• Geomechanics and Engineering
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• v.37 no.3
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• pp.293-306
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• 2024

Influenced by the alternating effects of dynamic and static pressure during the mining process of close range coal seams, the surrounding rock support of cross mining roadway is difficult and the deformation mechanism is complex, which has become an important problem affecting the safe and efficient production of coal mines. The paper takes the inclined longwall mining of the 10304 working face of Zhongheng coal mine as the engineering background, analyzes the key strata fracture mechanism of the large inclined right-angle trapezoidal mining field, explores the stress distribution characteristics and transmission law of the surrounding rock of the roadway affected by the mining of the inclined coal seam, and proposes a segmented and hierarchical support method for the cross mining roadway affected by the mining of the close range coal seam group. The research results indicate that based on the derived expressions for shear and tensile fracture of key strata, the ultimate pushing distance and ultimate suspended area of a right angle trapezoidal mining area can be calculated and obtained. Within the cross mining section, along the horizontal direction of the coal wall of the working face, the peak shear stress is located near the middle of the boundary. The cracks on the floor of the cross mining roadway gradually develop in an elliptical funnel shape from the shallow to the deep. The dual coupling support system composed of active anchor rod support and passive U-shaped steel shed support proposed in this article achieves effective control of the stability of cross mining roadways, which achieves effective control of floor by coupling active support and preventive passive support to improve the strength of the surrounding rock itself. The research results are of great significance for guiding the layout, support control, and safe mining of cross mining roadways, and to some extent, can further enrich and improve the relevant theories of roof movement and control.

• Journal of the Korean Institute of Gas
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• v.11 no.1 s.34
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• pp.29-33
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• 2007

This study investigates the use of $TiO_{2},\;Pd,\;Pt$, and In which greatly improves a sensitivity to trimethylamine gas. The metal-$SnO_{2}$ thick films were prepared by screen-printing method onto $Al_{2}O_{3}$ substrates with platinum electrode. The sensing characteristics were investigated by measuring the electrical resistance of each sensor in a test box as a function of detecting gas concentration. This was then used to detect trimethylamine, dimethylamine, and ammonia vapours within the concentration range of 100-1000ppm. The gas sensing properties of metal-$SnO_{2}$ mixed thick films depended on the content and variety of metal. It was found that sensitivity and selectivity of the films dopped with 1 wt% Pd and 10 wt% $TiO_{2}$ for trimethylamin gas showed the best result at $250^{\circ}C$.

• The Journal of Engineering Geology
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• v.25 no.1
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• pp.21-33
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• 2015

This study tests the potential of detecting small-magnitude earthquakes (~M3.0) and their precursors using a long-term groundwater-monitoring database. In groundwater records from April to June 2012, abnormal changes in water level, temperature, and electrical conductivity were identified in the bedrock monitoring wells of the Gimcheon-Jijwa, Gangjin-Seongjeon, and Gongju-Jeongan stations. These anomalies could be attributed to the M3.1 earthquake that occurred in the Youngdeok area on May 30th, although no linear relationship was found between the scale of changes and the distance between each monitoring station and the epicenter, which is attributed in part to the wide screen design of the monitoring wells. Groundwater monitoring networks designed specifically for monitoring earthquake impacts could provide better information on the safety of underground space and on the security of emergency water-resources in earthquake disaster areas.

• Journal of the Korean Society of Radiology
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• v.5 no.6
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• pp.357-361
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• 2011

In this study, the liquid-crystal optical modulation X-ray detection system using a CdS which is a family of II-IV compound semiconductor was proposed. The system consist of the detector, the signal processing part, the liquid-crystal driving parts, microcontroller, and I/O parts, and was designed to be suitable for miniaturization and portable. In addition, the system can measure a wide range X-ray by using the detecting range selection. In order to evaluate the performance of the proposed system, the CdS sensor's output characteristics were confirmed in accordance with changes of dose, and excellent correlation was determined. And also, the optical penetration ratio was discussed in accordance with changes of the applied voltage by measuring the change of the liquid-crystal in accordance with changes of the applied voltage. Through these results, the characteristics of the liquid-crystal optical modulation system such as the excellent reproducibility and the noise immunity were confirmed. And we considered that the CdS cell-based liquid-crystal optical modulated portable X-ray detection system could be applied to compact, low-cost, portable system.

• Journal of the Korean Geotechnical Society
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• v.29 no.3
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• pp.51-60
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• 2013

The slime, deposited in the bored pile due to falling soil particle, reduces the bearing capacity of bored pile and thus the stability of construction also decreases. The weight pendulum and iron have been used for estimating the slime thickness based on the subjective judgment and thus the previous method has a limitation of reliability. The objective of this paper is to suggest the method for estimating the slime thickness by using characteristics of electrical resistivity as scientific method. The temperature-compensation resistivity probe (TRP), which has a conical shape and the diameter of 35.7mm, is applied to the measurement of the electrical resistivity in the borehole during penetration. The field tests are carried out for estimating the slime thickness in the application site of bored pile. The slime thickness is calculated through the difference between excavation depth of borehole and measured data. Furthermore, the laboratory tests are also conducted for investigating effects of casing, time elapsing and relative density by using the specimen of slime. The laboratory test supporting the suggested method is reasonable for determining the slime depth. The paper suggests that the electrical resistivity method may be a useful method for detecting slime thickness and the method is expected to be applicable to various sites of bored piles.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.1
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• pp.27-35
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• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Fire Science and Engineering
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• v.32 no.6
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• pp.40-45
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• 2018

The omnidirectional surveillance camera uses the object detection algorithm to level the object by unit so that broadband surveillance can be performed using a fisheye lens and then, it was a field experiment with a system composed of an omnidirectional surveillance camera and a tracking (PTZ) camera. The omnidirectional surveillance camera accurately detects the moving object, displays the squarely, and tracks it in close cooperation with the tracking camera. In the field test of flame detection and temperature of the sensing camera, when the flame is detected during the auto scan, the detection camera stops and the temperature is displayed by moving the corresponding spot part to the central part of the screen. It is also possible to measure the distance of the flame from the distance of 1.5 km, which exceeds the standard of calorific value of 1 km 2,340 kcal. In the performance test of detecting the flame along the distance, it is possible to be 1.5 km in width exceeding $56cm{\times}90cm$ at a distance of 1km, and so it is also adaptable to forest fire. The system is expected to be very useful for safety such as prevention of intrinsic or surrounding fire and intrusion monitoring if it is installed in a petroleum gas storage facility or a storing place for oil in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.