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Design has always been seen as an inherently human activity and hard to automate. It requires a lot of traits that are seldom attributable to machines or algorithms. Consequently, the act of designing is also hard to assess. In particular in an educational context, the assessment of progress of design tasks performed by individuals or teams is difficult, and often only the outcome of the task is assessed or graded. There is a need to better understand, and potentially quantify, design progress. Natural Language Processing (NLP) is one way of doing so. With the advancement in NLP research, some of its models are adopted into the field of design to quantify a design class performance. To quantify and visualize design progress, the NLP models are often deployed to analyze written documentation collected from the class participants at fixed time intervals through the span of a course. This paper will explore several ways of using NLP in assessing design progress, analyze its advantages and shortcomings, and present a case study to demonstrate its application. The paper concludes with some guidelines and recommendations for future development. [ABSTRACT FROM AUTHOR]

Conceptual design is a key stage of product design and has received increasing attention in recent years. However, this stage is characterized by limited information, large uncertainty, and multidisciplinary aspects. Thus, increased workload and time cost are associated with conceptual design information acquisition; sometimes, it is difficult to develop novel solutions and the feasibility of the solutions obtained according to these limited and uncertain information is difficult to guarantee. Genetics-based design (GBD) is an effective approach to develop novel solutions and improve the reuse of knowledge, which is consistent with the goal of the conceptual design process. Product-gene acquisition is the premise and basis of GBD. At present, there are few reported studies in this area; most of the existing works are constrained by the structural aspects of the acquisition process, and there are limited studies on specific implementation techniques. To explore the specific implementation technologies of product-gene acquisition, an intelligent acquisition method based on K -means clustering and mutual information-based feature selection algorithm is proposed in this paper. The product genes defined in this paper are key product information that determines the nature of the product and influences the conceptual design process. Thus, solutions obtained according to them are more feasible than that based on limited and uncertain information. An illustrative example is presented. The results show that the proposed method can achieve intelligent acquisition of product genes to a certain extent. Further, the proposed method will allow designers to quickly search for the corresponding product genes when performing similar functional design tasks. [ABSTRACT FROM AUTHOR]

The use of topology optimization in the design of fluid dynamics systems is still in its infancy. With the decreasing cost of additive manufacture, the application of topology optimization in the design of structural components has begun to increase. This paper provides a method for using topology optimization to reduce the power dissipation of fluid dynamics systems, with the novelty of it being the first application of stochastic mechanisms in the design of 3D fluid–solid geometrical interfaces. The optimization algorithm uses the continuous adjoint method for sensitivity analysis and is optimized against an objective function for fluid power dissipation. The paper details the methodology behind a vanilla gradient descent approach before introducing stochastic behavior through a minibatch-based system. Both algorithms are then applied to a novel case study for an internal combustion engine's piston cooling gallery before the performance of each algorithm's resulting geometry is analyzed and compared. The vanilla gradient descent algorithm achieves an 8.9% improvement in pressure loss through the case study, and this is surpassed by the stochastic descent algorithm which achieved a 9.9% improvement, however this improvement came with a large time cost. Both approaches produced similarly unintuitive geometry solutions to successfully improve the performance of the cooling gallery. [ABSTRACT FROM AUTHOR]

This paper proposes a method to compare the performances of different methods for robust design optimization of computationally demanding models. Its intended usage is to help the engineer to choose the optimization approach when faced with a robust optimization problem. This paper demonstrates the usage of the method to find the most appropriate robust design optimization method to solve an engineering problem. Five robust design optimization methods, including a novel method, are compared in the demonstration of the comparison method. Four of the five compared methods involve surrogate models to reduce the computational cost of performing robust design optimization. The five methods are used to optimize several mathematical functions that should be similar to the engineering problem. The methods are then used to optimize the engineering problem to confirm that the most suitable optimization method was identified. The performance metrics used are the mean value and standard deviation of the robust optimum as well as an index that combines the required number of simulations of the original model with the accuracy of the obtained solution. These measures represent the accuracy, robustness, and efficiency of the compared methods. The results of the comparison show that sequential robust optimization is the method with the best balance between accuracy and number of function evaluations. This is confirmed by the optimizations of the engineering problem. The comparison also shows that the novel method is better than its predecessor is. [ABSTRACT FROM PUBLISHER]

The user's gaze can provide important information for human–machine interaction, but the analysis of manual gaze data is extremely time-consuming, inhibiting wide adoption in usability studies. Existing methods for automated areas of interest (AOI) analysis cannot be applied to tangible products with a screen-based user interface (UI), which have become ubiquitous in everyday life. The objective of this paper is to present and evaluate a method to automatically map the user's gaze to dynamic AOIs on tangible screen-based UIs based on computer vision and deep learning. This paper presents an algorithm for automated Dynamic AOI Mapping (aDAM), which allows the automated mapping of gaze data recorded with mobile eye tracking to the predefined AOIs on tangible screen-based UIs. The evaluation of the algorithm is performed using two medical devices, which represent two extreme examples of tangible screen-based UIs. The different elements of aDAM are examined for accuracy and robustness, as well as the time saved compared to manual mapping. The break-even point for an analyst's effort for aDAM compared to manual analysis is found to be 8.9 min gaze data time. The accuracy and robustness of both the automated gaze mapping and the screen matching indicate that aDAM can be applied to a wide range of products. aDAM allows, for the first time, automated AOI analysis of tangible screen-based UIs with AOIs that dynamically change over time. The algorithm requires some additional initial input for the setup and training, but analyzed gaze data duration and effort is only determined by computation time and does not require any additional manual work thereafter. The efficiency of the approach has the potential for a broader adoption of mobile eye tracking in usability testing for the development of new products and may contribute to a more data-driven usability engineering process in the future. [ABSTRACT FROM AUTHOR]

Constraint sets can become inconsistent in different contexts. For example, during a configuration session the set of customer requirements can become inconsistent with the configuration knowledge base. Another example is the engineering phase of a configuration knowledge base where the underlying constraints can become inconsistent with a set of test cases. In such situations we are in the need of techniques that support the identification of minimal sets of faulty constraints that have to be deleted in order to restore consistency. In this paper we introduce a divide and conquer-based diagnosis algorithm (FastDiag) that identifies minimal sets of faulty constraints in an overconstrained problem. This algorithm is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial. We compare the performance of FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis that shows the advantages of our approach. [ABSTRACT FROM PUBLISHER]

Feature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm. [ABSTRACT FROM AUTHOR]

Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task--method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach. [ABSTRACT FROM AUTHOR]

AbstractOntologies have been successfully applied as a semantic enabler of communication between both users and applications in fragmented, heterogeneous multinational business environments. In this paper we discuss the underlying principles, their current implementation status, and most importantly, their applicability to problems in the building information modeling domain. We introduce the development of an ontology for the building and construction sector based on the industry foundation classes. We discuss several approaches of lifting modeling information that is based on the express family of languages for data modeling onto a logically rigid and semantically enhanced ontological level encoded in the W3C Ontology Web Language. We exemplify the added value of such formal notation of building models by providing several examples where generic query and reasoning algorithms can be applied to problems that otherwise have to be manually hard-wired into applications for processing building information. Furthermore, we show how the underlying resource description framework and the set of technologies evolving around it can be tailored to the need of distributed collaborative work in the building and construction industry. [ABSTRACT FROM AUTHOR]

This paper proposes an aesthetic-driven evolutionary algorithm for user-centered design. The evolutionary algorithm is based on a genetic algorithm (GA). It is developed to work as an art form generator that enhances user's productivity and creativity through reproduction, evaluation, and selection. Users can input their preferences and guide the generating direction to the system. A two-step fitness function is developed to evaluate morphology and aesthetics of the generated art forms. Fractals created by an iterated function system are used for representing art forms in our process. Algorithmic aesthetics are developed based on the aesthetic measure theory, surveys of human preferences, and popular long-lasting symbols. The algorithmic aesthetics is used for evaluating aesthetics of art forms together with subjective nonquantifiable aspects, and placed in the fitness function. The GA basically creates two-dimensional art forms. However, any two-dimensional image can be included through the property of a condensation set of fractals. The proposed GA can increase design productivity by about 80%. Examples of jewelry designs and physical prototypes created by the proposed system are included. [ABSTRACT FROM AUTHOR]

Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods. [ABSTRACT FROM AUTHOR]

Today, in the field of architecture, bio-inspired algorithms can be used to design and seek solutions to design problems. Two of the most popular algorithms are the genetic algorithm (GA) and swarm intelligence algorithm. However, no study has examined the simultaneous use of these two bio-inspired algorithms in the field of architecture. Therefore, this study aims to test whether these two bio-inspired algorithms can work together. To this end, GA is used in this study to optimize the rule-based swarm algorithm for the conceptual design process. In this optimization test, the objective was to increase the surface area, and the constraints are parcel boundary and building height. Further, the forms associated with swarm agents were determined as variables. Following the case studies, the study concludes that the two bio-inspired algorithms can effectively work together. [ABSTRACT FROM AUTHOR]

A data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM. [ABSTRACT FROM AUTHOR]

Erection planning in shipbuilding is a highly complex process. When a process change happens for some reason, it is often difficult to identify how many factors are affected and estimate how sensitive these factors can be. To optimize the planning and replanning of the shipbuilding plan for the best production performance, a data-driven approach for shipbuilding erection planning is proposed, which is composed of an erection plan model, identification of major factors related to the erection plan, and a data-driven algorithm to apply shipbuilding operation data for creating plans and forecasting, for plan adjustment, future availabilities of shipyard resources including machines, equipment, and man power. Through data clustering, the relevant factors are identified as a result of plan change, and critical equipment health management is carried out through data-driven anomaly detection. A case study is implemented, and the result shows that the proposed data-driven method is able to reschedule the shipbuilding plans smoothly. [ABSTRACT FROM PUBLISHER]

A function-based keyword search is a concept generation methodology studied in the bioinspired design area that conveys textual biological inspiration for engineering design. Current keyword search methods are inefficient primarily due to the knowledge gap between engineering and biology domains. To improve current keyword search methods, we propose an algorithm that extracts and organizes morphology-based solutions from biological text. WordNet is utilized to discover morphological solutions in biological text. The novel algorithm also adapts latent semantic analysis and the expectation–maximization algorithm to categorize morphological solutions and group biological text. We introduce a novel penalty function that reflects the distance between functions (problems) and morphologies (solutions). The penalty function allows the algorithm to extract morphological solutions directly related to a design problem. We compare the output of the algorithm to manually extracted solutions for validation. A case study is included to exemplify the utility of the developed algorithm. Upon implementation of the algorithm, engineering designers can discover innovative solutions in biological text in a straightforward, efficient manner. [ABSTRACT FROM PUBLISHER]

The standard penetration test (SPT) is the most common test conducted in the field, and it is used to determine in situ properties of different soils. Although it is a matter of debate, these tests are also used for the determination of the consistency of fine-grained soils, whereby the test results can also be utilized to establish numerous empirical correlations to predict the strength of soils in the field. In this study, unsupervised clustering algorithms were employed to classify the SPT standard penetration resistance value (SPT-N) in the field. In this scope, shear strength and liquidity index parameters were used to classify the SPT-N values by taking the classification system of Terzaghi and Peck (1967) into consideration. The results showed that the input parameters were successful for classifying the SPT-N value to an acceptable degree of strength attribute. Therefore, in cases where the SPT tests are unreliable or could not be performed, laboratory tests on undisturbed specimens can give valuable information regarding the consistency and SPT-N value of the soil specimen under investigation. Data in this study is based on several tests that were conducted in a region; nevertheless, it is advised that the results of this study should be evaluated using global data. [ABSTRACT FROM AUTHOR]

Although many approaches to digital ink recognition have been proposed, most lack the flexibility and adaptability to provide acceptable recognition rates across a variety of problem spaces. This project uses a systematic approach of data mining analysis to build a gesture recognizer for sketched diagrams. A wide range of algorithms was tested, and those with the best performance were chosen for further tuning and analysis. Our resulting recognizer, RATA.Gesture, is an ensemble of four algorithms. We evaluated it against four popular gesture recognizers with three data sets; one of our own and two from other projects. Except for recognizer–data set pairs (e.g., PaleoSketch recognizer and PaleoSketch data set) the results show that it outperforms the other recognizers. This demonstrates the potential of this approach to produce flexible and accurate recognizers. [ABSTRACT FROM AUTHOR]

AbstractThe development of a fuzzy wavelet neural network (FWNN) for the prediction of electricity consumption is presented. The fuzzy rules that contain wavelets are constructed. Based on these rules, the structure of FWNN-based system is described. The FWNN system is applied for modeling and prediction of complex time series. The gradient algorithm and genetic algorithm are used for learning of FWNN parameters. The developed FWNN is applied for prediction of electricity consumption. This process has high-order nonlinearity. The statistical data for the last 10 years are used for the development of FWNN prediction model. The effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based prediction system and with the comparative simulation results of previous related models. [ABSTRACT FROM AUTHOR]