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Abstract: Pinned connections and journal bearings with cylindrical conforming contacts are widely used as fundamental building blocks in machines and civil structures of civilization. It is critical to determine their stress fields and contact performances under desired configurations in order to successfully design them. This paper presents a numerical model to deal with such contacts, particularly with the general configuration–double interfaces. Necessary formulae are developed and numerical procedures are described in this paper. Efficient numerical methods, i.e., the discrete convolution-fast Fourier transformation (DC-FFT) method and the conjugate gradient method (CGM), are implemented in the algorithm. Validations are conducted against the Persson’s results and finite element results and demonstrate excellent agreement. This contact analysis can be useful for design engineers to evaluate stress and contact pressure distribution. Furthermore, this efficient method of determining radial displacement can be applied in an elasto-hydrodynamic lubrication analysis. [Copyright &y& Elsevier]

Abstract: In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Regarding the solution of the problem, this paper introduces a greedy heuristic algorithm which is able to obtain a reasonable initial solution for the problem. This heuristic is then used to seed the initial population of the evolutionary algorithm, improving its performance. It is shown that the proposed seeded evolutionary approach is able to obtain very good solutions to this problem, which maximize the economical benefit which can be obtained from the wind farm. [Copyright &y& Elsevier]

This paper presents a modified bacterial foraging optimization algorithm called adaptive crossover bacterial foraging optimization algorithm (ACBFOA), which incorporates adaptive chemotaxis and also inherits the crossover mechanism of genetic algorithm. First part of the research work aims at improvising evaluation of the optimal objective function values. The idea of using adaptive chemotaxis is to make it computationally efficient and crossover technique is to search nearby locations by offspring bacteria. Four different benchmark functions are considered for performance evaluation. The purpose of this research work is also to investigate a face recognition algorithm with improved recognition rate. In this connection, we propose a new algorithm called ACBFO-Fisher. The proposed ACBFOA is used for finding optimal principal components for dimension reduction in linear discriminant analysis (LDA) based face recognition. Three well-known face databases, FERET, YALE and UMIST, are considered for validation. A comparison with the results of earlier methods is presented to reveal the effectiveness of the proposed ACBFO-Fisher algorithm. [ABSTRACT FROM AUTHOR]

In this paper we introduce a Bayesian analysis for mixture models with an unknown number of components via a new posterior split–merge MCMC algorithm. Our strategy for splitting is based on data in which allocation probabilities are calculated based on posterior distribution from the previously allocated observations. This procedure is easy to be implemented and determines a quick split proposal. The acceptance probability for split–merge movements are calculated according to metropolised Carlin and Chib’s procedure. The performance of the proposed algorithm is verified using artificial datasets as well as two real datasets. The first real data set is the benchmark galaxy data, while the second is the publicly available data set on Escherichia coli bacterium. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an approach to achieve fault diagnosis and Fault Tolerant Control in a typical bottle-filling plant using event based techniques is discussed. For this purpose, the plant is modeled using Hybrid Petri nets which enable study and analysis with regard to the working of the plant. Once effective modeling is done based on two different case studies considered, new algorithms are proposed to achieve fault diagnosis and Fault Tolerant Control on the models developed. Finally, performance measures with regard to the models proposed are evaluated to check the correctness of the models developed. Both analytical and numerical results are obtained which are highly useful to understand plant behavior. [Copyright &y& Elsevier]

Abstract: This paper deals with a kind of deployable truss antenna that consists of tetrahedral modules. The structure–electronic synthesis design method is proposed and mechanical performance and electrical performance of the antenna are researched together. The basic module and structural topology are described. The coordinate of each joint, the joint angles, the length of the struts and the pattern of the joint are formulated on the basis of the topology method. By employing these formulas, numerical models of deployed and folded configuration can be built. According to the feature of the deployable truss antenna, the numerical algorithm for computing the scattered field of the antenna is developed based on geometric optics theory. The radiation integral of reflector is discrete approximated when the projection area of triangular facet onto the caliber plane is meshed by rectangular grid. Combining these two components, the structure–electronic synthesis design method of antenna is presented. A 6-meter deployable truss antenna was designed by the corresponding design procedures. The results indicated that the structure parameter and radiation-patterns of the antenna can be designed and optimized by the synthesis design method. Additionally, a scaled model of the antenna was manufactured to illustrate its feasibility. [Copyright &y& Elsevier]

Abstract: In this paper an efficient Medium Term Conflict Detection and Resolution (MTCD&R) approach based on 4D trajectories to solve conflicts in a Terminal Maneuvering Area (TMA) is presented. The conflict detection subsystem (CD) is based on a Spatial Data Structure (SDS), avoiding non-efficient pairwise trajectory comparisons, and using a simplified wake vortex modeling through 4D tubes to detect time-based separation infringements between aircraft. The conflict resolution subsystem (CR) solves the detected conflicts with an efficient and dynamic 3D allocation of the arrival routes that takes into consideration the execution of Continuous Descent Approaches (CDAs). Algorithms have been tested with several stressing traffic scenarios (rush hour and saturation rush hour) taking place in a 3D simulation model of Gran Canaria Extended TMA. The resulting conflict-free trajectories have been validated for flyability conformance both with real A380 FMS avionics and with a certified B738 Full Flight Simulator. A new CR performance metric to measure the degree of runway utilization is also proposed in order to enable comparisons between different MTCD&R systems. Finally, a discussion about strengths and limitations of the algorithms for reducing controller’s workload while increasing airspace capacity of the future Single European Sky is outlined. [Copyright &y& Elsevier]

Abstract: In this paper a multi-level system design (MLS) algorithm is presented and utilized for a wind turbine system analysis. The MLS guides the decision making process for designing a complex system where many alternatives and many mutually competing objectives and disciplines need to be considered and evaluated. Mathematical relationships between the design variables and the multiple discipline performance objectives are developed adaptively as the various design considerations are evaluated and as the design is being evolved. These relationships are employed for rewarding performance improvement during the decision making process by allocating more resources and influence to the disciplines which exhibit the improvement. Simulation tools developed by the National Renewable Energy Laboratory (NREL) are employed in the wind turbine design analysis. The Cost Of Energy (COE) comprises the overall system level objective, while performance improvements at two technical design disciplines are pursued at the same time. The optimal design of the blade geometry for maximum Annual Energy Production (AEP), and the structural design of the blade for minimum bending moment at the root of the blade comprise the two technical design disciplines. Scalar metamodels are developed for linking the design variables with the performance metrics associated with the design of the blade geometry. Main characteristics of the wind turbine, namely, the rotor diameter, the rotational speed, the maximum rated power, the hub height, the structural characteristics of the blade, and the geometric characteristics of the blade (distribution of thickness, twist angle, and chord) are employed as design variables for the overall design analysis. The optimization results and the physical insight which can be gained through a sensitivity analysis for the optimal configuration are presented and discussed. [Copyright &y& Elsevier]

Abstract: In this paper motor current based torque feedback compensator is utilized in actuator space together with a closed loop impedance control algorithm instead of model based compensator to improve the transparency performance of haptic interfaces; moreover, a novel transparency evaluation metric is developed to evaluate the transparency performance of these devices. The proposed control algorithm is experimentally tested on a 1 DOF haptic device by employing a low-cost current sensor. It is also tested on a MATLAB/Simmechanics® model of a 2 DOF serial planar elbow type haptic manipulator to show that it is applicable to multi-DOF haptic systems. Free-motion, virtual-load and virtual-wall performance tests are conducted to compare the performance of the proposed control algorithm with the alternative algorithms by means of apparent inertial effects. The results show that the proposed algorithm significantly improves the transparency of the haptic devices. [Copyright &y& Elsevier]

Abstract: To mimic human tutors and provide optimal training, a cognitive tutoring agent should be able to continuously learn from its interactions with learners. An important element that helps a tutor better understand learner’s mistake is finding the causes of the learners’ mistakes. In this paper, we explain how we have designed and integrated a causal learning mechanism in a cognitive agent named CELTS (Conscious Emotional Learning Tutoring System) that assists learners during learning activities. Unlike other works in cognitive agents that used Bayesian Networks to deal with causality, CELTS’s causal learning mechanism is implemented using data mining algorithms that can be used with large amount of data. The integration of a causal learning mechanism within CELTS allows it to predict learners’ mistakes. Experiments showed that the causal learning mechanism help CELTS improve learners’ performance. [Copyright &y& Elsevier]

Abstract: This paper presents an algorithm based on nonsubsampled contourlet transform (NSCT) and Stein''s unbiased risk estimate with a linear expansion of thresholds (SURE-LET) approach for intensity image denoising. First, we analyzed the multiplicative noise model of intensity image and make the non-logarithmic transform on the noisy signal. Then, as a multiscale geometric representation tool with multi-directivity and shift-invariance, NSCT was performed to capture the geometric information of images. Finally, SURE-LET strategy was modified to minimize the estimation of the mean square error between the clean image and the denoised one in the NSCT domain. Experiments on real intensity images show that the algorithm has excellent denoising performance in terms of the peak signal-to-noise ratio (PSNR), the computation time and the visual quality. [Copyright &y& Elsevier]

Abstract: In this paper, we consider structure detection and parameter estimation of the nonlinear auto-regressive with exogenous inputs (NARX) model, using the EM (expectation–maximisation) algorithm. The parameter estimation step uses particle smoothing to obtain the necessary expectations in the E-step and the parameters are then estimated in closed form in the M-step. The model structure detection is performed using an -test, which makes use of the parameter information matrix (inverse of the covariance matrix), obtained from an augmentation of the EM algorithm. The steps for obtaining the information matrix are robust, guaranteeing a positive semi-definite information matrix to use in the structure detection step. For the case of unknown model orders, a method is proposed using the stochastic complexity (SC) information criterion for selecting between candidate models. The SC is composed of the information matrix (representing model complexity) and a likelihood estimate (representing model accuracy), which are both generated as byproducts of the augmented EM algorithm. Numerical results demonstrate that the EM approach performs well in comparison to a standard alternative based on orthogonal least squares, and also avoids the need to estimate a noise model for the case of measurement noise corrupted output signals. [Copyright &y& Elsevier]

Abstract: Implementing efficient inspection policies is much important for the organizations to reduce quality related costs. In this paper, a particle swarm optimization (PSO) algorithm is proposed to determine the optimal inspection policy in serial multi-stage processes. The policy consists of three decision parameters to be optimized; i.e. the stages in which inspection occurs, tolerance of inspection, and size of sample to inspect. Total inspection cost is adopted as the performance measure of the algorithm. A numerical example is investigated in two phases, i.e. fixed sample size and sample size as a decision parameter, to ensure the practicality and validity of the proposed PSO algorithm. It is shown that PSO gives better results in comparison with two other algorithms proposed by earlier works. [Copyright &y& Elsevier]

Abstract: It is difficult to evaluate the performance of the different companies feasibly and fairly only depending on experts. An evaluation model based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is built to solve this problem. In this model, the method of data processing and the evaluation algorithms are analyzed detailed. By means of the evaluation model, eight electric power supply bureau of Chongqing are evaluated in this paper, and the results show feasibly and reasonably. [Copyright &y& Elsevier]

Abstract: Power Conditioning System (PCS) in the photovoltaic system requires reliable islanding detection function (passive or active) in order to know electrical grid status and operate the grid connected inverter properly. This paper proposes a passive islanding detection algorithm based on the grid harmonic components. It utilizes the fact that the equivalent harmonic components at the point of common coupling (PCC) are changed according to the grid connection status. This method shows reduced Non-Detection Zone (NDZ) and fast detection time compared to the conventional approach. The mathematical modeling is performed and verified by experimental result using high performance DSP controller [Copyright &y& Elsevier]

Abstract: This paper introduces a solution, Device Management Load Balancing Solution (DMLBS) which includes system design and load balancing algorithm, to achieve load balancing on Device Management server. Our solution improves both user response time and throughput for a single Device Management server, while exposing one interface to clients. DMLBS achieves its performance by using dynamic adaptive load balancing strategy and designing a mathematic model to measure Device Management server load. With these means, compared to two well-known load balancing algorithms (LeastLoaded and Round-Robin), DMLBS provides outstanding performance on load balancing. [Copyright &y& Elsevier]

Summary: Despite all the progress made over the years on developing automatic methods for analysing hydrographs and measuring the performance of rainfall–runoff models, automatic methods cannot yet match the power and flexibility of the human eye and brain. Very simple approaches are therefore being developed that mimic the way hydrologists inspect and interpret hydrographs, including the way that patterns are recognised, links are made by eye, and hydrological responses and errors are studied and remembered. In this paper, a dynamic programming algorithm originally designed for use in data mining is customised for use with hydrographs. It generates sets of “rays” that are analogous to the visual links made by the hydrologist’s eye when linking features or times in one hydrograph to the corresponding features or times in another hydrograph. One outcome from this work is a new family of performance measures called “visual” performance measures. These can measure differences in amplitude and timing, including the timing errors between simulated and observed hydrographs in model calibration. To demonstrate this, two visual performance measures, one based on the Nash–Sutcliffe Efficiency and the other on the mean absolute error, are used in a total of 34 split-sample calibration–validation tests for two rainfall–runoff models applied to the Hodder catchment, northwest England. The customised algorithm, called the Hydrograph Matching Algorithm, is very simple to apply; it is given in a few lines of pseudocode. [ABSTRACT FROM AUTHOR]

Abstract: Performance prediction is crucial to increasing drill availability and reliability as well as facilitating further proactive maintenance. This paper presents a novel method for pattern recognition based performance prediction. The features of vibration signals are extracted by wavelet packet transform after de-noising, and then band energy based feature measures are identified by ant colony clustering analysis to form an input vector for the performance assessment model. Prediction of performance degradation trend of drill is carried out using feature mapping based on the established performance assessment model. Experimental results have shown that the proposed method can analyze drill degradation quantitatively, and predict problems before they occur. Furthermore, ant colony clustering algorithm is improved to adjust comparison probability dynamically and detect outliers. Compared with other ant colony clustering algorithms, the algorithm has higher convergence speed to meet requirements of real-time analysis as well as further improvement of accuracy. Finally, effectiveness and feasibility of the proposed method are verified using vibration signals acquired from a drill test bed. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the model reduction of positive systems. For a given stable positive system, our attention is focused on the construction of a reduced-order model in such a way that the positivity of the original system is preserved and the error system is stable with a prescribed performance. Based upon a system augmentation approach, a novel characterization on the stability with performance of the error system is first obtained in terms of linear matrix inequality (LMI). Then, a necessary and sufficient condition for the existence of a desired reduced-order model is derived accordingly. Furthermore, iterative LMI approaches with primal and dual forms are developed to solve the positivity-preserving model reduction problem. Finally, a compartmental network is provided to show the effectiveness of the proposed techniques. [Copyright &y& Elsevier]

Abstract: In this paper, we first propose two learning automata based decentralized dynamic guard channel algorithms for cellular mobile networks. These algorithms use learning automata to adjust the number of guard channels to be assigned to cells of network. Then, we introduce a new model for nonstationary environments under which the proposed algorithms work and study their steady state behavior when they use learning algorithm. It is also shown that a learning automaton operating under the proposed nonstationary environment equalizes its penalty strengths. Computer simulations have been conducted to show the effectiveness of the proposed algorithms. The simulation results show that the performances of the proposed algorithms are close to the performance of guard channel algorithm that knows all the traffic parameters. [Copyright &y& Elsevier]

Abstract: The paper proposes a new neuron model (geometric mean neuron model) with an aggregation function based on geometric mean of all inputs. Performance of the geometric mean neuron model was evaluated using various learning algorithms like the back-propagation and resilient propagation on various real life data sets. Comparison of the performance of this model was made with the performance of multilayer perceptron. It has been shown that the geometric mean based aggregation function with resilient propagation (RPROP) performs the best both in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a mathematical model is described and developed based on a set of algebraic equations which is capable of artificially generating normal events of the electrocardiogram (ECG) such as P-wave, QRS complex, T-wave or synthetic abnormal phenomena like ST-segment episodes (i.e. depression, elevation, and sloped ascending or descending) and repolarization abnormalities such as T-wave alternans (TWA). In order to demonstrate the flexibility of the presented model for the simulation of various ECG signals, fascicular ventricular tachycardia, rate dependent conduction block, and acute Q-wave infarctions of inferior and anterior-lateral walls are simulated. Then, using the proposed artificial ECG signal, two new developed QRS detection algorithms (Modified Hilbert transform-MHT and discrete wavelet curve length-DWCL algorithms) evolved, respectively based on the implementation of some modifications to the conventional Hilbert and discrete wavelet transforms of the ECG signal, are then evaluated. To meet this end, a mathematically-simulated ECG signal with a weak R-wave is embedded into a Gaussian noise with a wide range for variances (powers) and the MHT and DWCL algorithms are applied to the resulted signal to detect QRS complexes. In the next step, an artificial baseline wander is added to the ECG signal and the performance of the MHT and DWCL algorithms in cases of high baseline wander are studied. After conducting numerous simulations, it was concluded that for the values of signal to noise ratio (SNR) larger than −5dB and amplitudes of baseline wanders approximately less than 5 times of R-wave amplitude, the two detection algorithms will have acceptable performance while it is seen that in low (negative) SNR values, performance endurance of the DWCL based detection algorithm is higher than MHT method. The Matlab m-file script of the synthetic ECG generator will be freely available from the Appendix of this paper. [Copyright &y& Elsevier]

Abstract: The development of efficient on-line data processing and decision support algorithms is one of future trends of expert systems for machine condition monitoring research. This paper contributes to a lean model for machine performance assessment by combining an efficient signal processing algorithm, an effective feature selection criterion, and an intelligent assessment method. In the proposed model, firstly, a second generation wavelet packet transform is used to project raw signals into the wavelet domain; secondly, the Fisher criterion is applied to reduce redundant dimensions; eventually, a fuzzy c-means clustering method is used to assess and classify the performance of mechanical systems. The vibration signals from a rolling element bearing experiment has been used to verify both efficiency and effectiveness of the lean model. Compared with conventional methods, the lean model can reduce the time consumption of feature extraction by 49.7% and storage space or data transfer load related to the feature dimensionality by 97.7%, which indicates a great improvement in efficiency. [Copyright &y& Elsevier]

Abstract: Asynchronous logic is an important topic due to its interesting features of high performance, low noise and robustness to parameters variations. However, its performance evaluation and optimization are relatively challenging processes due to the dependencies between concurrent events. This paper introduces a framework mainly in the context of performance driven synthesis of asynchronous circuits including systems with choice using buffer insertion technique. In the proposed framework, a high-level description of the system is received in Verilog format powered by some special macros, and then the corresponding specification will be decomposed into smaller circuits which is possible to be directly mapped into predefined circuit templates. The proposed flow has the advantage of exploiting a new performance metric and presents an efficient methodology for static estimation of average performance of asynchronous circuits with choices at the template level. The selected performance model is a probabilistic timed Petri-Net that includes probabilistic choice places to capture the conditional behavior of the system. Since there are no any data handling during the proposed static analyzing leads to very fast performance estimation with enough precise results. The buffer insertion technique is properly encoded in a quantum genetic algorithm and then is exploited after decomposition step during the synthesis flow. This algorithm addresses the problem of identifying the number and location of inserted buffers needed in an asynchronous circuit with choice to satisfy a given average case performance constraint, thereby implicitly minimizes the area for a given average case performance. Experimental results on a set of real systems indicate that our proposed technique can achieve a 34% performance enhancement in average by forcing a 19% area penalty. [Copyright &y& Elsevier]

Abstract: In this study, we explored the potential of a hybrid model combining mathematical and statistical models. Mathematical models are capable of simulating microscopic phenomena but fail to explain the complicated situations in practical cases. On the other hand, statistical models are suitable to predict complex and non-linear behaviors. Thus, a hybrid model can have advantages in both mathematical and statistical models. This paper focusses on the techniques to combine mathematical models with statistical models. As a mathematical model, we have applied the Hagen–Poiseuille equation and filtration models modified with the critical flux concept to predict the performance of hollow fiber membranes. Statistical model such as ANN (artificial neural networks) was used to correlate operating conditions with membrane fouling. Experimental data were collected from a pilot plant using hollow fiber microfiltration membranes for surface water treatment. Different methods to hybridize mathematical and statistical models were compared to develop a feedforward guidance simulator. Comparison of model calculations with experimental results revealed that the hybrid model was useful to evaluate membrane fouling characteristics. An algorithm for process controller based on the hybrid model was also suggested as an initial step toward an “intelligent” membrane system. [Copyright &y& Elsevier]

Abstract: Replication is a key technology of distributed storage systems. In this paper, an indirect replication algorithm is proposed following the intrinsic characteristic of distributed storage systems and the peer-to-peer model. In the indirect replication algorithm, the data object is partitioned into several data blocks. These data blocks are encoded in order that there is data redundancy between data blocks. Comparing with the traditional replication algorithm, the indirect replication algorithm has less granularity of replication, less bandwidth and storage costs, and provides higher availability, durability, and security. The performance evaluation shows that the encoding and decoding times are proportional to the data size, and that the irregular cascade bipartite graphs are of great advantage in improving the success ratio of data recovery. Finally, if the number of data blocks used to recover the data object is larger than a certain value, the success ratio of data recovery approaches 100%. [Copyright &y& Elsevier]