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

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Retraction Note: Tolerance rough set firefly-based quick reduct

Haemodialysis (HD)-induced hypotension is still a severe complication in spite of all the progress in HD treatment. Because of its multifactor causes, HD-induced hypotension cannot be reliably prevented by conventional Ultrafiltration (UF) and sodium profiling in open-loop systems, as they are unable to adapt themselves to actual decreases in Blood Pressure (BP). Therefore, the ultimate goal of this paper is to build a biofeedback system dynamics software to provide automatic control of UF during HD. Furthermore, the treatment should improve patient comfort and be carried out without use of additional body sensors and without additional medication.

Ultrafiltration control during haemodialysis

In this chapter, an Active Disturbance Rejection Control (ADRC) is developed to trajectory tracking control of a Tricopter Unmanned Aerial Vehicle (UAV). The extended state observer (ESO) is an essential part of ADRC. The performance of ESO has direct impact on the performance of controlled system. This study addresses the design of ADRC for Tricopter UAV based on three types of extended state observers; one is the fractional order extended state observer (FOESO) and the two others are based on nonlinear extended state observer (NESO) and Super Twisting Extended State Observer (STESO). A performance comparison has been made between the observers in terms of robustness against variation of parameters and the capability to reject applied disturbance. Tunicate Swarm Algorithm Method (TSA) is used to tune the parameters of ADRC to reach minimum error in order to further improve the dynamic performance of the controlled system. Numerical simulations have been conducted to assess the effectiveness of proposed ADRC based on FOESO in the presence of uncertainty and external disturbance. 

Fractional Order Extended State Observer Enhances the Performance of Controlled Tri-copter UAV Based on Active Disturbance Rejection Control

In this research, a unique subspace data driven control for linear parameter changing system with scheduling parameters is presented. This control paves the way for investigating the nonlinear system based on the results regarding the linear system that are already known. Only the data matrix is utilized to represent the output prediction value in the future various time instants, while the input-output observation data matrix is used to identify Markov parameters in the form of state space forms. The cost function in data-driven control is then adjusted using the output prediction value. The optimal control input value of this quadratic cost function is solved using a parallel distribution technique, and the algorithm's iterative convergence is thoroughly examined. Finally, the DC motor, whose mass distribution factor is considered to be one linear parameter varying system, is controlled using the suggested subspace data driven control approach.

/pdf/subspace-data-driven-control-for-linear-parameter-varying-13xdim1n.pdf

Subspace Data-Driven Control for Linear Parameter Varying Systems

In view of the fact that the output is only disturbed by error in most of the current system studies, this article proposes a closed-loop variable system model with error (both input and output signals are disturbed by noise) and designs the controller of the system. In this study, minimum variance controller and self-correcting minimum variance controller are designed using minimum variance control. Then an example is given to evaluate the performance of the designed controller using minimum variance performance evaluation. Finally, the closed-loop variable error system is combined with the quadrotor UAV (unmanned aerial vehicle), and the position controller in the position control loop of the quadrotor UAV is designed. The experimental results show that the controller has good performance and can well meet the design needs.

/pdf/design-of-a-closed-loop-error-in-variable-system-controller-1ia5dfxi.pdf

Ahmad Taher Azar

Papers

Retraction Note: Tolerance rough set firefly-based quick reduct

Ultrafiltration control during haemodialysis

Fractional Order Extended State Observer Enhances the Performance of Controlled Tri-copter UAV Based on Active Disturbance Rejection Control

Subspace Data-Driven Control for Linear Parameter Varying Systems

Design of a Closed-Loop Error-in-Variable System Controller and Its Application in Quadrotor UAV