Stress Detection Methods Research Articles

Research on stress detection technology of long‐distance pipeline applying non‐magnetic saturation

In order to study the stress detection method on long-distance oil and gas pipeline, the distribution characteristics of the surface remanence signals in the stress concentration regions must be known. They were studied by using the magnetic domain model in the non-magnetic saturation state. The finite element method was used herein with the aim to analyse the static and mechanical characteristics of a ferromagnetic specimen. The variation law of remanence signal in stress concentration regions was simulated. The results show that a residue signal in the stress concentration region exists. In addition, a one-to-one correspondence in the non-magnetic saturation environment is evident. In the case of magnetic saturation, the remanence signal of the stress concentration region is covered and the signal cannot be recognised.

Vibration response analysis on stainless steel thin plate weldments

Dynamic responses of welded thin plates are influenced by welding residual stress, and improving this phenomenon understanding would turn possible to develop a new residual stress detection method. This research investigates natural frequency variations in welded thin plates. Experimental analysis was conducted for eight AISI316LSS plates, 6.30-mm thickness, bead on plate (BOP) and butt welded. Modal variations, comparing natural frequencies before and after welding, are negative, due to longitudinal compressive residual stress predomination. It was detected that higher variations occur for the first torsional mode and the variation for the longitudinal flexural mode is around null. It was observed that BOP welds do not always result in higher modal variations for higher heat inputs, as it occurs with butt welds. Moreover, it shows that, for the same heat inputs, modal variations were higher for butt welds than BOP tests. Numerical analysis was also performed, and theoretical results were compared to experiments.

Comparing the dynamic response of bead-on-plate and butt-welded plates

Some studies have been developed to demonstrate that welded plates have their natural frequencies reduced when comparing them to non-welded similar plates, which is caused by welding residual stress. This phenomenon can be used to develop a new welding residual stress detection method. This new method, analyzing the plate vibrational responses, would have the advantage of being nondestructive, when comparing to hole drilling, and simpler and cheaper than X-ray diffraction, ultrasound and neutron diffraction methods. Therefore, in the search of this new technique, more research is being developed and more progress is still required. Some experimental work in the field has used bead-on-plate weldments for data analysis. However, this work demonstrates that bead-on-plate weldments do not present the same vibrational responses due to welding residual stress than butt-welded plates. This experimental work compares the vibrational responses of two identical plates welded by two different manners: bead-on-plate and butt-welded. Two stainless steel AISI 316L plates, with 302 × 150.5 × 6.30 mm, were tested. First, the entire plates had their natural frequencies measured, using a laser vibrometer. Then, one plate was Gas Tungsten Arc welded bead-on-plate on its center. The other plate was wire Electrical Discharge Machining cut in its center and then butt-welded. There was no material addition, in order to avoid additional mass interference in the natural frequencies values. Finally, the natural frequencies were measured again and the variations were calculated for the first six vibrational modes. Even though it was employed the same heat inputs and welding parameters in both welds, it is shown that the butt weld had higher modal variations than the bead-on-plate. The higher modal variations in the butt-welded plate indicate that butt weld induced higher welding residual stresses in the plates than bead-on-plate. Therefore, this work demonstrates that researchers willing to advance in the studies about the new welding residual stress method using vibrational responses should be attentive to this disparity.

A hybrid filter-wrapper feature selection method for stress detection and monitoring among employees at workspaces

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Urban tree health assessment using airborne hyperspectral and LiDAR imagery

Urban trees provide valuable ecosystem services but are at the same time under continuous pressure due to unfavorable site conditions. In order to better protect and manage our natural capital, urban green managers require frequent and detailed information on tree health at the city wide scale. In this paper we developed a workflow to monitor tree defoliation and discoloration of broadleaved trees in Brussels, Belgium, through the combined use of airborne hyperspectral and LiDAR data. Individual trees were delineated using an object-based tree detection and segmentation algorithm primarily based on LiDAR data with an average accuracy of 91%. We constructed Partial Least Squares Regression (PLSR) models to derive tree chlorophyll content (RMSE = 2.8 μg/cm²; R² = 0.77) and Leaf Area Index (LAI; RMSE = 0.5; R² = 0.66) from the average canopy spectrum. Existing spectral indices were found to perform significantly worse (RMSE > 7 μg/cm² and >1.5 respectively), mainly due to contamination of tree spectra by neighboring background materials. In the absence of local calibration data, the applicability of PLSR to other areas, sensors and tree species might be limited. Therefore, we identified the best performing/least sensitive spectral indices and proposed a simple pixel selection procedure to reduce disturbing background effects. For LAI, laser penetration metrics derived from LiDAR data attained comparable accuracies as PLSR and were suggested instead. Detection of healthy and unhealthy trees based on remotely sensed tree properties matched reasonably well with a more traditional visual tree assessment (93% and 71% respectively). If combined with early tree stress detection methods, the proposed methodology would constitute a solid basis for future urban tree health monitoring programs.

EPR Technology as Sensitive Method for Oxidative Stress Detection in Primary and Secondary Keratinocytes Induced by Two Selected Nanoparticles.

Exogenous factors can cause an imbalance in the redox state of biological systems, promoting the development of oxidative stress, especially reactive oxygen species (ROS). To monitor the intensity of ROS production in secondary keratinocytes (HaCaT) by diesel exhaust particles and thermoresponsive nanogels (tNG), electron paramagnetic resonance (EPR) spectroscopy after 1 and 24 h of incubation, respectively, was applied. Their cytotoxicity was analyzed by a cell viability assay (XTT). For tNG an increase in the cell viability and ROS production of 10% was visible after 24 h, whereas 1 h showed no effect. A ten times lower concentration of diesel exhaust particles exhibited no significant toxic effects on HaCaT cells for both incubation times, thus normal adult human keratinocytes (NHK) were additionally analyzed by XTT and EPR spectroscopy. Here, after 24 h a slight increase of 18% in metabolic activity was observed. However, this effect could not be explained by the ROS formation. A slight increase in the ROS production was only visible after 1 h of incubation time for HaCaT (9%) and NHK (14%).

Monitoring stress with a wrist device using context

Being able to detect stress as it occurs can greatly contribute to dealing with its negative health and economic consequences. However, detecting stress in real life with an unobtrusive wrist device is a challenging task. The objective of this study is to develop a method for stress detection that can accurately, continuously and unobtrusively monitor psychological stress in real life. First, we explore the problem of stress detection using machine learning and signal processing techniques in laboratory conditions, and then we apply the extracted laboratory knowledge to real-life data. We propose a novel context-based stress-detection method. The method consists of three machine-learning components: a laboratory stress detector that is trained on laboratory data and detects short-term stress every 2min; an activity recognizer that continuously recognizes the user’s activity and thus provides context information; and a context-based stress detector that uses the outputs of the laboratory stress detector, activity recognizer and other contexts, in order to provide the final decision on 20-min intervals. Experiments on 55days of real-life data showed that the method detects (recalls) 70% of the stress events with a precision of 95%.

소리 정보를 이용한 철도 선로전환기의 스트레스 탐지

철도 선로전환기는 열차의 진로를 현재의 궤도에서 다른 궤도로 제어하는 장치이다. 선로전환기의 이상 상황은 탈선 등과 같은 심각한 문제를 발생할 수 있기 때문에, 선로전환기의 스트레스를 지속적으로 모니터링 하는 것은 매우 중요하다. 본 논문에서는 선로전환기가 작동할 때 발생하는 소리 정보를 이용하여 선로전환기의 스트레스를 탐지하는 시스템을 제안한다. 제안하는 시스템은 선로전환기의 동작 시 발생하는 소리 데이터로부터 자질 선택방법을 사용하여 스트레스 탐지에 유효한 감소된 차원의 자질 부분집합을 선택한 후, 기계학습의 대표적 모델인 SVM(Support Vector Machine)을 이용하여 선로전환기의 스트레스 상태 여부를 탐지한다. 테스트용 선로전환기를 실제 구동하며 수집한 소리 데이터를 이용하여, 본 논문에서 제안하는 시스템의 성능을 실험적으로 검증한 바 98%를 넘는 정확도를 확인하였다. Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure can significantly affect railway operations with potentially disastrous consequences, early stress detection of point machine is critical for monitoring and managing the condition of rail infrastructure. In this paper, we propose a stress detection method for point machine in railway condition monitoring systems using sound data. The system enables extracting sound feature vector subset from audio data with reduced feature dimensions using feature subset selection, and employs support vector machines (SVMs) for early detection of stress anomalies. Experimental results show that the system enables cost-effective detection of stress using a low-cost microphone, with accuracy exceeding 98%.

Magnetoelastic Effect-Based Transmissive Stress Detection for Steel Strips: Theory and Experiment

For the deficiencies of traditional stress detection methods for steel strips in industrial production, this paper proposes a non-contact stress detection scheme based on the magnetoelastic effect. The theoretical model of the transmission-type stress detection is established, in which the output voltage and the tested stress obey a linear relation. Then, a stress detection device is built for the experiment, and Q235 steel under uniaxial tension is tested as an example. The result shows that the output voltage rises linearly with the increase of the tensile stress, consistent with the theoretical prediction. To ensure the accuracy of the stress detection method in actual application, the temperature compensation, magnetic shielding and some other key technologies are investigated to reduce the interference of the external factors, such as environment temperature and surrounding magnetic field. The present research develops the theoretical and experimental foundations for the magnetic stress detection system, which can be used for online non-contact monitoring of strip flatness-related stress (tension distribution or longitudinal residual stress) in the steel strip rolling process, the quality evaluation of strip flatness after rolling, the life and safety assessment of metal construction and other industrial production links.

A Mobile Sensing Approach to Stress Detection and Memory Activation for Public Bus Drivers

The experience of daily stress among bus drivers has shown to affect physical and psychological health, and can impact driving behavior and overall road safety. Although previous research consistently supports these findings, little attention has been dedicated to the design of a stress detection method able to synchronize physiological and psychological stress responses of public bus drivers in their day-to-day routine work. To overcome this limitation, we propose a mobile sensing approach to detect georeferenced stress responses and facilitate memory recall of the stressful situations. Data were collected among public bus drivers in the city of Porto, Portugal (145 h, 36 bus drivers, +2300 km), and results supported the validation of our approach among this population and allowed us to determine specific stressor categories within certain areas of the city. Furthermore, data collected throughout the city allowed us to produce a citywide “stress map” that can be used for spotting areas in need of local authority intervention. The enriching findings suggest that our system can be a promising tool to support applied occupational health interventions for public bus drivers and guide authorities' interventions to improve these aspects in “future” cities.

Detection of Psychological Stress Using a Hyperspectral Imaging Technique

The detection of stress at early stages is beneficial to both individuals and communities. However, traditional stress detection methods that use physiological signals are contact-based and require sensors to be in contact with test subjects for measurement. In this paper, we present a method to detect psychological stress in a non-contact manner using a human physiological response. In particular, we utilize a hyperspectral imaging (HSI) technique to extract the tissue oxygen saturation (StO2) value as a physiological feature for stress detection. Our experimental results indicate that this new feature may be independent from perspiration and ambient temperature. Trier Social Stress Tests (TSSTs) on 21 volunteers demonstrated a significant difference $p\< 0.005$ and a large practical discrimination (d 1/4 1.37) between normalized baseline and stress StO2 levels. The accuracy for stress recognition from baseline using a binary classifier was 76.19 and 88.1 percent for the automatic and manual selections of the classifier threshold, respectively. These results suggest that the StO2 level could serve as a new modality to recognize stress at standoff distances.

Comparative physiological responses of Morella cerifera to RDX, TNT, and composition B contaminated soils

Surrounding vegetation is exposed to a variety of potentially toxic compounds due to unexploded ordnances leaching explosive compounds into the soil. These compounds are absorbed by roots, transported through the vascular system, and distributed throughout plant tissues. Research Demolition Explosive (RDX) (hexahydro-1,3,5-trinitro-1,3,5-triazine) and trinitrotoluene (TNT) (2-methyl-1,3,5-trinitrobenzene) are the most studied; however, mixtures of explosives are widespread in conventional munitions. Composition B (Comp B), a mixture of RDX and TNT, is the most common mixture. Our study objective was to quantify the comparative effects of RDX, TNT and Comp B on the physiology of an evergreen shrub, Morella cerifera. Adult M. cerifera plants were exposed for 7 weeks to soil amended with RDX up to 1500mgkg−1 dry soil, TNT up to 500mgkg−1 dry soil, and Comp B up to 750mgkg−1 dry soil. Stomatal conductance, photosynthesis, leaf water potential, leaf fluorescence, and contaminant uptake values were measured at the end of the experiment. As contaminant concentration increased, significant declines in photosynthesis and leaf fluorescence occurred for all compounds. Overall responses varied between contaminants and impacts of Comp B were largely reduced compared to either RDX or TNT. Of all physiological parameters, photosynthesis was most impacted, making it a sensitive indicator for the detection of explosives. Yet, the intricate relationships within normal physiological processes appear to be severed in the presence of explosives. These disparate responses in plant physiology may serve as a method for explosive contamination stress detection. Our results highlight the importance of studying real world munition mixtures.

Automatic drought stress detection in grapevines without using conventional threshold values

Aims Because the water status of grapevines strongly affects the quality of the grapes and resulting wine, automated and early drought stress detection is important. Plant measurements are very promising for detecting drought stress, but strongly depend on microclimatic changes. Therefore, conventional stress detection methods require threshold values which define when plants start sensing drought stress. There is however no unique method to define these values. In this study, we propose two techniques that overcome this limitation.

Using Active Canopy Sensing to Adjust Nitrogen Application Rate in Corn

Remote sensing that uses active sensors continue to be tested as an N stress detection method and provide in‐season rate adjustments to corn (Zea mays L.). The objectives of the study were to use active canopy sensing to vary in‐season N application at the V10 corn growth stage and compare applied N rate, grain yield, and nitrogen use efficiency (NUE) with N applied before planting. A fertilizer N rate study was conducted in 2009 and 2010 near Ames, IA. Pre‐plant N rates were 0 to 270 kg N ha−1 and then sensor‐based rates were applied in‐season at the V10 corn growth stage. Rainfall events occurred 3 to 5 d after N application each year, providing corn response to the in‐season applied N and increasing relative corn canopy biomass by the V13 growth stage. The pre‐plant applied nitrogen (PP‐N) 270 kg N ha−1 rate used as the N‐reference did not result in the highest green normalized difference vegetative index (GNDVI) or produce the highest grain yields. The pre‐plant plus sensor applied nitrogen (PP+S‐N) recovered corn yield from plant N stress at V10. The best yield recovery with deficit pre‐plant N and sensor‐directed N application rate occurred each year when no pre‐plant N had been applied. Overall, economic optimum nitrogen rate (EONR) and NUE in PP‐N and PP+S‐N were the same. The PP+S‐N application does give corn growers options for addressing in‐season N deficiency and protect yield potential when corn N need is uncertain or when soil N losses are unavoidable.

Thin-Film Residual Stress Detection Method Based on Flexible Hinges

The residual stress introduced in the thin-film process may caused some problems, especially after removing of the sacrificial layers below, the suspended structure may be bended due to the release. In this articles we will develop a new in-situ residual stress detection method based on flexible hinges for thin-film materials, then described the detection principle theoretically and simulated its properties by FEA method.

TEO-based speaker stress assessment using hybrid classification and tracking schemes

Speaker variability is known to have an adverse impact on speech systems that process linguistic content, such as speech and language recognition. However, speech production changes in individuals due to stress and emotions have similarly detrimental effect also on the task of speaker recognition as they introduce mismatch with the speaker models typically trained on modal speech. The focus of this study is on the analysis of stress-induced variations in speech and design of an automatic stress level assessment scheme that could be used in directing stress-dependent acoustic models or normalization strategies. Current stress detection methods typically employ a binary decision based on whether the speaker is or not under stress. In reality, the amount of stress in individuals varies and can change gradually. Using speech and biometric data collected in a real-world, variable-stress level law enforcement training scenario, this study considers two methods for stress level assessment. The first approach uses a nearest neighbor clustering scheme at the vowel token and sentence levels to classify speech data into three levels of stress. The second approach employs Euclidean distance metrics within the multi-dimensional feature space to provide real-time stress level tracking capability. Evaluations on audio data confirmed by biometric readings show both methods to be effective in assessment of stress level within a speaker (average accuracy of 55.6 % in a 3-way classification task). In addition, an impact of high-level stress on in-set speaker recognition is evaluated and shown to reduce the accuracy from 91.7 % (low/mid stress) to 21.4 % (high level stress).

Note from the editor: Special issue on speaker recognition

This special issue on speaker recognition has benefited from its exceptional guest editor, Hemant A. Patil, Associate Professor at Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), who accepted my invitation to help identify leading researchers in the area of speaker biometrics whose work merited publication in the Journal and to assemble their work in a tightly organized issue that would add some significant new findings to the literature on speaker recognition. Over thirty five researchers contributed to this special issue from among universities and research laboratories in the United States, Germany, France and India. Each paper was rigorously peer reviewed. Eleven papers were chosen for this issue out of forty submissions. Among the outstanding papers contained in this special issue are those that analyze how speaker stress and emotions can compromise the accuracy of performing speaker identification and verification tasks; the investigation of novel approaches to speaker modeling; and the exploration of various source and system features for speaker recognition and/or voice conversion. The issue begins with a discussion of new methods for analyzing stress-induced variations in speech and the design of an automatic stress level assessment scheme that could be used in directing stress-dependent acoustic models or normalization strategies. The authors point out that current stress detection methods typically employ a binary decision based on whether the speaker is or not under stress which can be misleading because stress levels can both vary as well as change gradually. In the real world, stress levels evolve sometimes with a fair degree of subtlety. The authors consider two methods for stress level assessment: The

A ${\rm Z}$-Axis Quartz Tuning Fork Micromachined Gyroscope Based on Shear Stress Detection

This paper proposes a novel quartz micromachined gyroscope. The structure is designed by using shear stress detection method which can simplify the sidewall electrodes obviously. Furthermore, a tuning fork is introduced by the structure to obtain better differential vibrations. In order to increase the sensitivity of the sensor, the sense beam is designed to be a symmetric tapered beam. The device was fabricated using quartz anisotropic wet etching process. The drive mode frequency is 14.99 kHz, and the quality factor is 7600 in air. The sense mode frequency is 14.25 kHz, and the quality factor is 600 in air. Therefore, this gyroscope can operate at atmosphere pressure properly. The sensor is tested on a rate table through a specially designed readout circuitry. The sensitivity is 23.9 and the nonlinearity is 1.1% in range of . The noise floor is 0.1 .

Remote sensing of nitrogen and water stress in wheat

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments ( P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers.