• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.9-21
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• 2022

In this paper, we developed a 100% detection system for entering and leaving vehicles by improving the detection rate of existing detection cameras based on the LiDAR sensor, which is one of the core technologies of the 4th industrial revolution. Since the currently operating parking lot depends only on the recognition rate of the license plate number of about 98%, there are various problems such as inconsistency in the entry/exit count, inability to make a reservation in advance due to inaccurate information provision, and inconsistency in real-time parking information. Parking status information should be managed with 100% accuracy, and for this, we built a parking lot entrance/exit detection system using LIDAR. When a parking system is developed by applying the LIDAR sensor, which is mainly used to detect vehicles and objects in autonomous vehicles, it is possible to improve the accuracy of vehicle entry/exit information and the reliability of the entry/exit count with the detected sensing information. The resolution of LIDAR was guaranteed to be 100%, and it was possible to implement so that the sum of entering (+) and exiting (-) vehicles in the parking lot was 0. As a result of testing with 3,000 actual parking lot entrances and exits, the accuracy of entering and exiting parking vehicles was 100%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.169-174
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• 2023

Currently, overall power usage is also increasing as power demand such as homes, offices, and factories increases. The increase in power use also raised interest in standby power as a change in awareness of energy saving appeared. Home and office devices are consuming power even in standby conditions. Accordingly, there is a growing need to reduce standby power, and it aims to have standby power of 1W or less. An intelligent outlet uses a near-field wireless network to connect to a home network and cut or reduce standby power of a lamp or appliance connected to an outlet. This research aims to develop a monitoring system and an intelligent outlet that can remotely monitor the amount of electricity used in a lighting lamp or a home appliance connected to an outlet using a short-range wireless network (Zigbee). Also, The intelligent outlet and monitoring system developed makes it possible for a user to easily cut off standby power by using a portable device. Intelligent outlets will not only reduce standby power but also be applicable to fire prevention systems. Devices that cut off standby power include intelligent outlets and standby power cutoff switches, so they will prevent short circuits and fires.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.30-30
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• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Journal of the Korean Applied Science and Technology
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• v.40 no.2
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• pp.258-267
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• 2023

In this study, to investigate the possibility of using ethanol extract of Petasites japonicus (PJE) as a functional material, we investigated the activity of improving skin barrier and inflammation through UVB-induced human keratinocyte (HaCaT cell). As a result of confirming the antioxidant effect through DPPH radical scavenging activity, ABTS⁺ radical scavenging activity, and hydrogen peroxide scavenging activity, it was confirmed that it had an antioxidant effect similar to that of ascorbic acid, a control, at a concentration of 1 mg/ml. As a result of confirming the mRNA expression of the production ability of filaggrin and aquaporin-3 in HaCaT cells induced by UVB, it was confirmed that the reduced expression level by UVB stimulation increased in a concentration-dependent manner when the PJE was treated. It was confirmed that the mRNA expression of TNF-𝛼 and IL-1𝛽 were increased by UVB stimulation and decreased when the PJE was treated. As a result of the migration assay, it was confirmed that the proliferation of skin keratinocytes and the recovery rate of wounds were increased in a concentration-dependent manner. Based on the experimental results, it suggests that Petasites japonicus can be used as a functional cosmetic product that can improve skin moisturizing and skin barrier function.

• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.45-52
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• 2022

Recently, interest in and demand for sensors that recognize physical activity and their products are increasing. In particular, the development of wearable materials that are flexible, stretchable, and able to detect the user's biological signals is drawing attention. In this study, an experiment was conducted to improve the dip-coating efficiency of a single-walled carbon nanotube dispersion solution after fine holes were made in a hydrophobic material with a micro needle. In this study, dip-coating was performed with a material that was not penetrated, and comparative analysis was performed. The electrical conductivity of the sensor was measured when the sensor was stretched using a strain universal testing machine (Dacell Co. Ltd., Seoul, Korea) and a multimeter (Keysight Technologies, Santa Rosa, CA, USA) was used to measure resistance. It was found that the electrical conductivity of a sensor that was subjected to needling was at least 16 times better than that of a sensor that was not. In addition, the gauge factor was excellent, relative to the initial resistance of the sensor, so good performance as a sensor could be confirmed. Here, the dip-coating efficiency of hydrophobic materials, which have superior physical properties to hydrophilic materials but are not suitable due to their high surface tension, can be adopted to more effectively detect body movements and manufacture sensors with excellent durability and usability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.443-451
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• 2010

This paper proposes the methodology about advisory safety speed based on real-time vehicular data collected from highway. The proposed model is useful information to drivers by appling seamless wireless communication and being collected from ECU(Engine Control Unit) equipment in every vehicle. Furthermore, this model also permits the use of realtime sensing data like as adverse weather and road-surface data. Here, the advisory safety speed is defined "the safety speed for drivers considering the time-dependent traffic condition and road-surface state parameter at uniform section", and the advisory safety speed model is developed by considering the parameters: inter-vehicles safe stopping distance, statistical vehicle speed, and real-time road-surface data. This model is evaluated by using the simulation technique for exploring the relationships between advisory safety speed and the dependent parameters like as traffic parameters(smooth condition and traffic jam), incident parameters(no-accident and accident) and road-surface parameters(dry, wet, snow). A simulation's results based on 12 scenarios show significant relationships and trends between 3 parameters and advisory safety speed. This model suggests that the advisory safety speed has more higher than average travel speed and is changeable by changing real-time incident states and road-surface states. The purpose of the research is to prove the new safety related services which are applicable in SMART Highway as traffic and IT convergence technology.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.113-123
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• 2023

This paper introduces the Cherry system, a user-independent blockchain donation system. This is a procedure that is delivered to the beneficiary's bank account through a virtual account when a donor makes a donation, so there is no difference from the existing donation delivery method from the user's point of view However, within the blockchain, Cherry Points, a virtual currency based on the user ID, are issued and delivered to the beneficiary, while all transactions and the beneficiary's usage history are managed on the blockchain. By adopting this method, there was an improvement in blockchain performance, with transaction processing exceeding 1,000 TPS in typical transaction condition and service completion within 21.3 seconds. By applying the automatic influence control algorithm to this system, the influence according to stake, which is an individual donation, is greatly reduced to 0.3 after 2 months, thereby concentrating influence could be controlled automatically. In addition, it was designed to enable micro tracking by adding a tracking function by timestamp to the donation ledger for each individual ID, which greatly improved the transparency in the use of donations. From a service perspective, existing blockchain donation systems were handled as limited donation delivery methods. Since it is a direct service in a user-independent method, convenience has been greatly improved by delivering donations in various forms.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.867-874
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• 2023

Recently, many problems are occurring in the bee ecosystem due to rapid climate change. The decline in the bee population and changes in the flowering period are having a huge impact on the harvest of bee-keepers. Since it is impossible to continuously observe the beehives in the hive with the naked eye, most people rely on knowledge based on experience about the state of the hive.Therefore, interest is focused on smart beekeeping incorporating IoT technology. In particular, with regard to swarming, which is one of the most important parts of beekeeping, we know empirically that the swarming time can be determined by the sound of the queen bee, but there is no way to systematically analyze this with data.You may think that it can be done by simply recording the sound of the queen bee and analyzing it, but it does not solve various problems such as various noise issues around the hive and the inability to continuously record.In this study, we developed a system that records queen bee sounds in a real-time cloud system and analyzes sound patterns.After receiving real-time analog sound from the hive through multiple channels and converting it to digital, a sound pattern that was continuously output in the queen bee sound frequency band was discovered. By accessing the cloud system, you can monitor sounds around the hive, temperature/humidity inside the hive, weight, and internal movement data.The system developed in this study made it possible to analyze the sound patterns of the queen bee and learn about the situation inside the hive. Through this, it will be possible to predict the swarming period of bees or provide information to control the swarming period.