Assuming the non availability of some observations and the availability of some stochastic linear constraints connecting the coefficients in a linearregression, the technique of mixed regression estimation is conside...
详细信息
Assuming the non availability of some observations and the availability of some stochastic linear constraints connecting the coefficients in a linearregression, the technique of mixed regression estimation is considered and a set of five unbiased estimators for the vector of coefficients is presented. They are compared with respect to the criterion of variance covariance matrix and conditions are obtained for the superiority of one estimator over the other.
This paper investigates an asymptotic distribution of the Akaike information criterion (AIC) and presents its characteristics in normal linear regression models. The bias correction of the AIC has been studied. It may...
详细信息
This paper investigates an asymptotic distribution of the Akaike information criterion (AIC) and presents its characteristics in normal linear regression models. The bias correction of the AIC has been studied. It may be noted that the bias is only the mean, i.e., the first moment. Higher moments are important for investigating the behavior of the AIC. The variance increases as the number of explanatory variables increases. The skewness and kurtosis imply a favorable accuracy of the normal approximation. An asymptotic expansion of the distribution function of a standardized AIC is also derived. (c) 2004 Elsevier B.V. All rights reserved.
The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked de...
详细信息
The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.
Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in...
详细信息
Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.
Processing and interpreting a large amount of data represents a great challenge for infrastructure health monitoring. This study demonstrates how to apply some available algorithms to the data measured by a practical ...
详细信息
Processing and interpreting a large amount of data represents a great challenge for infrastructure health monitoring. This study demonstrates how to apply some available algorithms to the data measured by a practical structural health monitoring system and how to evaluate the results. Short-term and long-term field tests were conducted on a steel highway bridge to monitor the structural behavior, including strain, displacement and temperature. The short-term test was carried out with a special vehicle crossing the bridge at different speeds. Measured dynamic strain responses are used to extract the dynamic characteristics of the structure by Eigensystem Realization Algorithm and to analyze the vehicle - bridge interaction properties by Wavelet Transform. In the long-term monitoring, strain time history and displacement versus temperature were recorded at several points. Cumulative fatigue damage model, extreme value distribution and linear regression model are, respectively, applied to the data and several indices are presented to evaluate the structural condition. Results indicate that the structural condition is deficient and further special inspection is required at the monitored bridge. The algorithms used in this paper can be applied as general structural health monitoring tools for processing of data measured on bridges.
This article discusses some properties of the first order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesi...
详细信息
This article discusses some properties of the first order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesis testing.
The afferent signals recorded with a multi-electrode cuff on the sciatic nerve were employed to investigate the possibility of extracting components ascending from the peroneal and tibial nerves. Two methods, an inver...
详细信息
The afferent signals recorded with a multi-electrode cuff on the sciatic nerve were employed to investigate the possibility of extracting components ascending from the peroneal and tibial nerves. Two methods, an inverse regressionmodel and principal component method, were studied. The parameters of inverse regressionmodel, determined by data collected in semistatic conditions, were validated by data collected in dynamic conditions. The results showed that the regressionmodel, which used only two channels of the sciatic recordings, was sufficient to separate the distal afferent components. The model, at the expense of requiring distal branch recordings for estimating model parameters, yielded better separation than the principal component method. In conclusion, peroneal and tibial afferent activity can be estimated from the sciatic nerve: the principal component method is suitable for applications focused on acquiring afferent information, whereas the inverse regressionmodel is better for applications in which stimulations will be applied to the branches. The estimation technique provides a powerful tool for in vivo investigation of sensory information transmitted in a peripheral nerve and facilitates implementation of advanced functional neuromuscular stimulation systems.
Preventing accidents caused by drowsiness behind the steering wheel is highly desirable but requires techniques for continuously estimating driver's abilities of perception, recognition and vehicle control abiliti...
详细信息
ISBN:
(纸本)0780387406
Preventing accidents caused by drowsiness behind the steering wheel is highly desirable but requires techniques for continuously estimating driver's abilities of perception, recognition and vehicle control abilities. This paper proposes methods for drowsiness estimation that combine the electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Results show that it is feasible to quantitatively monitor driver's alertness with concurrent changes in driving performance in a realistic driving simulator.
Mobile personal computing devices continue to proliferate and individualsâ reliance on them for day-to-day needs necessitate that these platforms be secure. Mobile computers are subject to a unique form of deni...
详细信息
Mobile personal computing devices continue to proliferate and individualsâ reliance on them for day-to-day needs necessitate that these platforms be secure. Mobile computers are subject to a unique form of denial of service attack known as a battery exhaustion attack, in which an attacker attempts to rapidly drain the battery of the device. Battery exhaustion attacks greatly reduce the utility of the mobile devices by decreasing battery life. If steps are not taken to thwart these attacks, they have the potential to become as widespread as the attacks that are currently mounted against desktop systems.
This thesis presents steps in the design of an intrusion detection system for detecting these attacks, a system that takes into account the performance, energy, and memory constraints of mobile computing devices. This intrusion detection system uses several parameters, such as CPU load and disk accesses, to estimate the power consumption of two test systems using multiple linear regression models, allowing us to find the energy used on a per process basis, and thus identifying processes that are potentially battery exhaustion attacks.
暂无评论