Search

This paper describes a noncontact acquisition system composed of several time synchronized laser height sensors, accelerometers, gyroscope, and so forth in order to collect the road profiles of vehicle riding on the unstructured roads. A method of correcting road profiles based on the accelerometer and gyroscope data is proposed to eliminate the adverse impacts of vehicle vibration and attitudes change. Because the power spectral density (PSD) of gyro attitudes concentrates in the low frequency band, a method called frequency division is presented to divide the road profiles into two parts: high frequency part and low frequency part. The vibration error of road profiles is corrected by displacement data obtained through two times integration of measured acceleration data. After building the mathematical model between gyro attitudes and road profiles, the gyro attitudes signals are separated from low frequency road profile by the method of sliding block overlap based on correlation analysis. The accuracy and limitations of the system have been analyzed, and its validity has been verified by implementing the system on wheeled equipment for road profiles’ measuring of vehicle testing ground. The paper offers an accurate and practical approach to obtaining unstructured road profiles for road simulation test.

This paper concerns the problem of fixed/finite-time synchronization of hybrid coupled dynamical networks. The considered dynamical networks with multilinks contain only one transmittal time-varying delay for each subnetwork, which makes us get hold of more interesting and practical points. Two kinds of delay-dependent feedback controllers with multilinks as well as appropriate Lyapunov functions are defined to achieve the goal of fixed-time synchronization and finite-time synchronization for the networks. Some novel and effective criteria of hybrid coupled networks are derived based on fixed-time and finite-time stability analysis. Finally, two numerical simulation examples are given to show the effectiveness of the results proposed in our paper.

A number of Suspen-Dome structures have been built, but there is some difficulty in using experimental data to obtain good modal parameters, especially modal damping. In this paper, an ANSYS numerical simulation of the 35.4 m span Suspen-Dome is presented. Firstly, the natural vibration characteristics of Suspen-Dome and dynamic response under some random forces were obtained. Then the results of the numerical simulation established that 60 modes are sufficient for a reasonable dynamic model. This model is used to represent the Suspen-Dome dynamic behavior, and OKID is then used to try to identify a model from simulated data. A 400-order model generated from OKID is shown to contain the 60 modes from ANSYS and is shown to give good predictions of the dynamic behavior of Suspen-Dome. The results of this paper can confirm that it can be a very efficient tool for the identification of models of Suspen-Dome dynamics.

This paper deals with the nonmonotone projection algorithm for constrained nonlinear equations. For some starting points, the previous projection algorithms for the problem may encounter slow convergence which is related to the monotone behavior of the iterative sequence as well as the iterative direction. To circumvent this situation, we adopt the nonmonotone technique introduced by Dang to develop a nonmonotone projection algorithm. After constructing the nonmonotone projection algorithm, we show its convergence under some suitable condition. Preliminary numerical experiment is reported at the end of this paper, from which we can see that the algorithm we propose converges more quickly than that of the usual projection algorithm for some starting points.

Copyright © 2016 Jiang Wu et al.This paper discusses the optimal preview control problem for a class of linear continuous stochastic control systems in the infinite horizon, based on the augmented error system method. Firstly, an assistant system is designed and the state equation is translated to the assistant system. Then, an integrator is introduced to construct a stochastic augmented error system. As a result, the tracking problem is converted to a regulation problem. Secondly, the optimal regulator is solved based on dynamic programming principle for the stochastic system, and the optimal preview controller of the original system is obtained. Compared with the finite horizon, we simplify the performance index. We also study the stability of the stochastic augmented error system and design the observer for the original stochastic system. Finally, the simulation example shows the effectiveness of the conclusion in this paper.

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.

Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data. Therefore to parallelize BPNN using distributed computing technologies is an effective way to improve the algorithm performance in terms of efficiency. However, traditional parallelization may lead to accuracy loss. Although several complements have been done, it is still difficult to find out a compromise between efficiency and precision. This paper presents a parallelized BPNN based on MapReduce computing model which supplies advanced features including fault tolerance, data replication, and load balancing. And also to improve the algorithm performance in terms of precision, this paper creates a cascading model based classification approach, which helps to refine the classification results. The experimental results indicate that the presented parallelized BPNN is able to offer high efficiency whilst maintaining excellent precision in enabling large-scale machine learning.

The mixed research design is a progressive methodological discourse that combines the advantages of quantitative and qualitative methods. Its possibilities of application are, however, dependent on the efficiency with which the particular research techniques are used and combined. The aim of the paper is to introduce the possible combination of Hypothesis with EyeTribe tracker. The Hypothesis is intended for quantitative data acquisition and the EyeTribe is intended for qualitative (eye-tracking) data recording. In the first part of the paper, Hypothesis software is described. The Hypothesis platform provides an environment for web-based computerized experiment design and mass data collection. Then, evaluation of the accuracy of data recorded by EyeTribe tracker was performed with the use of concurrent recording together with the SMI RED 250 eye-tracker. Both qualitative and quantitative results showed that data accuracy is sufficient for cartographic research. In the third part of the paper, a system for connecting EyeTribe tracker and Hypothesis software is presented. The interconnection was performed with the help of developed web application HypOgama. The created system uses open-source software OGAMA for recording the eye-movements of participants together with quantitative data from Hypothesis. The final part of the paper describes the integrated research system combining Hypothesis and EyeTribe.

When dealing with power system emergency caused by natural disaster, information survey is the important foundation of emergency power supply and repair. The information credibility would directly inflect the credibility and timeliness of electric system emergency work. Therefore, this paper starts the study from the point of information credibility and sets minimizing total cost and loss as the objective function, taking time constraint, transportation road constraint, information credibility constraint, and the parameters of different facilities into consideration and building a location and storage optimization model for electric system emergency facility. And a corresponding facility transportation model was built considering the power loss in demand points. The simulation result shows that the models proposed by this paper could satisfy the requirement of information credibility, improve the adaptability to demand change, and cut down total cost and loss.

Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS) and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with.

The main contribution of this paper is presenting a flexible solution to the box-constrained least squares problems. This solution is applicable to many existing problems, such as nonnegative matrix factorization, support vector machine, signal deconvolution, and computed tomography reconstruction. The key concept of the proposed algorithm is to replace the minimization of the cost function at each iteration by the minimization of a surrogate, leading to a guaranteed decrease in the cost function. In addition to the monotonicity, the proposed algorithm also owns a few good features including the self-constraint in the feasible region and the absence of a predetermined step size. This paper theoretically proves the global convergence for a special case of below-bounded constraints. Using the proposed mechanism, some valuable algorithms can be derived. The simulation results demonstrate that the proposed algorithm provides performance that is comparable to that of other commonly used methods in numerical experiment and computed tomography reconstruction.

Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur’s entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image’s spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur’s entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

This paper presents an analytical solution of the simply supported horizontally composite curved I-beam by trigonometric series considering the effect of partial interaction in the tangential direction. Governing equations and boundary conditions are obtained by using the Vlasov curved beam theory and the principle of minimum potential energy. The deflection functions and the Lagrange multiplier functions are expressed as trigonometric series to satisfy the governing equations and the simply supported constraints at both ends. The numerical results of deflections and forces which are obtained by this method are compared with both FEM results and experimental results, and the inaccuracy between the analytical solutions in this paper and the FEM results is small and reasonable.

The paper addresses particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy is proposed. In contrast with other label propagation strategies, the main contribution of this paper is to design the definition of the impact of node and take it into use. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectiveness of proposed strategy. Furthermore, this strategy is extended to signed networks, and the corresponding objective function which is called modularity density is modified to be used in signed networks. Experiments on real-life networks also demonstrate that it is an efficacious way to solve community detection problem.

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.

In order to present reliable mode and structure of connection between pole and base for breakaway sign, mode evaluation and structure design were conducted in this paper. AHP was adopted to evaluate connection modes between sign pole and base. Analytic hierarchy with double criterion layer and corresponding judgment matrix were established. Through qualitative analysis, powers of indexes were given and evaluation results were presented based on standardization process of each evaluation index. It shows that brittle connection mode comes in the first place, the plastic connection mode comes second, and rigid connection mode comes last. Two brittle connection structures were designed through the finite element simulation analysis. By ANSYS software, stress data were given for recessed and hole-type connection design structure. It can be found that maximum tensile stress data of two connection structures are all more than ultimate tensile strength of steel. Thus it can be concluded that two connection structures designed in this paper can be breakaway in collision and protect the safety of vehicle occupants.

A method to evaluate the stability of tunnel face is proposed in the framework of upper bound theorem. The safety factor which is widely applied in slope stability analysis is introduced to estimate the stability of tunnel face using the upper bound theorem of limit analysis in conjunction with a strength reduction technique. Considering almost all geomaterials following a nonlinear failure criterion, a generalized tangential technique is used to calculate the external work and internal energy dissipation in the kinematically admissible velocity field. The upper bound solution of safety factor is obtained by optimization calculation. To evaluate the validity of the method proposed in this paper, the safety factor is compared with those calculated by limit equilibrium method. The comparison shows the solutions derived from these two methods match each other well, which shows the method proposed in this paper can be considered as effective.

This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally. We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately. Then, we adopt the penalty factor to regulate the data imbalance effect on SVM classification algorithm. The achievements and scientific significance of this paper do not propose the best classifier or solution of imbalanced data set and just verify the validity and stability of proposed IDS resampling method. Experiments show that our method acquires obvious promotion effect in various imbalanced data sets.

The paper presents an optimization method for the ship hull principal dimensions scheme employing the fuzzy decision-making theory. First of all, the paper establishes the fuzzy decision-making model of the ship hull principal dimensions optimization, and then a series of ship hull principal dimensions schemes are accordingly constructed by employing the variable value method. On the basis of this, the fuzzy decision-making method is employed to evaluate the series ship hull principal dimensions schemes. Finally, the optimal ship hull principal dimensions scheme is obtained. The example demonstration verified the proposed method’s validity for ship hull principal dimensions optimization economic performance.

Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

Extreme learning machine (ELM) as an emerging technology has recently attracted many researchers’ interest due to its fast learning speed and state-of-the-art generalization ability in the implementation. Meanwhile, the incremental extreme learning machine (I-ELM) based on incremental learning algorithm was proposed which outperforms many popular learning algorithms. However, the incremental algorithms with ELM do not recalculate the output weights of all the existing nodes when a new node is added and cannot obtain the least-squares solution of output weight vectors. In this paper, we propose orthogonal convex incremental learning machine (OCI-ELM) with Gram-Schmidt orthogonalization method and Barron’s convex optimization learning method to solve the nonconvex optimization problem and least-squares solution problem, and then we give the rigorous proofs in theory. Moreover, in this paper, we propose a deep architecture based on stacked OCI-ELM autoencoders according to stacked generalization philosophy for solving large and complex data problems. The experimental results verified with both UCI datasets and large datasets demonstrate that the deep network based on stacked OCI-ELM autoencoders (DOC-IELM-AEs) outperforms the other methods mentioned in the paper with better performance on regression and classification problems.

Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverter based distributed generation is proposed. The proposed method let the inverter based distributed generation be equivalent toIθbus, which makes it suitable to calculate the power flow of distribution network with a current limited inverter based distributed generation. And the low voltage ride through capability of inverter based distributed generation can be considered as well in this paper. Finally, some tests of power flow and short circuit current calculation are performed on a 33-bus distribution network. The calculated results from the proposed method in this paper are contrasted with those by the traditional method and the simulation method, whose results have verified the effectiveness of the integrated method suggested in this paper.

This paper deals with the characteristic roots of different types of lattice matrices and proves that a matrix and its transpose have the same characteristic roots. Also this paper introduces the concept of similar lattice matrices and proves that similar lattice matrices have the same characteristic roots.

We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011) and Chen and Cao (2002).

This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.

An intelligent algorithm that simultaneously analyzes multiple rockfill parameters is proposed. First, the paper introduces the operation and monitoring condition of the Shuibuya concrete-faced rockfill dam (CFRD). Then the constitutive rockfill models and the FEM analysis procedure are introduced in this paper. Third, the MsPSO intelligent algorithm was adopted to inverse the rockfill parameters. The recalculated displacement of Shuibuya CFRD using the inversed rockfill parameters is presented, and a satisfactory result was obtained, indicating that the inversion method is correct and effective. The method developed in this paper can be adopted in any geotechnical engineering parameter inversion.

A focused crawler is topic-specific and aims selectively to collect web pages that are relevant to a given topic from the Internet. However, the performance of the current focused crawling can easily suffer the impact of the environments of web pages and multiple topic web pages. In the crawling process, a highly relevant region may be ignored owing to the low overall relevance of that page, and anchor text or link-context may misguide crawlers. In order to solve these problems, this paper proposes a new focused crawler. First, we build a web page classifier based on improved term weighting approach (ITFIDF), in order to gain highly relevant web pages. In addition, this paper introduces an evaluation approach of the link, link priority evaluation (LPE), which combines web page content block partition algorithm and the strategy of joint feature evaluation (JFE), to better judge the relevance between URLs on the web page and the given topic. The experimental results demonstrate that the classifier using ITFIDF outperforms TFIDF, and our focused crawler is superior to other focused crawlers based on breadth-first, best-first, anchor text only, link-context only, and content block partition in terms of harvest rate and target recall. In conclusion, our methods are significant and effective for focused crawler.

For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

Modular production and component commonality are two widely used strategies in the manufacturing industry to meet customers growing needs for customized products. Using these strategies, companies can enhance their performance to achieve optimal safety stock levels. Despite the importance of safety stocks in business competition, little attention has been paid to the way to reduce them without affecting the customer service levels. This paper develops a mathematical model to reduce safety stock levels in organizations that employ modular production. To construct the model, we take advantage of the benefits of aggregate inventories, standardization of components, component commonality, and Group Technology philosophy in regard to stock levels. The model is tested through the simulation of three years of operation of two modular product systems. For each system, we calculated and compared the safety stock levels for two cases: (1) under the only presence of component commonality and (2) under the presence of both component commonality and Group Technology philosophy. The results show a reduction in safety stock levels when we linked the component commonality with the Group Technology philosophy. The paper presents a discussion of the implications of each case, features of the model, and suggestions for future research.

In order to study the dynamic characteristics of urban public traffic network, this paper establishes the conventional bus traffic network and the urban rail traffic network based on the spaceRmodeling method. Then regarding these two networks as the subnetwork, the paper presents a new bilayer coupled public traffic network through the transfer relationship between subway and bus, and this model well reflects the connection between the passengers and bus operating vehicles. Based on the synchronization theory of coupling network with time-varying delay and taking “Lorenz system” as the network node, the paper studies the synchronization of bilayer coupled public traffic network. Finally, numerical results are given to show the impact of public traffic dispatching, delayed departure, the number of public bus stops between bus lines, and the number of transfer stations between two traffic modes on the bilayer coupled public traffic network balance through Matlab simulation.

This paper uses principal-agent theory to study the issue of incentivizing crowdsourcing communities. It proves that enterprises can generate innovation plans of high quality and expected utility using the crowdsourcing community. Outsourcers can encourage high-quality people to join by adopting a linear variable compensation scheme and make the low-quality people quit by requiring them to supply more effort. The paper also shows that enterprises’ participation in crowdsourcing community innovation can effectively improve their innovative ability and that it is necessary for enterprises to construct an effective and cooperative innovative system combining crowdsourcing community innovation and their own internal innovation.

Maritime freight shipping has increased significantly and air pollution from international ships has grown accordingly, having serious environmental effects all over the world. This paper analyzes the effects of the emission cost on ocean route choices, focusing on international container ships. First, the paper formulates a freight network model that captures decisions and interactions of ocean carriers and port terminal operators in the maritime freight transport system. Then, the emission cost is calculated based on an activity-based approach as a component of the ocean transportation cost function. A case study is examined to find if the emission cost affects ocean route choices. The results indicate that the optimal ocean route and transportation cost are changed distinctively due to the emission cost. The research discusses how the emission cost plays a role in route changes and why ocean carriers have to consider these costs in their routing decisions.

In order to optimize the signal timing for isolated intersection, a new method based on fuzzy programming approach is proposed in this paper. Considering the whole operation efficiency of the intersection comprehensively, traffic capacity, vehicle cycle delay, cycle stops, and exhaust emission are chosen as optimization goals to establish a multiobjective function first. Then fuzzy compromise programming approach is employed to give different weight coefficients to various optimization objectives for different traffic flow ratios states. And then the multiobjective function is converted to a single objective function. By using genetic algorithm, the optimized signal cycle and effective green time can be obtained. Finally, the performance of the traditional method and new method proposed in this paper is compared and analyzed through VISSIM software. It can be concluded that the signal timing optimized in this paper can effectively reduce vehicle delays and stops, which can improve traffic capacity of the intersection as well.

To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM). Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

The approach proposed in this paper, in contrast to the standard passive solution, focuses on active adaptation of energy absorbing structures (equipped with sensor system detecting, identifying the impact in advance, and controllable semi-active dissipaters) with high ability of adaptation to extreme overloading. The paper presents a concept of multi-folding structures providing the additional value of energy dissipation, due to the synergy of repetitive use of active elements (equipped with so-called structural fuses), according to pre-designed optimal distribution of yield stress levels, triggering the desired sequence of local collapses. The second example, an adaptive wind turbine, demonstrates the idea of controlling the value of peak dynamic force that is transferred to the support by means of a pneumatic system with a controllable on/off piezo-valve. An alternative for developing new blade materials is to introduce a semi-actively controlled connection between blade and hub with controllable characteristics. Finally, the paper discusses the possibility of real-time, dynamic mass identification, which is an important part of the process of load identification. Numerical simulations and experimental results are presented.

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.

Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means ofK-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

To study incremental machine learning in tensor space, this paper proposes incremental tensor discriminant analysis. The algorithm employs tensor representation to carry on discriminant analysis and combine incremental learning to alleviate the computational cost. This paper proves that the algorithm can be unified into the graph framework theoretically and analyzes the time and space complexity in detail. The experiments on facial image detection have shown that the algorithm not only achieves sound performance compared with other algorithms, but also reduces the computational issues apparently.

This paper presents a new hybrid global optimization algorithm, which is based on the wind driven optimization (WDO) and differential evolution (DE), named WDO-DE algorithm. The WDO-DE algorithm is based on a double population evolution strategy, the individuals in a population evolved by wind driven optimization algorithm, and a population of individuals evolved from difference operation. The populations of individuals both in WDO and DE employ an information sharing mechanism to implement coevolution. This paper chose fifteen benchmark functions to have a test. The experimental results show that the proposed algorithm can be feasible in both low-dimensional and high-dimensional cases. Compared to GA-PSO, WDO, DE, PSO, and BA algorithm, the convergence speed and precision of WDO-DE are higher. This hybridization showed a better optimization performance and robustness and significantly improves the original WDO algorithm.

This paper presents a wavelet-based method for estimating evolutionary power spectral density (EPSD) of nonstationary stochastic oscillatory processes and its application to field measured typhoon processes. The EPSD, which is deduced in a closed form based on the definition of the EPSD and the algorithm of the continuous wavelet transform, can be formulated as a sum of squared moduli of the wavelet functions in time domain modulated by frequency-dependent coefficients that relate to the squared values of wavelet coefficients and two wavelet functions with different time shifts. A parametric study is conducted to examine the efficacy of the wavelet-based estimation method and the accuracy of different wavelets. The results indicate that all of the estimated EPSDs have acceptable accuracy in engineering application and the Morlet transform can provide desirable estimations in both time and frequency domains. Finally, the proposed method is adopted to investigate the time-frequency characteristics of the Typhoon Matsa measured in bridge site. The nonstationary energy distribution and stationary frequency component during the whole process are found. The work in this paper may promote an improved understanding of the nonstationary features of typhoon winds.

The objective of superresolution is to reconstruct a high-resolution image by using the information of a set of low-resolution images. Recently, the variational Bayesian superresolution approach has been widely used. However, these methods cannot preserve edges well while removing noises. For this reason, we propose a new image prior model and establish a Bayesian superresolution reconstruction algorithm. In the proposed prior model, the degree of interaction between pixels is adjusted adaptively by an adaptive norm, which is derived based on the local image features. Moreover, in this paper, a monotonically decreasing function is used to calculate and update the single parameter, which is used to control the severity of penalizing image gradients in the proposed prior model. Thus, the proposed prior model is adaptive to the local image features thoroughly. With the proposed prior model, the edge details are preserved and noises are reduced simultaneously. A variational Bayesian inference is employed in this paper, and the formulas for calculating all the variables including the HR image, motion parameters, and hyperparameters are derived. These variables are refined progressively in an iterative manner. Experimental results show that the proposed SR approach is very efficient when compared to existing approaches.

In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC). By combining manifold learning and sparse coding techniques together, GRNMF_SC can efficiently extract the basic vectors from the data space, which preserves the intrinsic manifold structure and also the local features of original data. The target function of our method is easy to propose, while the solving procedures are really nontrivial; in the paper we gave the detailed derivation of solving the target function and also a strict proof of its convergence, which is a key contribution of the paper. Compared with sparseness constrained NMF and GNMF algorithms, GRNMF_SC can learn much sparser representation of the data and can also preserve the geometrical structure of the data, which endow it with powerful discriminating ability. Furthermore, the GRNMF_SC is generalized as supervised and unsupervised models to meet different demands. Experimental results demonstrate encouraging results of GRNMF_SC on image recognition and clustering when comparing with the other state-of-the-art NMF methods.

This paper presents a fault diagnosis application of the Latent Nestling Method to IGBTs. The paper extends the Latent Nestling Method based in Coloured Petri Nets (CPNs) to hybrid systems in such a manner that IGBTs performance can be modeled. CPNs allow for an enhanced capability for synthesis and modeling in contrast to the classical phenomena of combinational state explosion when Finite State Machine methods are applied. We present an IGBT model with different fault modes including those of intermittent nature that can be used advantageously as predictive symptoms within a predictive maintenance strategy. Ageing stress tests have been experimentally applied to the IGBTs modules and intermittent faults are diagnosed as precursors of permanent failures. In addition, ageing is validated with morphological analysis (Scanning Electron Microscopy) and semiqualitative analysis (Energy Dispersive Spectrometry)., This work was supported by the Spanish Ministerio de Ciencia y Tecnologia Project DPI2009-14744-C03-03, by Generalitat Valenciana Project GV/2010/018, and by Universitat Politecnica de Valencia Project PAID06-08.

Combining maintenance tasks into work packages is not only necessary for arranging maintenance activities, but also critical for the reduction of maintenance cost. In order to optimize the combination of maintenance tasks by fuzzy C-means clustering algorithm, an improved fuzzy C-means clustering model is introduced in this paper. In order to reduce the dimension, variables representing clustering centers are eliminated in the improved cluster model. So the improved clustering model can be directly solved by the optimization method. To optimize the clustering model, a novel nonlinear simplex optimization method is also proposed in this paper. The novel method searches along all rays emitting from the center to each vertex, and those search directions are rightlyn+1positive basis. The algorithm has both theoretical convergence and good experimental effect. Taking the optimal combination of some maintenance tasks of a certain aircraft as an instance, the novel simplex optimization method and the clustering model both exhibit excellent performance.

This paper investigates the meshing behavior of the roller screw, a mechanical transmission device characterized by threaded rollers that transfer a load between the nut and the screw, by analyzing the meshing characteristics between screw and rollers. This study seeks to establish a more accurate mathematical model for the thread surface by creating a modeling process in which the max radiuses of the threads are calculated more precisely. The contact line distribution and the contact location were also calculated in order to confirm the cross section of the meshing points. In the research presented in this paper, the actual transmission ratio is analyzed and the study results in a new method to calculate the actual transmission ratio. In this study, the helical angle and the vertex angle are proven to be of great significance after a careful analysis of their influence is conducted.

This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO) method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.

One novel control policy named selective siphon control policy is proposed to solve for deadlock problems of flexible manufacturing systems (FMSs). The new policy not only solves the deadlock problem successfully but also obtains maximally permissive controllers. According to our awareness, the policy is the first one to achieve the goal of obtaining maximally permissive controllers for all S3PR (one system of simple sequential processes with resources, S3PR) models in existing literature. However, one main problem is still needed to solve in their algorithm. The problem is that the proposed policy cannot check the exact number of maximally permissive states of a deadlock net in advance. After all iterating steps, the final maximally permissive states can then be known. Additionally, all legal markings are still to be checked again and again until all critical markings vanished. In this paper, one computationally improved methodology is proposed to solve the two problems. According to the experimental results, the computational efficiency can be enhanced based on the proposed methodology in this paper.

Clustering is one of the most commonly used approaches in data mining and data analysis. One clustering technique in clustering that gains big attention in clustering related research isk-means clustering such that the observation is grouped intokcluster. However, some obstacles such as the adherence of results to the initial cluster centers or the risk of getting trapped into local optimality hinder the overall clustering performance. The purpose of this research is to minimize the dissimilarity of all points of a cluster from gravity center of the cluster with respect to capacity constraints in each cluster, such that each element is allocated to only one cluster. This paper proposes an effective combination algorithm to find optimal cluster center for the analysis of data in data mining and a new combination algorithm is proposed to untangle the clustering problem. This paper presents a new hybrid algorithm, which is, based on cluster center initialization algorithm (CCIA), bees algorithm (BA), and differential evolution (DE), known as CCIA-BADE-K, aiming at finding the best cluster center. The proposed algorithm performance is evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in the literature confirm its superior performance and higher efficiency.

This paper addresses the robust stability of teleoperated systems under the four-channel architecture, affected by time-varying communication delays and using disturbance observers. It is based on our previous work which provides a framework for robust stability against delays with bounded variation and a bounded time-derivative, using structured singular values (SSV). The main new feature here is the inclusion of disturbance observers (DOBs). The DOB concept is well-documented and relevant to many applications, since only position (but not force) measurements are usually available. In this paper, we adapt two DOBs (master and slave) to our generic framework, by representing them as stable, fast filters affected by the uncertainty in the plant modelling. Our main result is an SSV test to verify robust stability. The simulation results confirm the usefulness of this approach.