• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.128-132
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• 1997

The conventional fire detection devices are operated after a processed fire phase, which are sensing only a high density of smoke level or high temperature heat. They are not so precision to detect a fire in the early phase to protect the facility from the fire. We need to develope a new high precision smoke detection system to keep expensive industial facilities most reliably from fire. A new optical precision smoke detection system was developed. It monitors very low level density of smoke psrticles in the air. It is operated continuously through many years without a stop or any malfunction. The developed precision smoke detection system will be installed in important industrial facilities,such as power plants, underground common tunnel,main control rooms,computer rooms etc.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Fire Science and Engineering
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• v.31 no.1
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• pp.58-62
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• 2017

There is a growing need for seismic resistance design. But it is controversial that standards of sway brace device in non-structural elements for buildings like pump waterway is vary widely. Therefore, in this study to get a valid range of sway brace device in seismic resistance design, using load test of sway brace device. As a result, load of safe range from 0 to 18.5 kN and under 29.4 kN, no structural fault of sway brace device. And using internet of things get a data of seismic resistance design from sensor node like accelerometer, GPS, tilt sensor and temperature sensor through steps of sampling and prediction. These results will be acceptable for monitoring system for seismic resistance in non-structural elements.

• Information Systems Review
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• v.12 no.2
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• pp.89-104
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• 2010

Recently, ubiquitous sensor network (USN) has been applied to many areas including environment monitoring. A few studies applied the USN to disaster prevention and emergency management, in particular, aiming to conserve cultural heritage. USN is an useful technology to do online real-time monitoring for the purpose of early detection of the fire which is a critical cause of damage and destruction of cultural heritages. It is necessary to online monitor the cultural heritages that human has a difficulty to access or their external appearance and beauty are important, by using the USN. However, there exists false warning from USN-based monitoring systems without human intervention. In this paper, we presented an alternative to resolve the problem by applying ontology. Our intelligent fire early detection systems for conserving cultural heritages are based on ontology and inference rules, and tested under laboratory environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.497-498
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• 2018

In the fire scene, it is difficult to see 1m ahead because of power failure, smoke and toxic gas, even with thermal imaging camera and Xenon searchlight. Analysis of the smoke particles in the fire scene shows that even if the smoke is $5{\mu}m$ or less in wavelength, it is difficult to obtain a front view when using a conventional thermal imaging camera if the visual distance exceeds 1 meter. In the case of black smoke with a particle wavelength of $5{\mu}m$ or more, a space permeation sensor technology using various sensors other than a single sensor is required because chemical materials, gas, and water molecules are mixed. Firefighters need a smoke detection technology for smoke detection and spatial information visualization for forward safety view.In this paper, we design the interface of the information processing system with 32bit CPU core and peripheral circuit. We also implemented and simulated the interface with Lidar sensor. Through this, we provide interface that can implement information processing system of human enhancement fire helmet in the future.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.49-54
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• 2010

The wooden cultural assets have the characteristics such as the fast spread of flame and leading to total destruction. Therefore, there is a need for a system for early countermeasure of recognized problem, along with the technological response for accurately recognizing the situation, for the prevention and early suppression of fire. To utilize such technology for detecting the situation through the latest ubiquitous technology and for a quick response to suppress fire, the ubiquitous sensor network (USN) technology, flame detector, image sensor, USN-based cultural asset disaster prevention management application case and malfunction identification system realization were examined in this study and the study result was presented focusing on the flame detector malfunction identification system for the ubiquitous-type cultural asset disaster prevention system.

• Journal of the Korean Institute of Educational Facilities
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• v.27 no.1
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• pp.3-10
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• 2020

Due to the frequent occurrence of large-scale disasters such as recent earthquakes, the problem of the safety of old school buildings has emerged. The need to secure safety management technology through constant monitoring is increasing in an attempt to supplement old school buildings with weak disaster response capabilities. Traditional research is approaching the development of an existing sensor-based risk precursor information monitoring system. However, unlike this, in this study, we will focus on the development of a data analysis platform as part of the development of a continuous monitoring system that can be prepared for earthquakes, collapses, and fires, based on constantly measured data. For this reason, the development of a safety diagnostic algorithm based on the optimal sensor-attached points and sensor data reflecting the fragile characteristics of old school buildings was derived. Utilizing this, a message and action manual system for each management / use entity of school buildings after retirement was constructed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.12
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• pp.935-943
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• 2015

This paper presents a method for early fire detection and fire position determination inside the nacelle of wind turbine generator system. The rising temperature and obscuration rates inside the nacelle were used as parameters for fire detection, which can minimize the possibility of a fire detection malfunction because these rising rates do not depend on the absolute values of temperature and obscuration. The fire position was determined using the time difference among various sensor positions for fire detection. The performance of the method was tasted using sets of experiments in a nacelle simulator.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.953-958
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• 2015

Raspberry Pi is a credit card-sized computer with support for a large number of input and output peripherals. This makes it the perfect platform for interaction with many different devices and for usage in a wide range of applications. When combined with Wi-Fi, it can communicate remotely, therefore increasing its suitability for the construction of wireless sensor nodes. In addition, data processing and decision-making can be based on artificial intelligence, what is performed in developed testbed on the example of monitoring and determining the confidence of fire. In this paper, we demonstrated the usage of Raspberry Pi as a sensor web node for fire-safety monitoring in a building. When the UV-flame sensors detect a flame as thin as that of a candle, the Raspberry Pi sends a push-message to notify the assigned smartphone of the on-site situation through the GCM server. A mobile app was developed to provide a real-time video streaming service in order to determine a false alarm. If an emergency occurs, one can immediately call for help.