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This paper is to propose a control scheme, a combination of the classical field-oriented control (FOC) technique and artificial intelligence (AI), to obtain constant torques in multiphase non-sinusoidal permanent magnet synchronous machine (PMSM) drives. Higher torque density, easier fabrication, and lower costs are several advantages of non-sinusoidal back electromotive force (back-EMF) machines over sinusoidal ones. However, multi-harmonics existing in back-EMFs possibly generate torque ripples, reducing torque quality of the drive. Therefore, in this paper, an adaptive linear neuron (ADALINE), a simple type of AI, is combined with the classical FOC technique to eliminate these torque ripples. The proposed control scheme is validated by numerical results with a seven-phase PMSM. In addition, these results are compared with an existing strategy to prove its effectiveness. This work has been achieved within the framework of CE2I project. CE2I is co-financed by European Union with the financial support of European Regional Development Fund (ERDF), French State and the French Region of Hautsde- France.

This study is to deal with unwanted current harmonics in rotating (d-q) frames of a 7-phase non-sinusoidal permanent magnet synchronous machine (PMSM) in a wye-connected winding topology. The machine is supplied by a 7-leg voltage source inverter (VSI) fed by a DC-bus voltage. In control, current responses are expected to properly track their references. However, several unwanted harmonics of the non-sinusoidal back electromotive force (back-EMF) and the inverter nonlinearity generate unwanted harmonic components in d-q currents. These current harmonics cannot be nullified by controllers such as conventional proportional-integral (PI) controllers. Consequently, the current responses cannot track their references. In this study, a combination of conventional PI controllers and simple adaptive linear neurons (ADALINEs) is proposed to eliminate these current harmonics, improving current control quality of the drive. The effectiveness of the proposed control structure is verified by experimental results. Thai Nguyen University of Technology and CE2I project

In order to have a full control on their processes, companies need to ensure real time monitoring and supervision using Key Performance Indicators (KPI). KPIs serve as a powerful tool to inform about the process flow status and objectives’ achievement. Although, experts are consulted to analyze, interpret, and explain KPIs’ values in order to extensively identify all influencing factors; this does not seem completely guaranteed if they only rely on their experience. In this paper, the authors propose a generic causality learning approach for monitoring and supervision. A causality analysis of KPIs’ values is hence presented, in addition to a prioritization of their influencing factors in order to provide a decision support. A KPI prediction is also suggested so that actions can be anticipated.

This paper proposes a new objective metric of visually induced motion sickness (VIMS) in the context of navigation in virtual environments (VEs). Similar to motion sickness in physical environments, VIMS can induce many physiological symptoms such as general discomfort, nausea, disorientation, vomiting, dizziness and fatigue. To improve user satisfaction with VR applications, it is of great significance to develop objective metrics for VIMS that can analyze and estimate the level of VR sickness when a user is exposed to VEs. One of the well-known objective metrics is the postural instability. In this paper, we trained a LSTM model for each participant using a normal-state postural signal captured before the exposure, and if the postural sway signal from post-exposure was sufficiently different from the pre-exposure signal, the model would fail at encoding and decoding the signal properly; the jump in the reconstruction error was called loss and was proposed as the proposed objective measure of simulator sickness. The effectiveness of the proposed metric was analyzed and compared with subjective assessment methods based on the simulator sickness questionnaire (SSQ) in a VR environment, achieving a Pearson correlation coefficient of .89. Finally, we showed that the proposed method had the potential to be deployed within a closed-loop system and get real-time performance to predict VR sickness, opening new insights to develop user-centered and customized VR applications based on physiological feedback. China Scholarship Council under grant No. 201708390014

International audience; Designing appealing products plays a key role in commercial success. Understanding the relationships between aesthetic properties and shape characteristics of a product can contribute to define user-friendly and interactive designing tools supporting the early design phases. This paper introduces a generic framework for mapping aesthetic properties to 3D free form shapes. The approach uses machine learning techniques to identify rules between the user-defined classifications of shapes and the geometric parameters of the underlying free form surfaces and to create an efficient classification model. The frameworkhas been set up and validated focusing on the flatness aesthetic property but is generic and can be applied to others. Several experiments have been conducted to understand if there is a consistency among people in the judgement of a specific aesthetic properties, if and to which extent the surrounding of the judged surface affects the perception consistency, and which are the surface geometric quantities influencing the perception. A graphic user interface has been designed to allow a fast classification of thousands of shapes automatically generated. The experiments have been conducted following a systematic methodology comparing two different approaches. The results confirm that the perception of flatness is commonly shared by the majority and the most relevant attributes have been identified. Additionally, it results that the surrounding information extension and context influence the perception of the flatness strengthening the classification consistency. The way those results can be used to design new interactive tools and to improve the product design process is discussed.

International audience; In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition.

International audience; Human pose estimation (HPE) methods based on convolutional neural networks (CNN) have demonstrated significant progress and achieved state-of-the-art results on human pose datasets. In this study, we aimed to assess the perfor-mance of CNN-based HPE methods for measuring anthropometric data. A Vicon motion analysis system as the reference system and a stereo vision system recorded ten asymptomatic subjects standing in front of the stereo vision system in a static posture. Eight HPE methods estimated the 2D poses which were transformed to the 3D poses by using the stereo vision system. Percentage of correct keypoints, 3D error, and absolute error of the body segment lengths are the evaluation measures which were used to assess the results. Percentage of correct keypoints – the stand-ard metric for 2D pose estimation – showed that the HPE methods could estimate the 2D body joints with a minimum accuracy of 99%. Meanwhile, the average 3D error and absolute error for the body segment lengths are 5 cm.

In a Smart Grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this arti- cle, we focus on the impact of the introduction of decentralized generation for the unit commitment problem (UC). Unit Commitment Problems consist in finding the optimal schedules and amounts of power to be generated by a set of gen- erating units in response to an electricity demand forecast. While this problem have received a significant amount of attention, classical approaches assume these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, ... In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment prob- lems. We introduce a novel distributed gradient descent algorithm which allow us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator. Programme ADEME - Réseaux électrique intelligent - Projet AgentVPP

International audience; Route planning is an important problem for many real-time applications in open and complex environments. The maritime domain is a relevant example of such environments where dynamic phenomena and navigation constraints generate difficult route finding problems. This paper develops a spatial data structure that supports the search for an optimal route between two locations while minimizing a cost function. Although various search algorithms have been proposed so far (e.g. breadth-first search, bidirectional breadth-first search, Dijkstra's algorithm, A*, etc.), this approach provides a bidirectional dynamic routing algorithm which is based on hexagonal meshes and an iterative deepening A* (IDA*) algorithm, and a front to front strategy using a dynamic graph that facilitates data accessibility. The whole approach is applied to the context of maritime navigation, taking into account navigation hazards and restricted areas. The algorithm developed searches for optimal routes while minimizing distance and computational time.

Part 1: Knowledge Sharing, Re-use and Preservation; International audience; Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented.

12th International Conference, CDVE 2015, Mallorca, Spain, September 20-23, 2015. Proceedings; International audience; With the fastidiously ever-increasing complexity of systems, the relentless, massive customisation of products and the mushrooming accumulation of legal documents (standards, policies and laws), we can observe a significant increase in requirements. We consider the tremendous volume of requirements as big data with which companies struggle to make strategic decisions early on. This paper proposes a collaborative requirement mining framework to enable the decision-makers of an Original Equipment Manufacturer (OEM) to gain insight and discover opportunities in a massive set of requirements so as to make early effective strategic decisions. The framework supports OEMs willing to uncover a subset of key requirements by distilling large unstructured and semi-structured specifications.

International audience; In model-driven engineering, model transformations are con- sidered a key element to generate and maintain consistency between re- lated models. Rule-based approaches have become a mature technology and are widely used in different application domains. However, in var- ious scenarios, these solutions still suffer from a number of limitations that stem from their injective and deterministic nature. This article pro- poses an original approach, based on non-deterministic constraint-based search engines, to define and execute bidirectional model transforma- tions and synchronizations from single specifications. Since these solely rely on basic existing modeling concepts, it does not require the intro- duction of a dedicated language. We first describe and formally define this model operation, called transformation as search, then describe a proof-of-concept implementation and discuss experiments on a reference use case in software engineering.