• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1065-1070
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• 2018

These days there are many researches on the vision based technology using deep learning. The lots of studies on the intelligent operation and maintenance for railway station system used technologies with vision analysis function. This paper analyzes the papers which studied the intelligent station system with vision analysis function for passengers and facilities monitoring, platform monitoring, fire monitoring, and effective operation and design. Also, this paper proposes research which uses the more powerful vision technology with deep-learning for smart railway station system.

• Korean Journal of Materials Research
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• v.20 no.3
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• pp.148-154
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• 2010

It is necessary to develop new methods to prevent catastrophic failure of structural material in order to avoid accidents and conserve natural and energy resources. Design of intelligent materials with a self-diagnosing function to prevent fatal fracture of structural materials was achieved by smart composites consisting of carbon fiber tows or carbon powders with a small value of ultimate elongation and glass fiber tows with a large value of ultimate elongation. The changes in electrical resistance of CF-GFRP/GFRP (carbon fiber and glass fiber-reinforced plastics/glass fiber-reinforced plastics) composites increased abruptly with increasing strain, and a tremendous change was seen at the transition point where carbon fiber tows were broken. Therefore, the composites were not to monitor damage from the early stage. On the other hand, the change in electrical resistance of CP-GFRP/GFRP (carbon powder dispersed in glass fiber-reinforced plastics/glass fiber-reinforced plastics) composites increased almost linearly in proportion to strain. CP-GFRP/GFRP composites are superior to CF-GFRP/GFRP composites in terms of their capability to monitor damage by measuring change in electrical resistance from the early stage of damage. However, the former was inferior to the latter as an application because of the difficulties of mass production and high cost. A method based on monitoring damage by measuring changes in the electrical resistance of structural materials is promising for improved reliability of the material.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.89-100
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• 2021

The conservation of stream habitats has been gaining more public attention and fish habitat suitability index (HSI) is an important measure for ecological stream habitat assessment. The fish habitat preference is affected not only by physical stream conditions but also by water quality of which HSI was not available due to the lack of field data. The purpose of this study is to estimate the HSI of Zacco platypus for water quality parameters of water temperature, dissolved oxygen (DO), and biochemical oxygen demand (BOD) using the water environment monitoring data provided by the Ministry of Environment (ME). Fish population data merged with water quality were constructed by spatio-temporal matching of nationwide water quality monitoring data with bio-monitoring data of the ME. Two types of the HSI were calculated by the Instream Flow and Aquatic Systems Group (IFASG) method and probability distribution (Weibull) fitting for the four major river basins. Both the HSIs by the IFASG and Weibull fitting appeared to represent the overall distribution and magnitude of fish population and this can be used in stream fish habitat evaluation considering water quality.

• Journal of Platform Technology
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• v.6 no.4
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• pp.41-48
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• 2018

Accidents involving infants and toddlers are occurring frequently, and if elementary measures are not smooth, they can lead to even greater accidents. Rapid elementary measures require an appropriate monitoring system, and it is necessary to build a system that can accurately monitor the child's living condition and notify the emergency situation immediately. Recently, with the development of IoT technology, various types of sensors are being developed in a very small size, and development of wearable devices connected with smart phones is accelerating with the development of smart phones. By using these wearable devices, it is possible to reduce the risk of infant accidents and to respond promptly in case of an accident. In this paper, we propose a wearable system that can monitor the infant condition and present a service to prevent infant accidents.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.11-16
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• 2019

Wireless sensor network is important for precision farming to monitor the growth environment of crops in open field, but radio signals are susceptible to different types of interference such as weather and physical objects. This paper designs and implements an environmental monitoring and weather forecast acquisition systems for smart field irrigation based on LoRa(Long Range) and then applies it to a test bed. And we evaluate the network reliability in terms of packet transmission success rate by comparing its condition on two criteria; the existence of obstacle or rain. The results show that much rain falls can affect on packet loss in LoRa field networks with obstacles.

• Smart Structures and Systems
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• v.28 no.6
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• pp.737-744
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• 2021

In this study, structural health monitoring (SHM) methods of carbon fiber reinforced plastics (CFRPs) were investigated using electrical resistance. The developed sensing technique monitored electrical resistance in accordance with the impact damage of a CFRP. The changes in electrical resistances with multiple electrode sets enabled SHM without extra sensors so that this technique can be called self-sensing. Moreover, this study proposed electrodes only at peripheral side of a structure to minimize the number of electrodes compared to those in an array which has square number of sensors as the sensing area increases. For the intensive investigation, electromechanical sensitivity in terms of electrode distance was analyzed and optimized under drop weight impact testing. Then, SHM methods with electrodes in an array and electrodes in peripheral edges were comparatively investigated. The developed methods successfully localized impact damages into 2D coordinates. Furthermore, damage severity can be shown with a damage map by calculating electrical resistance change ratio. Therefore, structural health self-sensing system using electrical resistance was successfully developed with the minimum number of electrodes.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.31-48
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• 2023

The popularization of smart devices and subsequent optimization of their sensing capacity has resulted in a novel mobile crowdsensing (MCS) pattern, which employs smart devices as sensing nodes by recruiting users to develop a sensing network for multiple-task performance. This technique has garnered much scholarly interest in terms of sensing range, cost, and integration. The MCS is prevalent in various fields, including environmental monitoring, noise monitoring, and road monitoring. A complete MCS life cycle entails task allocation, data collection, and data aggregation. Regardless, specific drawbacks remain unresolved in this study despite extensive research on this life cycle. This article mainly summarizes single-task, multi-task allocation, and space-time multi-task allocation at the task allocation stage. Meanwhile, the quality, safety, and efficiency of data collection are discussed at the data collection stage. Edge computing, which provides a novel development idea to derive data from the MCS system, is also highlighted. Furthermore, data aggregation security and quality are summarized at the data aggregation stage. The novel development of multi-modal data aggregation is also outlined following the diversity of data obtained from MCS. Overall, this article summarizes the three aspects of the MCS life cycle, analyzes the issues underlying this study, and offers developmental directions for future scholars' reference.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.25-37
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• 2024

The objective of this study was to create intelligent rail robots that are optimized for facility management and implement digital twin systems for smart road tunnel management. An autonomous surveillance system is formed by combining the sensing platform consisting of railing robots, fixed cameras and environmental detection sensors with the digital twin data platform technology for tunnel monitoring and early fire suppression. In order to develop mobile rail robots for fire extinguishing, we also designed and manufactured robots for extinguishing & monitoring and fire extinguishing devices, and then we examined the optimization of all parts. Our next step was to build a digital twin for road tunnel management by developing continuous image display system and implementing 3D modeling. After constructing prototypes, we attempted simulations by configuring abnormal symptom scenarios, such as vehicles fires. This study's proposal proposes high-accuracy risk prediction services that will enable intelligent management of risks in the tunnel with early response at each stage, using the data collected from the intelligent rail robots and digital twin systems.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Fashion & Textile Research Journal
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• v.10 no.1
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• pp.1-10
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• 2008