Speech transmission index (STI) is an objective measure of the acoustic properties of office environments and is used to specify norms for acceptable acoustic work conditions. Yet, the tasks used to evaluate the effects of varying STIs on work performance have often been focusing on memory (as memory of visually presented words) and reading tasks and may not give a complete view of the severity even of low STI values (i.e., when speech intelligibility is low). Against this background, we used a more typical office-work task in the present study. The participants were asked to write short essays (5 min per essay) in 5 different STI conditions (0.08; 0.23; 0.34; 0.50; and 0.71). Writing fluency dropped drastically and the number of pauses longer than 5 s increased at STI values above 0.23. This study shows that realistic work-related performance drops even at low STI values and has implications for how to evaluate acoustic conditions in school and office environments
Broadband noise is often used as a masking sound to combat the negative consequences of background speech on performance in open-plan offices. As office workers generally dislike broadband noise, it is important to find alternatives that are more appreciated while being at least not less effective. The purpose of experiment 1 was to compare broadband noise with two alternatives—multiple voices and water waves—in the context of a serial short-term memory task. A single voice impaired memory in comparison with silence, but when the single voice was masked with multiple voices, performance was on level with silence. Experiment 2 explored the benefits of multiple-voice masking in more detail (by comparing one voice, three voices, five voices, and seven voices) in the context of word processed writing (arguably a more office-relevant task). Performance (i.e., writing fluency) increased linearly from worst performance in the one-voice condition to best performance in the seven-voice condition. Psychological mechanisms underpinning these effects are discussed.
One of the crucial components in rail tracks is the rail fastening system, which acts as a means of fixing rails to the sleepers to maintain the track gauge and stability. Manual inspection and 2D visual inspection of fastening systems have predominated over the past two decades. However, both methods have drawbacks when visibility is obscured and are found to be relatively expensive in terms of cost and track possession. The present article presents the concept of a train-based differential eddy current (EC) sensor system for fastener detection. The sensor uses the principle of electromagnetic induction, where an alternating-current-carrying coil is used to create an EC on the rail and other electrically conductive material in the vicinity and a pick-up coil is used to measure the returning field. This paper gives an insight into the theoretical background and application of the proposed differential EC sensor system for the condition monitoring system of rail fasteners and shows experimental results from both laboratory and field measurements. The field measurements were carried out along a heavy-haul railway line in the north of Sweden. Results obtained from both the field measurements and from the lab tests reveal that that the proposed method was able to detect an individual fastening system from a height of 65 mm above the rail. Furthermore, missing clamps within a fastening system are detected by analysing a time domain feature of the measurement signal.
The rail fastening system forms an integral part of rail tracks, as it maintains the rail in a fixed position, upholding the track stability and track gauge. Hence, it becomes necessary to monitor their conditions periodically to ensure safe and reliable operation of the railway. Inspection is normally carried out manually by trained operators or by employing 2-D visual inspection methods. However, these methods have drawbacks when visibility is minimal and are found to be expensive and time consuming. In the previous study, the authors proposed a train-based differential eddy current sensor system that uses the principle of electromagnetic induction for inspecting the railway fastening system that can overcome the above-mentioned challenges. The sensor system includes two individual differential eddy current sensors with a driving field frequency of 18 kHz and 27 kHz respectively. This study analyses the performance of a machine learning algorithm for detecting and analysing missing clamps within the fastening system, measured using a train-based differential eddy current sensor. The data required for the study was collected from field measurements carried out along a heavy haul railway line in the north of Sweden, using the train-based differential eddy current sensor system. Six classification algorithms are tested in this study and the best performing model achieved a precision and recall of 96.64% and 95.52% respectively. The results from the study shows that the performance of the machine learning algorithms improved when features from both the driving channels were used simultaneously to represent the fasteners. The best performing algorithm also maintained a good balance between the precision and recall scores during the test stage.
Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.
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