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Occupant presence and behaviour have a large impact on building energy performance. With the availability of low cost and affordable sensors, accurate occupancy detection by combining sensor stream data with machine learning approaches becomes possible. In this paper, we propose a novel dynamical hidden semi-Markov model (D-HSMM) which can accurately detect occupancy pattern from sensor data stream...
In order to improve the prediction accuracy of cognitive radio spectrum and providing more reliable spectrum access for the subsequent spectrum detection, the dynamic fuzzy neural network is applied to predict the cognitive radio spectrum, and prove its feasibility. Simulation results show that the algorithm has higher accuracy than the general spectral prediction algorithm.
Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both static...
Sequential data modeling has received growing interests due to its impact on real world problems. Sequential data is ubiquitous -- financial transactions, advertise conversions and disease evolution are examples of sequential data. A long-standing challenge in sequential data modeling is how to capture the strong hidden correlations among complex features in high volumes. The sparsity and skewness...
In this paper, we realize a literature survey on the issue of community detection over time, first we present some basic concepts about networks modeled as graphs, then we state in an non exhaustive way the research fields arising from social networks. We present some of the existing models and methods to track communities over time. Community detection in networks is a prevailing subject in the area...
This paper presents a hierarchical feature extraction technique for non-stationary time-series data that is considered to be a slow-time scale mixture of time-series segments which are quasi-stationary at a faster time-scale. The problem is to model an unknown number of unique stationary segments at the low level while capturing their switching characteristics at a higher level. Symbolic Dynamic Filtering...
Human action analysis has achieved great success especially with the recent development of advanced sensors and algorithms that can effectively track the body joints. Temporal motion of body joints carries crucial information about human actions. However, current dynamic models typically assume stationary local transition and therefore are limited to local dynamics. In contrast, we propose a novel...
Tracking individual behaviors based on observations made from vast personal interaction network has become a major concern and interest for the policing community as well as for the business/commercial players. While the policing community resort to personal networks in order to predict and prevent adverse events, the commercial players want to track opportunities for online advertisement, market...
This paper describes a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic (time-series) data. The approach employs Cox proportional hazards model (Cox PHM) and soft dynamic multiple fault diagnosis algorithm (DMFD) for inferring the degraded state trajectories of components and to estimate their remaining useful life (RUL). This framework takes...
This paper is aim to improve the discrimination capability of LDA model through unsupervised feature selection. Experimental results show that if the interference of general word and general topic can be removed, the discrimination capability of LDA model will be increased. The key problem is how to find supervised information to evaluate features. The LDA topics are assumed reasonable. Therefore,...
We propose a new algorithm for sequence segmentation based on recent advances in semi-parametric sequence clustering. This approach implies the use of model-based distance measures between sequences, as well as a variant of spectral clustering specially tailored for segmentation. The method is highly flexible since it allows for the use of any probabilistic generative model for the individual segments...
Parameters of tracked video objects (for example: the angles of moving objects) are discrete random variables and the amount of data increases over time. In this paper we use a new method to analyze the parameter angle: the video frame is segmented into small sections and in each section the angle values during some time period are gathered. Through analysis the angle data in each section these angles...
This paper presents the theoretical framework of a new statistical model for phoneme recognition. In contrast with traditional HMMs, the posterior probability of a state sequence given an observation sequence is computed directly with the new model. The development of this paper is based on Maximum Entropy Markov Models (MEMMs[5]), appearing as a result of the application of Maximum Entropy principle...
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