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A variety of factors will affect the combination and distribution of the molds used in the production of prefabricated components on the mold table, and will produce many combination and distribution results. In order to solve such problems and quickly find the best distribution results of the molds on the mold table, this paper proposed a mold combination method based on the combination of backtracking method and improved Eclat algorithm. Firstly, it built a binary tree model based on the mold combination relationship in the historical data, searched the binary tree through the backtracking method, and obtained all theoretically feasible mold combination results. Then it pre-placed these combination results by using the improved BL positioning algorithm on the mold table with a fixed area, and screened out the mold combination results that met the actual placement conditions. It used Eclat algorithm to obtain the optimal combination set of mold adaptation from the selected combination results, determined the association rules between different types of molds,and used the rules to guide the allocation process of molds on the mold table in the new component production task. At the same time, this paper improved the Eclat algorithm by introducing the partition idea and the priori constraint idea based on probability, so as to improve its operation efficiency when dealing with large-scale data. Finally, through the simulation test of the case data in the prefabricated component manufacturing enterprise, it verified the effectiveness of the method to quickly solve the problem of mold table combination allocation. The results show that the mold combination allocation based on backtracking and DEclat algorithm method has a good effect on the utilization of the mold area, the reduction of the mold changing time and the reduction of the total completion time. [ABSTRACT FROM AUTHOR]

Community detection is significative in the complex network. This paper focuses on community detection based on clustering algorithms. We tend to find out the central nodes of the communities by centrality algorithms. Firstly, we define the distance between nodes using similarity. Then, a new centrality measuring the local density of nodes is put forward. Combining the independence of the centrality, the nodes in the network can be divided into four classes. Leveraging the product of centrality and independence, the central nodes in the network are easily identified. We also find that we can distinguish bridge nodes from central nodes using centrality and independence. This research designs a community detection algorithm combining centrality and independence. Simulation results reveal that our centrality is more effective than existing centralities in measuring local density and identifying community centers. Compared with other community detection algorithms, results prove the effectiveness of our algorithm. This paper just shows one application of independence of the centrality. There may be more useful applications of it. [ABSTRACT FROM AUTHOR]

Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level. [ABSTRACT FROM AUTHOR]

Built upon the methodology of "spatio-temporal context", a simple yet robust object tracking method is proposed for solving the occlusion problems in this paper. This algorithm makes full use of the context information of the object and its local background to calculate the features, which maximumlly improve the occlusion predictive response and recapture accuracy. Firstly, an early warning mechanism is adopted to realize the occlusion detection. Once the object is fully occluded, the object position with accurate motion information saved in the early warning is predicted and memory tracking model is used to delete the suspected object region, which reduces the matching complexity. Finally, a confidence strategy for similarity measurement is adopted to capture the suspected object when the object appears, and the optimal confidence is introduced to get an adaptive update model. Many simulation experiments in benchmark videos show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

As UWB high-precision positioning in NLOS environment has become one of the hot topics in the research of indoor positioning, this paper firstly presents a method for the smoothing of original range data based on the Kalman filter by the analysis of the range error caused by UWB signals in LOS and NLOS environment. Then, it studies a UWB and foot-mounted IMU fusion positioning method with the integration of particle filter with extended Kalman filter. This method adopts EKF algorithm in the kinematic equation of particle filters algorithm to calculate the position of each particle, which is like the way of running N (number of particles) extended Kalman filters, and overcomes the disadvantages of the inconformity between kinematic equation and observation equation as well as the problem of sample degeneration under the nonlinear condition of the standard particle filters algorithm. The comparison with the foot-mounted IMU positioning algorithm, the optimization-based UWB positioning algorithm, the particle filter-based UWB positioning algorithm, and the particle filter-based IMU/UWB fusion positioning algorithm shows that our algorithm works very well in LOS and NLOS environment. Especially in an NLOS environment, our algorithm can better use the foot-mounted IMU positioning trajectory maintained by every particle to weaken the influence of range error caused by signal blockage. It outperforms the other four algorithms described as above in terms of the average and maximum positioning error. [ABSTRACT FROM AUTHOR]

Based on wavelet transform and chaos algorithm, this paper presents a Network Traffic Forecasting Combination Model. The model introduces chaos algorithm for training the BP network and optimizing weights so as to avoid gradient descent algorithm that slowly converges and likely obtains local optimum results. Before forecasting, we first perform wavelet decomposition on the pretreated flow. Then, we utilize the FARIMA model and the improved Elman neural network model to forecast according to approximate components and detailed components, respectively. At last, we use the combination model for the network traffic forecasting. Simulation results confirmed the improved accuracy of the model, and comparing to traditional FARIMA model and wavelet neural network (WNN) model, the model can reduce the deviation. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that uses forecast encompassing tests for combining forecasts when there are a large number of forecasts that might enter the combination. The algorithm excludes a forecast from the combination if it is encompassed by another forecast. To assess the usefulness of this approach, an extensive empirical analysis is undertaken using a US macroeconomic dataset. The results are encouraging; the algorithm forecasts outperform benchmark model forecasts, in a mean square error (MSE) sense, in a majority of cases. The paper also compares the empirical performance of different approaches to forecast combination, and provides a rule-of-thumb cut-off point for the thick-modeling approach. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With advances in technology and the growing complexity of technological systems, the job of the reliability/system analyst has become more challenging as they have to study, characterize, measure and analyze the behavior of systems with the help of various traditional analytical (mathematical and statistical) techniques, which require knowledge of the precise numerical probabilities and component functional dependencies, information which is difficult to obtain. Even if data are available they are often inaccurate and are thus subject to uncertainty, i.e. historical records can only represent the past behavior and may be unable to predict the future behavior of the equipment. To cope with such situations, the knowledge-based approximate reasoning methodologies (KBARMs) provide necessary help. Among them, the fuzzy and grey methodologies are the most viable and effective tools for coping with imprecise, uncertain and subjective information in a consistent and logical manner. In this paper, the authors present a methodological and structured approach (which makes use of both qualitative and quantitative techniques) to model, analyze and predict the failurebehavior of two units, namely the forming and press units of a paper machine, using KBARMs. Various system parameters such as repair time, failure rate, mean time between failures, availability and expected number of failures are computed to quantify the system behavior in terms of fuzzy, crisp and defuzzified values. Furthermore, a risk ranking approach based on fuzzy and grey relational analysis is discussed to prioritize various failure causes associated with the components in failure mode and effects analysis (FMEA). Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper shows how probabilistic potential methods based on the Monte Carlo approach can be efficiently mapped onto an array of concurrent processors and how the resulting algorithm can provide rapid solutions of harmonic function problems. The method described here exploits the natural concurrency of the Monte Carlo approach. Computation is achieved by use of a low-cost array of transputers linked to a PC-AT. The resulting methodology is powerful and the ideas suggested should have application to many other modelling techniques used for field modelling. A microstrip geometry is used as the discussion vehicle throughout this paper. This geometry was selected since it exhibits a number of the important topological features required in a general problem, e.g. Dirichlet boundary conditions and mixed dielectric. The design of the parallel algorithm used is discussed in detail, and typical results are given. [ABSTRACT FROM AUTHOR]

This paper will motivate alternative combining schemes, provide a statistic for comparing alternative combinations out-of-sample and provide an example demonstrating these techniques. The evidence suggests the proposed procedures are likely to do no worse than other approaches and promise to do better under circumstances commonly encountered with economic data: integrated series and contaminated data. [ABSTRACT FROM AUTHOR]

In this paper, an identification scheme for sandwich systems with a dead zone is proposed. In this scheme, a so-called combinational input is proposed to transform the sandwich model with a dead zone into a model with separated parameters. Then, the recursive general identification algorithm can be applied to the parameter-estimation, since the transformed model has a form that is linearized in coefficients. Finally, the proposed method is applied to the identification of an X–Y moving positioning stage. [ABSTRACT FROM PUBLISHER]

Abstract  In this paper, a new feature extraction algorithm is developed based on canonical correlation analysis (CCA), called Local Discrimination CCA (LDCCA). The method considers a combination of local properties and discrimination between different classes. Not only the correlations between sample pairs but also the correlations between samples and their local neighborhoods are taken into consideration in LDCCA. Effective class separation is achieved by maximizing local within-class correlations and minimizing local between-class correlations simultaneously. Besides, a kernel version of LDCCA (KLDCCA) is proposed to cope with nonlinear problems in experiments. The experimental results on an artificial dataset, multiple feature databases and face databases including ORL, Yale, AR validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors—one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis tools. Aiming at bridging this gap and generalizing conventional statistical tools over tensors, this paper proposes a decoupled probabilistic algorithm, which is named Bayesian tensor analysis (BTA). Theoretically, BTA can automatically and suitably determine dimensionality for different modalities of tensor data. With BTA, a collection of 3-D faces can be well modeled. Empirical studies on expression retargeting also justify the advantages of BTA. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust optimization approach by combination of sensitivity analysis and sigma level estimation in multi-objective problems. In order to obtain robust Pareto-optima within acceptable computational costs, we actively reuse the already calculated data for the sensitivity analysis in multipoint search algorithm. The sigma level estimation is also utilized to select the significant solutions from a large number of Pareto-optima based on both the robustness and the performance. Some numerical results that verify the effectiveness of the proposed methods are presented. [ABSTRACT FROM AUTHOR]

This paper presents a general approach for designing array and tree integer multipliers with overflow detection. The overflow detection techniques are based on an analysis of the magnitudes of the input operands. The overflow detection circuits operate in parallel with a simplified multiplier to reduce the overall area and delay. [ABSTRACT FROM AUTHOR]

We present a new keyword extraction algorithm that applies to a single document without using a corpus. Frequent terms are extracted first, then a set of co-occurrences between each term and the frequent terms, i.e., occurrences in the same sentences, is generated. Co-occurrence distribution shows importance of a term in the document as follows. If the probability distribution of co-occurrence between term a and the frequent terms is biased to a particular subset of frequent terms, then term a is likely to be a keyword. The degree of bias of a distribution is measured by the χ2-measure. Our algorithm shows comparable performance to tfidf without using a corpus. [ABSTRACT FROM AUTHOR]

A Lyndon word is a primitive string which is lexicographically smallest among cyclic permutations of its characters. Lyndon words are used for constructing bases in free Lie algebras, constructing de Bruijn sequences, finding the lexicographically smallest or largest substring in a string, and succinct suffix-prefix matching of highly periodic strings. In this paper, we extend the concept of the Lyndon word to two dimensions. We introduce the 2D Lyndon word and use it to capture 2D horizontal periodicity of a matrix in which each row is highly periodic, and to efficiently solve 2D horizontal suffix-prefix matching among a set of patterns. This yields a succinct and efficient algorithm for 2D dictionary matching. We present several algorithms that compute the 2D Lyndon word that represents a matrix. The final algorithm achieves linear time complexity even when the least common multiple of the periods of the rows is exponential in the matrix width. [ABSTRACT FROM AUTHOR]

In the belief function theory, combination of reliable or unreliable information sources is concerned for a long time. Recently, Lefèvre and Elouedi proposed an operator called Combination With Adapted Conflict (CWAC) to synthesize all the knowledge of the initial belief functions. However, several problems are existed in the CWAC operator actually. The conflict obtained by using CWAC actually is not reasonable as an alarm in some situation and cannot truly reflect the opposition between the belief functions in the combination. In this paper, the existing problems of CWAC are exposed. And based on the spirit of original CWAC operator, an improved CWAC operator is proposed, which is more reasonable and effective. Some illustrative examples are given to show the effectiveness and strength of the proposed improved CWAC operator. [ABSTRACT FROM AUTHOR]

• We combine kernel alignment with subspace segmentation for MKC. • The algorithm automatically fuses unlabeled data from multiple sources. • The proposed algorithm is robust to noise and outliers. • An effective optimization method for the resultant optimization problem. • Experimental results demonstrate superior performance of the algorithm. Multiple kernel clustering (MKC) is an important research topic during the last few decades. It optimally combines a group of pre-specified base kernels to improve clustering performance. Though demonstrating promising performance in various applications, this task is still challenging due to lack of reliable discriminative guidance for the base kernel combination. Moreover, noise from either corrupted data or inappropriately selected base kernel parameters would undermine the intrinsic manifold and makes the problem even harder. In this paper, we integrate subspace segmentation into MKC and propose a robust subspace segmentation-based multiple kernel clustering (SS-MKC) algorithm to address these issues. In our formulation, we unify the constrained kernel polarization and subspace segmentation into a single procedure, where the resultant affinity matrix embedded with robust subspace structural information is utilized to guide the linear combination of base kernels. In addition, we carefully design the noise representation matrix as well as two sparse constraints, i.e., the ℓ 1 -norm and the probability constraints, to eliminate the adverse effect of noise among base kernels. We then propose a novel Alternative Direction Method of Multiplier (ADMM)-based algorithm to solve the resulting optimization problem. Extensive experiments have been conducted on both synthetic and public benchmark datasets, and the results well demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art MKC methods. [ABSTRACT FROM AUTHOR]

Road monitoring helps to control the regional traffic situation so as to adjust the traffic flow. Real-time panorama is conducive to timely treat traffic accidents and to greatly improve traffic capacity. This paper designs a 3D road scene monitoring framework based on real-time panorama. The system is the combination of large scale panorama, satellite map textures, and 3D scene model, in which users can ramble freely. This paper has the following contributions. Firstly, land-points were extracted followed bymotion detection, then comotion algorithm was applied to land-points from adjacent cameras, and homography matrix was constructed. Secondly, reference camera was chosen and transformed to overhead viewpoint; subsequentlymultiviewsweremorphed to the same viewpoint and stitched to panorama. Finally, the registration based on high-precision GPS information between 2Droad panorama and 3D scene model was also proposed. The proposed framework has been successfully applied to a large road intersection monitoring. Experimental results are furnished at the end of the paper. [ABSTRACT FROM AUTHOR]

This paper gives a bird's-eye view of the various ingredients that make up a modern, model-checking-based approach to performability evaluation: Markov reward models, temporal logics and continuous stochastic logic, model-checking algorithms, bisimulation and the handling of non-determinism. A short historical account as well as a large case study complete this picture. In this way, we show convincingly that the smart combination of performability evaluation with stochastic model-checking techniques, developed over the last decade, provides a powerful and unified method of performability evaluation, thereby combining the advantages of earlier approaches. [ABSTRACT FROM AUTHOR]

An LMI-based method for the integrated system identification and controller design is proposed in the paper. We use the fact that a class of a system identification problem results in an LMI optimization problem. By combining LMIs for the system identification and those to obtain a discrete time controllerwe propose a framework to integrate two steps for themodel-based control system design, that is, the system identification and the controller synthesis. The framework enables us to obtain a good model for control and a model-based feedback controller simultaneously in the sense of the closed-loop performance. An iterative design algorithm similar to so-called Windsurfer Approach is presented. [ABSTRACT FROM AUTHOR]

Abstract: Co-association matrix has been a useful tool in many clustering ensemble techniques as a similarity measure between objects. In this paper, we introduce the weighted-association matrix, which is more expressive than the traditional co-association as a similarity measure, in the sense that it integrates information from the set of partitions in the clustering ensemble as well as from the original data of object representations. The weighted-association matrix is the core of the two main contributions of this paper: a natural extension of the well-known evidence accumulation cluster ensemble method by using the weighted-association matrix and a kernel based clustering ensemble method that uses a new data representation. These methods are compared with simple clustering algorithms as well as with other clustering ensemble algorithms on several datasets. The obtained results ratify the accuracy of the proposed algorithms. [Copyright &y& Elsevier]

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State-based systems and modal logics for reasoning about them often heterogeneously combine a number of features such as non-determinism and probabilities. In this paper, we show that the combination of features can be reflected algorithmically, and we develop modular decision procedures for heterogeneous modal logics. The modularity is achieved by formalising the underlying state-based systems as multi-sorted coalgebras and associating both a logical and algorithmic description with a number of basic building blocks. Our main result is that logics arising as combinations of these building blocks can be decided in polynomial space provided this is also the case for the components. By instantiating the general framework to concrete cases, we obtain PSpace decision procedures for a wide variety of structurally different logics, describing, for example, Segala systems and games with uncertain information. [ABSTRACT FROM AUTHOR]

The celebrated theorem established by Martin Davis, Hilary Putnam, and Julia Robinson in 1961 states that every effectively enumerable set of natural numbers has an exponential Diophantine representation. This theorem was improved by the author in two ways: However, it remains unknown whether these two improvements could be combined, that is, whether every effectively enumerable set has a single-fold (or at least finite-fold) Diophantine representation. In the paper, we discuss known results about single-fold exponential Diophantine representations, their applications, possible approaches to improving them to the case of genuine Diophantine representations, and what would follow if such improvement is impossible. Bibliography: 27 titles. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a deterministic algorithm that can construct a higher-order neuron representation for an arbitrary -variable Boolean function with a fan-in less than , and provides related theoretical results. When the logic constants True and False are identified by +1 and −1, an -variable Boolean function is identified by a unique dichotomy of the -dimensional hypercube. With this equivalence, all -variable Boolean functions can be uniquely represented by linear combinations of monomials, the products of input variables. A polynomial function whose sign matches the truth table of a given Boolean function is said to sign-represent that Boolean function. The artificial neural units that implement this sign-representation scheme are often called higher-order neurons or polynomial threshold units. This paper investigates the freedom provided by the sign-representation framework in terms of the fan-in of these artificial neural units. In particular, we look for sign-representations with a small number of monomials. Although there are methods developed for finding a reduced set of monomials to represent Boolean functions, there are no deterministic algorithms for computing non-trivial solutions with guarantees on the number of monomials in the found sign-representations. This work fills this gap by providing deterministic algorithms which are guaranteed to find solutions with fewer than monomials for -variable Boolean functions. Although the algorithms presented here are computationally costly, it is expected that several research directions can be spawned from the current study, such as reducing the bound and devising efficient algorithms for finding sign-representations with a small number of monomials. [Copyright &y& Elsevier]

We introduce the notion of λ-regularities in strings that consist of λ-covers and λ-seeds, and study three λ-regularities problems – the λ-cover problem, the general λ-cover problem and the λ-seed problem in this paper. λ-regularities can be viewed as generalized string regularities in the sense that a set of λ repetitive strings rather than a single repeated string are considered. We first present a general algorithm for computing all the λ-combinations of a given string, since they serve as candidates for both λ-covers and λ-seeds. The running time of this algorithm is O(n^2). Relying on this result, we answer the above mentioned three problems all in O(n^2) time. [ABSTRACT FROM AUTHOR]

This paper presents a family of bitmap algorithms that address the problem of counting the number of distinct header patterns (flows) seen on a high-speed link. Such counting can be used to detect DoS attacks and port scans and to solve measurement problems. Counting is especially hard when processing must be done within a packet arrival time (8 ns at OC-768 speeds) and, hence, may perform only a small number of accesses to limited, fast memory. A naive solution that maintains a hash table requires several megabytes because the number of flows can be above a million. By contrast, our new probabilistic algorithms use little memory and are fast. The reduction in memory is particularly important for applications that run multiple concurrent counting instances. For example, we replaced the port-scan detection component of the popular intrusion detection system Snort with one of our new algorithms. This reduced memory usage on a ten minute trace from 50 to 5.6 MB while maintaining a 99.77% probability of alarming on a scan within 6 s of when the large-memory algorithm would. The best known prior algorithm (probabilistic counting) takes four times more memory on port scan detection and eight times more on a measurement application. This is possible because our algorithms can be customized to take advantage of special features such as a large number of instances that have very small counts or prior knowledge of the likely range of the count. [ABSTRACT FROM AUTHOR]

This paper presents a new approach that uses the maximum model distance (MMD) method for the adaptation of Hidden Markov models (HMMs). This method has the same framework as it is used for constructing speech recognizers with abundant data, and work effectively with any amount of adaptation data . All parameters of the HMMs with or without the adaptation data could be adapted. If the adaptation data is sufficient, then the adapted models will gradually become a speaker-dependent one. Both the dialect and the speaker adaptation experiments were conducted to investigate the effectiveness of the proposed algorithm. In the speaker adaptation experiments, up to 65.55% phoneme error reduction was achieved, and the MMD could reduce the phoneme error by 16.91% even only one adaptation utterance is available. [ABSTRACT FROM AUTHOR]

In this paper we will study deformations of A∞-algebras. We will also answer questions relating to Moore algebras which are one of the simplest nontrivial examples of an A∞-algebra. We will compute the truncated Hochschild cohomology of odd Moore algebras and classify them up to a unital weak equivalence. We will construct miniversal deformations of particular Moore algebras and relate them to the universal odd and even Moore algebras. Finally we will conclude with an investigation of formal one-parameter deformations of an A∞-algebra. [ABSTRACT FROM AUTHOR]

Since Balas and Zemel in the 1980s introduced the so-called core problem as an efficient tool for solving the Knapsack Problem, all the most successful algorithms have applied this concept. Balas and Zemel proved that if the weights in the core are uniformly distributed then there is a high probability for finding an optimal solution in the core. Items outside the core may be fathomed because of reduction rules.This paper demonstrates that generally it is not reasonable to assume a uniform distribution of the weights in the core, and it is experimentally shown that the heuristic proposed by Balas and Zemel does not find as good solutions as expected. Also, other algorithms that solve some kind of core problem may be stuck by difficult cores. This behavior has apparently not been noticed before because of unsufficient testing.Capacities leading to difficult problems are identified for several categories of instance types, and it is demonstrated that the hitherto applied test instances are easier than the average. As a consequence we propose a series of new randomly generated test instances and show how recent algorithms behave when applied to these problems. [ABSTRACT FROM AUTHOR]

This article summarizes four formulations of the composite body method for the inertia matrix of a manipulator in the earlier works and presents a new formulation. These five formulations all use the first moments and the inertia tensors of composite bodies about the origin of the local frame. This paper also presents an algorithm for computing these first moments and inertia tensors. This algorithm utilizes a set of minimal linear combinations of inertia parameters instead of the natural inertia parameters, so that a number of redundant computations are saved. It is found that the new algorithm for the first moments and the inertia tensors of composite bodies is computationally superior to the others in the literature. On the other hand, two among the five formulations for the inertia matrix are more efficient than the other three as well as the others in the literature. The new formulation is one of these two most efficient formulations, and is specially adequate to a manipulator with some translational joints. ©1999 John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In this paper we approach the problem of computing the characteristic polynomial of a matrix from the combinatorial viewpoint. We present several combinatorial characterizations of the coefficients of the characteristic polynomial in terms of walks and closed walks of different kinds in the underlying graph. We develop algorithms based on these characterizations and show that they tally with well-known algorithms arrived at independently from considerations in linear algebra. [ABSTRACT FROM AUTHOR]

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to present the concepts underlying an approach to data analysis using a hierarchical classification. The data can be provided by observation, experiment or knowledge. We begin by presenting the classical view of the context of data representation, in which the algorithm of hierarchical ascendant construction of the classification tree is set. The main notion in our method is one of `similarity'. The latter must be elaborated in the best way, taking into account the mathematical nature of the objects to be compared. In this elaboration, we adopt a set theoretic and combinatoric representation of the descriptive attributes, which are interpreted in terms of relations. On the other hand, we introduce a probability scale for similarity measurement by using a likelihood concept. [ABSTRACT FROM AUTHOR]

The standard method for constructing generating vectors for good lattice point sets is the component-by-component construction. Numerical experiments have shown that the generating vectors found by these constructions sometimes tend to have recurring components, which can lead to the problem of having projections with all lattice points lying on the main diagonal. In this paper we combine methods of Dick and Kritzer to avoid this problem with a reduced fast component-by-component construction. That is, we give a variation of the standard component-by-component construction which avoids repeated components and simultaneously results in a considerable speed-up in comparison to the standard construction. [ABSTRACT FROM AUTHOR]

This work characterizes and optimizes an outlier detection algorithm to identify potentially invalid scores produced by jury members while grading engineering design projects. The paper describes the original algorithm and the associated adjudication process in detail. The impact of the various conditions in the algorithm on the false positive and false negative rates is explored. A response surface design is performed to optimize the algorithm using a data set from Fall 2010. Finally, the results are tested against a data set from Fall 2011. It is shown that all elements of the original algorithm (the base rule and the three additional conditions) play a role in the algorithm's performance and should be included in the algorithm. Because there is significant interaction between the base rule and the additional conditions, many acceptable combinations that balance the FPR and FNR can be found, but no true optimum seems to exist. The performance of the best optimizations and the original algorithm are similar. Therefore, it should be possible to choose new coefficient values for jury populations in other cultures and contexts logically and empirically without a full optimization as long as the algorithm assumptions are valid and the limitations for its use are well understood. [ABSTRACT FROM AUTHOR]

With the gradual increase in the number of GNSS systems and the improvement of functions, in addition to the single-system navigation and timing service, the integrated navigation and positioning service among multiple systems can provide users with more accurate and stable positioning results, arousing more attention from the workers in GNSS field. Compatibility and interoperability among different systems has become a trend in the development of GNSS. Compatibility and interoperability between systems require a uniform time scale. Therefore, the measurement and forecasting of time deviations in GNSS systems is particularly important. This paper first studies the multi-system fusion location model and proposes an adaptive GNSS fusion PPP algorithm based on parameter equivalent reduction. Then, the method of fusion PPP is used to monitor the time difference of GNSS. Finally, the effectiveness of the improved algorithm and time difference monitoring method is verified by practical examples. [ABSTRACT FROM AUTHOR]

The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework. [ABSTRACT FROM AUTHOR]

Classification is a long standing problem in computing. There are two broad kinds of classifiers, frequency based and geometry based. Frequency based classifiers often ignore the geometry underlying the data. Conversely, geometry based classifiers take into account the frequency only indirectly. This paper presents a classification algorithm which considers explicitly geometric and statistical characteristics of the data and combines them into a class representation. Reported here are initial experiments with this algorithm using two well known data sets, both with and without noise. The results show that the proposed algorithm is less sensitive to the training data set than other classifiers. [ABSTRACT FROM AUTHOR]

Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short-term load forecasting (STLF) application. However, the good performance of kernel-based method depends on the selection of an appropriate kernel function that fits the learning target, unsuitable kernel function or hyper-parameters setting may lead to significantly poor performance. To get the optimal kernel function of STLF problem, this paper proposes a kernel-based SVR combination model by using a novel individual model selection algorithm. Moreover, the proposed combination model provides a new way to kernel function selection of SVR model. The performance and electric load forecast accuracy of the proposed model are assessed by means of real data from the Australia and California Power Grid, respectively. The simulation results from numerical tables and figures show that the proposed combination model increases electric load forecasting accuracy compared to the best individual kernel-based SVR model. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper proposes a new method to optimize a Switched Reluctance Motor (SRM). [•] A combination of SOA and GA with Finite Element Method (FEM) analysis is employed to solve the SRM design optimization. [•] The results show that optimized SRM obtains higher average torque and higher efficiency. [Copyright &y& Elsevier]

Abstract: The control theory and automation technology cast the glory of our era. Highly integrated computer chip and automation products are changing our lives. Mathematical models and parameter estimation are basic for automatic control. This paper discusses the parameter estimation algorithm of establishing the mathematical models for dynamic systems and presents an estimated states based recursive least squares algorithm, and the states of the system are computed through the Kalman filter using the estimated parameters. A numerical example is provided to confirm the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper demonstrates the color compensation algorithm to reduce the color distortion caused by mismatches between the reference gamma value of a dimming algorithm and the display gamma values of an LCD panel in a low power adaptive dimming scheme. In 2010, we presented the Y r Y g Y b algorithm, which used the display gamma values extracted from the luminance data of red, green, and blue sub-pixels, Y r, Y g, and Y b, with the simulation results. It was based on the ideal panel model where the color coordinates were maintained at the fixed values over the gray levels. Whereas, this work introduces an X r Y g Z b color compensation algorithm which obtains the display gamma values of red, green, and blue from the different tri-stimulus data of X r, Y g, and Z b, to obtain further reduction on the color distortion. Both simulation and measurement results ensure that a X r Y g Z b algorithm outperforms a previous Y r Y g Y b algorithm. In simulation which has been conducted at the practical model derived from the measured data, the X r Y g Z b scheme achieves lower maximum and average color difference values of 3.7743 and 0.6230 over 24 test picture images, compared to 4.864 and 0.7156 in the Y r Y g Y b one. In measurement of a 19-inch LCD panel, the X r Y g Z b method also accomplishes smaller color difference values of 1.444072 and 5.588195 over 49 combinations of red, green, and blue data, compared to 1.50578 and 6.00403 of the Y r Y g Y b at the backlight dimming ratios of 0.85 and 0.4. [Copyright &y& Elsevier]