Successful design processes and tools are vital for the success of any design project, particularly when developing aerospace, automotive and other complex systems that can entail imposing design constraints to meet desired objectives. These constraints, coupled with a lack of uniform strategies to define, acquire and process the interaction between designers and tools, add new challenges to the design process. So appropriate processes and tools that allow problem designers to assist in framing and resolving complex design problems can extend the power of the individual working memory, according to previous research. This current research investigates the behavior of engineers working on a parameter design experiment. In the study, 30 subjects solved parameter design problems with both coupled and uncoupled variables. Results showed a relationship among designers’ actions and other features such as gender, recorded error, problem complexity, and performance. These findings can guide future research into engineering design and can inform ideas for better strategies for various aspects of parameter designing.
After the occurrence of a hurricane, assessing damage is extremely important for the emergency managers so that relief aid could be provided to afflicted people. One method of assessing the damage is to determine the damaged and the undamaged buildings post-hurricane. Normally, damage assessment is performed by conducting ground surveys, which are time-consuming and involve immense effort. In this paper, transfer learning techniques have been used for determining damaged and undamaged buildings in post-hurricane satellite images. Four different transfer learning techniques, which include VGG16, MobileNetV2, InceptionV3 and DenseNet121, have been applied to 23,000 Hurricane Harvey satellite images, which occurred in the Texas region. A comparative analysis of these models has been performed on the basis of the number of epochs and the optimizers used. The performance of the VGG16 pre-trained model was better than the other models and achieved an accuracy of 0.75, precision of 0.74, recall of 0.95 and F1-score of 0.83 when the Adam optimizer was used. When the comparison of the best performing models was performed in terms of various optimizers, VGG16 produced the best accuracy of 0.78 for the RMSprop optimizer.
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