Experimental study of the delayed threshold phenomenon in a semiconductor laser

An experimental study of the delayed threshold phenomenon in a Vertical Extended Cavity Semiconductor Emitting Laser is carried out. Under modulation of the pump power, the laser intensity exhibits a hysteresis behavior in the vicinity of the threshold. The temporal width of this hysteresis is measured as a function of the modulation frequency, and is proved to follow the predicted scaling law. A model based on the rate equations is derived and used to analyze the experimental observations. A frequency variation of the laser around the delayed threshold and induced by the phase-amplitude coupling is predicted and estimated

Experimental characterization of behavior laws for titanium alloys: application to Ti5553

The aim of this paper is to study the machinability of a new titanium alloy: Ti-5AL-5Mo-5V-3CR used for the production of new landing gear. First, the physical and mechanical properties of this material will be presented. Second, we show the relationship between material properties and machinability. Third, the Ti5553 will be compared to Ti64. Unless Ti64 is α+β alloy group and Ti5553 is a metastable, we have chosen to compare these two materials. Ti64 is the most popular of titanium alloys and many works were been made on its machining. After, we have cited the Ti5553 properties and detailed the behavior laws. They are used in different ways: with or without thermal softening effect or without dynamic terms. The goal of the paper is to define the best cutting force model. So, different models are compared for two materials (steel and titanium alloy). To define the model, two methods exist that we have compared. The first is based on machining test; however the second is based on Hopkinson bar test. These methods allow us to obtain different ranges of strain rate, strain and temperature. This comparison will show the importance of a good range of strain rate, strain and temperature for behavior law, especially in titanium machining

Unsupervised and semi-supervised fuzzy clustering with multiple kernels.

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Recently, kernel-based clustering has been proposed to perform clustering in a higher-dimensional feature space spanned by embedding maps and corresponding kernel functions. Although good results were obtained using the Gaussian kernel function, its performance depends on the selection of the scaling parameter among an extensive range of possibilities. This step is often heavily influenced by prior knowledge about the data and by the patterns we expect to discover. Unfortunately, it is often unclear which kernels are more suitable for a particular task. The problem is aggravated for many real-world clustering applications, in which the distributions of the different clusters in the feature space exhibit large variations. Thus, in the absence of a priori knowledge, a single kernel selected from a predefined group is sometimes insufficient to represent the data. One way to learn optimal scaling parameters is through an exhaustive search of one optimal scaling parameter for each cluster. However, this approach is not practical since it is computationally expensive, especially when the data includes a large number of clusters and when the dynamic range of possible values of the scaling parameters is large. Moreover, the evaluation of the resulting partition in order to select the optimal parameters is not an easy task. To overcome the above drawbacks, we introduce two novel fuzzy clustering techniques that use Multiple Kernel Learning to provide an elegant solution for parameter selection. The Fuzzy C-Means with Multiple Kernels algorithm (FCMK) simultaneously finds the optimal partition and the cluster-dependent kernel combination weights that reflect the intrinsic structure of the data. The Relational Fuzzy Clustering with Multiple Kernels (RFCMK) learns the kernel combination weights by optimizing the relational dissimilarities. Consequently, the learned kernel combination weights reflect the relative density, size, and position of each cluster with respect to the other clusters. We also extended FCMK and RFCMK to the semi-supervised paradigms. We show that the incorporation of prior knowledge in the unsupervised clustering task in the form of a small set of constraints on which instances should or should not reside in the same cluster, guides the unsupervised approaches to a better partitioning of the data and avoid local minima, especially for high dimensional real world data. All of the proposed algorithms are optimized iteratively by dynamically updating the partition and the kernel combination weights in each iteration. This makes these algorithms simple and fast. Moreover, our algorithms are formulated to work on both vector and relational data. This makes them applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. We also introduced two relational fuzzy clustering with multiple kernel algorithms for large data to deal with the scalability issue of RFCMK. The random sample and extend RFCMK (rseRFCMK) computes cluster prototypes from a smaller sample of randomly selected objects, and then extends the partition to the remainder of the data. The single pass RFCMK (spRFCMK) sequentially loads manageable sized chunks, clustering the chunks in a single pass, and then combining the results from each chunk. Our extensive experiments show that RFCMK and SS-RFCMK outperform existing algorithms. In particular, we show that when data include clusters with various intrinsic structures and densities, learning kernel weights that vary over clusters is crucial in obtaining a good partition

Experimental study of coated carbide tools behaviour: application for Ti-5-5-5-3 turning

The goal of this paper is to study the relation between the input data (conditions and geometry of cut) and answers (wear of tool, forces and cutting temperatures) when machining the Ti-5-5-5-3 alloy treated. This study has shown that the cutting process is different and that the slip forces are preponderates. Compared with other materials, the specific cutting pressure is higher and does not vary according to the cutting speed but depend on feed rate. Moreover, both edge preparation and feed rate have an influence on cutting force direction. Besides, cutting temperatures are high and almost similar to those provided by high speed machining with low cutting speed. Finally, we have shown that failure modes are different from those obtained when machining other titanium alloys. Built-up edge is the most deteriorating phenomenon and no flank wear was met in our study context

Optimization of pocket machining strategy in HSM

Our two major concerns, which should be taken into consideration as soon as we start the selecting the machining parameters, are the minimization of the machining time and the maintaining of the high-speed machining machine in good state. The manufacturing strategy is one of the parameters which practically influences the time of the different geometrical forms manufacturing, as well as the machine itself. In this article, we propose an optimization methodology of the machining strategy for pockets of complex forms. For doing this, we have developed analytic models expressing the feed rate of the cutting tools trajectory. Then, we have elaborated an optimization method based on the analysis of the different critical parameters so as to distinguish the most suitable strategies to calculate the cutting time and define the machine dynamics. To validate our results, we have compared them to the experimental ones and also to those found in literature

Automatic target recognition with convolutional neural networks.

Automatic Target Recognition (ATR) characterizes the ability for an algorithm or device to identify targets or other objects based on data obtained from sensors, being commonly thermal. ATR is an important technology for both civilian and military computer vision applications. However, the current level of performance that is available is largely deficient compared to the requirements. This is mainly due to the difficulty of acquiring targets in realistic environments, and also to limitations of the distribution of classified data to the academic community for research purposes. This thesis proposes to solve the ATR task using Convolutional Neural Networks (CNN). We present three learning approaches using WideResNet-28-2\cite{wrn} as a backbone CNN. The first method uses random initialization of the network weights. The second method explores transfer learning. Finally, the third approach relies on spatial transformer networks \cite{stn} to enhance the geometric invariance of the model. To validate, analyze and compare our three proposed models, we use a large-scale real benchmark dataset that includes civilian and military vehicles. These targets are captured at different viewing angles, different resolutions, and different times of the day. We evaluate the effectiveness of our methods by studying their robustness to realistic case scenarios where no ground truth data is available and targets are automatically detected. We show that the method that uses spatial transformer networks achieves the best results and demonstrates the most robustness to various perturbations that can be encountered in real applications