Ahmed M. Alqaissi

Bio: Ahmed M. Alqaissi is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Adaptive neuro fuzzy inference system. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Implementation of an intelligent SINS navigator based on ANFIS

Abstract: In this work an intelligent navigator developed to overcome the limitations of existing Strapdown Inertial Navigation Systems (SINS) algorithm. This system is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). As in previous work, which is based on Artificial Neural Network, the window based weight updating strategy was used, and the intelligent navigator evaluated using several SINS hypothetical field tests data. And the results show that the intelligent navigator based on ANFIS more powerful compared with other (traditional and intelligent navigator based on ANN).

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Abstract: Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

INS/GNSS integration using recurrent fuzzy wavelet neural networks

Abstract: In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages.

The use of adaptive network-based fuzzy inference system for marine AHRS

Abstract: A kind of marine strapdown attitude and heading reference system (AHRS) based on the principle of strapdown inertial navigation system (INS) is discussed here. With an electromagnetic (EM) log aided, the oscillations included in the attitude and heading errors are bounded by damping network. Furthermore, in order to decrease attitude and heading errors aroused by EM log measurements, we introduce an adaptive network-based fuzzy inference system to control the damping ratio automatically in terms of the vessel maneuvers conditions. The results of test demonstrate the validity of proposed method.