A study on the usefulness of Support Vector Machines for the realtime computational simulation of soft biological organs

Realistic and realtime computational simulation of soft biological organs (e.g., liver, kidney) is necessary when one tries to build a quality surgical simulator that can simulate surgical procedures involving these organs. Since the realistic simulation of these soft biological organs should account for both nonlinear material behavior and large deformation, achieving realistic simulations in realtime using continuum mechanics based numerical techniques necessitates the use of a supercomputer or a high end computer cluster which are costly. Hence there is a need to employ soft computing techniques like Support Vector Machines (SVMs) which can do function approximation, and hence could achieve physically realistic simulations in realtime by making use of just a desktop computer. Present work tries to simulate a pig liver in realtime. Liver is assumed to be homogeneous, isotropic, and hyperelastic. Hyperelastic material constants are taken from the literature. An SVM is employed to achieve realistic simulations in realtime, using just a desktop computer. The code for the SVM is obtained from [1]. The SVM is trained using the dataset generated by performing hyperelastic analyses on the liver geometry, using the commercial finite element software package ANSYS. The methodology followed in the present work closely follows the one followed in [2] except that [2] uses Artificial Neural Networks (ANNs) while the present work uses SVMs to achieve realistic simulations in realtime. Results indicate the speed and accuracy that is obtained by employing the SVM for the targeted realistic and realtime simulation of the liver.

Identification of fault location in power distribution system with distributed generation using support vector machines

This paper presents a multi-class support vector machine (SVMs) approach for locating and diagnosing faults in electric power distribution feeders with the penetration of Distributed Generations (DGs). The proposed approach is based on the three phase voltage and current measurements which are available at all the sources i.e. substation and at the connection points of DG. To illustrate the proposed methodology, a practical distribution feeder emanating from 132/11kV-grid substation in India with loads and suitable number of DGs at different locations is considered. To show the effectiveness of the proposed methodology, practical situations in distribution systems (DS) such as all types of faults with a wide range of varying fault locations, source short circuit (SSC) levels and fault impedances are considered for studies. The proposed fault location scheme is capable of accurately identify the fault type, location of faulted feeder section and the fault impedance. The results demonstrate the feasibility of applying the proposed method in practical in smart grid distribution automation (DA) for fault diagnosis.

Effect of FIB milling on MEMS SOI cantilevers

Stress induced by Focused Ion Beam (FIB) milling of cantilevers fabricated on silicon-on-insulator (SOI) wafer has been studied. Milling induces stress gradients ranging from -10MPa/μm to -120MPa/μm, depending on the location of cantilevers from the point of milling. Simulations were done to estimate the stress in the milled cantilevers.

Resource usage monitoring for KVM based virtual machines

Realization of cloud computing has been possible due to availability of virtualization technologies on commodity platforms. Measuring resource usage on the virtualized servers is difficult because of the fact that the performance counters used for resource accounting are not virtualized. Hence, many of the prevalent virtualization technologies like Xen, VMware, KVM etc., use host specific CPU usage monitoring, which is coarse grained. In this paper, we present a performance monitoring tool for KVM based virtualized machines, which measures the CPU overhead incurred by the hypervisor on behalf of the virtual machine along-with the CPU usage of virtual machine itself. This fine-grained resource usage information, provided by the above tool, can be used for diverse situations like resource provisioning to support performance associated QoS requirements, identification of bottlenecks during VM placements, resource profiling of applications in cloud environments, etc. We demonstrate a use case of this tool by measuring the performance of web-servers hosted on a KVM based virtualized server.

Automatic Data Allocation and Buffer Management for Multi-GPU Machines

Multi-GPU machines are being increasingly used in high-performance computing. Each GPU in such a machine has its own memory and does not share the address space either with the host CPU or other GPUs. Hence, applications utilizing multiple GPUs have to manually allocate and manage data on each GPU. Existing works that propose to automate data allocations for GPUs have limitations and inefficiencies in terms of allocation sizes, exploiting reuse, transfer costs, and scalability. We propose a scalable and fully automatic data allocation and buffer management scheme for affine loop nests on multi-GPU machines. We call it the Bounding-Box-based Memory Manager (BBMM). BBMM can perform at runtime, during standard set operations like union, intersection, and difference, finding subset and superset relations on hyperrectangular regions of array data (bounding boxes). It uses these operations along with some compiler assistance to identify, allocate, and manage data required by applications in terms of disjoint bounding boxes. This allows it to (1) allocate exactly or nearly as much data as is required by computations running on each GPU, (2) efficiently track buffer allocations and hence maximize data reuse across tiles and minimize data transfer overhead, and (3) and as a result, maximize utilization of the combined memory on multi-GPU machines. BBMM can work with any choice of parallelizing transformations, computation placement, and scheduling schemes, whether static or dynamic. Experiments run on a four-GPU machine with various scientific programs showed that BBMM reduces data allocations on each GPU by up to 75% compared to current allocation schemes, yields performance of at least 88% of manually written code, and allows excellent weak scaling.

Study the effect of electrical and mechanical shock loading on liberation and milling characteristics of mineral materials

Milling is an energy intensive process and it is considered as one of the most energy inefficient processes. Electrical and mechanical shock loading can be used to develop a pre-treatment methodology to enhance energy efficiency of comminution and liberation of minerals. Coal and Banded Hematite Jasper (BHJ) Iron ores samples were taken for the study to know the effect of shock loading. These samples were exposed to 5 electric shocks of 300 kV using an electric shock loading device. A diaphragmless shock tube was used to produce 3 and 6 compressed air shocks of Mach number 2.12 to treat the coal and Iron ore samples. Microscopic, comminution and liberation studies were carried out to compare the effectiveness of these approaches. It was found that electric shock loading can comminute the coal samples more effectively and increases the yield of carbon by 40% at 1.6 gm/cc density over the untreated coal samples. Mechanical shock loading showed improved milling performance for both the materials and 12.90% and 8.1% reduction in the D-80 of the particles was observed during grinding for treated samples of coal and iron, respectively. Liberation of minerals in BHJ Iron ore was found unaffected due to low intensity of the mechanical shock waves and non conductivity of minerals. Compressed air based shock loading is easier to operate than electrical shock loading and it needs to be explored further to improve the energy efficacy of comminution. (C) 2014 Elsevier Ltd. All rights reserved.

Chemical-free graphene by unzipping carbon nanotubes using cryo-milling

Current paper reports synthesis of chemical free graphene by unzipping of the carbon nanotubes (CNTs) using high strain rate deformation at 150K. A specially designed cryomill operating at 150 K was used for the experiments. The mechanism of unzipping was further explored using molecular dynamics (MD) simulations. Both experimental and simulation results reveal two modes of unzipping through radial and shear loading. (C) 2015 Elsevier Ltd. All rights reserved.

Optical and dielectric study of strontium modified barium zirconium titanate ceramic prepared by high energy ball milling

Strontium modified barium zirconium titanate with general formula Ba1-xSrxZr0.05Ti0.95O3 ceramics have been prepared by solid state and high energy ball milling technique. The X-ray diffraction and Rietveld refinement studies show that all the compositions have single phase symmetry. The composition BaZr0.05Ti0.95O3 shows orthorhombic symmetric with space group Amm2. The structure changes from orthorhombic to tetragonal with strontium doping up to x = 0.3 and with further addition, changes to cubic. The scanning electron micrographs show that the grain size decreases with increase in strontium content. The temperature dependent dielectric behavior shows three phase transition in the parent material which merges with an increase in Sr content The transition temperature and dielectric constant decreases with an increase in Sr concentration. The phase transition becomes more diffused with increment in doping concentration. The ferroelectric behavior of the ceramics is studied by the hysteresis loop. The optical behavior is studied by the UV-visible spectroscopy and found that the optical band gap increases with Sr concentration. (C) 2015 Elsevier B.V. All rights reserved.

Stabilization of the high-temperature and high-pressure cubic phase of ZnO by temperature-controlled milling

The reversible transition of wurtzite to rock salt phase under pressure is well reported in literature. The cubic phase is unstable under ambient conditions both in the bulk and in nanoparticles. This paper reports defect-induced stabilization of cubic ZnO phase in sub 20 nm ZnO particles and explores their optical properties. The size reduction was achieved by ball milling in a specially designed mill which allows a control of the milling temperature. The process of synthesis involved both variation of milling temperature (including low temperature similar to 150 K) and impact pressure. We show that these have profound influence in the introduction of defects and stabilization of the cubic phase. A molecular dynamics simulation is presented to explain the observed results. The measured optical properties have further supported the observations of defect-induced stabilization of cubic ZnO and reduction in particle size.

Two-dimensional Cartesian air-gap element (CAGE) for dynamic finite-element modeling of electrical machines with a flat air gap

This paper extends the air-gap element (AGE) to enable the modeling of flat air gaps. AGE is a macroelement originally proposed by Abdel-Razek et al.for modeling annular air gaps in electrical machines. The paper presents the theory of the new macroelement and explains its implementation within a time-stepped finite-element (FE) code. It validates the solution produced by the new macroelement by comparing it with that obtained by using an FE mesh with a discretized air gap. It then applies the model to determine the open-circuit electromotive force of an axial-flux permanent-magnet machine and compares the results with measurements.

A general design method for electric machines using magnetic circuit model considering the flux saturation problem

This paper proposes an analytical approach that is generalized for the design of various types of electric machines based on a physical magnetic circuit model. Conventional approaches have been used to predict the behavior of electric machines but have limitations in accurate flux saturation analysis and hence machine dimensioning at the initial design stage. In particular, magnetic saturation is generally ignored or compensated by correction factors in simplified models since it is difficult to determine the flux in each stator tooth for machines with any slot-pole combinations. In this paper, the flux produced by stator winding currents can be calculated accurately and rapidly for each stator tooth using the developed model, taking saturation into account. This aids machine dimensioning without the need for a computationally expensive finite element analysis (FEA). A 48-slot machine operated in induction and doubly-fed modes is used to demonstrate the proposed model. FEA is employed for verification.