• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1470-1475
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• 2009

Until now for the safety of structures and equipment monitoring technology to measure the amount of the physical, if that is the one, one-point or single-source target is one the most. Therefore, becoming more numerous and complex to measure the amount of physical measurement technology that is comprehensive and complex, multi-source concepts to the monitoring of a multi-sensing technology is required. Have the same characteristics of multi-source multi-use space such as a multi-structure of facilities/equipment is. The people's safety in a multi-use facility will be directly related to life and even a little carelessness can lead to large-scale disaster occurs because of several factors, risks and to manage detect in advance the development of an intelligent monitoring technology is essential. Therefore, this study shows that multiple structures/facilities to improve the quality of human life in research to maintain a safe and comfortable living space for multi-source intelligence to the development of monitoring technology to achieve that goal, and the ubiquitous sensor network system on the basis of the wireless transmission module, and multiple research facilities/equipment for the ultra-small sensors for health monitoring study was performed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1758-1764
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• 2012

A large number of wire rope has been used in various inderstiries as Cranes and Elevators from expanding the scale of the industrial market. But now, the management of wire rope is used as manually operated by rope replacement from over time or after the accident.It is caused to major accidents as well as economic losses and personal injury. Therefore its time to need periodic fault diagnosis of wire rope or supply of real-time monitoring system. Currently, there are several methods has been reported for fault diagnosis method of the wire rope, to find out the feature point from extracting method is becoming more common compared to time wave and model-based system. This method has implemented a deterministic modeling like the observer and neural network through considering the state of the system as a deterministic signal. However, the out-put of real system has probability characteristics, and if it is used as a current method on this system, the performance will be decreased at the real time. And if the random noise is occurred from unstable measure/experiment environment in wire rope system, diagnostic criterion becomes unclear and accuracy of diagnosis becomes blurred. Thus, more sophisticated techniques are required rather than deterministic fault diagnosis algorithm. In this paper, we developed the fault diagnosis of the wire rope using probability density estimation techniques algorithm. At first, The steady-state wire rope fault signal detection is defined as the probability model through probability distribution estimate. Wire rope defects signal is detected by a hall sensor in real-time, it is estimated by proposed probability estimation algorithm. we judge whether wire rope has defection or not using the error value from comparing two probability distribution.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.789-796
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• 2006

Context-aware is the core in ubiquitous environment of sensor network to support intelligent and contextual adaptation service. The new context information model is demanded to support context-aware applications. The model should not depend on a specified application and be shareable between applications in the same environment. Also, it should support various context representation and complex context-aware. In this paper, we define the context information according to context-aware process. Also we design the knowledge of domain as well as applications using ontologies and rules. The domain spatial ontology and application knowledge are represented using the spatial object model and the rules of expanded ontologies, respectively. The expression of abundant spatial ontology represents the context information about distance between objects and adjacent object as well as the location of the object. The proposed context information model which is able to exhibit various spatial context and recognizes complex spatial context through the existing GIS. This model shows that it can adapt to a large scale outdoor context-aware applications such as air pollution and prevention of disasters as well as various context-aware applications.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.10
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• pp.259-268
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• 2018

Due to the development of cities and the increase of vehicles, effective control of parking space management service in cities is needed. However, the existing parking lot management system does not provide limited or convenient service in terms of space and time. In this paper, we propose distributed parking space management service based on large scale LPWA (Low-Power Wide-Area). The parking sensor collects parking space information from the parking lot and is transmitted over a low-power wide network. All parking data is processed and analyzed in the IoT cloud. Through a parking space management service system in all cities, users are given the temporal convenience of determining the parking space and the area efficiency of the parking space.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.1
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• pp.69-80
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• 2021

The new town in Korea, developed as a large-scale housing plan, has created urban ecological corridors to provide habitat and movement routes to wildlife and to promote natural ecological flow. This study aimed to investigate the use of wildlife in 10 ecological corridors in Gwanggyo New Town through camera trap technology and confirm effectiveness by identifying environmental factors affecting the use of wildlife's urban ecological corridors. Our researchers installed 20 unmanned sensor cameras at each the entrance and exit of the ecological corridors, and monitored urban wildlife for 10 weeks. According to the monioring results, the main species in Gwanggyo New Town were identified not only raccons, cats, water deer, korean hare and avain but also magpies, dove, eurasian tree sparrow, ring-necked pheasant, and eurasian jay. The number of uses ecological corridors of urban residents was 801(13.49%), as high as that of urban wildlife (1,140, 19.20%), which was judged to have disturbed the use of ecological corridors by wildlife. However, most dominant species of urban wildlife are nocturnal so that, it was judged that they share home range with urban residents at a time interval. In addition, according to the correlation analysis results between the mammal using rate of the urban ecological corridors and environmental factors(ecological corridor-specific length, ecological corridor-specific width, cover degree, shielding degree, connected green area, separation of movement routes, and presence of streetlights), environmental factors were not statistically significant. However, the more the area of green space connected to ecological corridors, the more increasing the mammal using rate of ecological corridor(r=0.71, p<0.05). Therefore, the area of green space connected to the ecological corridors that is associated with rate of wildlife using corridors should be considered as a priority when developing an urban ecological corridors. In the future, this study will extend the observation period of the ecological corridors and continuously accumulate data by adding the number of observation cameras. Furthermore, it is expected that the results of this study can be used as basic data for the standards for urban ecological corridors installation.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1307-1307
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• 2024

Recent research on heavy equipment has been conducted for the purposes of enhanced safety, productivity improvement, and carbon neutrality at construction sites. A sensor-based approach is being explored to monitor the location and movements of heavy equipment in real time. However, it poses significant challenges in terms of time and cost as multiple sensors should be installed on numerous heavy equipment at construction sites. In addition, there is a limitation in identifying the collaboration or interference between two or more heavy equipment. In light of this, a vision-based deep learning approach is being actively conducted to effectively respond to various working conditions and dynamic environments. To enhance the performance of a vision-based activity recognition model, it is essential to secure a sufficient amount of training datasets (i.e., video datasets collected from actual construction sites). However, due to safety and security issues at construction sites, there are limitations in adequately collecting training dataset under various situations and environmental conditions. In addition, the videos feature a sequence of multiple activities of heavy equipment, making it challenging to clearly distinguish the boundaries between preceding and subsequent activities. To address these challenges, this study proposed a domain adaptation in vision-based transfer learning for automated excavator activity recognition utilizing 3D ResNet (residual deep neural network). Particularly, this study aimed to identify the optimal depth of 3D ResNet (i.e., the number of layers of the feature extractor) suitable for domain adaptation via fine-tuning process. To achieve this, this study sought to evaluate the activity recognition performance of five 3D ResNet models with 18, 34, 50, 101, and 152 layers, which used two consecutive videos with multiple activities (5 mins, 33 secs and 10 mins, 6 secs) collected from actual construction sites. First, pretrained weights from large-scale datasets (i.e., Kinetic-700 and Moment in Time (MiT)) in other domains (e.g., humans, animals, natural phenomena) were utilized. Second, five 3D ResNet models were fine-tuned using a customized dataset (14,185 clips, 60,606 secs). As an evaluation index for activity recognition model, the F1 score showed 0.881, 0.689, 0.74, 0.684, and 0.569 for the five 3D ResNet models, with the 18-layer model performing the best. This result indicated that the activity recognition models with fewer layers could be advantageous in deriving the optimal weights for the target domain (i.e., excavator activities) when fine-tuning with a limited dataset. Consequently, this study identified the optimal depth of 3D ResNet that can maintain a reliable performance in dynamic and complex construction sites, even with a limited dataset. The proposed approach is expected to contribute to the development of decision-support systems capable of systematically managing enhanced safety, productivity improvement, and carbon neutrality in the construction industry.