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

The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

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The CMS experiment at the CERN LHC

An Introduction to MultiAgent Systems.

The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

/pdf/smart-grid-the-new-and-improved-power-grid-a-survey-4jypbbgv3j.pdf

Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

In the scope of a recently launched European Research Project, a team of experts from public laboratories and TSO is in charge of defining the concepts and methodological approaches to design and analyse the technical and economic feasibility of future HVDC grids. This work aims at identifying, assessing and comparing several possible HVDC network topologies, with appropriate control and protection schemes, able to collect wind energy on large areas, transmit it at the best points to the AC grid and provide the necessary ancillary services for optimising the DC / AC interconnection in normal and disturbed conditions. The methodology adopted for the study and presented in this paper will focus on three main items: 1. identify and assess the economic drivers for the development of off-shore HVDC networks 2. identify the requirements for an optimal operation of the AC / DC interconnected power systems under normal and emergency conditions 3. conceptualise the coordinated control / command and protection plans for HVDC networks This paper gives a comprehensive view of the issues and tasks to be addressed during the run of the project.

/pdf/economic-and-technical-criteria-for-designing-future-off-4bi75htcie.pdf

Economic and technical criteria for designing future off-shore HVDC grids

The proposed testbed of the cyber‐power system consists of power system simulation, substation automation, and the SCADA system. Scenarios for substation cyber security intrusions and anomaly detection concepts have been proposed. An attack tree method can be used to identify vulnerable substations and intrusions through remote access points. Specific substation vulnerability scenarios have been tested. Temporal anomaly is determined by data and information acquired at different time points. This is a metric to determine the anomaly between two snapshots. In a distributed intrusion detection algorithm, distributed agents are trained with a large number of scenarios and intended for real‐time applications. In a distributed environment, if an anomaly is detected by one agent, it is able to distribute critical information to other agents in the network.

An intrusion and defense testbed in a cyber-power system environment

On August 14, 2003, the U.S. faced the largest blackout in history, which left over 50 million people without electricity in eight U.S. states and part of Canada. This paper investigates the effects of the blackout on the security values of the U.S. electric utilities and manufacturing firms in the electric power equipment industry, using an event study method. The results of this empirical study show that the electric utilities were negatively affected, but the electrical equipment manufacturing firms were significantly, positively affected.

Empirical Analysis of the Impact of 2003 Blackout on Security Values of U.S. Utilities and Electrical Equipment Manufacturing Firms

PENET: A practical method and tool for integrated modeling of security attacks and countermeasures

The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed.

https://www.mdpi.com/1996-1073/9/4/297/pdf

Chen-Ching Liu

Papers

Economic and technical criteria for designing future off-shore HVDC grids

An intrusion and defense testbed in a cyber-power system environment

Empirical Analysis of the Impact of 2003 Blackout on Security Values of U.S. Utilities and Electrical Equipment Manufacturing Firms

PENET: A practical method and tool for integrated modeling of security attacks and countermeasures

Smart Distribution Systems