• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.10
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• pp.797-808
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• 2019

In a closed workplace, the management of the workplace is important because the environmental data at the workplace has a great influence on the safety of workers. Today's industrial sites are transformed into data-based factories that collect and analyze data through sensors in those sites, requiring a management system to ensure safety. In general, a safety management system stores and manages data on a central server associated with a database. Since such management system introduces high possibility of forgery and loss of data, workers often suspect the reliability of the information on the management system. In this paper, we present a worker safety management system based on the EOS blockchain which is considered as third-generation blockchain technology. The developed system consists of a set of smart contracts on the EOS blockchain and 3 decentralized applications associated with the blockchain. According to the roles of users, the worker and manager applications respectively perform the process of initiating or terminating tasks as blockchain transactions. The entire transaction history is distributed and stored in all nodes participating in the blockchain network, so forgery and loss of data is practically impossible. The system administrator application assigns the account rights of workers and managers appropriate for performing the functions, and specifies the safety standards of IoT data for ensuring workplace safety. The IoT data received from sensor platforms in workplaces and the information on initiation, termination or approval of tasks assigned to workers, are explicitly stored and managed in the EOS smart contracts.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.28-36
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• 2021

It is known that buried rebars inside concrete structures are protected from corrosion due to passive layer. It is very important to delay the timing of corrosion or evaluate a detection of corrosion initiation for the purpose of cost-beneficiary service life of a structure. In this study, corrosion monitoring was performed on concrete specimens considering 3 levels of cover depth(60 mm, 45 mm, and 30 mm), W/C(water to cement) ratio(40.0%, 50.0%, and 60.0%) and chloride concentration(0.0%, 3.5%, and 7.0%). OCP(Open Circuit Potential) was measured using agar-based socket type sensors. The OCP measurement showed the consistent behavior where the potential was reduced in wet conditions and it was partially recovered in dry conditions. In the case of 30 mm of cover depth for most W/C ratio cases, the lowest OCP value was measured and rapid OCP recovery was evaluated in increasing cover depth from 30 mm to 45 mm, since cover depth was an effective protection against chloride ion ingress. As the chloride concentration increased, the effect on the cover depth tended to be more dominant than the that of W/C ratio. After additional monitoring and physical evaluation of chloride concentration after specimen dismantling, the proposed system can be improved with increasing reliability of the corrosion monitoring.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.4
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• pp.253-263
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• 2021

Concrete structures are damaged by aging and external environmental factors. This type of damage is to appear in the form of cracks, to proceed in the form of spalling. Such concrete damage can act as the main cause of reducing the original design bearing capacity of the structure, and negatively affect the stability of the structure. If such damage continues, it may lead to a safety accident in the future, thus proper repair and reinforcement are required. To this end, an accurate and objective condition inspection of the structure must be performed, and for this inspection, a sensor technology capable of detecting damage area is required. For this reason, we propose a deep learning-based image processing algorithm that can detect spalling. To develop this, 298 spalling images were obtained, of which 253 images were used for training, and the remaining 45 images were used for testing. In addition, an improved loss function and data augmentation technique were applied to improve the detection performance. As a result, the detection performance of concrete spalling showed a mean intersection over union of 80.19%. In conclusion, we developed an algorithm to detect concrete spalling through a deep learning-based image processing technique, with an improved loss function and data augmentation technique. This technology is expected to be utilized for accurate inspection and diagnosis of structures in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.764-771
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• 2018

Domestic and foreign agricultural environments nowadays are undergoing various changes such as aging of agricultural population, increase of earned population, rapid climate change, diversification of agricultural product distribution structure, depletion of water resources and limited cultivation area. In order to respond to various environmental changes in recent agriculture, practical use of Smart Greenhouse to easily record, store and manage crop production information such as crop growing information, growth environment and agriculture work log, Interest is growing. In this paper, we propose a system that collects the situation information necessary for growth such as temperature, humidity, solar radiation, CO2 concentration, and monitor the collected data, which can be measured in the rhizosphere of the crop. We have developed a system that collects data such as temperature, humidity, radiation, and growth environment data, which are measured by data obtained from the rhizosphere measuring section of a growing crop and measured by a sensor, and transmitted to a wireless communication gateway of 400 MHz. We developed the integrated SW that can monitor the rhythm environment data and visualize the data by using cloud based data. We can monitor by graph format and data format for visualization of data. The existing smart farm managed crops and facilities using only the data within the farm, and this study suggested the most efficient growth environment by collecting and analyzing the weather and growth environment of the farms nationwide.

• Journal of IKEEE
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• v.23 no.2
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• pp.743-746
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• 2019

In this paper we proposed a new ocean radiation automatic monitoring system. The proposed system has the following characteristics: First, using NaI + PVT mixed detectors, the response speed is fast and precision analysis is possible. Second, the application of temperature compensation algorithm to scintillator-type sensors does not require additional cooling devices and enables stable operation in the changing ocean environment. Third, since cooling system is not needed, electricity consumption is low, and electricity can be supplied reliably by utilizing solar energy, which can be installed at the observation deck of ocean environment. Fourth, using GPS and wireless communications, accurate location information and real-time data transmission function for measurement areas enables immediate warning response in the event of nuclear accidents such as those involving neighboring countries. The results tested by the authorized testing agency to assess the performance of the proposed system were measured in the range of $5{\mu}Sv/h$ to 15mSv/h, which is the highest level in the world, and the accuracy was determined to be ${\pm}8.1%$, making normal operation below the international standard ${\pm}15%$. The internal environmental grade (waterproof) was achieved, and the rate of variation was measured within 5% at operating temperature of $-20^{\circ}C$ to $50^{\circ}C$ and stability was verified. Since the measured value change rate was measured within 10% after the vibration test, it was confirmed that there will be no change in the measured value due to vibration in the ocean environment caused by waves.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.5
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• pp.657-673
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• 2019

A monitoring method based on acoustic emission (AE) sensor has been widely used to evaluate the damage of structures in underground rock. The acoustic emission signal generated from cracking in material is analyzed as various acoustic emission parameters in time and frequency domain. To investigate from initial crack generation to final failure of rock material, it is important to understand the characteristics of acoustic emission parameters according to the stress ratio and rock strength. In this study, uniaxial compression tests were performed using very strong and weak rock specimen in order to investigate the acoustic emission parameters when the failure of specimen occurred. In the results of experimental tests, the event, root-mean-square (RMS) voltage, amplitude, and absolute energy of very strong rock specimen were larger than those of the weak rock specimen with an increase of stress ratio. In addition, the acoustic emission parameters related in frequency were more affected by specification (e.g., operation and resonant frequency) of sensors than the stress ratio or rock strength. It is expected that this study may be meaningful for evaluating the damage of underground rock when the health monitoring based on the acoustic emission technique will be performed.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.627-636
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• 2021

Although many studies on ship detection using synthetic aperture radar (SAR) satellite images are being conducted around the world, there are still very few employing SAR microsatellites, as most of the microsatellites are optical satellites. Recently, the ICEYE and Capella Space have embarked on the development of microsatellites with SAR sensor, and similar projects are being initiated globally in line with the flow of the new space era [e.g., for the ICEYE: 18 satellites (~2021); Capella Space: 36 satellites (~2023); and the Coast Guard SAR: 32 satellites in the early development stage]. In preparation for these new systems, it is important to review the SAR microsatellite system and the recent advances in this technology. Accordingly, in this paper, the current status and characteristics of optical and SAR microsatellite constellation operation are described, and studies using them are investigated. In addition, based on the status and characteristics of the representative SAR microsatellites, specifically the ICEYE and Capella systems, methods for using SAR microsatellite data for ship detection applications are described. Our results confirm that the SAR microsatellites operate as a constellation and have the advantages of short revisit cycles and quick provision of high-resolution images. With this technology, we expect SAR microsatellites to contribute greatly to the monitoring a wide-area target vessel, in which the spatiotemporal resolution of the imagery is especially important.

• Journal of Applied Biological Chemistry
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• v.63 no.4
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• pp.347-355
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• 2020

Several sensors have been developed for soil and plants to assess plant stress due to climate change. Therefore, the objective of the study is to nondestructively evaluate temperature stress on plant by monitoring climatic and soil conditions and plant responses using various sensors. Plant responses were monitored by electrical conductivity in plant stem and sap flow rate. Electrical conductivity in plant stem reflects the physiological activity of plants including water and ion transport. Fully grown Brassica oleracea var. italica was exposed to 20/15 ℃ (day/night) with 16 h photoperiods as a control, low temperature 15/10 ℃, and high temperature 35/30 ℃ while climatic, soil, and plant conditions were monitored. Electrical conductivity in plant stem and sap flow rate increased during the day and decreased at night. Under low temperature stress, electrical conductivity in plant stem of Brassica oleracea var. italica was lower than control while under high temperature stress, it was higher than control indicating that water and ion transport was affected. However, chlorophyll a and b increased in leaves subjected to low temperature stress and there was no significant difference between high temperature stressed leaves and control. Free proline contents in the leaves did not increase under low temperature stress, but increased under high temperature stress. Proline synthesis in plant is a defense mechanism under environmental stress. Therefore, Brassica oleracea var. Italica appears to be more susceptible to high temperature stress than low temperature.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.