• 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.

• Interaction and multiscale mechanics
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• v.2 no.2
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• pp.153-175
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• 2009

Damage detection methods using structural dynamic responses have received much attention in the past decades. For bridge and offshore structures, these methods are usually based on beam models. To ensure the successful application of these methods, it is necessary to examine the sensitivity of modal properties to structural damages. To this end, an analytic solution is presented of the modal properties of simply-supported Euler-Bernoulli beams that contain a general damage with no additional assumptions. The damage can be a reduction in the bending stiffness or a loss of mass within a beam segment. This solution enables us to thoroughly discuss the sensitivities of different modal properties to various damages. It is observed that the lower natural frequencies and mode shapes do not change so much when a section of the beam is damaged, while the mode of rotation angle and curvature modes show abrupt change near the damaged region. Although similar observations have been reported previously, the analytical solution presented herein for clarifying the mechanism involved is considered a contribution to the literature. It is helpful for developing new damage detection methods for structures of the beam type.

• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.103-108
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• 2012

Radiofrequency(RF) signal is a key medium to the most of the present wireless communication devices including RF identification devices(RFID) and smart sensors. However, the most critical barrier to overcome in RFID application is in the failure rate in detection. The most notable improvement in the detection was from the introduction of EPC Class1 Gen2 protocol, but the fundamental problems in the physical properties of the RF signal drew less attention. In this work, we focused on the physical properties of the RF signal in order to understand the failure rate by noting the existence of the ground planes and noise sources in the real environment. By using the mathematical computation software, Maple, we simulated the distribution of the electromagnetic field from a dipole antenna when ground planes exist. Calculations showed that the dark area can be formed by interference. We also constructed a test system to measure the failure rate in the detection of a RFID transponder. The test system was composed of a fixed RFID reader and an EPC Class1 Gen2 transponder which was attached to a scanner to sweep in the x-y plane. Labview software was used to control the x-y scanner and to acquire data. Tests in the laboratory environment showed that the dark area can be as much as 43 %. One who wants to use RFID and smart sensors should carefully consider the extent of the dark area.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.5
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• pp.463-470
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• 2015

When a camera installed on a vehicle is used, estimation of the camera pose including tilt, roll, and pan angle with respect to the world coordinate system is important to associate camera coordinates with world coordinates. Previous approaches using huge calibration patterns have the disadvantage that the calibration patterns are costly to make and install. And, previous approaches exploiting multiple vanishing points detected in a single image are not suitable for automotive applications as a scene where multiple vanishing points can be captured by a front camera is hard to find in our daily environment. This paper proposes a camera pose estimation method. It collects multiple images of lane markings while changing the horizontal angle with respect to the markings. One vanishing point, the cross point of the left and right lane marking, is detected in each image, and vanishing line is estimated based on the detected vanishing points. Finally, camera pose is estimated from the vanishing line. The proposed method is based on the fact that planar motion does not change the vanishing line of the plane and the normal vector of the plane can be estimated by the vanishing line. Experiments with large and small tilt and roll angle show that the proposed method outputs accurate estimation results respectively. It is verified by checking the lane markings are up right in the bird's eye view image when the pan angle is compensated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.11
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• pp.919-925
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• 2016

In this study, the Lamb wave mixing technique, which is basised on advanced research on the nonlinear bulk wave mixing technique, is applied for corrosion detection. To demonstrate the validity of the Lamb wave mixing technique, an experiment was performed with normal and corroded specimens. Comparison group in an experimentation are selected to mode and frequency with dominant in-plane displacement and out-of-plane displacement of Lamb waves. The results showed that the Lamb wave mixing technique can monitor corrosion defects, and it has a trend similar to that of the conventional Lamb wave technique. It was confirmed that the dominant displacement and mode matching the theory were generated. Flaw detectability is determined depending on displacement ratio instead of using the measurement method and mode selection.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.115.2-115.2
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• 2011

UWISH2 (UKIRT Widefield Infrared Survey for $H_2$) is an unbiased, narrow-band imaging survey of the Galactic plane in the $H_2$ 1-0 S(1) emission line at $2.122{\mu}m$ using the Wide-Field Camera (WFCAM) at the United Kingdom Infrared Telescope (UKIRT). The survey covers about 150 square degrees of the first Galactic quadrant ($10^{\circ}$ < l < $65^{\circ}$; $-1.3^{\circ}$ < b < $+1.3^{\circ}$). The images have a $5{\sigma}$ detection limit of point sources of K~18 mag and the surface brightness limit is $10^{-19}\;W\;m^{-2}$ $arcsec^{-2}$. The survey operation began on 28 July 2009 and has completed on 17 August 2011. We have been studying the supernova remnants (SNRs) in the UWISH2 survey area. Among the known 274 Galactic SNRs, the survey area includes 65 SNRs or 24 percent of the known SNRs. The wide-field and high-quality UWISH2 images allow us to identify both the diffuse extended and compact $H_2$ emission associated with SNRs, which is useful for understanding their physical environment and evolution. The continuum is subtracted from the narrow-band $H_2$ images using the K-band continuum images obtained as part of the UKIDSS GPS (UKIRT Infrared Deep Sky Survey of the Galactic Plane). So far, we have inspected 42 SNRs, and found distinct H2 emission in 14 SNRs. The detection rate is 33%. Some of the SNRs show bright, complex, and interesting structures that have never been reported in previous studies. In this report, we present our identification scheme and preliminary results.

• Computers and Concrete
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• v.33 no.5
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• pp.535-544
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• 2024

In the rapidly advancing landscape of computer vision (CV) technology, there is a burgeoning interest in its integration with the construction industry. Camera calibration is the process of deriving intrinsic and extrinsic parameters that affect when the coordinates of the 3D real world are projected onto the 2D plane, where the intrinsic parameters are internal factors of the camera, and extrinsic parameters are external factors such as the position and rotation of the camera. Camera pose estimation or extrinsic calibration, which estimates extrinsic parameters, is essential information for CV application at construction since it can be used for indoor navigation of construction robots and field monitoring by restoring depth information. Traditionally, camera pose estimation methods for cameras relied on target objects such as markers or patterns. However, these methods, which are marker- or pattern-based, are often time-consuming due to the requirement of installing a target object for estimation. As a solution to this challenge, this study introduces a novel framework that facilitates camera pose estimation using standardized materials found commonly in construction sites, such as concrete forms. The proposed framework obtains 3D real-world coordinates by referring to construction materials with certain specifications, extracts the 2D coordinates of the corresponding image plane through keypoint detection, and derives the camera's coordinate through the perspective-n-point (PnP) method which derives the extrinsic parameters by matching 3D and 2D coordinate pairs. This framework presents a substantial advancement as it streamlines the extrinsic calibration process, thereby potentially enhancing the efficiency of CV technology application and data collection at construction sites. This approach holds promise for expediting and optimizing various construction-related tasks by automating and simplifying the calibration procedure.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.442-450
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• 2014

This paper is a study of the propagation characteristic of matching liquids in the skin-covered breast model. In order to evaluate the matching liquids, we investigated six kinds of matching liquids applied to proposed 1-D breast model from frequency range of 3~6 GHz. A uniform plane wave is projected / transmitted inside the multi-layered breast model. Then the propagation characteristics inside the model and the transmission loss of each matching liquids were analyzed. The studying method presented in the paper can be used in the breast cancer detection system, the field of cancer detection using human tissue and the field of other medical devices. This paper was applied to the breast cancer detection system. Consequently, these studies could be used to determine the suitable type of matching liquids for breast cancer detection system and to apply useful for performance analysis.

• Plant Journal
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• v.17 no.3
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• pp.37-41
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• 2021

P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.