Method for Fault Diagnosis and Speed Control of PMSM

In the field of fault tolerance estimation, the increasing attention in electrical motors is the fault detection and diagnosis. The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis. It has now turned out to be essential to diagnose faults at their very inception; as unscheduled machine downtime can upset deadlines and cause heavy financial burden. In this paper, fault diagnosis and speed control of permanent magnet synchronous motor (PMSM) is proposed. Elman Neural Network (ENN) is used to diagnose the fault of permanent magnet synchronous motor. Both the fault location and fault severity are considered. In this, eccentricity fault may occur in the motor. To control the speed of the permanent magnet synchronous motor, Dolphin Swarm Optimization (DSO) algorithm is used. The proposed work is simulated by using MATLAB in terms of amplitude, speed and torque. The comparison graph of speed vs. torque obtained by the proposed method gives better result compared to the other existing techniques. The proposed work is also compared with Particle Swarm Optimization (PSO) and Elephant Herding Optimization (EHO) algorithm. The proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm Optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.     

Improved fractional augmented control strategies for continuously stirred tank reactors

Controlling concentration and temperature of continuously stirred tank reactor (CSTR) is an extremely challenging task in chemical process industries. This is because conventional unity feedback schemes do not guarantee stable operating conditions for the CSTR process. Therefore, fractional calculus is augmented with multi-loop control to achieve enhanced stability and closed-loop performance than unity feedback structure in this work. Accordingly, three different fractional-order-based novel multi-loop control structures are proposed based on time domain analysis. In two of the proposed strategies, fractional-order PID controller (FOPID) and internal model control (IMC)-based FOPID controller with fractional filter are used in the inner-loop. Moreover, fractional-order-based Lyapunov stability rule of model reference adaptive control (MRAC) is used in outer-loops for both of the above mentioned methods. Finally, an advanced multi-loop predictor with FOPD controller in the inner-loop and FOIMC controller in the outer-loop is also proposed. In this work, fractional-order, filter parameters, and FOPID settings are obtained by minimizing multi-objective functions using modified particle swarm optimization (PSO) algorithm. Closed-loop responses and control efforts of the proposed control strategies are compared with that of FO-Lyapunov-based MRAC scheme. Quantitative performance analysis is also carried out on all proposed methods based on error metrics and total variation of the control signal.     

Performance of CPU/GPU compiler directives on ISO/TTI kernels

GPUs are slowly becoming ubiquitous devices in High Performance Computing, as their capabilities to enhance the performance per watt of compute intensive algorithms as compared to multicore CPUs have been identified. The primary shortcoming of a GPU is usability, since vendor specific APIs are quite different from existing programming languages, and it requires a substantial knowledge of the device and programming interface to optimize applications. Hence, lately a growing number of higher level programming models are targeting GPUs to alleviate this problem. The ultimate goal for a high-level model is to expose an easy-to-use interface for the user to offload compute intensive portions of code (kernels) to the GPU, and tune the code according to the target accelerator to maximize overall performance with a reduced development effort. In this paper, we share our experiences of three of the notable high-level directive based GPU programming models—PGI, CAPS and OpenACC (from CAPS and PGI) on an Nvidia M2090 GPU. We analyze their performance and programmability against Isotropic (ISO)/Tilted Transversely Isotropic (TTI) finite difference kernels, which are primary components in the Reverse Time Migration (RTM) application used by oil and gas exploration for seismic imaging of the sub-surface. When ported to a single GPU using the mentioned directives, we observe an average 1.5–1.8x improvement in performance for both ISO and TTI kernels, when compared with optimized multi-threaded CPU implementations using OpenMP.     

Homogeneous dynamical systems theory

We study controlled homogeneous dynamical systems and derive conditions under which the system is perspective controllable. We also derive conditions under which the system is observable in the presence of a control over the complex base field. In the absence of any control input, we derive a necessary and sufficient condition for observability of a homogeneous dynamical system over the real base field. The observability criterion obtained generalizes a well known Popov-Belevitch-Hautus rank criterion to check the observability of a linear dynamical system. Finally, we introduce rational, exponential, interpolation problems as an important step toward solving the problem of realizing homogeneous dynamical systems with minimum state dimensions     

Understanding and designing for intermediated information tasks in India

It's important to consider both primary and secondary users when designing for intermediated interaction scenarios in India and elsewhere in the developing world. However, most of this research has focused on supporting users in the developed world who are voluntarily collaborating on a computing task. Many users in India, especially those from disadvantaged classes, have only partial or no physical access to computing devices. We refer to these users as secondary users to distinguish them from the primary users that the interface design process traditionally considers. Secondary users must interact with information resources via a proxy primary user who has the required access rights and skills. The proxy's filtering and funneling decisions limit the secondary users' information-seeking behavior; the secondary user might also have an unequal power relationship with the proxy. Therefore, secondary users might never know the full scope of actions and knowledge available to them. If we are to realize the egalitarian potential of computing, we must consider secondary users in the design process. We must develop technologies that recognize the needs and aspirations of all classes of users, including those without direct access to the user interface. In fact, by designing user interfaces explicitly supporting intermediated tasks, both primary and secondary users can benefit.     

Forking genetic algorithms: GAs with search space division schemes

In this article, we propose a new type of genetic algorithm (GA), the forking GA (fGA), which divides the whole search space into subspaces, depending on the convergence status of the population and the solutions obtained so far. The fGA is intended to deal with multimodal problems that are difficult to solve using conventional GAs. We use a multipopulation scheme that includes one parent population that explores one subspace and one or more child populations exploiting the other subspace. We consider two types of fGAs, depending on the method used to divide the search space. One is the genotypic fGA (g-fGA), which defines the search subspace for each subpopulation, depending on the salient schema within the genotypic search space. The other is the phenotypic fGA (p-fGA), which defines a search subspace by a neighborhood hypercube around the current best individual in the phenotypic feature space. Empirical results on complex function optimization problems show that both the g-fGA and p-fGA perform well compared to conventional GAs. Two additional utilities of the p-fGA are also studied briefly.     

On the asymptotic performance of IDA*

Since best‐first search algorithms such as A* require large amounts of memory, they sometimes cannot run to completion, even on problem instances of moderate size. This problem has led to the development of limited‐memory search algorithms, of which the best known is IDA*. This paper presents the following results about IDA* and related algorithms: 1) The analysis of asymptotic optimality for IDA* in [R.E. Korf, Optimal path finding algorithms, in: Search in Artificial Intelligence, eds. L. Kanal and V. Kumar (Springer‐Verlag, 1988) pp. 200-222] is incorrect. There are trees satisfying the asymptotic optimality conditions given in [R.E. Korf, Optimal path finding algorithms, in: Search in Artificial Intelligence, eds. L. Kanal and V. Kumar (Springer‐Verlag, 1988) pp. 200-222] for which IDA* is not asymptotically optimal. 2) To correct the above problem, we state and prove necessary and sufficient conditions for asymptotic optimality of IDA* on trees. On trees not satisfying our conditions, we show that no best‐first limited‐memory search algorithm can be asymptotically optimal. 3) On graphs, IDA* can perform quite poorly. In particular, there are graphs on which IDA* does Ω(2^2N) node expansions where N is the number of nodes expanded by A*. This revised version was published online in June 2006 with corrections to the Cover Date.     

Fast computation of smallest enclosing circle with center on a query line segment

Here we propose an efficient algorithm for computing the smallest enclosing circle whose center is constrained to lie on a query line segment. Our algorithm preprocesses a given set of n points P={p₁,p₂,…,pn} such that for any query line or line segment L, it efficiently locates a point c on L that minimizes the maximum distance among the points in P from c. Roy et al. [S. Roy, A. Karmakar, S. Das, S.C. Nandy, Constrained minimum enclosing circle with center on a query line segment, in: Proc. of the 31st Mathematical Foundation of Computer Science, 2006, pp. 765-776] have proposed an algorithm that solves the query problem in O(log²n) time using O(nlogn) preprocessing time and O(n) space. Our algorithm improves the query time to O(logn); but the preprocessing time and space complexities are both O(n²).     

Deep neural networks for automatic grain-matrix segmentation in plane and cross-polarized sandstone photomicrographs

Applied Intelligence - Grain segmentation of sandstone that is partitioning the grain from its surrounding matrix/cement in the thin section is the primary step for computer-aided mineral...     

Proportional navigation guidance using predictive and time delay control

A new formulation of the proportional navigation guidance law using the continuous time nonlinear predictive control approach is proposed. The guidance law needs information about the target acceleration for its implementation, which is generally not available. In this paper, this problem is addressed by estimating the target acceleration using the time delay control (TDC). The effectiveness of the guidance law and the estimation of the target acceleration is demonstrated by simulation in a realistic scenario against a highly maneuvering target.