• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of the Microelectronics and Packaging Society
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• v.25 no.4
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• pp.149-154
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• 2018

This paper describes the fabrication and heat transfer property of 50 watts rated LED array module where multiple chips are mounted on chip-on-board type ceramic-metal hybrid substrate with high heat dissipation property for high power street and anti-explosive lighting system. The high heat transfer ceramic-metal hybrid substrate was fabricated by conformal coating of thick film glass-ceramic and silver pastes to form insulation and conductor layers, using thick film screen printing method on top of the high thermal conductivity aluminum alloy heat-spreading panel, then co-fired at $515^{\circ}C$. A comparative LED array module with the same configuration using epoxy resin based FR-4 PCB with thermalvia type was also fabricated, then the thermal properties were measured with multichannel temperature sensors and thermal resistance measuring system. As a result, the thermal resistance of the ceramic-metal hybrid substrate in the $4{\times}9$ type LEDs array module exhibited about one third to the value as that of FR-4 substrate, implying that at least triple performance of heat transfer property as that of FR-4 substrate was realized.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.203-215
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• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.

• Journal of Life Science
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• v.31 no.2
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• pp.237-247
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• 2021

Immune responses in the central nervous system (CNS) function as the host's defense system against pathogens and usually help with repair and regeneration. However, chronic and exaggerated neuroinflammation is detrimental and may create neuronal damage in many cases. The NOD-, LRR-, and pyrin domain―containing 3 (NLRP3) inflammasome, a kind of NOD-like receptor, is a cytosolic multiprotein complex that consists of sensors (NLRP3), adaptors (apoptosis-associated speck like protein containing a caspase recruitment domain, ASC) and effectors (caspase 1). It can detect a broad range of microbial pathogens along with foreign and host-derived danger signals, resulting in the assembly and activation of the NLRP3 inflammasome. Upon activation, NLRP3 inflammasome leads to caspase 1-dependent secretion of the pro-inflammatory cytokines IL-1β and IL-18, as well as to gasdermin D-mediated pyroptotic cell death. NLRP3 inflammasome is highly expressed in CNS-resident cell types, including microglia and astrocytes, and growing evidence suggests that NLRP3 inflammasome is a crucial player in the pathophysiology of several neuroinflammatory and psychiatric diseases, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, traumatic brain injury, amyotrophic lateral sclerosis, and major depressive disorder. Thus, this review describes the molecular mechanisms of NLRP3 inflammasome activation and its crucial roles in the pathogenesis of neurological disorders.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.119-133
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• 2022

Due to the improvement of surveying, mapping and communication techniques, various apps for road direction guides and vehicle navigations have been developed. Although such a development has impacted on walking and driving, there is a limit to improving the daily convenience of the socially impaired people. This is mainly due to the fact that the software have been developed for normal pedestrians and drivers. Therefore, visually impaired people still have problems with the confusion of direction and/or non-provision of risk factors in walking. This study aimed to propose a scheme which constructs data for mobility-impaired or traffic-impaired people based on various geospatial information. The factors and components related to walking for the visually impaired are selected by geospatial data and a walking route guidance network that can be applied to a commercial software. As a result, it was confirmed that road direction guidance would be possible if additional contents, such as braille blocks (dotted/linear), sound signals, bus stops, and bollards are secured. In addition, an initial version of the application software was implemented based on the suggested data model and its usefulness was evaluated to a visually impaired person. To advance the stability of the service in walking for the visually impaired people, various geospatial data obtained by multiple institutes are necessary to be combined, and various sensors and voice technologies are required to be connected and utilized through ICT (Information and Communications Technologies) technology in near future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.93-104
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• 2023

Currently, the permanent road traffic volume surveys under Road Act are conducted using a intrusive Automatic Vehicle Classification (AVC) equipments to classify 12 categories of vehicles. However, intrusive AVC equipment inevitably have friction with vehicles, and physical damage to sensors due to cracks in roads, plastic deformation, and road construction decreases the operation rate. As a result, accuracy and reliability in actual operation are deteriorated, and maintenance costs are also increasing. With the recent development of ITS technology, research to replace the intrusive AVC equipment is being conducted. However multiple equipments or self-built DB operations were required to classify 12 categories of vehicles. Therefore, this study attempted to prepare a method for classifying 12 categories of vehicles using vehicle specification information of the Vehicle Management Information System(VMIS), which is collected and managed in accordance with Motor Vehicle Management Act. In the future, it is expected to be used to upgrade and diversify road traffic statistics using vehicle specifications such as the introduction of a road traffic survey system using Automatic Number Plate Recognition(ANPR) and classification of eco-friendly vehicles.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.311-323
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• 2023

Oil spill accidents can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. In the case of oil spill detection using satellite imagery, it is possible to detect a wide range of oil spill areas by utilizing the information collected from various sensors equipped on the satellite. Previous studies have analyzed the reflectance of oil at specific wavelengths and have developed an oil spill index using bands within the specific wavelength ranges. When analyzing multiple images before and after an oil spill for monitoring purposes, a significant amount of time and computing resources are consumed due to the large volume of data. By utilizing Google Earth Engine, which allows for the analysis of large volumes of satellite imagery through a web browser, it is possible to efficiently detect oil spills. In this study, we evaluated the applicability of four types of oil spill indices in the area of various land cover using Sentinel-2 MultiSpectral Instrument data and the cloud-based Google Earth Engine platform. We assessed the separability of oil spill areas by comparing the index values for different land covers. The results of this study demonstrated the efficient utilization of Google Earth Engine in oil spill detection research and indicated that the use of oil spill index B ((B3+B4)/B2) and oil spill index C (R: B3/B2, G: (B3+B4)/B2, B: (B6+B7)/B5) can contribute to effective oil spill monitoring in other regions with complex land covers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.155-163
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• 2023

Real-time monitoring technology is critical for ensuring the safety and reliability of nuclear power plant structures. However, the current seismic monitoring system has limited system identification capabilities such as modal parameter estimation. To obtain global behavior data and dynamic characteristics, multiple sensors must be optimally placed. Although several studies on optimal sensor placement have been conducted, they have primarily focused on civil and mechanical structures. Nuclear power plant structures require robust signals, even at low signal-to-noise ratios, and the robustness of each mode must be assessed separately. This is because the mode contributions of nuclear power plant containment buildings are concentrated in low-order modes. Therefore, this study proposes an optimal sensor placement methodology that can evaluate robustness against noise and the effects of each mode. Indicators, such as auto modal assurance criterion (MAC), cross MAC, and mode shape distribution by node were analyzed, and the suitability of the methodology was verified through numerical analysis.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.123-135
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• 2013

The reflectance observed in the visible channels of a geostationary meteorological satellite can be used to calculate the amount of cloud by comparing the reflectance with the observed solar radiation data at the ground. Using this, the solar radiation arriving at the surface can be estimated. This study used the Meteorological Imager (MI) reflectance observed at a wavelength of 675 nm and the Geostationary Ocean Color Imager (GOCI) reflectance observed at similar wavelengths of 660 and 680 nm. Cloudy days during a typhoon and sunny days with little cloud cover were compared using observation data from the geostationary satellite. Pixels that had more than 40% reflectance in the satellite images showed less than 0.3 of the cloud index and blocked more than 70% of the solar energy. Pixels that showed less than 15% reflectance showed more than 0.9 of the cloud index and let through more than 90% of the solar energy to the surface. The calculated daily accumulated solar radiation was compared with the observed daily accumulated solar radiation in 22 observatories of the Korean Meteorological Administration. The values calculated for the COMS and MTSAT MI sensors were smaller than the observation and showed low correlations of 0.94 and 0.93, respectively, which were smaller than the 0.96 correlation coefficient calculated for the GOCI sensor. The RMSEs of MTSAT, COMS MI and GOCI calculation results showed 2.21, 2.09, 2.02 MJ/$m^2$ in order. Comparison of the calculated daily accumulated results from the GOCI sensor with the observed data on the ground gave correlations and RMSEs for cloudy and sunny days of 0.96 and 0.86, and 1.82 MJ/$m^2$ and 2.27 MJ/$m^2$, respectively, indicating a slightly higher correlation for cloudy days. Compared to the meteorological imager, the geostationary ocean color imager in the COMS satellite has limited observation time and observation is not continuous. However, it has the advantage of providing high resolution so that it too can be useful for solar energy analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.