Air-conditioning systems (ACs) are essential in hot and humid climates to ensure acceptable ambient air quality as well as thermal comfort for buildings users. It is essential to improve refrigeration system performance without increasing the effects of global warming potential (GWP) and ozone depletion potential (ODP). The main objective of this study is to evaluate the performance of an air conditioning system that operates with a liquid suction heat exchanger (LSHX) through implementing refrigerants with zero OPD and low GWP (i.e., R134a and R1234yf). Liquid suction heat exchanger (LSHX) was added to an automobile air conditioning system (AACS).When Liquid suction heat exchanger was added to the cycle, primary results indicated t

In this paper, an experimental analysis of conventional air-cooled and microchannel condensers in automotive vapor compression refrigeration cycle concerning heat transfer coefficient and energy using R134a as a refrigerant was presented. The performance of two condensers and cycles tested regarding ambient temperature which it was varied from 40^oC to 65^oC, while the indoor temperature and load have been set to be 23^oC and 2200 W respectively. Results showed that the microchannel condenser has 224 % and 77 % higher refrigerant side and air side heat transfer coefficient respectively than the coefficients of the conventional condenser. Thus, the COP, in case of using the microchannel

Leap Motion Controller (LMC) is a gesture sensor consists of three infrared light emitters and two infrared stereo cameras as tracking sensors. LMC translates hand movements into graphical data that are used in a variety of applications such as virtual/augmented reality and object movements control. In this work, we intend to control the movements of a prosthetic hand via (LMC) in which fingers are flexed or extended in response to hand movements. This will be carried out by passing in the data from the Leap Motion to a processing unit that processes the raw data by an open-source package (Processing i3) in order to control five servo motors using a micro-controller board. In addition, haptic setup is proposed using force sensors (F

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

The development of microcontroller is used in monitoring and data acquisition recently. This development has born various architectures for spreading and interfacing the microcontroller in network environment. Some of existing architecture suffers from redundant in resources, extra processing, high cost and delay in response. This paper presents flexible concise architecture for building distributed microcontroller networked system. The system consists of only one server, works through the internet, and a set of microcontrollers distributed in different sites. Each microcontroller is connected through the Ethernet to the internet. In this system the client requesting data from certain side is accomplished through just one server that is in