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Over the last few decades, action recognition applications have attracted the growing interest of researchers, especially with the advent of RGB-D cameras. These applications increasingly require fast processing. Therefore, it becomes important to include the computational latency in the evaluation criteria. In this paper, we propose a novel human action descriptor based on skeleton data provided by RGB-D cameras for fast action recognition. The descriptor is built by interpolating the kinematics of skeleton joints (position, velocity and acceleration) using a cubic spline algorithm. A skeleton normalization is done to alleviate anthropometric variability. To ensure rate invariance which is one of the most challenging issues in action recognition, a novel temporal normalization algorithm called Time Variable Replacement (TVR) is proposed. It is a change of variable of time by a variable that we call Normalized Action Time (NAT) varying in a fixed range and making the descriptors less sensitive to execution rate variability. To map time with NAT, increasing functions (called Time Variable Replacement Function (TVRF)) are used. Two different Time Variable Replacement Functions (TVRF) are proposed in this paper: the Normalized Accumulated kinetic Energy (NAE) of the skeleton and the Normalized Pose Motion Signal Energy (NPMSE) of the skeleton. The action recognition is carried out using a linear Support Vector Machine (SVM). Experimental results on five challenging benchmarks show the effectiveness of our approach in terms of recognition accuracy and computational latency.

Considerable efforts have been made to find a reliable model able to accurately describe and predict the thermal behavior of the indoor environment of a building. Such a model is essential, in particular, for designing climate control strategies for optimizing both the comfort level and the energy consumption. However, a building is a complex system characterized by a nonlinear thermal behavior, so the task to find such a reliable model is rather difficult. This paper aims at overcoming some of these difficulties by presenting a data driven approach based on a switched system identification to detect and model the thermal behavior of a building zone during normal usage. The proposed technique relies on a PieceWise AutoRegressive model with eXogeneous inputs (PWARX) consisting of a set of sub-models with each one of them describing a certain configuration/state of the dwelling, e.g. turning the heating ON/OFF or opening/closing windows, doors and shutters. The approach is data-driven, easy to implement and its computational time is inferior to creating a detailed model under a specialized software. Therefore, it is particularly suitable for providing a quick description of the thermal behavior of existing buildings for which it is possible to install sensors and perform measurements. Using the available measurements, the algorithm is able to detect various configurations as will be shown by two numerical examples. Such a collection of sub-models provides a better temperature estimate than using ARX models, so it will eventually allow to select better strategies for improving energy efficiency. © 2019 Elsevier Ltd

Gradual patterns that capture co-variation of complex attributes in the form “when X increases/decreases, Y increases/decreases” play an important role in many real world applications where huge volumes of complex numerical data must be handled. More recently, they have received attention from the data mining community for exploring temporal data and methods have been defined to automatically extract gradual patterns from temporal data. However, to the best of our knowledge, no method has been proposed to extract gradual patterns that always appear at the identical time intervals in the sequences of temporal data, despite the knowledge that such patterns may bring for certain applications such as e-commerce. This paper proposes to extract co-variations of periodically repeating attributes from the sequences of temporal data that we call seasonal gradual patterns. We discuss the specific features of these patterns and propose an approach for their extraction by exploiting a motif mining algorithm in a sequence, and justify its applicability to the gradual case. Illustrative results obtained from a real world data set are described and show the interest for such patterns.

This paper introduces a novel approach for real-time classification of human activities using data from inertial sensors embedded in a smartphone. We propose a hierarchical classification scheme to recognize seven classes of activities including postural transitions. Its structure has three internal nodes composed of three Support Vector Machines (SVMs) classifiers, each one is associated with a set of activities. Moreover, each SVMs is fed with a feature vector from an adapted and optimal frequency band. Experimental results conducted on a challenging publicly available dataset named SBHAR show that our method is effective and outperforms various state-of-the-art approaches. We also show the suitability of our method to recognize postural transitions.

An implementation of a precise flood forecasting is becoming increasingly urgent considering the growing number of climatic disasters. Several solutions of water asset management based or not on optimization and control exist but they require primarily on a good prediction of the volumes triggered by rainfall after a storm. Numerous studies are dedicated to this challenging issue that have this main objective of how to predict the runoff by using the right model with taking into account the nonlinearities induced by the geographical features of the catchment. This paper present an alternative to the large scale hydrological mathematical model based on offline and recursive/online parameter estimation of data-driven linear and nonlinear models. The updating of the model parameters allows to handle the variations due to the past rainfall quantities and to the weather forecasting.

International audience; In order to enhance the performance of electromagnetic interference (EMI) filters, it is necessary to identify high-frequency parasitic elements of their passive components, mainly those related to the coupled inductors. motivated by this issue, in this work a realistic high-frequency model is proposed for the coupled inductors. Actually, using interval analysis in particular the forward-backward contractor, a set-membership algorithm has been developed to estimate systematically the parasitic elements linked with the magnetic components. The main advantages of this algorithm compared to the fitting methods are the values of the estimated parameters are always positive and the corrupted data are taken into account. The comparison of the simulation results and the experimental data allows us to validate the proposed method.

The paper presents an online strategy for sensor and/or actuator fault detection and isolation applied to a dam-gallery. A recursive subspace identification algorithm is used to estimate the dam-gallery model parameters. The main contribution consists in developing a specific identification scheme, insensitive to a certain type of faults. That is, the identified parameters are invariant to the faults. A fault estimation procedure is proposed to detect potential faults. The proposed approach appears to be suitable for open channel systems for which the characteristics are not easily measurable.

Most of the studies related to the special class of systems called Switched Linear Parameter Varying (LPV) systems are considered in terms of multi-Lyapunov analysis and control design. This issue attracted a lot of attention since a single controller with acceptable performance may not exist for LPV systems with a large parameter variation region. Even more, the mode sequence has mostly considered a priori known and independent of the system parameters and the regression vector. In this paper, a methodology is investigated for the estimation of the discrete state and parameters for a class of discrete-time (DT) Switched LPV systems. A clustering technique based on an interpretation of belief functions combined to the least squares algorithm is used to estimate the scheduled parameters. The simulation example demonstrates the effectiveness of the identification method by giving results in terms of discrete state estimation, parameter estimation and output fit.

With the availability of the recent human skeleton extraction algorithm introduced by Shotton et al. [1], an interest for skeleton-based action recognition methods has been renewed. Despite the importance of the low-latency aspect in applications, it can be noted that the majority of recent approaches has not been evaluated in terms of computational cost. In this paper, a novel fast and accurate human action descriptor named Kinematic Spline Curves (KSC) is introduced. This descriptor is built by interpolating the kinematics of joints (position, velocity and acceleration). To overcome the anthropometric and the execution rate variability, we respectively propose the use of a skeleton normalization and a temporal normalization. For this purpose, a new temporal normalization method based on the Normalized Accumulated kinetic Energy (NAE) of the human skeleton is suggested. Finally, the classification step is performed using a linear Support Vector Machine (SVM). Experimental results on challenging benchmarks show the efficiency of our approach in terms of recognition accuracy and computational latency.

Issues in Energy Efficiency of Data Centers (DC) are important, due to the cumulative effects of the increase in the DCs number and in the energy consumption per center. Developing new design recommendations to improve a cooling system efficiency, commonly quantified by the PUE metric (Power Usage Effectiveness) is one objective of the Green IT organizations. For existing DCs, without considering the optimization of the IT workload, a possible way to improve the DC's energy efficiency is to adjust the cooling setpoints. In this paper, a methodology based on predictive models is used to optimize the PUE by improving the cooling setting. The modeling approach consists in exploiting the temperatures and energy measurements at various operating conditions to predict the PUE behavior using data-driven models commonly called black box models. The optimization procedures are based on the simulation of these models in order to estimate the best working conditions.

This paper presents the real-time emulation of a hydrogen-production process for assessment of an active wind- energy conversion system. The hardware-in-the-loop emulator of the electrolyzer consists of a power-electronic stage and a control stage. In the control board, the algorithmic equations of the electrolyzer modeling and its control should be implemented, as well as the emulator's power converter control. The causal ordering graph is used to model the electrolyzer and its auxiliary equipment. This model is capable of characterizing the relations among the different physical quantities and can be used to design the control system, ensuring an efficient and reliable operation of the electrolyzer. The proposed control method can manage the power and hydrogen flows. The simulation results have highlighted the variation domains and the relations among different physical quantities. The experimental results of the real-time emulation is based on a PHOEBUS advanced alkaline electrolyzer and shows the same electrical characteristics in real time with hardware.

In this paper, we are concerned by the improvement of the safety and the reliability of dynamical systems subjected to slow degradations. We propose a new prognosis strategy which aims at an efficient predictive maintenance by providing an estimation of the future state of the system. The prognosis method is based on an appropriated supervision technique which consists in drift tracking of the dynamical systems using AUDyC an auto-adaptative dynamical classifier. The proposed prognosis method is compared with a prognosis method based on the ANFIS approach (Adaptive Neuro Fuzzy Inference Sytem). These two prognosis methods are implemented and applied to a temperature controller.

This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment.

In this paper we propose a strategy for fault detection and isolation without any fixed model of the system to be supervised. The proposed approach is based on the identification of the parameters characterizing the system without any a priori knowledge. Our contribution consists in developing a specific identification scheme that is insensitive to a certain type of faults. The identified parameters are then invariant to the presence of actuator or sensor faults. Thereafter, a fault estimation procedure is proposed in order to detect sensor or actuator faults. The paper ends with a simulation example which highlights the effectiveness of the proposed approach.

Person identification is an important but still challenging problem in video surveillance. This work designs a completely automatic appearance-based person identification system, which has the ability to achieve new person discovery and classification. The proposed system consists of three modules: background and silhouette separation; feature extraction and selection; and online person identification. The Self-Adaptive Kernel Machine (SAKM) algorithm is used to differentiate existing persons who can be classified from new persons who have to be learnt and added. A new video database with 22 persons is created in real-life environments. The experimental results show that the proposed system achieves satisfying recognition rates of over 90% on person classification with novelty identification.

This paper presents the application of an online identification neural technique to the prediction of the in-situ daily performance of solar collectors. First, it is shown that the use of the Laplace transform helps to find the order of an approximated model; the input of the studied system being the solar radiation. Then it is shown that a Neural Network Output Error (NNOE) model can be accurate using the right size of the regression vector; the learning database consisting of the data obtained during a half day. Finally, it is shown that a Multiple Inputs Single Output (MISO) NNOE model can be accurate; the inputs being the solar radiation and the thermal heat loss conductance that varies with the wind velocity. In any case the differential between the actual value of the daily energy and the value computed by a neural model (SISO—Single Input Single Output) or MISO) is less than 0.5%.

A new supervision system consisting of three modules is presented. The main novelty is the first module that corresponds to a modelling task. This module, which uses the auto-adaptive and dynamical clustering (AUDyC) neural network, allows us to continuously analyse and classify the functioning state of the monitored system using a dynamical modelling of all known modes (good/bad functioning modes represent different classes). The second module exploits these models of the functioning modes in order to detect “fast” and “slow” deviations. From membership degrees and from the information extracted by the monitoring module, the third module, dedicated to the diagnostics, informs the user about the functioning conditions of the system. In this paper, the main characteristics of the AUDyC and its abilities to model on-line non-stationary data are presented. Then, the description of the supervision system is given and some experimental results stemmed from a supervision application of a hydraulic system are discussed.

International audience; Simulation of retail space is a growing topic in the multi-agent systems community. Those systems vary depending on many issues, such as the type of store, or type of behaviour. The kind of issue that is wished to be simulated, or the type of data used to build the simulation, are other subjects of variations. Our ambition is to develop a simulator using a generic model, based on real data easy to collect. In this paper, we focus on the agent model. We develop it and make some experiments to test it.

In this paper, a fault diagnosis architecture based on a dynamical clustering algorithm is developed to detect and isolate faults in wind turbines. The challenge is to deal with different kinds of faults. Constraints on the time of detection are also added in the sense that a fault must be detected as soon as possible. Also, limited historical data corresponding only to normal operating modes are available. Our methodology is based on a data-driven model and is therefore not dependent of the physical models in the wind turbine. It consists of extracting, from sensor measurements, features that are fed into a dynamical clustering algorithm. The latter learns process behaviors characterized by clusters with the ability to update, recursively, the parameters of these clusters. These parameters are used to create detection signals and health indicators used for diagnosis.

International audience; Due to the decrease of sensor and actuator prices and their ease of installation, smart homes and smart environments are more and more exploited in automation and health applications. In these applications, activity recognition has an important place. This article presents a general architecture that is responsible for adapting automation for the different users of the smart home while recognizing their activities. For that, semi-supervised learning algorithms and Markov-based models are used to determine the preferences of the user considering a combination of: (1) observations of the data that have been acquired since the start of the experiment and (2) feedback of the users on decisions that have been taken by the automation. We present preliminarily simulated experimental results regarding the determination of preferences for a user.

This paper proposes a method for human action classification by utilizing correlations between SURF based descriptors. This approach provides us a novel type of descriptor that can be used for action classification. The method proposed is tested using an SVM classification technique. For evaluation purposes, the KTH action recognition dataset, which is a standard benchmark for this area is used as it is one of the most well known and challenging dataset. The method proposed was able to successfully classify different action classes.

The multi-agent simulation consists in using a set of interacting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. It is considered now as an alternative to classical simulations based on analytical models. But, its implementation remains difficult, particularly in terms of behaviors extraction and agents modelling. This task is usually performed by the designer who has some expertise and available observation data on the process. In this paper, we propose a novel way to make use of the observations of real world agents to model simulated agents. The modelling is based on clustering techniques. Our approach is illustrated through an example in which the behaviors of agents are extracted as trajectories and destinations from video sequences analysis. This methodology is investigated with the aim to apply it, in particular, in a retail space simulation for the evaluation of marketing strategies. This paper presents experiments of our methodology in the context of a public area modelling.

The goal of this paper is to contribute to the realization of a system able to recognize people in video surveillance images. The context of this study is to classify a new frame including a person into a set of already known people, using an incremental classifier. To reach this goal, we first present the feature extraction and selection that have been made on appearance based on features (from color and texture), and then we introduce the incremental classifier used to differentiate people from a set of 20 persons. This incremental classifier is then updated at each new frame with the new knowledge that has been presented. With this technique, we achieved 92% of correct classification on the used database. These results are then compared to the 99% of correct classification in the case of a nonincremental technique and these results are explained. Some future works will try to rise the performances of incremental learning the one of non-incremental ones.

The analysis of digital video content is of fundamental importance for efficient browsing, indexing and retrieval of video database in order to facilitate user's access to relevant data. An essential first step is the parsing of the video content into visually-coherent segments, called shots. In this paper we propose an efficient approach for shot change detection and shot modeling based on a new Switched AutoRegressive (SAR) model identification technique. We make the assumption that pixel intensities of all the frames obey a SAR model where each linear sub-model of the SAR model corresponds to a shot and each discrete state corresponds to a different event in the video. Finally, experimental results on three different video sequences show the performance and the feasibility of the proposed approach.

In this paper we propose an online multi-category support vector classifier dedicated to non-stationary environment. Our algorithm recursively discriminates between datasets of three or more classes, one sample at a time. With its incremental and decremental procedures, it can achieve an efficient update of the decision function after the incorporation/elimination of the incoming/oldest data. The key idea is to keep the KKT conditions of one single optimization problem satisfied, while adding or eliminating data. Compared to the QP approach, our classifier is able to provide accurate results. The performance of the proposed algorithm is shown on synthetic and experimental data.

In this paper we present an online recursive clustering algorithm based on incremental and decremental Support Vector Machine (SVM). Developed to learn evolving clusters from non-stationary data, it is able to achieve an efficient multi-class clustering in a non-stationary environment. With a new similarity measure and different procedures (Creation, Adaptation: incremental and decremental learning, Fusion and Elimination) this classifier can provide optimal updated models of data.

In the context of evolutionary data classification, dynamical modeling techniques are useful to continuously learn clusters models. Dedicated to on-line clustering, the AUDyC (Auto-adaptive and Dynamical Clustering) algorithm is an unsupervised neural network with auto-adaptive abilities in nonstationary environment. These particular abilities are based on specific learning rules that are developed into three stages: “Classification”, “Evaluation” and “Fusion”. In this paper, we propose a new densities merge mechanism to improve the “Fusion” stage in order to avoid some local optima drawbacks of Gaussian fitting. The novelty of our approach is to use an ambiguity rule of fuzzy modelling with new merge acceptance criteria. Our approach can be generalized to any type of fuzzy classification method using Gaussian models. Some experiments are presented to show the efficiency of our approach to circumvent to AUDyC NN local optima problems.

This paper presents a kernel-based clustering algorithm called SAKM (Self-Adaptive Kernel Machine) that is developed to learn continuously evolving clusters from non-stationary data. Dedicated to online clustering in multi-class environment, this algorithm is based on an unsupervised learning process with self-adaptive abilities. This process is achieved through three main stages: clusters creation (with an initialization procedure), online clusters adaptation and clusters fusion. Thanks to a new specific kernel-induced similarity measure, the SAKM algorithm is attractive to be very computationally efficient in online applications. At the end, some experiments illustrate the capacities of our algorithm in non-stationary environment.

In the context of pattern recognition area, a small number of clustering techniques are dedicated to the on-line classification of non-stationary data. This paper presents a new algorithm designed with specific properties for the dynamical modeling of classes. This algorithm, called AUDyC (Auto-adaptive and Dynamical Clustering), is based on an unsupervised neural network with full auto-adaptive abilities. The classes modeling is obtained using Gaussian prototypes. Thanks to specific learning strategies, prototypes and classes are created, adapted or eliminated in order to incorporate new knowledge from on-line data. To do that, new learning rules have been developed into three stages: "Classification", "Fusion" and "Evaluation". The results show the real abilities of the AUDyC network to track classes and then to model their evolutions thanks to the adaptation of the prototypes parameters.

This paper presents the application of an online identification neural technique to the prediction of the in-situ daily performance of solar collectors. First, it is shown that the use of the Laplace transform helps to find the order of an approximated model; the input of the studied system being the solar radiation. Then it is shown that an NNOE model can be more accurate using the right size of the regression vector; the learning database consisting of the data obtained during half a day. Finally, it is shown that a multiple inputs single output (MISO) NNOE model can be accurate; the inputs being the solar radiation and the thermal heat loss conductance that depends on the wind velocity. In any case the differential between the actual value of the daily energy and the value computed by a neural model is less than 0.5%.

This paper proposes an improvement of the Cluster Detection and Labeling Neural Network. The original classifier criterion has been modified by introducing Elliptical Basis Functions (EBF) as transfer function of the hidden neurons. In the original CDL network, a similarity criterion is used to determine the membership to prototypes and then to classes. By introducing EBF, we have introduced degrees of membership leading to elliptic shape of classes. In this paper, the functioning of the original CDL network is summarized. Then, the improvements of the architecture in terms of network architecture, neuron activation function and learning stages are described. We present the improvement with EBF and the modification of the auto-adaptation neural network abilities. As validations of our architecture, we illustrate its benefits in comparison with the original CDL network.