IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Visualization is the post processing stage in Knowledge Discovery Process (KDP) to simplify the process of understanding, abstraction, and diminishing the size of mined information, patterns, and/or knowledge. Pre-mining and mining stages of KDP seem as preprocessing steps for visualization engine. Visualization is a complex process because it needs a formal definition of complicated rules to translate large volumes of data into graphic formats. In this research, the term Visual KDP, VKDP was suggested, in which the benefits of visualization techniques have been utilized before, during, and after the data mining stage. To prove the validity and applicability of VKDP approach it is applied to the most important task of Data Mining; Association Rules Mining. The process of finding the appropriate visualization techniques is not a trivial one. Therefore, many visualization techniques are proposed for different levels of Association Rule (AR) mining, i.e., for database under mining, intermediate result or mining level, and mined rule level. Bipartite graph is proposed as a new technique to visualize the database under mining in addition to many variations in horizontal and vertical layouts. Also, networks of concepts are proposed as a new visualization technique to visualize the mined frequent itemsets, while the two-dimensional matrix, directed graph, and rule-item approach are adopted as visualization techniques to visualize the mined rules.

This paper proposes a neural controller to guide a nonholonomic mobile robot during trajectory tracking. The structure of the controller used consists of two models that describe the kinematical mobile robot system. These models are modified Elman neural networks (MENN) and feed forward multi-layer perceptron (MLP). The modified Elman neural networks model is trained with two stages; off-line and on-line, in order to guarantee that the outputs of the model accurately represent the actual outputs of the mobile robot system. The neural model, after being trained, acts as the position and orientation predictor. The feed forward multi-layer perceptron neural networks controller is trained on-line to find the inverse kinematical model, which controls the outputs of the mobile robot system. The general back propagation algorithm is used to learn the feed forward kinematics neural controller and the predictor. The results obtained from the conducted simulation show the effectiveness of the proposed neural control algorithm. This is demonstrated by the minimized tracking error and the smoothness of the control signal obtained.

In this research the design of a Model Predictive Controller (MPC) is investigated for regulating Mean Arterial Pressure (MAP) by infusing the Sodium Nitro-Prusside (SNP) drug to the mathematical model of human patients during surgical operations where the blood pressure suffers from sudden rises due to multiple causes. The MPC is designed and compared with digital Proportional-Integral (PI) controller. MPC is chosen due to its known ability of estimating an optimal control action and dealing with input, state constraints and with disturbances. Simulation results for three patients' MAP models controlled by MPC and PI controllers that are designed for a sensitive patient model are analyzed and compared. Both controllers give a satisfactory response but MPC is preferred due to its optimally estimated actions.

The relatively new field of Multiwavelets shows promise in removing some of the
limitations of wavelets. This paper introduces a new human face recognition using the
combination of Multiwavelet transform (MWT) and multidimension-Two Activation Function
Wavelet Network (MD-TAFWN). After taking the MWT of the image, it is required to divide the
approximate quarter into four parts and rearrange them in 3D form. Next, this 3D data will be fed
into a proposed MD-Two Activation Function Wavelet Network. This is for face image. For the
fingerprint image,it is required to divide the approximate quarter into four parts and rearrange
them in 3D form. Next, this 3D data will be fed into a proposed MD-Two Activation Function
Wavelet Network.The proposed transform is considered as a feature extractor of the decomposed
reference images with different frequency sub bands, and amid-range frequency sub band for data
image to the representation of the given image. Evaluations have generally shown that the
technique of the combination for Discrete Multi-wavelet Transform (DMWT) and the Two
Activation Function Wavelet Network (MD-TAFWN) is interesting and promising. The results
obtained showed that the combination technique outperformed. other conventional methods that
use a given transform with neural Network (NN). It results in a perfect recognition of 100% to a
data base which consists of 100 human face images.

This paper is concerned with the problem of boiler drum wall temperature
estimation to limit thermal stresses. The boiler drum of AL-Mussiab thermal power
station is taken as a case study. It deals with the fundamental issue that must be
considered when applying a linear theory (i.e the Kalman filter) to practical non-
linear problems. The Kalman filter is applied to estimate boiler drum wall
temperature using the outer surface temperature of boiler drum wall (measurable)
variable. The classical estimator (Kalman filter) is simple but it requires a good
dynamic model to give reliable results. The numerical test results showed that the
estimator is efficient and works well and it converges to the correct conditions (to
within ±1°C) in time about half an hour from boiler heating start up. The estimator is
robust where 100% error in initial conditions did not seriously influence the
transient time to produce correct estimated output.

Practical hydraulic control systems are nonlinear, high-order and parameters sensitive systems. On the other hand, usually the customer demands are difficult to achieve without some type of tradeoffs among these demands. Therefore, the burden of designing an optimal controller will be so complicated, and a suboptimal controller seems to be preferable. However, the validation of such design requires a detailed mathematical model of the hydraulic system and actual values of parameters. In this paper, a mathematical model of a hypothetical hydraulic system is derived first. Then, for the linearized model, a suboptimal controller is designed based on the LQR techniques. A Simulink model of the overall controlled system is utilized to simulate the closed-loop performance. The stable very fast response indicates the validity of the proposed procedure of design.

This work presents a novel simulation methodology applied to a human arm. It is aimed to allow the robotic system to perform complex movement operations of human arm. The human arm is represented by using virtual reality (VR). The work includes mathematical modeling of the direct kinematics ,inverse kinematic and the dynamics of the human arm .The model permits direct forward dynamics simulation, which accurately predicts hand position, also presents a solution to the inverse problem of determining set of joints angle to achieve a given position or motion. The method is implemented in the 3D space and uses the Simulink/ MATLAB Ver.2009a approach. This methodology can be used with different robots to test the behavior and control laws.

This paper presents indoor propagation modeling for wireless local area network at 2.4GHz. Different propagation path loss models are simulated such as the International Telecommunication Union (ITU) and the log-distance path loss models. The ITU model is tested in residential or office environments, and is examined; wether the position of the Access Point (AP) and the WLAN cards on the same floor or in another floors. Log-distance path loss model is tested and simulated; from this model the effects of the shadowing deviation can be seen. Practical measurements will be taken by using a laptop equipped with the NETSTUMBLER 0.40 software to see the impression of walls and doors. Different charts are illustrated to view the responses of ITU and log-distance path loss models versus distances, path loss, transmitting power and receiving power. Practical data are also plotted in different cases, for open area between AP and mobile user and in a random positions case.

This article presents a proposed design and implementation of an 8-bit Arithmetic Multiplier based on FPGA (Field Programmable Gate Array). The design is implemented a schematic FPGA way using CPLD (Complex Programmable Logic Device) development board SN-PLDE2. The development board contains an FPGA device EPF8282ALC4-4 (5000 gates account) of Altera FLEX8000 family (Flexible Logic Element MatriX) with the other necessary peripherals. The proposed design is achieved under MAXplus2 V10.1 software for FPGA programming. The designed arithmetic multiplier is tested using an experiment board (SN-PLDE3A). The results show both efficient usage and high performance including the accuracy and the fast operation.

The paper presents the novel application of Particle Swarm optimization (PSO) for the optimal tuning of the new PID controller which is called generalized PID (GPID). In 2009, Zhao Xiaodong, Li Yongqiang, Xue Anke proposed a generalized PID(GPID) to improve the time response and control quality of the traditional PID control algorithm This paper applies the Particle Swarm Optimization(PSO) algorithm for GPID controllers. The main goal is to eliminate the steady state error of the system and minimize the error performance index. The method searches the GPID parameter that realizes the expected step response of the plant. The expected response is defined by the overshoot ratio, the rising time, the settling time. The numerical result and the experiment result show the effectiveness of the proposed tuning method when the results are compared with the Traditional PID Controller.