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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

For the purpose of motor fault
real-time monitoring, this research developed a nano-silicon ni- tride film based
magnetic field (MF) sensor, and applied this sensor in MF detection of two common faults. Through experiment, it turned out that arc discharge and slot discharge
occur in motor fault produce MF with certain laws. This result proved the feasibility
of the sensor and sensing method in MF analysis, and revealed possibility of a new
method in fault detection.

/pdf/development-of-magnetic-field-sensor-and-motor-fault-57atrkbmbh.pdf

Development of Magnetic Field Sensor and Motor Fault Monitoring Application

The emerging prosumers have changed the way traditional distribution systems operate. While harvesting the benefits of Distributed Energy Resources (DERs), prosumers’ self-dispatching and internal energy trading can also bring technical challenges and economic loss to the distribution system operator (DSO). To this end, a DSO-prosumers cooperated scheduling framework for the transactive distribution system, which considers the impact of prosumers’ peer-to-peer (P2P) energy trading, is proposed in this paper. First, by incorporating the P2P energy trading among prosumers into scheduling, prosumers and the DSO can cooperate to optimize the power flow. Meanwhile, the Nash bargaining theory is introduced to achieve the optimal allocation of interests. Then, to improve the computational efficiency, the established bargaining problem is decomposed into two subproblems, i.e., the optimal physical power flow problem, and the virtual power flow bargaining problem. Finally, in order to protect individual privacy, each problem is solved via the alternating direction method of multipliers (ADMM) algorithm in a distributed manner. Case study based on the IEEE 33-bus test system verifies the effectiveness of the proposed method.

Incorporating P2P Trading Into DSO's Decision-Making: A DSO-Prosumers Cooperated Scheduling Framework for Transactive Distribution System

A number of emerging techniques for power system analysis have been described in the previous chapters of this book. However, given the complexity and ever increasing uncertainties of the power industry, there are always new challenges and consequently new techniques that are needed as well. The major initiatives in the power industry of this decade are no doubt renewable energy and more recently, the smart grid. These new challenges have already encouraged engineers and researchers to explore more emerging techniques. Given the fast changing environment, some of the techniques may become more and more established for power system analysis. These rapid changes also result into the wide diversity in the emerging techniques; consequently, this book can only cover some of these techniques. However, it is expected that these techniques discussed in the book can provide a general overview of the recent advances in power system analysis. As the technology advances, continuous study in this area is expected. This chapter summarizes some of the key techniques discussed in the book. The trends of emerging techniques are also given, followed by a list of topics for further reading.

Conclusions and Future Trends in Emerging Techniques

To improve the autonomous deformation capability of morphing aircraft, a new method was proposed, which established an autonomous deformation strategy knowledge base based on deep learning. A longitudinal nonlinear dynamic model of variable-sweep-wing unmanned aerial vehicle (UAV) was obtained by using multi-rigid-body modeling. DATCOM was used to perform aerodynamic analysis, and then aerodynamic coefficient fitting formula was calculated. Based on prior knowledge, a deformation strategy was designed, which could generate training data for deep learning. A deep neural network (DNN), so-called autonomous deformation strategy knowledge base, was trained by using these data, which could output optimal sweep angle according to current flight states and flight mission. Simulation results show that this DNN gives morphing U A V a better speed maneuverability in deceleration task and a better directional maneuverability in turning task, which can enhance UA V's combat capability.

Research on UAV autonomous deformation strategy based on deep learning

In this paper, we study on evaluating physical education teaching quality based on the entropy model. The entropy model is a measure of the uncertainty associated with a random variable, which is a powerful tool in many research fields. Next, a physical education teaching quality evaluation method is designed by obtaining the maximum information entropy based on the index systems, which include 12 categories index, such as include continuous variables effect factors and discrete variables effect factors. To demonstrate the effectiveness of the proposed method in physical education teaching quality evaluation, an experiment is conducted on ten datasets to make performance evaluation comparing with the results obtained by the students evaluation and experts evaluation.

Zhao Yang Dong

Papers

Development of Magnetic Field Sensor and Motor Fault Monitoring Application

Incorporating P2P Trading Into DSO's Decision-Making: A DSO-Prosumers Cooperated Scheduling Framework for Transactive Distribution System

Conclusions and Future Trends in Emerging Techniques

Research on UAV autonomous deformation strategy based on deep learning

Risk Based Identification of Cascading Chains Based on Generalized Line Outage Distribution Factors