Performance Modeling based on Multidimensional Surface Learning for Performance Predictions of Parallel Applications in Non-Dedicated Environments

Modeling the performance behavior of parallel applications to predict the execution times of the applications for larger problem sizes and number of processors has been an active area of research for several years. The existing curve fitting strategies for performance modeling utilize data from experiments that are conducted under uniform loading conditions. Hence the accuracy of these models degrade when the load conditions on the machines and network change. In this paper, we analyze a curve fitting model that attempts to predict execution times for any load conditions that may exist on the systems during application execution. Based on the experiments conducted with the model for a parallel eigenvalue problem, we propose a multi-dimensional curve-fitting model based on rational polynomials for performance predictions of parallel applications in non-dedicated environments. We used the rational polynomial based model to predict execution times for 2 other parallel applications on systems with large load dynamics. In all the cases, the model gave good predictions of execution times with average percentage prediction errors of less than 20%

Friction and transfer layer formation in polymer-steel tribo-system: Role of surface texture and roughness parameters

In the present investigation, various kinds of surface textures were attained on the steel plates. Roughness of the textures was varied using various grinding or polishing methods. The surface textures were characterized in terms of roughness parameters using an optical profilometer. Then experiments were conducted using an inclined pin-on-plate sliding apparatus to identify the role of surface texture and its roughness parameters on coefficient of friction and transfer layer formation. In the experiments, a soft polymer (polypropylene) was used for the pin and hardened steel was used for the plate. Experiments were conducted at a sliding velocity of 2 minis in ambient conditions under both dry and lubricated conditions. The normal load was varied from 1 to 120 N during the tests. The morphologies of the worn surfaces of the pins and the formation of a transfer layer on the steel plate surfaces were observed using a scanning electron microscope. Based on the experimental results, it was observed that the transfer layer formation and the coefficient of friction along with its two components, namely adhesion and plowing, were controlled by the surface texture of the harder mating surfaces and were less dependent of surface roughness (R(a)) of the harder mating surfaces. The effect of surface texture on the friction was attributed to the variation of the plowing component of friction for different surfaces. Among the various surface roughness parameters studied, the mean slope of the profile, Delta(a), was found to most accurately characterize variations in the friction and wear behavior. (C) 2011 Elsevier B.V. All rights reserved.

Analysis of Strain Rates and Microstructural Evaluation during Metal Forming: Role of Surface Texture and Friction

The surface texture of a die plays an important role in friction during metal forming. In the present study, unidirectional and random surface finishes were produced on hardened steel plate surfaces. To understand the influence of surface texture on friction, experiments were conducted using Al-Mg alloy pins that slid against steel plates of different surface textures. In the sliding experiments, a high coefficient of friction was observed when the pins slid perpendicular to the unidirectional grinding marks and low friction occurred when the pins slid on the random surfaces. Finite element simulations were performed using the measured friction values to understand the stress and strain evolutions in the deforming material using dies with various friction. The numerical results showed that the states of stress and strain rates are strongly influenced by the friction at the interface and hence would influence the final material microstructure. To substantiate the numerical results, laboratory compression tests were conducted. Different surface textures were obtained in order to experience different friction values at different locations. A large variation in the microstructure at these locations was observed during experiments, verifying that surface texture and die friction significantly influence fundamental material formation behavior.

Influence of Surface Texture and Roughness of Softer and Harder Counter Materials on Friction During Sliding

Surface texture influences friction during sliding contact conditions. In the present investigation, the effect of surface texture and roughness of softer and harder counter materials on friction during sliding was analyzed using an inclined scratch testing system. In the experiments, two test configurations, namely (a) steel balls against aluminum alloy flats of different surface textures and (b) aluminum alloy pins against steel flats of different surface textures, are utilized. The surface textures were classified into unidirectionally ground, 8-ground, and randomly polished. For a given texture, the roughness of the flat surfaces was varied using grinding or polishing methods. Optical profilometer and scanning electron microscope were used to characterize the contact surfaces before and after the experiments. Experimental results showed that the surface textures of both harder and softer materials are important in controlling the frictional behavior. The softer material surface textures showed larger variations in friction between ground and polished surfaces. However, the harder material surface textures demonstrated a better control over friction among the ground surfaces. Although the effect of roughness on friction was less significant when compared to textures, the harder material roughness showed better correlations when compared to the softer material roughness.

Synthetic molecular machines for active self-assembly : prototype algorithms, designs, and experimental study

Computer science and electrical engineering have been the great success story of the twentieth century. The neat modularity and mapping of a language onto circuits has led to robots on Mars, desktop computers and smartphones. But these devices are not yet able to do some of the things that life takes for granted: repair a scratch, reproduce, regenerate, or grow exponentially fast–all while remaining functional.

This thesis explores and develops algorithms, molecular implementations, and theoretical proofs in the context of “active self-assembly” of molecular systems. The long-term vision of active self-assembly is the theoretical and physical implementation of materials that are composed of reconfigurable units with the programmability and adaptability of biology’s numerous molecular machines. En route to this goal, we must first find a way to overcome the memory limitations of molecular systems, and to discover the limits of complexity that can be achieved with individual molecules.

One of the main thrusts in molecular programming is to use computer science as a tool for figuring out what can be achieved. While molecular systems that are Turing-complete have been demonstrated [Winfree, 1996], these systems still cannot achieve some of the feats biology has achieved.

One might think that because a system is Turing-complete, capable of computing “anything,” that it can do any arbitrary task. But while it can simulate any digital computational problem, there are many behaviors that are not “computations” in a classical sense, and cannot be directly implemented. Examples include exponential growth and molecular motion relative to a surface.

Passive self-assembly systems cannot implement these behaviors because (a) molecular motion relative to a surface requires a source of fuel that is external to the system, and (b) passive systems are too slow to assemble exponentially-fast-growing structures. We call these behaviors “energetically incomplete” programmable behaviors. This class of behaviors includes any behavior where a passive physical system simply does not have enough physical energy to perform the specified tasks in the requisite amount of time.

As we will demonstrate and prove, a sufficiently expressive implementation of an “active” molecular self-assembly approach can achieve these behaviors. Using an external source of fuel solves part of the the problem, so the system is not “energetically incomplete.” But the programmable system also needs to have sufficient expressive power to achieve the specified behaviors. Perhaps surprisingly, some of these systems do not even require Turing completeness to be sufficiently expressive.

Building on a large variety of work by other scientists in the fields of DNA nanotechnology, chemistry and reconfigurable robotics, this thesis introduces several research contributions in the context of active self-assembly.

We show that simple primitives such as insertion and deletion are able to generate complex and interesting results such as the growth of a linear polymer in logarithmic time and the ability of a linear polymer to treadmill. To this end we developed a formal model for active-self assembly that is directly implementable with DNA molecules. We show that this model is computationally equivalent to a machine capable of producing strings that are stronger than regular languages and, at most, as strong as context-free grammars. This is a great advance in the theory of active self- assembly as prior models were either entirely theoretical or only implementable in the context of macro-scale robotics.

We developed a chain reaction method for the autonomous exponential growth of a linear DNA polymer. Our method is based on the insertion of molecules into the assembly, which generates two new insertion sites for every initial one employed. The building of a line in logarithmic time is a first step toward building a shape in logarithmic time. We demonstrate the first construction of a synthetic linear polymer that grows exponentially fast via insertion. We show that monomer molecules are converted into the polymer in logarithmic time via spectrofluorimetry and gel electrophoresis experiments. We also demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism. This shows the growth of a population of polymers in logarithmic time. We characterize the DNA insertion mechanism that we utilize in Chapter 4. We experimentally demonstrate that we can control the kinetics of this re- action over at least seven orders of magnitude, by programming the sequences of DNA that initiate the reaction.

In addition, we review co-authored work on programming molecular robots using prescriptive landscapes of DNA origami; this was the first microscopic demonstration of programming a molec- ular robot to walk on a 2-dimensional surface. We developed a snapshot method for imaging these random walking molecular robots and a CAPTCHA-like analysis method for difficult-to-interpret imaging data.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Prediction of scoliosis curve type based on the analysis of trunk surface topography

Scoliosis treatment strategy is generally chosen according to the severity and type of the spinal curve. Currently, the curve type is determined from X-rays whose acquisition can be harmful for the patient. We propose in this paper a system that can predict the scoliosis curve type based on the analysis of the surface of the trunk. The latter is acquired and reconstructed in 3D using a non invasive multi-head digitizing system. The deformity is described by the back surface rotation, measured on several cross-sections of the trunk. A classifier composed of three support vector machines was trained and tested using the data of 97 patients with scoliosis. A prediction rate of 72.2% was obtained, showing that the use of the trunk surface for a high-level scoliosis classification is feasible and promising.

Properties of Support Vector Machines

Support Vector Machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed Support Vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this paper we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending only on the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that there are at most m+1 margin vectors and observe that m+1 SVs are usually sufficient to fully determine the decision surface. For relatively small m this latter result leads to a consistent reduction of the SV number.

Studies on the cooling minimum quantity and conventional cooling at hardened steels in grinding process

The purpose of this work is to explain the concept of cutting fluids reasonable usage through the fluid minimum quantity in grinding processes. on that purpose, the development of a new nozzle and an own and adequate methodology should be required in order to obtain good results and compare them to the conventional methods. The analysis of the grinding wheel/cutting fluid performance was accomplished from the following input parameters: flow rate variation by nozzle diameter changes (three diameters values: 3mm, 4mm and 5mm), besides the conventional round nozzle already within the machine. Integral oil and a synthetic emulsion were used as cutting fluids and a conventional grinding wheel was employed. The workpieces were made of steel VC 131, tempered and quenched with 60HRc. Thus, as the flow rate and the nozzle diameter changes, keeping steady fluid jet velocity (equal to cutting velocity), attempted to find the best machining conditions, with the purpose to obtain a decrease on the cutting fluid volume, taking into consideration the analysis of the process output variables such as cutting strength, cutting specific energy, grinding wheel wear and surface roughness. It was verified that the 3mm diameter optimized nozzle and the integral oil, in general, was the best combination among all proposed.

Improving minimum quantity lubrication in CBN grinding using compressed air wheel cleaning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of the Influence of Sparkout Time on Grinding Using Several Lubrication/Cooling Methods

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Investigation on Surface Finishing of Components Ground with Lapping Kinematics: Lapgrinding Process

Over the last three decades, researchers have responded to the demands of industry to manufacture mechanical components with geometrical tolerance, dimensional tolerance, and surface finishing in nanometer levels. The new lapgrinding process developed in Brazil utilizes lapping kinematics and a flat grinding wheel dressed with a single-point diamond dresser in agreement with overlap factor (U(d)) theory. In the present work, the influences of different U(d) values on dressing (U(d) = 1, 3 e 5) and grain size of the grinding wheel made of silicon carbide (SiC = 800, 600 e 300 mesh) are analyzed on surface finishing of stainless steel AISI 420 flat workpieces submitted to the lapgrinding process. The best results, obtained after 10 minutes of machining, were: average surface roughness (Ra) 1.92 nm; 1.19 mu m flatness deviation of 25.4 mm diameter workpieces and mirrored surface finishing. Given the surface quality achieved, the lapgrinding process can be included among the ultra-precision finishing processes and, depending on the application, the steps of lapping followed by polishing can be replaced by the proposed abrasive process.

Surface finishing of flat pieces when submitted to lapping kinematics on abrasive disc dressed under several overlap factors

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Influence of the grinding wheel's dive speed on the damage of rectified hardened steels

The world tendency is the increase of the productivity and the production of pieces more and more sophisticated, with high degree of geometric and dimensional tolerances, with good surface finish and low cost. Rectification is responsible for the final finish in the machining process of a material. However, damages generated in this production phase affect all the resources used in the previous processes. Great part of the problems happennig in the rectification process is due to the enormous temperature generated in this activity because of the machining conditions. The dive speed, which is directly related to the productivity, is considered responsible for the damages that occur during rectification, limiting its values to those that do not cause such damages. In this work, through the variation of the dive speed in the process of cylindrical grinding of type ABNT D6 steel, rationalizing the application of two cutting fluids and using a CBN (cubic boron nitrate) abrasive wheel with vitrified blond, the influence of the dive speed on the surface damages of hardened steels was evaluated. The results allowed to say that the dive speed, associated to an efficient cooling and lubrication, didn't provoke thermal damages (including heated zones, cracks and tension stresses) to the material. Residual stresses and the roughness of rectified materials presented a correlation with the machining conditions. The work concluded that it is possible to increase the productivity without provoking damages in the rectified components.

Advanced ceramics: Evaluation of the mechanism of stock removal and ground surface quality

The aim of this work is to evaluate the mechanism of stock removal and the ground surface quality of advanced ceramics grounded by a plane tangential grinding process with diamond grinding wheels. The analysis of the grinding performance was done regarding the cutting surface wear behavior of the grinding wheel for ceramic workpieces. The discussion about the results emphasized the wear mechanism of the grinding wheel cutting surface and the cutting phenomenology of the grinding process. The grounded surface was evaluated using Scanning Electron Microscopy (SEM). © 1999 Society of Automotive Engineers, Inc.