Dynamical mining of ever-changing user requirements: A product design and improvement perspective

Previous studies carried out customer surveys by questionnaires to collect data for analyzing consumer requirements. In recent years, a large and growing body of literature has investigated the extraction of customer requirements and preferences from online reviews. However, since customer requirements change dynamically over time, traditional studies failed to obtain the change data of customer requirements and opinions based on sentiments expressed in reviews. In this paper, a new method for dynamically mining user requirements is proposed, which is used to analyze the changing behavior of product attributes and improve product design. Dynamic mining differs from the traditional need acquisition mainly in three aspects: (1) it involves dynamically mining user requirements over time (2) it adds changes in manufacturers’ opinions to the analysis (3) it allows for product improvement strategies based on the changing behavior of product attributes. First, text mining is adopted to collect customer and manufacturer review data for different time periods and extract product attributes. A Natural Language Processing tool is used to measure the importance weight and sentiment score of product attributes. Second, an approach for dynamically mining user requirements is introduced to classify product attributes and analyze the changes of attribute data in three categories over time. Finally, an improvement strategy for next-generation product design is developed based on the changing behavior of attributes. Moreover, a case study on vehicles based on online reviews was conducted to illustrate the proposed methodology. Our research suggests that the proposed approach can accurately mine customer requirements and lead to successful product improvement strategies for next-generation products.     

A framework for the design and verification of software measurement methods

At the core of any engineering discipline is the use of measures, based on ISO standards or on widely recognized conventions, for the development and analysis of the artifacts produced by engineers. In the software domain, many alternatives have been proposed to measure the same attributes, but there is no consensus on a framework for how to analyze or choose among these measures. Furthermore, there is often not even a consensus on the characteristics of the attributes to be measured.In this paper, a framework is proposed for a software measurement life cycle with a particular focus on the design phase of a software measure. The framework includes definitions of the verification criteria that can be used to understand the stages of software measurement design. This framework also integrates the different perspectives of existing measurement approaches. In addition to inputs from the software measurement literature the framework integrates the concepts and vocabulary of metrology. This metrological approach provides a clear definition of the concepts, as well as the activities and products, related to measurement. The aim is to give an integrated view, involving the practical side and the theoretical side, as well as the basic underlying concepts of measurement.     

An approach to the automatic design of multiple classifier systems

Multiple classifier systems (MCSs) based on the combination of outputs of a set of different classifiers have been proposed in the field of pattern recognition as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them are accurate and make different errors. Therefore, the fundamental need for methods aimed to design “accurate and diverse” classifiers is currently acknowledged. In this paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed at selecting the subset made up of the most accurate and diverse classifiers. A proof of the optimality of the proposed design approach is given. Reported results on the classification of multisensor remote sensing images show that this approach allows the design of effective multiple classifier systems.     

Integration of design and manufacturing for structural shape optimization

This paper presents an integrated design and manufacturing approach that supports shape optimization of structural components. The approach starts from a primitive concept stage, where boundary and loading conditions of the structural component are given to the designer. Topology optimization is conducted for an initial structural layout. The discretized structural layout is smoothed using parametric B-Spline surfaces. The B-Spline surfaces are imported into a CAD system to construct parametric solid models for shape optimization. Virtual manufacturing (VM) techniques are employed to ensure that the optimized shape can be manufactured at a reasonable cost. The solid freeform fabrication (SFF) system fabricates physical prototypes of the structure for design verification. Finally, a computer numerical control (CNC) machine is employed to fabricate functional parts as well as mold or die for mass production of the structural component. The main contribution of the paper is incorporating manufacturing into the design process, where manufacturing cost is considered for design. In addition, the overall design process starts from a primitive stage and ends with functional parts. A 3D tracked vehicle roadarm is employed throughout this paper to illustrate the overall design process and various techniques involved.     

A multi-objective problem based on fuzzy inference with application to parametric design of an electrophotographic system

This paper proposes a systematic approach to conduct system-level optimal parametric design for a class of electrophotographic systems. A conventional monochrome laser printer serves as the platform for verification of the proposed optimal design approach. Besides performance, we incorporate two other practical indices, i.e., cost and energy consumption, into design objectives and formulate a multi-objective optimization problem. A fuzzy inference system is established to provide the nonlinear or linguistic mapping between the decision variables and the design objectives. For comparative purpose, the problem is solved using both single-objective and multi-objective optimization algorithms. Note that the proposed approach is also applicable to other complex systems.     

Design of software systems based on axiomatic design

The ability to utilize fully automated flexible manufacturing systems (FMS) or develop reliable computer-integrated manufacturing (CIM) systems will depend on our ability to develop reliable and reusable software for large complex systems on a timely basis. To date, software design has not gone very far beyond the ad hoc trial-and-error stage. Consequently, the development of software is slow, expensive, unreliable, and unmanageable. The purpose of this paper is to provide a scientific basis for designing software. The approach used here is that of axiomatic design, which is based on two design axioms: the Independence Axiom and the Information Axiom. The axiomatic approach is based on the recognition of the following common elements in design: the existence of independent domains (i.e. the consumer domain, the functional domain, the physical domain, and the process domain); the need to map between various domains during the design process; the decomposition of the characteristic vectors (i.e. functional requirements, design parameters, and process variables) in their respective domains; the zig-zagging required between the domains for decomposition; and the need to satisfy the design axioms during the design process. The axiomatic approach discussed in this paper provides decision making tools for software design in addition to systematic means of knowledge and data representation, synthesis and analysis of software, and the construction of the module-junction structure diagram.     

Aircraft morphing wing design by using partial topology optimization

A morphing wing concept has been investigated over the last decade because it can effectively enhance aircraft aerodynamic performance over a wider range of flight conditions through structural flexibility. The internal structural layouts and component sizes of a morphing aircraft wing have an impact on aircraft performance i.e. aeroelastic characteristics, mechanical behaviors, and mass. In this paper, a novel design approach is proposed for synthesizing the internal structural layout of a morphing wing. The new internal structures are achieved by using two new design strategies. The first design strategy applies design variables for simultaneous partial topology and sizing optimization while the second design strategy includes nodal positions as design variables. Both strategies are based on a ground structure approach. A multiobjective optimization problem is assigned to optimize the percentage of change in lift effectiveness, buckling factor, and mass of a structure subject to design constraints including divergence and flutter speeds, buckling factors, and stresses. The design problem is solved by using multiobjective population-based incremental learning (MOPBIL). The Pareto optimum results of both strategies lead to different unconventional wing structures which are superior to their conventional counterparts. From the results, the design strategy that uses simultaneous partial topology, sizing, and shape optimization is superior to the others based on a hypervolume indicator. The aeroelastic parameters of the obtained morphing wing subject to external actuating torques are analyzed and it is shown that it is practicable to apply the unconventional wing structures for an aircraft.     

Expert system approach in design of mechanical components

The present investigation is aimed toward the development of knowledge-based aids for the design of mechanical systems. We have developed and implemented the knowledgebased aid system, which includes MEET and DPMED. The basic approach of MEET follows along the lines ofDesign=Refinement+ Constraint Propagation. This approach has been proven successful in the circuit design domain. Our attempts to utilize MEET have convinced us that we need to extend this methodology to solve mechanical design problems. The DPMED methodology has been applied to design gear-pairs, v-belts, bearings, and shafts. Rules for selecting materials, critical design criteria, and so on are incorporated as part of the rule-system. In order for DPMED to select the design parameter values within the feasible design space, design criteria need to be investigated. Based on these criteria and input/output specifications, DPMED attempts to perform parameter selections. DPMED uses a general hill-climbing algorithm to guide the search.     

Highly dependable concurrent programming using design for verification

There has been significant progress in automated verification techniques based on model checking. However, scalable software model checking remains a challenging problem. We believe that this problem can be addressed using a design for verification approach based on design patterns that facilitate scalable automated verification. In this paper, we present a design for verification approach for highly dependable concurrent programming using a design pattern for concurrency controllers. A concurrency controller class consists of a set of guarded commands defining a synchronization policy, and a stateful interface describing the correct usage of the synchronization policy. We present an assume-guarantee style modular verification strategy which separates the verification of the controller behavior from the verification of the conformance to its interface. This allows us to execute the interface and behavior verification tasks separately using specialized verification techniques. We present a case study demonstrating the effectiveness of the presented approach.     

基于多项式符号代数方法的高层次数据通路的等价验证

基于BDD或布尔SAT的等价验证方法虽然能够成功验证低层次门级电路,但却难以满足高层次设计验证要求.由此,以多项式符号代数为理论基础,提出了一个高层次数据通路的等价验证算法.深入研究了使用多项式表达式描述复杂数据通路行为的方法,得到了高层次数据通路的多项式集合表示的一般形式.从多项式集合公共零点的角度定义了高层次数据通路的功能等价,给出了一个基于Gr(o)bner基计算的有效代数求解算法.针对不同基准数据通路的实验结果表明了该算法的有效性.     

Structuring ship design project approval mechanism towards installation of operator-system interfaces via fuzzy axiomatic design principles

Managing the verification of primary design projects for ship machinery systems is one of the crucial stages in ship building processes. In particular, the design of operator-system interfaces such as remote controls, displays, alarms, workstations, and labels requires a high level of technical expertise and systematic control. This paper focuses on structuring a ship design project approval mechanism (SDPAM) based on fuzzy axiomatic design (FAD) methodology which is concerned with the system design of relevant interfaces on an engine room control console. Design characteristics such as accessibility and operational requirements are treated as “hard” constraints being encountered in this complex problem. Hence, the proposed methodology requires performance assessment using fuzzy values. This study provides original contributions to the existing approval procedures of classification societies, which are non-governmental construction and classification organizations in the shipping industry. Furthermore, the study proposes a new approach to handle uncertainty and vagueness by an axiomatic design extension to ship project execution problems.     

An intelligent decision theoretic approach to producibility optimization in conceptual design

Product optimization involves selecting design, manufacturing, and support attributes that can produce the best system. Producibility or manufacturability is the term often used to describe the relative ease of manufacturing a product. In complex systems, productibility optimization is a very difficult process, particularly when the values of many attributes are restricted by constraints. One challenge is to develop more universal producibility metrics for the conceptual design phase when design information is limited and drawings are nondimensional. This paper develops a new method for producibility optimization in conceptual design based on a combination of both decision theoretic and expert system techniques. Decision theoretic techniques provide the means to model the design for producibility problem in a manner that can deal with risk, uncertainty, and user (or corporate) preferences, and can effectively integrate diverse factors to provide a measure of the overall worth of a design. The particular decision theoretic approach employed is based on multi-attribute utility theory. An illustrative example of the methodology is applied to the conceptual design of a structural composite part.     

Using transformations and verification in circuit design

We show how machine-checked verification can support an approach to circuit design based on two kinds of refinement. This approach starts with a conceptually simple (but inefficient) initial design and uses a combination of ad hoc refinement and algorithmic transformation to produce a design that is more efficient (but more complex).We present an example in which we start with a simplified CPU design and derive an efficient pipelined form, including circuitry for reverting the effects of partially executed instructions when a successful branch is detected late in the pipeline. The algorithmic stage of our derivation applies a transormation, retiming, that has been proven to preserve functional behavior in the general case. The ad hoc stage requires special justification, which we supply in the form of a machine-checked formal verification.     

Reduced representations of vector-valued coupling variables in decomposition-based design optimization

Decomposition-based optimization strategies decouple a system design problem and introduce coupling variables as decision variables that manage communication among subproblems. The computational cost of such approaches is comparable to that of the equivalent, yet usually unsuccessful, attempts to solve the coupled system directly when the coupling variables consist of a small, finite number of scalars. When the coupling variables are infinite-dimensional quantities, such as functional data, implementing decomposition-based optimization strategies may become computationally challenging. Discretization is typically applied, transforming infinite-dimensional variables into finite-dimensional ones represented as vectors. A large number of discretized points is often necessary to ensure a sufficiently accurate representation of the functional data, and so the dimensionality of these vector-valued coupling variables (VVCVs) can become prohibitively large for decomposition-based design optimization. Therefore, it is desirable to approximate the VVCVs with a reduced dimension representation that improves optimization efficiency while preserving sufficient accuracy. We investigate two VVCV representation techniques, radial-basis function artificial neural networks and proper orthogonal decomposition, and implement each in an analytical target cascading problem formulation for electric vehicle powertrain system optimization. Specifically, both techniques are applied to VVCVs associated with motor boundary torque curves and power loss maps and are assessed in terms of dimensionality reduction, computational expense, and accuracy.     

Theorem prover approach to semistructured data design

The wide adoption of semistructured data has created a growing need for effective ways to ensure the correctness of its organization. One effective way to achieve this goal is through formal specification and automated verification. This paper presents a theorem proving approach towards verifying that a particular design or organization of semistructured data is correct. We formally specify the semantics of the Object Relationship Attribute data model for Semistructured Data (ORA-SS) modeling notation and its correctness criteria for semistructured data normalization using the Prototype Verification System (PVS). The result is that effective verification on semistructured data models and their normalization can be carried out using the PVS theorem prover.     

Multiobjective optimization–based fault‐tolerant flight control system design

The loss of measurements used for controller scheduling or envelope protection in modern flight control systems due to sensor failures leads to a challenging fault‐tolerant control law design problem. In this article, an approach to design such a robust fault‐tolerant control system, including full envelope protections using multiobjective optimization techniques, is proposed. The generic controller design and controller verification problems are derived and solved using novel multiobjective hybrid genetic optimization algorithms. These algorithms combine the multiobjective genetic search strategy with local, single‐objective optimization to improve convergence speed. The proposed strategies are applied to the design of a fault‐tolerant flight control system for a modern civil aircraft. The results of an industrial controller verification and validation campaign using an industrial benchmark simulator are reported.     

Computational modeling of effects of intravascular stent design on key mechanical and hemodynamic behavior

Atherosclerosis, a condition related to cholesterol build-up and thickening of the inner wall of the artery, narrows or occludes the artery lumen. A stent is a miniature medical device deployed in a stenotic artery to restore the blood flow. In this paper, we propose to apply the parametric design concept onto the stent design and integrate it with the developed FEA/CFD models to evaluate several key clinically-relevant functional attributes recommended by the FDA. These key clinical attributes include stresses/strains, fatigue resistance, radial strength, expansion recoil, and wall shear stresses, which have yet to be systematically investigated. Finite element models were developed to predict the mechanical integrity of a balloon-expandable stent at various stages such as crimping onto a balloon catheter, stent expansion, radial strength to resist blood vessels from collapsing, and service life in the human body when subjected to pulsatile blood pressure. Computational fluid dynamics models were developed to predict the wall shear stress distribution in stented arteries. A stent parametric analysis was conducted using the integrated computational schemes to systematically evaluate the effects of varying stent design parameters on key clinically-relevant functional attributes. Each stent design parameter was varied in its dimension from ?30% to +30% (compared to the standard case) for sensitivity studies in attempts to find the most dominant design parameter for each key clinical attribute. The developed design/analytical schemes allow us to gain deeper insight into the fundamental stent issues and evaluate the mechanical/hemodynamic behavior of various stent designs.