1,972 research outputs found

    Power system stability scanning and security assessment using machine learning

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    Future grids planning requires a major departure from conventional power system planning, where only a handful of the most critical scenarios is analyzed. To account for a wide range of possible future evolutions, scenario analysis has been proposed in many industries. As opposed to the conventional power system planning, where the aim is to find an optimal transmission and/or generation expansion plan for an existing grid, the aim in future grids scenario analysis is to analyze possible evolution pathways to inform power system planning and policy making. Therefore, future grids’ planning may involve large amount of scenarios and the existing planning tools may no longer suitable. Other than the raised future grids’ planning issues, operation of future grids using conventional tools is also challenged by the new features of future grids such as intermittent generation, demand response and fast responding power electronic plants which lead to much more diverse operation conditions compared to the existing networks. Among all operation issues, monitoring stability as well as security of a power system and action with deliberated preventive or remedial adjustment is of vital important. On- line Dynamic Security Assessment (DSA) can evaluate security of a power system almost instantly when current or imminent operation conditions are supplied. The focus of this dissertation are, for future grid planning, to develop a framework using Machine Learning (ML) to effectively assess the security of future grids by analyzing a large amount of the scenarios; for future grids operation, to propose approaches to address technique issues brought by future grids’ diverse operation conditions using ML techniques. Unsupervised learning, supervised learning and semi-supervised learning techniques are utilized in a set of proposed planning and operation security assessment tools

    A human motion feature based on semi-supervised learning of GMM

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    Using motion capture to create naturally looking motion sequences for virtual character animation has become a standard procedure in the games and visual effects industry. With the fast growth of motion data, the task of automatically annotating new motions is gaining an importance. In this paper, we present a novel statistic feature to represent each motion according to the pre-labeled categories of key-poses. A probabilistic model is trained with semi-supervised learning of the Gaussian mixture model (GMM). Each pose in a given motion could then be described by a feature vector of a series of probabilities by GMM. A motion feature descriptor is proposed based on the statistics of all pose features. The experimental results and comparison with existing work show that our method performs more accurately and efficiently in motion retrieval and annotation

    Template update algorithms and their application to face recognition systems in the deep learning era

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    Biometric technologies and facial recognition systems are reaching a very high diffusion for authentication in personal devices and public and private security systems, thanks to their intrinsic reliability and user-friendliness. However, although deep learning-based facial features reached a significant level of compactness and expressive power, the facial recognition performance still suffers from intra-class variations such as ageing, different facial expressions, different poses and lighting changes. In the last decade, several "adaptive" biometric systems have been proposed to deal with this problem. Unfortunately, adaptive methods usually lead to a growth of the system in terms of memory and computational complexity and involve the risk of inserting impostors among the templates. The first goal of this PhD thesis is the presentation of a novel template-based self-update algorithm, able to keep over time the expressive power of a limited set of templates. This classification-selection approach overcomes the problem of manual updating and stringent computational requirements. In the second part of the thesis, we analyzed if and to what extent this "optimized" self-updating strategy improves the facial recognition performance, especially in application contexts where the facial biometric trait undergoes great changes due to the passage of time. In contexts of long-term use, in fact, the high representativeness of the deep features may not be enough and this is usually overcome with a re-enrollment phase. For this reason, one of our goals was to evaluate how much an automatic template updating system could compete with human-in-the-loop in terms of performance. To simulate situations of long-term use in which the temporal variability of biometric data is high, we acquired a new dataset collected by using frames of some videos in YouTube related to Daily Photo Projects: people take a picture every day for a certain period of time, usually to show how their appearance is changing. The temporal information present in this new dataset allowed us to evaluate how long a facial feature can remain representative depending on the context and the recognition system. Extensive experiments on different datasets and using different facial features are conducted to define the contexts of applicability and the usefulness of adaptive systems in the deep learning era

    Self-labeling techniques for semi-supervised time series classification: an empirical study

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    An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context
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