• KIPS Transactions on Computer and Communication Systems
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• v.5 no.3
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• pp.71-78
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• 2016

Recently, The interest about underwater acoustic communication is increase such as marine resources, disaster prevention, weather prediction, and so on. Because the underwater acoustic communication uses a water as media, the underwater acoustic communication has a lot of restrictions. Although the underwater acoustic communication is hard, it is important to consider the security. In this paper, we estimate the performance of cryptographic algorithms(AES, ARIA, and LEA) on a various devices, available in underwater acoustic communication, and analysis the results. This result will be provide effective data confidentiality for underwater communication.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.2
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• pp.195-204
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• 2017

Predicting the failure life of automated manufacturing systems can reduce overall downtime, maintenance costs, and total plant operation costs. Therefore, there is a growing interest in fatigue failure mechanisms as the safety or service life assessment of manufacturing systems becomes an important issue. In particular, fretting fatigue is caused by repeated tangential stresses that are generated by friction during small amplitude oscillatory movements or sliding between two surfaces pressed together in intimate contact. Previous studies in fretting fatigue have observed size effects related to contact width such that a critical contact width exists where there is drastic change in the fretting fatigue life. However, most of them are the two-dimensional finite element analyses based on the plane strain assumption. The purpose of this study is to investigate the contact size effects on the three-dimensional finite element model of a finite width of a flat specimen and a cylindrical pad exposed to fretting fatigue. The contact size effects were analyzed by means of the stress and strain averages at the element integration points of three-dimensional finite element model. This study shows that the fretting fatigue life of manufacturing systems can be predicted by three-dimensional finite element analysis based on SWT critical plane model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1613-1631
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• 2011

Several metrics have been reported in the literature to assess stereo image quality, mostly based on visual attention or human visual sensitivity based distortion prediction with the help of disparity information, which do not consider the combined aspects of human visual processing. In this paper, visual attention and depth assisted stereo image quality assessment model (VAD-SIQAM) is devised that consists of three main components, i.e., stereo attention predictor (SAP), depth variation (DV), and stereo distortion predictor (SDP). Visual attention is modeled based on entropy and inverse contrast to detect regions or objects of interest/attention. Depth variation is fused into the attention probability to account for the amount of changed depth in distorted stereo images. Finally, the stereo distortion predictor is designed by integrating distortion probability, which is based on low-level human visual system (HVS), responses into actual attention probabilities. The results show that regions of attention are detected among the visually significant distortions in the stereo image pair. Drawbacks of human visual sensitivity based picture quality metrics are alleviated by integrating visual attention and depth information. We also show that positive correlation with ground-truth attention and depth maps are increased by up to 0.949 and 0.936 in terms of the Pearson and the Spearman correlation coefficients, respectively.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.121-132
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• 1994

A GIS based tactical terrain analysis system named ATTAS(Army Tactical Terrain Analysis Software) has been designed and implemented to support the field commanders for enhancing the capabiliy of their unit and efficiency of weapon system. This system is designed to provide computer graphics environment in which the analyst can interactively operate the entire analyzing process such as selecting the area of interest, performing analysis functions, simulating required battlefield operation and display the results. This system can be divided into three major sections; the terrain analysis modules, utilites, and graphic editor. The terrain analysis module inclused surface analysis, line of sight analysis, enemy disposition, 3D display, radar coverage, logistic route analysis, shortest path analysis, atmospheric phenomena prediction, automated IPB (Inteligence preparation of Battlefield), and other applied analysis. A combination of 2D and 3D computer graphics techniques using the X-window system with OSF/Motif in UNIX workstation was adopted as the user interface. The integration technique of remotely sensed images and GIS data such as precision registration, overlay, and on-line editing was developed and implemented. An efficient image and GIS data management technique was also developed and implemented using Oracle Database Management System.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.71-83
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• 1994

Recently, an interest in the uncertainty analysis on measurement and prediction has been growing. An uncertainty analysis method is applied to the P.O.W test where error sources, estimated errors, their propagation route and their sensitivities to the uncertainty items are clearly illustrated. The uncertainty range for the results obtained from the HMRI Propeller Open Water test is within ${\pm}1%$ which is assumed to be lower than an usual measurement error range of ${\pm}1%$. It has been noticed that the uncertainty analysis can be used quite usefully for detecting dominant error-sources and hence improving the experimental measurement accuracy.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2788-2802
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• 2021

Multigroup cross section (MG XS) generation by the UNIST in-house Monte Carlo (MC) code MCS for fast reactor analysis using nodal diffusion codes is reported. The feasibility of the approach is quantified for two sodium fast reactors (SFRs) specified in the OECD/NEA SFR benchmark: a 1000 MW_th metal-fueled SFR (MET-1000) and a 3600 MW_th oxide-fueled SFR (MOX-3600). The accuracy of a few-group XSs generated by MCS is verified using another MC code, Serpent 2. The neutronic steady-state whole-core problem is analyzed using MCS/RAST-K with a 24-group XS set. Various core parameters of interest (core k_eff, power profiles, and reactivity feedback coefficients) are obtained using both MCS/RAST-K and MCS. A code-to-code comparison indicates excellent agreement between the nodal diffusion solution and stochastic solution; the error in the core k_eff is less than 110 pcm, the root-mean-square error of the power profiles is within 1.0%, and the error of the reactivity feedback coefficients is within three standard deviations. Furthermore, using the super-homogenization-corrected XSs improves the prediction accuracy of the control rod worth and power profiles with all rods in. Therefore, the results demonstrate that employing the MCS MG XSs for the nodal diffusion code is feasible for high-fidelity analyses of fast reactors.

• Korean Chemical Engineering Research
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• v.56 no.6
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• pp.878-883
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• 2018

A circulating fluidized bed (CFB) has been used in various chemical industries because of good heat and mass transfer. In addition, the methanol to olefins (MTO) process requiring the CFB reactor has attracted a great deal of interest due to steep increase of oil price. To design a CFB reactor for MTO pilot process, therefore, we has examined the hydrodynamic properties of spherical catalysts with different particle size and developed a correlation equation to predict catalyst holdup in a riser of CFB reactor. The hydrodynamics of micro-spherical catalysts with average particle size of 53, 90 and 140 mm was evaluated in a $0.025m-ID{\times}4m-high$ CFB riser. We also developed a model described by a decay coefficient to predict solid hold-up distribution in the riser. The decay coefficient developed in this study could be expressed as a function of Froude number and dimensionless velocity ratio. This model could predict well the experimental data obtained from this work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.61-66
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• 2022

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.