Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field. However, this method may not be a straight-forward task considering several issues such as original network size or large differences between the source and target domain. In this paper, we perform transfer learning for semantic segmentation of off-road driving environments using a pre-trained segmentation network called DeconvNet. We explore and verify two important aspects regarding transfer learning. First, since the original network size was very large and did not perform well for our application, we proposed a smaller network, which we call the light-weight network. This light-weight network is half the size to the original DeconvNet architecture. We transferred the knowledge from the pre-trained DeconvNet to our light-weight network and fine-tuned it. Second, we used synthetic datasets as the intermediate domain before training with the real-world off-road driving data. Fine-tuning the model trained with the synthetic dataset that simulates the off-road driving environment provides more accurate results for the segmentation of real-world off-road driving environments than transfer learning without using a synthetic dataset does, as long as the synthetic dataset is generated considering real-world variations. We also explore the issue whereby the use of a too simple and/or too random synthetic dataset results in negative transfer. We consider the Freiburg Forest dataset as a real-world off-road driving dataset.

A magneto-rheological (MR) fluid damper is a semi-active control device that has recently begun to receive more attention in the vibration control community. However, the inherent nonlinear nature of the MR fluid damper makes it challenging to use this device to achieve high damping control system performance. The development of an accurate modeling method for a MR fluid damper is necessary because of its unique characteristics. Our goal was to develop an alternative method for modeling an MR fluid damper by using a self tuning fuzzy (STF) method based on neural technique. The behavior of the researched damper is directly estimated through a fuzzy mapping system. To improve the accuracy of the STF model, a back propagation and a gradient descent method are used to train online the fuzzy parameters to minimize the model error function. A series of simulations were done to validate the effectiveness of the suggested modeling method when compared with the data measured from experiments on a test rig with a researched MR fluid damper. Finally, modeling results show that the proposed STF interference system trained online by using neural technique could describe well the behavior of the MR fluid damper without need of calculation time for generating the model parameters.

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Summary We report the autochthonous existence of Vibrio cholerae in coastal waters of Iceland, a geothermally active country where cholera is absent and has never been reported. Seawater, mussel and macroalgae samples were collected close to, and distant from, sites where geothermal activity causes a significant increase in water temperature during low tides. Vibrio cholerae was detected only at geothermal‐influenced sites during low‐tides. None of the V. cholerae isolates encoded cholera toxin (ctxAB) and all were non‐O1/non‐O139 serogroups. However, all isolates encoded other virulence factors that are associated with cholera as well as extra‐intestinal V. cholerae infections. The virulence factors were functional at temperatures of coastal waters of Iceland, suggesting an ecological role. It is noteworthy that V. cholerae was isolated from samples collected at sites distant from anthropogenic influence, supporting the conclusion that V. cholerae is autochthonous to the aquatic environment of Iceland.

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