An approximate method for evaluating hypersingular integrals

This paper describes a new method for direct numerical evaluation of multidimensional hypersingular integrals assigned on smooth curves and surfaces. These integrals arise when the boundary integral equations are used to solve problems of mechanics, electrodynamics, aerodynamics, etc. The hypersingular integrals are considered, in the sense of Hadamard, as finite parts. The main advantage of the proposed method is the numerical computation of the hypersingular integrals by the direct application of the developed cubature formulas, thus requiring little analytical pre-work. The method is not restricted to the type of problem however and may be easily applied to any hypersingular integrals. The convergence of the proposed technique has been proved and error estimates are given. An illustrative example demonstrates the accuracy and efficiency of the method.     

Automatic generation of explanations: AGE

Explaining how engineering devices work is important to students, engineers, and operators. In general, machine generated explanations have been produced from a particular perspective. This paper introduces a system called automatic generation of explanations (AGE) capable of generating causal, behavioral, and functional explanations of physical devices in natural language. AGE explanations can involve different user selected state variables at different abstraction levels. AGE uses a library of engineering components as building blocks. Each component is associated with a qualitative model, information about the meaning of state variables and their possible values, information about substances, and information about the different functions each component can perform. AGE uses: (i) a compositional modeling approach to construct large qualitative models, (ii) causal analysis to build a causal dependency graph, (iii) a novel qualitative simulation approach to efficiently obtain the system's behavior on large systems, and (iv) decomposition analysis to automatically divide large devices into smaller subsystems. AGE effectiveness is demonstrated with different devices that range from a simple water tank to an industrial chemical plant.     

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.     

An integrated job release and scheduling approach on parallel machines: An application in electric wire-harness industry

This paper presents a new job release (JR) and scheduling methodology for one-stage parallel machines where sequence dependent setup times exist. A decision support system (DSS) based on job release is developed in order to enable the application of the methodology. First, mathematical programming models for both job release and job scheduling are devised. Then, due to the NP-hard nature of the problems, heuristics are proposed. As for the interaction between JR and scheduling, job scheduling is integrated with job release for the proposed heuristic solutions so that the capacity achievement provided by scheduling can be utilized for job release. In brief, product design characteristics oriented scheduling affects JR in the proposed approach. Moreover, value stream mapping (VSM) approach is used with the aim of stating the effect of the proposed methodology. Furthermore, the presented methodology was applied in a real life electric wire-harness production system. The application, based on 120-day production data, revealed that the proposed methodology provided 25% decrease in in-plant manufacturing lead time.     

Diffusion on Some Simple Stratified Spaces

A variety of different imaging techniques produce data which naturally lie in stratified spaces. These spaces consist of smooth regions of maximal dimension glued together along lower dimensional boundaries. Diffusion processes are important as they can be used to represent noise in statistical models on spaces for which standard parametric probability distributions do not exist. We consider particles undergoing Brownian motion in some low dimensional stratified spaces, and obtain analytic solutions to the heat equation specifying the distribution of particles. These solutions play the role of prototypical distributions for studying behaviour near singularities. While probabilistic reasoning can be used to solve the heat equation in some straightforward cases, more generally we construct solutions from eigenfunctions of the Laplacian. Specifically, we solve the heat equation on: open books; two-dimensional cones; the Petersen graph with unit edge length; and the cone of this graph which corresponds to a space of evolutionary trees.     

Robust sparse principal component analysis

The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximizes the l1-norm variance,which is less sensitive to noise and outlier. To ensure sparsity,lp-norm（0 p 1） constraint,which is more general and effective than l1-norm,is considered. A simple yet efficient algorithm is developed against the proposed model. The complexity of the algorithm approximately linearly increases with both of the size and the dimensionality of the given data,which is comparable to or better than the current sparse PCA methods. The proposed algorithm is also proved to converge to a reasonable local optimum of the model. The efficiency and robustness of the algorithm is verified by a series of experiments on both synthetic and digit number image data.     

A Novel Filtering Based Recursive Estimation Algorithm for Box-Jenkins Systems

International Journal of Control, Automation and Systems - This paper presents a novel filtering based multi-innovation estimation algorithm for output-error autoregressive moving average (i.e.,...