Aydin Azizi

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

Bifurcation Behavior of a Capacitive Micro-Beam Suspended between Two Conductive Plates

In this paper, bifurcation and pull-in phenomena of a capacitive micro switch suspended between two stationary plates have been studied. The governing dynamic equation of the switch has been attained using Euler Bernoulli beam theorem. Due to the nonlinearity of the electrostatic force, the analytical solution for the derived equation is not available. So the governing differential equation has been solved using combined Galerkin weighted residual and Step-By-Step Linearization Methods (SSLM). To obtain the fixed points and study the local and global bifurcational behavior of the switch, a mass-spring model has been utilized and adjusted so that to have similar static/dynamic characteristics with those of Euler-Bernoulli beam model (in the first mode). Using 1-DOF model, mathematical and physical equilibrium points of the switch have been obtained for three different cases. It is shown that the pull-in phenomenon in the present micro-switch can be occurred due to a pitchfork or transcritical bifurcations as well as saddle node bifurcation which are transpired in the classical micro-switches. And for some cases primary and secondary pull-in phenomena are observed where the first one is due to a transcritical bifurcation and the second one is due to a saddle node bifurcation. In addition the dynamic response of the switch to a step DC voltage has also been studied and the results show that in contrast to the classical microswitches, the ratio of the dynamic pull-in to the static one depends on the gaps and voltages ratio where for the classical one is approximately a constant value.

Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

Optimal Trajectory Planning for Parallel Robots Considering Time-Jerk

A method for optimization in trajectory planning of 3RUU robot manipulators is presented in this paper. At first, to get the optimal trajectory, position analyses has been done on the 3RUU robot, then an objective function which have two terms is minimized: first term relevant to the total execution time and another one relevant to the integral of the squared jerk (defined as the derivative of the acceleration toward time) along the trajectory and this Guarantees that the obtained trajectory is smooth. This technique let to calculate the kinematic constraints on the motion of the robot, defined as upper limits on the absolute values of velocity, acceleration and jerk. , the total execution time does not require to be set priori. The algorithm has been tested in simulation and in comparison with other important trajectory planning techniques it has been given good results.

Buckling Control of Morphing Composite Airfoil Structure using Multi-stable Laminate by Piezoelectric Sensors/Actuators

In the present work, an unsymmetric laminated plate with surface bonded piezoelectric sensors, and actuators has been considered. Piezoelectric sensor were used to monitor the load and deformation bifurcation occurs. Monitoring the shape and load of a morphing structure is essential to ascertain that the structure is properly deployed and it is not loaded excessively ,thus, preventing structural to failure. A piezoceramic actuator is used to provide activation load and to force the structure to change its stability state from one to another. A non-linear finite element model based on the layerwise displacement theory considering the electro-mechanical coupling effects of piezoelectric elements has been developed for simulation purposes. A control mechanism is also employed to actively control the shape of the structure. It is observed that, utilizing multistable composite to design a morphing structure may significantly reduce the energy required for changing the shape. Further controlling the buckling phenomena using piezoelectric sensor and actuator along with an ON/OFF controller can effectively and efficiency enhance the performance of the morphing structure during manoeuver.

Modeling of Dermal Wound Healing-Remodeling Phase by Neural Networks

Wound healing is a complex biological process dependent on multiple variables: tissue oxygenation, wound size, contamination, etc. Many of these factors depend on multiple factors themselves. Mechanisms for some interactions between these factors are still unknown, presenting a barrier for scientists intending to model wound healing using an object-based programming approach. In this paper we focus on the neural networks and regard them as function approximators, and attempt to simulate remodeling phase of dermal wound healing process using neural networks as an intelligence technique.

Designing of Artificial Intelligence Model-Free Controller Based on Output Error to Control Wound Healing Process

The complexity of biological systems demands the use of appropriate controllers in order to control the final quality of the system based on the effects of inputs of the system. Wound healing is a complex biological process dependent on multiple variables: tissue oxygenation, wound size, contamination, etc. Many of these factors depend on multiple factors themselves. Mechanisms for some interactions between these factors are still unknown. The artificial intelligence appears as an interesting alternative to control such systems and to satisfy the desired requirement. In this paper we try to simulate and control wound healing process with focusing on remodeling phase by neural networks as an intelligence technique. For these purposes some materials like mathematical modeling, finite elements method, and effect of external forces on the scar tissue are used here.

Modeling mechanical properties of FSW thick pure copper plates and optimizing it utilizing artificial intelligence techniques

Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminum, magnesium, zinc and copper alloys. In this process, parameters play a major role in deciding the weld quality these parameters. Using predictive modelling for mechanical properties of FSW not only reduce experiments but also is created standard model for predict outcomes. Therefore, this paper is undertaken to develop a model to predict the microstructure and mechanical properties of FSW. The proposed model is based on Ring Probabilistic logic Neural Network (RPLNN) and optimize it utilizing Genetic Algorithms (GA). The simulation results show that performance of the RPLNN algorithm with utilizing Genetic Algorithm optimizing technique compared to real data is reliable to deal with function approximation problems, and it is capable of achieving a solution in few convergence time steps with powerful and reliable results.

Introducing Genetic Algorithm as an Intelligent Optimization Technique

The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GA operates on a population of potential solutions applying the principle of survival of the fittest to produce (hopefully) better and better approximations to a solution. Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies. Here, in this paper genetic algorithm is introduced as an optimization technique.

Introducing Neural Networks as a Computational Intelligent Technique

. Neural networks have been applied very successfully in the identification and control of dynamic systems. The universal approximation capabilities of the multilayer perceptron have made it a popular choice for modeling nonlinear systems and for implementing general-purpose nonlinear controllers. In this paper we try to model and control the mass-spring-damper mechanism as a 1 DOF system using neural networks. The control architecture used in this paper is Model reference controller (MRC) as one of the popular neural network control architectures.