Post-buckling dynamic behavior of self-assembled 3D microstructures

The paper presents the snap-through phenomenon in the case of micro fabricated clamped-clamped buckled beam. This dynamic post-buckling behavior is likely to occur in 3D microstructures when they are subjected to large vibration amplitudes. The main difference between this work and previous studies is the MEMS specific beam dimension, especially the large initial deflection of the buckled beam that involves the inversion of the two first resonance frequencies. The mathematical development allows showing how the vibration amplitude of the supporting base affects the post-buckling dynamic behavior of the beam. For each frequency, the limit between the stable behavior and the snap-through behavior is evaluated. Moreover, the effect of environment is taken into account from the damping point of view. Samples are fabricated and the experiment is described. Measurements are compared to the theoretical approach and the results are in good agreement with the proposed model.     

Optimization of forging processes using Finite Element simulations

During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to the design of feasible forming processes. Coupling FEM to mathematical optimization algorithms offers a promising opportunity to design optimal metal forming processes rather than just feasible ones. In this paper Sequential Approximate Optimization (SAO) for optimizing forging processes is discussed. The algorithm incorporates time-consuming nonlinear FEM simulations. Three variants of the SAO algorithm—which differ by their sequential improvement strategies—have been investigated and compared to other optimization algorithms by application to two forging processes. The other algorithms taken into account are two iterative algorithms (BFGS and SCPIP) and a Metamodel Assisted Evolutionary Strategy (MAES). It is essential for sequential approximate optimization algorithms to implement an improvement strategy that uses as much information obtained during previous iterations as possible. If such a sequential improvement strategy is used, SAO provides a very efficient algorithm to optimize forging processes using time-consuming FEM simulations.     

Multivariable linearizing control of an industrial sugar crystallization process

This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach proposes a setpoint tracking for the crystal mass/concentration couple. In this purpose, a model dedicated to last stage crystallization is designed, without consideration of crystal size distribution. The controlled variables, unavailable, are obtained using an extended Luenberger observer. The observer is validated on industrial data and shows good performance in both convergence rate and accuracy. The performance of the proposed linearizing strategy, which application to cane sugar crystallization constitutes a real novelty, is tested via simulation. The good performance in setpoint tracking, even in presence of noise, disturbances and modeling error, allows to consider a significant improvement of the global productivity.     

A modular system for global and local abnormal event detection and categorization in videos

This paper presents a modular system for both abnormal event detection and categorization in videos. Complementary normalcy models are built both globally at the image level and locally within pixels blocks. Three features are analyzed: (1) spatio-temporal evolution of binary motion where foreground pixels are detected using an enhanced background subtraction method that keeps track of temporarily static pixels; (2) optical flow, using a robust pyramidal KLT technique; and (3) motion temporal derivatives. At the local level, a normalcy MOG model is built for each block and for each flow feature and is made more compact using PCA. Then, the activity is analyzed qualitatively using a set of compact hybrid histograms embedding both optical flow orientation (or temporal gradient orientation) and foreground statistics. A compact binary signature of maximal size 13 bits is extracted from these different features for event characterization. The performance of the system is illustrated on different datasets of videos recorded on static cameras. The experiments show that the anomalies are well detected even if the method is not dedicated to one of the addressed scenarios.     

Surface force apparatus for nanorheology under large shear strain

We describe a surface force apparatus designed to probe the rheology of a nanoconfined medium under large shear amplitudes (up to 500 microm). The instrument can be operated in closed loop, controlling either the applied normal load or the thickness of the medium during shear experiments. Feedback control allows us to greatly extend the range of confinement/shear strain attainable with the surface force apparatus. The performances of the instrument are illustrated using hexadecane as the confined medium.     

Using genetic algorithms for early schedulability analysis and stress testing in real-time systems

Reactive real-time systems have to react to external events within time constraints: Triggered tasks must execute within deadlines. It is therefore important for the designers of such systems to analyze the schedulability of tasks during the design process, as well as to test the system's response time to events in an effective manner once it is implemented. This article explores the use of genetic algorithms to provide automated support for both tasks. Our main objective is then to automate, based on the system task architecture, the derivation of test cases that maximize the chances of critical deadline misses within the system; we refer to this testing activity as stress testing. A second objective is to enable an early but realistic analysis of tasks' schedulability at design time. We have developed a specific solution based on genetic algorithms and implemented it in a tool. Case studies were run and results show that the tool (1) is effective at identifying test cases that will likely stress the system to such an extent that some tasks may miss deadlines, (2) can identify situations that were deemed to be schedulable based on standard schedulability analysis but that, nevertheless, exhibit deadline misses.

Tracking mobile users in wireless networks via semi-supervised colocalization

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.