• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.237.2-237.2
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• 2013

ReRAM cell, also known as conductive bridging RAM (CBRAM), is a resistive switching memory based on non-volatile formation and dissolution of conductive filament in a solid electrolyte [1,2]. Especially, Chalcogenide-based ReRAM have become a promising candidate due to the simple structure, high density and low power operation than other types of ReRAM but the uniformity of switching parameter is undesirable. It is because diffusion of ions from anode to cathode in solid electrolyte layer is random [3]. That is to say, the formation of conductive filament is not go through the same paths in each switching cycle which is one of the major obstacles for performance improvement of ReRAM devices. Therefore, to control of nonuniform conductive filament formation is a key point to achieve a high performance ReRAM. In this paper, we demonstrated the enhanced repeatable bipolar resistive switching memory characteristics by spreading the Ag nanocrystals (Ag NCs) on amorphous GeSe layer compared to the conventional Ag/GeSe/Pt structure without Ag NCs. The Ag NCs and Ag top electrode act as a metal supply source of our devices. Excellent resistive switching memory characteristics were obtained and improvement of voltage distribution was achieved from the Al/Ag NCs/GeSe/Pt structure. At the same time, a stable DC endurance (>100 cycles) and an excellent data retention (>104 sec) properties was found from the Al/Ag NCs/GeSe/ Pt structured ReRAMs.

• Journal of Korean Society on Water Environment
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• v.38 no.3
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• pp.143-149
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• 2022

Membrane distillation (MD) has emerged as a sustainable desalination technology to solve the water and energy problems faced by the modern society. In particular, the surface wetting properties of the membrane have been recognized as a key parameter to determine the performance of the MD system. In this study, a novel surface modification technique was developed to induce a Janus-type hydrophilic/hydrophobic layer on the membrane surface. The hydrophilic layer was created on a porous PVDF membrane by vapor phase polymerization of the pyrrole monomer, forming a thin coating of polypyrrole on the membrane walls. A rigid polymeric coating layer was created without compromising the membrane porosity. The hydrophilic coating was then followed by the in-situ growth of siloxane nanoparticles, where the condensation of organosilane provided quick loading of hydrophobic layers on the membrane surface. The composite layers of dual coatings allowed systematic control of the surface wettability of porous membranes. By the virtue of the photothermal property of the hydrophilic polypyrrole layer, the desalination performance of the coated membrane was tested in a solar MD system. The wetting properties of the dual-layer were further evaluated in a direct-contact MD module, exploring the potential of the Janus membrane structure for effective and low-energy desalination.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.123-131
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• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.3
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• pp.19-29
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• 2024

Total organic carbon (TOC) represents the total amount of organic carbon contained in water and is a key water quality parameter used, along with biochemical oxygen demand (BOD) and chemical oxygen demand (COD), to quantify the amount of organic matter in water. In this study, a model to predict TOC was developed using XGBoost (XGB), a representative ensemble machine learning algorithm. Independent variables for model construction included water temperature, pH, electrical conductivity, dissolved oxygen concentration, BOD, COD, suspended solids, total nitrogen, total phosphorus, and discharge. To quantitatively analyze the impact of various water quality parameters used in model construction, the feature importance of input variables was calculated. Based on the results of feature importance analysis, items with low importance were sequentially excluded to observe changes in model performance. When built by sequentially excluding items with low importance, the performance of the model showed a root mean squared error-observation standard deviation ratio (RSR) range of 0.53 to 0.55. The model that applied all input variables showed the best performance with an RSR value of 0.53. To enhance the model's field applicability, models using relatively easily measurable parameters were also built, and the performance changes were analyzed. The results showed that a model constructed using only the relatively easily measurable parameters of water temperature, electrical conductivity, pH, dissolved oxygen concentration, and suspended solids had an RSR of 0.72. This indicates that stable performance can be achieved using relatively easily measurable field water quality parameters.

• Journal of Energy Engineering
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• v.25 no.1
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• pp.69-75
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• 2016

Bubble size is a key parameter for an accurate prediction of bubble behaviours in the multi-dimensional two-phase flow. In the current STAR CCM+ CFD code, a mechanistic bubble size model $S{\gamma}$ is available for the prediction of bubble size in the flow channel. As another model, Yun model is developed based on DEBORA that is subcooled boiling data in high pressure. In this study, numerical simulation for the gas-liquid two-phase flow was conducted to validate and confirm the performance of $S{\gamma}$ model and Yun model, using the commercial CFD code STAR CCM+ ver. 10.02. For this, local bubble models was evaluated against the air-water data from DEDALE experiments (1995) and Hibiki et al. (2001) in the vertical pipe. All numerical results of $S{\gamma}$ model predicted reasonably the two-phase flow parameters and Yun model is needed to be improved for the prediction of air-water flow under low pressure condition.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.1
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• pp.1-9
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• 2015

This paper presents a design method for optimizing the focused beam characteristics, which are mainly determined by the condenser lenses in a scanning electron microscopy (SEM) design. Sharply reducing the probe diameter of electron beams by focusing the condenser lens (i.e., the rate of condensation) is important because a small probe diameter results in high-performance demagnification. This study explored design parameters that contribute to increasing the SEM resolution efficiently using lens analysis and the ray tracing method. A sensitivity analysis was conducted based on those results to compare the effects of these parameters on beam focusing. The results of this analysis on the design parameters for the beam characteristics can be employed as basic key information for designing a column in SEM.

• Structural Engineering and Mechanics
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• v.56 no.4
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• pp.507-534
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• 2015

The present work investigates the behavior of the embankment models resting on soft soil reinforced with ordinary and stone columns encased with geogrid. Model tests were performed with different spacing distances between stone columns and two lengths to diameter ratios (L/d) of the stone columns, in addition to different embankment heights. A total number of 42 model tests were carried out on a soil with undrianed shear strength $${\sim_\sim}10kPa$$. The models consist of stone columns embankment at s/d equal to 2.5, 3 and 4 with L/d ratio equal 5 and 8. Three embankment heights; 200 mm, 250 mm and 300 mm were tested for both tests of ordinary (OSC) and geogrid encased stone columns (ESC). Three earth pressure cells were used to measure directly the vertical effective stress on column at the top of the middle stone column under the center line of embankment and on the edge stone column for all models while the third cell was placed at the base of embankment between two columns to measure the vertical effective stress in soft soil directly. The performance of stone columns embankments relies upon the ability of the granular embankment material to arch over the 'gaps' between the stone columns spacing. The results showed that the ratio of the embankment height to the clear spacing between columns (h/s-d) is a key parameter. It is found that (h/s-d)<1.2 and 1.4 for OSC and ESC, respectively; (h is the embankment height, s is the spacing between columns and d is the diameter of stone columns), no effect of arching is pronounced, the settlement at the surface of the embankment is very large, and the stress acting on the subsoil is virtually unmodified from the nominal overburden stress. When $(h/s-d){\geq}2.2$ for OSC and ESC respectively, full arching will occur and minimum stress on subsoil between stone columns will act, so the range of critical embankment height will be 1.2 (h/sd) to 2.2 (h/s-d) for both OSC and ESC models.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.767-773
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• 2014

The carrier-to-noise power ratio is a key parameter for determining the reliability of PVT (Position, Velocity, and Time) solutions which are obtained by a GNSS (Global Navigation Satellite System) receiver. It is also used for locking a tracking loop, deciding the re-acquisition process, and processing advanced navigation in the receiver subsystem. The representative carrier-to-noise power ratio estimation schemes are the narrowband-wideband power ratio method (NW), the MM (Moment Method), and Beaulieu's method (BL). The NW scheme is the most classical one for commercial GNSS receivers. It is often used as an authoritative benchmark for assessing carrier-to-noise power estimation schemes. The MM scheme is the least biased solution among them, and the BL scheme is a simpler scheme than the MM scheme. This paper focuses on the less biased estimation with low complexity when the residual phase noise remains, then proposes a novel carrier-to-noise power ratio estimation scheme with low complexity for GNSS receivers. The asymptotic bias of the proposed scheme is derived and compared with others, and the simulation results demonstrate that the complexity of the proposed scheme is lowest among them, while the estimation performance of the proposed scheme is similar to those of the BL and MM schemes in normal and high gained reception environments.

• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.801-810
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• 2011

This paper proposes a new lossless data embedding algorithm on spatial domain of digital images. A single key parameter is required to embed and extract data in the algorithm instead of embedding any additional information such as the location map. A $3{\times}3$ window slides over the cover image by one pixel unit, and one bit can be embedded at each position of the window. So, the ideal embedding capacity equals to the number of pixels in an image. For further increase of embedding capacity, new weight parameters for the estimation of embedding target pixels have been used. As a result, significant increase in embedding capacity and better quality of the message-embedded image in high capacity embedding have been achieved. This algorithm is verified with simulations.