• Electrical & Electronic Materials
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• v.11 no.11
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• Journal of the Korea Concrete Institute
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• v.18 no.2 s.92
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• pp.293-298
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• 2006

The reinforced concrete(RC) structures strengthened with fiber reinforced plastic(FRP) has been accepted by the construction engineering community for rehabilitation. FRP composites can present many advantages like a corrosion resistance, strength-weight ratio, relatively short application time, and cost effectiveness. The beams under design load, however, are cracked and result in degrading the strength. It is difficult to recognize cracks and deflections on the surface of the concrete members retrofitted with FRP through the life cycle. For these reasons, if they result in the effects, which were below the expected strength, we must monitor the state of concrete structures all the time in order to take an appropriate measure. Fiber Bragg Grating(FBG) sensor excel as monitoring of investigating the stress state of the retrofitted beams with FRP. The main objective of this study is to measure strain by experiment and analyze the behavior of RC beams retrofitted with FRP using FBG sensor. The kinds of FRP which were used in research are carbon, glass and improved hybrid FRP(IFRP) that has capacity than any other FRP. Other variables are the length of FRP, the number of sheet.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.1-18
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• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.442-451
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• 2008

The evaluation of field degree of consolidation on soft clays has been an important problem in geotechnical areas. Monitoring either settlements or pore water pressures has been widely applied in the filed, but occasionally they have some problems. This study addresses the suggestion and application of another method for evaluating the degree of consolidation using shear wave velocities. A research site where soft clay layers were consolidated by surcharging loads was chosen. Laboratory tests were performed to determine the relation between shear wave velocity and effective stress. Field seismic tests were conducted several times during the consolidation of the clay layers. The tests results show that the shear wave velocity increased significantly as clays consolidated. The shear wave velocities at each field stress states were derived from the laboratory results and the degree of consolidation was evaluated by comparing the shear wave velocities obtained by laboratory and field seismic methods. In most stress states, the degree of consolidation evaluated using the shear wave velocity matched well with that obtained from field settlement record, showing the potential of applying the method using shear waves in the evaluation of field degree of consolidation on soft clay deposits.

• Geomechanics and Engineering
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• v.22 no.1
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• pp.1-9
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• 2020

This paper presents an investigation of the ground response of a gob-side gateroad suffering mining stress induced by a 21 m-thick coal seam extraction. A field observation, including entry convergence and stress changes monitoring, was first conducted in the tailgate 8209. The observation results of entry convergence showed that, during the adjacent panel 8210 retreating period, the deformation of the gob-side gateroad experienced a continuous increase stage, subsequently, an accelerating increase stage, and finally, a slow increase stage. However, strong ground response, including roof bending deflection, rib extrusion and floor heave, occurred during the current panel 8209 retreating period, and the maximum floor heave reached 1530 mm. The stress changes within coal mass of the two ribs demonstrated that the gateroad was always located in the stress concentrated area, which responsible for the strong response of the tailgate 8209. Subsequently, a hydraulic fracture technique was proposed to pre-fracture the two hard roofs above the tailgate 8209, thus decreasing the induced disturbance on the tailgate. The validity of the above roof treatment was verified via field application. The finding of this study could be a reference for understanding the stability control of the gob-side gateroad in extra thick coal seams mining.

• Ocean and Polar Research
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• v.30 no.1
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• pp.109-117
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• 2008

Application of biomarkers for assessing marine environmental health risk is a relatively new field. According to the National Research Council and the World Health Organization, biomarkers can be divided into three classes: biomarkers of exposure, biomarkers of effect, and biomarkers of susceptibility. In order to assess exposure to or effect of the environmental pollutants on marine ecosystem, the following set of biomarkers can be examined: detoxification, oxidative stress, biotransformation products, stress responses, apoptosis, physiological metabolisms, neuromuscular responses, reproductions, steroid hormones, antioxidants, genetic modifications. Since early 1990s, several biomarker research groups have developed health indices of marine organisms to be used for assessing the state of the marine environment. Biomarker indices can be used to interpret data obtained from monitoring biological effects. In this review, we will summarize Health assessment Index, Biomarker Index, Bioeffect Assessment Index and Generalized Linear Model. Measurements of biomarker responses and development of biomarker index in marine organisms from contaminated sites offer great a lot of information, which can be used in environmental monitoring programs, designed for various aspects of ecosystem risk assessment.

• Smart Structures and Systems
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• v.1 no.4
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• pp.385-404
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• 2005

The paper presents a model to calculate reinforcement strain using measured crack width in members under applied tension, flexure, and/or shear stress. Crack mapping using a new type of distributed coaxial cable sensors for health monitoring of large-scale civil engineering infrastructure was recently proposed and developed by the authors. This paper shows the results and performance of such sensors mounted on near surface of two flexural beams and a large scale reinforced concrete box girder that was subjected to cyclic combined shear and torsion. The main objectives of this health monitoring study was to correlate the sensor's response to strain in the member, and show that magnitude of the signal's reflection coefficient is related to increases in applied load, repeated cycles, cracking, and reinforcement yielding. The effect of multiple adjacent cracks, and signal loss was also investigated. The results shown in this paper are an important step in using the sensors for crack mapping and determining reinforcement strain for in-situ structures.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.259-270
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• 2022

This study explores the use of the recently developed "strain-sensing smart skin" (S⁴) method for noncontact strain measurements on cement-based samples. S⁴ sensors are single-wall carbon nanotubes dilutely embedded in thin polymer films. Strains transmitted to the nanotubes cause systematic shifts in their near-infrared fluorescence spectra, which are analyzed to deduce local strain values. It is found that with cement-based materials, this method is hampered by spectral interference from structured near-infrared cement luminescence. However, application of an opaque blocking layer between the specimen surface and the nanotube sensing film enables interference-free strain measurements. Tests were performed on cement, mortar, and concrete specimens with such modified S⁴ coatings. When specimens were subjected to uniaxial compressive stress, the spectral peak separations varied linearly and predictably with induced strain. These results demonstrate that S⁴ is a promising emerging technology for measuring strains down to ca. 30 𝜇𝜀 in concrete structures.

• Journal of Korea Water Resources Association
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• v.36 no.5
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• pp.839-849
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• 2003

Drought is one of the major environmental disasters. Weather data, particularity rainfall, are currently the primary source of information widely used for drought monitoring. However, weather data are often from a very sparse meteorological network, incomplete and/or not always available in good time to enable relatively accurate and timely drought detection. Data from remote sensing platforms can be used to complements weather data in drought. Therefore, data obtained from the Advanced Very High Resolution Radiometer(AVHRR) sensor on board the NOAA polar-orbiting satellites have been studied as a tool for drought monitoring. The normalized difference vegetation index(NDVI)-based vegetation condition index(VCI) were used in this study These indices showed their excellent ability to detect vegetation stress due to drought. The results clearly show that temporal and spatial characteristics of drought in Korea can be detected and mapped by the VCI index.