• Fire Science and Engineering
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• v.29 no.4
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• pp.39-48
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• 2015

This research was done to develop a duct-dedicated intelligent fire detection system to prevent fires and minimize fire damage of the industrial duct having a high fire risk. To understand the fire hazards of the ducts, the analysis was centered on the Daegu Textile Industrial Complex, where industrial ducts are used frequently. With this in the background, dedicated fire detectors and fire alarm control panel, which can prevent fires and to minimize fire damages to the ducts, were designed and produced, after which the performance was confirmed. As a result of performance experiments, it was shown that a duct-dedicated intelligent fire detection system had excellent adaptability and temperature accuracy. Through real-time temperature monitoring of the inside of the ducts, it was confirmed that duct fires could be efficiently extinguished by stepwise control of linkage facilities according to the setting temperature.

• Plant Journal
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• v.4 no.3
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• pp.66-72
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• 2008

The vibration of steam turbine is caused by Mass unbalance, Shaft misalignment, Oil whip and rubbing etc. But in turbine which is normally operated and maintained, the Mass unbalance component possesses the greatest portion. Our power plant has two steam turbines in capacity of 200 MW and 135 MW respectively and each turbine is supported by 6 journal bearings. However, we had many difficulties because the vibration amplitude of #3 and #4 Bearings was high during the start-up and operation mode change of steam turbine. But, with this study, we completely solved the vibration problem caused by the mass unbalance of #1 steam turbine. Until a recent date, #3 and #4 bearings which support high pressure turbine for #1 steam turbine had shown about $135{\mu}m$ in vibration amplitude (sometimes it increased to $221{\mu}m$ maximum. alarm: 6 mils, trip: 9 mils) at base load. After applying the study, they decreased to about $45{\mu}m$ maximum. It is a result from that we did not change the setting value of bearing alignment and only changed the assembly position of internal parts in Synchro clutch coupling rachet wheel which links between high pressure turbine and low pressure turbine, and increased the internal gap and machining of the Pawl cage surface. In the operation of steam turbine, if the vibration value increases by 1X, we should reduce the vibration of bearing by weight balancing. However, unless the vibration of bearing is declined by the balancing, we will have to disassemble and check the component and find the cause. In this study, we researched the way to lower mass unbalance that is 1X vibration component which has the greatest portion of vibration generated by steam turbine and we got good result by applying the findings of this study.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.232-235
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• 2008

The vibration of steam turbine is caused by Mass Unbalance, Shaft Misalignment, Oil Whip and Rubbing etc. but in turbine which is normally operated and maintained, the Mass Unbalance component possesses the greatest portion. Our power plant has two steam turbines in capacity of 200MW and 135MW respectively and each turbine is supported by 6 journal bearings. However, we had many difficulties because the vibration amplitude of No 3 and 4 Bearings was high during the start-up and operation mode change of steam turbine. But, with this study, we completely solved the vibration problem caused by the mass unbalance of No 1 steam turbine. Until a recent date, No 3 and 4 bearings which support high pressure turbine for No 1 steam turbine had shown about 135${\mu}$m in vibration amplitude (sometimes it increased to 221${\mu}$m maximum. alarm: 6mils, trip: 9mils) at base load. After applying the study, they decreased to about 40${\mu}$m maximum. It is a result from that we did not change the setting value of Bearing Alignment and only changed the assembly position of internal parts in Synchro Clutch Coupling Rachet Wheel which links between high pressure turbine and low pressure turbine, and increased the internal gap and machining of the Pawl stopper surface. In the operation of steam turbine, if the vibration value increases by 1X, we should reduce the vibration of bearing by weight balancing. However, unless the vibration of bearing is declined by the balancing, we will have to disassemble and check the component and find the cause. In this study, We researched the way to lower mass unbalance that is 1X vibration component which has the greatest portion of vibration generated by steam turbine and We got good result by applying the findings of this study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1718-1724
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• 2017

Recently, Smart plugs for real time monitoring of household appliances based on IoT(Internet of Things) have been activated. Through this, consumers are able to save energy by monitoring real-time energy consumption at all times, and reduce power consumption through alarm function based on consumer setting. In this paper, we measure the alternating current from a wall power outlet for real-time monitoring. At this time, the current pattern for each household appliance was classified and it was experimented with deep learning to determine which product works. As a result, we used a cross validation method and a bootstrap verification method in order to the classification performance according to the type of appliances. Also, it is confirmed that the cost function and the learning success rate are the same as the train data and test data.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.381-386
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• 2018

Objectives: This study aimed to describe the process of utilizing a mobile application for ecological momentary assessment (EMA) to collect data on stress and mood in daily life setting. Methods: A mobile application for the Android operating system was developed and installed with a set of questions regarding momentary mood and stress into a smartphone of a participant. The application sets alarms at semi-random intervals in 60-minute blocks, four times a day for 7 days. After obtaining all momentary affect and stress, the questions to assess the usability of the mobile EMA application were also administered. Results: The data were collected from 97 police officers working in Gyeonggi Province of South Korea. The mean completion rate was 60.0% ranging from 3.5% to 100%. The means of positive and negative affect were 18.34 of 28 and 19.09 of 63. The mean stress was 17.92 of 40. Participants responded that the mobile application correctly measured their affect ($4.34{\pm}0.83$) and stress ($4.48{\pm}0.62$) of 5-point Likert scale. Conclusions: Our study investigated the process of utilizing a mobile application to assess momentary affect and stress at repeated times. We found challenges regarding adherence to the research protocol, such as completion and delay of answering after alarm notification. Despite this inherent issue of adherence to the research protocol, the EMA still has advantages of reducing recall bias and assessing the actual moment of interest at multiple time points that improves ecological validity.

• Journal of the Korean Society of Safety
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• v.38 no.3
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• pp.35-42
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• 2023

This study analyzed the current and temperature characteristics of major components of an induction motor during normal and abnormal operations as functions of the difference in the rated capacities of medium and large-sized motors widely used in industrial settings. The temperature rise equation of the induction motor winding was derived through locked-rotor operation experiments and linear regression analysis. When the ambient temperature is 40 ℃, the time to reach 155 ℃, the temperature limit of the insulation class (F class) of the winding of the induction motor, was confirmed to be 48 seconds for the 2.2 kW induction motor and 39 seconds for the 3.7 kW induction motor. This means that when the rated capacity is large or the installation environment is high temperature, the time to reach the temperature limit of the insulation class during locked-rotor operation is short, and the risk of insulation deterioration and fire is high. In addition, even if the EOCR (Electronic Over Current Relay) is installed, if the setting time is excessively set, the EOCR does not operate even if the normal and locked-rotor operation of the induction motor is repeated, and the temperature limit of the insulation grade of the winding of the induction motor is exceeded. The results of this study can be used for preventive measures such as the promotion of electrical and mechanical measures for the failure of induction motors and fire prevention in industrial sites, or the installation of fire alarm systems.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.865-870
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• 2023

Recently, accidents caused by black ice, a road freezing phenomenon caused by natural power, are increasing. Black ice is difficult to identify directly with the human eye and is more likely to misunderstand it as standing water, so there is a high accident rate caused by car sliding. To solve this problem, this paper presents a method of detecting black ice centered on LiDAR sensors. With a small, inexpensive, and high-accuracy light detection and ranging (LiDAR) sensor, the temperature and inclination angle are set differently to detect black ice and asphalt by setting different reflection angles of asphalt and black ice differently in temperatures and inclinations. The LIDARO carried out in the study points out that additional research and improvement are needed to increase accuracy, and through this, more reliable black ice detection methods can be suggested. This method suggests a method of detecting black ice through early system design research by preventing accidents caused by black ice in advance.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.