• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.10 s.103
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• pp.1202-1210
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• 2005

This paper develops a computer model for estimating the bump characterisitcs of a cat)over type large-sized truck. The truck is composed of front and rear suspension systems, a frame, a cab, and ten tires. The computer model is developed using MSC.ADAMS. A shock absorber, a rubber bush, and a leaf spring affect a lot on the dynamic characteristic of the vehicle. Their stiffness and damping coefficient are measured and used as input data of the computer model. Leaf springs in the front and rear suspension systems are modeled by dividing them three links and joining them with joints. To improve the reliability of the developed computer model, the frame is considered as a flexible body. Thus, the frame is modeled by finite elements using MSC.PATRAN. A mode analysis is performed with the frame model using MSC.NASTRAN in order to link the frame model to the computer model. To verify the reliability of the developed computer model, a double wheel bump test is performed with an actual vehicle. In the double wheel bump, vortical displacement, velocity, acceleration are measured. Those test results are compared with the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5039-5061
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• 2017

Cloud storage becomes a new trend that more and more users move their data to cloud storage servers (CSSs). To ensure the security of cloud storage, many cloud auditing schemes are proposed to check the integrity of users' cloud data. However, most of them are based on public key infrastructure, which leads to complex certificates management and verification. Besides, most existing auditing schemes are inefficient when user uploads a large amount of data or a third party auditor (TPA) performs auditing for multiple users' data on different CSSs. To overcome these problems, in this paper, we propose an efficient and secure auditing scheme based on identity-based cryptography. To relieve user's computation burden, we introduce a proxy, which is delegated to generate and upload homomorphic verifiable tags for user. We extend our auditing scheme to support auditing for dynamic data operations. We further extend it to support batch auditing in multiple users and multiple CSSs setting, which is practical and efficient in large scale cloud storage system. Extensive security analysis shows that our scheme is provably secure in random oracle model. Performance analysis demonstrates that our scheme is highly efficient, especially reducing the computation cost of proxy and TPA.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.55-62
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• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• Aerospace Engineering and Technology
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• v.2 no.2
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• pp.1-10
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• 2003

An airship is statically unstable, because it has no wing, comparatively small tail and large hull. Hence, an accurate prediction of dynamic stability is critical. In this study, dynamic stability data of the Mid-Size Airship is acquired through forced oscillation wind tests. The test was done in BAR LAMP which is Birhle Applied Research Inc's facility located in Germany. The test was composed with 16 static runs and 26 dynamic runs. As a result, dynamic characteristics of the airship depends on sideslip angle, angular rate and its direction as well as angle of attack. Generally, it is obtained that 3 directional moments have damping, but normal force, side force, and cross-derivatives are unstable. The dynamic derivatives are not sensitive to control surfaces, but have nonlinear dependency on sideslip angle.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.124-133
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• 1997

Pile load test is good for estimating pile bearing capacity, but using this method is limited by time and cost required. Dynamic and static method is more convenient and economical, but confidence of estimated value by dynamic and static method are lower than that of pile load test. After being compared pile bearing capacity data obtained from pile load test with those of other two methods, the results from this study were summarised as follows. For allowable bearing capacity values greater than 175t per pile, bearing capacity acquired from static method was higher than that acquired from pile load test, whereas bearing capacity acquired from pile load test was higher than that acquired from static method for values under 175 per pile. It was that variance of bearing capacity was large when bearing capacity obtained by dynamic method was higher than 250t. Also bearing capacity based on dynamic method was higher than that based on pile load test. Allowable bearing capacity get from dynamic method suggested that carefull precautions are necessary in application for allowable bearing capacity values higher than 2S0ton per pile.

• Journal of the Korean Magnetic Resonance Society
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• v.9 no.2
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• pp.93-102
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• 2005

Purpose: To develop an advanced non-linear curve fitting (NLCF) algorithm for dynamic susceptibility contrast study of brain. Materials and Methods: The first pass effects give rise to spuriously high estimates of $K^{trans}$ in voxels with large vascular components. An explicit threshold value has been used to reject voxels. Results: By using this non-linear curve fitting algorithm, the blood perfusion and the volume estimation were accurately evaluated in T2*-weighted dynamic contrast enhanced (DCE)-MR images. From the recalculated each parameters, perfusion weighted image were outlined by using modified non-linear curve fitting algorithm. This results were improved estimation of T2*-weighted dynamic series. Conclusion: The present study demonstrated an improvement of an estimation of kinetic parameters from dynamic contrast-enhanced (DCE) T2*-weighted magnetic resonance imaging data, using contrast agents. The advanced kinetic models include the relation of volume transfer constant $K^{trans}\;(min^{-1})$ and the volume of extravascular extracellular space (EES) per unit volume of tissue $\nu_e$.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.41-47
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• 2008

The nonlinear structural analyses of high-aspect-ratio structures were performed. For the high-aspect-ratio structures, it is important to understand geometric nonlinearity due to large deflections. To consider geometric nonlinearity, finite element analyses based on the large deflection beam theory were introduced. Comparing experimental data and the present nonlinear analysis results, the current results were proved to be very accurate for the static and dynamic behaviors for both isotropic and anisotropic beams.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.131-143
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• 2007

The current trends in information technology and intelligent systems use data mining techniques to discover patterns and extract rules from distributed databases. In distributed environment, the extracted rules from data mining techniques can be used in dynamic replications, adaptive load balancing and other schemes. However, transmission of large data through the system can cause errors and unreliable results. This paper proposes the intelligent distributed platform based on dynamic group binding using mobile agents which addresses the use of intelligence in distributed environment. The proposed grouping service implements classification scheme of objects. Data compressor agent and data miner agent extracts rules and compresses data, respectively, from the service node databases. The proposed algorithm performs preprocessing where it merges the less frequent dataset using neuro-fuzzy classifier before sending the data. Object group classification, data mining the service node database, data compression method, and rule extraction were simulated. Result of experiments in efficient data compression and reliable rule extraction shows that the proposed algorithm has better performance compared to other methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.

• Smart Structures and Systems
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• v.20 no.2
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• pp.115-126
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• 2017

The modal identification of large civil structures such as bridges under the ambient vibrational conditions has been widely investigated during the past decade. Many operational modal analysis methods have been proposed and successfully used for identifying the dynamic characteristics of the constructed bridges in service. However, there is very limited research available on reliable criteria for the robustness of these identified modal parameters of the bridge structures. In this study, two time-domain operational modal analysis methods, the data-driven stochastic subspace identification (SSI-DATA) method and the covariance-driven stochastic subspace identification (SSI-COV) method, are employed to identify the modal parameters from field recorded ambient acceleration data. On the basis of the SSI-DATA method, the modal contribution indexes of all identified modes to the measured acceleration data are computed by using the Kalman filter, and their applicability to evaluate the robustness of identified modes is also investigated. Here, the benchmark problem, developed by Hong Kong Polytechnic University with field acceleration measurements under different excitation conditions of a cable-stayed bridge, is adopted to show the effectiveness of the proposed method. The results from the benchmark study show that the robustness of identified modes can be judged by using their modal contributions to the measured vibration data. A critical value of modal contribution index of 2% for a reliable identifiability of modal parameters is roughly suggested for the benchmark problem.