Gliding Motion for an Underwater Gliding Snake-like Robot

International Journal of Control, Automation and Systems - An underwater gliding snake-like robot (UGSR) combines the advantages of an underwater glider (UG) and an underwater snake-like robot...     

Stability Analysis for Time-delay Systems via a Novel Negative Condition of the Quadratic Polynomial function

This paper studies the stability analysis problem for time-varying delay systems. An appropriate Lyapunov-Krasovskii functional (LKF) is constructed where its derivative is a quadratic polynomial function of the delay. A novel negative condition of the mentioned quadratic function with two variable parameters is developed to ensure that the LKF derivative is negative, reducing conservatism on some similar results. Besides, an extended version of Bessel-Legendre inequality is introduced to be employed in the stability analysis of time-varying delay systems. Then, some stability criteria with less conservatism are derived for two kinds of the time-varying delay. Finally, the effectiveness of the proposed stability criteria is demonstrated through three examples.

     

Robust Control of Networked System and Its Application

International Journal of Control, Automation and Systems - In this paper, the stability issue of networked system with dynamical topology in intelligent vehicle is studied. The systems are...