There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Data Mining Practical Machine Learning Tools and Techniques

In the last few decades, several control techniques are deployed for the stabilization, regulation, and control of linear and nonlinear dynamical systems. For linear autonomous systems, it is easy to find a control Lyapunov function for stability and optimization problems. However, finding a suitable control Lyapunov function is a challenging problem for nonlinear control systems. The backstepping control method is a recursive design procedure that links the choice of a control Lyapunov function with the design of a feedback controller and guarantees global asymptotic stability of strict feedback systems. The active backstepping control method is a practical tool to overcome the limitations of the feedback linearization approach in the control literature. The block backstepping control method is a general backstepping control method with more applicability in the control literature. The adaptive backstepping control method is a modified form of backstepping control method that uses estimates for unknown parameters in the systems. The robust backstepping control method is an effective backstepping technique for control systems with uncertainties. In this chapter, we describe an introduction to backstepping control with some basic examples and simulations. We also state some basic results for active backstepping control method.

An introduction to backstepping control

Jerk chaotic systems have many applications in science and engineering. In the past few decades, significant attention has been paid on the modeling of new jerk differential equations arising in engineering. An important model of a plasma torch jerk differential equation exhibiting chaos was reported by Ghorui et al. (2000) . The Ghorui jerk system is a recent example of a jerk chaotic system consisting of a just cubic nonlinearity and linear terms in its dynamics. This work announces a new plasma torch jerk chaotic system by adding a quadratic nonlinearity to the Ghorui jerk chaotic system. We show that the new plasma torch jerk system has a self-excited chaotic attractor with three unstable saddle-foci rest points. We give a detailed bifurcation analysis of the new plasma torch chaotic jerk system. We also report that the new plasma torch chaotic jerk system has multistability and coexisting chaotic attractors. As control applications, we use active backstepping control laws for the global chaos stabilization and global chaos synchronization for the new plasma torch chaotic jerk system. Finally, we design an electronic circuit of the new plasma torch chaotic jerk system using Multisim for practical implementation in engineering.

A new multistable plasma torch chaotic jerk system, its dynamical analysis, active backstepping control, and circuit design

Backlash is one of several discontinuities found in different kinds of systems. It can be found in actuators of different types, such as mechanical and hydraulic, giving way to unwanted effects in the system behavior. Proportional integral (PI) loop shaping control design implementing a describing function to find the limit cycle oscillations and the appropriate control gain selection by particle swarm optimization is developed. Therefore, a frequency domain approach is implemented for the control of nonlinear system of any kind such as robotics, mechatronics, and other kind of mechanisms, electrical motors, etc. Finally, in order to corroborate the theoretical background explained in this chapter, the stabilization of a cart-pendulum system with the proposed control strategy is shown.

Proportional Integral Loop Shaping Control Design With Particle Swarm Optimization Tuning

Urea kinetic modelling (UKM) has been generally accepted as a method for quantifying hemodialysis (HD) treatment. During hemodialysis, reduction in the urea concentration in the intracellular fluid (ICF) compartment will lag behind that in the extra cellular fluid (ECF) compartment, and following the end of dialysis, a ”rebound” in the blood level of urea will occur where it continues to rise due to diffusion of urea from the ICF to ECF to establish an equilibrium state. Because of compartment effects, the dose of dialysis with regard to urea removal is significantly overestimated from immediate post-dialysis urea concentrations, because 30 to 60 min are required for concentration gradients to dissipate and for urea concentrations to equilibrate across body water spaces during the post-dialysis period. To avoid the delay of waiting for an equilibrated post-dialysis sample, it became necessary to describe and to quantitate effects causing the urea compartmentalization during dialysis; two-pool modeling approaches have been developed that more accurately reflect the amount of urea removed. This in turn gives more adequate measures not only of dialysis adequacy, but also of the protein catabolic rate, an important nutritional measure that is clinically monitored in dialysis patients. This chapter discusses the double pool urea kinetic models and regional blood flow models in order to understand the concept of urea rebound.

Double Pool Urea Kinetic Modeling

Considering the amount of unmanned aerial vehicles (UAV) and the forthcoming new kinds of UAV’s, it is important to design a control strategy for different kinds of UAV’s tasks to provide a fast, robust, reliable, and accurate control approaches for trajectory tracking of any kind of UAVs. For these reasons, this paper is proposed a decoupled lateral-longitudinal control strategy for unmanned aerial vehicles. This research study begins with the dynamic model derivation of the separated unmanned aerial vehicle dynamics, this means, by separating the longitudinal dynamics and lateral dynamics. This objective is achieved by obtaining the longitudinal and lateral kinematics, and on this basis, the longitudinal dynamics are obtained by the linear coordinates in the longitudinal plane along with the pitch angle. Something similar is done to obtain the lateral kinematics in which the roll and yaw angle along with the linear displacement in y are obtained. For the lateral and longitudinal dynamics, derivation of two Lagrangians is derived for each case, and then by the Euler-Lagrange formulation, the respective dynamic models are obtained. The decoupled controller for the longitudinal and lateral dynamics is obtained first by providing an extra state variable that considers the errors between the desired roll, pitch, and yaw angle and the obtained angles, to provide the required actuator position and attitude. A numerical experiment is provided in which the desired reference profile is implemented. As it is corroborated, the action of the controller for the longitudinal and lateral dynamics in their respective loops drives the variables to the required final value.

Ahmad Taher Azar

Papers

An introduction to backstepping control

A new multistable plasma torch chaotic jerk system, its dynamical analysis, active backstepping control, and circuit design

Proportional Integral Loop Shaping Control Design With Particle Swarm Optimization Tuning

Double Pool Urea Kinetic Modeling

Decoupled Lateral-Longitudinal Dynamic Modeling and Control of Unmanned Aerial Vehicles