Construction cost estimators are confronted with the challenging task of estimating the cost of constructing one of a kind facilities. They must first recognize the design conditions of the facility design that are important (i.e., incur a cost) and then determine how the design conditions affect the cost of construction. Current product models of facility designs explicitly represent components, attributes of components, and relationships between components. These designer-focused product models do not represent many of the cost-driving features of building product models, such as penetrations and component similarity. Previous research efforts identify many of the different features that affect construction costs, but they do not provide a formal and general way for practitioners to represent the features they care about according to their preferences. This paper presents the formal ontology we developed to represent construction knowledge about the cost-driving features of building product models. The ontology formalizes three classes of features, defines the attributes and functions of each feature type, and represents the relationships between the features explicitly. The descriptive semantics of the model allow estimators to represent their varied preferences for naming features, specifying features that result from component intersections and the similarity of components, and grouping features that affect a specific construction domain. A software prototype that implements the ontology enables estimators to transform designer-focused product models into estimator-focused, feature-based product models. Our tests show that estimators are able to generate and maintain cost estimates more accurately, consistently, and expeditiously with feature-based product models than with industry standard product models.

The data storage capacities of modern process automation systems have grown rapidly. Nowadays, the systems are able to frequently carry out even hundreds of measurements in parallel and store them in databases. However, these data are still rarely used in the analysis of processes. In this article, preparation of the raw data for further analysis is considered using feature extraction from signals by piecewise linear modeling. Prior to modeling, a preprocessing phase that removes some artifacts from the data is suggested. Because optimal models are computationally infeasible, fast heuristic algorithms must be utilized. Outlines for the optimal and some fast heuristic algorithms with modifications required by the preprocessing are given. In order to illustrate utilization of the features, a process diagnostics framework is presented. Among a large number of signals, the procedure finds the ones that best explain the observed short-term fluctuations in one signal. In the experiments, the piecewise linear modeling algorithms are compared using a massive data set from an operational paper machine. The use of piecewise linear representations in the analysis of changes in one real process measurement signal is demonstrated.

This paper presents an approach to the automatic generation of electromechanical engineering designs. We apply messy genetic algorithm (GA) optimization techniques to the evolution of assemblies composed of LegoTM structures. Each design is represented as a labeled assembly graph and is evaluated based on a set of behavior and structural equations. The initial populations are generated at random, and design candidates for subsequent generations are produced by user-specified selection techniques. Crossovers are applied by using cut and splice operators at the random points of the chromosomes; random mutations are applied to modify the graph with a certain low probability. This cycle continues until a suitable design is found. The research contributions in this work include the development of a new GA encoding scheme for mechanical assemblies (Legos), as well as the creation of selection criteria for this domain. Our eventual goal is to introduce a simulation of electromechanical devices into our evaluation functions. We believe that this research creates a foundation for future work and it will apply GA techniques to the evolution of more complex and realistic electromechanical structures.

In the construction industry, projects are becoming increasingly large and complex, necessitating multiple subcontractors. Traditional centralized coordination techniques used by general contractors become insufficient as subcontractors perform most work and provide their own resources. When subcontractors cannot provide enough resources, they hinder their own performance, that of other subcontractors, and ultimately the entire project. Thus, projects need a new distributed coordination approach wherein all of the concerned subcontractors can respond to changes and reschedule a project dynamically. This paper presents a new distributed coordination framework for project schedule changes (DCPSC) that is based on an agent-based negotiation approach wherein software agents evaluate the impact of changes, simulate decisions, and give advice on behalf of the human subcontractors. A case example demonstrates the significance of the DCPSC. It compares two centralized coordination methodologies used in current practice to the DCPSC framework. We demonstrate that our DCPSC framework always finds a solution that is better than or equal to any of two centralized coordination methodologies.

Selective disassembly is an important issue in industrial and mechanical engineering for environmentally conscious manufacturing. This paper presents an intelligent selective disassembly approach based on ant colony algorithms, which take inspiration from the behavior of real ant colonies and are used to solve combinatorial optimization problems. For diverse assemblies, the algorithm generates different amounts of ants cooperating to find disassembly sequences for selected components, minimizing the reorientation of assemblies and removal of components. A candidate list that is composed of feasible disassembly operations, which are derived from a disassembly matrix of products, guides sequence construction in the implicit solution space and ensures the geometric feasibility of sequences. Preliminary implementation results show the effectiveness of the proposed method.

We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mechanism to progressively relax or tighten the constraint. The relaxation or tightening of constraint guides the GA to the subspace for further iteration. The system referred to as the iterative GA learning module is useful for learning an efficient fuzzy control algorithm based on a predefined linguistic terms set. The overall approach was applied to learn a fuzzy algorithm for a water bath temperature control. The simulation results demonstrate the effectiveness of the approach in automating an industrial process.

An evolutionary model for nonroutine design is presented, which is called hierarchical coevolution. The requirements for an evolutionary model of nonroutine design are provided, and some of the problems with existing approaches are discussed. Some of the ways in which these problems have been addressed are examined in terms of the design knowledge required by evolutionary processes. Then, a synthesis of these approaches as a hierarchical coevolutionary model of nonroutine design is presented and the manner in which this model addresses the requirements of an evolutionary design model is discussed. An implementation in the domain of space planning provides an example of a hierarchical design problem.

The exponential growth of the Internet and increasing communication and computational power have created many opportunities for advancing engineering, manufacturing, and business activities. Among them are electronic catalogs. These have become basic information resources to a number of people, ranging from shoppers looking for personal items to engineers selecting electromechanical parts to build a product. Although rich in content, current catalog systems are limited both in search quality and in realizing the full potential of the retrieved information. The active catalog system brings a conceptually new idea to electronic commerce by providing a new, computationally usable, catalog information environment about components and their use in applications. It utilizes a rich body of domain knowledge to facilitate access and retrieval of component information. The utility of retrieved information is enhanced by using it to rapidly construct simulation programs and test alternatives, supporting a “try before you buy” paradigm in which users evaluate candidate components within simulations of their design. We describe services provided in the active catalog system to support engineers in selecting and evaluating electromechanical components and subsystems. The services include mechanisms for creating queries for parts based on their intended use rather than merely parametric specifications, refining those queries to take account of constraints imposed by domain knowledge, providing multimodal information to help engineers assess and compare candidate parts, and generating simulation models for candidate parts and integrating them to provide simulation models for candidate systems.

Genetic algorithm (GA) and singular value decomposition (SVD) are deployed for the optimal design of both Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of adaptive neurofuzzy inference systems (ANFIS) networks that are used for modeling of the explosive cutting process of plates by shaped charges. The aim of such modeling is to show how the depth of penetration varies with the variation of important parameters, namely, the apex angle, standoff, liner thickness, and mass of charge. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models and their Gaussian membership functions in premise parts are determined by a GA.

Within the engineering design community there is support for further research into the development of improved approaches to design management. Such research has lead to coordination being identified as an important and pervasive characteristic of many existing approaches (e.g., concurrent engineering and work-flow management). In this article, operational design coordination is proposed as the basis for an improved approach. This article also presents a novel integrated approach that incorporates the key elements of operational design coordination: coherence, communication, task management, resource management, schedule management, and real-time support. Through unifying these key elements, this approach provides an integrated means of managing design in a controlled and harmonious fashion. The approach also provides knowledge of the constituent techniques involved in operational design coordination, the interrelationships and dynamic interactions between them, and the knowledge used and maintained within and between them. The approach has been realized within an agent-oriented system called the Design Coordination System, which provides a systematic means of simultaneously coordinating operational management tasks and technical design tasks. To evaluate the approach, the system has been applied to an industrial case study involving the computational process of turbine blade design. This application has been shown to enable the structured undertaking of interrelated tasks by allocating and using resources of varying performance efficiency in an optimized fashion in accordance with dynamically derived schedules in a coherent, appropriate, and timely manner. This is achieved by managing tasks, their dependencies, and the information required to undertake them. In addition, the approach enables and sustains the continuous optimized use of resources by monitoring, forecasting, and disseminating resource performance efficiency. The approach facilitates dynamic scheduling and the subsequent enactment of the resulting schedules. Decision making for rescheduling is also incorporated within the approach such that it is only performed as and when appropriate. If rescheduling is performed, it is done so in parallel with task enactment such that resources continue to be utilized in an optimized manner.

This paper presents a novel approach for integrating arrays with access time ${\cal O}$(1) into functional languages. It introduces n-dimensional arrays combined with a type system that supports hierarchies of array types with varying shape information as well as a shape-invariant form of array comprehension called WITH-loop. Together, these constructs allow for a programming style similar to that of array programming languages such as APL. We use Single Assignment C (SAC), a functional C-variant aimed at numerical applications that is based on the proposed design, to demonstrate that programs written in that style can be compiled to code whose runtime performance is competitive with that of hand-optimized Fortran programs. However, essential prerequisites for such performance figures are a shape inference system integrated in the type system as well as several high-level optimizations. Most notably of these is With Loop Folding, an optimization technique for eliminating intermediate arrays.

We propose regular expression pattern matching as a core feature of programming languages for manipulating XML. We extend conventional pattern-matching facilities (as in ML) with regular expression operators such as repetition (*), alternation (|), etc., that can match arbitrarily long sequences of subtrees, allowing a compact pattern to extract data from the middle of a complex sequence. We then show how to check standard notions of exhaustiveness and redundancy for these patterns. Regular expression patterns are intended to be used in languages with type systems based on regular expression types. To avoid excessive type annotations, we develop a type inference scheme that propagates type constraints to pattern variables from the type of input values. The type inference algorithm translates types and patterns into regular tree automata, and then works in terms of standard closure operations (union, intersection, and difference) on tree automata. The main technical challenge is dealing with the interaction of repetition and alternation patterns with the first-match policy, which gives rise to subtleties concerning both the termination and precision of the analysis. We address these issues by introducing a data structure representing these closure operations lazily.

First-order unification algorithms (Robinson, 1965) are traditionally implemented via general recursion, with separate proofs for partial correctness and termination. The latter tends to involve counting the number of unsolved variables and showing that this total decreases each time a substitution enlarges the terms. There are many such proofs in the literature (Manna & Waldinger, 1981; Paulson, 1985; Coen, 1992; Rouyer, 1992; Jaume, 1997; Bove, 1999). This paper shows how a dependent type can relate terms to the set of variables over which they are constructed. As a consequence, first-order unification becomes a structurally recursive program, and a termination proof is no longer required. Both the program and its correctness proof have been checked using the proof assistant LEGO (Luo & Pollack, 1992; McBride, 1999).

This paper describes a practical exercise set to an introductory functional programming course. The exercise is to implement a small game involving a space ship in an asteroids belt, after the fashion of the classic Asteroids arcade game. The positive experience suggests that interactive graphics programs of this kind make good and entertaining programming exercises for functional programming courses.

The paper deals with on-line obstacle avoidance in an unstructured environment based on the force strategy. The obstacle avoidance is considered as a control problem. We discuss three approaches regarding the sensors used to detect the obstacles. First we investigate how obstacles can be avoided without using any sensors to detect them. To solve the problem we make use of a very basic principle: an action causes a reaction. For backdrivable manipulators we propose a controller which ensures stiff behaviour in the task space and compliant behaviour in the null space. Using such control the tracking capabilities in the task space can be preserved and the redundant degrees of freedom are used to avoid the obstacle after the collision. The tactile sensors are proposed to be used as the alternative for stiff systems. A tactile sensor detects an obstacle and the controller generates the avoiding motion. Last, we present a virtual forces approach which can be applied to the systems with proximity sensors. The objective is to assign each point on the body of the manipulator, which is close to the obstacle force component in a direction that is away from the obstacle. The proposed formulation avoids the problem of singular configurations and is very suitable when many obstacles are present in the neighbourhood of the manipulator. The computational efficiency of the proposed algorithms allows real-time application in a unstructured or time-varying environment. The efficiency of the proposed control algorithms is illustrated by simulations of highly redundant planar manipulators and by experiments on a four link planar manipulator.

Kinematic calibration is essential to improve the accuracy of the manipulator. This paper presents a complete description of the Gough platform modeling and a unified scheme to identify its kinematic parameters. The interest of this formulation is that it may be applied whatever information is available on the state of the robot (measurement or constraints) without using the kinematics to obtain the basic system of constraint equations. Moreover, the scheme may be applied for all parallel robots. We propose to experiment and compare three methods of calibration, using either (or both) external measurement and internal redundant sensors. Finally, we show how to reduce the initial error in pose determination by 99% for the Hexapode 300, CMW's machining center, and validate the choice of a self-calibration method in an industrial context.

A novel mobile robot FTIR (Finned Tube Inspection Robot) for finned tube inspecting is presented in this paper. FTIR can move in both x- and y-direction on the top layer of the tubing that is installed in layers. The snake-like arms mounted on the robot can be lowered into the narrow space between the tubes and the tubing can be inspected through the device installed in the end of the arms. The moving mechanisms, inspecting device applied in narrow space and control system are introduced.

Stroke is a common condition resulting in 30,000 people per annum left with significant disability. In patients with severe arm paresis after stroke, functional recovery in the affected arm is poor. Inadequate intensity of treatment is cited as one factor accounting for the lack of arm recovery found in some studies. Given that physical therapy resource is limited, strategies to enhance the physiotherapists' efforts are needed. One approach is to use robotic techniques to augment movement therapy.

A three degree-of-freedom pneumatic robot has been developed to apply physiotherapy to the upper limb. The robot has been designed with a workspace encompassing the reach-retrieve range of the average male. Control experiments have applied force and then position only controllers to the pneumatic robot. These controllers are combined to form a position-based impedance control strategy on all degrees of freedom of the robot. The impedance controller performance was found to be dependent upon the specified impedance parameters. Initial experiments attaching the device to human subjects have indicated great potential for the device.