• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1048-1051
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• 1996

In this paper, we present real-time S/W architecture and techniques such as real-time scheduling, synchronization, and etc. for implementation of a real-time control system for driving multi-motors. Simulation shows feasibility of real-time S/W techniques presented here.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.2 s.332
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• pp.83-88
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• 2005

We propose the design method for the VLSI architecture of FNNPDS combined PDS(partial distance search) and FNNS(fast nearest neighbor search), which are used to fast encoding in vector quantization, and obtain the results that FNNPDS(fast nearest neighbor partial distance search) is faster method than the conventional methods by simulation. In simulations, we investigate timing diagrams described searching time of the nearest codevector for an input vector, and compare the average clock cycles per input vector for Lena and Peppers images. According to the result of simulations, the number of the clock cycle of FNNPDS was reduced to $79.2\%\~11.7\%$ as compared with the number using the conventional techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.358-362
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• 2004

According to the rapid advance of computer and communication techniques, the request of mobile internet services is highly increasing. However, the main obstacles for mobile vector map service environments, are large data volume and narrow wireless bandwidth. Among the many possible solutions, spatial data compression technique may contribute to reduce the load of bandwidth and client response time. This thesis proposes two methods for spatial data compression. The one is relative coordinates transformation method, and the other is client coordinates transformation method. And, this thesis also proposes the system architecture for experiments. The two compression methods could be evaluated the compression effect and the response time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.944-946
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• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. We modified the existing Support vector machine algorithm to fit into the distributed hadoop architecture system for localization of a sensor node. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.1
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• pp.61-70
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• 2012

A construction project is very costly and takes a long time to make investment and yield profit. For this reason, financial institutions are cautious about financing construction projects. Meanwhile, a construction company needs financing from financial institutions to cover a large expense of a construction project. Thus, there is likely to be a close correlation between financing conditions and business operation of a construction company. To examine the relationship, variables were identified that are related to insolvency of a construction company and changes in financing conditions. The analysis period is between the second quarter of 2001 and the fourth quarter of 2010. Data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Office, and Construction Economy Research Institute of Korea (CERIK). In terms of methodology, VECM (Vector Error Correction Model) was used to analyze dynamic relationship between changes in financing conditions and insolvency of a construction company based on the identified variables. The hypothesis was that changes in financing conditions would significantly affect business of a construction company, but, the analysis did not find a close relation between the two factors. However, it was shown that poor business of a construction company affects financing conditions adversely.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.47-52
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• 2010

This study presents a modified scheme for the radar image simulation of sea waves. A simulated radar image was obtained by taking into account the dot product of the directed vector from the radar and the normal vector of the sea surface. Moreover, to calculate the radar image, we used the radar received power and radar cross section. To demonstrate the effectiveness of the proposed scheme, the wave spectrum from field data was utilized to obtain the simulated sea waves. The radar image was simulated using numerically generated sea waves. The wave statistics from the simulation agrees comparatively with those of the original field data acquired by real radar measurements.

• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.4
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• pp.137-142
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• 1999

The purpose of this study is to develope a CAD-based tool for rasterization of polygonal vector map in AutoCAD. To identity the layer property of polygonal entity with user-defined coordinates as topology, algorithm in processing entity data of selection set that intersected with scan line was used, and the layers were extracted sequentially by sorted intersecting points in data-list. In addition to the functions for querying and modifying topology, two options for mapping were set up to construct plan projection type and to change meshes' properties in existing DTM data. In case of plan projection type, user-defined cell size of 3DFACE mesh is available for more detailed edge, and topological draping on landform can be executed in case of referring DTM data as an AutoCAD's drawing. The concept of algorithm was simple and clear, but some unexpectable errors were found in detecting intersected coordinates that were AutoCAD's error, not the utility's. Also, the routines to check these errors were included in algorithmic processing. Developed utility named MESHMAP was written in entity data control functions of AutoLISP language and dialog control language(DCL) for the purpose of user-oriented interactive usage. MESHMAP was proved to be more effective in data handling and time comparing with GRIDMAP module in LANDCADD which has similar function.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.623-629
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• 2012

During the construction of crossing engineering one of the important measures to ensure the safety of subway operation is the implementation of deformation surveying to the existing subway tunnel. Guangzhou new subway line 2 engineering which crosses the existing tunnel is taken as the background. How to achieve intelligent and automatic deformation surveying forecast during the subway tunnel construction process is studied. Because large amount of surveying data exists in the subway construction, deformation analysis is difficult and prediction has low accuracy, a subway intelligent deformation prediction model based on the PBIL and support vector machine is proposed. The PBIL algorithm is used to optimize the exact key parameters combination of support vector machine though probability analysis and thereby the predictive ability of the model deformation is greatly improved. Through applications on the Guangzhou subway across deformation surveying deformation engineering the prediction method's predictive ability has high accuracy and the method has high practicality. It can support effective solution to the implementation of the comprehensive and accurate surveying and early warning under subway operation conditions with the environmental interference and complex deformation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2045-2049
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• 2008

In this paper, we propose an novel architecture for H.264 High Profile Encoder Intra Prediction module. This designed module can be operated in 306 cycle for one-macroblock. To verify the Encoder architecture, we developed the reference C from JM 13.2 and verified the our developed hardware using test vector generated by reference C. We adopt plan removal and SAD calculation to reduce the Hardware cost and cycle. The designed circuit can be operated in 133MHz clock system, and has 250K gate counts using TSMC 0.18 um process including SRAM memory.