• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2008년 영문 학술대회
• /
• pp.557-560
• /
• 2008

A two-dimensional particle-in-cell(PIC) simulation with a Monte-Carlo Collision(MCC) has been developed to investigate the discharge characteristics of the acceleration channel of a HET. The dynamics of electrons and ions are treated with PIC method at the time scale of electrons in order to investigate the particle transport. The densities of charged particles are coupled with Poisson's equation. Xenon neutrals are injected from the anode and experience elastic, excitation, and ionization collisions with electrons, and are scattered by ions. These collisions are simulated by using an MCC model. The effects of control parameters such as magnetic field profile, electron current density, and the applied voltage have been investigated. The secondary electron emission on the dielectric surface is also considered.

• 한국컴퓨터정보학회논문지
• /
• 제29권4호
• /
• pp.39-46
• /
• 2024

많은 연구자들이 다양한 모델을 이용하여 물의 수질을 평가하기 위해 노력하고 있다. 평가 모델에는 결측값이 없는 데이터셋이 필요하지만, 관측 데이터셋에는 결측값이 다수 포함되는 것이 현실이다. 단순히 결측값을 삭제하는 방법은 경우에 따라 기저 데이터의 분포를 왜곡시키고 모델의 예측성능에도 편의(bias)를 불러올 위험성이 있다. 본 연구에서는 수질 데이터의 결측값 처리에 적합한 기법을 탐색하기 위해, 기존의 KNN과 MICE Imputation, 그리고 생성형 신경망 모델인 Autoencoder와 Denoising Autoencoder를 기반으로 몇 가지 대치 기법을 실험하였다. 실험 결과, KNN과 MICE Imputation의 결과를 평균한 Combined Imputation이 실측치에 가장 가깝게 값을 추정하였으며, 이 기법을 적용하여 결측값을 처리한 관측 데이터셋을 support vector machine과 ensemble 기반의 분류 모델로 평가한 결과, 결측값을 삭제했을 때에 비해 Accuracy, F1 score, ROC-AUC score, 그리고 MCC(Mathews Correlation Coefficient) 지표가 향상되었다.

• 한국지반공학회:학술대회논문집
• /
• 한국지반공학회 2000년도 봄 학술발표회 논문집
• /
• pp.669-676
• /
• 2000

The estimation of consolidation rate is one of the important factors in the construction on soft clayey deposits. A number of researches are carried out to predict the consolidation behavior in field, however, most of the results show the discrepancies between the prediction and observation. This paper analyzes consolidation behavior of normally consolidated clay in K/sub o/ condition with 2-dimensional drainage by use of the numerical methods. Elastic and elastic-plastic finite element analyses are compared in terms of the dissipation of excess pore pressure. These results are also compared with Terzaghi-Rendulic's equation that is implemented by finite difference method. The consolidation time calculated by using elastic model is found to be similar to the result of Terzaghi-Rendulic's equation. The consolidation predicted by MCC model takes more time than other cases. Initial increase of excess pore pressure in radial drainage can be shown, however, this phenomenon does not have a significant effect on tile final consolidation time.

• Geomechanics and Engineering
• /
• 제20권2호
• /
• pp.147-154
• /
• 2020

A novel approach for predicting lateral displacement caused by pile installation in anisotropic clay is presented, on the basis of the cylindrical and spherical cavities expansion theory. The K₀-based modified Cam-clay (K₀-MCC) model is adopted for the K₀-consolidated clay and the process of pile installation is taken as the cavity expansion problem in undrained condition. The radial displacement of plastic region is obtained by combining the cavity wall boundary and the elastic-plastic (EP) boundary conditions. The predicted equations of lateral displacement during single pile and multi-pile installation are proposed, and the hydraulic fracture problem in the vicinity of the pile tip is investigated. The comparison between the lateral displacement obtained from the presented approach and the measured data from Chai et al. (2005) is carried out and shows a good agreement. It is suggested that the presented approach is a useful tool for the design of soft subsoil improvement resulting from the pile installation.

• Geomechanics and Engineering
• /
• 제29권1호
• /
• pp.1-11
• /
• 2022

A semi-analytical solution to responses of overconsolidated (OC) unsaturated soils surrounding an expanding spherical cavity under constant suction condition is presented. To capture the elastoplastic hydro-mechanical property of OC unsaturated soils, the unified hardening (UH) model for OC unsaturated soil is adopted in corporation with a soil-water characteristic curve (SWCC) and two suction yield surfaces. Taking the specific volume, radial stress, tangential stress and degree of saturation as the four basic unknowns, the problem investigated is formulated by solving a set of first-order ordinary differential equations with the help of an auxiliary variable and an iterative algorithm. The present solution is validated by comparing with available solution based on the modified Cam Clay (MCC) model. Parametric studies reveal that the hydraulic and mechanical responses of spherical cavity expanding in unsaturated soils are not only coupled, but also affected by suction and overconsolidation ratio (OCR) significantly. More importantly, whether hydraulic yield will occur or not depends only on the initial relationship between suction yield stress and suction. The presented solution can be used for calibration of some insitu tests in OC unsaturated soil.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권12호
• /
• pp.4345-4363
• /
• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• International Journal of Computer Science & Network Security
• /
• 제23권8호
• /
• pp.177-189
• /
• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• 반도체디스플레이기술학회지
• /
• 제17권1호
• /
• pp.76-83
• /
• 2018

The electron energy probability function (EEPF) is of significant importance since the plasma chemistry such as the rate of ionization is determined by the electron energy distribution function. It is usually assumed to be Maxwell distribution for 0-D global model. Meanwhile, it has been observed experimentally that the form of EEPF of Ar plasma changes from being two-temperature to Druyvesteyn like as the gas pressure increases. Thus, to apply the 0-D global model of Maxwellian distribution to the non-Maxwellian plasma, we investigated the relative contribution of two distinct electrons with different temperatures. The contributions of cold/hot electrons to the equilibrium state of the plasma have attracted interest and been researched. The contributions to the power and particle balance of cold/hot electrons were studied by comparing the result of the global model considering all combinations of electron temperatures with that of 1-D Particle-in-Cell and Monte Carlo collision (PIC-MCC) simulation and the results of studies were analyzed physically. Furthermore, comparisons term by term for variations of the contribution of cold/hot electrons at different driving currents are presented.

• 한국태양에너지학회 논문집
• /
• 제29권1호
• /
• pp.58-63
• /
• 2009

In this paper, the power loss due to PV module mismatch in PV string is analyzed and a mismatch compensation method is proposed to improve the efficiency of PV system. The analysis of mismatch loss using PV model simulation reveals that the mismatch module may decrease the total efficiency because the MPPT function of power conditioner make the PV system operate at the local maximum point. The mismatch loss can be severe if the maximum power point current of mismatch module is less than that of string. The proposed compensation method which is simply implemented with a buck type converter shows the possibility to remove the mismatch loss. The effectiveness of the analysis and compensation method is verified by a prototype experiment.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2015년도 춘계학술발표대회
• /
• pp.494-496
• /
• 2015

개인의 의료 정보는 개인의 프라이버시에 관련되므로 민감하게 취급되어야 하는 정보이다. 이러한 개인정보 유출은 유출된 정보의 해당 당사자의 사회적 고립과 정보의 질에 따라 당사자의 생명도 위협하게 되므로 철저한 판리가 필요하다. 따라서 의사, 간호사, 환자, 일반인 등의 사용자 식별을 통해 병원 기록의 접근 통제 및 사용 권한에 따른 정보의 암호화 수준과 해당 정보에 특화된 역할기반 접근제어(Role-Based Access Control)를 제정해야 한다. 환자 자신이 자신의 의료정보를 특정한 사람에게 접근 권한을 주어 확인할 수도 있게 하고 그 외의 다른 부분들도 제어 할 수 있게 권한을 부여 할 수 있어야 한다. 본 논문은 현재 의료 및 진찰 정보 관리를 위해 RBAC모델을 기반으로 의료정보보호를 위한 접근제어 방법을 분석하고 각 정보의 객체들과 사용자 간의 효율적인 역할 분담과 한계를 통해 의료 정보의 보호방안을 고찰한다.