• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.89-97
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• 2022

Recently, various problems such as reduced productivity, insufficient inflow of skilled manpower, reduced quality, and concerns about increasing safety accidents have appeared in the domestic construction industry. Stakeholder of construction project are considering the Off-Site Construction (OSC) method rather than the conventional on-site construction as an alternative. Despite the importance of decision making in the early stage of the adoption of OSC, there is a lack of methodologies for rational decision making. In this study, a decision making system has been developed to derive the final construction cost score by deriving the project characteristics, selecting the construction difficulty index, and developing a cost model for each construction method alternatives to calculate the standard construction cost. Using this system, the OSC method can be effectively evaluated in terms of its feasibility in the early stage of construction

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.205-212
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• 2023

In the modern society, the extreme weather caused by climate change has brought about exceptional damage in succession over the world due to the use of fossil fuels, and infectious diseases such as COVID-19 worsen the quality of human life. It is urgently necessary to reduce green-house gas and use new renewable energy. The global environmental pollution should be decreased by reducing the use of fossil fuels and using new renewable energy. This paper suggests a system which can function for the environment of four seasons, safety and communication, through the photovoltaic power-based intelligent CCTV, internet and WiFi, and cooling and heating systems, and can optimally manage power, through the real-time monitoring of the production and the consumption of the photovoltaic power. It suggests a hybrid generation system supporting diesel generation without discontinuation in the case of emergency such as system power outage caused by cold waves, typhoons and natural disasters in which the photovoltaic power generating system cannot be used.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1106-1113
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• 2013

For patients who have senile mental disorder such as dementia, quantity of excercise and amount of sunlight are important clue for dose and the treatment. Therefore, monitoring health information of daily life is necessary for patients' safety and healthy life. Portable & wearable sensor device and server configuration monitoring data are needed to provide these services for patients. Watch-type device(smart watch) which patients wear and server system are developed in this paper. Smart watch developed includes GPS, accelerometer and illumination sensor, and can obtain real time health information by measuring the position of patients, quantity of exercise and amount of sunlight. Server system includes the sensor data analysis algorithm and web server that doctor and protector can monitor through sensor data acquired from smart watch. The proposed data analysis algorithm acquires quantity of exercise information and detects step count in patients' motion acquired from acceleration sensor and to verify this, the three cases with fast pace, slow pace, and walking pace show 96% of the experimental result. If developed u-Healthcare System for dementia patients is applied, more high-quality medical service can be provided to patients.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.208-217
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• 2024

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.226-234
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• 2021

The objective of this study is to establish the fundamental design data for axial load-displacement relationship under axial monotonic or cyclic responses of seismic damping·isolation (SDI) units developed for ceiling structures. The main parameters include the installation of a spring, the number of rubber layer, prestress stress of bolts for connector between the spring and rubbers, and loading type. Test results showed that SDI units with a spring in the core and higher prestress stress of bolts tended to be higher stiffness at the ascending branch and more ductile behavior at the descending branch. This trends more notable for the specimens under monotonic load rather than cyclic loads. Consequently, the energy dissipation of SDI unit can be optimally designed with the following conditions: installation of a spring within 3-layer rubbers and prestress applied to the bolts at 10% of their yielding strength . When compared with the experimental tension capacity of the developed SDI units, the predictions by JIS B 2704-1 and KDS 31 00 are conservative under monotonic loading but higher by approximately 10% under cyclic loading.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.67-76
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• 2023

In this study, in order to enhance the joint capacity between the existing reinforced concrete (R/C) frame and the reinforcement member, we proposed a novel concept of Internal Composite Seismic Strengthening Method (CSSM) for seismic retrofit of existing domestic medium-to-low-rise R/C buildings. The Internal CSSM rehabilitation system is a type of strength-enhancing reinforcement systems, to easily increase the ultimate horizontal shear capacity of R/C structures without seismic details in Korea, which show shear collapse mechanism. Two test specimens of full-size two-story R/C frame were fabricated based on an existing domestic R/C building without seismic details, and then retrofitted by using the proposed CSSM seismic system; therefore, one control test specimen and one test specimen reinforced with the CSSM system were used. Pseudo-dynamic testing was carried out to evaluate seismic strengthening effects, and the seismic response characteristics of the proposed system, in terms of the maximum shear force, response story drift, and seismic damage degree compared with the control specimen (R/C bare frame). Experiment results indicated that the proposed CSSM reinforcement system, internally installed to the existing R/C frame, effectively enhanced the horizontal shear force, resulting in reduced story drift of R/C buildings even under a massive earthquake.

• Convergence Security Journal
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• v.17 no.3
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• pp.59-64
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• 2017

Korea is experiencing a rapid increase in the number of elderly living alone accompanying the aging society problem, a nd is making efforts to solve the problem through the policy of 'living alone u-care service'. The purpose of this study is to propose a better u-Care service improvement method by applying new technology to improve the user experience of ucare service for the health and safety of the elderly living alone. First, the improvement of u-Care service for elderly livin g alone by applying IoT technology. It provides remote monitoring service using health information data measured through wearable device, and transmits personal health status to medical institution by using personal device such as smart phone, so that remote medical consultation or telemedicine can be connected in the future. Second, improvement of u-Care service through consideration of emotional stability of elderly living alone as well as simple safety and health care through applica tion of emotional service robot technology.It is expected that it will be able to help independent living of one person's elde rly person in the future by providing caring function service to existing u-care service providing service.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.185-192
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• 2019

The introduction of smart technology provides accuracy, safety, and efficiency to both physicians and patients. Although interest in a clear aligner is increasing among users worldwide, the current clear aligner requires a visit to the hospital every one or two weeks for replacement, which is a very cumbersome process. There is also confusion among dentists and patients because about 40 to 80 devices are made, and calibration is done based on the order and duration of the clear aligner. Therefore, this study designed and developed a clear aligner management system so that communication between the patient and dentist can be smoothly performed by inserting the QR code into the transparent correction device. As a result, the size of the QR code was recognized as $6{\ast}6mm^2$ which can be used in the oral and the recognition distance was 100% within 12 cm. Since the dentist can remotely manage the patient with the proposed system and improve the correction effect, it is possible to manage patients abroad, as well as domestically.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.689-693
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• 2023

Domestic bridges with a service life of more than 30 years are expected to account for approximately 54% of all bridges within the next 10 years. As bridges rapidly deteriorate, it is necessary to establish an appropriate maintenance plan. Recent domestic and international research have focused on the integration of BIM to digitize bridge maintenance information and then enhance accessibility and usability of the information. Accordingly, this study developed a BIM-FEM interoperability algorithm for bridge decks to convert maintenance information into data and efficiently manage the history of maintenance. After creating an initial crack BIM based on an exterior damage map, bridge specification and damage information were linked to a numerical analysis that performs damage analysis considering damage scenarios and design loads. The spread of cracks obtained from the analysis results were updated into the BIM. Based on the damage spread information on the BIM, an automated technology was also developed to assess both the current and future condition ratings of the bridge deck. This approach can enable an efficient maintenance of the deck using the history data from bridge inspection and diagnosis as well as future information on cracks and defects. The expected early detection and prevention would ultimately improve the lifespan and safety of bridges.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.916-923
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• 2023

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site 'A', objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site 'B' showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.