Dynamic highlight line generation for locally deforming NURBS surfaces

The highlight line model is a powerful tool in assessing the quality of a surface. Efficient highlight line generation is especially important for an interactive design environment. In this paper, a method for dynamic generation of highlight lines on a locally deforming NURBS surface is presented. The method generates frames of the deforming surface and the corresponding highlight lines by directly modifying the current highlight lines using a Taylor expansion technique, instead of going through a tracing process. The highlight lines computation process adopted here enables a unified distance surface to generate all highlight lines in the highlight line family. The computation process is facilitated by looking up pre-calculated information of the tessellation mesh and an indexing technique for the distance surface. The indexing technique is presented to determine when the highlight line model should be re-generated and, to facilitate the highlight line re-generation process. The new technique is suitable for interactive design environments and animation applications as the updating process takes only one subtraction and one vector inner product to get the new parameters for each new node.     

A direct approach for subdivision surface fitting from a dense triangle mesh

This article presents a new and direct approach for fitting a subdivision surface from an irregular and dense triangle mesh of arbitrary topological type. All feature edges and feature vertices of the original mesh model are first identified. A topology- and feature-preserving mesh simplification algorithm is developed to further simplify the dense triangle mesh into a coarse mesh. A subdivision surface with exactly the same topological structure and sharp features as that of the simplified mesh is finally fitted from a subset of vertices of the original dense mesh. During the fitting process, both the position masks and subdivision rules are used for setting up the fitting equation. Some examples are provided to demonstrate the proposed approach.     

Rough and finish tool-path generation for NC machining of freeform surfaces based on a multiresolution method

Progressive fitting and multiresolution tool path generating techniques are proposed in this paper, by which multi-level (LOD) models fitting for different subsets of sampled points are obtained, and then multiresolution rough-cut and finish-cut tool paths are generated based on the LOD models. The advantages of the proposed method are: (1) the user need not care for data reduction in CAD modeling; (2) final result is obtained by interpolating two lower-level reconstructed surfces, and each lower multiresolution CAD representation can be used to generate rough-cut tool paths; (3) different manufacturing requirements utilize different level models to generate tool paths; (4) selective refinement can be applied by interpolating selceted areas at different levels of details. The key avantage of the prograssive fitting algorithm is that it can use different level surfaces to generate adaptive rough-cut and finishi-cut tool path curves directly. Therefore, based on the proposed techniques, tool path length is reduced. Sharp concers are smothed out and large tools can be selected for rough machining. The efficiency of this algorithm has been demonstrated, and it results in a 20% reduction in machining time.     

A mapping-based approach to eliminating self-intersection of offset paths on mesh surfaces for CNC machining

Geometrically, a tool path can be generated by successively offsetting its adjacent path on the surface with a given path interval, which preferably starts from one of the surface boundaries or a primary curve. The key issues involved in offset path planning are the generation of raw offset paths and the elimination of the self-intersection of raw offset paths. Most researches available in this area are focused on how to generate the raw offset paths, however, the latter, especially how to eliminate the self-intersection of the offset paths on mesh surfaces, has not been sufficiently addressed. In this paper, a mapping-based approach to eliminating the self-intersection of offset paths is proposed for the CNC machining of mesh surfaces. The method first flattens the mesh surface onto a predefined plane by using a mesh mapping technique, and then taking the mapping as a guide, the offset paths are also naturally mapped onto the plane, from which those invalid self-intersection loops can be effectively identified and eliminated. To handle the issue of self-intersection for all types of offset path, a notion of local loop is introduced to detect and eliminate the invalid self-intersection loops. After that the planar paths are inversely mapped into the physical space and the final tool paths used for the machining of mesh surface are obtained. Meanwhile, in order to improve the kinematic and dynamic performance of the machine tool when machining along the generated offset paths, a method for rounding the sharp corners of tool paths, which result from the process of eliminating the self-intersection of raw offset paths, is also preliminarily investigated. Finally, the proposed method is validated by the results of simulations and machining experiments.     

A new approach to automatic and a priori mesh adaptation around circular holes for finite element analysis

Through our research on the integration of finite element analysis in the design and manufacturing process with CAD, we have proposed the concept of mesh pre-optimization. This concept consists in converting shape and analysis information in a size map (a mesh sizing function) with respect to various adaptation criteria (refining the mesh around geometric form features, minimizing the geometric discretization error, boundary conditions, etc.). This size map then represents a constraint that has to be respected by automatic mesh generation procedures. This paper introduces a new approach to automatic mesh adaptation around circular holes. This tool aims at optimizing, before any FEA, the mesh of a CAD model around circular holes. This approach, referred to as “a priori” mesh adaptation, should not be regarded as an alternative to adaptive a posteriori mesh refinement but as an efficient way to obtain reasonably accurate FEA results before a posteriori adaptation, which is particularly interesting when evaluating design scenarios. The approach is based on performing many offline FEA analyses on a reference case and deriving, from results and error distributions obtained, a relationship between mesh size and FEA error. This relationship can then be extended to target user specified FEA accuracy objectives in a priori mesh adaptation for any distribution of circular holes. The approach being purely heuristic, fulfilling FEA accuracy objectives, in all cases, cannot be theoretically guaranteed. However, results obtained using varying hole diameters and distributions in 2D show that this heuristic approach is reliable and useful. Preliminary results also show that extension of the method can be foreseen towards a priori mesh adaptation in 3D and mesh adaptation around other types of 2D features.     

Computing on rays: A parallel approach for surface mesh modeling from multi-material volumetric data

Ray representation (Ray-rep) of a solid has been studied and used in the solid modeling community for many years because of its compactness and simplicity. This paper presents a parallel approach for mesh surface modeling from multi-material volume data using an extended Ray-rep as an intermediate, where every homogeneous region is enclosed by a set of two-manifold surface meshes on the resultant model. The approach consists of three major algorithms: firstly, an algorithm is developed to convert the given multi-material volumetric data into a Ray-rep for heterogeneous solid; secondly, filtering algorithm is exploited to process the rays of heterogeneous solid in parallel; and lastly, the adaptive mesh surfaces are generated from the Ray-rep through a dual-contouring like algorithm. Here the intermediate surfaces between two constituent materials can be directly extracted without building the volumetric mesh, and the manifold topology is preserved on each surface patch. Furthermore, general offset surface can be easily computed in this paradigm by designing a special parallel operator for the rays.     

A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms

Natural resource allocation is a complex problem that entails difficulties related to the nature of real world problems and to the constraints related to the socio-economical aspects of the problem. In more detail, as the resource becomes scarce relations of trust or communication channels that may exist between the users of a resource become unreliable and should be ignored. In this sense, it is argued that in multi-agent natural resource allocation settings agents are not considered to observe or communicate with each other. The aim of this paper is to study multi-agent learning within this constrained framework. Two novel learning methods are introduced that operate in conjunction with any decentralized multi-agent learning algorithm to provide efficient resource allocations. The proposed methods were applied on a multi-agent simulation model that replicates a natural resource allocation procedure, and extensive experiments were conducted using popular decentralized multi-agent learning algorithms. Experimental results employed statistical figures of merit for assessing the performance of the algorithms with respect to the preservation of the resource and to the utilities of the users. It was revealed that the proposed learning methods improved the performance of all policies under study and provided allocation schemes that both preserved the resource and ensured the survival of the agents, simultaneously. It is thus demonstrated that the proposed learning methods are a substantial improvement, when compared to the direct application of typical learning algorithms to natural resource sharing, and are a viable means of achieving efficient resource allocations.     

A new functional for variational mesh generation and adaptation based on equidistribution and alignment conditions

A new functional is presented for variational mesh generation and adaptation. It is formulated based on combining the equidistribution and alignment conditions into a single condition with only one dimensionless parameter. The functional is shown to be coercive but not convex. A solution procedure using a discrete moving mesh partial differential equation is employed. It is shown that the element volumes and altitudes of a mesh trajectory of the mesh equation associated with the new functional are bounded away from zero and the mesh trajectory stays nonsingular if it is so initially. Numerical examples demonstrate that the new functional performs comparably as an existing one that is also based on the equidistribution and alignment conditions and known to work well but contains an additional parameter.     

A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture

Neural Computing and Applications - Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based...     

A heuristic approach to robot path planning based on task requirements using a genetic algorithm

In order to enhance integration between CAD and robots, wer propose a scheme to plan kinematically feasible paths in the presence of obstacles based on task requirements. Thus, the feasibility of a planned path from a CAD system is assured before the path is sent for execution. The proposed scheme uses a heuristic approach to deal with a rather complex search space, involving high-dimensional C-space obstacles and task requirements specified in Cartesian space. When the robot is trapped by the local minimum in the potential field related to the heuristic, a genetic algorithm is then used to find a proper intermediate location that will guide it to escape out of the local minimum. For demonstration, simulations based on using a PUMA-typed robot manipulator to perform different tasks in the presence of obstacles were conducted. The proposed scheme can also be used for mobile robot planning. The paper falls into Category (5). Please address correspondence to the second author. This work was supported in part by the National Science Council, Taiwan, R.O.C., under grant NSC 82-0422-E-009-403.     

A hybrid approach based on genetic algorithms and (max, +) algebra for network applications

The following work addresses the problem of scheduling operations on a flow network, as well as alignment (path) allocation. This is a multi-objective problem, and this paper proposes a solution method through a hybrid approach based on a genetic algorithm in conjunction with (max, +) algebra. A concise system abstraction is proposed through a non-linear (max, +) model. This model describes the main optimization constraints which dictate the behavior of the mutation and crossover operations in the genetic algorithm. Additionally, each individual in the population represents the value assignment of the decision variables, which linearizes the (max, +) model. A hierarchic genetic structure is proposed for individuals such that variable dependence is modeled. For each individual, the (max, +)-linear model is solved through a matrix product which determines the daters for alignment enabling for transfer operations. The study is extendable to complex net-structured systems of different nature.     

A formal approach based on UML and B for the specification and development of database applications

This article describes a formal approach to specify and develop database applications. This approach consists of two complementary phases. In the first phase, B specifications are automatically generated from UML class, state and collaboration diagrams describing the data and the transactions of the system we are developing. In the second phase, these specifications are successively refined until they become close enough to a relational implementation. The tool supporting this approach is implemented as an extension of the Rational Rose tool to develop and visualize graphical (UML) and formal (B) notations in a single environment.     

A general approach for the machining quality evaluation of S-shaped specimen based on POS-SQP algorithm and Monte Carlo method

In S-shaped specimens which are frequently used to reflect the machining ability of machine tools, the surface error refers to the distance between the points on the actual machining surface to their relevant points on the design surface. The proper measurement of this error is crucial for evaluating the machining quality of S-shaped specimens. During the process of error measurement, improper registration between the measurement coordinate system and the design coordinate system, as well as neglected uncertainty remain the main obstacles for the quality evaluation of S-shaped specimens. This study proposes a general method for the high-precision machining quality evaluation of S-shaped specimens that overcomes both problems. By applying the non-uniform rational B-spline (NURBS) surface molding technology, the surface of S-shaped specimen was reconstructed. Based on the minimum area principle and the particle swarm optimization-sequential quadratic programming (POS-SQP) algorithm, a surface error model of S-shaped specimen was developed. This model minimizes the maximum distance of the transacted measurement points to the design surface. It can be used to obtain the optimal registration matrix of the measurement coordinate system, with minimal surface error of S-shaped specimen. Additional common algorithms were also adopted to search the optimal registration matrix for comparison. Accounting for the random characteristics of basic parameters and the nonlinearity of surface error model, an uncertainty model of the surface error of S-shaped specimen was established based on the Monte Carlo method. This could obtain the actual tolerance zone of the surface error, according to which, the allowable tolerance zone of the surface error was optimized and a defined evaluation result of machining quality of S-shaped specimen was obtained. Then, a general approach for the evaluation of the machining quality of S-shaped specimen was developed based on POS-SQP algorithm and Monte Carlo method. This approach was implemented in a case study though a series of experiments. The experimental results identified the proposed approach as effective in improving the measurement quality and the evaluation of the machining quality of S-shaped specimens can thus be performed within an allowable tolerance zone.     

A new algorithm based on spline in tension approximation for 1D quasi-linear parabolic equations on a variable mesh

In this paper, we propose a new two-level implicit method of order two in time and four in space directions, based on spline in tension approximation for the numerical solution of one space dimensional quasi-linear parabolic partial differential equation on a uniform mesh. We have discussed the derivation of the proposed method in detail and have also discussed the stability analysis for a model problem. We have extended the method to non-uniform mesh. Numerical results are given to illustrate the usefulness of the proposed methods.     

A practical approach for active camera coordination based on a fusion-driven multi-agent system

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.     

A randomised approach for NARX model identification based on a multivariate Bernoulli distribution

The identification of polynomial NARX models is typically performed by incremental model building techniques. These methods assess the importance of each regressor based on the evaluation of partial individual models, which may ultimately lead to erroneous model selections. A more robust assessment of the significance of a specific model term can be obtained by considering ensembles of models, as done by the RaMSS algorithm. In that context, the identification task is formulated in a probabilistic fashion and a Bernoulli distribution is employed to represent the probability that a regressor belongs to the target model. Then, samples of the model distribution are collected to gather reliable information to update it, until convergence to a specific model. The basic RaMSS algorithm employs multiple independent univariate Bernoulli distributions associated to the different candidate model terms, thus overlooking the correlations between different terms, which are typically important in the selection process. Here, a multivariate Bernoulli distribution is employed, in which the sampling of a given term is conditioned by the sampling of the others. The added complexity inherent in considering the regressor correlation properties is more than compensated by the achievable improvements in terms of accuracy of the model selection process.     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

A risk management ontology for Quality-by-Design based on a new development approach according GAMP 5.0

A new approach to the development of a risk management ontology is presented. This method meets the requirements of a pharmaceutical Quality by Design approach, good manufacturing practice and good automated manufacturing practice. The need for a risk management ontology for a pharmaceutical environment is demonstrated, and the term “ontology” is generally defined and described with regard to the knowledge domain of quality risk management.To fulfill software development requirements defined by good manufacturing practice regulations and good automated manufacturing practice 5.0 for the novel development approach, we used a V-model as a process model, which is discussed in detail. The development steps for the new risk management ontology, such as requirement specification, conceptualization, formalization, implementation and validation approach, are elaborated.     

A novel communication system approach for a Smart City based on the human nervous system

The massive introduction of internet, architect of the Internet of Thingsparadigm, was expected to facilitate the spread of people out of the urban spaces. However, the reality is that increasingly people are moving to cities, which generates many challenges for the cities of tomorrow. In this context, the Smart Cityconcept emerges, where Information and Communication Technologies have a key role. This situation has revealed an important issue that needs to be addressed: the lack of an ubiquitous communication architecture able to deal with the expected requirements of a SC. To this end, a wide review of the existing communication frameworks and city services have been performed, which showed that existing proposals are classical fixed ad-hocsolutions for very specific problems with no resilience and common thread. By rethinking the nature of cities and considering them as living organisms, it is possible to relate the nervous systems with the communication architecture of a city. Therefore, this work proposes a novel communication architecture, ubiquitous and resilient, inspired in the human nervous system by the definition of Smart Gateways, able to satisfy the needs of a real SCand adaptable to the growing and specific requirements of every single city. Additionally, the research also includes the simulation of the communication channel with different technologies.