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Discussion of “Structural Damage Detection from Wavelet Coefficient Sensitivity with Model Errors” by S. S. Law, X. Y. Li, and Z. R. Lu
Publisher: American Society of Civil Engineers
Ontology-Based Environmental Effectiveness Knowledge Application System for Optimal Reliability Design
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Environmental effectiveness refers to the influence and harm on products and materials resulting from the effects of various environmental factors. In their actual usage in a complex environment, products are put forward ...
EcoRacer: Game Based Optimal Electric Vehicle Design and Driver Control Using Human Players
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: We compare the performance of human players against that of the efficient global optimization (EGO) algorithm for an NPcomplete powertrain design and control problem. Specifically, we cast this optimization problem as an ...
Topology Generation for Hybrid Electric Vehicle Architecture Design
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Existing hybrid powertrain architectures, i.e., the connections from engine and motors to the vehicle output shaft, are designed for particular vehicle applications, e.g., passenger cars or city buses, to achieve good fuel ...
Optimal Stiffness Design for an Exhaustive Parallel Compliance Matrix in Multiactuator Robotic Limbs
Publisher: The American Society of Mechanical Engineers (ASME)
Hardware-in-the-Loop Simulation System for Multi-Intersection Traffic Signal Control
Publisher: American Society of Civil Engineers
Abstract: To evaluate the performance of a traffic signal control system quickly and efficiently, a hardware-in-the-loop simulation system of multi-intersection traffic signal is developed. With the hardware-in-the-loop approach, a ...
When Crowdsourcing Fails: A Study of Expertise on Crowdsourced Design Evaluation
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Crowdsourced evaluation is a promising method of evaluating engineering design attributes that require human input. The challenge is to correctly estimate scores using a massive and diverse crowd, particularly when only a ...
Improving Design Preference Prediction Accuracy Using Feature Learning
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Quantitative preference models are used to predict customer choices among design alternatives by collecting prior purchase data or survey answers. This paper examines how to improve the prediction accuracy of such models ...