• Journal of Sensor Science and Technology
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• v.4 no.4
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• pp.16-22
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• 1995

Catalytic combustion type gas sensor was fabricated by using ${\gamma}-Al_{2}O_{3}$, Pd catalyst and some binders for metane and propane detection. Using the gas sensor, digital gas meter was manufactured and tested for sensing performance. The fabricated sensor had power consumption of 700mW with applied voltage of dc 2V and the output voltage of the sensor was about 700mV for propane of 1,000ppm and 500mV for methane of 1,000ppm. In 10 cycle injection of the gases of 2,400ppm, The digital meter showed good sensitivity, linearity, and reproductivity with precision of ${\pm}25ppm({\pm}1%)$.

• Analytical Science and Technology
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• v.25 no.6
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• pp.375-381
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• 2012

In this study, we have developed a cryogenic concentration system for the analysis of non-methane hydrocarbons (NMHCs) in air sample. NMHCs with nmol/mol level of concentrations in the atmosphere were quantified by a comparative method with certified reference gas mixture. GC/FID with cryogenic concentration system operated in the range from $-67^{\circ}C$ to $180^{\circ}C$ was adopted. After cryogenic concentration, a fast desorption by heating could achieve a large amount of sample injection into the GC/FID. The linearity of the system was verified with the peak areas of NMHCs of the concentration amount of sample. The reproducibility is less than 10% and the limit of detection (LOD) is 0.1 nmol/mol. For the application of this system, we analyzed NMHCs in gas samples taken from air and soil in Daejeon, Seosan and Goheung.

• Journal of the Korean Vacuum Society
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• v.10 no.1
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• pp.119-126
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• 2001

Hydrogenated amorphous carbon (a-C:H) films were deposited by ECR-PECVD (electron cyclotron resonance-plasma enhanced chemical vapor deposition) method with deposition conditions such as ECR plasma source power, gas composition of methane and hydrogen, deposition time and substrate bias voltage. The hydrogen content in the films has been measured by ERDA (elastic recoil detection analysis) using 2.5 MeV $He^{++}$ ion beam. From the results of AES (Auger electron spectroscopy), RBS (Rutherford backscattering spectrometry) and ERDA, the composition elements of deposited film were confirmed the carbon atom and the hydrogen atom. It was observed by FTIR (Fourier transform infrared) that the hydrogen contents in the film varied according to the deposition conditions. In deposition condition of substrate bias voltage, the hydrogen contents were decreased remarkably because the amount of dehydrogenation in films was increased as the substrate bias voltage increased. In the rest deposition conditions, the hydrogen contents in the film were measured in the range 45~55%.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.973-980
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• 2015

With their wide geographical distribution, unconventional resources are continuously compared against conventional resources, but their development is expanding because TRRs (Technical Recoverable Resources) are similar to conventional resources. In particular, there is active development of unconventional gas resources such as shale gas, tight gas, CBM (coalbed methane) and gas hydrate. However, it is difficult to calculate the material properties of unconventional resources, especially the gas content, with current geophysical logging technology. Additionally, some overseas companies have monopolies on related equipment and materials. Therefore, this study developed a reservoir PCS (Pressure Core Sampler). It can collect core samples without gaseous loss by maintaining high pressure from the moment the core is sampled and record pressure and temperature in real time. Successful performance testing was also carried out for official verification of the manufactured PCS. The reservoir PCS will contribute to the acquisition of geophysical well logging data as well as accurate and reliable cores.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.224-231
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• 2001

Hydrogenated amorphous carbon films were deposited by ERC-PECVD with deposition conditions, such as ECR power, gas composition of methane and hydrogen, deposition time, and substrate bias voltage. The characteristics of the film were analyzed using the AES, ERDA, FTIR. Raman spectroscopy and micro hardness tester. From the results of AES and ERDA, the elements in the deposited film were confirmed as carbon and hydrogen atoms. FTIR spectroscopy analysis shows that the atomic bonding structure of a-C:H film consisted of sp³and sp²bonding, most of which is composed of sp³bonding. The structure of the a-C:H films changed from CH₃bonding to CH₂or CH bonding as deposition time increased. We also found that the amount of dehydrogenation in a-C:H films was increased as the bias voltage increased. Raman scattering analysis shows that integrated intensity ratio (I/sub D//I/sub G/) of the D and G peak was increased as the substrate bias voltage increased, and films hardness was increased.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• Journal of Animal Science and Technology
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• v.50 no.3
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• pp.391-400
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• 2008

Odor management is significantly concerned with sustainable livestock production because odor nuisance is a primary cause for complaint to neighbors. This study was conducted to measure the concentration of odorous compounds, odor intensity, and odor offensiveness at unit process in animal waste treatment facility combined composting and methane fermentation process by an instrumental analysis and direct olfactory method. Ammonia, sulfur-containing compounds, and volatile fatty acid were analyzed at each process units and boundary area in summer and winter, respectively. Higher concentration of odorants occurred in the summer than in the winter due to high ambient temperature. The maximum concentration of odorants was detected in composting pile when mixed manure was being turned followed by inlet, curing, outlet, and screen & packing process. Highest concentration of detected odorous compounds was ammonia ranging from 3.4 to 224.7 ppm. Among the sulfur-containing compounds measured, hydrogen sulfide was a maximum level of 2.3 ppm and most of them exceeded reported odor detection thresholds. Acetic acid was the largest proportion of VFA generated, reaching a maximum of 51 to 89%, followed by propionic and butyric acid at 1.9 to 35% and 1.8 to 15%, respectively. Malodor assessment by a human panel appeared a similar tendency in instrumental analysis data. Odor quotient for predicting major odor-causing compounds was calculated by dividing concentrations measured in process units by odor detection thresholds. In the composting process, hydrogen sulfide, ammonia, dimethyl sulfide, and methyl mercaptan were deeply associated with odor-causing compounds, while the major malodor compounds in the inlet process were methyl mercaptan, hydrogen sulfide, and butyric acid.

• Journal of Sensor Science and Technology
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• v.6 no.6
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• pp.451-457
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• 1997

The thick films of oxide semiconductors such as $WO_{3}$, $SnO_{2}$ and ZnO for the $NO_{2}$ detection of sub-ppm range have been prepared and their characteristics were investigated. It is showed that the optimum operating temperatures of the sensors are $300^{\circ}C$ and $220{\sim}260^{\circ}C$ for $WO_{3}$-based and $SnO_{2}$-based thick films, and ZnO-based thick films, respectively. Since the resistance of ZnO-based thick films are extremely high($>10^{6}{\Omega}$), the signal to noise ratio was comparatively low. In order to determine the selectivity, the films are exposed to the interfering gases such as ozone, ammonia, methane and the mixture of carbon monoxide and propane. $WO_{3}$-ZnO(3 wt.%) and $SnO_{2}-WO_{3}$(3 wt.%) thick film sensors show high sensitivity, good selectivity, excellent reproducibility and the linearity of $NO_{2}$ concentration versus sensor resistance. The preliminary results clearly demonstrated that the sensor can be successfully applied for the detection of $NO_{2}$ in sub-ppm range.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.285-294
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• 2022

In order to accurately calculate greenhouse gas emissions in the agricultural field, Korea has been developing national-specific emission factors through direct measurement of gas fluxes using the closed-chamber method. In the rice paddy, only national-specific emission factors for methane (CH₄) have been developed. It is thus necessary to develop those for nitrous oxide (N₂O) affected by the application of nitrogen fertilizer. However, since the concentration of N₂O emission from rice cultivation is very low, the QA/QC methods such as method detection and practical quantification limits are important. In this study, N₂O emission from a rice paddy was evaluated affected by the amount of nitrogen fertilizer, by taking into account both method detection and practical quantification limits for N₂O concentration. The N₂O emission from a rice paddy soils affected by the nitrogen fertilizer application was estimated in the following order. The method detection limit (MDL) of N₂O concentration was calculated at 95% confidence level based on the pooled standard deviation of concentration data sets using a standard gas with 98 nmol mol^-1 N₂O 10 times for 3 days. The practical quantification limit (PQL) of the N₂O concentration is estimated by multiplying 10 to the pooled standard deviation. For the N₂O flux data measured during the rice cultivation period in 2021, the MDL and PQL of N₂O concentration were 18 nmol mol^-1 and 87 nmol mol^-1, respectively. The measured values above the PQL were merely about 12% of the total data. The cumulative N₂O emission estimated based on the MDL and PQL was higher than the cumulative emission without nitrogen fertilizer application. This research would contribute to improving the reliability in quantification of the N₂O flux data for accurate estimates of greenhouse gas emissions and uncertainties.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.69 no.3
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• pp.154-162
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• 2024

In response to the increasing impact of climate change on agriculture, various cultivation technologies have been recently developed to improve agricultural productivity and reduce carbon emissions for carbon neutrality. This study presents an algorithm for estimating rice planting density in agriculture using drone-captured images and deep learning-based image analysis technology. The algorithm utilizes images collected from various paddies; these images are processed through pre-processing steps and serve as training data for the YOLOv5x deep learning model. The trained model demonstrated high precision and recall, effectively estimating the position information of rice plants in each image. By accurately estimating the position of rice plants based on the central coordinates in diverse unpaved environments, the model allowed for estimation of rice plant density in each paddy, producing values closely aligned with actual measurements. Moreover, the algorithm proposed in this study provides a novel approach for precise determination of rice planting density based on the position information of rice plants in the images. Analysis of drone footage from different regions capturing portions of paddies revealed that the developed algorithm exhibited a significant correlation (R² =0.877) with actual planting density. This finding suggests the potential effective application of the algorithm in real-world agricultural settings. In conclusion, we believe that this research contributes to the ongoing digital transformation in agriculture by offering a valuable technology that supports the goals of enhancing efficiency, mitigating methane emissions, and achieving carbon neutrality, in response to the challenges posed by climate change.