Mental fatigue recognition by heart rate variability analysis in a photoplethysmography (PPG) device in terms of pulse rate variability (PrV) is gathering interest for stress relief and well-being applications. To rea...
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ISBN:
(数字)9798331516451
ISBN:
(纸本)9798331516468
Mental fatigue recognition by heart rate variability analysis in a photoplethysmography (PPG) device in terms of pulse rate variability (PrV) is gathering interest for stress relief and well-being applications. To realize mental fatigue recognition systems, many studies have been conducted, which showed the high accuracy of such systems evaluations. However, there are limitations in generalization due to the lack of consideration of the physiological mechanism. In this study, we developed a framework for investigating mental fatigue recognition using physiologically interpretable deep neural network (dNN) in a wrist-worn PPG device. Furthermore, we developed a novel feature extraction method that captures the temporal accumulation of arousal states to consider the perspective of cognitive resource consumption. The results suggest that the method we developed improved the mental fatigue recognition performance of the wrist-worn PPG device throughout our experiments (an accuracy of 0.90 and an F1 score of 0.91). In addition, it was observed that the method for the wrist-worn PPG device enhanced the contribution of HrV indices, known to be associated with cognitive function. The findings demonstrate that the proposed approach enhances not only accuracy but also the physiological interpretability of the model in PPG devices.
The temperature is as high as 500 °C at the bottom of shale oil wells during in-situ development. during shale oil production, the fluids with high temperature are transmitted to the wellhead, and the wellbo...
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rapid kick detection is an essential part of rig safety. Identifying potential kick events in time is valuable and is an urgent and critical task forrig personnel andremote Operation Support Center(OSC) users. A new...
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ISBN:
(纸本)9781959025269
rapid kick detection is an essential part of rig safety. Identifying potential kick events in time is valuable and is an urgent and critical task forrig personnel andremote Operation Support Center(OSC) users. A new monitoring system has been developed by firstly calibrating the active system volume for pipe and cuttings displacement during drilling and then by detecting a statistically significant trend in the observeddata rather than to rely solely on absolute values and threshold crossing to maximize the sensitivity whilst minimizing the number of false alarms. More than hundreds of historical kick alarms were reviewed and the corresponding mud logging data before the events occurred were displayed in log format against time for clear identification of anomalies in three different tracks: one track for pit volume, one for flow-in and flow-out, the third hole depth, bit depth and block position. By applying an automatedrule-based system, the states of rig activities are also displayed simultaneously by detection of bit movement, pump, rotation status and other elements. It is noticed that the data absolute value, data noise, andrig operations such as sudden string movements and flow transients has significant interference to the detection of kick event and that the differential flow is more sensitive early kick indicator than pit volume in most of the historical cases. A pit volume calibrated fordrill pipe and cuttings displacement during drilling and a trend analysis by applying Page-Hinkley method for monitoring both active system volumes anddifferential flows are established and the monitoring system is updated. The system has been tested andrefined with the data flows of the historical cases in labs to maximize the sensitivity whilst minimizing the number of false alarms. A significant reduction of false positive alarms is achieved by reducing triggering of 1-2 alarms per well per week while maintaining a 100% true positive identification rate by compa
The involved object of systematic innovation and management is neither a single goal nor a single meth-od. Firstly, the research object is a systematic and organic whole composed of multiple node elements with a certa...
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China is rich in tight oil resources, with a wide distribution range and a large amount of resources, making it one of the key areas for strategic replacement of future oil reserves and production. In response to issu...
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In recent days, many people die of pulmonary embolism (PE) complications, respiratory problems that form in the deep lung. Covid-19 is the leading cause of preventable hospital death for trauma patients anddespite 0....
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downhole vibration, such as backward or chaotic whirling, is one of the primary causes fordrill string failure during drilling operation. Understanding drilling dynamics is crucial fordetermining the mechanism of vi...
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ISBN:
(纸本)9781959025269
downhole vibration, such as backward or chaotic whirling, is one of the primary causes fordrill string failure during drilling operation. Understanding drilling dynamics is crucial fordetermining the mechanism of vibration and fatigue accumulation so as to mitigate risk of drill string failure. drilling dynamics or vibration data can either be obtained from real-time measurement or simulation based on dynamics model, which plays a crucial role fordrilling optimization in pre-drill planning phase. An advanced 4ddrilling dynamics model, which considers the interaction between drill bit and formation, impact and friction between drill string and wellbore, constraints exerted on drill string anddrill bit, is proposed to simulate the distribution and variation of 3ddisplacement, velocity, acceleration as well as the stress state of entire drill string within specified time period. This time domain model allows exploration of the full range of dynamics response underdifferent drilling scenarios, which helps to optimize the performance andreliability of BHA as well as WOB/rPM selection. Moreover, drill string fatigue life prediction can also be achieved by combing simulated stress state and fatigue accumulation theory. Simulation shows that BHA with smaller- sizeddrill collar or fewer stabilizers are more susceptible to backward and even chaotic whirling while drilling. Also, inappropriate WOB/rPM combinations such as low WOB with high rPM could also cause the onset of severe lateral vibration. High frequency bending stress, mainly concentrated on BHA and caused by lateral vibration, is the main reason forrapid fatigue accumulation and continuous severe vibration could even result in drill string fatigue failure within hours. These findings helped to justify the 5 consecutive BHA failure and 1 washout while drilling the 171/2" wellbore with 7621 ft length of an ultra-deep well. Simulation also showed that downhole lateral vibration severity was significantly redu
We propose a hybrid analysis method that uses a general SPICE simulator to perform high-speed and high-precision conduction noise analysis of flyback converter circuits. Time domain analysis, generally used for conduc...
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ISBN:
(数字)9784885523472
ISBN:
(纸本)9798350349498
We propose a hybrid analysis method that uses a general SPICE simulator to perform high-speed and high-precision conduction noise analysis of flyback converter circuits. Time domain analysis, generally used for conducted noise analyses, is disadvantaged by its long analysis time. Frequency domain analysis, which requires a shorter analysis time, is also used for conducted noise analyses, but it struggles to reproduce the operating characteristics of semiconductors. The hybrid analysis method enables frequency domain analysis that considers semiconductor operating characteristics by using the results of a time domain analysis as a noise source. The results of our proposed hybrid analysis method matched the results of a time domain analysis across the entire analyzed bandwidth, from $1-100 \mathrm{MHz}$. The analysis time was about $1/10$ of that of the time domain analysis.
Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem...
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In recent years, there has been a meteoric rise in the amount of digital information stored in the biomedical industry due to the rapid growth of the internet and other information technologies. Automated biomedical t...
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