An Investigation of Speech Features, Plant System Alarms, and Operator–System Interaction for the Classification of Operator Cognitive Workload During Dynamic Work

Objective To investigate speech features, human–machine alarms, and operator–system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios. Background Theories and models of cognitive workload are critical for the design and evaluation of human–machine systems. Unfortunately, there are very few nonintrusive cognitive workload measures available for realistic dynamic human–machine interaction. Method The study was conducted in a full-scope control room research simulator of an advanced nuclear reactor. Six crews, each consisting of three operators, participated in 12 scenarios. The operators rated their workload every second minute. Machine learning algorithms were trained to estimate operators’ workload based on crew communication, operator–system interaction, and system alarms. Results Random Forest (RF) utilizing speech and system features achieved an accuracy of 67% on test data. Utilizing speech features only, the accuracy achieved was 63%. The most important speech features were pitch, amplitude, and articulation rate. A 61% accuracy was achieved when alarms and operator–system interaction features were used. The most important features were the number of alarms and amount of operator–system interaction. Accuracy for algorithms trained for each operator ranged from 39% to 98%, with an average of 72%. For a majority of analyses performed, RF and extreme gradient boosting (XGB) outperformed other algorithms. Conclusion The results demonstrate that the features investigated and machine learning models developed provide a potential for the dynamic nonintrusive measurement of cognitive workload. Application The approach presented can be developed for nonintrusive workload measurement in real-world human–machine applications, simulator-based training, and research.

Design and Development of Weigh-In-Motion Using Vehicular Telematics

Identifying overloaded vehicles on a highway is essential for the safety of vehicles on the road as well as for the performance monitoring of highway infrastructure and planning. Traffic enforcement uses various weigh-in-motion (WIM) methods. Since Vehicular Telematics (VT) is favoured in the transport industry, using it for building a new WIM system to infer the payload of a vehicle at any road segment would be beneficial for the transport industry. This paper presents the effort taken to use VT data from onboard diagnostics modules and smartphones to infer the payload of a vehicle. The experiment done to find the correlation between VT data and the payload of a vehicle is discussed. Feature engineering was done; nine different settings were tested to find the best regression model. A multiple nonlinear regression model produced significant a p value of 6.322e-08 and an R-squared value of 0.8736. Results support the notion of using the VT data for nonintrusive measurement of the weight of a vehicle in motion.

Ultrasonic Measurement of Velocity Profile on Bubbly Flow Using a Single Resonant Frequency

The present paper describes a measurement technique for phase-separated velocity profile measurements in the two-phase bubbly flow. The Ultrasonic Velocity Profiler (UVP) method which is nonintrusive measurement, is applied to obtain an instantaneous velocity profile of liquid and bubble separately by using only one resonant frequency. To achieve this target, developed algorithm, which can decompose frequency component of the Doppler signal affected by liquid and bubble, is applied in the UVP system to obtain and separate instantaneous velocity profile of both phases. For confirming the applicability of modified measurement system, the developed UVP was used for the measurement of the velocity profile in bubbly flow on vertical pipe flow apparatus, the measurement accuracy was validated by UVP Original and Particle Image Velocimetry (PIV) method. Finally, the UVP was applied to experiment for observing velocity distribution of both phases in a bubble column.

Infrared Thermometry in Winter Road Maintenance

There is significant interest among road authorities in measuring pavement conditions to perform appropriate winter road maintenance. The most common monitoring methods are based on pavement-mounted sensors. This study’s hypothesis is that the temperature distribution in a pavement can be measured by means of a nonintrusive method to retrieve the topmost pavement temperature values. By utilizing the latest infrared (IR) technology, it is possible to retrieve additional information concerning both road temperatures and road conditions. The authors discovered that surface temperature readings from IR sensors are more reliable than data retrieved from traditional surface-mounted sensors during wet, snowy, or icy road conditions. It was also possible to detect changes in the road condition by examining how the temperatures in wheel tracks and in between the wheel tracks differ from a reference dry road condition. The conclusion was that nonintrusive measurement of the road temperature is able to provide an increase in relation to the knowledge about both the road temperature and the road condition. Another conclusion was that the surface temperature should not be considered as being equal to the ground temperatures retrieved from traditional surface-mounted sensors except under conditions of dry, stable roadways.