• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.173-175
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• 2005

In this paper, we propose a data encoding algorithm for two-dimensional barcode system. In general, one-dimensional barcode is just a key which can access detailed information to the host computer database. But the two-dimensional barcode is a new technology which can obtain high density information without access to the host computer database. We implemented encoding algorithm for Data Matrix Barcode which is the most widely used among the many kind of two-dimensional barcodes. And we marked to a real object using Digital Signal Processor(DSP) Marking System. The performance of proposed algorithm is verified through the result of marking work.

• Journal of electromagnetic engineering and science
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• v.7 no.1
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• pp.1-6
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• 2007

Recently, Mc(Multi-code) modulation/demodulation(modem) technique has been explored for high speed data transmission in wireless environment. The conventional Mc modem generates some side effects such as allocating Walsh codes, which motivates to propose a novel Mc modem method with sub-code. Our proposed system should expanded the size of sub-code to provide high-rate data transmission, which also affect adversely to the performance of the system with high PAPR(Peak to Average Power Ratio). Thus, in this paper, we propose a novel pre-coded Multi-Dimensional HCOC(High Capacity Orthogonal Code) Mc modem technique to reduce the high PAPR, which enables the performance improvement. This proposed system can be easily designed by concatenating HCOC Mc modem with the generic Mc modem. The pre-coding technique that is used in this paper is CAC(Constant Amplitude Coding), that helps the system maintain the constant transmission power and reduce the maximum transmission power.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.1-12
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• 2003

Recently, there have been many indexing schemes for multimedia data such as image, video data. But recent database applications, for example data mining and multimedia database, are required to support multi-user environment. In order for indexing schemes to be useful in multi-user environment, a concurrency control algorithm is required to handle it. So we propose a concurrency control algorithm that can be applied to CBF (cell-based filtering method), which uses the signature of the cell for alleviating the dimensional curse problem. In addition, we extend the SHORE storage system of Wisconsin university in order to handle high-dimensional data. This extended SHORE storage system provides conventional storage manager functions, guarantees the integrity of high-dimensional data and is flexible to the large scale of feature vectors for preventing the usage of large main memory. Finally, we implement the web-based image retrieval system by using the extended SHORE storage system. The key feature of this system is platform-independent access to the high-dimensional data as well as functionality of efficient content-based queries. Lastly. We evaluate an average response time of point query, range query and k-nearest query in terms of the number of threads.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.435-446
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• 2020

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the non-destructive identification of crop diseases.

• Nuclear Engineering and Technology
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• v.30 no.3
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• pp.273-286
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• 1998

As there has arisen a concern that failure of the high burnup fuel under the reactivity-insertion accident(RIA) may occur at the energy lower than the expected, fuel behavior under the rod ejection accident in a typical Westinghouse-designed 950 MWe PWR was analyzed by using the three dimensional nodal transient neutronics code, PANBOX2 and the transient fuel rod performance analysis code, FRAP-T6. Fuel failure criteria versus the burnup was conservatively derived taking into account available test data and the possible fuel failure mechanisms. The high burnup and longer cycle length fuel loading scheme of a peak rod turnup of 68 MWD/kgU was selected for the analysis. Except three dimensional core neutronics calculation, the analysis used the same core conditions and assumptions as the conventional zero dimensional analysis. Results of three dimensional analysis showed that the peak fuel enthalpy during the rod ejection accident is less than one third of that calculated by the conventional zero dimensional analysis methodology and the fraction of fuel failure in the core is less than 4 %. Therefore, it can be said that the current design limit of less than 10 percent fuel failure and maintaining the core coolable geometry would be adequately satisfied under the rod ejection accident, even though the conservative fuel failure criteria derived from the test data are applied.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.217-220
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• 2007

LLE(Locally Linear Embedding) has been proposed to compute low dimensional, neighborhood preserving embeddings of high dimensional data. Here, we should perform whole processes of LLE when untrained patterns are presented. In this paper, we propose a training of MLPs(Multilayer Perceptrons) to perform the mapping of LLE from high dimensional data to low dimensional ones.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.2
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• pp.259-269
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• 1999

A new algorithm for measuring 3-D velocity components of high speed flows were developed using a digital image processing technique. The measuring system consists of three CCD cameras an optical instrument called AOM a digital image grabber and a host computer. The images of mov-ing particles arranged spatially on a rotation plate are taken by two or three CCD cameras and are recorderd onto the image grabber or a video tape recoder. The three-dimensionl velocity com-ponents of the particles are automatically obtained by the developed algorithm In order to verify the validity of this technique three-dimensional velocity data sets obtained from a computer simu-lation of a backward facing step flow were used as test data for the algorithm. an uncertainty analysis associated with the present algorithm is systematically evaluated, The present technique is proved to be used as a tookl for the measurement of unsteady three-dimensional fluid flows.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1272-1274
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• 2003

In this paper, we present a new algorithm for high dimensional data reduction based on wavelet decomposition and Genetic Algorithm (GA). Comparative results show the superiority of our algorithm for dimensionality reduction and accuracy improvement.

• Journal of the Korean Society of Visualization
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• v.1 no.2
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• pp.58-70
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• 2003

Meteorological data contains observation and numerical weather prediction model output data. The computerized analysis and visualization of meteorological data often requires very high computing capability due to the large size and complex structure of the data. Because the meteorological data is frequently formed in multi-variables, 3-dimensional and time-series form, it is very important to visualize and analyze the data in 3D spatial domain in order to get more understanding about the meteorological phenomena. In this research, we developed interactive 3-dimensional visualization techniques for visualizing meteorological data on a PC environment such as volume rendering, iso-surface rendering or stream line. The visualization techniques developed in this research are expected to be effectively used as basic technologies not only for deeper understanding and more exact prediction about meteorological environments but also for scientific and spatial data visualization research in any field from which three dimensional data comes out such as oceanography, earth science, and aeronautical engineering.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.643-656
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• 2016

High dimensional data are frequently encountered in various fields where the number of variables is greater than the number of samples. It is usually necessary to select variables to estimate regression coefficients and avoid overfitting in high dimensional data. A penalized regression model simultaneously obtains variable selection and estimation of coefficients which makes them frequently used for high dimensional data. However, the penalized regression model also needs to select the optimal model by choosing a tuning parameter based on the model selection criterion. This study deals with the bias effect of LASSO regression for model selection criteria. We numerically describes the bias effect to the model selection criteria and apply the proposed correction to the identification of biomarkers for lung cancer based on gene expression data.