Dynamic problems of the theory of elasticity for layers and semilayers with cavities

We present a solution methodology for dynamic problems of the theory of elasticity based on the fundamental (F)-solutions approach for layers and semilayers containing cavities. Under the proposed solution framework boundary-value problems for three-dimensional cylindrical bodies are reduced to well-studied systems of one-dimensional singular integral equations. With the aid of the integral Fourier transform in time, we study the problem of impulse loading at the sides of cavities. We also demonstrate how the combination of the proposed methodology with the approach of reflections can be used for the solution of analogous problems for semi-infinite layers.     

Managing object schemas in a distributed database architecture for concurrent engineering

Abstract

In recent years, object database systems have emerged as a new technology, integrating object‐oriented paradigms with transaction and query capabilities of conventional database systems. The strength of distributed object database systems is further enhanced by new generations of powerful workstations. The synergy between object databases and the networked workstation environment, however, brings forth a new set of challenges. In this paper, we address the issues of managing object schemas in a cooperative network environment. After reviewing existing solutions, we present a more dynamic approach to the problem of distributed schema management. We also show how this approach can serve as a general framework for supporting various schema management policies.     

Capturing intelligence as a reusable framework for manufacturing decision processes

A reusable framework consisting of hierarchical knowledge representation, preliminary design, iterative modification, four information flow and reasoning paths, and solution validation is conceived as a common substrate for addressing multiple components in manufacturing decision processes. The problems are represented in a state-space framework. An investment is made to design a rich representational scheme and to discriminate the promising solution states by utilizing its many implicit constraints in contrast to investing in heuristics operating on a more simplified representation of the problem. Although isolated segments of the described framework (e.g. hierarchical problem solving, abstraction) have been previously mentioned in knowledge-based problem solving, the framework distinguishes itself by exploring the nature of the interaction of these concepts in actually obtaining end results for manufacturing problems. Although hard to quantify, it is stated that the involved ‘intelligence’ from the manufacturing systems integration standpoint is the amount of reusability in the framework for different components of manufacturing decision processes. The reusability of the framework is illustrated by two such components: (i) integration of design and process planning, and (ii) facilities layout.     

Application of evolution strategies for the solution of an inverse problem in near-field optics

We introduce an inversion procedure for the characterization of a nanostructure from near-field intensity data. The method proposed is based on heuristic arguments and makes use of evolution strategies for the solution of the inverse problem as a nonlinear constrained-optimization problem. By means of some examples we illustrate the performance of our inversion method. We also discuss its possibilities and potential applications.     

A framework for data-driven structural analysis in general elasticity based on nonlinear optimization: The dynamic case

In this article, we present an extension of the formulation recently developed by the authors to the structural dynamics setting. Inspired by a structure-preserving family of variational integrators, our new formulation relies on a discrete balance equation that establishes the dynamic equilibrium. From this point of departure, we first derive an “exact” discrete-continuous nonlinear optimization problem that works directly with data sets. We then develop this formulation further into an “approximate” nonlinear optimization problem that relies on a general constitutive model. This underlying model can be identified from a data set in an offline phase. To showcase the advantages of our framework, we specialize our methodology to the case of a geometrically exact beam formulation that makes use of all elements of our approach. We investigate three numerical examples of increasing difficulty that demonstrate the excellent computational behavior of the proposed framework and motivate future research in this direction.     

Numerical approaches for collaborative data processing

We present an approach to uncertainty propagation in dynamic systems, exploiting information provided by related experimental results along with their models. The approach relies on a solution mapping technique to approximate mathematical models by polynomial surrogate models. We use these surrogate models to formulate prediction bounds in terms of polynomial optimizations. Recent results on polynomial optimizations are then applied to solve the prediction problem. Two examples which illustrate the key aspects of the proposed algorithm are given. The proposed algorithm offers a framework for collaborative data processing among researchers. This work was supported by the National Science Foundation, Information Technology Research Program, Grant No. CTS-0113985.     

The study of an Easy-to-Use DBR and BM system

The concept and advantages of the Drum–Buffer–Rope (DBR) scheduling and buffer management (BM) system are now widely accepted and recognized by the industrial communities. Therefore, there are several types of commercial DBR and BM business solution software such as OPT21?, DISASTER?, Visual DBR?, Synchrono?, Drummer?, etc., on the market. However, prior to implementation of any of these, factories must first gather a complete data set for their perspective throughput nets. This means that the users will be required to enter and maintain a complicated database and the degree of difficulty of the entire software system implementation process will also increase. Furthermore, it is also unrealistic to maintain the accuracy of these dynamic data in the fast-paced and competitive business environment. Therefore, in this article, we have proposed the Easy-to-Use DBR and BM system concept. The term ‘Easy-to-Use’ refers to the fact that users will be required to enter and maintain a minimal set of fundamental data to satisfy the factories’ operation needs. The system framework mentioned in our article contains the full and complete function of the DBR and BM system but with very limited data maintenance by the users. Therefore, with less procedural complexity, this system can produce a higher range of operational application and can attract higher interest from the users. The concept of simplified throughput net design proposed in our article cannot only be used as a reference when factories develop their own information systems, but can also provide a new design model and algorithm for the system software developers as well. Since the database maintenance is significantly simplified, the factories can quickly adapt to any changes in the dynamic, rapid-changing, and highly competitive production environment. In the article, we first describe the concept of the Easy-to-Use DBR and BM system and the steps to simplify the information system by using a simple throughput net. We then explain the system framework and design methodology. At the end, we have used a prototype system to demonstrate and to verify the applicability and efficiency of the concept and framework mentioned in our article.     

The influence of socialisation and absorptive capacity on buyer’s innovation performance

In contrast to prior literature, we hypothesise that socialisation with suppliers plays a key role in the success of buyer’s innovation programmes. Using the theoretical lens of social capital theory and knowledge-based view of a firm; we propose a framework of antecedents and consequences of socialisation. We use data from a large sample of firms in Australia to test our framework. We also corroborate our results with a small sample of qualitative case studies that were carried out in conjunction with the large-scale empirical study. Taken together, the results supported the claim of the importance of socialisation as well as absorptive capacity in influencing innovation performance of buyers. The results suggest that managers ought to consider socialisation as a mechanism to enhance collaborative competence which in turn helps innovation performance via tapping the potential of absorptive capacity of knowledge.     

Implementation of knowledge-based system for iron core inductor design

We describe the implementation of a knowledge-based system for designing iron core inductors. We identify the underlying issues related to the implementation of such a system. We employ a recursive design methodology based on imitating an expert human designer that is suitable for embedding in the knowledge-based system. A hybrid knowledge base, using both frame-based and rule-based paradigms, together with assertions and procedural attachment, captures inductor-oriented design expertise. The paper establishes a general framework whereby dedicated knowledge-based systems for electromagnetic design can be built. It presents results that illustrate the performance of the developed system, particularly in comparison to the standard design procedure found in the literature.     

An exact approach for portfolio selection with transaction costs and rounds

We consider a single-period mean-safety portfolio selection problem with transaction costs and integer constraints on the quantities selected for the securities (rounds). We propose an exact approach based on the partition of the initial problem into two subproblems and the use of a simple local search heuristic to obtain an initial solution. To the best of our knowledge, no optimal algorithms have been proposed in the literature for this problem. The proposed approach is simple, general and easily adaptable to other problems. An extensive experimental analysis based on real data from the main international Stock Exchange Markets is performed. The results show, on average, an impressive improvement with respect to the computational time and space memory required by CPLEX 7.0. We also show that the solution of the first subproblem can be used on its own as an extremely effective heuristic.     

显著区域检测的建筑物检索

建筑物特征的自动描述以及建筑物的检索在自动导航和自动定位系统中都有着非常重要的作用。传统的彩色直方图的描述和检索方法丢掉了建筑物重要的结构特征信息。为了充分利用这些结构信息,提出了一种显著区域检测的建筑物检索方法。首先利用尺度显著区域检测技术提取建筑物的结构特征信息,然后用一种改进的EMD(Earth Mover′s Distance)距离来衡量各个建筑物的结构信息之间的差异,最后进行检索。实验结果表明该方法是有效的。     

A unifying framework for quantifying the nature of animal interactions

Collective phenomena, whereby agent–agent interactions determine spatial patterns, are ubiquitous in the animal kingdom. On the other hand, movement and space use are also greatly influenced by the interactions between animals and their environment. Despite both types of interaction fundamentally influencing animal behaviour, there has hitherto been no unifying framework for the models proposed in both areas. Here, we construct a general method for inferring population-level spatial patterns from underlying individual movement and interaction processes, a key ingredient in building a statistical mechanics for ecological systems. We show that resource selection functions, as well as several examples of collective motion models, arise as special cases of our framework, thus bringing together resource selection analysis and collective animal behaviour into a single theory. In particular, we focus on combining the various mechanistic models of territorial interactions in the literature with step selection functions, by incorporating interactions into the step selection framework and demonstrating how to derive territorial patterns from the resulting models. We demonstrate the efficacy of our model by application to a population of insectivore birds in the Amazon rainforest.     

Multi-objective genetic algorithm for solving N-version program design problem

N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently.We formulate the optimal design problem of NVP as a bi-objective 0–1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process.The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.     

Sparse $$\ell _{1}$$ regularisation of matrix valued models for acoustic source characterisation

We present a strategy for the recovery of a sparse solution of a common problem in acoustic engineering, which is the reconstruction of sound source levels and locations applying microphone array measurements. The considered task bears similarities to the basis pursuit formalism but also relies on additional model assumptions that are challenging from a mathematical point of view. Our approach reformulates the original task as a convex optimisation model. The sought solution shall be a matrix with a certain desired structure. We enforce this structure through additional constraints. By combining popular splitting algorithms and matrix differential theory in a novel framework we obtain a numerically efficient strategy. Besides a thorough theoretical consideration we also provide an experimental setup that certifies the usability of our strategy. Finally, we also address practical issues, such as the handling of inaccuracies in the measurement and corruption of the given data. We provide a post processing step that is capable of yielding an almost perfect solution in such circumstances.     

Inverse problem in optical diffusion tomography. IV. Nonlinear inversion formulas

We continue our study of the inverse scattering problem for diffuse light. In contrast to our earlier work, in which we considered the linear inverse problem, we now consider the nonlinear problem. We obtain a solution to this problem in the form of a functional series expansion. The first term in this expansion is the pseudoinverse of the linearized forward-scattering operator and leads to the linear inversion formulas that we have reported previously. The higher-order terms represent nonlinear corrections to this result. We illustrate our results with computer simulations in model systems.     

Manufacturing perspective of enterprise application integration: the state of the art review

Manufacturing enterprise today has become a matter of effective and efficient application of information technology and knowledge-based engineering. Several new manufacturing paradigms such as virtual enterprise (or extended enterprise) and mass customization have resulted in a highly distributed and autonomous manufacturing system. On the one hand, this will increase the competitiveness of a firm in terms of quickly meeting dynamic changes in the market; on the other hand, this will also increase the difficulty of integrating different information and knowledge systems residing in each member firm. This integration is also called enterprise application integration (EAI) (here the term application means information systems or software systems for supporting manufacturing or service activities). The methodology for EAI has been studied for at least a decade; but no satisfactory solution has been found from a practical viewpoint. EAI is becoming even more difficult due to the boom in various enterprise information and knowledge systems (and also to ever increasing competition in the technical software market). The study presented in this paper conducted a critical analysis of existing solutions to EAI. We consider EAI as having two generic issues: semantic integration and syntactic integration. The main problem of semantic integration reduces to the general problem of enterprise or business modelling. The main problem of syntactic integration reduces to the general problem of software architecture of enterprise applications which enables interoperability between any two EAs. While the first issue is studied in the manufacturing field, the second issue is studied in the computing engineering field. In this paper, both issues are put together under the context of EAI and studied. The result of the study, together with our experience with one Canadian manufacturing firm, has led to the identification of several issues to be addressed in the future. We also outline possible ways to approach these issues.     

On describing the solution sets of generalized Nash games with shared constraints

In this paper we use parametric variational inequality problems for the purpose of describing entire solution sets of generalized Nash games with shared constraints. We prove two theoretical results and we introduce a computational method that practitioners can implement in applied problems modeled as generalized Nash games, under assumptions present in the current literature. Further, we give illustrative examples of how our computational technique is used to derive solution sets of known generalized Nash games previously not solved by existing techniques. We close with the presentation of an applied problem formulated as a generalized Nash game, namely a model of a joint implementation environmental accord between countries. We discuss the possible advantages of modeling it within a generalized Nash game framework.     

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.     

A Decision Support System for Solving Multi‐Objective Redundancy Allocation Problems

The Redundancy Allocation Problem (RAP) is a reliability optimization problem in designing series‐parallel systems. The reliability optimization process is intended to select multiple components with appropriate levels of redundancy by maximizing the system reliability under some predefined constraints. Several methods have been proposed to solve the RAPs. However, most of these methods often treat RAP as a single objective problem of maximizing the system reliability (or minimizing the system design cost). We propose a Decision Support System for solving Multi‐Objective RAPs. Initially, we use the Technique for Order Performance by Similarity to Ideal Solution method to reduce the multiple objective dimensions of the problem. We then propose an efficient ε‐constraint method to generate non‐dominated solutions on the Pareto front. Finally, we use a Data Envelopment Analysis model to prune the non‐dominated solutions. A benchmark case is presented to assess the performance of the proposed system, demonstrate the applicability of the proposed framework, and exhibit the efficacy of the procedures and algorithms. Copyright © 2013 John Wiley & Sons, Ltd.