Automatic prediction of interest point stability

Department Head: L. Darrell Whitley.2009 Spring.Includes bibliographical references (pages 67-72).Many computer vision applications depend on interest point detectors as a primary means of dimensionality reduction. While many experiments have been done measuring the repeatability of selective attention algorithms [MTS+05, BL02, CJ02, MP07, SMBI98], we are not aware of any method for predicting the repeatability of an individual interest point at runtime. In this work, we attempt to predict the individual repeatability of a set of 106 interest points produced by Lowe's SIFT algorithm [Low03], Mikolajczyk's Harris-Affine [Mik02], and Mikolajczyk and Schmid's Hessian-Affine [MS04]. These algorithms were chosen because of their performance and popularity. 17 relevant attributes are recorded at each interest point, including eigenvalues of the second moment matrix, Hessian matrix, and Laplacian-of-Gaussian score. A generalized linear model is used to predict the repeatability of interest points from their attributes. The relationship between interest point attributes proves to be weak, however the repeatability of an individual interest point can to some extent be influenced by attributes. A 4% improvement ofmean interest point repeatability is acquired through two related methods: the addition of five new thresholding decisions and through selecting the N best interest points as predicted by a GLM of the logarithm of all 17 interest points. A similar GLM with a smaller set of author-selected attributes has comparable performance. This research finds that improving interest point repeatability remains a hard problem, with an improvement of over 4% unlikely using the current methods for interest point detection. The lack of clear relationships between interest point attributes and repeatability indicates that there is a hole in selective attention research that may be attributable to scale space implementation

FireProt: web server for automated design of thermostable proteins

There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot.Web of Science45W1W399W39

Implementation of prediction algorithms to reduce latency in control of switching converters

Using pulse-width modulation (PWM) to control switching converters such as a dual active bridge introduces latency into a system [1]. Latency in a switching converter decreases the effective bandwidth that the converter can be switched at and for the case of a closed loop control system decreases the effective closed loop system stability. This delay can be attributed to sampling of the control signal and calculation time for the PWM. If a prediction scheme is implemented in this calculation, the next switching time could be predicted to remove any latency. There are effectively two steps in order to achieve this prediction: 1.) analog to digital conversion of the control signal; 2.) prediction of next point of interest in the control signal [1]. In this paper, several different methods of prediction are compared in order to determine the relative effectiveness of different prediction techniques. Prediction of future data points of the control signal can reduce latency in PWM creation and thereby improve the stability and/or control bandwidth of the system

Extending The Lossy Spring-Loaded Inverted Pendulum Model with a Slider-Crank Mechanism

Spring Loaded Inverted Pendulum (SLIP) model has a long history in describing running behavior in animals and humans as well as has been used as a design basis for robots capable of dynamic locomotion. Anchoring the SLIP for lossy physical systems resulted in newer models which are extended versions of original SLIP with viscous damping in the leg. However, such lossy models require an additional mechanism for pumping energy to the system to control the locomotion and to reach a limit-cycle. Some studies solved this problem by adding an actively controllable torque actuation at the hip joint and this actuation has been successively used in many robotic platforms, such as the popular RHex robot. However, hip torque actuation produces forces on the COM dominantly at forward direction with respect to ground, making height control challenging especially at slow speeds. The situation becomes more severe when the horizontal speed of the robot reaches zero, i.e. steady hoping without moving in horizontal direction, and the system reaches to singularity in which vertical degrees of freedom is completely lost. To this end, we propose an extension of the lossy SLIP model with a slider-crank mechanism, SLIP- SCM, that can generate a stable limit-cycle when the body is constrained to vertical direction. We propose an approximate analytical solution to the nonlinear system dynamics of SLIP- SCM model to characterize its behavior during the locomotion. Finally, we perform a fixed-point stability analysis on SLIP-SCM model using our approximate analytical solution and show that proposed model exhibits stable behavior in our range of interest.Comment: To appear in The 17th International Conference on Advanced Robotic

Selection of design and operational parameters in spindle-holder-tool assemblies for maximum chatter stability by using a new analytical model

In this paper, using the analytical model developed by the authors, the effects of certain system design and operational parameters on the tool point FRF, thus on the chatter stability are studied. Important conclusions are derived regarding the selection of the system parameters at the stage of machine tool design and during a practical application in order to increase chatter stability. It is demonstrated that the stability diagram for an application can be modified in a predictable manner in order to maximize the chatter-free material removal rate by selecting favorable system parameters using the analytical model developed. The predictions of the model, which are based on the methodology proposed in this study, are also experimentally verified

A Modeling approach for analysis and improvement of spindle-holder-tool assembly dynamics

The most important information required for chatter stability analysis is the dynamics of the involved structures, i.e. the frequency response functions (FRFs) which are usually determined experimentally. In this study, the tool point FRF of a spindle-holder-tool assembly is analytically determined by using the receptance coupling and structural modification techniques. Timoshenko’s beam model is used for increased accuracy. The spindle is also modeled analytically with elastic supports representing the bearings. The mathematical model is used to determine the effects of different parameters on the tool point FRF and to identify contact dynamics from experimental measurements. The applications of the model are demonstrated and the predictions are verified experimentally

High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression

Motivation: The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to instable and non convergent methods due to inappropriate computational frameworks. We hereby propose a stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS). Results: We start by proposing a new solution for the sparse PLS problem that is based on proximal operators for the case of univariate responses. Then we develop an adaptive version of the sparse PLS for classification, which combines iterative optimization of logistic regression and sparse PLS to ensure convergence and stability. Our results are confirmed on synthetic and experimental data. In particular we show how crucial convergence and stability can be when cross-validation is involved for calibration purposes. Using gene expression data we explore the prediction of breast cancer relapse. We also propose a multicategorial version of our method on the prediction of cell-types based on single-cell expression data. Availability: Our approach is implemented in the plsgenomics R-package.Comment: 9 pages, 3 figures, 4 tables + Supplementary Materials 8 pages, 3 figures, 10 table

Analytical modeling of spindle-tool dynamics on machine tools using Timoshenko beam model and receptance coupling for the prediction of tool point FRF

Regenerative chatter is a well-known machining problem that results in unstable cutting process, poor surface quality and reduced material removal rate. This undesired self-excited vibration problem is one of the main obstacles in utilizing the total capacity of a machine tool in production. In order to obtain a chatter-free process on a machining center, stability diagrams can be used. Numerically or analytically, constructing the stability lobe diagram for a certain spindleholdertool combination implies knowing the system dynamics at the tool tip; i.e., the point frequency response function (FRF) that relates the dynamic displacement and force at that point. This study presents an analytical method that uses Timoshenko beam theory for calculating the tool point FRF of a given combination by using the receptance coupling and structural modication methods. The objective of the study is two fold. Firstly, it is aimed to develop a reliable mathematical model to predict tool point FRF in a machining center so that chatter stability analysis can be done, and secondly to make use of this model in studying the effects of individual bearing and contact parameters on tool point FRF so that better approaches can be found in predicting contact parameters from experimental measurements. The model can also be used to study the effects of several spindle, holder and tool parameters on chatter stability. In this paper, the mathematical model, as well as the details of obtaining the system component (spindle, holder and tool) dynamics and coupling them to obtain the tool point FRF are given. The model suggested is veried by comparing the natural frequencies of an example spindleholdertool assembly obtained from the model with those obtained from a nite element software

FireProt: Web server for automated design of thermostable proteins

Stable proteins are used in numerous biomedical and biotechnological applications. Unfortunately, naturally occurring proteins cannot usually withstand the harsh industrial environment, since they are mostly evolved to function at mild conditions. Therefore, there is a continuous interest in increasing protein stability to enhance their industrial potential. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. A much higher degree of stabilization can be achieved by the construction of the multiple-point mutants. Here, we present the FireProt method [1] and the web server [2] for the automated design of multiple-point mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen bioinformatics tools, including several force field calculations. Highly reliable designs of the thermostable proteins are constructed by two distinct protein engineering strategies, based on the energy and evolution approaches and the multiple-point mutants are checked for the potentially antagonistic effects in the designed protein structure. Furthermore, time demands of the FireProt method are radically decreased by the utilization of the smart knowledge-based filters, protocol optimization, and effective parallelization. The server is complemented with an interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable proteins. The server is freely available at http://loschmidt.chemi.muni.cz/fireprot. 1. Bednar, D., Beerens, K., Sebestova, E., Bendl, J., Khare, S., Chaloupkova, R., Prokop, Z., Brezovsky, J., Baker, D., Damborsky, J., 2015: FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants. PLOS Computational Biology 11: e1004556. 2. Musil, M., Stourac, J., Bendl, J., Brezovsky, J., Prokop, Z., Zendulka, J., Martinek, T., Bednar, D., Damborsky, J., 2017, FireProt: Web Server for Automated Design of Thermostable Proteins, Nucleic Acids Research, in press, doi: 10.1093/nar/gkx285