Showing papers in "Journal of The Franklin Institute-engineering and Applied Mathematics in 2007"
TL;DR: An overview of digital image processing and pattern analysis techniques to address several areas in CAD of breast cancer, including: contrast enhancement, detection and analysis of calcifications, detection of masses and tumors, analysis of bilateral asymmetry, and detection of architectural distortion is presented.
Abstract: Mammography is the best available tool for screening for the early detection of breast cancer. Mammographic screening has been shown to be effective in reducing breast cancer mortality rates: screening programs have reduced mortality rates by 30–70%. Mammograms are difficult to interpret, especially in the screening context. The sensitivity of screening mammography is affected by image quality and the radiologist's level of expertise. Computer-aided diagnosis (CAD) technology can improve the performance of radiologists, by increasing sensitivity to rates comparable to those obtained by double reading, in a cost-effective manner. Current research is directed toward the development of digital imaging and image analysis systems that can detect mammographic features, classify them, and provide visual prompts to the radiologist. Radiologists would like the ability to change the contrast of a mammogram, either manually or with pre-selected settings. Computer techniques for detecting, classifying, and annotating diagnostic features on the images would be desirable. This paper presents an overview of digital image processing and pattern analysis techniques to address several areas in CAD of breast cancer, including: contrast enhancement, detection and analysis of calcifications, detection and analysis of masses and tumors, analysis of bilateral asymmetry, and detection of architectural distortion. Although a few commercial CAD systems have been released, the detection of subtle signs of breast cancer such as global bilateral asymmetry and focal architectural distortion remains a difficult problem. We present some of our recent works on the development of image processing and pattern analysis techniques for these applications.
340 citations
TL;DR: The honey-bee mating optimization (HBMO) algorithm is presented and tested with a nonlinear, continuous constrained problem with continuous decision and state variables to demonstrate the efficiency of the algorithm in handling the single reservoir operation optimization problems.
Abstract: In recent years, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Ease of use, broad applicability, and global perspective may be considered as the primary reason for their success. The honey-bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is presented and tested with a nonlinear, continuous constrained problem with continuous decision and state variables to demonstrate the efficiency of the algorithm in handling the single reservoir operation optimization problems. It is shown that the performance of the model is quite comparable with the results of the well-developed traditional linear programming (LP) solvers such as LINGO 8.0. Results obtained are quite promising and compare well with the final results of the other approach.
287 citations
TL;DR: A switching control strategy is employed to get around the smooth stabilization issue (difficulty) associated with nonholonomic systems when the initial state of system is known and a dynamic output feedback controller is developed with a filter of observer gain.
Abstract: This paper deals with chained form systems with strongly nonlinear disturbances and drift terms. The objective is to design robust nonlinear output feedback laws such that the closed-loop systems are globally exponentially stable. The systematic strategy combines the input-state-scaling technique with the so-called backstepping procedure. A dynamic output feedback controller for general case of uncertain chained system is developed with a filter of observer gain. Furthermore, two special cases are considered which do not use the observer gain filter. In particular, a switching control strategy is employed to get around the smooth stabilization issue (difficulty) associated with nonholonomic systems when the initial state of system is known.
119 citations
TL;DR: This paper presents a mix-integer linear model of a CVRP with split services and heterogeneous fleet and suggests that the proposed model enables users to establish routes to serve all given customers using the minimum number of vehicles and maximum capacity.
Abstract: We address a capacitated vehicle routing problem (CVRP) in which the demand of a node can be split on several vehicles celled split services by assuming heterogeneous fixed fleet. The objective is to minimize the fleet cost and total distance traveled. The fleet cost is dependent on the number of vehicles used and the total unused capacity. In most practical cases, especially in urban transportation, several vehicles transiting on a demand point occurs. Thus, the split services can aid to minimize the number of used vehicles by maximizing the capacity utilization. This paper presents a mix-integer linear model of a CVRP with split services and heterogeneous fleet. This model is then solved by using a simulated annealing (SA) method. Our analysis suggests that the proposed model enables users to establish routes to serve all given customers using the minimum number of vehicles and maximum capacity. Our proposed method can also find very good solutions in a reasonable amount of time. To illustrate these solutions further, a number of test problems in small and large sizes are solved and computational results are reported in the paper.
80 citations
TL;DR: A new stochastic fading channel model called cascaded Weibull fading is introduced and the associated capacity is derived in closed form and the statistical basis of the lognormal distribution is investigated.
Abstract: A new stochastic fading channel model called cascaded Weibull fading is introduced and the associated capacity is derived in closed form. This model is generated by the product of independent, but not necessarily identically distributed, Weibull random variables (RVs). By quantifying the convergence rate of the central limit theorem as pertaining to the multiplication of Weibull distributed RVs, the statistical basis of the lognormal distribution is investigated. By performing Kolmogorov–Smirnov tests, the null hypothesis for this product to be approximated by the lognormal distribution is studied. Another null hypothesis is also examined for this product to be approximated by a Weibull distribution with properly adjusted statistical parameters.
73 citations
TL;DR: Experimental results demonstrate that SVM classifiers with the proposed automatic parameter tuning systems and the SOM–RBF classifier can be efficient tools for breast cancer detection, with the detection accuracy up to 98 %.
Abstract: In this paper, we consider the benefits of applying support vector machines (SVMs), radial basis function (RBF) networks, and self-organizing maps (SOMs) for breast cancer detection. The Wisconsin diagnosis breast cancer (WDBC) dataset is used in the classification experiments; the dataset was generated from fine needle aspiration (FNA) samples through image processing. The 1-norm C-SVM ( L 1 -SVM), 2-norm C-SVM ( L 2 -SVM), and υ -SVM classifiers are applied, for which the grid search based on span error estimate (GSSEE), gradient descent based on validation error estimate (GDVEE), and gradient descent based on span error estimate (GDSEE) are developed to improve the detection accuracy. The gradient descent (GD) tuning method based on the span error estimate (SEE) is employed for the L 2 -SVM classifier because of its reachable smooth nonlinearity. Such a GDSEE tuning system also has the advantage of saving available samples for the training procedure. The SOM–RBF classifier is developed to improve the performance of only the SOM learning procedure based on distance comparison, in which the RBF network is employed to process the clustering result obtained by the SOM. Experimental results demonstrate that SVM classifiers with the proposed automatic parameter tuning systems and the SOM–RBF classifier can be efficient tools for breast cancer detection, with the detection accuracy up to 98 % .
71 citations
TL;DR: The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers.
Abstract: A proposed particle swarm classifier has been integrated with the concept of intelligently controlling the search process of PSO to develop an efficient swarm intelligence based classifier, which is called intelligent particle swarm classifier (IPS-classifier) This classifier is described to find the decision hyperplanes to classify patterns of different classes in the feature space An intelligent fuzzy controller is designed to improve the performance and efficiency of the proposed classifier by adapting three important parameters of PSO (inertia weight, cognitive parameter and social parameter) Three pattern recognition problems with different feature vector dimensions are used to demonstrate the effectiveness of the introduced classifier: Iris data classification, Wine data classification and radar targets classification from backscattered signals The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers
70 citations
TL;DR: In this note, the problem of solution to the matrix equation AX+XTC=B is considered by the Moore–Penrose generalized inverse matrix and a general solution to this equation is obtained.
Abstract: In this note, the problem of solution to the matrix equation AX+XTC=B is considered by the Moore–Penrose generalized inverse matrix. A general solution to this equation is obtained. At the same time, some useful conclusions are made, which play important roles in the linear system theories and applications.
68 citations
TL;DR: It is concluded that ANN can predict, to a great degree of accuracy, the shear resistance of rectangular R/C beams and it is a viable tool for carrying out parametric study of shear behavior of R/ C beams.
Abstract: Artificial neural network (ANN) has been used in several engineering application areas including civil engineering. The use of ANN to predict the behavior of reinforced concrete (R/C) members, using the vast amount of experimental data as a test-bed for learning and verification of results, proved to be a viable method for carrying out parametric studies. This paper presents application of ANN for predicting the shear resistance of rectangular R/C beams. Six parameters that influence the shear resistance of beams, mainly shear-span-to-depth ratio, concrete strength, longitudinal reinforcement, shear reinforcement, beam depth and beam width, are used as input for the ANN. A back propagation neural network (BPNN) with different activation functions is used and their results are compared. The sigmoid function with variable threshold is adopted due to its accuracy of prediction. The ANN prediction and the measured experimental values are compared with the shear strength predictions of ACI318-02 and BS8110 codes. A sensitivity study of the parameters that affect shear strength of R/C beams is carried out and the underlying complex nonlinear relationships among these parameters were investigated. Shear response curves and surfaces based on these parameters were generated. It is concluded that ANN can predict, to a great degree of accuracy, the shear resistance of rectangular R/C beams and it is a viable tool for carrying out parametric study of shear behavior of R/C beams.
68 citations
TL;DR: The general principles of the integral sliding mode compensator design are modified to yield the basic control algorithm oriented to time-delay systems, which is then applied to robustify the optimal regulator.
Abstract: This paper presents the optimal regulator for a linear system with state delay and a quadratic criterion. The optimal regulator equations are obtained using the maximum principle. Performance of the obtained optimal regulator is verified in the illustrative example against the best linear regulator available for linear systems without delays. Simulation graphs demonstrating better performance of the obtained optimal regulator are included. The paper then presents a robustification algorithm for the obtained optimal regulator based on integral sliding mode compensation of disturbances. The general principles of the integral sliding mode compensator design are modified to yield the basic control algorithm oriented to time-delay systems, which is then applied to robustify the optimal regulator. As a result, the sliding mode compensating control leading to suppression of the disturbances from the initial time moment is designed. The obtained robust control algorithm is verified by simulations in the illustrative example.
67 citations
TL;DR: This article proposes a new LMI estimation method based on recent results from the mathematical theory of moments that exploits the advantages of rational Lyapunov functions to enhance the estimates.
Abstract: In this article we deal with the classical problem of estimating the domain of attraction (DOA) of autonomous dynamical systems. We propose a new LMI estimation method based on recent results from the mathematical theory of moments. In contrast to previous works we exploit the advantages of rational Lyapunov functions to enhance the estimates. Several examples illustrate the estimation method.
TL;DR: Results show that the use of histogram matched (HM) image give better performance than using the green channel image when employing the two-dimensional matched filter to detect retinal blood vessels.
Abstract: A new pre-processing method for colour fundus images with adaptive contribution of the red channel is proposed. Based on a condition that is developed in this paper, this method utilises the intensity information from both red and green channels instead of using only the green channel as in the usual practice. The histogram matching is used to modify the histogram of the green channel by using the histogram of the red channel (of the same retinal image) to obtain a new processed image having the advantages of both channels. This method can be used to correct non-uniform illumination in colour fundus images or as a pre-processing step in the automatic analysis of retinal images. Results show that the use of histogram matched (HM) image give better performance than using the green channel image when employing the two-dimensional matched filter to detect retinal blood vessels. At specificity of 90%, in case of abnormal images, sensitivity increased from 76% when using the green channel image to 82% when using the HM image compared with 81% when using the piece-wise threshold probing method. In case of normal images, at the same specificity, the sensitivity obtained when using green channel image or HM image was 87% compared with 88% for the piece-wise threshold probing method.
TL;DR: The existence conditions of the robust fault detection filter for nonlinear time-delay systems with unknown inputs are presented in terms of linear matrix inequality (LMI) formulation, independently of time delay.
Abstract: In this paper, the robust fault detection filter (RFDF) design problems are studied for nonlinear time-delay systems with unknown inputs. Firstly, a reference residual model is introduced to formulate the robust fault detection filter design problem as an H∞ model-matching problem. Then appropriate input/output selection matrices are introduced to extend a performance index to the time-delay systems in time domain. The reference residual model designed according to the performance index is an optimal residual generator, which takes into account the robustness against disturbances and sensitivity to faults simultaneously. Applying robust H∞ optimization control technique, the existence conditions of the robust fault detection filter for nonlinear time-delay systems with unknown inputs are presented in terms of linear matrix inequality (LMI) formulation, independently of time delay. An illustrative design example is used to demonstrate the validity and applicability of the proposed approach.
TL;DR: This paper considers the semilinear viscoelastic equation u tt - Δ u + ∫ 0 t g ( t - τ ) Δ u ( τ ) d τ to establish a uniform decay result under weaker conditions on the relaxation function g.
Abstract: In this paper we consider the semilinear viscoelastic equation u tt - Δ u + ∫ 0 t g ( t - τ ) Δ u ( τ ) d τ + | u | γ u = 0 , in a bounded domain, and establish a uniform decay result under weaker conditions on the relaxation function g.
TL;DR: Lyapunov's second method is used to show that uniform boundedness and convergence to zero of all solutions of this equation together with their derivatives of the first order are shown.
Abstract: In this paper, the second order non-linear differential equation x ¨ + a ( t ) f ( x , x ˙ ) x ˙ + b ( t ) g ( x ) = p ( t , x , x ˙ ) is considered, and Lyapunov's second method is used to show that uniform boundedness and convergence to zero of all solutions of this equation together with their derivatives of the first order.
TL;DR: A new methodology to quantify the levels of consciousness that takes advantage of the fractal and self-similarity properties of the electroencephalogram (EEG) signal and significantly reduces computational complexity and produces faster reaction to transients in patients’ consciousness levels compared to other algorithms and technologies.
Abstract: The depth of anesthesia estimation has been of great interest in recent decades. In this paper, we present a new methodology to quantify the levels of consciousness. Our algorithm takes advantage of the fractal and self-similarity properties of the electroencephalogram (EEG) signal. We have studied the effect of anesthetic agents on the rate of the signal fluctuations. By translating these fluctuations with detrended fluctuation analysis (DFA) algorithm to fractal exponent, we could describe the dynamics of brain during anesthesia. We found the optimum fractal-scaling exponent by selecting the best domain of box sizes, which have meaningful changes with different depth of anesthesia. Experimental results confirm that the optimal fractal-scaling exponent on the raw EEG data can clearly discriminate between awake to moderate and deep anesthesia levels and have robust relation with the well-known depth of anesthesia index (BIS). When the patient's cerebral states change from awake to moderate and deep anesthesia, the fractal-scaling exponent increases from 0.8 to 2 approximately. Moreover, our new algorithm significantly reduces computational complexity and produces faster reaction to transients in patients’ consciousness levels compared to other algorithms and technologies.
TL;DR: The algorithm was found to distinguish effectively between signals representing patients with chronic obstructive pulmonary disease, those with pulmonary fibrosis and normal individuals, with orders of magnitude differences between the groups.
Abstract: The stethoscope is widely used for listening to heart sounds (cardiology), lung sounds (chest medicine) and digestive, bowel sounds, etc. An obvious development from this is to seek to automate the process by which a physician listens to and interprets the sounds. In parallel with signal processing developments, medical usage of diagnostic instrumentation is changing. The study described in this paper aimed to prove the feasibility of an automatic stethoscope for particular screening and monitoring algorithms, for which we envisage genuine clinical applicability. The first algorithm is to enable non-specialists to screen for pulmonary fibrosis. The algorithm was found to distinguish effectively between signals representing patients with chronic obstructive pulmonary disease (COPD), those with pulmonary fibrosis and normal individuals. A clear demarcation was observed between signals from patients in different diagnostic categories, with orders of magnitude differences between the groups. The second application is the monitoring of asthmatic patients. The proportion of wheeze within a given time recording is an objective indicator of the patient's condition, which can be used to monitor the person's progress during treatment. The proposed usage of each algorithm is demonstrated through a conceptual device, in which the ability to objectively quantify the patient's condition represents a powerful opportunity to improve clinical outcomes.
TL;DR: The paper develops a sliding mode control law that provides robustness against model imperfection and uncertainty, and provides an implicit stability proof.
Abstract: Manipulating flexible objects stirs a great deal of interest due to the potential applications in industry. Most previous research work with multiple manipulators, however, focused on developing control strategies for the manipulation of rigid bodies. This paper seeks to develop simple yet practical and efficient control scheme that enables cooperating arms to handle a flexible beam. Specifically the problem studied herein is that of two arms rigidly grasping a flexible beam and capable of generating forces/moments in such a way as to move a flexible beam along a predefined trajectory. The paper develops a sliding mode control law that provides robustness against model imperfection and uncertainty. It also provides an implicit stability proof. Given the bounds of uncertainty in the model of the flexible beam and choosing a switching surface that enforces trajectory tracking, a control algorithm is designed to push the states to remain on the switching surface. Simulation results for two three joint arms moving a flexible beam are presented to validate the theoretical results.
TL;DR: The functional correlation between the subthalamic nucleus (STN) and muscle in Parkinsonian tremor is dynamic, bi-directional, and dependent on the tremor status.
Abstract: Functional correlation between oscillatory neural and muscular signals during tremor can be revealed by coherence estimation. The coherence value in a defined frequency range reveals the interaction strength between the two signals. However, coherence estimation does not provide directional information, preventing the further dissection of the relationship between the two interacting signals. We have therefore investigated causal correlations between the subthalamic nucleus (STN) and muscle in Parkinsonian tremor using adaptive Granger autoregressive (AR) modeling. During resting tremor we analyzed the inter-dependence of local field potentials (LFPs) recorded from the STN and surface electromyograms (EMGs) recorded from the contralateral forearm muscles using an adaptive Granger causality based on AR modeling with a running window to reveal the time-dependent causal influences between the LFP and EMG signals in comparison with coherence estimation. Our results showed that during persistent tremor, there was a directional causality predominantly from EMGs to LFPs corresponding to the significant coherence between LFPs and EMGs at the tremor frequency; and over episodes of transient resting tremor, the inter-dependence between EMGs and LFPs was bi-directional and alternatively varied with time. Further time–frequency analysis showed a significant suppression in the beta band (10–30 Hz) power of the STN LFPs preceded the onset of resting tremor which was presented as the increases in the power at the tremor frequency (3.0–4.5 Hz) in both STN LFPs and surface EMGs. We conclude that the functional correlation between the STN and muscle is dynamic, bi-directional, and dependent on the tremor status. The Granger causality and time–frequency analysis are effective to characterize the dynamic correlation of the transient or intermittent events between simultaneously recorded neural and muscular signals at the same and across different frequencies.
TL;DR: The method proposes an algorithm which consists of biorthogonal expansions over two redundant dictionaries which is created for training wavelet networks in order to provide an efficient coordinate system maximizing the Cross Entropy function between two complementary classes.
Abstract: The paper deals with a method of constructing orthonormal bases of coordinates which maximize, through redundant dictionaries (frames) of biorthogonal bases, a class separability index or distances among classes. The method proposes an algorithm which consists of biorthogonal expansions over two redundant dictionaries. Embedded classes are often present in multiclassification problems. It is shown how the biorthogonality of the expansion can really help to construct a coordinate system which characterizes the classes. The algorithm is created for training wavelet networks in order to provide an efficient coordinate system maximizing the Cross Entropy function between two complementary classes. Sine and cosine wavelet packets are basis functions of the network. Thanks to their packet structure, once selected the depth of the tree, an adaptive number of basis functions is automatically chosen. The algorithm is also able to carry out centering and dilation of the basis functions in an adaptive way. The algorithm works with a preliminary extracted feature through shrinkage technique in order to reduce the dimensionality of the problem. In particular, our attention is pointed out for time-frequency monitoring, detection and classification of transients in rail vehicle systems and the outlier problem. In the former case the goal is to distinguish transients as inrush current and no inrush current and a further distinction between the two complementary classes: dangerous inrush current and no dangerous inrush current. The proposed algorithm is used on line in order to recognize the dangerous transients in real time and thus shut-down the vehicle. The algorithm can also be used in a general application of the outlier detection. A similar structure is used in developed algorithms which are currently integrated in the inferential modeling platform of the unit responsible for Advanced Control and Simulation Solutions within ABB's (Asea Brown Boveri) industry division. It is shown how impressive and rapid performances are achieved with a limited number of wavelets and few iterations. Real applications using real measured data are included to illustrate and analyze the effectiveness of the proposed method.
TL;DR: Comparison of the two robust controllers for a multivariable vertical short take-off and landing (VSTOL) aircraft system are designed and compared and it is shown that LQG method requires rate feedback to increase damping of closed-loop system, while H-infinity controller meets the same performance for step response.
Abstract: In this paper two robust controllers for a multivariable vertical short take-off and landing (VSTOL) aircraft system are designed and compared. The aim of these controllers is to achieve robust stability margins and good performance in step response of the system. LQG/LTR method is a systematic design approach based on shaping and recovering open-loop singular values while mixed-sensitivity H-infinity method is established by defining appropriate weighting functions to achieve good performance and robustness. Comparison of the two controllers show that LQG method requires rate feedback to increase damping of closed-loop system, while H-infinity controller by only proper choose the weighting functions, meets the same performance for step response. Output robustness of both controllers is good but H-infinity controller has poor input stability margin. The net controller order of H-infinity is higher than the LQG/LTR method and the control effort of them is in the acceptable range. (c) 2006 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
TL;DR: The existence of the abstract nonlinear mixed Volterra–Fredholm integro-differential system of the type x ′ is proved by using the application of the topological transversality theorem known as Leray–Schauder alternative and rely on a priori bounds of solutions.
Abstract: Dhakne and Kendre [On abstract nonlinear mixed Volterra–Fredholm integro-differential equations, Presented Paper in the International Conference at IIT-Bombay, 11–13 December, 2004] has proved the existence of the abstract nonlinear mixed Volterra–Fredholm integro-differential system of the type x ′ ( t ) = f t , x ( t ) , ∫ 0 t k ( t , s , x ( s ) ) d s , ∫ 0 T h ( t , s , x ( s ) ) d s , x ( 0 ) = x 0 ∈ X ; t ∈ J = [ 0 , T ] . In this short article, we have studied sufficient conditions for controllability of semi-linear mixed Volterra–Fredholm-type integro-differential systems in Banach space of the type x ′ ( t ) = Ax ( t ) + ( Bu ) ( t ) + f t , x ( t ) , ∫ 0 t g ( t , s , x ( s ) ) d s , ∫ 0 T h ( t , s , x ( s ) ) d s , x ( 0 ) = x 0 , t ∈ J = [ 0 , T ] , where the state x ( . ) takes values in a Banach space X and the control function u ( . ) is given in L 2 ( J , U ) , with U as a Banach space. Here A is the infinitesimal generator of a strongly continuous semigroup in a Banach space X . B is a bounded linear operator from U into X . The result is obtained by using the application of the topological transversality theorem known as Leray–Schauder alternative and rely on a priori bounds of solutions. An example is provided to illustrate the theory.
TL;DR: This paper addresses the problem of robust stabilization for uncertain discrete-time singular large-scale systems with parameter uncertainties with sufficient conditions for the solvability of the problem, and the parameterization of desired state feedback controllers is given.
Abstract: This paper addresses the problem of robust stabilization for uncertain discrete-time singular large-scale systems with parameter uncertainties. The system under consideration is not necessarily regular. The parameter uncertainties are assumed to be time invariant, but norm-bounded. The purpose of the robust stabilization problem is to design state feedback controllers such that, for all admissible uncertainties, the closed-loop system is regular, causal and stable. In terms of strict LMIs, sufficient conditions for the solvability of the problem is presented, and the parameterization of desired state feedback controllers is also given. A numerical example is given to demonstrate the applicability of the proposed design approach.
TL;DR: Numerical simulation results confirm that the new feedback controller using time delay is efficient in controlling Hopf bifurcation.
Abstract: In this paper, we consider the problem of Hopf bifurcation control for a complex network model with time delays. We know that for the system without control, as the positive gain parameter of the system passes a critical point, Hopf bifurcation occurs. To control the Hopf bifurcation, a time-delayed feedback controller is proposed to delay the onset of an inherent bifurcation when such bifurcation is undesired. Furthermore, we can also change the stability and direction of bifurcating periodic solutions by choosing appropriate control parameters. Numerical simulation results confirm that the new feedback controller using time delay is efficient in controlling Hopf bifurcation.
TL;DR: It is shown that there is a sequence of generalized eigenfunctions, which forms a Riesz basis for the Hilbert state space, and as a consequence, the exponential stability of the system is concluded.
Abstract: In this paper, we study the Riesz basis property of the generalized eigenfunctions of a one-dimensional hyperbolic system in the energy state space. This characterizes the dynamic behavior of the system, particularly the stability, in terms of its eigenfrequencies. This system is derived from a thermoelastic equation with memory type. The asymptotic expansions for eigenvalues and eigenfunctions are developed. It is shown that there is a sequence of generalized eigenfunctions, which forms a Riesz basis for the Hilbert state space. This deduces the spectrum-determined growth condition for the C 0 -semigroup associated with the system, and as a consequence, the exponential stability of the system is concluded.
TL;DR: This work presents an automatic system for the extraction of MUAPs based on generative topographic mapping (GTM), which is a recently developed technique for data clustering and visualization and shows that the system is capable of accurately extractingMUAPs from the EMG.
Abstract: The extraction of motor unit action potentials (MUAPs) from electromyographic (EMG) signals (also known as EMG decomposition) is an important step in investigations aiming to obtain information on control strategies of the neuromuscular system and its state. For instance, the analysis of the shape of MUAPs and their frequency of occurrence may be used as an additional tool in the detection of some neuromuscular disorders. Although MUAPs can be manually extracted from the EMG, such a procedure is often time consuming and prone to error. In this context, systems which aim to automate the extraction of MUAPs play an important role. First, they allow for the reduction in the processing time of signals, and secondly, they introduce consistency across analyses. In this work, we present an automatic system for the extraction of MUAPs based on generative topographic mapping (GTM), which is a recently developed technique for data clustering and visualization. The system is composed of several signal processing units, including signal filtering and detection, feature selection, data clustering and visualization. Its input is a time-series, representing EMG activity, and its output is the visualization of MUAPs obtained through GTM. The performance of the system was assessed via the analysis of synthetic and experimental EMG signals, detected by means of concentric needle and surface electrodes, collected from healthy subjects executing muscle contractions with distinct levels of force. Our results show that the system is capable of accurately extracting MUAPs from the EMG.
TL;DR: A sequential method that uses the ordinal optimization to select a subset G randomly from the search space that contains the best simulated systems with high probability to guarantee that this subset contains thebest systems it needs to be relatively large.
Abstract: In this paper, we consider the problem of selecting a subset of k systems that is contained in the set of the best s simulated systems when the number of alternative systems is huge. We propose a sequential method that uses the ordinal optimization to select a subset G randomly from the search space that contains the best simulated systems with high probability. To guarantee that this subset contains the best systems it needs to be relatively large. Then methods of ranking and selections will be applied to select a subset of k best systems of the subset G with high probability. The remaining systems of G will be replaced by newly selected alternatives from the search space. This procedure is repeated until the probability of correct selection (a subset of the best k simulated systems is selected) becomes very high. The optimal computing budget allocation is also used to allocate the available computing budget in a way that maximizes the probability of correct selection. Numerical experiments for comparing these algorithms are presented.
TL;DR: A new algorithm to solve the velocity field control formulation in the robot operational space is introduced and is based on a hierarchical structure that results of using the kinematic control concept and a joint velocity controller.
Abstract: In the velocity field control approach the robot motions are specified through a vectorial function that assigns the desired velocity to each point of the configuration space. In other words, a velocity field defines the robot desired velocity in the operational space as a function of its current position. In this paper is introduced a new algorithm to solve the velocity field control formulation in the robot operational space. The proposed approach assumes only joint position measurements and is based on a hierarchical structure that results of using the kinematic control concept and a joint velocity controller. To estimate the joint velocity, nonlinear filtering of the joint position is used.
TL;DR: It is shown that the center of the minimal circle is in the convex hull of P, and an analysis of the case of three points is used to find sharp estimates on the diameter of the minimum circle in terms of theiameter of P.
Abstract: A number of numerical codes have been written for the problem of finding the circle of smallest radius in the Euclidean plane that encloses a finite set P of points, but these do not give much insight into the geometry of this circle. We investigate geometric properties of the minimal circle that may be useful in the theoretical analysis of applications. We show that a circle C enclosing P is minimal if and only if it is rigid in the sense that it cannot be translated while still enclosing P. We show that the center of the minimal circle is in the convex hull of P. We use this rigidity result and an analysis of the case of three points to find sharp estimates on the diameter of the minimal circle in terms of the diameter of P.
TL;DR: Profiles such as fluid flow rate with a change in differential pressure and pressure build-up data with time, signify that the PD technique can achieve maximum wellbore productivity when compared to the PS technique.
Abstract: The objective of perforating the petroleum wells is to maximize well productivity. A good connectivity between the wellbore and formation can lead up to achieve that goal. However, the conventional method of perforation, which basically involves the use of explosive charges, rarely meets the expected well productivity. It is mainly because of the formation of a region of reduced permeability around the perforation tunnel. To offset such and several other shortcomings of present practices, a new technique is required, the highlight of this paper. Described as the ‘perforation by drilling’ (PD), this new technique is examined and compared for its performance with that of another technique, ‘Perforation by Shooting’ (PS). For this, the experimental and numerical results of the PS technique on cylindrical sand samples of varying amounts of strength and porosity are studied. Moreover, in order to achieve a so-called ‘perfect perforation’, results are compared with the ‘Casting technique’. Three different samples were selected for the measurement of fluid flow rate and differential pressure across the perforation using a ‘geotechnical digital system’ (GDS), which is a triaxial testing device. Profiles such as fluid flow rate with a change in differential pressure and pressure build-up data with time, signify that the PD technique can achieve maximum wellbore productivity when compared to the PS technique. Results also indicate that at a 100 kPa differential pressure, the PS, PD and Casting techniques can achieve 0.20, 0.65 and 1.00 mL/s fluid flow rates respectively across a sample. The paper also implements a 1-D time dependent porous media flow model to simulate flow across the perforated cylindrical samples created by the PS, PD and Casting techniques. Results show a good consistency between the experimental and numerical approaches.