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

The integration of wind turbines (WTs) in variable speed drive systems belongs to the main factors causing low stability in electrical networks. Therefore, in order to avoid this issue, WTs hybridization with a storage system is a mandatory. This paper investigates WT system operating at variable speed. The system contains of a permanent magnet synchronous generator (PMSG) supported by a battery storage system (BSS). To enhance the quality of active and reactive power injected into the network, direct power control (DPC) scheme utilizing space-vector modulation (SVM) technique based on proportional-integral (PI) control is proposed. Meanwhile, to improve the rendition of this method (DPC-SVM-PI), the rooted tree optimization technique (RTO) algorithm-based controller parameter identification is used to achieve PI optimal gains. To compare the performance of RTO-based controllers, they were implemented and tested along with some other popular controllers under different working conditions. The obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion (THD), overshoot percentage, settling time, rise time, average active power value, overall efficiency, and active power steady-state error. 

/pdf/rooted-tree-optimization-for-wind-turbine-optimum-control-1qejqbil.pdf

Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

Drinking water contains chemical, microbiological, and other contaminants. A healthy adult drinks about 10-12 liters of water per week, this water goes across a selective barrier of the gastrointestinal tract, and excess chemicals are removed by the healthy kidney. In contrast, with a typical three times a week hemodialysis protocol, a dialysis patient is exposed to more than 300 liters of water weekly, the water passes through the nonselective dialyzer membrane, and there is no kidney to maintain the normal balance of chemicals. Moreover, the highly permeable high-flux membrane used today increases the risk of increased load of contaminants passing through the membrane and into the blood. Some common contaminants have been shown to be injurious to patients. Thus, the water for dialysis must be purified of these contaminants prior to its use by the proportioning system of the dialysis machine to make the final dialysate. This chapter covers the purpose of water purification before it is used for dialysis. It describes the components of a water treatment system, how the system is monitored, and the common contaminants found in water.

Hemodialysis Water Treatment System

This paper presents an asynchronous distributed switched controller for robotic systems with a dynamic model derivation based on fractional-order Euler–Lagrange formulation. This study begins with the dynamic model derivation of a two links robotic manipulator by a fractional-order Euler–Lagrange formulation. This objective is achieved by selecting an appropriate Lagrangian, considering the linear and angular kinetic energy and linear potential energy. The kinetic and potential energies are determined so that in the case of kinetic energy, a fractional-order operator is utilized for a linear and angular velocity, and a gradient operator is used to generate potential energy, which is important for this purpose to have a spatial derivative. The fractional-order Euler–Lagrange formulation is used to obtain a decoupled position and orientation fractional-order dynamics which achieves a novel fractional-order dynamic model representation to model many physical models with more fidelity. Then an asynchronous distributed switched controller is designed by dividing first the time interval into two parts. Then, by selecting two Lyapunov functionals for each time interval, the asynchronous switched control laws are obtained to ensure exponential stability. In opposition to other studies, a novel switching law is proposed by implementing the required set topology. The objective of this study is only to stabilize the studied robotic mechanism in its equilibrium points. A numerical experiment is shown to validate the theoretical results obtained in this study, proving the proposed control strategy’s effectiveness. 

Fractional-Order Euler–Lagrange Dynamic Formulation and Control of Asynchronous Switched Robotic Systems

In this paper, a loop shaping controller design methodology for single input and a single output (SISO) system is proposed. The theoretical background for this approach is based on complex elliptic functions which allow a flexible design of a SISO controller considering that elliptic functions have a double periodicity. The gain and phase margins of the closed-loop system can be selected appropriately with this new loop shaping design procedure. The loop shaping design methodology consists of implementing suitable filters to obtain a desired frequency response of the closed-loop system by selecting appropriate poles and zeros by the Abel theorem that are fundamental in the theory of the elliptic functions. The elliptic function properties are implemented to facilitate the loop shaping controller design along with their fundamental background and contributions from the complex analysis that are very useful in the automatic control field. Finally, apart from the filter design, a PID controller loop shaping synthesis is proposed implementing a similar design procedure as the first part of this study.

https://www.mdpi.com/2297-8747/26/1/21/pdf

Ahmad Taher Azar

Papers

Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

Hemodialysis Water Treatment System

Fractional-Order Euler–Lagrange Dynamic Formulation and Control of Asynchronous Switched Robotic Systems

Robust H∞ Loop Shaping Controller Synthesis for SISO Systems by Complex Modular Functions

Overview of Biomedical Engineering