• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.275-279
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• 2023

Throughout the 20th century, the transition of pig farms from extensive to intensive commercial operations amplified the risk of disease transmission, particularly involving African swine fever (ASF). Real-time temperature monitoring systems have emerged as essential tools for early ASF diagnosis. In this paper, we introduce new real-time temperature monitoring ear tags (RTMEs) modeled after existing ear tag designs. Our crafted Pig-Temp platforms have three primary advantages. First, they can be effortlessly attached to pig ears, ensuring superior compatibility. Second, they enable real-time temperature detection, and the data can be displayed on a personal computer or smartphone application. Furthermore, they demonstrate excellent measurement accuracy, ranging from 98.9% to 99.8% at temperatures between 2.2 and 360℃. A linear regression approach enables fever symptoms associated with ASF to be identified within 3 min using RTMEs. The communication range extends to approximately 12 m (452 m²), enabling measurements from an estimated 75 to 2,260 pigs per gateway. These newly developed Pig-Temp platforms offer singifcant enhancement of early ASF detection.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1431-1440
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• 2024

The study aimed to develop a laser-based distance meter (LDM) to improve water surface identification for clinical MeV electron beam dosimetry, as inaccurate water surface determination can lead to imprecise positioning of ionization chambers (ICs). The LDM consisted of a laser ranging sensor, a signal processing microcontroller, and a tablet PC for data acquisition. I₅₀ (the water depth at which ionization current drops to 50 % of its maximum) measurements of electron beams were performed using six different types of ICs and compared to other water surface identification methods. The LDM demonstrated reproducible I₅₀ measurements with a level of 0.01 cm for all six ICs. The uncertainty of water depth was evaluated at 0.008 cm with the LDM. The LDM also exposed discrepancies between I₅₀ measurements using different ICs, which was partially reduced by applying an optimum shift of IC's point of measurement (POM) or effective point of measurement (EPOM). However, residual discrepancies due to the energy dependency of the cylindrical chamber's EPOM caused remained. The LDM offers straightforward and efficient means for precision water surface identification, minimizing reliance on individual operator skills.

• Tuberculosis and Respiratory Diseases
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• v.50 no.3
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• pp.310-319
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• 2001

Background : The peak flowmeter is very useful in monitoring of out-patients as well as those in emergency departments because of its convenience and simplicity with low cost. There have been many studies aimed at determining the accuracy and reproducibility of the peak flow meter in normal population. However, there is a paucity of reports regarding its accuracy in patients with chronic obstructive pulmonary disease(COPD) or asthma. The accuracy of the peak expiratory flow(PEF) measured with a mini-Wright peak flowmeter was assessed by a comparison with the results of a mass flow sensor. Methods : The PEF measurements were performed in 108 patients aged 19-82 years presenting with either a chronic obstructive lung disease or asthma before and after inhaling salbutamol. The PEF measurements from the mini-Wright flowmeter were compared with those obtained by the calibrated mass flow sensor. Results : The average of the readings taken by the mini-Wright meter were 37-39 l/min higher than those taken by the mass flow sensor. The average percentage error of the mini-Wright meter were higher, ranging less than 300 l/min. The mean of the differences between the values obtained using both instruments (the bias)$\pm$limits of agreement(${\pm}2$ SD) were $37.1{\pm}90\;l/min$ for the PEF(p<0.001). Conclusions : The mini-Wright peak flowmeter overestimated the flows in patients with COPD or asthma. It was also found that the accuracy of the mini-Wright peak flowmeter decreased in its mid to low range. The limits of agreement are wide and the difference between the two instruments is significant. Therefore, the measurements made between the two types of machines in patients with asthma or COPD cannot be used interchangeably.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.2
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• pp.104-115
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• 1989

In an effort to clarify the wetted patterns of sandy loam soil under trickle irrigation conditions, the distance of wetted zone, infiltration capacity and soil wetted patterns, etc. were measured by gypsum block as soil moisture sensor located every 5 cm vertically and horizontaly in the soil bin under the such conditions as a). irrigation rates set to 2, 4, 6, 8 liters per hour b). total amount of water applied fixed to 14.62 liters per soil bin c) the hearing force of soil measured by plate penetrometer ranging from 1.04 to 1.22kg/cm$_2$ The results can be summarized as follows ; 1. The wetted distance in horizontal direction(H), the wetted distance in vertical direction(D), the horizontal infiltration capacity (iH) and the vertical infiltration capacity(in)could by explained as a function of time t. 2. The horizontal wetted distance (H) is explained by an exponetial function H= a$.$ t where b was found ranging from 021 to 026 under surface trickle irrigation, which was considered a lotlower than the classical value of 0.5 and these measurements were indifferent to the increasing irrigation rates. 3. As for the surface trickle irrigation where horizontal infiltration capacity(iH) is explained as iH = A $.$ t h, the coefficient A increases with respect to irrigation rates within the limits of 0.89~1.34. 4. In terms of surface trickle irrigation of the ratio of Dm Which is maximum vertical wetted distance to Hm, which is maximum horizontal wetted distance, found to be within range of 1.0 to 1.21. It was also noted that the value of Dm decreses when irrigation rates increases while the value of Hm changes the opposite direction. 5. The optimum location of sensors from emitter for surface trickle irrigation should he inside of hemisphere whose lateral radius is 28~30cm long and vertical radius is 10~12cm long. The distance between emitters should be within 60cm long. 6. In the study of vertical wetted distance( D) where D= a $.$ tb, the exponential coefficient b ranged from 0.61 to 0.75 in surface trickle irrigation, and from 0A9 to 0.68 for subsurface trickle irrigation. These measurements showed an increasing tendency to with respect to irrigation rates. 7. In case of vertical infiltration capacity( in), where iD= A $.$ t 1-h, the coefficient A for surface trickle irrigation found to be within range of 0.16 to 0.19 and did not show any relationships with varying degree of irrigation rates. However, the coefficient was varying from 0.09 to 0.22 and showed a tendency to increase vis-a-vis irrigation rates for subsurface trickle irrigation, in contrast. 8. In the observation of subsurface trickle irrigation, it was found that Dm/Hm ratio was within 1.52 to 1.91 and showed a decreasing tendency with respect to increasing rates of irrigation. 9. The location of sensors for subsurface trickle irrigation follows same pattern as above, with vertical distance from emitter being 10~17cm long and horizontal 22~25cm long. The location of emitter should be 50 cm. 10.The relationship between VS which is the volume of wetted soil and Q which is the total amount of water when soil is reached field capacity could be explained as VS= 2.914Q0.91and the irrigation rates showed no impacts on the above relationship.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.1-14
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• 2024

An image-reconstruction technology, involving the deployment of an unmanned mobility equipped with high-speed LiDAR (Light Detection And Ranging) has been proposed to reconstruct the shape of abandoned mine. Unmanned mobility operation is remarkably useful in abandoned mines fraught with operational difficulties including, but not limited to, obstacles, sludge, underwater and narrow tunnel with the diameter of 1.5 m or more. For cases of real abandoned mines, quadruped robots, quadcopter drones and underwater drones are respectively deployed on land, air, and water-filled sites. In addition to the advantage of scanning the abandoned mines with 2D solid-state lidar sensors, rotation of radiation at an inclination angle offers an increased efficiency for simultaneous reconstruction of mineshaft shapes and detecting obstacles. Sensor and robot posture were used for computing rotation matrices that helped compute geographical coordinates of the solid-state lidar data. Next, the quadruped robot scanned the actual site to reconstruct tunnel shape. Lastly, the optimal elements necessary to increase utility in actual fields were found and proposed.

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.41-47
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• 2007

The first geostationary satellite made by Korea, COMS, has the three different payload ; Meteorological sensor, Oceanographic sensor and Ka-band communication payload. There are Meteorological & Ocean Data Communication Subsystem(MODCS) and Telemetry, Command and Ranging Subsystem(TC&R) as other RF radiation sources. MODCS transmits and receives Meteo and Ocean measurement data from/to earth using L-band and TC&R using S-band. The Ka-band communication payload will provide high-speed multimedia services and communication services for natural disaster such as prediction, prevention, and recovery services in the government communications network.Ka-band beacon is for the earth antenna pointing and the experiment of rain fading. This paper gives the analysis results about the mutual radiation effect on Ka-band communication payload, Ka-band beacon, MODCS and TC&R. Up/Down link power and coupling factor including the geometrical position and distance of antenna, filter rejection and degradation factor due to the different polarization are considered. The results show MODCS and TC&R are compatible for Ka-band communication payload and Ka-band beacon does not interfere with MODCS and TC&R normal operation.

• Journal of the Korean Society for Railway
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• v.18 no.5
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• pp.401-409
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• 2015

Defects generated in a railroad track that guides the railroad vehicle have the characteristic of growing fast; as such, the detection technology for railroad track defects is very important because defects can eventually cause mass disasters like derailments. In this study, a speed-up test facility was fabricated to investigate the feasibility of using magnetic flux leakage (MFL) technology for defect detection in a railroad track under speed-up condition; a test was conducted using a railroad track specimen with defects. For this purpose, an MFL sensor head dedicated to the configuration of the railroad was designed and test specimens with artificial defects on their surfaces were manufactured. Using the test facility, a speed-up test ranging from 4km/h to 12km/h was performed and defects including locations were successfully detected from MFL signals induced by defects with enhanced visibility by differentiating raw MFL signals. In the future, it should be possible to apply this system to a high-speed railroad inspection car by improving the lift-off stability that is necessary for speed-up of the developed MFL sensor system.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.723-730
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• 2013

This paper presents the design of an arm gesture recognition system using Kinect sensor. A variety of methods have been proposed for gesture recognition, ranging from the use of Dynamic Time Warping(DTW) to Hidden Markov Models(HMM). Our system learns a unique HMM corresponding to each arm gesture from a set of sequential skeleton data. Whenever the same gesture is performed, the trajectory of each joint captured by Kinect sensor may much differ from the previous, depending on the length and/or the orientation of the subject's arm. In order to obtain the robust performance independent of these conditions, the proposed system executes the feature transformation, in which the feature vectors of joint positions are transformed into those of angles between joints. To improve the computational efficiency for learning and using HMMs, our system also performs the k-means clustering to get one-dimensional integer sequences as inputs for discrete HMMs from high-dimensional real-number observation vectors. The dimension reduction and discretization can help our system use HMMs efficiently to recognize gestures in real-time environments. Finally, we demonstrate the recognition performance of our system through some experiments using two different datasets.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.