The main aim of this work is to develop and implement an automatic anomaly detection algorithm for meteorological time-series. To achieve this goal we develop an approach to constructing an ensemble of anomaly detectors in combination with adaptive threshold selection based on artificially generated anomalies. We demonstrate the efficiency of the proposed method by integrating the corresponding implementation into "Minimax-94" road weather information system.

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Conventional methods for determining and monitoring the viscosity of oils are time-consuming, expensive, and in some instances, technically unfeasible. These limitations can be avoided using low-field nuclear magnetic resonance (LF-NMR) relaxometry. However, due to the chemical dissimilarity of oils and various temperatures these oils are exposed to, as well as LF-NMR equipment limitations, the commonly used models fail to perform at a satisfactory level, making them impractical for use in heavy oil and bitumen reservoirs and in environments with large temperature oscillations (e.g., mechanical systems). We present a framework that combines supervised learning algorithms with domain knowledge for synthesizing new features to improve model forecasts using only one NMR parameterT 2 geometric mean. Two principal methods were considered, support vector regression (SVR) and gradient boosted trees (GBRT). Models were trained using the experimental data from our previous studies and literature data combining conventional oils, heavy oils, and bitumens from various reservoirs in Canada and United States. The models’ performance was compared against four other intelligent algorithms and four well-known empirical NMR models against which the SVR- and GBRT-based models achieved the highest statistical scores. These two models can be used for oil viscosity prediction in conventional and heavy oil reservoirs with a wide range of oil viscosities and in situations where high precision is needed, such as in the determination of viscosity of petroleum distillates or for monitoring of oil viscosity in mechanical systems. The proposed framework can also be applied to determine other physicochemical properties of oils by LF-NMR, where the application of supervised learning is usually impractical due to the limited volume of experimental data.

Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios. Unfortunately, CAD model retrieval is limited by the availability of models in standard 3D shape collections (e.g., ShapeNet). In this work, we address this shortcoming by introducing CAD-Deform 1 , a method which obtains more accurate CAD-to-scan fits by non-rigidly deforming retrieved CAD models. Our key contribution is a new non-rigid deformation model incorporating smooth transformations and preservation of sharp features, that simultaneously achieves very tight fits from CAD models to the 3D scan and maintains the clean, high-quality surface properties of hand-modeled CAD objects. A series of thorough experiments demonstrate that our method achieves significantly tighter scan-to-CAD fits, allowing a more accurate digital replica of the scanned real-world environment while preserving important geometric features present in synthetic CAD environments.

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