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.

Optimization of renewable distributed generation's (DGs) uncertainty can make the profit of electricity market more certain. Struggles are continuously in progress to develop the tools for accurate prediction of power output from renewable DGs. This paper presents an uncertainty analysis method for optimal day-ahead power dispatch of islanded microgrid (MG) based on net demand forecasting method. Firstly a multiobjective optimization algorithm has developed to optimally place and size the DGs. Then wind power uncertainty is handled and analyzed under the stochastic programming. Optimal power dispatch strategy has been performed for islanded microgrid. IEEE 33-bus standard test system has been converted into microgrid with the integration of wind power and conventional DGs.

Uncertainty Analysis for Day Ahead Stochastic Power Dispatch of Islanded Microgrid

In order to solve the control problem of morphing aircraft under anti-jamming requirement, this paper proposed a new type of active disturbance rejection controller (ADRC) with optimized parameters. First, a longitudinal nonlinear dynamic model of a variable-swept-wing aircraft was established. Then, a set of control schemes with cascaded structure were proposed and summarized, and the decoupling control of speed and altitude was realized through the throttle opening and elevator deflection angle respectively. Next, for the controller of each loop, an extended state observer was designed to estimate all states in real time, the backstepping method was used to design the output feedback control law, and the dynamic inversion method was used to solve the control non-affine problem. Finally, the particle swarm optimization (PSO) algorithm was used to optimize controller parameters. The simulation results show that the PSO algorithm converges well when optimizing the controller parameters, and the parameter-optimized ADRC can track the command well in the case of configuration change and interference existence.

Active Disturbance Rejection Control of Morphing Aircraft with PSO-Based Parameter Optimization

An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact on aggregator's scheduling decision and each customer's cost, while solar energy fluctuation can cause an overvoltage problem in distribution networks. However, the probability distributions of these uncertainties always have errors, even in emerging data-based methods. There is no stochastic method using real data with an out-of-sample guarantee suitable for this distributed approach so far to help an aggregator avoid price risk and manage customers' energy against solar energy fluctuation. To address these unsolved issues, we propose a data-driven Wasserstein distributionally robust formulation of the aggregator's agent and customer's agent respectively. The Wasserstein metric is employed to construct the Wasserstein ambiguity set. The mathematical models are then reformulated equivalently to convex programming respectively so that the operating model can be solved by the off-the-shelf solver. To improve the efficiency of the distributed solving framework, an alternating optimization procedure (AOP) process is proposed to overcome the issue caused by binary variables in the alternating direction method of multipliers (ADMM). The proposed operation framework is verified on the modified IEEE 33-bus distribution network and realistic single-feeder LV network. 

A Customer-centric Distributed Data-driven Stochastic Coordination Method for Residential PV and BESS

This paper proposes a XML-based power heterogeneous data integrating framework. The tool uses XML to describe and model the objects of power system in a similar way with rational database. It helps transform the heterogeneous data belonging to different power organizations to the data with uniform format stored as XML files. The tool can enable the heterogeneous data integrating and exchanging of power system. Thus various distributed application can be developed base on this framework. The practical application proved the efficiency of proposed framework.

A Novel XML-Based Power Resource Modeling Framework for Power System Heterogeneous Data Integrating

Distribution network reconfiguration has been widely applied to enhance resilience of active distribution networks (ADNs) after extreme contingency events. While distributed generation (DG) units are connected to the network after network reconfiguration, uncertain loads and renewable power outputs may cause power imbalance and frequency deviation in each islanded network. To address this challenge, this paper proposes a reconfiguration method considering system frequency and a practical voltage frequency dependent load model. In addition, this paper considers the full reconfiguration potential that each branch has one switch to be controlled more flexibly, thus restoring loads to the maximal level. To address the proposed intractable load restoration problem, an advanced alternative optimization algorithm is proposed. Thus, a resilience enhancement method for ADNs under the post-event state is proposed. Case study conducted on a 33-bus system verifies the effectiveness of the proposed method on load restoration with consideration of uncertainty realizations and frequency deviations.

Zhao Yang Dong

Papers

Uncertainty Analysis for Day Ahead Stochastic Power Dispatch of Islanded Microgrid

Active Disturbance Rejection Control of Morphing Aircraft with PSO-Based Parameter Optimization

A Customer-centric Distributed Data-driven Stochastic Coordination Method for Residential PV and BESS

A Novel XML-Based Power Resource Modeling Framework for Power System Heterogeneous Data Integrating

Reconfiguration Based Load Restoration in Active Distribution Networks with A Voltage Frequency Dependent Load Model