• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.129-145
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• 2022

Purpose: This study aims at preparing an external evacuation system by setting up situation that may occur outside buildings in case of large-scale fire at buildings such as multiuse facilities and presenting appropriate response procedures and action instructions for evacuees and facility managers. Method: Major matters are summarized based on various situations which may occur outside in case of fire and the contents of fire manual. Necessary factors including risk alert standards in the event of fire and the role of building occupants are classified and then important issues are summarized. In addition, the definition of fire-related outside shelters and external evacuation routes are showed, and then the applicability to the shelters and the routes are reviewed for old apartments in Jung-gu among multi-dense facilities. Result: Four stages (attention, caution, alert, serious) for standards of fire risk warning are established with the results of the investigation and analysis, and guidelines for behavior for evacuees, facility owners, residents, managers are summarized and presented. In addition, the concept and role of external shelters are divided into primary to the third shelters, and matters related to the definition of each shelter and the establishment of evacuation routes are presented, and then considered them carefully. Conclusion: This study has highlighted the importance of suggesting a systematic plan to secure the safety for evacuees outside space of buildings with disorder and difficulty to control in the event of fire. Therefore, we are confident that it will be useful in making an integrated manual for inside and outside buildings.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.49-55
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• 2022

In this paper, we propose an obstacle detection method that can operate robustly even in external environmental factors such as weather. In particular, we propose an obstacle detection system that can accurately inform dangerous situations in AR through DB-based feature matching and RANSAC-based multiplane method. Since the approach to detecting obstacles based on images obtained by RGB cameras relies on images, the feature detection according to lighting is inaccurate, and it becomes difficult to detect obstacles because they are affected by lighting, natural light, or weather. In addition, it causes a large error in detecting obstacles on a number of planes generated due to complex terrain. To alleviate this problem, this paper efficiently and accurately detects obstacles regardless of lighting through DB-based feature matching. In addition, a criterion for classifying feature points is newly calculated by normalizing multiple planes to a single plane through RANSAC. As a result, the proposed method can efficiently detect obstacles regardless of lighting, natural light, and weather, and it is expected that it can be used to secure user safety because it can reliably detect surfaces in high and low or other terrains. In the proposed method, most of the experimental results on mobile devices reliably recognized indoor/outdoor obstacles.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.111-123
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• 2022

Artificial intelligence, which is developing as the core of an intelligent information society, is bringing convenience and positive life changes to humans. However, with the development of artificial intelligence, human rights and property are threatened, and ethical problems are increasing, so alternatives are needed accordingly. In this study, the most controversial artificial intelligence ethics problem in the dysfunction of artificial intelligence was aimed at researching and developing artificial intelligence ethical measurement indicators to protect human personality rights and property first under artificial intelligence ethical principles and components. In order to research and develop artificial intelligence ethics measurement indicators, various related literature, focus group interview(FGI), and Delphi surveys were conducted to derive 43 items of ethics measurement indicators. By survey and statistical analysis, 40 items of artificial intelligence ethics measurement indicators were confirmed and proposed through descriptive statistics analysis, reliability analysis, and correlation analysis for ethical measurement indicators. The proposed artificial intelligence ethics measurement indicators can be used for artificial intelligence design, development, education, authentication, operation, and standardization, and can contribute to the development of safe and reliable artificial intelligence.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.4
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• pp.65-72
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• 2022

Recently, damage to buildings due to earthquakes in Korea occurred mainly in school buildings and Piloti-type multi-family houses, highlighting the need for seismic retrofit for buildings of the same type. In the early days of the seismic retrofit project for school facilities, various patented methods using dampers as a ductile seismic retrofit method were applied without sufficient verification procedures. However, in 「School Facility Seismic Performance Evaluation and Retrofit Manual, 2021」, when the patented method is applied, it must be applied through a separate strict verification procedure, and instead, the strength/stiffness retrofit method was induced as a general method. In practice,when evaluating seismic performance for retrofit by infilled steel frame with brace, the analysis model is constructed by directly connecting only the steel brace to the existing RC member. However, if the frame is removed from the analysis model of the infilled steel frame with brace, the force reduction occurring on the existing RC member near the retrofit is considered to be very large, and this is judged to affect the review of whether to retrofit the foundation or not. Therefore, in this study, preliminary analysis with variables such as whether or not steel frame is taken into account and frame link method for the analysis model of RC school building retrofitted by infilled steel frame with brace and nonlinear analysis for actual 3-story school building was performed, and basic data for rational analysis model setting were presented by comparing preliminary analysis and pushover analysis results for each variable.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.185-191
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• 2022

In this study, we propose a novel super-resolution neural network based on U-Net, residual neural network, and sub-pixel convolution. To prevent the loss of detailed information due to the max pooling of U-Net, we propose down-sampling and connection using sub-pixel convolution. This uses all pixels in the filter, unlike the max pooling that creates a new feature map with only the max value in the filter. As a 2×2 size filter passes, it creates a feature map consisting only of pixels in the upper left, upper right, lower left, and lower right. This makes it half the size and quadruple the number of feature maps. And we propose two methods to reduce the computation. The first uses sub-pixel convolution, which has no computation, and has better performance, instead of up-convolution. The second uses a layer that adds two feature maps instead of the connection layer of the U-Net. Experiments with a banchmark dataset show better PSNR values on all scale and benchmark datasets except for set5 data on scale 2, and well represent local area patterns.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.327-338
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• 2022

This study aims to empirically and statistically predict soil depths across areas affected by landslides. Using soil depth measurements from a landslide area in Korea, two sets of soil depths are calculated using a Z-model based on terrain elevation and a probabilistic statistical model. Both sets of calculation results are applied to derive landslide risk using the saturated infiltration depth ratio of the soil layer. This facilitates analysis of the infiltration of rainfall into soil layers for a rainfall event. In comparison with the probabilistic statistical model, the Z-model yields soil depths that are closer to measured values in the study area. Landslide risk assessment in the study area based on soil depth predictions from the two models shows that the percentage of first-grade landslide risk assessed using soil depths from the probabilistic statistical model is 2.5 times that calculated using soil depths from the Z-model. This shows that soil depths directly affect landslide risk assessment; therefore, the acquisition and application of local soil depth data are crucial to landslide risk analysis.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.51-59
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• 2022

This study aims to investigate the changes in health status of inpatients before and after a fall accident, and it is a retrospective study using data from 328 inpatients who fell from January 1, 2016 to December 31, 2020, reported to the patient safety reporting system. The average age of the study subjects was 68.57(±14.13), and those in their 70s accounted for the most at 30.49%. Falls occurred on average 13.86(±25.03) days after hospitalization, and the time when the most falls occurred was between 22:30 and 06:59 with 42.99%. Before and after a fall during hospitalization, bowel problems (x²=314.0, p<.001), urination problems (x²=284.0, p<.001), intravenous fluid therapy (x²=85.16, p<.001), and walking (x²=69.77. p<.001), bedridden state (x²=51.60, p< .001), mental state and performance (x²=17.52, p<.001) patient's attitude (x²=220.17, p<.001), there was a statistically significant difference. It is necessary to develop an appropriate method and education program for fall prevention in hospital by considering the individual characteristics of inpatient.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1037-1051
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• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

• Journal of Navigation and Port Research
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• v.45 no.6
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• pp.306-313
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• 2021

National fishing harbour is as a refuge for fishing boats and a breakwater lighthouse is installed as a functional facility in consideration of harbour identification and the safety of passing vessels. In this study, the service level of breakwater lighthouse (234 units) was estimated based on the traffic volume of 105 national fishing harbour. For this purpose, the evaluation items were determined, the fishing harbour standard index was calculated (Fs=1), the proximity of fishing harbour was identified and the function (service level) of the breakwater lighthouse was estimated in the following order. However, national fishing harbour differed in size, traffic volume and fishing vessel capacity. Therefore, 105 national fishing harbour were divided into three groups through cluster analysis. The cluster analysis was based on the service level factors of the breakwater lighthouse, such as the number of weeding fishing vessels, tonnage of fishing vessels, the number of incoming and outgoing vessels per year, and fishing vessel capacity. As a result of the estimation, the service level of the breakwater lighthouse (light tower height, visual height, visual range, interval) was 10.50m, 16.50m, 7.00mile, 5.5sec for group 1, and 10.67m, 16.16m, 8.33mile, and 6.0sec for group 2, The three groups are 11.53m, 16.75m, 6.75mile and 5.0sec. The results of this study can be used as useful basic data for improving the service level of traffic vessels when a breakwater lighthouse is built in a fishing harbour 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.