• 한국BIM학회 논문집
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• 제11권3호
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• pp.45-54
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• 2021

The augmented reality (AR) has recently became an attractive technology in construction industry, which can play a critical role in realizing smart construction concepts. The AR has a great potential to help construction workers access digitalized information about design and construction more flexibly and efficiently. Though several AR applications have been introduced for on-site made to enhance on-site and off-site tasks, few are utilized in actual construction fields. This paper proposes a new AR framework that provides on-site managers with an opportunity to easily access the information about construction resources such as workers and equipment. The framework records videos with the camera installed on a wearable AR device and streams the video in a server equipped with high-performance processors, which runs an object detection algorithm on the streamed video in real time. The detection results are sent back to the AR device so that menu buttons are visualized on the detected objects in the user's view. A user is allowed to access the information about a worker or equipment appeared in one's view, by touching the menu button visualized on the resource. This paper details implementing parts of the framework, which requires the data transmission between the AR device and the server. It also discusses thoroughly about accompanied issues and the feasibility of the proposed framework.

• 드라이브 ㆍ 컨트롤
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• 제19권3호
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• pp.23-31
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• 2022

Traffic safety issues have recently been seriously magnified, due to child deaths in apartment complexes and parking lots. Accordingly, traffic safety technologies are being developed to recognize dangerous situations on living roads and to provide warning services. In this study, a collision detection algorithm was developed to prevent collision accidents between moving objects, by using object type and location information provided from CCTV monitoring devices. To determine the exact collision between moving objects, an object movement model was developed to predict the range of movement by considering the moving characteristics of the object, and a collision detection algorithm was developed to efficiently analyze the presence and location of the collision. The developed object movement model as well as the collision detection algorithm were simulated, in a virtual space of an actual living road to verify performance and derive supplementary matters.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2007년도 동계학술발표대회 논문집
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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.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• Nuclear Engineering and Technology
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• 제45권6호
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• pp.795-802
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• 2013

For the improvement of APR1400 Diverse Protection System (DPS) design, the Advanced DPS (ADPS) has recently been developed to enhance the fault tolerance capability of the system. Major fault masking features of the ADPS compared with the APR1400 DPS are the changes to the channel configuration and reactor trip actuation equipment. To minimize the fault occurrences within the ADPS, and to mitigate the consequences of common-cause failures (CCF) within the safety I&C systems, several fault avoidance design features have been applied in the ADPS. The fault avoidance design features include the changes to the system software classification, communication methods, equipment platform, MMI equipment, etc. In addition, the fault detection, location, containment, and recovery processes have been incorporated in the ADPS design. Therefore, it is expected that the ADPS can provide an enhanced fault tolerance capability against the possible faults within the system and its input/output equipment, and the CCF of safety systems.

• 대한토목학회논문집
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• 제41권4호
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• pp.441-447
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• 2021

건설현장에서는 수많은 중장비와 작업자가 다양한 작업을 동시다발적으로 수행하기 때문에 복잡하고 위험한 상황이 자주 발생한다. 복잡한 현장에서 중장비가 단독으로 작업할 경우 운전자의 시야제한, 판단오류 등으로 인해 안전사고가 발생할 수 있으며, 이에 따라 중장비는 신호수와의 상호작용을 통해 주변 상황에 대한 정보를 수집하면서 작업을 수행해야 한다. 중장비를 자동으로 모니터링하고 위험상황을 탐지하기 위해 많은 컴퓨터비전 기술들이 개발되었지만, 기존의 방법들은 중장비 단독작업 인식에 필요한 중장비와 신호수 간 상호작용을 고려하지 않았다는 한계가 있다. 이러한 한계를 보완하기 위해 본 연구는 중장비-신호수 간의 상호작용을 고려한 컴퓨터비전 기반 중장비의 단독작업 자동 인식 모델을 제안함을 목표로 한다. 개발된 모델을 검증하기 위해 실제 건설현장으로부터 영상 데이터를 수집하여 실험을 수행하였다. 그 결과, 학습된 모델은 중장비와 사람을 83.4 %의 정확도로 인식하였고, 일반 작업자와 신호수를 84.2 %의 정확도로 분류하였으며, 중장비-신호수 간 상호작용 또한 95.1 %의 높은 정확도로 분석하였다. 본 연구결과는 건설현장에서 위험한 상황을 초래할 수 있는 중장비의 단독작업을 사전에 탐지하여 안전사고를 예방할 수 있다.

• 국제초고층학회논문집
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• 제9권4호
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• pp.335-342
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• 2020

This article comes up with a new method which is based on the visual characteristic of the objects and machine learning technology to achieve semi-automated recognition of the personnel, machine & materials of the construction sites. Balancing the real-time performance and accuracy, using Faster RCNN (Faster Region-based Convolutional Neural Networks) with transfer learning method appears to be a rational choice. After fine-tuning an ImageNet pre-trained Faster RCNN and testing with it, the result shows that the precision ratio (mAP) has so far reached 67.62%, while the recall ratio (AR) has reached 56.23%. In other word, this recognizing method has achieved rational performance. Further inference with the video of the construction of Huoshenshan Hospital also indicates preliminary success.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1138-1145
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• 2022

While pursuing digitalization and paperless projects, the construction industry needs to settle on how to make the most of digitized data and information. On-site workers, who currently rely on paper documents to check and review design and construction plans, will need alternative ways to efficiently access the information without using any paper. Augmented Reality is a potential solution where the information customized to a user is aligned with the physical world. This paper proposes the Augmented Reality framework to deliver the information on on-site resources (e.g., workers and equipment) using head-mounted devices. The proposed framework was developed by interoperating Augmented Reality-supported devices and a digital twin platform in which all information related to ongoing tasks is accumulated in real-time. On-site resources appearing in the user's field of view are automatically detected by an object detection algorithm and then assigned to the corresponding information by matching the data in the digital twin platform. Preliminary experiments show the feasibility of the proposed framework. Worker detection results can be visualized on HoloLens 2 in near real-time, and the matching process obtained the accuracy greater than 88%.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2008년도 하계학술발표대회 논문집
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• pp.48-53
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• 2008

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. A major use of building energy management system(BEMS) is for monitoring plant and building's energy consumption. National building management system (N-BMS) presented in this study links buildings into a network group in order to monitor and control the buildings. 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.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.721-727
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• 2022

Construction safety remains an ongoing concern, and project managers have been increasingly forced to cope with myriad uncertainties related to human operations on construction sites and the lack of a skilled workforce in hazardous circumstances. Various construction fatality monitoring systems have been widely proposed as alternatives to overcome these difficulties and to improve safety management performance. In this study, we propose an intelligent, automatic control system that can proactively protect workers using both the analysis of big data of past safety accidents, as well as the real-time detection of worker non-compliance in using personal protective equipment (PPE) on a construction site. These data are obtained using computer vision technology and data analytics, which are integrated and reinforced by lessons learned from the analysis of big data of safety accidents that occurred in the last 10 years. The system offers data-informed recommendations for high-risk workers, and proactively eliminates the possibility of safety accidents. As an illustrative case, we selected a pilot project and applied the proposed system to workers in uncontrolled environments. Decreases in workers PPE non-compliance rates, improvements in variable compliance rates, reductions in severe fatalities through guidelines that are customized according to the worker, and accelerations in safety performance achievements are expected.