Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.

An SIMD machine for low-level vision

This paper presents an SIMD machine which has been tuned to execute low-level vision algorithms employing the relaxation labeling paradigm. Novel features of the design include: 1. (1) a communication scheme capable of window accessing under a single instruction. 2. (2) flexible I/O instructions to load overlapped data segments; and 3. (3) data-conditional instructions which can be nested to an arbitrary degree. A time analysis of the stereo correspondence problem, as implemented on a simulated version of the machine using the probabilistic relaxation technique, shows a speed up of almost N2 for an N × N array of PEs.

DTC Technique for Induction Machine based Integrated Starter-Generator of an Automobile

This paper mainly concentrates on the application of the direct torque control (DTC) technique for the induction machine based integrated startergenerator (ISG) for automobile applications. It also discusses in brief about the higher DC bus voltage requirements in the automobiles i.e. present 14V system vs. 42V system to meet the power requirements, modes of operation of ISG, electric machine and the drive selection for the ISG,description of DTC technique, simulation and experimental results, and implementation.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

Dynamic response of latex modified concrete machine foundations

New materials in concrete constructions have been widely used to improve various properties such as impact resistance, strength and durability. Polymer modified concrete is one of the new materials which has been developed for potential application in the construction industry. This Paper describes the use of polymer latex for foundation blocks subjected to dynamic loads. Experiments were conducted using ordinary concrete and latex modified concrete footings of three different thicknesses, for three static loads at four excitation levels. Experimental results have revealed that the amplitude of resonance is reduced considerably in the latex modified concrete footings.

Minimizing total completion time on a batch processing machine with job families

We consider the problem of minimizing the total completion time on a single batch processing machine. The set of jobs to be scheduled can be partitioned into a number of families, where all jobs in the same family have the same processing time. The machine can process at most B jobs simultaneously as a batch, and the processing time of a batch is equal to the processing time of the longest job in the batch. We analyze that properties of an optimal schedule and develop a dynamic programming algorithm of polynomial time complexity when the number of job families is fixed. The research is motivated by the problem of scheduling burn-in ovens in the semiconductor industry

Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity

The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio, state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (sigma'(v0)), soil type, shear wave velocity (V-s) and earthquake parameters such as peak horizontal acceleration (a(max)) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are sigma'(v0), soil type. V-s, a(max) and M. In the other model based on shear wave velocity alone uses V-s, a(max) and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model. (C) 2010 Elsevier B.V. All rights reserved.

Modeling and Field Oriented Control of Salient Pole Wound Field Synchronous Machine in Stator Flux Coordinates

This paper describes the field oriented control of a salient pole wound field synchronous machine in stator flux coordinates. The procedure for derivation of flux linkage equations along any general rotating axes including stator flux axes is given. The stator flux equations are used to identify the cross-coupling occurring between the axes due to saliency in the machine. The coupling terms are canceled as feedforward terms in the generation of references for current controllers to achieve good decoupling during transients. The design of current controller for stator-flux-oriented control is presented. This paper proposes the method of extending rotor flux closed loop observer for sensorless control of wound field synchronous machine. This paper also proposes a new sensorless control by using stator flux closed loop observer and estimation of torque angle using stator current components in stator flux coordinates. Detailed experimental results from a sensorless 15.8 hp salient pole wound field synchronous machine drive are presented to demonstrate the performance of the proposed control strategy from a low speed of 0.8 Hz to 50 Hz.

Five-level inverter scheme for an open-end winding induction machine with less number of switches

Higher level of inversion is achieved with a less number of switches in the proposed scheme. The scheme proposes a five-level inverter for an open-end winding induction motor which uses only two DC-link rectifiers of voltage rating of Vdc/4, a neutral-point clamped (NPC) three-level inverter and a two-level inverter. Even though the two-level inverter is connected to the high-voltage side, it is always in square-wave operation. Since the two-level inverter is not switching in a pulse width modulated fashion and the magnitude of switching transient is only half compared to the convention three-level NPC inverter, the switching losses and electromagnetic interference is not so high. The scheme is experimentally verified on a 2.5 kW induction machine.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Machine recognition of printed Kannada text

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.