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

Power Quality Improvement for Grid-Connected Photovoltaic Panels Using Direct Power Control

In order to overcome the problem of unmodeled dynamics, design of accurate control strategies for trajectory tracking or force control is necessary. Starting from the mathematical modeling stage, it’s necessary to select, design and implement robust control strategies to eliminate this problem. On the other side, for different kinds of applications, time varying loads are important to be considered because of different applications of robotics in industry and other fields. For these reasons, in this study, the design of an adaptive integral sliding mode force controller for robotic manipulators is proposed. An adaptive control law is designed by selecting an integral sliding surface and by using a Lyapunov function. The respective adaptive gains are obtained taking into account the parametric uncertainties that represent the unmodeled system’s dynamics which are found in the inertia, mass and center of mass terms of the robot. Taking into account the robustness of the adaptive sliding mode force control, the time varying loads or external disturbances that can be found in the end effector and on the robot structure that can occur in a specific task are suppressed satisfactorily. The simulation results demonstrated that the adaptive integral sliding mode controller ensures the closed loop stability of the system while following the end effector time varying profile.

Adaptive Integral Sliding Mode Force Control of Robotic Manipulators with Parametric Uncertainties and Time-Varying Loads

Convolution neural network based automatic localization of landmarks on lateral x-ray images

Improvement of fuel cell MPPT performance with a fuzzy logic controller

Measuring post dialysis urea rebound (PDUR) requires a 30- or 60-minute post-dialysis sampling, which is inconvenient. This paper presents a novel technique for predicting equilibrated urea concentration and post dialysis urea rebound in the form of a Takagi-Sugeno-Kang fuzzy inference system. The advantage of this neuro-fuzzy hybrid approach is that it doesn't require 30-60-minute post-dialysis urea sample. Adaptive neuro-fuzzy inference system (ANFIS) was constructed to predict equilibrated urea (Ceq) taken at 60 min after the end of the hemodialysis (HD) session in order to predict PDUR. The accuracy of the ANFIS was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), PDUR and equilibrated dialysis dose (eqKt/V). The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms artificial neural networks and other traditional urea kinetic models (UKM).

Ahmad Taher Azar

Papers

Power Quality Improvement for Grid-Connected Photovoltaic Panels Using Direct Power Control

Adaptive Integral Sliding Mode Force Control of Robotic Manipulators with Parametric Uncertainties and Time-Varying Loads

Convolution neural network based automatic localization of landmarks on lateral x-ray images

Improvement of fuel cell MPPT performance with a fuzzy logic controller

Neuro-Fuzzy System for Post-Dialysis Urea Rebound Prediction