iFEED: Interactive Feature Extraction for Engineering Design

2016 ◽  
Author(s):  
Hyunseung Bang ◽  
Daniel Selva
Keyword(s):  
Knowledge Discovery ◽  
Design Space ◽  
Information Overload ◽  
Human Subjects ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
Trade Offs ◽  
Design Variables ◽  
Design Selection ◽  
High Level

One of the major challenges faced by the decision maker in the design of complex engineering systems is information overload. When the size and dimensionality of the data exceeds a certain level, a designer may become overwhelmed and no longer be able to perceive and analyze the underlying dynamics of the design problem at hand, which can result in premature or poor design selection. There exist various knowledge discovery and visual analytic tools designed to relieve the information overload, such as BrickViz, Cloud Visualization, ATSV, and LIVE, to name a few. However, most of them do not explicitly support the discovery of key knowledge about the mapping between the design space and the objective space, such as the set of high-level design features that drive most of the trade-offs between objectives. In this paper, we introduce a new interactive method, called iFEED, that supports the designer in the process of high-level knowledge discovery in a large, multiobjective design space. The primary goal of the method is to iteratively mine the design space dataset for driving features, i.e., combinations of design variables that appear to consistently drive designs towards specific target regions in the design space set by the user. This is implemented using a data mining algorithm that mines interesting patterns in the form of association rules. The extracted patterns are then used to build a surrogate classification model based on a decision tree that predicts whether a design is likely to be located in the target region of the tradespace or not. Higher level features will generate more compact classification trees while improving classification accuracy. If the mined features are not satisfactory, the user can go back to the first step and extract higher level features. Such iterative process helps the user to gain insights and build a mental model of how design variables are mapped into objective values. A controlled experiment with human subjects is designed to test the effectiveness of the proposed method. A preliminary result from the pilot experiment is presented.

Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Adaptive Sampling ◽  
Design Space ◽  
Fundamental Problem ◽  
Domain Boundary ◽  
Sampling Technique ◽  
Standard Test ◽  
Worst Case ◽  
Trade Offs ◽  
Design Variables

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

An Experimental Study of Continuous and Discrete Visualization Paradigms for Interactive Trade Space Exploration

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Brian J. German ◽  
Karen M. Feigh ◽  
Matthew J. Daskilewicz
Keyword(s):  
Design Space ◽  
Human Subjects ◽  
Optimal Designs ◽  
Full Potential ◽  
Design Metrics ◽  
Functional Relationships ◽  
Design Variables ◽  
Visualization Tools ◽  
Interactive Data ◽  
Trade Space Exploration

Software tools that enable interactive data visualization are now commonly available for engineering design. These tools allow engineers to inspect, filter, and select promising alternatives from large multivariate design spaces based upon an examination of the tradeoffs between multiple objectives. There are two general approaches for visually representing data: (1) discretely, by plotting a sample of designs as distinct points; and (2) continuously, by plotting the functional relationships between design variables and design metrics as curves or surfaces. In this paper, we examine these two approaches through a human subjects experiment. Participants were asked to complete two design tasks with an interactive visualization tool: one by using a sample of discrete designs and one by using a continuous representation of the design space. Metrics describing the optimality of the design outcomes, the usage of different graphics, and the task workload were quantified by mouse tracking, user process descriptions, and analysis of the selected designs. The results indicate that users had more difficultly in selecting multiobjective optimal designs with common continuous graphics than with discrete graphics. The findings suggest that innovative features and additional usability studies are required in order for continuous trade space visualization tools to achieve their full potential.

scholarly journals A Framework for System Design Optimization Based on Maintenance Scheduling With Prognostics and Health Management

2013 ◽  
Author(s):  
Bo Yang Yu ◽  
Tomonori Honda ◽  
Syed Zubair ◽  
Mostafa H. Sharqawy ◽  
Maria C. Yang
Keyword(s):  
Health Management ◽  
Operating Conditions ◽  
Maintenance Scheduling ◽  
Prognostics And Health Management ◽  
Maintenance Strategies ◽  
Design Component ◽  
Trade Offs ◽  
Design Variables ◽  
System Design Optimization ◽  
High Level

The optimal maintenance scheduling of systems with degrading components is highly coupled with the design of the system and various uncertainties associated with the system, including the operating conditions, the interaction of different degradation profiles of various system components, and the ability to measure and predict degradation using prognostics and health management (PHM) technologies. Due to this complexity, designers need to understand the correlations and feedback between the design variables and lifecycle parameters to make optimal decisions. A framework is proposed for the high level integration of design, component degradation, and maintenance decisions. The framework includes constructing screening models for rapid design evaluation, defining a multi-objective robust optimization problem, and using sensitivity studies to compare trade-offs between different design and maintenance strategies. A case example of power plant condenser is used to illustrate the proposed framework and advise how designers can make informed comparisons between different design concepts and maintenance strategies under highly uncertain lifecycle conditions.

An Example Performance Trade-Off Evaluation Using the Integration Framework for Architecture Development (IFAD) for Conceptual Vehicle Design

2005 ◽  
Author(s):  
Xiaoyu Gu ◽  
Peter A. Fenyes
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Design ◽  
Vehicle Performance ◽  
Integration Framework ◽  
Trade Offs ◽  
Design Variables ◽  
Design Changes ◽  
High Level ◽  
Architecture Development ◽  
Geometrical Dimensions

The Integration Framework for Architecture Development (IFAD) is an integrated framework that provides fast and consistent discipline analysis results and identifies discipline consequences corresponding to vehicle design changes. This information is valuable for balancing and integration in the early design phase. In this paper, the IFAD framework is utilized to conduct an example multi-objective multi-disciplinary optimization to evaluate vehicle performance trade-offs for a hypothetical vehicle. We consider design changes on high-level geometrical dimensions including front overhang, rear overhang and vehicle width at rocker. We also study vehicle configurations including choice of materials and tires and choice of powertrains. A commonly used multi-objective genetic algorithm (MOGA) technique, Non-dominated Sorting Genetic Algorithm (NSGAII [1]) is chosen because of the mixed types of design variables involved (i.e., continuous design variables representing high-level geometrical dimensions and discrete design variables representing vehicle configurations such as powertrain selection and material choice). Vehicle performance analyses in a range of disciplines such as geometry, aerodynamics and energy are carried out automatically through IFAD. The use of response surface modeling (RSM) is desired due to the large number of evaluations typical for a MOGA application. A comparison of the engineering performance trade-offs based on two different sets of performance objectives is presented.

Pakistan's Export Possibilities

1973 ◽  
Vol 12 (1) ◽  
pp. 1-30
Author(s):  
Syed Nawab Haider Naqvi
Keyword(s):  
Economic Growth ◽  
Foreign Aid ◽  
Policy Maker ◽  
Time Horizon ◽  
Price Level ◽  
Balance Of Payments ◽  
Domestic Saving ◽  
Trade Offs ◽  
High Level

The recent uncertainties about aid flows have underscored the need for achieving an early independence from foreign aid. The Perspective Plan (1,965-85) had envisaged the termination of Pakistan's dependence on foreign aid by 1985. However, in the context of West Pakistan alone the time horizon can now be advanced by several years with considerable confidence in its economy to pull the trick. The difficulties of achieving independence from foreign aid can be seen by reference to the fact that aid flows make it possible for the policy-maker to pursue such ostensibly incompatible objectives as a balance in international payments (i.e., foreign aid finances the balance of payments), higher rates of economic growth (Lei, it pulls up domestic saving and investment levels), a high level of employment (i.e., it keeps the industries working at a fuller capacity than would otherwise be the case), and a reasonably stable price level (i.e., it lets a higher level of imports than would otherwise be possible). Without aid, then a simultaneous attainment of all these objectives at the former higher levels together with the balance in foreign payments may become well-nigh impos¬sible. Choices are, therefore, inevitable not for definite places in the hierarchy of values, but rather for occasional "trade-offs". That is to say, we will have to" choose how much to sacrifice for the attainment of one goal for the sake of somewhat better realization of another.

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Evaluation of Static Mapping for Dynamic Space-Shared Multi-task Processing on FPGAs

2021 ◽  
Author(s):  
Umar Ibrahim Minhas ◽  
Roger Woods ◽  
Georgios Karakonstantis
Keyword(s):  
High Performance ◽  
Design Space Exploration ◽  
Design Space ◽  
System Throughput ◽  
Design Parameters ◽  
Temporal Constraints ◽  
Shared Resources ◽  
Task Processing ◽  
High Level ◽  
Performance Computing

AbstractWhilst FPGAs have been used in cloud ecosystems, it is still extremely challenging to achieve high compute density when mapping heterogeneous multi-tasks on shared resources at runtime. This work addresses this by treating the FPGA resource as a service and employing multi-task processing at the high level, design space exploration and static off-line partitioning in order to allow more efficient mapping of heterogeneous tasks onto the FPGA. In addition, a new, comprehensive runtime functional simulator is used to evaluate the effect of various spatial and temporal constraints on both the existing and new approaches when varying system design parameters. A comprehensive suite of real high performance computing tasks was implemented on a Nallatech 385 FPGA card and show that our approach can provide on average 2.9 × and 2.3 × higher system throughput for compute and mixed intensity tasks, while 0.2 × lower for memory intensive tasks due to external memory access latency and bandwidth limitations. The work has been extended by introducing a novel scheduling scheme to enhance temporal utilization of resources when using the proposed approach. Additional results for large queues of mixed intensity tasks (compute and memory) show that the proposed partitioning and scheduling approach can provide higher than 3 × system speedup over previous schemes.

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

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