• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.435-442
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• 2017

The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4865-4885
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• 2019

We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line(s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.

• Safety and Health at Work
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• v.11 no.2
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• pp.173-177
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• 2020

Background: Indoor air dampness microbiota (DM) is a big health hazard. Sufficient evidence exists that exposure to DM causes new asthma or exacerbation, dyspnea, infections of upper airways and allergic alveolitis. Less convincing evidence has yet been published for extrapulmonary manifestations of dampness and mold hypersensitivity syndrome). Methods: We investigated the prevalence of extrapulmonary in addition to respiratory symptoms with a questionnaire in a cohort of nurses and midwives (n = 90) exposed to DM in a Helsinki Obstetric Hospital. The corresponding prevalence was compared with an unexposed cohort (n = 45). Particular interest was put on neurological symptoms and multiple chemical sensitivity. Results: The results show that respiratory symptoms were more common among participants of the study vs. control cohort, that is, 80 vs 29%, respectively (risk ratio [RR]: 2.56, p < 0.001). Symptoms of the central or peripheral nervous system were also more common in study vs. control cohort: 81 vs 11% (RR: 6.63, p < 0.001). Fatigue was reported in 77 vs. 24%, (RR: 3.05, p < 0.001) and multiple chemical sensitivity in 40 vs. 9%, (RR: 3.44, p = 0.01), the so-called "brain fog", was prevalent in 62 vs 11% (RR: 4.94, p < 0.001), arrhythmias were reported in 57 vs. 2.4% (RR: 19.75, p < 0.001) and musculoskeletal pain in 51 vs 22% (RR: 2.02, p = 0.02) among participants of the study vs. control cohort, respectively. Conclusion: The results indicate that the exposure to DM is associated with a plethora of extrapulmonary symptoms. Presented data corroborate our recent reports on the health effects of moist and mold exposure in a workplace.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.9 no.1
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• pp.33-54
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• 2003

The aim of this study was to develop a model that could be used in the design of modified atmosphere packaging (MAP) for peaches. Respiratory data at 5, 10, $20^{\circ}C$ for peaches were gathered and altered for create useful respiration model. Packaging materials were conventional low density polyethylene and polypropylene with anti-fog, and anti-fungi treatments, and thickness was $30{\mu}m$ and $50{\mu}m$ each. Permeability tests were performed to find their oxygen, carbon dioxide, water vapor transmission rate as increases in temperature. Test results were then converted to logarithm format for MAP modeling. The maximum rate of oxygen uptake increased with increasing temperature. Optimum gas composition in the package system for fruits were set according to literature and upper or lower limits of oxygen and dioxide established. To predict gas composition at certain storage time, weight of fruits, film thickness, film type, and other variables, respiration rate was studied at various storage conditions. The results of tests were used to calculate Cameron's model and converted to a cubic estimation equation. The validity of the model was tested experimentally by observing actual atmospheric changes inside packages. This result of study may be useful for designing dynamic gas exchange MAP systems for similar agricultural products.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.712-736
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• 2022

To understand the characteristics of low-level clouds (CLs), environmental variables are composited on each CL using individual surface observations and six-hourly upper-air meteorologies around the globe. Individual CLs has its own distinct environmental conditions. Over the eastern subtropical and western North Pacific Ocean in JJA, stratocumulus (CL5) has a colder sea surface temperature (SST), stronger and lower inversion, and more low-level cloud amount (LCA) than the climatology whereas cumulus (CL12) has the opposite characteristics. Over the eastern subtropical Pacific, CL5 and CL12 are influenced by cold and warm advection within the PBL, respectively but have similar cold advection over the western North Pacific. This indicates that the fundamental physical process distinguishing CL5 and CL12 is not the horizontal temperature advection but the interaction with the underlying sea surface, i.e., the deepening-decoupling of PBL and the positive feedback between shortwave radiation and SST. Over the western North Pacific during JJA, sky-obscuring fog (CL11), no low-level cloud (CL0), and fair weather stratus (CL6) are associated with anomalous warm advection, surface-based inversion, mean upward flow, and moist mid-troposphere with the strongest anomalies for CL11 followed by CL0. Over the western North Pacific during DJF, bad weather stratus (CL7) occurs in the warm front of the extratropical cyclone with anomalous upward flow while cumulonimbus (CL39) occurs on the rear side of the cold front with anomalous downward flow. Over the tropical oceans, CL7 has strong positive (negative) anomalies of temperature in the upper troposphere (PBL), relative humidity, and surface wind speed in association with the mesoscale convective system while CL12 has the opposite anomalies and CL39 is in between.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.6-13
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• 2022

The Ministry of Land, Infrastructure and Transport (MoLIT) and the Korean Automobile Testing and Research Institute (KATRI) are supporting the development of Lv.3 automated vehicle (hereinafter, AV) technology by constructing an automated driving pilot city (as known as K-City) equipped with total 5 evaluation environments (urban, motorway, suburban, community road, and autonomous parking facility) which is a test bed exclusively for AV (2017~2018). An upgrade project is in a progress to materialize harsh environments such as bad weather (rain, fog, etc.) and reproduction of communication jamming (GPS blocking, etc.) with the purpose of supporting the development of Lv.4 connected & automated vehicle (hereinafter, CAV) technology (2019~2022). We intend to proactively establish a national level standard for CAV test-bed and test road requirements, test method, etc. for establishment of a road map for the construction of the test bed which is being promoted step by step and analyze and, when required, benchmark the case of China that has announced and is utilizing it. Through this, we plan to define standardized requirements (evaluation facility, evaluation system, etc.) on the test bed for the development of Lv.4/4+ CAV technology and utilize the same for the design and construction of a test bed, establishment of a road map for the construction of a real car-based test environment related to the support for autonomous driving service substantiation, etc. through provision of an evaluation environment utilizing K-City, and the establishment of a K-City upgrade strategies, etc.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5D
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• pp.635-644
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• 2009

Image processing technique in the outdoor environment is very sensitive, and it tends to lose a lot of accuracy when it rapidly changes by outdoor environment. Therefore, in order to calculate accurate traffic information using the traffic monitoring system, we must resolve removing shadow in transition time, Distortion by the vehicle headlights at night, noise of rain, snow, and fog, and occlusion. In the research, we developed a system to calibrate the amount of traffic, speed, and time occupancy by using image processing technique in a variety of outdoor environments change. This system were tested under outdoor environments at the Gonjiam test site, which is managed by Korea Institute of Construction Technology (www.kict.re.kr) for testing performance. We evaluated the performance of traffic information, volume counts, speed, and occupancy time, with 4 lanes (2 lanes are upstream and the rests are downstream) from the 16th to 18th December, 2008. The evaluation method performed as based on the standard data is a radar detection compared to calculated data using image processing technique. The System evaluation results showed that the amount of traffic, speed, and time occupancy in period (day, night, sunrise, sunset) are approximately 92-97% accuracy when these data compared to the standard data.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• Atmosphere
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• v.31 no.2
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• pp.241-249
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• 2021

A smart city utilizes data collected from various sensors through the internet of things (IoT) and improves city operations across the urban area. Recently substantial research is underway to examine all aspects of data that requires for the smart city operation. Atmospheric data are an essential component for successful smart city implementation, including Urban Air Mobility (UAM), infrastructure planning, safety and convenience, and traffic management. Unfortunately, the current level of conventional atmospheric data does not meet the needs of the new city concept. New and innovative approaches to developing high spatiotemporal resolution of observational and modeling data, resolving the complex urban structure, are expected to support the future needs. The geographic information system (GIS) integrates the atmospheric data with the urban structure and offers information system enhancement. In this study we proposed the necessity and applicability of the high resolution urban meteorological dataset based on heavy fog cases in the smart city region (e.g., Sejong and Pusan) in Korea.

• Journal of Environmental Science International
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• v.22 no.6
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• pp.745-759
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• 2013

The characteristics of meteorological conditions and air pollution were investigated in a valley city (Yangsan) on bad visibility days (from 05:00 to 09:00 LST) of the cold half year (November 2008 to April 2009). This analysis was performed using the hourly observed data of meteorological variables (temperature, wind speed and direction, relative humidity, and 2 m and 10 m temperature) and air pollutants ($NO_2$, $SO_2$, $PM_{10}$, and $O_3$). In addition, visibility data based on visual measurements and a visibility meter were used. The bad visibility days were classified into four types: fog, mist, haze, and the mixture (mist+haze). The results showed that the bad visibility days of the four types in the valley city were observed to be more frequently (about 50% of the total study period (99 days except for missing data)) than (27%) those near coastal metropolitan city (Busan). The misty days (39%) in the valley city were the most dominant followed by the hazy (37%), mixture (14%), and foggy days (10%). The visibility degradation on the misty days in Yangsan was closely related to the combined effect of high-level relative humidity due to the accumulation of water vapor from various sources (e.g. river, stream, and vegetation) and strong inversion due to the development of surface radiative cooling within the valley. On the hazy days, the visibility was mainly reduced by the increase in air pollutant (except for $O_3$) concentrations from the dense emission sources under local conditions of weaker winds from the day before and stronger inversion than the misty days. The concentrations of $NO_2$, $PM_{10}$, and $SO_2$ (up to +36 ppb, $+25{\mu}g/m^3$, and +7 ppb) on the hazy days were a factor of 1.4-2.3 higher than those (+25 ppb, $+14{\mu}g/m^3$, and +3 ppb) on the misty days.