• International conference on construction engineering and project management
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• 2020.12a
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• pp.387-396
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• 2020

Wearable biosensors have the potential to non-invasively and continuously monitor seniors' stress in their daily interaction with the urban environment, thereby enabling to address the stress and ultimately advance their outdoor mobility. However, current wearable biosensor-based stress detection methods have several drawbacks in field application due to their dependence on batch-learning algorithms. First, these methods train a single classifier, which might not account for multiple subjects' different physiological reactivity to stress. Second, they require a great deal of computational power to store and reuse all previous data for updating the signle classifier. To address this issue, we tested the feasibility of online multi-task learning (OMTL) algorithms to identify multiple seniors' stress from electrodermal activity (EDA) collected by a wristband-type biosensor in a daily trip setting. As a result, OMTL algorithms showed the higher test accuracy (75.7%, 76.2%, and 71.2%) than a batch-learning algorithm (64.8%). This finding demonstrates that the OMTL algorithms can strengthen the field applicability of the wearable biosensor-based stress detection, thereby contributing to better understanding the seniors' stress in the urban environment and ultimately advancing their mobility.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.433-440
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• 2016

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%.

• Steel and Composite Structures
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• v.19 no.1
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• pp.131-152
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• 2015

Easy detection and evaluation of defect in the tube structure is a continuous problem and remains a significant demand in tube inspection technologies. This study is aimed to automate defect detection using the pattern recognition approach based on the classification of high frequency stress wave signals. The stress wave signals from vibrational impact excitation on several tube conditions were captured to identify the defect in ASTM A179 seamless steel tubes. The variation in stress wave propagation was captured by a high frequency sensor. Stress wave signals from four tubes with artificial defects of different depths and one reference tube were classified using the autoregressive (AR) algorithm. The results were demonstrated using a dendrogram. The preliminary research revealed the natural arrangement of stress wave signals were grouped into two clusters. The stress wave signals from the healthy tube were grouped together in one cluster and the signals from the defective tubes were classified in another cluster. This approach was effective in separating different stress wave signals and allowed quicker and easier defect identification and interpretation in steel tubes.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.883-902
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• 2023

Water is critical to the health and productivity of fruit trees. Efficient monitoring of water stress is essential for optimizing irrigation practices and ensuring sustainable fruit production. Short-range sensing can be reliable, rapid, inexpensive, and used for applications based on well-developed and validated algorithms. This paper reviews the recent advancement in fruit tree water stress detection via short-range sensing, which can be used for irrigation scheduling in orchards. Thermal imagery, near-infrared, and shortwave infrared methods are widely used for crop water stress detection. This review also presents research demonstrating the efficacy of short-range sensing in detecting water stress indicators in different fruit tree species. These indicators include changes in leaf temperature, stomatal conductance, chlorophyll content, and canopy reflectance. Short-range sensing enables precision irrigation strategies by utilizing real-time data to customize water applications for individual fruit trees or specific orchard areas. This approach leads to benefits, such as water conservation, optimized resource utilization, and improved fruit quality and yield. Short-range sensing shows great promise for potentially changing water stress monitoring in fruit trees. It could become a useful tool for effective fruit tree water stress management through continued research and development.

• Journal of Biosystems Engineering
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• v.24 no.6
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• pp.539-546
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• 1999

Some stress for a plant could be detected to a certain degree by plant physiological measuring technique of the state of the art. The capability of early detection of my measuring system depends on kind of plant and kind and level of stress. The objectives of this study were to evaluate the capability of several fast and intact type plant stress detection systems to detect nitrogen deficiency of cucumber in the field. A series of experiment was carried out with four kinds of intact type measuring devices - a chlorophyll content meter, a chlorophyll fluorescence measurement system, an infrared thermometer and an optical spectrometer. The experiments resulted that the chlorophyll content meter could detect the stress of N deficiency at a confidence level higher than 95% on 3rd day for the earliest case and the detection of high precision was possible from 7th day after the stress was applied. The chlorophyll fluorescence measurement system detected the stress at a confidence level higher than 95% on 3rd day for the earliest case but the detection was not as much precise as the chlorophyll content meter. Leaf temperature measurement noted very poor results to detect the stress. Using the spectrometer, sensitive wavelength regions to detect the stress were searched and found out as 562∼564 nm, 700∼724 nm and 1,886∼1,894 nm. With the spectrometer using any of wavelength within the sensitive wavelength region, detection of the stress at a confidence level higher than 95% was possible from 3rd or 4th day after the stress was applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.613-617
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• 2021

According to the growth of the service industry, stresses from emotional labor workers have been emerging as a social problem, thereby so-called the Emotional Labor Protection Act was implemented in 2018. However, insufficient substantial protection systems for emotional workers emphasizes the necessity of a digital stress management system. Thus, in this paper, we suggest a stress detection system for customer service representatives based on deep learning facial expression recognition. This system consists of a real-time face detection module, an emotion classification FER module that deep-learned big data including Korean emotion images, and a monitoring module that only visualizes stress levels. We designed the system to aim to monitor stress and prevent mental illness in emotional workers.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.355-356
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• 2000

Three 'stress probe' plasmids were constructed and characterized which utilize a green fluorescent protein (CFP) as a non-invasive reporter to elucidate Escherichia coli cellular stress responses in quiescent or 'resting' cells. Facile detection of cellular stress levels was achieved by fusion of three heat shock stress protein promoter elements, those of the heat shock transcription factor ${\sigma}^{32}$, pretense subunit ClpB, and chaperone DnaK, to the reporter gene $gfp_{uv}$. When perturbed by chemical or physical stress (such as heat shock, nutrient (amino acid) limitation, addition of IPTG, acetic acid, ethanol, phenol, antifoam, and salt (osmotic shock), the E. coli cells produced GFPuv which was easily detected from within the cells as emitted green fluorescence. A temporal and amplitudinal mapping of these responses was performed, demonstrating regions where quantitative delineation of cell stress was afforded.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1131-1135
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• 2010

The development of modern civilization result from the abundance of material. Yet modern people live with chronic mild stress. Excessive chronic mild stress leads to various diseases. From the risk of the disease in order to protect our bodies need to manage chronic mild stress. The purpose of this study is to inspection the effectiveness of detecting in chronic mild stress using the Multi-sensor system. The Multi-sensor system is designed that can be measure three kinds of vital signals of chronic mild stress for the detection. First Photoplethysmogram(PPG), second Electro Dermal Activity(EDA), third Skin Temperature(SKT). The ages and occupations exposed to chronic mild stress, people often use out of this system was applied to dairy products(Pen). In addition, vital signals that occur when the variety of noise was used to remove the accelerometer. Chronic mild stress by the analysis of measured vital signals from Multi-sensor system to the measurement information to a PC to a wireless transmission(Bluetooth). In this study, using Multi-sensor system writing conditions and a variety of situations in the movement to measure vital signals and measurement results verified the accuracy and reliability. Through this measure chronic mild stress in everyday life and managing to maintain will help more healthy lifestyle.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.4
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• Journal of the Korean Society for Nondestructive Testing
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• v.11 no.1
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• pp.23-30
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• 1991

In this study the bonding quality evaluation of FRP honeycomb structure was performed by the ultrasonic C-Scan method and stress wave factor measurements. These NDT techniques could be well applied to the disbonding detection of FRP honeycomb structures. Especially, stress wave factor (SWF) measurement is expected to be a useful technique in field applications.