Exact maximum likelihood estimation for non-stationary periodic time series models

Time series models with parameter values that depend on the seasonal index are commonly referred to as periodic models. Periodic formulations for two classes of time series models are considered: seasonal autoregressive integrated moving average and unobserved components models. Convenient state space representations of the periodic models are proposed to facilitate model identification, specification and exact maximum likelihood estimation of the periodic parameters. These formulations do not require a priori (seasonal) differencing of the time series. The time-varying state space representation is an attractive alternative to the time-invariant vector representation of periodic models which typically leads to a high dimensional state vector in monthly periodic time series models. A key development is our method for computing the variance-covariance matrix of the initial set of observations which is required for exact maximum likelihood estimation. The two classes of periodic models are illustrated for a monthly postwar US unemployment time series.     

Local exact controllability for Berger plate equation

We study the exact controllability of a nonlinear plate equation by the means of a control which acts on an internal region of the plate. The main result asserts that this system is locally exactly controllable if the associated linear Euler–Bernoulli system is exactly controllable. In particular, for rectangular domains, we obtain that the Berger system is locally exactly controllable in arbitrarily small time and for every open and nonempty control region.     

Reliability design of thermally actuated MEMS switches based on V-shape beams

This paper presents the design and fabrication of the thermally actuated MEMS switches based on out-of-plane V-beams. The purpose of this research is to analyze the mechanical response of a V-thermal actuator fabricated from aluminum in order to improve the accuracy in response and to increase the switch lifetime. The actuation of this kind of switches is based on the thermal displacement of the mobile electrode under thermal load that is generated when the actuation voltage is applied. It can be used either as a capacitive switch or as a metal-to-metal one. The displacement of the mobile electrode for a given temperature is analytically calculated and validated both numerically and experimentally. Experimental investigations are performed on a macro-scale sample using a 3D digital image correlation measuring system, a heating source and a thermal camera for temperature monitoring. The first fabrication steps of the MEMS switch based on the V-beam thermal actuator are presented. The out-of-plane V-beams thermal MEMS switches can be monolithically integrated in RF applications.

     

Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems

This paper proposes new stability analysis and convergence results applied to the Iterative Feedback Tuning (IFT) of a class of Takagi–Sugeno–Kang proportional-integral-fuzzy controllers (PI-FCs). The stability analysis is based on a convenient original formulation of Lyapunov’s direct method for discrete-time systems dedicated to discrete-time input affine Single Input-Single Output (SISO) systems. An IFT algorithm which sets the step size to guarantee the convergence is suggested. An inequality-type convergence condition is derived from Popov’s hyperstability theory considering the parameter update law as a nonlinear dynamical feedback system in the parameter space and iteration domain. The IFT-based design of a low-cost PI-FC is applied to a case study which deals with the angular position control of a direct current servo system laboratory equipment viewed as a particular case of input affine SISO system. A comparison of the performance of the IFT-based tuned PI-FC and the performance of the PI-FC tuned by an evolutionary-based optimization algorithm shows the performance improvement and advantages of our IFT approach to fuzzy control. Real-time experimental results are included.     

Superelastic Multimaterial Electronic and Photonic Fibers and Devices via Thermal Drawing

Electronic and photonic fiber devices that can sustain large elastic deformation are becoming key components in a variety of fields ranging from healthcare to robotics and wearable devices. The fabrication of highly elastic and functional fibers remains however challenging, which is limiting their technological developments. Simple and scalable fiber‐processing techniques to continuously codraw different materials within a polymeric structure constitute an ideal platform to realize functional fibers and devices. Despite decades of research however, elastomeric materials with the proper rheological attributes for multimaterial fiber processing cannot be identified. Here, the thermal drawing of hundreds‐of‐meters long multimaterial optical and electronic fibers and devices that can sustain up to 500% elastic deformation is demonstrated. From a rheological and microstructure analysis, thermoplastic elastomers that can be thermally drawn at high viscosities (above 10³ Pa s), allowing the encapsulation of a variety of microstructured, soft, and rigid materials are identified. Using this scalable approach, fiber devices combining high performance, extreme elasticity, and unprecedented functionalities, allowing novel applications in smart textiles, robotics, or medical implants, are demonstrated.     

Laser-based <Emphasis Type="Italic">in situ</Emphasis> embedding of metal nanoparticles into bioextruded alginate hydrogel tubes enhances human endothelial cell adhesion

Alginate is a widely used hydrogel in tissue engineering owing to its simple and non-cytotoxic gelation process, ease of use, and abundance. However, unlike hydrogels derived from mammalian sources such as collagen, alginate does not contain cell adhesion ligands. Here, we present a novel laser ablation technique for the in situ embedding of gold and iron nanoparticles into hydrogels. We hypothesized that integration of metal nanoparticles in alginate could serve as an alternative material because of its chemical biofunctionalization ability (coupling of RGD ligands) to favor cell adhesion. Cytocompatibility and biofunctionality of the gels were assessed by cell culture experiments using fibroblasts and endothelial cells. Nanoparticles with an average particle size of 3 nm (gold) and 6 nm (iron) were generated and stably maintained in alginate for up to 6 months. Using an extrusion system, several centimeter-long alginate tubes with an outer diameter of approximately 3 mm and a wall thickness of approximately 150 μm were manufactured. Confocal microscopy revealed homogeneously distributed nanoparticle agglomerates over the entire tube volume. Endothelial cells seeded on iron-loaded gels showed significantly higher viability and an increased degree of spreading, and the number of attached cells was also elevated in comparison to the control and gold-loaded alginates. We conclude that laser-based in situ integration of iron nanoparticles (?0.01 wt.%) in alginate is a straightforward method to generate composite materials that favor the adhesion of endothelial cells. In addition, we show that nanoparticle integration does not impair the alginate’s gelation and 3D biofabrication properties.

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The spatial statistics of structural magnetic resonance images: application to post-acquisition quality assessment of brain MRI images

This report describes a new quality evaluation method for structural magnetic resonance images (MRI) of the brain. Pixels in MRI images are regarded as regionalized random variables that exhibit distinct and organized geographic patterns. We extract geo-spatial local entropy features and build three separate Gaussian distributed quality models upon them using 250 brain MRI images of different subjects. The MRI images were provided by Alzheimer's disease neuroimaging initiative (ADNI). Image quality of a test image is predicted in a three-step process. In the first step, three separate geo-spatial feature vectors are extracted. The second step standardizes each quality model using corresponding geo-spatial feature vector extracted from the test image. The third step computes image quality by transforming the standardized score to probability. The proposed method was evaluated on images without perceived distortion and images degraded by different levels of motion blur and Rician noise as well as images with different configurations of bias fields. Based on the performance evaluation, our proposed method will be suitable for use in the field of clinical research where quality evaluation is required for the brain MRI images acquired from different MRI scanners and different clinical trial sites before they are fed into automated image processing and image analysis systems.     

基于深度学习的事件抽取研究综述

事件抽取是从非结构化的自然语言文本中自动抽取用户感兴趣的事件信息, 并以结构化的形式表示出来. 事件抽取是自然语言处理与理解中的重要方向, 在政府公共事务管理、金融业务、生物医学等不同领域有着很高的应用价值. 根据对人工标注数据的依赖程度, 目前基于深度学习的事件抽取方法主要分为两类: 有监督和远程监督学习方法. 对当前深度学习中事件抽取技术进行了全面的综述. 围绕有监督中CNN、RNN、GAN、GCN与远程监督等方法, 系统地总结了近几年的研究情况, 并对不同的深度学习模型的性能进行了详细对比与分析. 最后, 对事件抽取面临的挑战进行了分析, 针对研究趋势进行了展望.     

Intelligent Choice of Machine Learning Methods for Predictive Maintenance of Intelligent Machines

Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are explained. Then factors for the forecast are explained. Requirements for the data are collected and algorithms for machine learning are presented. Based on the relationships found, a process model is presented that shows a fast implementation of the predictive maintenance for machines.     

Efficient benchmarking of algorithm configurators via model-based surrogates

The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. However, the proper evaluation of new algorithm configuration (AC) procedures (or configurators) is hindered by two key hurdles. First, AC scenarios are hard to set up, including the target algorithm to be optimized and the problem instances to be solved. Second, and even more significantly, they are computationally expensive: a single configurator run involves many costly runs of the target algorithm. Here, we propose a benchmarking approach that uses surrogate scenarios, which are computationally cheap while remaining close to the original AC scenarios. These surrogate scenarios approximate the response surface corresponding to true target algorithm performance using a regression model. In our experiments, we construct and evaluate surrogate scenarios for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems. We generalize previous work by building surrogates for AC scenarios with multiple problem instances, stochastic target algorithms and censored running time observations. We show that our surrogate scenarios capture overall important characteristics of the original AC scenarios from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate.