Search

Abstract How to observe the state of a network from a limited number of measurements has become an important issue in complex networks, engineering, communication, epidemiology, etc. Under some scenarios, it is either unfeasible or unnecessary to observe the entire network. Therefore, we investigate the target observation of a network in this paper. We propose a target minimal dominating set problem corresponding to target observation, which is a natural generalization of classical minimal dominating set problem. Three algorithms are proposed to approximate the minimum set of occupied nodes sufficient for target observation. Extensive numerical results on computer-generated random networks and real-world networks demonstrate that the proposed algorithms offer superior performance in identification of a target minimal dominating set. Highlights • We propose a target minimum dominating set problem on networks. • We propose three solving algorithms. • We compare two mechanisms for selection the target nodes. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a data-driven online entry guidance framework is proposed. Based on the proposed framework, a novel neural network based online predictive guidance algorithm for high lifting vehicles is developed, which combines the benefits of the existing predictive guidance and the neural network predictor. By introducing the neural network predictor, the proposed algorithm can effectively overcome the long-standing contradiction between guidance accuracy and real-time guidance of existing numerical predictive guidance methods. Take the augmented predictor–corrector guidance algorithm as guidance pattern, a large number of sample trajectory data can be generated by performing full envelop trajectory simulations with different perturbation terms. Based on the sample data, the mapping relationship between the real-time flight states of high lifting vehicles and guidance commands is approximated by multi-layer feedforward neural network. By substituting the off-line trained neural network predictor for the trajectory integrations of each guidance cycle in the augmented algorithm, the proposed algorithm can successfully realize the online precision guidance for high lifting vehicles. The simulation results for nominal and dispersed cases show that the proposed algorithm has better performance on real-time capability and robustness than the existing numerical predictive guidance methods, and it is suitable for engineering practice. [ABSTRACT FROM AUTHOR]

The recently introduced Artificial Hummingbird Algorithm (AHA) exhibits competitive performance in developing optimization concerns. However, AHA has an imbalance between exploration and utilization abilities, often prematurely converging with low precision. Therefore, in this paper, a multi-strategy boosted hybrid artificial hummingbird algorithm called LCAHA combined with sinusoidal chaotic map strategy, Lévy flight, cross, and update foraging strategy is proposed. Firstly, LCAHA is initialized by the sinusoidal chaotic map strategy to obtain a population with better ergodicity. Secondly, introducing the Lévy flight can boost the diversity of the population, control premature convergence and boost the stability of the population. Then, two new strategies, cross foraging and update foraging, are introduced. The introduction of new foraging strategies further enhances the exploration and developmental capabilities. These three strategies work together to improve the overall performance of the AHA. Finally, the performance of the LCAHA is examined on 23 classical test suites, the CEC2017, CEC2019, and CEC2020 test suites, and six engineering optimization cases. The numerical experimental results show that LCAHA provides very promising numerical results in solving challenging optimization problems. The algorithm is applied to two spacecraft trajectory optimization cases. The experimental results demonstrate the applicability and potential of the LCAHA in solving practical applications. [ABSTRACT FROM AUTHOR]

Haptic technologies allow for adding a new “touching” modality into virtual scenes. 3D reconstruction of a real life scene results, however, often in millions of polygons which cannot be simultaneously visualized and haptically rendered. In this paper, we propose a way of haptic interaction with real life scenes where multiple original images of the real scenes are augmented with reconstructed polygon meshes. We present our solution to the problems of haptic model alignment with the images and interactive haptic rendering of large polygon meshes with reconstruction artifacts. In particular, the presented collision detection algorithm is not restricted by any hypothesis and robust enough to support smooth interaction with millions of polygons. The feasibility and usability of the proposed solution is evaluated in a user study. [ABSTRACT FROM AUTHOR]

The paper presents an attempt to proceed to a general concept of software environment for fast and consistent multi-task simulation of EM transients (engineering simulator for tokamak applications). As an example, the ITER tokamak is taken to introduce a computational technique. The strategy exploits parallel processing with optimized simulation algorithms based on using of influence functions and superposition principle to take full advantage of parallelism. The software has been tested on a multi-core supercomputer. The results were compared with data obtained in TYPHOON computations. A discrepancy was found to be below 0.4%. The computation cost for the simulator is proportional to the number of observation points. An average computation time with the simulator is found to be by hundreds times less than the time required to solve numerically a relevant system of differential equations for known software tools. [ABSTRACT FROM AUTHOR]

Abstract: The design of engineering systems under probabilistic model uncertainty using stochastic simulation techniques is discussed in this paper. A recently developed algorithm, called Stochastic Subset Optimization (SSO), is adopted for solution of the associated optimization problem. SSO is based on formulation of an augmented stochastic design problem and relies on simulation of samples from an auxiliary probability density function to establish a sensitivity analysis for the design variables. This stochastic sampling requires repeated evaluation of system model response and corresponds to the computationally most intensive part of the algorithm. A Moving Least Squares (MLS) response surface approximation is introduced here for reduction of this computational burden. The paper focuses on the efficient integration of the MLS approximation within SSO. Specifically, adaptive selection of the MLS weights is proposed exploiting information directly available from SSO to quantify the importance of the uncertain model parameters and the design variables in affecting the system response. A measure based on relative information entropy concepts is introduced for this purpose and its efficient evaluation is examined in detail. The selection of support points for generating the response surface is also addressed, and a novel measure is finally introduced for evaluating the efficiency of the response surface approximation in terms of the stochastic sampling accuracy. [Copyright &y& Elsevier]

Abstract: This paper presents a practical algorithm for the first-order reliability method (FORM) to deal with the implicit nature of a limit-state function (LSF) in reliability assessment of stability of a working highway tunnel, which is constructed in a mountainous area in China. First, a mechanical model to determine the LSF that is not explicitly known with complex non-linear behaviour is formulated for the primary support provided by the combination of shotcrete and rockbolts. After reviewing concisely the basic concepts relevant to the FORM, the central difference approximation is subsequently introduced to estimate the partial derivatives of the LSF. By consideration of Taylor’s formula, this LSF can be transformed into an equation involving a single unknown described as the reliability index, and then the resulting solution procedure for determination of the reliability index can be rendered based on the derivation rule of compound function. A flow for tunnel reliability problem posed by the implicit LSF in applying the FORM is further developed. Eventually, the proposed methodology for the LSF in the non-explicit circumstance is used to perform the probabilistic evaluation for such a working tunnel where the choices of values of step length coefficient affecting the calculation results are suggested. Comparisons are made with the Monte-Carlo simulation method (MCSM) to assess the computational accuracy and efficiency of the algorithm proposed in this paper. It is shown that the MCSM used to obtain the “exact” solutions entails the formidable computational effort, whereas the current algorithm that alleviates the computational labour dramatically can provide an efficient way of implementing reliability calculations for the implicit LSF and offer results which are found to be in satisfactory agreement with the “exact” ones when the step length coefficient is properly taken. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the resolution of multiobjective optimization problems as a tool in engineering design. In the literature, the solutions of this problems are based on the Pareto frontier construction. Therefore, substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The normalized normal constraint is a recent contribution that generates a well-distributed Pareto frontier. Nevertheless, these methods are susceptible of improvement or modifications to obtain the same level of results more efficiently. This paper proposes a modification of the original normalized normal constraint method using a genetic algorithms in the optimization task. The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss–Newton optimization methods which are used by the original normalized normal constraint method. [Copyright &y& Elsevier]

Abstract: This paper introduces a new honeycomb based domain representation and SIGMOID material function to model continuum topology optimization domains with fixed grids. The two-point connectivity ensured with hexagonal cells is shown to circumvent singularities related to checkerboard patterns and point flexures that are typically observed with one-point connected rectangular cells. The novel SIGMOID function is developed to assign the ‘solid’ material for very low values of design variables (probabilities) and ‘void’ material for those further lower than the threshold, thus encouraging the binary material assignment. The performance of the SIGMOID function is compared with the previously proposed SIMP and PEAK material assignment functions. It is shown that both SIMP and PEAK functions can be over-restrictive in that the material is assigned only for probabilities close to one. The proposed modeling is suitable for topology optimization objectives wherein the number of constraints is large and gradient-based algorithms are chosen. Numerous examples on material layout determination for compliant mechanisms are solved with flexibility-stiffness and flexibility-strength multi-criteria formulations to illustrate the essence of this paper. [Copyright &y& Elsevier]

Abstract: In this paper, we consider vulnerable systems which can have different states corresponding to different combinations of available elements composing the system. Each state can be characterized by a performance rate, which is the quantitative measure of a system''s ability to perform its task. Both the impact of external factors (attack) and internal causes (failures) affect system survivability, which is determined as the probability of meeting a given demand. In order to increase the system''s survivability a multilevel protection can be applied to its subsystems. In such systems, the protected subsystems are destroyed by external impacts only if all of the levels of their protection are destroyed. The paper describes an algorithm for evaluating the survivability of series–parallel systems with arbitrary configuration of multilevel protection. The algorithm is based on a composition of Boolean and the Universal Generating Function techniques. The adaptation of the algorithm for numerical implementation is suggested. Illustrative examples are presented. [Copyright &y& Elsevier]

Abstract: This paper expands the use of network flows to the multidimensional case, in which network flows are associated with vectors, instead of the conventionally used scalar values. A method for solving a multidimensional max-flow problem is systematically developed, on the basis of the primal-dual algorithm. It is demonstrated that upon reduction to a one-dimensional case, the method is transformed to the known Ford and Fulkerson algorithm. This multidimensional flow network can be applied to a variety of engineering applications, as the variables underlying engineering systems frequently possess a vector form. In this paper, the multidimensional max-flow problem is shown to correspond to the problem of plastic analysis of trusses. [Copyright &y& Elsevier]

Abstract: The main aim of this paper is to present an improved algorithm of “Branch and Bound” method for control structure screening. The new algorithm uses a best-first search approach, which is more efficient than other algorithms based on depth-first search approaches. Detailed explanation of the algorithms is provided in this paper along with a case study on Tennessee–Eastman process to justify the theory of branch and bound method. The case study uses the Hankel singular value to screen control structure for stabilization. The branch and bound method provides a global ranking to all possible input and output combinations. Based on this ranking an efficient control structure with least complexity for stabilizing control is detected which leads to a decentralized proportional controller. [Copyright &y& Elsevier]

It is very important to develop effective strategies for process industry to implement feasible scheduling while the process bottlenecks work optimally. However, the bottlenecks adopted by the existed scheduling strategies are often partial bottlenecks, local bottlenecks, or even deceptive bottlenecks, which are basically not key constraints to achieve optimal objective, so that the corresponding scheduling strategies are not perfect, and the further improvement and evaluation can hardly be proposed. So, it is a valuable issue to analyze the bottlenecks both in academic and engineering fields. This paper aims at the minimum cost problem of generalized network flow model to define and analyse three classes of bottlenecks based on generalized network simplex algorithm, and the corresponding search algorithms are proposed in this paper. The obtained bottlenecks cannot only be used to determine whether the object cost will be increased or decreased, but also be used to propose the corresponding strategy to evaluate the improvement on the network flow model for scheduling. Finally, a typical example is discussed. [Copyright &y& Elsevier]

Hydraulic fracturing of rocks has various engineering applications. However, there has been limited research into crack propagation prediction by three dimensional analytical techniques. This paper discusses such a technique for predicting the propagation surface of a pressurized circular crack subjected to various loading conditions. The propagation surfaces predicted from the proposed crack front propagation algorithm align well with published results. The suggested method consumes only a fraction of the time needed for a numerical simulation, and therefore it could be useful in assisting the design of hydraulic fracturing operations. [ABSTRACT FROM AUTHOR]

In this paper, a self-adaptive classification learning hybrid JAYA and Rao-1 algorithm, which is called EHRJAYA, is proposed for solving large-scale numerical problems and real-world complex engineering optimization problems. JAYA algorithm and Rao-1 algorithm are two kinds of algorithms with simple structure and superior performance, which have the characteristics of no particular controlling parameters. In EHRJAYA the evolution strategies of the two algorithms are selected through a random selection mechanism. Then, a novel self-adaptive classification learning strategy is proposed, which fully utilizes information from different individuals. On this basis, two different adaptive coefficients are introduced to guide the population towards the optimal individual and away from the worst individual. Finally, combining the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed and ability to jump out of local optimum of the algorithm are greatly improved. To verify the performance of the proposed EHRJAYA, 59 complex functions from the CEC2014 and CEC2017 competitions are solved by EHRJAYA. Then, EHRJAYA and more than 20 algorithms with superior performance jointly solve ten challenging real-world engineering optimization problems. Experimental results show that the proposed EHRJAYA can obtain optimal results with the least computational resources in most cases. Therefore, in the face of these problems, effective solutions can be provided by EHRJAYA. [ABSTRACT FROM AUTHOR]

Highlights: [•] The paper presents a new method for microscopic egress analysis in case of fire. [•] It considers occupant’s characteristics, building properties and env. conditions. [•] The egress analysis is integrated into the BIM environment. [•] An integrated model-based numerical fire simulation is deployed for env. conditions. [•] A wayfinding algorithm permanently calculates and evaluates the potential ways. [Copyright &y& Elsevier]

Abstract: Different kinds of algorithms can be chosen so as to compute elementary functions. Among all of them, it is worthwhile mentioning the shift-and-add algorithms due to the fact that they have been specifically designed to be very simple and to save computer resources. In fact, almost the only operations usually involved with these methods are additions and shifts, which can be easily and efficiently performed by a digital processor. Shift-and-add algorithms allow fairly good precision with low cost iterations. The most famous algorithm belonging to this type is CORDIC. CORDIC has the capability of approximating a wide variety of functions with only the help of a slight change in their iterations. In this paper, we will analyze the requirements of some engineering and industrial problems in terms of type of operands and functions to approximate. Then, we will propose the application of shift-and-add algorithms based on CORDIC to these problems. We will make a comparison between the different methods applied in terms of the precision of the results and the number of iterations required. [Copyright &y& Elsevier]

Abstract: Underwater robots have been an emerging research area being at the intersection of the field of robotics and oceanic engineering. Their applications include environmental monitoring, oceanographic mapping, and infrastructure inspections in deep sea. In performing these tasks, the ability of autonomous navigation is the key to a success, especially with the limited communications in underwater environments. Considering the highly dynamic and three-dimensional environments, the autonomous navigation technologies including path planning and tracking have been one of the interesting but challenging tasks in the field of study. Cameras have not been at the center of attention as an underwater sensor due to the limited detection ranges and the poor visibility. Use of visual data from cameras, however, is still an attractive method for underwater sensing and it is especially effective in the close range detections. In this paper, the vision-based object detection and tracking techniques for underwater robots have been studied in depth. In order to overcome the limitations of cameras and to make use of the full advantages of image data, a number of approaches have been tested. The topics include color restoration algorithm for the degraded underwater images, detection and tracking methods for underwater target objects. The feasibilities of the proposed algorithms have been demonstrated in the experiments with an underwater robot platform and the results have been analyzed both qualitatively and quantitatively. [Copyright &y& Elsevier]

Abstract: Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration. [Copyright &y& Elsevier]

Abstract: Many real life problems can be stated as a minimax optimization problem, such as the problems in economics, finance, management, engineering and other fields. In this paper, we present an algorithm with nonmonotone strategy and second-order correction technique for minimax optimization problems. Using this scheme, the new algorithm can overcome the difficulties of the Maratos effect occurred in the nonsmooth optimization, and the global and superlinear convergence of the algorithm can be achieved accordingly. Numerical experiments indicate some advantages of this scheme. [Copyright &y& Elsevier]

Abstract: This paper presents the results of a survey to show the applicability of an approach based on a combination of distribution-free tolerance interval and genetic algorithms for testing and maintenance optimization of safety-related systems based on unavailability and cost estimation acting as uncertain decision criteria. Several strategies have been checked using a combination of Monte Carlo (simulation)––genetic algorithm (search-evolution). Tolerance intervals for the unavailability and cost estimation are obtained to be used by the genetic algorithms. Both single- and multiple-objective genetic algorithms are used. In general, it is shown that the approach is a robust, fast and powerful tool that performs very favorably in the face of noise in the output (i.e. uncertainty) and it is able to find the optimum over a complicated, high-dimensional nonlinear space in a tiny fraction of the time required for enumeration of the decision space. This approach reduces the computational effort by means of providing appropriate balance between accuracy of simulation and evolution; however, negative effects are also shown when a not well-balanced accuracy–evolution couple is used, which can be avoided or mitigated with the use of a single-objective genetic algorithm or the use of a multiple-objective genetic algorithm with additional statistical information. [Copyright &y& Elsevier]

This paper presents an extensive study of a new metaheuristics algorithm called Smell Agent Optimization (SAO) on some CEC numerical optimization benchmark functions and Hybrid Renewable Energy System (HRES) engineering problems. The SAO implements the relationships between a smell agent and an object evaporating a smell molecule. These relationships are modelled into three separate modes called the sniffing, trailing and random modes. The sniffing mode simulates the smell perception capability of the agent as the smell molecules diffuse from a smell source towards the agent. The trailing mode simulates the capability of the agent to track the part of the smell molecules until its source is identified. Whereas, the random mode is a strategy employed by the agent to avoid getting stuck in local minima. Thirty-seven commonly used CEC benchmark functions, and HRES engineering problem are tested, and results are compared with six other metaheuristics methods. Experimental results revealed that the SAO can find the global optimum in 76% of the benchmark functions. Similarly, statistical results showed that the SAO also obtained the most cost effective HRES design compared to the benchmarked algorithms. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: This paper is devoted to the robust adaptive control of solvable single-input single-output impulse-free linear time-invariant singular dynamic systems of known orders and unknown parameterizations subject to single external point delays. The control law is of pole-placement type and based on input/output measurements and parametrical estimation only. The parametrical estimation algorithm incorporates adaptation dead zones to prevent against potential instability caused by disturbances and unmodeled dynamics. The Weierstrass canonical form is investigated in detail concerning the properties of controllability and observability via testable conditions of the given arbitrary state-space realization of the same order. Robust closed-loop stability is proved under similar conditions to those used for (nonsingular) standard systems. [Copyright &y& Elsevier]

Abstract: In order to improve the prediction capability of spring-back in the computational analysis of sheet metal forming processes, a stress–strain constitutive formulation of non-linear combined hardening rule has been proposed in this paper according to non-linear kinematic hardening theory of Lemaitre and Chaboche and Hill''s 1948 anisotropic yielding function. Traditionally, Young''s modulus is considered as a constant in engineering application and numerical simulation. In fact, it decreases with plastic deformation. So the effect of the change of Young''s modulus with plastic strain on spring-back is considered in the constitutive model. The algorithm of stress update is elastic prediction, plastic correcting and radial returning. Numerical results and experimental results show that the proposed constitutive model significantly improves the prediction accuracy of spring-back. [Copyright &y& Elsevier]

Abstract: This paper is intended to introduce the Bayesian network in general and the Naïve-Bayes classifier in particular as one of the most successful classification systems to simulate damage detection in engineering materials. A method for feature subset selection has also been introduced too. The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naïve-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. The data sets were conducted based on artificial damages created in quasi isotopic laminated composites of the AS4/3501-6 graphite/epoxy system and ball bearing of the type 6204 with a steel cage. The Naïve-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials. [Copyright &y& Elsevier]

Abstract: Graph data in modern scientific and engineering applications are often too large to fit in the computer’s main memory. Input/output (I/O) complexity is a major research issue in this context. Minimization of the number of I/O operations (in external memory graph algorithms) is the main focus of current research while classical (internal memory) graph algorithms were designed to minimize temporal complexity. In this paper, we propose an external memory depth first search algorithm for general grid graphs. The I/O-complexity of the algorithm is , where , is the sorting I/O-complexity, is the memory size, and is the block size. The best known algorithm for this class of graph is the standard (internal memory) DFS algorithm with appropriate block (sub-grid) I/O-access. Its I/O-complexity is . Recently, the authors proposed an algorithm for solid grid graphs. [Copyright &y& Elsevier]

Abstract: This paper addresses multi-objective optimization from the viewpoint of real-world engineering designs with lots of specifications, where robust and global optimization techniques need to be applied. The problem used to illustrate the process is the design of non-linear control systems with hundreds of performance specifications. The performance achieved with a recent multi-objective evolutionary algorithm (MOEA) is compared with a proposed scheme to build a robust fitness function aggregation. The proposed strategy considers performances in the worst situations: worst-case combination evolution strategy (WCES), and it is shown to be robust against the dimensionality of specifications. A representative MOEA, SPEA-2, achieved a satisfactory performance with a moderate number of specifications, but required an exponential increase in population size as more specifications were added. This becomes impractical beyond several hundreds. WCES scales well against the problem size, since it exploits the similar behaviour of magnitudes evaluated under different situations and searches similar trade-offs for correlated objectives. Both approaches have been thoroughly characterized considering increasing levels of complexity, different design problems, and algorithm configurations. [Copyright &y& Elsevier]

Abstract: Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity. [Copyright &y& Elsevier]

Abstract: When studying crashworthiness problems, contact simulation can be the source of a number of problems. A first one is the discontinuities in the normal evolution for a boundary discretized by finite elements. Another problem is the treatment of the contact forces that can introduce numerical energy in the system. In this paper, we propose to combine a method of discontinuity smoothing with the energy–momentum consistent scheme that recently appeared in the literature. [Copyright &y& Elsevier]

Abstract: One drawback of genetic algorithm is that it may spend much computation time in the encoding and decoding processes. Also, since genetic algorithm lacks hill-climbing capacity, it may easily fall in a trap and find a local minimum not the true solution. In this paper, a novel adaptive real-parameter simulated annealing genetic algorithm (ARSAGA) that maintains the merits of genetic algorithm and simulated annealing is proposed. Adaptive mechanisms are also included to insure the solution quality and to improve the convergence speed. The performance of the proposed operators has been discussed in detail and compared to other operators, and the performance of the proposed algorithm is demonstrated in 16 benchmark functions and two engineering optimization problems. Due to their versatile characteristics, these examples are suitable to test the ability of the proposed algorithm. The results indicate that the global searching ability and the convergence speed of this novel hybrid algorithm are significantly better, even though small population size is used. Also, the proposed algorithm has good application to engineering optimization problems. Hence, the proposed algorithm is efficient and improves the drawbacks of genetic algorithm. [Copyright &y& Elsevier]

Abstract: Applying software engineering concepts can improve the quality of any software development, and this is even more dramatic for complex, large and sophisticated software, such as meshing tools. Software product families are series of related products that make intensive reuse of already developed components. Object-oriented design promotes reusability, so it is specially suited for designing the structure of product families. In this paper we present an object-oriented design of a product family of meshing tools, where all family members share the software structure. By instantiating the structure with particular algorithms and parameters, we can easily produce different tools of the family. A good family design allows us not only to combine existing algorithms but also to easily incorporate new ones, improving software family evolution. We show how the family design is used for the generation of finite element and finite volume meshing tools, as well as a new tool for image processing. [Copyright &y& Elsevier]

Abstract: The ROCK algorithm is an agglomerative hierarchical clustering algorithm for clustering categorical data [Guha S., Rastogi, R., Shim, K., 1999. ROCK: A robust clustering algorithm for categorical attributes. In: Proc. IEEE Internat. Conf. Data Engineering, Sydney, March 1999]. In this paper we prove that under certain conditions, the final clusters obtained by the algorithm are nothing but the connected components of a certain graph with the input data-points as vertices. We propose a new algorithm QROCK which computes the clusters by determining the connected components of the graph. This leads to a very efficient method of obtaining the clusters giving a drastic reduction of the computing time of the ROCK algorithm. We also justify that it is more practical for specifying the similarity threshold rather than specifying the desired number of clusters a priori. The QROCK algorithm also detects the outliers in this process. We also discuss a new similarity measure for categorical attributes. [Copyright &y& Elsevier]

Abstract: The locking phenomenon, or the degeneracy, has long been the potential defectiveness of the Complex Method. A locked optimization process leads to not only unreliable optimum but waste of time. In this paper, a modified Complex algorithm is suggested and assessed by four test functions. The results showed that the improved Complex Method equipped with the new unlocking mechanisms demonstrates excellent abilities to avoid locking phenomenon. Besides, it reduces the time of processing to about 80% to reach the optimum with the same accuracy. Moreover, a practical example of a common engineering problem is also given to show its applicability. [Copyright &y& Elsevier]

Abstract: The paper reviews the development of process systems engineering from a personal view-point, selecting concepts from the past which have given rise to new paradigms, and hence led to improved understanding or advances in solution techniques, but which either still pose unresolved problems or hold the promise of further developments. [Copyright &y& Elsevier]

While scanning a complex part in reverse engineering, it is not possible to acquire all part of the scanned surface. Data are inevitably missing due to the complexity of the scanned part or imperfect scanning process. Missing scanned data cause holes in the created triangular mesh, so that a hole-free mesh model is prerequisite for fitting watertight surfaces. Although a number of hole filling algorithms have been investigated, they enable to fill holes only on the smooth regions of a model. They are not always robust in the regions of high curvature. This paper proposes a novel methodology that can automatically fill complex polygonal holes with a piecewise manner. It incrementally splits a complex hole into several simple holes with respect to the 3D shape of the hole boundary, and then it consecutively fills each divided simple hole with planar triangulation method until the entire complex hole is firmly closed. Finally smoothing and subdivision techniques are applied for enhancing the hole triangles. The newly created vertices and triangles are added to their respective lists and the topology information is updated. The method has proven to be robust and effective from the result of test with a variety of complex holes. Examples are given and discussed to validate the methodology. [Copyright &y& Elsevier]

Abstract: The use of discrete data to represent engineering structures as derivatives from intersecting components requires algorithms to perform Boolean operations between groups of triangulated surfaces. In the intersection process, an accurate and efficient method for the determination of intersection lines is a crucial step for large scale and complex surface intersections. Given the node numbers at the vertices of the triangles, the neighbour relationship is first established. A background grid is employed to limit the scope of searching for candidate triangles that may intersect. This will drastically reduce the time of geometrical check for intersections between triangles, making the surface intersection and mesh generation a quasi-linear process with respect to the number of elements involved. The intersection lines are determined by the robust algorithm based on tracing the neighbours of intersecting triangles. In the determination of intersection between two triangles, four fundamental cases are identified and treated systematically to enhance robustness and reliability. In this paper, the consistent treatment of mesh generation along intersection lines is emphasized. The procedure ensures that all mesh generation operations are carried out on the surface concerned without leaving the surface so that elements generated will always be on the surface. Five examples on a great variety of surface and mesh characteristics are given to illustrate the efficiency and robustness of the algorithm. [Copyright &y& Elsevier]

Machining of low-rigidity components is a key process in industries such as aerospace, marine engineering and power engineering. The part deflection caused by the cutting force due to the flexible part structure reduces the validity of the CAM output and leads to additional machining errors that are difficult to predict and control. The paper reports a modelling methodology and integration architecture for multi-step simulation of cutting processes of low-rigidity components incorporating a finite element analysis (FEA)-based component model, FE analysis tool, force model and material removal algorithm. The FEA-based data model of low-rigidity component is proposed based on describing key object-oriented classes such as component, element, node and force to create a common integrated decision making environment. Each object has unique decision making methods associated with it that allow seamless integration in simulating the part behaviour during machining. Two iterative algorithms are proposed within the simulation environment for cutting force prediction and material removal simulation. A prototype version of the simulation environment has been developed using C++, and the feasibility of the proposed approach has been illustrated using practical examples backed up by experimental data. [Copyright &y& Elsevier]

In this paper we present a novel method for the stable inversion of Mellin convolution operators. Although the inverse operator is explicitly known for this class of operators, regularization techniques have to be used due to the ill-posedness of the problem. Considering operators which are continuously invertible between the space L2[0,∞] and a Mellin–Sobolev space H*s[0,∞], we propose a combination of a data smoothing algorithm and an exact inversion for the order optimal regularization of the equation. As application, we derive the Mellin transform of the capillary pressure operator and analyze its mapping properties. Finally, we present some numerical results. [Copyright &y& Elsevier]

Boundary representation models reconstructed from 3D range data suffer from various inaccuracies caused by noise in the data and by numerical errors in the model building software. The quality of such models can be improved in a beautification step, where geometric regularities need to be detected and imposed on the model, and defects requiring topological change need to be corrected. This paper considers changes to the topology such as the removal of short edges, small faces and sliver faces, filling of holes in the surface of the model (arising due to missing data), adjusting pinched faces, etc. A practical algorithm for detecting and correcting such problems is presented. Analysis of the algorithm and experimental results show that the algorithm is able to quickly provide the desired changes. Most of the time required for topological beautification is spent on adjusting the geometry to agree with the new topology. [Copyright &y& Elsevier]

This paper presents a new method to compute constructive solid geometry (CSG) tree representations of an object whose faces consist of planar and non-planar surfaces. The algorithm described accepts as input a valid boundary representation of an object consisting of piecewise implicit surfaces, and computes a halfspace CSG representation of the object. A class of objects that are describable by the surfaces bounding them are valid input for the algorithm of this work, although methods currently exist to compute the additional information necessary to process non-describable quadric objects as well. This work builds on and complements the other work in this area, in which dominating halfspaces are used to simplify the b-rep to CSG conversion process. We include factored faces to enable the factorization of dominating halfspaces throughout the algorithm. Thus, an efficient disjoint decomposition of the solid is obtained as a matter of course in the algorithm, so that CSG minimization is generally not necessary.This work is motivated by reverse engineering of mechanical parts, in which a model of a part is recovered from information obtained by some sort of sensing technique (e.g. CAT scanning, laser range finding). The recovery of a valid CSG-tree description of an object from a boundary representation of it can provide useful information to an engineer in the area of reverse engineering and in other areas related to solid modeling as well. The CSG tree also provides a relatively neutral representation that can enhance form feature recognition and translation. [Copyright &y& Elsevier]

Systems, structures, and components of Nuclear Power Plants are subject to Technical Specifications (TSs) that establish operational limitations and maintenance and test requirements with the objective of keeping the risk associated to the plant within the limits imposed by the regulatory agencies. Recently, in an effort to improve the competitiveness of nuclear energy in a deregulated market, modifications to maintenance policies and TSs are being considered within a risk-informed viewpoint, which judges the effectiveness of a TS, e.g. a particular maintenance policy, with respect to its implications on the safety and economics of the system operation.In this regard, a recent policy statement of the US Nuclear Regulatory Commission declares appropriate the use of Probabilistic Risk Assessment models to evaluate the effects on the system of a particular TS. These models rely on a set of parameters at the component level (failure rates, repair rates, frequencies of failure on demand, human error rates, inspection durations, and others) whose values are typically affected by uncertainties. Thus, the estimate of the system performance parameters corresponding to a given TS value must be supported by some measure of the associated uncertainty.In this paper we propose an approach, based on the effective coupling of genetic algorithms and Monte Carlo simulation, for the multiobjective optimization of the TSs of nuclear safety systems. The method transparently and explicitly accounts for the uncertainties in the model parameters by attempting to minimize both the expected value of the system unavailability and its associated variance. The costs of the alternative TSs solutions are included as constraints in the optimization. An application to the Reactor Protection Instrumentation System of a Pressurized Water Reactor is demonstrated. [Copyright &y& Elsevier]

This paper describes a robust support vector machines (SVMs) classification methodology, which can offer superior classification performance for important process engineering problems. The method incorporates efficient tuning procedures based on minimization of radius/margin and span bound for leave-one-out errors. An agent-based asynchronous teams (A-teams) software framework, which combines Genetic-Quasi-Newton algorithms for the optimization is highly successful in obtaining the optimal SVM hyper-parameters. The algorithm has been applied for classification of binary as well as multi-class real world problems. [Copyright &y& Elsevier]