• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1014-1015
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• 2008

Safety critical systems are those in which a failure can have serious and irreversible consequences. Nowadays digital technology has been rapidly applied to critical system such as railways, airplanes, nuclear power plants, and vehicles. The main difference between analog system and digital system is that the software is the key component of the digital system. The digital system performs more varying and highly complex functions efficiently compared to the existing analog system because software can be flexibly designed and implemented. The flexible design make it difficult to predict the software failures. This paper reviews safety standard and criteria for safety critical system such as railway system and suggests development process, ordering management and assessment process for railway software with more detail description.

• Archives of Pharmacal Research
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• v.20 no.4
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• pp.324-329
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• 1997

AB-type amphiphilic copolymers (abbreviated as LE) composed of poly (L-leucine) (PLL) as the A component and poly (ethylene oxide) (PEO) as the B component were synthesized by the ring-opening polymerization of L-leucine N-carboxy-anhydride initiated by methoxy polyoxyethylene amine $(Me-PEO-NH_2)$ and characterized. Core-shell type nanoparticles were prepared by the diafiltration method. Particle size distribution obtained by dynamic light scattering was dependent on PLL composition and the size for LE-1, LE-2 and LE-3 was $369.6{\pm}267$, $523.4{\pm}410$ and $561.2{\pm}364 nm$, respectively. Shapes of the nanoparticies observed by transmission electron microscope (TEM) were almostly spherical. The critical micelle concentration (CMC) of the nanoparticles determined by a fluorescence probe technique was dependent on the composition of hydrophobic PLL, and the CMC for LE-1, LE-2 and LE-3 was $2.0{\times}10^{-6},1.7{\times}10^{-6}$ and $1.5{\times}10^{-6}(mol/l) $, respectively. Clonazepam release from core-shell type nanoparticles in vitro was dependent on PLL composition and drug loading content.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.506-516
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• 1998

In this paper, an algorithm is proposed to detect the malfunction of plasma-etching characteristics using EPD signal trajectories. EPD signal trajectories offer many information on plasma-etching process state, so they must be considered as the most important data sets to predict the wafer states in plasma-etching process. A recent work has shown that EPD signal trajectories were successfully incorporated into process modeling through critical parameter extraction, but this method consumes much effort and time. So Principal component analysis(PCA) can be applied. PCA is the linear transformation algorithm which converts correlated high-dimensional data sets to uncorrelated low-dimensional data sets. Based on this reason neural network model can improve its performance and convergence speed when it uses the features which are extracted from raw EPD signals by PCA. Wafer-state variables, Critical Dimension(CD) and uniformity can be estimated by simulation using neural network model into which EPD signals are incorporated. After CD and uniformity values are predicted, proposed algorithm determines whether malfunction values are produced or not. If malfunction values arise, the etching process is stopped immediately. As a result, through simulation, we can keep the abnormal state of etching process from propagating into the next run. All the procedures of this algorithm can be performed on-line, i.e. wafer to wafer.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.11-19
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• 2014

Modern systems development becomes more and more complicated due to the need on the ever-increasing capability of the systems. In addition to the complexity issue, safety concern is also increasing since the malfunctions of the systems under development may result in the accidents in both the test and evaluation phase and the operation phase. Those accidents can cause disastrous damages if explosiveness gets involved therein such as in weapon systems development. The subject of this paper is on how to incorporate safety requirements in the design of safety-critical systems. As an approach, a useful system structure using the method of design structure matrix (DSM) is studied while reflecting the need on systems safety. Specifically, the effects of system components failure are analyzed and numerically modeled first. Also, the system components are identified and their interfaces are represented using a component DSM. Combining the results of the failure analysis and the component DSM leads to a modified DSM. By rearranging the resultant DSM, a modular structure is derived with safety requirements incorporated. As a case study, application of the approach is also discussed in the development of a military UAV plane.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.451-460
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• 2012

Natural disasters often lead to regional failures that can cause network nodes and links co-located in a large geographical area to fail. Novel approaches are required to assess the network vulnerability under such regional failures. In this paper, we investigate the vulnerability of networks by considering the geometric properties of regional failures and network nodes. To evaluate the criticality of node locations and determine the critical areas in a network, we propose the concept of ${\alpha}$-critical-distance with a given failure impact ratio ${\alpha}$, and we formulate two optimization problems based on the concept. By analyzing the geometric properties of the problems, we show that although finding critical nodes or links in a pure graph is a NP-complete problem, the problem of finding critical areas has polynomial time complexity. We propose two algorithms to deal with these problems and analyze their time complexities. Using real city-level Internet topology data, we conducted experiments to compute the ${\alpha}$-critical-distances for different networks. The computational results demonstrate the differences in vulnerability of different networks. The results also indicate that the critical area of a network can be estimated by limiting failure centers on the locations of network nodes. Additionally, we find that with the same impact ratio ${\alpha}$, the topologies examined have larger ${\alpha}$-critical-distances when the network performance is measured using the giant component size instead of the other two metrics. Similar results are obtained when the network performance is measured using the average two terminal reliability and the network efficiency, although computation of the former entails less time complexity than that of the latter.

• Journal of The Korean Astronomical Society
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• v.33 no.1
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• pp.1-17
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• 2000

We have performed extensive simulations of response of gaseous disk in barred galaxies using SPH method. The gravitational potential is assumed to be generated by disk, bulge, halo, and bar. The mass of gaseous disk in SPH simulation is assumed to be negligible compared to the stellar and dark mass component, and the gravitational potential generated by other components is fixed in time. The self-gravity of the gas is not considered in most simulations, but we have made a small set of simulations including the self-gravity of the gas. Non-circular component of velocity generated by the rotating, non-axisymmetric potential causes many interesting features. In most cases, there is a strong tendency of concentration of gas toward the central parts of the galaxy. The morphology of the gas becomes quite complex, but the general behavior can be understood in terms of simple linear approximations: the locations and number of Lindblad resonances play critical role in determining the general distribution of the gas. We present our results in the form of 'atlas' of artificial galaxies. We also make a brief comment on the observational implications of our calculations. Since the gaseous component show interesting features while the stellar component behaves more smoothly, high resolution mapping using molecular emission line for barred galaxies would be desirable.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.749-756
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• 2010

The signal space diversity is one of the promising transmission techniques in next generation mobile TV service. However, DVB-T2 does not consider the multiple antennas (MIMO) so that the cyclic Q-delay method, a component interleaver in DVB-T2, causes a critical issue in detecting symbols at the receiver side by increasing the inter-symbol dependency. To solve this problem, the component-swapping technique is proposed, which limits the inter-symbol dependency in order to reduce detection complexity. In this paper, the achievable gain of a component-swapping technique combined with 16K LDPC code defined in DVB-T2 is evaluated by computer simulations. From the results, the gain is confirmed in terms of BER and receive complexity compared to legacy component interleaver methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Earthquakes and Structures
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• v.1 no.1
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• pp.43-67
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• 2010

A gradient-based evolutionary optimization methodology is presented for finding the optimal design of both the added dampers and their supporting members to minimize an objective function of a linear multi-storey structure subjected to the critical ground acceleration. The objective function is taken as the sum of the stochastic interstorey drifts. A frequency-dependent viscoelastic damper and the supporting member are treated as a vibration control device. Due to the added stiffness by the supplemental viscoelastic damper, the variable critical excitation needs to be updated simultaneously within the evolutionary phase of the optimal damper placement. Two different models of the entire damper unit are investigated. The first model is a detailed model referred to as "the 3N model" where the relative displacement in each component (i.e., the spring and the dashpot) of the damper unit is defined. The second model is a simpler model referred to as "the N model" where the entire damper unit is converted into an equivalent frequency-dependent Kelvin-Voigt model. Numerical analyses for 3 and 10-storey building models are conducted to investigate the characters of the optimal design using these models and to examine the validity of the proposed technique.

• Analytical Science and Technology
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• v.24 no.6
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• pp.519-526
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• 2011

Chemometrics using near-infrared (NIR) and Raman spectroscopy have found significant uses in a variety quantitative and qualitative analyses of pharmaceutical products in complex matrixes. Most of the pharmaceutical can be measured directly with little or no sample preparation using these spectroscopic methods. During pharmaceutical manufacturing process, analytical techniques with no or less sample preparation are very critical to confirm the quality. This study showed NIR and Raman spectroscopy with principal component analysis (PCA) was very effective for the blending processing control. It is of utmost importance to evaluate critical parameters related to quality of products during pharmaceutical processing. The blending is confirmed by off-line determination of active pharmaceutical ingredient (API) by a conventional method such as high performance liquid chromatography (HPLC) and UV spectroscopy. These analytical methods are time-consuming and ineffective for real time control. This study showed the possibility for the determination of blend uniformity end-point of CR tablets with the use of both NIR and Raman spectroscopy. The samples were acquired from six positions during blending processing with U-type blender from 0 to 30 min. Using both collected NIR and Raman spectral data, principal component analysis (PCA) was used to follow the uniformity of blending and finally determine the end-point. The variation of homogeneity of six samples during blending was clearly found and blend uniformity end-point was successfully confirmed in the domains of principal component (PC) scores.