The rapid increase in both the quantity
and complexity of data
that are being generated daily in the field of environmental science
and engineering (ESE) demands accompanied advancement in data analytics.
Advanced data analysis approaches, such as machine learning (ML),
have become indispensable tools for revealing hidden patterns or deducing
correlations for which conventional analytical methods face limitations
or challenges. However, ML concepts and practices have not been widely
utilized by researchers in ESE. This feature explores the potential
of ML to revolutionize data analysis and modeling in the ESE field,
and covers the essential knowledge needed for such applications. First,
we use five examples to illustrate how ML addresses complex ESE problems.
We then summarize four major types of applications of ML in ESE: making
predictions; extracting feature importance; detecting anomalies; and
discovering new materials or chemicals. Next, we introduce the essential
knowledge required and current shortcomings in ML applications in
ESE, with a focus on three important but often overlooked components
when applying ML: correct model development, proper model interpretation,
and sound applicability analysis. Finally, we discuss challenges and
future opportunities in the application of ML tools in ESE to highlight
the potential of ML in this field.
A great many living beings, such as aquatics and arthropods, are equipped with highly sensitive flow sensors to help them survive in challenging environments. These sensors are excellent sources of inspiration for developing application-driven artificial flow sensors with high sensitivity and performance. This paper reviews the bio-inspirations on flow sensing in nature and the bio-mimicking efforts to emulate such sensing mechanisms in recent years. The natural flow sensing systems in aquatics and arthropods are reviewed to highlight inspirations at multiple levels such as morphology, sensing mechanism and information processing. Biomimetic hair flow sensors based on different sensing mechanisms and fabrication technologies are also reviewed to capture the recent accomplishments and to point out areas where further progress is necessary. Biomimetic flow sensors are still in their early stages. Further efforts are required to unveil the sensing mechanisms in the natural biological systems and to achieve multi-level bio-mimicking of the natural system to develop their artificial counterparts.
This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.
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