• Earthquakes and Structures
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• v.27 no.4
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• pp.331-344
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• 2024

The performance of reinforced concrete buildings subjected to earthquake excitations depends on the structural behaviour of the superstructure as well as the type of foundation and the properties of soil on which the structure is founded. The consideration of the effects due to the interaction between the structure and soil- foundation alters the seismic response of reinforced concrete buildings subjected to earthquake motion. Evaluation of the structural response of buildings for quantitative assessment of the seismic fragility has been a demanding problem for the engineers. Present research deals with development of fragility curve for building specific vulnerability assessment based on different damage parameters considering the effect of soil-structure interaction. Incremental Dynamic Analysis of fixed base and flexible base RC building models founded on different soil conditions was conducted using finite element software. Three sets of fragility curves were developed with maximum roof displacement, inter storey drift and plastic energy dissipated as engineering demand parameters. The results indicated an increase in the likelihood of exceeding various damage limits by 10-40% for flexible base condition with soft soil profiles. Fragility curve based on energy dissipated showed a higher probability of exceedance for collapse prevention damage limit whereas for lower damage states, conventional methods showed higher probability of exceedance. With plastic energy dissipated as engineering demand parameter, it is possible to track down the intensity of earthquake at which the plastic deformation starts, thereby providing an accurate vulnerability assessment of the structure. Fragility modification factors that enable the transformation of existing fragility curves to account for Soil-Structure Interaction effects based on different damage measures are proposed for different soil conditions to facilitate a congenial vulnerability assessment for buildings with flexible base conditions.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.3
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• pp.210-218
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• 2018

In order to generate meshes automatically for finite element analysis of complex structures such as aircraft, a large number of triangular elements are typically created. However, triangular elements are less accurate than rectangular elements, so it is difficult to obtain a reliable solution. This problem can be improved through the meshfree method using the back cell integration. However, this method also causes some problems such as over-use of the integration points and inefficiency of the integral domain. In order to improve these problems, a method of performing integration by setting the integral area based on a node basis has been proposed, but in the case of incompressible material problems, the numerical accuracy deteriorates due to the vibration phenomenon of the solution. Therefore, in this paper, the modified meshfree method is proposed which sets the integral domain as an element domain instead of the nodal domain, and the proposed method improves the numerical instability caused by the conventional meshfree method without decreasing the accuracy regardles of the shape of integral domain. The effectiveness of the modified meshfree method is verified by using 2-D examples.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.35-46
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• 2010

Sequential pattern mining is one of the essential data mining tasks, and it is widely used to analyze data generated in various application fields such as web-based applications, E-commerce, bioinformatics, and USN environments. Recently data generated in the application fields has been taking the form of continuous data streams rather than finite stored data sets. Considering the changes in the form of data, many researches have been actively performed to efficiently find sequential patterns over data streams. However, conventional researches focus on reducing processing time and memory usage in mining sequential patterns over a target data stream, so that a research on mining more interesting and useful sequential patterns that efficiently reflect the characteristics of the data stream has been attracting no attention. This paper proposes a mining method of sequential patterns over data streams with a gap constraint, which can help to find more interesting sequential patterns over the data streams. First, meanings of the gap for a sequential pattern and gap-constrained sequential patterns are defined, and subsequently a mining method for finding gap-constrained sequential patterns over a data stream is proposed.

• Tribology and Lubricants
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• v.35 no.1
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• pp.44-51
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• 2019

The paper presents the rotordynamic performance measurements and model predictions of a fuel cell electric vehicle (FCEV) air compressor supported on gas foil bearings (GFBs). The rotor has an impeller on one end and a thrust runner on the other end. The front (impeller side) and rear (thrust side) gas foil journal bearings (GFJBs) are located between the impeller and thrust runner to support the radial loads, and a pair of gas foil thrust bearings are located on both sides of the thrust runner to support the axial loads. The test GFJBs have a partial arc shim foil installed between the top foil and bump strip layers to enhance hydrodynamic pressure generation. During the rotordynamic performance tests, two sets of orthogonally installed eddy-current displacement sensors measure the rotor radial motions at the rotor impeller and thrust ends. A series of speed-up and coast-down tests to 100k rpm demonstrates the dominant synchronous (1X) rotor responses to imbalance masses without noticeable subsynchronous motions, which indicates a rotordynamically stable rotor-GFB system. Finite element analysis of the rotor determines the rotor free-free (bending) natural modes and frequencies well beyond the maximum rotating frequency. The predicted damped natural frequencies and damping ratios of the rotor-GFB system reveal rotordynamic stability over the speeds of interest. The imbalance response predictions show that the predicted critical speeds and rotor amplitudes strongly agree with the test measurements, thus validating the developed rotordynamic model.

• Wind and Structures
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• v.34 no.1
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• pp.127-136
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• 2022

In this work a multi-fidelity non-intrusive polynomial chaos (MF-NIPC) has been applied to a structural wind engineering problem in architectural design for the first time. In architectural design it is important to design structures that are safe in a range of wind directions and speeds. For this reason, the computational models used to design buildings and bridges must account for the uncertainties associated with the interaction between the structure and wind. In order to use the numerical simulations for the design, the numerical models must be validated by experi-mental data, and uncertainties contained in the experiments should also be taken into account. Uncertainty Quantifi-cation has been increasingly used for CFD simulations to consider such uncertainties. Typically, CFD simulations are computationally expensive, motivating the increased interest in multi-fidelity methods due to their ability to lev-erage limited data sets of high-fidelity data with evaluations of more computationally inexpensive models. Previous-ly, the multi-fidelity framework has been applied to CFD simulations for the purposes of optimization, rather than for the statistical assessment of candidate design. In this paper MF-NIPC method is applied to flow around a rectan-gular 5:1 cylinder, which has been thoroughly investigated for architectural design. The purpose of UQ is validation of numerical simulation results with experimental data, therefore the radius of curvature of the rectangular cylinder corners and the angle of attack are considered to be random variables, which are known to contain uncertainties when wind tunnel tests are carried out. Computational Fluid Dynamics (CFD) simulations are solved by a solver that employs the Finite Element Method (FEM) for two turbulence modeling approaches of the incompressible Navier-Stokes equations: Unsteady Reynolds Averaged Navier Stokes (URANS) and the Large Eddy simulation (LES). The results of the uncertainty analysis with CFD are compared to experimental data in terms of time-averaged pressure coefficients and bulk parameters. In addition, the accuracy and efficiency of the multi-fidelity framework is demonstrated through a comparison with the results of the high-fidelity model.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.518-528
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• 2014

In this paper, we propose a method to recognize the action direction of human by developing 4D space-time (4D-ST, [x,y,z,t]) features. For this, we propose 4D space-time interest points (4D-STIPs, [x,y,z,t]) which are extracted using 3D space (3D-S, [x,y,z]) volumes reconstructed from images of a finite number of different views. Since the proposed features are constructed using volumetric information, the features for arbitrary 2D space (2D-S, [x,y]) viewpoint can be generated by projecting the 3D-S volumes and 4D-STIPs on corresponding image planes in training step. We can recognize the directions of actors in the test video since our training sets, which are projections of 3D-S volumes and 4D-STIPs to various image planes, contain the direction information. The process for recognizing action direction is divided into two steps, firstly we recognize the class of actions and then recognize the action direction using direction information. For the action and direction of action recognition, with the projected 3D-S volumes and 4D-STIPs we construct motion history images (MHIs) and non-motion history images (NMHIs) which encode the moving and non-moving parts of an action respectively. For the action recognition, features are trained by support vector data description (SVDD) according to the action class and recognized by support vector domain density description (SVDDD). For the action direction recognition after recognizing actions, each actions are trained using SVDD according to the direction class and then recognized by SVDDD. In experiments, we train the models using 3D-S volumes from INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset and recognize action direction by constructing a new SNU dataset made for evaluating the action direction recognition.