• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.171-172
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• 2020

Recently, the need for smart construction technologies related to the Fourth Industrial Revolution has been increasing in order to improve productivity of the construction industry. The Ministry of Land, Infrastructure and Transport has established Smart construction technology road map to commercialize the smart construction, and research and development is also underway. However, due to the lack of cost estimation standards for such smart construction technologies to be deployed to actual sites, smart construction technologies are not actively applied to construction sites. In particular, cost estimation standards are needed for construction machinery equipment with ICT technology that is currently available for commercialization. Therefore, as a preliminary study for the development of smart construction cost estimation standards, a case study was conducted on ICT construction estimation standards in Japan and present them as basic data for standards in Korea.

• Journal of Drive and Control
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• v.19 no.2
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• pp.11-16
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• 2022

It is important to measure the excavator's work productivity that estimates the bucket's payloads on a process. If the bucket isn't filled at every working cycle, the excavator's operator has to drive the machine more to achieve his work quota. If bucket is filled over with the load, the other way around, the transferred object has to spread out on the workplace. That causes additional work to clean the site. This paper proposes a method that can estimate the bucket's payload to improve the excavator's work productivity. This method assumes that the excavator is a lumped mass system. And it uses a 3 points angle (boom link, arm link, swing) and 2 points pressure (boom cylinder's input port and output port) of measurable data. Depending on assumptions, the bucket's payload can be calculated by the payload's motion equation. And this suggested method can be verified by simple experiments.

• ETRI Journal
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• v.31 no.6
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• pp.806-808
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• 2009

A new computationally efficient algorithm-based propagator method for two-dimensional (2-D) direction-of-arrival (DOA) estimation is proposed, which uses two parallel uniform linear arrays. The algorithm takes advantage of the special structure of the array which enables 2-D DOA estimation without pair matching. Simulation results show that the proposed algorithm achieves very accurate estimation at a computational cost 4 dB lower than that of standard methods.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.222-229
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• 2022

Although stochastic programming and feedback control approaches could efficiently mitigate the overdue risks caused by inherent uncertainties in ground conditions, the lack of formal representations of planners' rationales for resource allocation still prevents planners from applying these approaches due to the inability to consider comprehensive resource allocation policies for hard rock tunnel projects. To overcome the limitations, the authors developed an ontology that represents the project duration estimation rationales, considering the impacts of ground conditions, excavation methods, project states, resources (i.e., given equipment fleet), and resource allocation policies (RAPs). This ontology consists of 5 main classes with 22 subclasses. It enables planners to explicitly and comprehensively represent the necessary information to rapidly and consistently estimate the excavation durations during construction. 10 rule sets (i.e., policies) are considered and categorized into two types: non-progress-related and progress-related policies. In order to provide simplified information about the remaining durations of phases for progress-related policies, the ontology also represents encoding principles. The estimation of excavation schedules is carried out based on a hypothetical example considering two types of policies. The estimation results reveal the feasibility, potential for flexibility, and comprehensiveness of the developed ontology. Further research to improve the duration estimation methodology is warranted.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.12 no.1
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• pp.53-62
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• 2018

In this study, we developed a smart 1RM system for national health management and physical fitness, which enables quantitative 1RM measurement in various types of exercise using digital pulley technology, and to test the effect on training by using it. We developed the smart 1RM system, which is composed of portable muscle strength measuring device, Bluetooth communication based mobile phone data transmission and circuit diagram, and height adjustable system body. We recruited the 30 participants with 20th aged and divided into training and non-performing groups with 15 participants randomly. The participants performed 5 sets of elbow, lumbar, knee extension / flexion 10 times using smart 1RM system and the experimental period was 3 days a week for a total of 8 weeks. The experimental results showed that the maximum strength of the elbow, lumbar, and knee joints was significantly improved before and after maximal muscle strength training in the training group. Oxygen intakes during 1RM exercise mode showed 10.91% than endurance. To verify the validity of the smart 1RM maximal strength data, the reliability was 0.895 (* p <0.00). This study can be applied to the early rehabilitation treatment of the elderly and rehabilitation patients more quantitatively using the national health care.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.180-186
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• 2022

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the smart farming technology in the field of agriculture.

• Journal of Platform Technology
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• v.10 no.1
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• pp.3-10
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• 2022

In high-risk workplaces where communication is not possible, such as deforestation, it is necessary to use equipment that monitors the worker's situation in real time and obtains information according to the worker's location in case of an emergency. This paper analyzes the development and demonstration experiments of smart hard hats for deforestation workers to maintain a safe working environment. The developed smart helmet identifies the location of the worker based on the UWB signal for location estimation, and it is necessary to keep the distance between the workers not too close. UWB, Gyro, and LoRa are used to communicate even in the communication shadow area. It is used to provide a safe working environment such as improved construction to reduce worker risks and risks in forest working environments.

• Journal of KIBIM
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• v.12 no.4
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• pp.61-69
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• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.460-466
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• 2018

For the efficient energy consumption in an urban railway station, it is necessary to know the patterns of electrical loads for each usage in detail. The electrical loads in an urban railway station have different characteristics from other normal electrical load, such as the peak load timing during a day. The lighting, HVAC, communication, and commercial loads make up large amount of electrical load for equipment in an urban railway station, and each of them has the unique specificity. These loads for each usage were estimated without measuring device by the polynomial regression method with big data such as total amount of electrical load and weather data. In the simulation with real data, the optimal polynomial regression model was third order polynomial regression model with 9 or 10 independent variables.

• Smart Structures and Systems
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• v.30 no.4
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.