• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.11
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• pp.591-598
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• 2009

The service quality with the various building equipments tends to depend on the individual superintendent's capability and efforts generally and this often lead to poor maintenances and managements of them. N-BMS(Networked Building Management System), proposed in this study, is a new type of building management method, which links principal equipments within many buildings into a communication network and carries out several significant missions such as fault detection, deterioration diagnosis and control the equipments and so on, as well as monitoring which is the main and unique purpose of the conventional BMS. How to construct the N-BMS was considered to keep performance of equipments high and thus to save energy and resource. LCC(Life Cycle Cost) based analysis was also performed in order to verify the effects of some maintenance and management works for the building HVAC systems.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.149-154
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• 2007

Now, in Korea, the performances of the building service equipment relay on the individual superintendent's share for the assessment of performance, fault detection, deterioration diagnosis of the building service equipment. As the result, very different quality of the performance or the durability of equipment is being obtained with his skill and effort and it is also not easy to assess that quality. This finally lead to the waste of labor force and the operating cost due to the high-cost, low-efficiency system. How to construct the N-BMS was considered to save energy, resource and to conserve performance of building service equipment. The FEMIS, facility, energy/environmental management & information system, for building service offer management process integrated with BAS, FMS and EMS and so on.

• International Journal of High-Rise Buildings
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• v.9 no.4
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• pp.351-360
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• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.229-230
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• 2022

According to the national building status, there are a total of 2.89 million buildings that are over 30 years old after completion, and the number is increasing by more than 70,000 to 80,000 buildings every year. As a result, the demand for demolition works is also increasing, and more than 3 to 4 collapse accidents occur steadily every year during demolition work. Major causes of accidents include non-compliance with plans, negligence of on-site supervisors, and failure to secure structural safety. Due to the strengthening of the Severe Disaster Punishment Act, there is growing interest in the demand for secure management of collapse detection during demolition works. Therefore, this study aims to investigate the applicability of real-time safety monitoring systems using a total station capable of 3D automatic displacement measurement in building demolition work for securing structural safety by the load changes during the demolition process.

• Journal of Radiation Protection and Research
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• v.16 no.1
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• pp.15-24
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• 1991

Radon-222 exhalation rate from several domestic building materials were experimentally measured by using radon cup method, in which a CR-39 plastic is used as a passive radon detector. The radon detection factor of CR-39 detector determined in a series of calibration experiments was $0.164{\pm}0.005(tracks\;cm^{-2}/Bq\;d\;m^{-3})$, which is consistent with those reported by other investigators. The radon exhalation rates of several building materials (brick, red brick, concrete block, granite plate, concrete floor and wall) ranges from $6.8{\times}10^{-6}\;(granite plate)\;to\;75.0{\times}10^{-6}Bq/m^2-sec(brick)$ with the increasing order of granite plate, red brick, concrete wall, concrete block, concrete floor and brick. It showed that the CR39 radon cup can be efficiently utilized in measuring the radon-222 gas exhalation rate from building materials.

• Korean Institute of Interior Design Journal
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• v.16 no.1 s.60
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• pp.126-134
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• 2007

This study researches the effectiveness of virtual experiences as an evaluation method in design. For the study, I researched the result on the spatial recognition before, abstracted visual objects and factors in recognition process and made up 43 inquiries for the evaluation through the experts group. Besides, set the analytical code between recognition factor and spatial constituents, made analysis the result from it. From April 1st to June 30th in 2004, the virtual model of educational place, college main building and city library was established as a subject of the research and studied the result. The methods that are used for the study are frequent analysis, T-test, reliability analysis, factor analysis etc. and SPSSWIN Package is used for the statistic analysis. There were two times of analysis on the evaluation to analyze the effect of virtual models on the web basis, one group evaluated the virtual building same as real and the other group evaluated the building site. Then analyzed the differences and correlation between the two. In result, college building, the first subject of study, and the second city library shows that made little differences between on the spot evaluation and virtual evaluation. Therefore, it is considered that the design evaluation with virtual model on the web basis has a vantage. But some problems founded that solved between preceding and present research through the feedback. It is as follows, 1. detection of problems that insufficience validity in the first survey. 2. found some not-validity inquiries. 3. detection of necessities that qualitative improvement and technical repletion in virtual environment.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.238-246
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• 2013

In this paper, a robot vision technique is presented to detect obstacles, particularly approaching humans, in the images acquired by a mobile robot that autonomously navigates in a narrow building corridor. A single low-cost color camera is attached to the robot, and a trapezoidal area is set as a region of interest (ROI) in front of the robot in the camera image. The lower parts of a human such as feet and legs are first detected in the ROI from their appearances in real time as the distance between the robot and the human becomes smaller. Then, the human detection is confirmed by detecting his/her face within a small search region specified above the part detected in the trapezoidal ROI. To increase the credibility of detection, a final decision about human detection is made when a face is detected in two consecutive image frames. We tested the proposed method using images of various people in corridor scenes, and could get promising results. This method can be used for a vision-guided mobile robot to make a detour for avoiding collision with a human during its indoor navigation.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.43-52
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• 2011

With the rapid development of technology, skyscrapers are widely spread and they are tightly coupled. If fire occurs in a building, it is easily spread to neighboring buildings, resulting in the large number of victims and property damages. To remove fire disasters, the need for early fire detection techniques is increasing. To detect fire, detecting devices for heat, smoke, and flame have been used widely. However, this paper surveys and presents the latest research which focuses on early smoke and flame detection algorithms and systems with camera's input images. In addition, this paper implements and evaluates the performance of these flame and smoke detection algorithms with several types of movies.

• Journal of KIBIM
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• v.9 no.1
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• pp.42-51
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• 2019

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.