• Smart Structures and Systems
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• v.6 no.5_6
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• pp.525-543
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• 2010

Testing and validation processes are critical tasks in developing a new hardware platform based on a new technology. This paper describes a series of experiments to evaluate the performance of a newly developed MEMS-based wireless sensor node as part of a wireless sensor network (WSN). The sensor node consists of a sensor board with four accelerometers, a thermometer and filtering and digitization units, and a MICAz mote for control, local computation and communication. The experiments include calibration and linearity tests for all sensor channels on the sensor boards, dynamic range tests to evaluate their performance when subjected to varying excitation, noise characteristic tests to quantify the noise floor of the sensor board, and temperature tests to study the behavior of the sensors under changing temperature profiles. The paper also describes a large-scale deployment of the WSN on a long-span suspension bridge, which lasted over three months and continuously collected ambient vibration and temperature data on the bridge. Statistical modal properties of a bridge tower are presented and compared with similar estimates from a previous deployment of sensors on the bridge and finite element models.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1053-1056
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• 1999

Video On Demand servers generally require massive disk storages for storing many video data. Many researches have been done on the topics of efficient allocation of movies in disks. This paper, We describe efficient disk placement techniques and implement storage system with SCSI and PCI Bus interface for efficient data handling. This paper also proposes a logical zone reconstruction method for the SCAN data placement technique. The proposed method reconstructs physical zones into logical zones by split and merge operations.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.110.3-110
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• 2002

$\textbullet$ Lane detection by TFALDA $\textbullet$ SVM for large scale data and multiclass classification problem $\textbullet$ TLC Classification $\textbullet$ Lateral offset estimation by IPT $\textbullet$ Lane departure warning by a fuzzy system $\textbullet$ Experimental results by HiLS $\textbullet$ Conclusion

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.5-6
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• 2007

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• Journal of Radiation Industry
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• v.11 no.1
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• pp.7-11
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• 2017

RI-Biomics is a fusion technology in radiation fields for evaluating in-vivo dynamics such as absorption, distribution, metabolism and excretion (RI-ADME) of new drugs and materials using radioisotopes and quantitative evaluation of their efficacy. RI-Biomics information is being provided by RIBio-Info developed as information system for distributing its information and three requirements for improving RIBio-Info system have been derived through reviewing recent big data trends in this study. Three requirements are defined as resource, technology and manpower, and some reviews for applying big data in RIBio-In system are suggested. Fist, applicable external big data have to be obtained, second, some infrastructures for realizing applying big data to be expanded, and finally, data scientists able to analyze large scale of information to be trained. Therefore, an original technology driven to analyze for atypical and large scale of data can be created and this stated technology can contribute to obtain a basis to create a new value in RI-Biomics field.

• Architectural research
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• v.4 no.1
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• pp.17-23
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• 2002

Daylight simulation methods play an important role for the prediction of daylight illuminances in underground spaces. This daylighting project is designed to compare daylight prediction methods for the application of large underground spaces. In this study, actual measurements were conducted under overcast and clear sky conditions. Also, computer simulations by Radiance, Superlite IEA 2.0 program and scale model testings were conducted to be compared with measured data. Simulation results show the data by Radiance, Superlite IEA 2.0 and the scale model are similar to the measured data in two underground spaces in Seoul. Overall results show that Radiance and superlite IEA 2.0 proved to be useful to predict daylight illuminances even in big underground spaces.

• Journal of Korean Society of Rural Planning
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• v.8 no.1 s.15
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• pp.94-104
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• 2002

In spite of the importance of the farm area in Seoul, in providing fresh vegetables, a pleasant environment and a good quality of life for residents, rapid urbanization and industrialization have greatly reduced the farm area. The purpose of this study is to examine farmers' intentions and attitudes to provide supporting data for planning the strategy of urban agricultural development. All the collected data was analyzed using the contingency tables and the Chi-square test using the SAS computer statistical package. Based on analysis of the survey data, the leaseholders were found to be more satisfied with their job than the landowning farmers. Also, the small-scale farmers with green houses showed greater job satisfaction than the ordinary large-scale farmers. Farmers' views on the farming in Seoul were different depending on their status. Therefore, agricultural strategies in there should be considered their different attitudes.

• Journal of Environmental Impact Assessment
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• v.2 no.2
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• pp.73-83
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• 1993

Increasing concern for the environment in Korea has led to the demand that major policies and large-scale development projects be subjected to detailed impact assessment. This paper reports on the state of data related to the prediction of the environmental impact (EIA) to emphasize the importance of data quality. Environmental impact statements (EIS) consulted with the Ministry of Environment of Korea were analyzed from 1981 through 1992. Many of assessors used existing data and collected supplementary data from field survey. Most of the results of EIA are presented directly or summarized on maps and as graphics. For the national purpose, large source of quality-controlled data such as atmospheric data have been developed, However, there are the deficiency in data to analyze the impact of human activity, and data gaps and incompatibilities among systems. Consequently, the development of data bank systems including computer database and remotely-sensed satellite data is required to improve the quality of data which are relevant to EIA. The data bank system should be organized meaningfully in minimum time with a least cost, and measurement standards must be made explicit. Geographical information systems (GIS) are applicable to the graphic presentation or to the impact prediction model.