• Journal of Korea Society of Waste Management
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• v.35 no.7
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• pp.616-626
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• 2018

The quality standards of solid refuse fuel (SRF) define the values for 12 physico-chemical properties, including moisture, lower heating value, and metal compounds, according to Article 20 of the Enforcement Rules of the Act on Resource Saving and Recycling Promotion. These parameters are evaluated via various SRF Quality Test Methods, but problems related to the heavy metal content have been observed in the microwave acid digestion method. Therefore, these methods and their applicability need improvement. In this study, the appropriate testing conditions were derived by varying the parameters of microwave acid digestion, such as microwave power and pre-treatment time. The pre-treatment of SRF as a function of the microwave power revealed an incomplete decomposition of the sample at 600 W, and the heavy metal content analysis was difficult to perform under 9 mL of nitric acid and 3 mL of hydrochloric acid. The experiments with the reference materials under nitric acid at 600 W lasted 30 minutes, and 1,000 W for 20 or 30 minutes were considered optimal conditions. The results confirmed that a mixture of SRF and an acid would take about 20 minutes to reach $180^{\circ}C$, requiring at least 30 minutes of pre-treatment. The accuracy was within 30% of the standard deviation, with a precision of 70 ~ 130% of the heavy metal recovery rate. By applying these conditions to SRF, the results for each condition were not significantly different and the heavy metal standards for As, Pb, Cd, and Cr were satisfied.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.66-74
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• 2021

The objective of this study is to evaluate the practical application potential and limitations of the modified fly ash(MFA) by vibration grinding as a partial replacement of ordinary portland cement(OPC). The test parameters investigated were the replacement level of fly ash(FA) and FA for OPC, varying from 10% to 40%, and curing temperatures of 5, 20, and 40℃. The various characteristics(including slump, air content, bleeding, setting time, compressive strength development, and hydration products) of MFA concrete were measured and then compared with those of the concrete with conventional FA. Test resul ts showed that the MFA prefers to FA in reducing the bl eeding of fresh concrete and enhancing the compressive strength gain at an early age. The compressive strength ratios between MFA and FA concrete specimens at an age of 1 day were 135%, 146%, and 111% at the curing temperatures of 5, 20, and 40℃, respectively. The corresponding ratios at an age of 28 days were approximately 110%, regardless of the curing temperatures. The X-ray diffraction analysis also revealed less calcium hydroxide products in MFA pastes than in FA pastes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.5
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• pp.126-134
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• 2020

In the case of an earthquake, the fluid storage structure generates hydraulic pressure due to the fluctuation of the fluid. At this time, the hydraulic pressure of the fluid changes not only the peaked acceleration of the earthquake but also the sloshing height of the fluid free water surface. Factors influencing this change in load include the shape of the seismic wave, the maximum seismic strength, the size of the fluid storage structure, the width of the structure, and the height of the fluid. In this study, the effect of the ratio between the height of the fluid and the width of the structure was investigated on the fluctuation characteristics of the fluid. 200mm and 140mm of fluid were placed in a water storage tank with a width of 500mm, and a real seismic wave was applied to measure the shape of the fluctuation of the fluid free water surface. The similarity between the experiment and the analysis was verified through the S.P.H(Smoothed Particle Hydrodynamic) technique, one of the numerical analysis techniques. It was confirmed that the free water surface of the fluid showed a similar shape, through comparison of experiment and analysis. And based on this results, SPH technique was applied to analyze the fluctuation shape of the fluid free water surface while varying the ratio between the fluid height and the structure width. An equation to predict the maximum and minimum heights of the fluid free water surface during an earthquake was proposed, and it was confirmed that the error between the maximum and minimum heights of the fluid free water surface predicted by the proposed equation was within a maximum of 3%.

• Journal of Applied Biological Chemistry
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• v.64 no.1
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• pp.75-81
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• 2021

Recent gut microbiota studies have revealed the important roles of gut microbiota for our health. Increasing numbers of health functional foods have been developed every year. Development of functional food often includes ex- and in-vivo experiment to verify the beneficial effects of the functional food. To investigate effects of functional food on gut microbiota, animal models were often conducted. Beneficial effects of food can be evaluated based on how gut microbiota was shifted by food, which results in either increase in beneficial bacteria, decrease in potentially pathogenic bacteria or both. As animal experiments are generally time-consuming and laborious, we investigate how well in-vitro investigation of fecal microbiota may reflect dietary health benefits. Here, we tested 15 kinds of diets using two human subjects' fecal materials. Our results showed varying gut microbiota shifts according to diets, which suggested generally known beneficial diets (i.e. Kimchi, Chunggukjang) increased Lactobacillus and Bifidobacterium. Therefore, we suggest that in vitro fecal microbiota analysis could be used to evaluate beneficial effects of diets. Moreover, this method may be ideal to establish personalized diet.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.159-168
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• 2022

Catalytic activity in ammonia decomposition reaction was studied on Mo-Al nitride obtained through temperature programmed nitridation of calcined Mo-Al mixed oxide prepared by varying the MoO₃ quantity in the range of 10-50 wt%. N₂ sorption analysis, X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS) and H₂-temperature programmed reduction (H₂-TPR), and transmission electron microscopy (TEM) to investigate the physicochemical properties of the prepared catalyst were performed. After calcination at 600 ℃, the XRD of Mo-Al oxide showed γ-Al₂O₃ and Al₂(MoO₄)₃ phases, and the nitride after nitridation showed an amorphous form. The specific surface area after nitridation by topotactic transformation of MoO₃ to nitride was increased due to the formation of Mo nitride, and the Mo nitride was observed to be supported on γ-Al₂O₃. As for the catalytic activity in the ammonia decomposition reaction, 40 wt% MoO₃ showed the best activity, and as the nitridation time increases, the activity increased, and thus the activation energy decreased.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.421-435
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• 2022

As natural disasters have been increasing due to climate change, sustainable solutions are in need to alleviate the degree of drought hazard, assess and project the drought influence based on future climate change scenarios. In assessing drought risk, socio-economic factors of the region must be considered along with meteorological factors. This study categorized drought hazard, exposure, and vulnerability as three major components of drought risk according to the Intergovernmental panel on Climate Change (IPCC) risk assessment framework, and selected indices for each component to quantify the drought risk in South Korea according to the mid-size basins. Combinations of climate scenarios (Representative Concentration Pathway; RCP 2.6 and RCP 8.5) and socio-economic scenarios (Shared Socio-economic Pathways; SSP 1, SSP2 and SSP3) for the near future (2030-2050) ant the far future (2080-2099) were utilized in drought risk analysis, and results were compared with the historical data (1986-2005). In general, the drought risks for all scenarios shows large increases as time proceeds to the far furture. In addition, we analyzed the rank of drought hazard, exposure, vulnerability for drought risk, and each of their contribution. The results showed that the drought hazard is the most contributing component to the increase of drought risk in future and each basin shows varying contributing components. Finally, we suggested countermeasures for each basin according to future climate change scenarios, and thus this study provides made the basis for establishing drought management measures.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Applied Chemistry for Engineering
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• v.33 no.1
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• pp.28-37
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• 2022

The lignite and bituminous coal are mainly used in thermal power plant. They exhaust green house gas (GHG) such as CO₂, and become deplete, thus require alternative energy resources. To solve the problem, the hydrochar production from biomass is suggested. In this study, both hydrothermal carbonization (HTC) and solvothermal carbonization (STC) were used to produce high quality hydrochar. To improve the reactivity of water solvent process in HTC, STC process was conducted using ethanol solution. The experiments were carried out by varying the solid-liquid ratio (1:4, 1:8, 1:12), reaction temperature (150~300 ℃) and retention time (15~120 min) using kenaf. The characteristic of hydrochar was analyzed by EA, FT-IR, TGA and SEM. The carbon content of hydrochar increased up to 48.11%, while the volatile matter decreased up to 39.34%. Additionally, the fuel characteristic of hydrochar was enhanced by reaction temperature. The results showed that the kenaf converted to a fuel by HTC and STC process, which can be used as an alternative energy source of coal.