Driving performance and road sign identification by multifocal contact lens wearers in a driving simulator

PurposeThe purpose of this study was to compare sign identification distances and driving performance metrics in presbyopic participants while wearing multifocal contact lenses (MFCL) and while wearing progressive addition lens (PAL) spectacles.Methods19 presbyopic participants completed PAL spectacle assessments and contact lens fitting and follow up visits before driving assessments began. These assessments occurred in a simulator equipped with a full-sized sedan on a motion platform and a 260 degree screen. Participants completed the driving task with PAL and with MFCL. Participants followed a lead car and identified signs at various distances from the road. For the two wearing conditions, comparisons of the distance along the road at which signs were identified were made using repeated measures ANOVA. Paired t-tests were used to compare driving performance for the two conditions.ResultsThere was no statistical difference in sign identification distance between PAL and MFLC for signs 32.0 m from the road side of the road (182 ± 46 m for MFCL; 205 ± 45 m for PAL; P = 0.07) or 51.4 m from the side of the road (204 ± 43 m for MFCL; 216 ± 36 m for PAL; P = 0.3). Only signs 70.2 m from the roadside showed a significant difference (207 ± 42 m with MFCL; 232 ± 39 m with PAL; P = 0.01), All distances were greater than those required to safely stop a vehicle. There were no significant differences in the driving performance metrics between the refractive corrections.ConclusionDriving performance metrics were similar for MFCL and PAL spectacles. Sign identification distances with both eyewear types were well within the distances required for safe vehicle stopping.     

Efficient framework for mobile walkthrough application

The past few years have witnessed a dramatic growth in the number and variety of graphics intensive mobile applications, which allow users to interact and navigate through large scenes such as historical sites, museums and virtual cities. These applications support many clients and impose a heavy requirement on network resources and computational resources. One key issue in the design of cost efficient mobile walkthrough applications is the data transmission between servers and mobile client devices. In this paper, we propose an effective progressive mesh transmission framework that stores and divide scene objects into different resolutions. In this approach, each mobile device progressively receives and processes only the object’s details matching its display resolution which improves the overall system’s response time and the user’s perception. A fine grained cache mechanism is used to keep the most frequently requested objects’ details in the device memory and consequently reduce the network traffic. Experiments, in simulated and real world environment, are used to illustrate the effectiveness of the proposed framework under various settings of the virtual scene and mobile device configuration. Experimental results show that the proposed framework can improve the walkthrough system performance in mobile devices, with a relatively small overhead.     

High-precision tracking differentiator via generalized discrete-time optimal control

An enhanced discrete-time tracking differentiator (TD) with high precision based on discrete-time optimal control (DTOC) law is proposed. This law takes the form of state feedback for a double-integral system that adopts the Isochronic Region approach. There, the control signal sequence is determined by a linearized criterion based on the position of the initial state point on the phase plane. The proposed control law can be easily extended to the TD design problem by combining the first-state variable of the double-integral system with the desired trajectory. To improve the precision of the discretization model, we introduced a zero-order hold on the control signal. We also discuss the general form of DTOC law by analysing the relationship between boundary transformations and boundary characteristic points. After comparing the simulation results from three different TDs, we determined that this new TD achieves better performance and higher precision in signal-tracking filtering and differentiation acquisition than do existing TDs. Also the comparisons of the computational complexities between the proposed DTOC law and normal one are demonstrated. For confirmation of its utility, we processed raw phasor measurement units data via the proposed TD. In the absence of complex power system modelling and historical data, it was verified that the proposed TD is suitable for applications of real-time synchrophasor estimations, especially when the states are corrupted by noise.     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

Soft sensor for real-time cement fineness estimation

This paper describes the design and implementation of soft sensors to estimate cement fineness. Soft sensors are mathematical models that use available data to provide real-time information on process variables when the information, for whatever reason, is not available by direct measurement. In this application, soft sensors are used to provide information on process variable normally provided by off-line laboratory tests performed at large time intervals. Cement fineness is one of the crucial parameters that define the quality of produced cement. Providing real-time information on cement fineness using soft sensors can overcome limitations and problems that originate from a lack of information between two laboratory tests. The model inputs were selected from candidate process variables using an information theoretic approach. Models based on multi-layer perceptrons were developed, and their ability to estimate cement fineness of laboratory samples was analyzed. Models that had the best performance, and capacity to adopt changes in the cement grinding circuit were selected to implement soft sensors. Soft sensors were tested using data from a continuous cement production to demonstrate their use in real-time fineness estimation. Their performance was highly satisfactory, and the sensors proved to be capable of providing valuable information on cement grinding circuit performance. After successful off-line tests, soft sensors were implemented and installed in the control room of a cement factory. Results on the site confirm results obtained by tests conducted during soft sensor development.     

Assessment of subcritical crack growth in hydrogen-containing environment by the parameters of acoustic emission signals

Estimation models (differential equations, initial and final conditions) for determining the crack propagation kinetics in hydrogen-containing environments using the acoustic emission (AE) signal parameters are proposed. The formulation of these models is based on the main ideals of the AE method, dependence between the crack increment area and a sum of AE-signals amplitude, main criteria of fracture mechanics and laws of thermodynamics.     

Sensitivity analysis of steel buildings subjected to column loss

In this study, the sensitivity of design parameters of steel buildings subjected to progressive collapse is studied. To this end, design parameters such as yield strengths of beams, columns, and braces, live load, elastic modulus, and damping ratio were considered as random variables. The Monte Carlo simulation, the Tornado Diagram analysis, and the First-Order Second Moment method were applied to deal with the uncertainties involved in the design parameters. The analysis results showed that among the design variables beam yield strength was ultimately the most important design parameter in the moment-resisting frame buildings while the column yield strength was the most important design parameter in the dual system building. Sensitivity of the vertical displacement to uncertain member strength showed that progressive collapse mechanisms of the moment-resisting frame buildings and the dual system building completely differed due to different patterns of the vertical load redistribution.     

Pushdown resistance as a measure of robustness in progressive collapse analysis

This paper presents a technique termed ‘pushdown analysis’ that can be used to investigate the robustness of building systems by computing residual capacity and establishing collapse modes of a damaged structure. The proposed method is inspired by the pushover method commonly used in earthquake engineering. Three variants of the technique, termed uniform pushdown, bay pushdown and incremental dynamic pushdown, are suggested and exercised using nonlinear analysis on 10-story steel moment frames designed for moderate and high levels of seismic risk. Simulation results show that the frame designed for high seismic risk is more robust than the corresponding one designed for moderate seismic risk. The improved performance is attributed to the influence of seismic detailing, specifically, the presence of reduced beam sections and stronger columns. It is shown that the dynamic impact factors associated with column removal are significantly lower than the commonly used value of 2.0 and are in line with lower values in the guidelines recently proposed by the US Department of Defense. The study suggests that seismic ‘fuses’ can play a role in the design for robustness and a discussion of the implications of this observation is provided.