International Conference on Modelling, Identification and Control 

About: International Conference on Modelling, Identification and Control is an academic conference. The conference publishes majorly in the area(s): Control theory & Control system. Over the lifetime, 1602 publications have been published by the conference receiving 5551 citations.

JOE : A mobile, Inverted Pendulum

Abstract: The Industrial Electronics Laboratory at the Swiss Federal Institute of Technology (EPFL) in Lausanne has built a prototype of a revolutionary two-wheeled vehicle. Due to its configuration with two coaxial wheels, each of which is coupled to a DC Motor, the vehicle is able to do stationary U-turns. A control system, made up of two decoupled state space controllers, pilots the motors so as to keep the system in equilibrium. See http://leiwww.epfl.ch/joe for a video demonstration of JOE.

A novel mutual authentication scheme for Internet of Things

Abstract: This paper presents a novel mutual identity authentication scheme which can be applied securely in Internet of Things. Based on secure hash algorithm(SHA), feature extraction and elliptic curve cryptography(ECC), we propose an asymmetric mutual authentication scheme between the platform and the terminal node, which imposes light computation and communication cost, through security analysis it is also shown that the proposed scheme is secure and feasible for applications in the Internet of Things.

Improved threshold denoising method based on wavelet transform

Abstract: There is much noise in the channel during signal transmission. The signals are easily polluted when they are transmitted in it. So they can't be received correctly. In order to get the correct signals, the polluted signals should be processed to reduce noises and improve the quality when they reach the receiver. After analyzing the theory of wavelet transform and the characteristics of traditional soft threshold and hard threshold wavelet denoising, an improved threshold denoising method based on wavelet transform is adopted to improve the quality of a signal. It is one dimensional and has been polluted by noise. The method overcomes the discontinuous in hard-threshold denoising and reduces the permanent bias in soft-threshold denoising. At last soft threshold denoising, hard threshold denoising and the improved threshold denoising are all adopted to reduce noise in the same polluted signal by software simulation. The results show that the improved threshold denoising method is superior to the traditional soft threshold and hard-threshold wavelet denoising methods in improving SNR and decreasing RMSE.

Sliding mode control technique for multi-switching synchronization of chaotic systems

Abstract: In this manuscript, a sliding mode control technique is proposed for multi-switching synchronization of chaotic systems in which state variables of a master system are synchronized with different state variables of a slave system. Based on the sliding mode control technique and Lyapunov stability theory, the suitable controllers are designed for different switches. To implement this methodology, Lorenz chaotic system is taken as a master system and Pehlivan chaotic system is taken as a slave system. Simulation results are carried out to justify the theoretical results and revealed that the error dynamics tends to zero and required synchronization is obtained.

Review of genetic programming in modeling of machining processes

Abstract: The mathematical modeling of machining processes has received immense attention and attracted a number of researchers because of its significant contribution to the overall cost and quality of product. The literature study demonstrates that conventional approaches such as statistical regression, response surface methodology, etc. requires physical understanding of the process for the erection of precise and accurate models. The statistical assumptions of such models induce ambiguity in the prediction ability of the model. Such limitations do not prevail in the nonconventional modeling approaches such as Genetic Programming (GP), Artificial Neural Network (ANN), Fuzzy Logic (FL), Genetic Algorithm (GA), etc. and therefore ensures trustworthiness in the prediction ability of the model. The present work discusses about the notion, application, abilities and limitations of Genetic Programming for modeling of machining processes. The characteristics of GP uncovered from the current review are compared with features of other modeling approaches applied to machining processes.