Exercise-based rehabilitation for chronic conditions such as cardiovascular disease, diabetes, and chronic obstructive pulmonary disease, constitutes a key element in reducing patient symptoms and improving health sta...
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The design of manufacturing systems can see dramatic improvements through the use of digital technologies for modeling and simulation prior to deployment. At the 2017 ASME International Manufacturing Science and Engin...
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The ultimate goal of a baby detection task concerns detecting the presence of a baby and other objects in a sequence of 2D images, tracking them and understanding the semantic contents of the scene. Recent advances in...
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Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world appl...
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Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.
In this paper, we propose a transfer learning based method to classify PCB images into two classes - i) True Defect and ii) Pseudo Defect A pre-trained Inception-V3 model is used for transfer learning and the mid-leve...
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ISBN:
(纸本)9781538666876
In this paper, we propose a transfer learning based method to classify PCB images into two classes - i) True Defect and ii) Pseudo Defect A pre-trained Inception-V3 model is used for transfer learning and the mid-level representations of the PCB images are extracted from the output of an intermediate layer of the pre-trained model. These mid-level representations of the PCB images are used to train a small adaptation network, which makes the overall network suitable for classification of PCB images. In order to tackle overfitting, regularization strategies are implemented. The results of our experiment, conducted on real world PCB images, show a significant improvement in the classification accuracy as compared to previous work.
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and ...
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In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to...
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The segmentation of a shape into a series of meaningful parts is a fundamental problem in shape analysis and part-based object representation. However, it is difficult to make the result of shape segmentation accord w...
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Brain morphometry study plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surface...
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