• Journal of the Korea Concrete Institute
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• v.14 no.1
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• pp.126-135
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• 2002

Plate bonding technique has been widely used in strengthening of existing concrete structures, although it has often a serious problem of premature falure such as interface separation and rip-off. However, this premature failure problem has not been well explored yet especially in view of local failure mechanism around the interface of plate ends. The purpose of the present study is, therefore, to identify the local failure of strengthened plates and to derive a separation criterion at the interface of plates. To this end, a comprehensive experimental program has been set up. The double lap pull-out tests considering pure shear force and half beam tests considering combined flexure-shear force were performed. The main experimental parameters include plate thickness, adhesive thickness, and plate end arrangement. The strains along the longitudinal direction of steel plates have been measured and the shear stress were calculated from those measures strains. The effects of plate thickness, bonded length, and plate end treatment have been also clarified from the present test results. Nonlinear finite element analysis has been performed and compared with test results. The Interface properties are also modeled to present the separation failure behavior of strengthened members. The cracking patterns as well as maximum failure loads agree well with test data. The relation between maximum shear and normal stresses at the interface has been derived to propose a separation failure criterion of strengthened members. The present study allows more realistic analysis and design of externally strengthened flexural member with steel plates.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.1
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• pp.65-73
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• 2022

The proper estimation of physical and chemical properties of Earth materials and their structures at high pressure and high temperature conditions is key to the full understanding of diverse geological processes in Earth and planetary interiors. Multi-anvil press - high-pressure generating device - provides unique information of Earth materials under compression, mainly relevant to Earth's upper mantle. The quantitative estimation of the relationship between the oil load within press and the actual pressure conditions within the sample needs to be established to infer the planetary processes. Such pressure-load calibration has often been based on the phase transitions of crystalline earth materials with known pressure conditions; however, unlike at high temperature conditions, phase transitions at low (or room) temperatures can be sluggish, making the calibration at such conditions challenging. In this study, we explored the changes in Al coordination environments of permanently densified pyrope glasses upon the cold compression using the high-resolution ²⁷Al MAS and 3QMAS NMR. The fractions of highly coordinated Al in the cold compressed pyrope glasses increase with increasing oil load and thus, the peak pressure condition. Based on known relationship between the peak pressure and the Al coordination environment in the compressed pyrope glasses at room temperature, we established a room temperature pressure-load calibration of the 14/8 HT assembly in 1,100-ton multi-anvil press. The current results highlight the first pressure-load calibration of any high pressure device using high-resolution NMR. Irreversible structural densification upon cold compression observed for the pyrope glasses provides insights into the deformation and densification mechanisms of amorphous earth materials at low temperature and high pressure conditions within the subducting slabs.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.727-736
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• 2022

Ice wedges are subsurface ice mass structures that formed mainly by freezing precipitation with airborne dust and surrounding soil particles flowed through the active layer into the cracks growing by repeating thermal contractions in the deeper permafrost layer over time. These ice masses characteristically contain high concentrations of solutes and solids. Because of their unique properties and distribution, the possibility of harnessing ice wedges as an alternative archive for reconstructing paleoclimate and paleoenvironment has been recently suggested despite limited studies. It is imperative to preserve the physicochemical properties of the ice wedge (e.g., solute concentration, mineral particles) without any potential alteration to use it as a proxy for reconstructing the paleo-information. Thawing the ice wedge samples is prerequisite for the assessment of their physicochemical properties, during which the paleo-information could be unintentionally altered by any methodological artifact. This study examined the effect of thawing conditions and procedures on the physicochemical properties of solutes and solid particles in ice wedge samples collected from Cyuie, East Siberia. Four different thawing conditions with varying temperatures (4 and 23℃) and oxygen exposures (oxic and anoxic) for the ice wedge sample treatment were examined. Ice wedge samples thawed at 4℃ under anoxic conditions, wherein biological activity and oxidation were kept to a minimum, were set as the standard thawing conditions to which the effects of temperature and oxygen were compared. The results indicate that temperature and oxygen exposure have negligible effects on the physicochemical characteristics of the solid particles. However, the chemical features of the solution (e.g., pH, electric conductivity, alkalinity, and concentration of major cations and trace elements) at 4℃ under oxic conditions were considerably altered, compared to those measured under the standard thawing conditions. This study shows that the thawing condition of ice wedge samples can affect their chemical features and thereby the geochemical information therein for the reconstruction of the paleoclimate and/or paleoenvironment.

• Journal of Bio-Environment Control
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• v.32 no.2
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• pp.148-156
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• 2023

This study was conducted in an indoor cultivation room and chamber where environmental control is possible to investigate the effect of temperature and irrigation interval on photosynthesis, growth and growth analysis of potted seedling cucumber. The light intensity (70 W·m^-2) and humidity (65%) were set to be the same. The experimental treatments were six combinations of three different temperatures, 15/10℃, 25/20℃, and 35/25℃, and two irrigation intervals, 100 mL per day (S) and 200 mL every 2 days (L). The treatments were named 15S, 15L, 25S, 25L, 35S, and 35L. Seedlings at 0.5 cm in height were planted in pots (volume:1 L) filled with sandy loam and treated for 21 days. Photosynthesis, transpiration rate and stomatal conductance at 14 days after treatment were highest in 25S. These were higher in S treatments with a shorter irrigation interval than L treatments. Total amount of irrigation water was supplied evenly at 2 L, but the soil moisture content was highest at 15S and lowest at 25S > 15L > 25L, 35S and 35L in that order. Humidity showed a similar trend at 15/10℃ (61.1%) and 25/20℃ (67.2%), but it was as high at 35/25℃ (80.5%). Cucumber growth (plant height, leaf length, leaf width, chlorophyll content, leaf area, fresh weight and dry weight) on day 21 was the highest in 25S. Growth parameters were higher in S with shorter irrigation intervals. Yellow symptom of leaf was occurred in 89.9% at 35S and 35L, where the temperature was high. Relative growth rate (RGR) and specific leaf weight (SLA) were high at 25/20℃ (25S, 25L), RGR tended to be high in the S treatment, and SLA in the L treatment. Water use efficiency (WUE) was high in the order of 25S, 25L > 15S > 15L, 35S, and 35L. As a result of the above, the growth and WUE were high at the temperature of 25/20℃.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.