• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.169-177
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• 2011

As demand for efficiency in handling dynamic XML data grows, new dynamic XML labeling schemes have been researched. The key idea of the dynamic XML labeling scheme is to find ancestor-descendent-sibling relationships and to minimize memory space to store total label, response time and range of relabeling incurred by update operations. The prime number labeling scheme is a representative scheme which supports dynamic XML documents. It determines the ancestor-descendant relationships between two elements by a simple divisibility test of labels. When a new element is inserted into the XML data using this scheme, it does not change the label values of existing nodes. However, since each prime number must be used exclusively, labels can become significantly large. Therefore, in this paper, we introduce a novel technique to effectively reduce the problem of label overflow. The suggested idea is based on tree decomposition. When label overflow occurs, the full tree is divided into several sub-trees, and nodes in each sub-tree are separately labeled. Through experiments, we show the effectiveness of our scheme.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1126-1132
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• 2009

Bridge structures are designed to support ordinary loadings such as vehicles, wind, temperature and current as well as unexpected loadings like earthquakes and storm. Especially, the displacement of Flexible bridges like an suspension bridge under ordinary loading conditions is necessary to be monitored. In case of long span bridges, there are some difficulties in monitoring the displacement of center of the main span using traditional laser displacement sensors. In this study, the static and dynamic displacement responses due to vehicle loadings were measured by DGPS(differential global positioning system) technique. The displacement response data were compared with data obtained from traditional laser displacement sensors so that the static and dynamic behavior of the bridge under vehicle loadings was examined and the applicability of the displacement response measurement using DGPS technique was verified. The static and dynamic loading test for an self-anchored suspension bridge, So-rok Bridge, was performed using vehicles. The displacement response from DGPS technique and that from laser displacement sensors of the bridge monitoring system were compared. The amplitude of white noise from DGPS based measurement was about 7 mm and that of laser displacement sensor based measurement was about 3 mm. On the other hand, dynamic behavior of the center of main span from DGPS based measurement showed better agreement with influence line of the bridge than that from laser displacement sensors. In addition, there were some irregular and discontinuous variation of data due to the instability of GPS receivers or frequent appearance of GPS satellites. Post-processing via the reference station close to an observation post provided by NGII(National Geographic Information Institute) will be a counter-plan for these defects.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.1
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• pp.21-27
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• 2013

Manual wheelchair propulsion can lead to pain and injuries of users due to mechanical inefficiency of wheelchair propulsion motion. The kinetic analysis of the upper limbs during manual wheelchair propulsion needs to be studied. A two dimensional inverse dynamic model of upper limbs was developed to compute the joint torque during manual wheelchair propulsion. The model was composed of three segments corresponding to upper arm, lower arm and hand. These segments connected in series by revolute joints constitute open chain mechanism in sagittal plane. The inverse dynamic method is based on Newton-Euler formalism. The model was applied to data collected in experiments. Kinematic data of upper limbs during wheelchair propulsion were obtained from three dimensional trajectories of markers collected by a motion capture system. Kinetic data as external forces applied on the hand were obtained from a dynamometer. The joint rotation angles and joint torques were computed using the inverse dynamic model. The developed model is for upper limbs biomechanics and can easily be extended to three dimensional dynamic model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.219-222
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• 2011

There are number of methods that track the movement of an object or the change of state, such as Kalman filter, particle filter, dynamic clustering, and so on. Amongst these method, dynamic clustering method is an useful way to track cluster across multiple data frames and analyze their trend. In this paper we suggest the similarity-based dynamic clustering method, and verifies it's performance by simulation. Proposed dynamic clustering method is how to determine the same clusters for each continuative frame. The same clusters have similar characteristics across adjacent frames. The change pattern of cluster's characteristics in each time frame is throughly studied. Clusters in each time frames are matched against each others to see their similarity. Mamdani fuzzy model is used to determine similarity based matching algorithm. The proposed algorithm is applied to radar reflectivity data over time domain. We were able to observe time dependent characteristic of the clusters.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.1
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• pp.39-55
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• 1993

Modal Analysis is the process of characterizing the dynamic properties of an elastic structure by identifying its modes of vibration. A mode of vibration is a global property of an elastic structure. That is, a mode has a specific natural frequency and damping factor which can be identified from response data at practically any point on a structure, and it has a characteristic mode shape which identifies the mode spatially over the entire structure. Modal testing is able to be performed on structural and mechanical structure in an effort to learn more about their elastic behavior. Once the dynamic properties of a structure are known its behavior can be predicted and therefore controlled or corrected. Resonant frequencies, damping factors and mode shape data can be used directly by a mechanical designer to pin point weak spots in a structure design, or this data can also be used to confirm or synthesize equations of motion for the elastic structure. These differential equations can be used to simulate structural response to know input forces and to examine the effects of pertubations in the distributed mass, stiffness and damping properties of the structure in more detail. In this paper the measurement of transfer functions in digital form, and the application of digital parameter identification techniques to identify modal parameters from the measured transfer function data are discussed. It is first shown that the transfer matrix, which is a complete dynamic model of an elastic plate structure can be written in terms of the structural modes of vibration. This special mathematical form allows one to identify the complete dynamics of the structure from a much reduced set of test data, and is the essence of the modal approach to identifying the dynamics of a structure. Finally, the application of transfer function models and identification techniques for obtaining modal parameters from the transfer function data are discussed. Characteristics on vibration response of elastic plate structure obtained from the dynamic analysis by Finite Element Method are compared with results of modal analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1615-1622
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• 2009

Generally, the Terminal Based Navigation System(TBNS) used embedded road data searches a path that has less qualitative than The Center Based Navigation System(CBNS). TBNS has not used real time road data but it is recently able to use it with technique such as TPEG. However, it causes to increase a cost of exploring by using real time road data for improvement quality of a path, because of limited performance. In this paper, we propose a Dynamic Heuristic to improve quality of path in the TBNS. Dynamic Heuristic(DH) is not fixed data and is dynamically modified using transferred real time road data from server. In this paper, we propose path-lading algorithm with Maximum Speed Dynamic Heuristic (DH-MAX) and do an experiment. The DH-MAX is to be used the highest speed as DH, in real map divided by same size. And proposed algorithm searches path using the priority searching only of the fixed data, but also the highest speed with real time information. In the performance test, the quality of path is enhanced but the cost of searching is increased than A* algorithm.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.125-136
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• 2005

The goal of this paper is to develop freeway Origin-Destination (OD) demand estimation model using VDS and AVI data. The formulation of methodology proposed in this paper includes traffic flow technique to be able to remove the bi-level problem and optimal solution algorithm using a kalman filter algorithm. The proposed dynamic OD estimation model use ilk and off-ramp volumes collected from VDS and partial OD collected from AVI data to raise the accuracy of dynamic OD estimation. The proposed model is evaluated by using the real-time data of SOHAEAN freeway, South Korea. The result of the proposed dynamic OD estimation model based on VDS and AVI data is better than that of based on VDS data. The more AVI systems are equipped at on and off-ramp, the more excellent result of estimation accuracy is expected.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.103-111
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• 2023

This paper proposes a new data correction technique that transforms anomalies in time series data into normal values. With the recent development of IT technology, a vast amount of time-series data is being collected through sensors. However, due to sensor failures and abnormal environments, most of time-series data contain a lot of anomalies. If we build a predictive model using original data containing anomalies as it is, we cannot expect highly reliable predictive performance. Therefore, we utilizes the LSTM-GAN model to detect anomalies in the original time series data, and combines DTW (Dynamic Time Warping) and GAN techniques to replace the anomaly data with normal data in partitioned window units. The basic idea is to construct a GAN model serially by applying the statistical information of the window with normal distribution data adjacent to the window containing the detected anomalies to the DTW so as to generate normal time-series data. Through experiments using open NAB data, we empirically prove that our proposed method outperforms the conventional two correction methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3A
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• pp.317-324
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• 2000

In this paper, we propose an interpolation technique to rain attenuation data which represents dynamic characteristics by time variations. By using this technique, it is possible to sample the rain attenuation data at an arbitrary time interval, and thus it would play an important role in developing adaptive transmission scheme for countermeasuring rain attenuation. We propose the interpolation technique which can synthesizes rain attenuation data by extracting the most proper parameters required to emulate the dynamic characteristics of rain attenuation. Interpolation results to measured data of I minute time interval will be demonstrated, and it is shown that more exact performance evaluation of adaptive transmission scheme to countermeasure rain attenuation can be achieved.

• Journal of KIISE
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• v.44 no.8
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• pp.842-853
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• 2017

Streaming data is a sequence of data samples that are consistently generated over time. The data distribution or concept can change over time, and this change becomes a factor to reduce the performance of a classification model. Adaptive incremental learning can maintain the classification performance by updating the current classification model with the weight adjusted according to the degree of concept drift. However, selecting the proper weight value depending on the degree of concept drift is difficult. In this paper, we propose a dynamic ensemble method based on adaptive weight adjustment according to the degree of concept drift. Experimental results demonstrate that the proposed method shows higher performance than the other compared methods.