Cancellation of bulging effect on mould level in continuous casting: Experimental validation

During the continuous casting of steel, several disturbances occur and affect all the parts of the caster, including the mould where the molten steel has to be stable for good quality of the final product. Especially at high casting velocities, the bulging generates important level fluctuations which cannot be efficiently rejected by the classical controllers. To address these problems, this paper proposes an architecture combining disturbances estimation and compensation techniques. It operates as an additional module and does not need any change of the main controller currently implemented. The entire structure has been tested successfully through several simulations and by means of intensive water model experiments.     

Maintaining consistent results of continuous queries under diverse window specifications

Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolving–yet restricted–set of tuples and thus provide timely and incremental results. Although sliding windows get frequently employed in many user requests, additional types like partitioned or landmark windows are also available in stream processing engines. In this paper, we set out to study the existence of monotonic-related semantics for a rich set of windowing constructs in order to facilitate a more efficient maintenance of their changing contents. After laying out a formal foundation for expressing windowed queries, we investigate update patterns observed in most common window variants as well as their impact on adaptations of typical operators (like windowed join, union or aggregation), thus offering more insight towards design and implementation of stream processing mechanisms. Furthermore, we identify syntactic equivalences in algebraic expressions involving windows, to the potential benefit of query optimizations. Finally, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window specifications, providing concrete evidence of its significance.     

Stochastic differential equations driven by a Wiener process and fractional Brownian motion: Convergence in Besov space with respect to a parameter

A stochastic differential equation involving both a Wiener process and fractional Brownian motion, with nonhomogeneous coefficients and random initial condition, is considered. The coefficients and initial condition depend on a parameter. The assumptions on the coefficients and the initial condition supplying continuous dependence of the solution on a parameter, with respect to the Besov space norm, are established.     

基于FPGA的逻辑分析仪连续触发模块设计

该文介绍一种逻辑分析仪数据捕获模式的设计原理与实现方法。该模式主要用于嵌入式软件的测试分析,它不再采用采样的方式来采集数据,而是利用软件插桩与硬件设置触发字相结合的方法来监视系统总线。当程序运行到插桩点时,硬件将产生触发,此时才会主动地到总线上将相应数据与对应的具体时问捕获回来,从而实现有效数据的精确捕获。此外,在FPGA内部采用双RAM交替读写的方式来暂存数据,在不中断程序运行的情况下,实现有效数据的长时间实时存取与分析,提高嵌入式软件性能分析的范围和质量。采用FPGA作为构成硬件架构的基本功能器件,提高了工作速度,使整个设计具具有集成度高、性能稳定、调试方便等特点。该模块在实际应用中得到了验证,并取得了良好的效果。     

Frequency-domain identification of continuous-time ARMA models from sampled data

The subject of this paper is the direct identification of continuous-time autoregressive moving average (CARMA) models. The topic is viewed from the frequency domain perspective which then turns the reconstruction of the continuous-time power spectral density (CT-PSD) into a key issue. The first part of the paper therefore concerns the approximate estimation of the CT-PSD from uniformly sampled data under the assumption that the model has a certain relative degree. The approach has its point of origin in the frequency domain Whittle likelihood estimator. The discrete- or continuous-time spectral densities are estimated from equidistant samples of the output. For low sampling rates the discrete-time spectral density is modeled directly by its continuous-time spectral density using the Poisson summation formula. In the case of rapid sampling the continuous-time spectral density is estimated directly by modifying its discrete-time counterpart.     

Using a neural network for predicting the average grain size in friction stir welding processes

In the paper the microstructural phenomena in terms of average grain size occurring in friction stir welding (FSW) processes are focused. A neural network was linked to a finite element model (FEM) of the process to predict the average grain size values. The utilized net was trained starting from experimental data and numerical results of butt joints and then tested on further butt, lap and T-joints. The obtained results show the capability of the AI technique in conjunction with the FE tool to predict the final microstructure in the FSW joints.     

A MEMS affinity glucose sensor using a biocompatible glucose-responsive polymer

We present a MEMS affinity sensor that can potentially allow long-term continuous monitoring of glucose in subcutaneous tissue for diabetes management. The sensing principle is based on detection of viscosity changes due to affinity binding between glucose and poly(acrylamide-ran-3-acrylamidophenylboronic acid) (PAA-ran-PAAPBA), a biocompatible, glucose-specific polymer. The device uses a magnetically driven vibrating microcantilever as a sensing element, which is fabricated from Parylene and situated in a microchamber. A solution of PAA-ran-PAAPBA fills the microchamber, which is separated from the surroundings by a semi-permeable membrane. Glucose permeates through the membrane and binds reversibly to the phenylboronic acid moiety of the polymer. This results in a viscosity change of the sensing solution, which is obtained by measuring the damped cantilever vibration using an optical lever setup, allowing determination of the glucose concentration. Experimental results demonstrate that the device is capable of detecting glucose at physiologically relevant concentrations from 27 mg/dL to 324 mg/dL. The glucose response time constant of the sensor is approximately 3 min, which can be further improved with device design optimization. Excellent reversibility and stability are observed in sensor responses, as highly desired for long-term, stable continuous glucose monitoring.     

Extending process automation systems with multi-agent techniques

This paper presents a design of a process automation system extended with multi-agent systems (MAS) and experiments with its implementation. According to this design, MAS can be used to extend the functionality of ordinary process automation systems at higher levels of control. Anticipated benefits of this include enhanced reconfigurability, responsiveness and flexibility of the resulting automation system. The design also takes into account particular characteristics of process automation. An agent platform for process automation is presented as a basis for applying MAS. A FIPA-compliant agent platform is extended with process automation specific functionality. The platform utilizes a hierarchical agent organization and a BDI-agent model. Two applications are implemented using the platform. One of these shows how the techniques of distributed planning can be applied in discrete control. The other provides a model for supervisory continuous control using the techniques of distributed search. Experiments performed with a laboratory test environment using the applications are presented. They are able to demonstrate the feasibility of the approach in test scenarios.     

基于动态层次聚类的离散化算法的研究

连续属性离散化问题是机器学习的重要方面,是数据预处理问题之一.提供的基于动态层次聚类的离散化算法是层次聚类算法的一种改进.对该算法进行定性分析-对随机采集数据根据相似度进行聚类分析,得到论域的一种划分.通过实验表明,基于动态层次聚类的离散化算法对连续属性的划分更加合理,更加有效.     

A model for simulating the performance and irrigation of green stormwater facilities at residential scales in semiarid and Mediterranean regions

Impervious areas change hydrological processes, reducing infiltration and evapotranspiration, and increasing direct runoff. Stormwater practices using green infrastructure are implemented locally to control runoff and preserve the hydrological cycle. Applying these techniques in semiarid and Mediterranean regions requires accounting for aspects related to the maintenance of green areas. This study develops the Integrated Hydrological Model at Residential Scale, a continuous model for representing the performance and irrigation of green stormwater facilities at residential scales. Among other relevant process, the model simulates evaporation from bare soil and redistribution between soil layers. Different components of the model were tested using laboratory and numerical experiments, and then an application to a case study and a sensitivity analysis were carried out. The model identifies significant differences in the performance of a rain garden with different vegetation, climate and irrigation practices and provides good insight for the maintenance needs of green infrastructure for runoff control.