ZHU XING – författare

Near-field optics, dealing with the interaction between optical field and matter in the nanometric region, has become an interdisciplinary field spaning physics, chemistry, materials science, electrical engineering and high density data storage. This book reflects the recent status of this rapidly growing field. It discusses the basic theories, instrumentation, novel probes, theoretical simulations, and the application of near-field optics to the fields of condensed matter physics, new materials, information storage, atom photonics, etc. It provides an overview of the research on near-field optics in the 1990s.

This book focuses on the application of machine learning in slope stability assessment. The contents include: overview of machine learning approaches, the mainstream smart in-situ monitoring techniques, the applications of the main machine learning algorithms, including the supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, deep learning, ensemble learning, etc., in slope engineering and landslide prevention, introduction of the smart in-situ monitoring and slope stability assessment based on two well-documented case histories, the prediction of slope stability using ensemble learning techniques, the application of Long Short-Term Memory Neural Network and Prophet Algorithm in Slope Displacement Prediction, displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks, seismic stability analysis of slopes subjected to water level changes using gradient boosting algorithms, efficient reliability analysis of slopes in spatially variable soils using XGBoost, efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms, as well as the future work recommendation.The authors also provided their own thoughts learnt from these applications as well as work ongoing and future recommendations.