AR order selection in the case when the model parameters are estimated by forgetting factor least-squares algorithms

During the last decades, the use of information theoretic criteria (ITC) for selecting the order of autoregressive (AR) models has increased constantly. Because the ITC are derived under the strong assumption that the measured signals are stationary, it is not straightforward to employ them in combination with the forgetting factor least-squares algorithms. In the previous literature, the attempts for solving the problem were focused on the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the predictive least squares (PLS). In connection with PLS, an ad hoc criterion called SRM was also introduced. In this paper, we modify the predictive densities criterion (PDC) and the sequentially normalized maximum likelihood (SNML) criterion such that to be compatible with the forgetting factor least-squares algorithms. Additionally, we provide rigorous proofs concerning the asymptotic approximations of four modified ITC, namely PLS, SRM, PDC and SNML. Then, the four criteria are compared by simulations with the modified variants of BIC and AIC.     

Past‐Free[ze] reachability analysis: reaching further with DAG‐directed exhaustive state‐space analysis

Model‐checking enables the automated formal verification of software systems through the explicit enumeration of all the reachable states. While this technique has been successfully applied to industrial systems, it suffers from the state‐space explosion problem because of the exponential growth in the number of states with respect to the number of interacting components. In this paper, we present a new reachability analysis algorithm, named Past‐Free[ze], that reduces the state‐space explosion problem by freeing parts of the state‐space from memory. This algorithm relies on the explicit isolation of the acyclic parts of the system before analysis. The parallel composition of these parts drives the reachability analysis, the core of all model‐checkers. During the execution, the past states of the system are freed from memory making room for more future states. To enable counter‐example construction, the past states can be stored on external storage. To show the effectiveness of the approach, the algorithm was implemented in the OBP Observation Engine and was evaluated both on a synthetic benchmark and on realistic case studies from automotive and aerospace domains. The benchmark, composed of 50 test cases, shows that in average, 75% of the state‐space can be dropped from memory thus enabling the exploration of up to 14 times more states than traditional approaches. Moreover, in some cases, the reachability analysis time can be reduced by up to 25%. In realistic settings, the use of Past‐Free[ze] enabled the exploration of a state‐space 4.5 times larger on the automotive case study, where almost 50% of the states are freed from memory. Moreover, this approach offers the possibility of analyzing an arbitrary number of interactions between the environment and the system‐under‐verification; for instance, in the case of the aerospace example, 1000 pilot/system interactions could be analyzed unraveling an 80 GB state‐space using only 10 GB of memory. Copyright © 2016 John Wiley & Sons, Ltd.     

Enabling high‐speed asynchronous data extraction and transfer using DART

As the complexity and scale of applications grow, managing and transporting the large amounts of data they generate are quickly becoming a significant challenge. Moreover, the interactive and real‐time nature of emerging applications, as well as their increasing runtime, make online data extraction and analysis a key requirement in addition to traditional data I/O and archiving. To be effective, online data extraction and transfer should impose minimal additional synchronization requirements, should have minimal impact on the computational performance and communication latencies, maintain overall quality of service, and ensure that no data is lost. In this paper we present Decoupled and Asynchronous Remote Transfers (DART), an efficient data transfer substrate that effectively addresses these requirements. DART is a thin software layer built on RDMA technology to enable fast, low‐overhead, and asynchronous access to data from a running simulation, and supports high‐throughput, low‐latency data transfers. DART has been integrated with applications simulating fusion plasma in a Tokamak, being developed at the Center for Plasma Edge Simulation (CPES), a DoE Office of Fusion Energy Science (OFES) Fusion Simulation Project (FSP). A performance evaluation using the Gyrokinetic Toroidal Code and XGC‐1 particle‐in‐cell‐based FSP simulations running on the Cray XT3/XT4 system at Oak Ridge National Laboratory demonstrates how DART can effectively and efficiently offload simulation data to local service and remote analysis nodes, with minimal overheads on the simulation itself. Copyright © 2010 John Wiley & Sons, Ltd.     

On some properties of the NML estimator for Bernoulli strings

This paper proves the monotonicity of the sequence , where C_n denotes the normalization coefficient in the universal Normalized Maximum Likelihood (NML) model for the Bernoulli class. The main result is used to find a non-asymptotic estimation of logC_n.     

Sensitivity minimization for arbitrary SISO distributed plants

In this note we write down an explicit formula for the computation of the optimal sensitivity of an arbitrary single input/single output, linear -invariant distributed plant, We moreover discuss the uniqueness of the optimal compensator and work out a design example.     

Potential Computational Benefits of Geometric, Kinematic, and Behavioral Downsampling of Microscopic Traffic Network Simulation

This paper introduces the concept of microscopic simulation system scalability for the purpose of reducing the computational requirements of microsimulation modeling of large-scale traffic networks. This exploratory stage of research investigates scalability of both lane-changing and car-following behavior. The main objective of the proposed methodology is to create a reduced-scale network (microcosm) that retains most of the significant characteristics of the full-scale network (prototype). To achieve this objective a systematic downsampling procedure has been applied to a case study of a one-lane homogeneous freeway corridor in order to create a geometrically, kinematically, and behaviorally equivalent microcosm environment. This paper examines the scalability of lane-changing behavior, assuming a shifted negative exponential headway distribution, and investigates the scalability of car-following behavior under various operating conditions and downsampling ratios. The paper focuses on the tradeoff between performance and scalability of microscopic simulation systems. For each of the 48 cases considered, optimal behavioral parameters were determined based on two optimization methods: (1) Microscopic based on minimization of trajectory errors in both environments and (2) macroscopic derived from minimization of density errors in both environments throughout the simulation period. The results show that both optimization solutions were consistent in determining the optimal behavioral parameters.     

Exergetic Comparative Analysis of Ammonia and Carbon Dioxide Two-Stage Cycles for Simultaneous Cooling and Heating

The paper deals with the comparative analysis of the performance of cooling and heating systems operating with NH3 （ammonia） or CO2 （carbon dioxide）, both natural refrigerants. The study is based on the exergetic analysis that points out the location and the magnitude of a system malfunction. Both systems, with NH3 or CO2 operate in two stages. The exergetic analysis gives the direction of the structural optimization. The exergetic analysis has shown that the best structural schematic is not the same for the two agents. The exergetic analysis points out that the largest exergy destruction in the CO2 cycle is due to the throttling process and offers solutions to diminish it.     

Influence of sodium dodecyl sulfate and cetyl trimethylammonium bromide upon calcium carbonate precipitation on bacterial cellulose

Calcium carbonate was deposed on bacterial cellulose (BC) never-dried membranes in the presence of different concentrations of sodium dodecyl sulfate (SDS) and cetyl trimethylammonium bromide (CTAB) by a precipitation reaction between aqueous solutions of calcium chloride (CaCl₂) and sodium carbonate (Na₂CO₃) containing, or not, surfactant in their composition. Different shapes of crystals were obtained from rhombohedral ones to flowerlike, depending on surfactant type and concentration. From the two surfactants tested, SDS has a greater influence on calcium carbonate morphology than CTAB. The only polymorph obtained in all studied cases was calcite. The composite films BC-calcite were characterized by means of scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and color measurements. The obtained BC-calcium carbonate composites could be used in paper manufacturing.     

Wheat flour dough Alveograph characteristics predicted by Mixolab regression models

BACKGROUND: In Romania, the Alveograph is the most used device to evaluate the rheological properties of wheat flour dough, but lately the Mixolab device has begun to play an important role in the breadmaking industry. These two instruments are based on different principles but there are some correlations that can be found between the parameters determined by the Mixolab and the rheological properties of wheat dough measured with the Alveograph. RESULTS: Statistical analysis on 80 wheat flour samples using the backward stepwise multiple regression method showed that Mixolab values using the ‘Chopin S’ protocol (40 samples) and ‘Chopin + ’ protocol (40 samples) can be used to elaborate predictive models for estimating the value of the rheological properties of wheat dough: baking strength (W), dough tenacity (P) and extensibility (L). The correlation analysis confirmed significant findings (P < 0.05 and P < 0.01) between the parameters of wheat dough studied by the Mixolab and its rheological properties measured with the Alveograph. CONCLUSION: A number of six predictive linear equations were obtained. Linear regression models gave multiple regression coefficients with $R_{\rm {adjusted}}^{2} > 0.70$ for P, $R_{\rm {adjusted}}^{2} > 0.70$ for W and $R_{\rm {adjusted}}^{2} > 0.38$ for L, at a 95% confidence interval. Copyright © 2011 Society of Chemical Industry     

Softwood heating in radio frequency fields

The aim of this study was to investigate the heating rate of lodgepole pine (Pinus contorta) and western red cedar (Thuja plicata) during radio frequency (RF) heating. Wood specimens (40 × 150 × 1000 mm³) with various moisture content and power density were heated using a laboratory size RF dryer at a frequency of 40.7 MHz, until shell temperature reached 56?°C that is approved as a lethal temperature for phytosanitation. Heating rate was positively correlated with power density and negatively correlated with moisture content. The ratio of heating rate to power density had a negative correlation with moisture content and density in both pine and cedar. The regression lines for moisture content had good coefficient of determination (R ²) values of 0.63 and 0.61?°C?m³/min kW (pine), 0.50 and 0.55?°C?m³/min kW (red cedar) in both shell and core, respectively. The results demonstrate that the ratio of heating rate to power density is a useful parameter to estimate heating rate. The derived empirical equations made possible the calculation of the heating rate within test conditions applied to this study. The initial temperature rise in shell and core allowed a rather accurate determination of local power density. One dimensional mathematical model to describe the heating rate of wood during RF heating that was derived from the governing heat transfer equation combined with internal heat generation was developed and verified. The model using local power density had high R ² of 0.71 and 0.93 in both shell and core, respectively, indicating that the model was able to predict heating rate of wood with various moisture content under the known power density distribution.