Power System Oscillations.- Linear Control in Power Systems.- Test System Model.- Power System Stabilizers.- Multiple-Model Adaptive Control Approach.- Simultaneous Stabilization.- Mixed-Sensitivity Approach Using Linear Matrix Inequalities.- Normalized ?? Loop-Shaping Using Linear Matrix Inequalities.- ?? Control For Time-Delayed Systems.

Robust Control in Power Systems

Supplier selection in agile supply chains: An information-processing model and an illustration

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article.

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Optimization Methods for Electric Power Systems: An Overview

Fuzzy logic, neural networks, and evolutionary computation are the core methodologies of soft computing (SC). SC is causing a paradigm shift in engineering and science fields since it can solve problems that have not been able to be solved by traditional analytic methods. In addition, SC yields rich knowledge representation, flexible knowledge acquisition, and flexible knowledge processing, which enable intelligent systems to be constructed at low cost. This paper reviews applications of SC in several industrial fields to show the various innovations by TR, HMIQ, and low cost in industries that have been made possible by the use of SC. Our paper intends to remove the gap between theory and practice and attempts to learn how to apply soft computing practically to industrial systems from examples/analogy, reviewing many application papers.

/pdf/industrial-applications-of-soft-computing-a-review-45ao4h93ry.pdf

Industrial applications of soft computing: a review

There is provided an information processing device including a display section configured to display a first object in a virtual three-dimensional space having a depth direction of a display screen, an operation section configured to acquire an operation for moving the first object in at least the depth direction, and a controller configured to move the first object on the display screen in accordance with the acquired operation, to execute, when a region of the first object overlaps a first overlap determination region, a first process to one or both of the first and second objects, and to execute, when the region of the first object overlaps a second overlap determination region, a second process to one or both of the first and second objects. The first overlap determination region may be a region obtained by extending the second overlap determination region in at least the depth direction.

Information processing device, information processing method, and program

Summary form only given as follows. This paper presents a new approach for real-time tuning the parameters of a conventional power system stabilizer (PSS) using a radial basis function (RBF) network. The RBF network is trained using an orthogonal least squares (OLS) learning algorithm. Investigations reveal that the required number of RBF centers depends on spread factor, /spl beta/ and the number of training patterns. Studies show that a parsimonious RBF network can be obtained by presenting a relatively smaller number of training patterns, generated randomly and spreadover the entire operating domain. Investigations reveal that the dynamic performance of the system with an RBF network adaptive PSS (RBFAPSS) is virtually identical to that of an artificial neural network based adaptive PSS (ANNAPSS). The dynamic performance of the system with RBFAPSS is quite robust over a wide range of loading conditions and equivalent reactance X/sub c/.

Radial basis function (RBF) network adaptive power system stabilizer

System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.

Visual Descriptors Based Video Quality Assessment Using Outlier Model

The paper presents a systematic approach for designing a radial basis function (RBF) network based adaptive power system stabilizer using orthogonal least squares learning algorithm. The training patterns are generated over a wide range in machine real/reactive power output and terminal voltage using linearized model of the system. Investigations reveal that the required number of RBF centers heavily depend on the spread factor /spl beta/ and tolerance expressed as sum of squared errors. Studies reveal that the dynamic performance of the system with radial basis function network adaptive power system stabilizer (RBFAPSS) is superior to that with a conventional PSS. Moreover, RBFAPSS provides optimum performance for a wide range in loading conditions and large perturbations.

Orthogonal least squares learning algorithm based radial basis function (RBF) network adaptive power system stabilizer

A method and system for calculating a blur artifact in a video are disclosed. The video includes a series of frames captured at a predefined interval of time. The frames include one or more pixels. Calculating the blur artifact in the video includes identifying a focused area in a set of frames. Further, edges are detected in each of the frames. Furthermore, the blur artifact is calculated as a ratio of number of blurred pixels and total number edge pixels.

Method and system for calculating blur artifact in videos

Systems and Methods for efficient and reliable detection of error blocks in a video based on detecting one or more candidate blocks in a region of interest and then verifying the block error on the basis of the patterns formed inside the candidate block and its distinction from the surrounding blocks spatially and/or temporally

Shekhar Madnani

Papers

Radial basis function (RBF) network adaptive power system stabilizer

Visual Descriptors Based Video Quality Assessment Using Outlier Model

Orthogonal least squares learning algorithm based radial basis function (RBF) network adaptive power system stabilizer

Method and system for calculating blur artifact in videos

Methods and Systems for Detecting Block Errors in a Video