• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.644-652
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• 2022

During the pyroprocessing operation, various signals can be collected by process monitoring (PM). These signals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguards of electrorefining operation was examined based on the use of machine learning for detecting off-normal operations. The off-normal operation, in this study, is defined as co-deposition of key elements through reduction on cathode. The monitored process signal selected for PM was cathode potential. The necessary data were produced through electrodeposition experiments in a laboratory molten salt system. Model-based cathodic surface area data were also generated and used to support model development. Computer models for classification were developed using a series of recurrent neural network architectures. The concept of transfer learning was also employed by combining pre-training and fine-tuning to minimize data requirement for training. The resulting models were found to classify the normal and the off-normal operation states with a 95% accuracy. With the availability of more process data, the approach is expected to have higher reliability.

• Animal Bioscience
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• v.34 no.4
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• pp.734-742
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• 2021

Objective: The present study aimed to investigate the occurrence and species of coagulase-positive staphylococci (CoPS) and coagulase-negative staphylococci (CoNS) in retail pork meat samples collected during nationwide monitoring. The staphylococcal isolates were characterized for antimicrobial and zinc chloride resistance and enterotoxigenic potential. Methods: A total of 260 pre-packaged pork meat samples were collected from 35 retail markets in 8 provinces in Korea for isolation of staphylococci. Antimicrobial and zinc chloride resistance phenotypes, and genes associated with the resistance phenotypes were determined on the isolates. Furthermore, the presence and distribution of 19 staphylococcal enterotoxin (SE) genes and enterotoxin-like genes among the pork-associated staphylococci were determined by multiplex polymerase chain reaction-based assays using the specific primer sets. Results: A total of 29 staphylococcal strains (29/260, 11.1%) were isolated from samples of retail pork meat, 24 (83%) of which were CoNS. The four CoNS species identified were S. saprophyticus (n = 16, 55%), S. sciuri (n = 3, 10%), S. warneri (n = 3, 10%), and S. epidermidis (n = 2, 7%). Among the 29 isolates, four methicillin-resistant CoNS (MR-CoNS; three S. sciuri and one S. epidermidis) and one methicillin-resistant CoPS (MR-CoPS; one S. aureus) were identified. In addition, a relatively high level of tetracycline (TET) resistance (52%) was confirmed in CoNS, along with a predominant distribution of tet(K). The most prevalent SEs were sep (45%), and sen (28%), which were carried by 81% of S. saprophyticus. Conclusion: These findings suggest that CoNS, especially S. saprophyticus strains, in raw pork meat could be a potential risk factor for staphylococcal food poisoning (SFP), and therefore, requires further investigation to elucidate the role of SEls in SFP and virulence of the pathogen. Our results also suggest that CoNS from raw pork meat may act as a source for transmission of antimicrobial resistance genes such as staphylococcal cassette chromosome mec and tet(K).

• Asian Journal of Atmospheric Environment
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• v.7 no.2
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• pp.105-113
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• 2013

We constructed and tested an airborne peroxyacetyl nitrate (PAN) monitoring system based on luminol chemiluminescence detection with fast gas chromatography. This system allowed for simultaneous measurement of PAN and nitrogen dioxide ($NO_2$) with a time resolution of <2 min. Actual sample masses within the fixed volume sample loop at various altitudes and temperatures were adjusted to standard atmosphere, using measured pressures and temperatures. The airborne PAN measurement system was evaluated during two field studies above the southern Korean Peninsula in August and October 2009. The detection limit based on the ISO approach was 0.035 ppbv PAN, well below the observed concentrations of 0.185-1.49 ppbv during these studies. Under these conditions, the PAN mixing ratios were positively correlated with $O_x$ ($O_x=O_3+NO_2$), with slopes varying between 0.014 and 0.033 and intercepts between 22.6 and 55.1 ppbv $O_x$. The intercepts corresponded roughly to background $O_x$ mixing ratios in central Europe; however, the slopes were above the range of slopes reported in other studies. We also enhanced the durability, safety, and ease of maintenance of the PAN monitoring system by redesigning the structure of the conventional luminol cell.

• Journal of Ecology and Environment
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• v.34 no.4
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• pp.411-423
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• 2011

This study was conducted to quantify soil $CO_2$ efflux using the continuous measurement method and to examine the applicability of an automatic continuous measurement system in a Korean deciduous broad-leaved forest. Soil respiration rate (Rs) was assessed through continuous measurements during the 2004-2005 full growing seasons using an automatic opening/closing chamber system in sections of a Gwangneung temperate deciduous forest, Korea. The study site was an old-growth natural mixed deciduous forest approximately 80 years old. For each full growth season, the annual Rs, which had a gap that was filled with data using an exponential function derived from soil temperature (Ts) at 5-cm depth, and Rs values collected in each season were 2,738.1 g $CO_2$ $m^{-2}y^{-1}$ in 2004 and 3,355.1 g $CO_2$ $m^{-2}y^{-1}$ in 2005. However, the diurnal variation in Rs showed stronger correlations with Ts (r = 0.91, P < 0.001 in 2004, r = 0.87, P < 0.001 in 2005) and air temperature (Ta) (r = 0.84, P < 0.001 in 2004, r = 0.79, P < 0.001 in 2005) than with deep Ts during the spring season. However, the temperature functions derived from the Ts at various depths of 0, -2, -5, -10, and -20 cm revealed that the correlation coefficient decreased with increasing soil depth in the spring season, whereas it increased in the summer. Rs showed a weak correlation with precipitation (r = 0.25, P < 0.01) and soil water content (r = 0.28, P < 0.05). Additionally, the diurnal change in Rs revealed a higher correlation with Ta than that of Ts. The $Q_{10}$ values from spring to winter were calculated from each season's dataset and were 3.2, 1.5, 7.4, and 2.7 in 2004 and 6.0, 3.1, 3.0, and 2.6 in 2005; thus, showing high fluctuation within each season. The applicability of an automatic continuous system was demonstrated for collecting a high resolution soil $CO_2$ efflux dataset under various environmental conditions.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.151-166
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• 2024

Coastal debris presents a significant environmental threat globally. This research sought to improve the monitoring methods for coastal debris by employing deep learning and remote sensing technologies. To achieve this, an object detection approach utilizing the You Only Look Once (YOLO)v8 model was implemented to develop a comprehensive image dataset for 11 primary types of coastal debris in our country, proposing a protocol for the real-time detection and analysis of debris. Drone imagery was collected over Sinja Island, situated at the estuary of the Nakdong River, and analyzed using our custom YOLOv8-based analysis program to identify type-specific hotspots of coastal debris. The deployment of these mapping and analysis methodologies is anticipated to be effectively utilized in managing coastal debris.

• Journal of environmental and Sanitary engineering
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• v.8 no.2
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• pp.25-48
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• 1993

International concern over the environmental pollution is ever increasing, and diversified countermeasures must be devised in Korea also. Global trend, damages, problems and countermeasures with respect to issues mentioned in the Rio Declaration, such as prevention of ozone layer destruction, reduction of migratory atmospheric pollution between neighboring countries, and prevention of global greenhouse effect, were discussed in this report. Conclusion of the report is summarized as follows : A. Measurement, Planning and Monitoring (1) Development and implementation of a global network for measurement and monitoring from the global aspects such factors as related to acid rain(Pioneer substances, pH, sulfate, nitrate), effect of global temperature(Air temperature, $CO_2$, $CH_4$, CFC, $N_2O$) and destruction of ozone layer($CFC_S$). (2) Establishment of network system via satellite monitoring movement of regional air mass, damage on the ozone layer and ground temperature distribution. B. Elucidation of Present State (1) Improvement and development of devices for carbon circulation capable of accurately forecasting input and output of carbon. (2) Developmental research on chemical reactions of greenhouse gas in the air. (3) Improvement and development of global circulation model(GCM) C. Impact Assessment Impact assessment on ecosystem, human body, agriculture, floodgate, land use, coastal ecology, industries, etc. D. Preventive Measures and Technology Development (1) Development and consumption of new energy (2) Development of new technology for removal of pioneer substances (3) Development of substitute matter for $CFC_S$ (4) Improvement of agriculture and forestry means to prevent the destruction of ozone layer and the greenhouse effect of the globe (5) Improvement of housing to prevent the destruction of ozone layer and the greenhouse effect of the globe (6) Development of new technology for probing underground water (7) Preservation of forest (8) Biomass 5. Policy Development (1) Development of strategy model (2) Development of long term forecast model (3) Development of penalty charge effect and expense evaluation methods (4) Feasibility study on regulations By establishing the above mentioned measures for environmentally sound and sustainable development to establish the right to live for humankind and to preserve the one and only earth.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.116-126
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• 2002

Discharge pattern and water quality were investigated in the drainage water from about 10 ha of groundwater-irrigated paddy field in the growing season of 2001. Total discharge quantity was about 1,117.2 mm in which about 75% was caused by management drainage due to cultural practice of paddy rice farming and the rest by rainfall runoff where total rainfall was about 515 mm. Dry-day sampling data showed wide variations in constituent concentrations with average of 26.14 mg/L, 0.37 mg/L, 3.54 mg/L at the inlet, and 43.60 mg/L, 0.34 mg/L, 3.58 mg/L at the outlet for CO $D_{cr}$ , T-P, and T-N, respectively. Wet-day sampling data demonstrated that generally CO $D_{cr}$ followed the discharge pattern and T-P was in opposite to the discharge pattern, but T-N did not show apparent pattern to the discharge. Discharge and load are in strong relationship. And based on regression equation, pollutant loads from groundwater irrigation area are estimated to be 288.34, 1.17, and 5.45 kg/ha for CO $D_{cr}$ , T-P, and T-N, respectively, which was relatively lower than the literature value from surface water irrigation area which implies that groundwater irrigation area might use less irrigation water and result in less drainage water, Therefore, total pollutant load from paddies irrigation with groundwater could be significantly lower than that with surface water. This study shows that agricultural drainage water management needs a good care of drainage outlet as well as rainfall runoff. This study was based on limited monitoring data of one year, and further monitoring and successive analysis are recommended for more generalized conclusion.

• Journal of the Korean Geotechnical Society
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• v.29 no.5
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• pp.53-64
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• 2013

Resistivity monitoring is based on the fact that a change in the porosity leads to the changes in water content and fine particles, which alter the electrical resistivity. At every embankment dam, internal erosion always occurs as time passes. The internal erosion generally develops into piping over a long time by backward erosion and concentrated leak, and finally leads to dam failure. Resistivity is known to be very sensitive to the changes in porosity in embankment dams. Thus resistivity monitoring is a reasonable method to find out the leakage zone. However, resistivity is strongly influenced by seasonal variation of temperature, TDS of reservoir water and water level. In this paper. we first installed electrodes permanently at the center of the crest. The electrical resistivity monitoring data was acquired every 6 hours from Apr. 3, 2011 to July. 31, 2012. To analyze the characteristics of monitoring data, each resistivity data was calculated from up to 2,950 data sets. The result indicated a seasonal resistivity variation due to related temperature. Finally, a quantitative method to estimate porosities of the embankment dam from the resistivity monitoring data was analyzed. The applicability and reliability were verified and the importance of electrical resistivity monitoring for obtaining reliable result was emphasized.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.2
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• pp.55-63
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• 2018

This study analyzed the applications of near real-time drought monitoring using satellite rainfall for the Korean Peninsula and un-gaged basins. We used AWS data of Yongdam-Dam, Hoengseong-Dam in Korea area, the meteorological station of Nakhon Rachasima, Pak chong for test-bed to evaluate the validation and the opportunity for un-gaged basins. In addition, we calculated EDI (Effective doought index) using the stations and co-located PERSIANN-CDR, TRMM (Tropical Rainfall Measurement Mission) TMPA (The TRMM Multisatellite Precipitation Analysis), GPM IMERG (the integrated Multi-satellitE Retrievals for GPM) rainfall data and compared the EDI-based station data with satellite data for applications of drought monitoring. The results showed that the correlation coefficient and the determination coefficient were 0.830 and 0.914 in Yongdam-dam, and 0.689 and 0.835 in Hoengseng-Dam respectively. Also, the correlation coefficient were 0.830, 0.914 from TRMM TMPA datasets and compasion with 0.660, 0.660 based on PERSIANN-CDR and TRMM data in nakhon and pakchong station. Our results were confirmed possibility of near real-time drought monitoring using EDI with daily satellite rainfall for un-gaged basins.