• KIPS Transactions on Computer and Communication Systems
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• v.2 no.12
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• pp.541-552
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• 2013

Recently software security of the smart phone is an important issue in the field of information science and technology due to fast propagation of smart technology in our life. The smart phone as the security critical systems which are utilizing in terminal systems of the banking, ubiquitous home management, airline passengers screening, and so on are related to the risk of costs, risk of loss, risk of availability, and risk by usage. For the security issues, software hazard analysis of smart phone is the key approaching method by use of observed failures. In this paper, we propose an efficient integrative framework for software security analysis of the smart phone using Fault Tree Analysis (FTA) and Failure Mode Effect Analysis (FMEA) to gain a convergence security and reliability analysis technique on hand handle devices. And we discuss about that if a failure mode effect analysis performs simpler, not only for improving security but also reducing failure effects on this smart device, the proposed integrative framework is a key solution.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.4
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• pp.160-167
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• 2021

Seabed deformation due to the fault failure have both a spatial variation and temporal history. When the faulting process initiates at a certain point beneath seabed, the failure spreads out to neighboring points, resulting in temporal changes of deformation. In particular, such a process induces tsunami waves from the vertical motion of seabed. The uprising speed of seabed affects the formation of initial surface profile, eventually altering the arrival time and runup of tsunamis at the coast. In this work, we developed a numerical model that can simulate the generation and propagation of tsunami waves by considering the horizontal and vertical changes of seabed in an active and dynamic manner. For the verification of the model, it was applied to the 2011 Tohoku-oki earthquake in Japan and the results confirmed that the accuracy was improved compared to the existing passive and static model.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.51-57
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• 2004

An intelligent performance diagnostic computer program of a gas turbine using the NN(Neural Network) was developed. Recently on-condition performance monitoring of major gas path components using the GPA(Gas Path Analysis) method has been performed in analyzing of engine faults. However because the types and severities of engine faults are various and complex, it is not easy that all fault conditions of the engine would be monitored only by the GPA approach Therefore in order to solve this problem, application of using the NNs for learning and diagnosis would be required. Among then, a BPN (Back Propagation Neural Network) with one hidden layer, which can use an updating learning rate, was proposed for diagnostics of PT6A-62 turboprop engine in this work.

• Journal of Advanced Navigation Technology
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• v.8 no.1
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• pp.26-37
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• 2004

Consistent and realistic error covariance information is important for position estimation, error analysis, fault detection, and integer ambiguity resolution for differential GNSS. In designing a position domain carrier-smoothed-code filter where incremental carrier phases are used for time-propagation, formulation of consistent error covariance information is not easy due to being bounded and temporal correlation of propagation noises. To provide consistent and correct error covariance information, this paper proposes two recursive filter algorithms based on carrier-smoothed-code techniques: (a) the stepwise optimal position projection filter and (b) the stepwise unbiased position projection filter. A Monte-Carlo simulation result shows that the proposed filter algorithms actually generate consistent error covariance information and the neglection of carrier phase noise induces optimistic error covariance information. It is also shown that the stepwise unbiased position projection filter is attractive since its performance is good and its computational burden is moderate.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Steel and Composite Structures
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• v.31 no.6
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• pp.575-589
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• 2019

The main focus of this study is to numerically investigate the influence of strong earthquake and tsunami-induced wave impact on the response and behavior of a cable-stayed steel bridge with large caisson foundations, by assuming that the earthquake and the tsunami come from the same fault motion. For this purpose, a series of numerical simulations were carried out. First of all, the tsunami-induced flow speed, direction and tsunami height were determined by conducting a two-dimensional (2D) tsunami propagation analysis in a large area, and then these parameters obtained from tsunami propagation analysis were employed in a detailed three-dimensional (3D) fluid analysis to obtain tsunami-induced wave impact force. Furthermore, a fiber model, which is commonly used in the seismic analysis of steel bridge structures, was adopted considering material and geometric nonlinearity. The residual stresses induced by the earthquake were applied into the numerical model during the following finite element analysis as the initial stress state, in which the acquired tsunami forces were input to a whole bridge system. Based on the analytical results, it can be seen that the foundation sliding was not observed although the caisson foundation came floating slightly, and the damage arising during the earthquake did not expand when the tsunami-induced wave impact is applied to the steel bridge. It is concluded that the influence of tsunami-induced wave force is relatively small for such steel bridge with large caisson foundations. Besides, a numerical procedure is proposed for quantitatively estimating the accumulative damage induced by the earthquake and the tsunami in the whole bridge system with large caisson foundations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.1
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• pp.109-116
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• 1994

Tsunamis generated by of the 1992 Flores Island Earthquake in Indonesia caused tremendous casualties and damages. This tsunami event was hindcasted via numerical tsunami models. Initial conditions were taken from fault parameters from Havard CMT (Centroid Moment Tensor) solution and additional subaqueous slump consideration at the Inner Hading Bay and Riang Krok, Leworahang coasts. The computed results showed general agreements with observations made by the International Tsunami Survey Group. Subsequently a runup model was developed to investigate catastrophic runup at southern shore of the Babi Island with fine grid resolution of 50 m. Computed results were recorded to construct rendered images for video animation. The computer-graphic aided video animation showed a remarkable reproduction of tsunami propagation and runup at southern coast of the Babi Island.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2958-2968
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• 2000

To effectively supervise WDM systems requires that the alarm signals for each optical transport network layer is considered. The related recommendations and studies for alarm signals are defining fundamental functions without specifying the structure and configuration of the supervisory channel. In this paper, we propose the detailed alarm signals and propagation flows for each optical transport network layer. We also describe the structure of overhead and the configuration of supervisory channel. Our proposals are based on the ITU T requirements of optical transport network and the supervisory schemes of terminal & add-drop systems. We show that our proposed schemes effectively performs supervisory functions for various fault conditions which will occur in terminal & add-drop typed WDM systems.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.121-130
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• 2011

Recent research indicates that transient errors will increasingly become a critical concern in microprocessor design. As embedded processors are widely used in reliability-critical or noisy environments, it is necessary to develop cost-effective fault-tolerant techniques to protect processors against transient errors. The register file is one of the critical components that can significantly affect microprocessor system reliability, since registers are typically accessed very frequently, and transient errors in registers can be easily propagated to functional units or the memory system, leading to silent data error (SDC) or system crash. This paper focuses on investigating the impact of register file soft errors on system reliability and developing cost-effective techniques to improve the register file immunity to soft errors. This paper proposes the register vulnerability factor (RVF) concept to characterize the probability that register transient errors can escape the register file and thus potentially affect system reliability. We propose an approach to compute the RVF based on register access patterns. In this paper, we also propose two compiler-directed techniques and a hybrid approach to improve register file reliability cost-effectively by lowering the RVF value. Our experiments indicate that on average, RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly, without sacrificing the register value integrity.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.