• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3301-3318
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• 2016

Online social networks (OSNs) have changed the way people communicate with each other. An OSN usually encourages the participants to provide personal information such as real names, birthdays and educational background to look for and establish friendships among them. Some users are unwilling to reveal personal information on their personal pages due to potential privacy concerns, but their friends may inadvertently reveal that. In this work, we investigate the possibility of leaking personal information on Facebook in an unintentional and involuntary manner. The revealed information may be useful to malicious users for social engineering and spear phishing. We design the inference methods to find birthdays and educational background of Facebook users based on the interactions among friends on Facebook pages and groups, and also leverage J-measure to find the inference rules. The inference improves the finding rate of birthdays from 71.2% to 87.0% with the accuracy of 92.0%, and that of educational background from 75.2% to 91.7% with the accuracy of 86.3%. We also suggest the sanitization strategies to avoid the private information leakage.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.439-440
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• 2018

본 논문에서는 제작한 가상현실 게임의 소개와 적용기술에 대해여 논한다. HTC VIVE를 이용하여 강력한 주요 게임 개발엔진 중 하나인 Unreal engine4를 이용하여 쉽게 접근 할 수 있는 VR 리듬게임 구현을 목적으로 한다. 플랫폼의 한계의 벗어나 HUD로 좀 더 현실감과 몰입을 요구하는 게임개발을 목표로 리듬감 증진 혹은 운동효과도 기대할 수 있다. 사운드 플러그를 이용하여 주파수별 스펙트럼을 시각화하여 재생되는 음원중 스펙트럼 값이 조건을 만족하면, 노트가 자동으로 생성되고 일정 시간이 경과한 후 사라지거나, 플레이어가 타격하여 점수를 획득 하는 방식으로 진행되며, 플레이어가 노트를 맞을 경우 체력값이 단계별로 낮아져 게임이 종료된다. 더 많은 곡과 맵을 추가하여 흥미소요를 늘릴 수 있으며, 단순타격이 아닌 조건이나 임무를 부여 다양성과 복잡함을 추가해 낮은 접근성과 높은 정복성을 가질 수 있다.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2353-2360
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• 2020

An anodic electrophoretic deposition (EPD) technique is used to create a uniform TiO₂ thin film coating on boiling thin steel plates (1.1 mm by 90 mm). All of the effective parameters except time of the EPD method are kept constant. To investigate the effect of gamma irradiation on the critical heat flux (CHF), the test specimens were irradiated in a gamma cell to different doses ranging from 100 to 300 kGy, and then SEM and BET analysis were performed. For each coated specimen, the contact angle and capillary length were measured. The specimens were then tested in a boiling pool for CHF and boiling heat transfer coefficient. It was observed that irradiation significantly decreases the maximum pore diameter while it increases the porosity, pore surface area and pore volume. These surface modifications due to gamma irradiation increased the CHF of the nano-coated surfaces compared to that of the unirradiated surfaces. The heat transfer coefficient (HTC) of the nano-coated surfaces irradiated at 300 kGy increased from 83 to 160 kW/(㎡ K) at 885 kW/㎡ wall heat flux by 100%. The CHF of the irradiated (300 kGy) and unirradiated surfaces are 2035 kW/㎡ and 1583 kW/㎡, respectively, an increase of nearly 31%.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.4
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• pp.94-105
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• 2021

A graphical user interface (GUI) program for steam tracing system selection was developed by using a theoretical model. We derived the model on the basis of the one-dimensional heat transfer theory of conduction and convection through a composite wall. Computational fluid dynamics (CFD) and experiments were performed for validation at steam temperatures of 120.4[℃] and 158.9[℃]. The temperature of a pipe's outer surface obtained through CFD matched well with that predicted by the proposed model for both conditions. By contrast, the experiment results showed a small error at 120.4[℃] and a large error at 158.9[℃] because of the melting of the heat transfer compound and water phase transition. Thus, the steam temperature range of the proposed model is below 120.4[℃].

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.19-22
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• 2021

본 논문은 군과 경찰 등에서 수행하는 시뮬레이션 사격 훈련 중 스크린 사격과 VR 사격이 혼합된 복합 훈련 환경에서 사용할 수 있는 일체형 시뮬레이션 사격 서비스에 대해 연구하였다. 이 서비스는 스크린과 HMD에 투사되는 목표물에 대해 사격 훈련이 가능하도록 설계한 서비스로 하나의 가상공간에서 사용할 경우 스크린을 이용한 개인 사격 훈련과 VR을 이용한 팀 사격 훈련이 동시에 가능하다. 일체형 시뮬레이션 총기의 총구에 비가시 적외선 레이저 모듈을 장착하여 스크린 사격이 가능하도록 하고, 총열 상단에 HTC Vive Tracker를 장착하여 VR 사격에서 훈련자의 자세 및 위치를 인식하게 한다. 총열 하단에는 사용자의 행동을 제어하는 손잡이 형태의 인터페이스 모듈을 장착한다. 비가시 적외선 레이저 모듈과 인터페이스 모듈에서 발생한 신호는 일체형 시뮬레이션 총기의 총열에 장착한 무선 통신 모듈에 의해 블루투스 방식으로 통합 제어 장치로 전달되어 처리함으로써 혼합 환경에서 사용이 가능하다. 스크린 사격과 VR 사격이 동시에 가능한 혼합 환경에서 시험한 결과 운용이 가능한 것을 확인하였다. 일체형 시뮬레이션 사격 서비스는 기존 사람 중심의 훈련 서비스에서 자동차 또는 함정 등과 같은 조종 훈련 플랫폼과 연동하여 운용할 수 있는 기술 발전이 필요하다.

• Journal of Soil and Groundwater Environment
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• v.25 no.1
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• pp.95-105
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• 2020

In this study, iron nanoparticles impregnated hydrochar (FeNPs@HC) was synthesized using lignocellulosic waste and simple one-pot synthetic method. During hydrothermal carbonization (HTC) process, the mixture of lignocellulosic waste and ferric nitrate (0.1~0.5 M) as a precursor of iron nanoparticles was added and heated to 220℃ for 3 h in a teflon sealed autoclave, followed by calcination at 600℃ in N₂ atmosphere for 1 h. For the characterization of the as-prepared materials, X-ray diffraction (XRD), cation exchange capacity (CEC), fourier transform infrared spectrometer (FT-IR), Brunauer-Emmett-Teller (BET), transmission electron microscope (TEM), Energy Dispersive X-ray Spectroscopy (EDS) were used. The change of Fe(III) concentration in the feedstock influenced characteristics of produced FeNPs@HC and removal efficiency towards As(V) and Pb(II). According to the Langmuir isotherm test, maximum As(V) and Pb(II) adsorption capacity of Fe_0.25NPs@HC were found to be 11.81 and 116.28 mg/g respectively. The results of this study suggest that FeNPs@HC can be potentially used as an adsorbent or soil amendment for remediation of groundwater or soil contaminated with arsenic and cation heavy metals.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.115-120
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• 2015

$K_2CO_3$-based dry regenerable sorbents were prepared by spray-drying techniques to improve mass produced $K_2CO_3-Al_2O_3$ sorbents (KEP-CO2P, hereafter), and then tested for their $CO_2$ sorption capacity by a $2,000Nm^3/h$ (0.5 MWe) $CO_2$ capture pilot plant built for Unit 3 of the Hadong thermal power station in 2010. Each of the sample sorbents contained 35 wt.% $K_2CO_3$ as the active materials with various support materials such as $TiO_2$, MgO, Zeolite 13X, $Al_2O_3$, $SiO_2$ and hydrotalcite (HTC). Their physical properties and reactivity were tested to evaluate their applicability to a fluidized-bed or fast transport-bed $CO_2$ capture process. The $CO_2$ sorption capacity and percentage utilization of $K_2CO_3$-MgO based sorbent, Sorb-KM2, was $8.6g-CO_2/100g$-sorbents and 90%, respectively, along with good mechanical strength for fluidized-bed application. Sorbs-KM2 and KT were almost completely regenerated at $140^{\circ}C$. No degradation of Sorb-KM by $SO_2$ added as a pollutant in flue gas was observed during a cycle test.

• Journal of KIISE
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• v.42 no.5
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• pp.573-579
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• 2015

Nationwide supercomputing infrastructures in Korea consist of geographically distributed supercomputing clusters. We developed High-Throughput Computing as a Service(HTCaaS) based on these distributed national supecomputing clusters to facilitate the ease at which scientists can explore large-scale and complex scientific problems. In this paper, we present our mechanism for dynamically managing computing resources and show its effectiveness through a case study of a real scientific application called drug repositioning. Specifically, we show that the resource utilization, accuracy, reliability, and usability can be improved by applying our resource management mechanism. The mechanism is based on the concepts of waiting time and success rate in order to identify valid computing resources. The results show a reduction in the total job completion time and improvement of the overall system throughput.