Adversarials−1: detecting adversarial inputs with internal attacks

Deep neural networks are very successful in various demanding tasks, e.g. in image and speech classification. Nevertheless, they are vulnerable to attacks where the input is slightly modified, which leads to misclassification. In this thesis a new attack scenario is introduced to perform such attacks against road signs without physical manipulation. Then a defence strategy is presented, whose basic idea is to manipulate an unknown input internally. Based on the internal manipulation it is decided whether the initial input was original or already manipulated. In the second case the original class can be restored by the internal manipulation. Experiments show that this procedure can be applied in both image and speech classification. Finally, it is shown that the method can also be used to detect more general out-of-distribution input

Electrical Components for Marine Renewable Energy Arrays: A Techno-Economic Review

This paper presents a review of the main electrical components that are expected to be present in marine renewable energy arrays. The review is put in context by appraising the current needs of the industry and identifying the key components required in both device and array-scale developments. For each component, electrical, mechanical and cost considerations are discussed; with quantitative data collected during the review made freely available for use by the community via an open access online repository. This data collection updates previous research and addresses gaps specific to emerging offshore technologies, such as marine and floating wind, and provides a comprehensive resource for the techno-economic assessment of offshore energy arrays