• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.190-195
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• 1987

This approach to the transmultiplexer is for the 12-channel TDM/FDM translation system with the polyphase network and the FDCT. For the reduction of the conversion time the 14-point FDCT algorithm is used and the polyphase network which translate the protorype filter into the channel filtrs required in each channel is designed. The prototype filters is designed by the IIR/FIR hybrid filter. The number of numerator terms of the hybrid filter is very large compaired to the denomiator terms. Because of symmetrical properties for numerator terms, required multiplication rate is 0.11396x10**6M/sec.ch. and reduced to 25%-45% of the rate required in the other papers. The proposed system is simulated with the computer and by the results it is proved that the proposed conversion method is valid.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.336-337
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• 2007

The energy conversion characteristics of $TiO_2$ paste of dye-sensitized solar cell (DSSC) was investigated. In the case of DSSC without a binder, the current density increased due to the development of porosity. As for DSSC with a binder, the fill factor increased due to the development of network among the particles. The energy conversion efficiency of 7.2% was obtained due to the porosity and the network as for DSSC with the mixed binder (Vol. 50:50).

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.283-287
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• 2022

Machine Learning is the most popular method used in data science. Growth of data is not only numeric data but also text data. Most of the algorithm of supervised and unsupervised machine learning algorithms use numeric data. Now it is required to convert text data into numeric. There are many techniques for this conversion. Researcher confuses which technique is best in what situation. Here in proposed work BOW (Bag-of-Words) and TF-IDF (Term-Frequency-Inverse-Document-Frequency) has been studied based on different features to determine best method. After experimental results on text data, TF-IDF and BOW both provide better performance at range from 100 to 150 number of features.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• Proceedings of the Korean Vacuum Society Conference
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• 2009.08a
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• pp.102-102
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• 2009

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.227-234
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• 2016

When Chinese character used in Korea, so did the characters' pronunciation, so many Korean Chinese characters today have similar pronunciation with Chinese, but since Korean and Chinese pronunciations were preserved and developed in different alphabets, the written letter of the pronunciation also differs. This study on Chinese education, has constructed and implemented an easy way to study Chinese pronunciations by creating conversion rule set between Chinese pronunciation, Chinese Hanyu latin Pinyin and Korean chinese character pronunciation consisting of an initial sound, a medial vowel, and a final consonant. This study has established web version and application version of this conversion rule set education system to enhance Chinese education.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1125-1128
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• 2003

This paper presents the design method of a Colpitts type oscillator with coplanar waveguide(CPW) structures in the range of Ka-band frequency for transmitter and receiver modules. Series short stubs of CPW patterns provide inductances and capacitances in the range of Ka-band which can be expressed as a CLC-$\pi$ equivalent circuit. The experimentation has employed ro4003 substrates as a CPW substrate which has a dielectric constant of 3.38 and a signal and ground space of 100um. A method of momentum simulation for the CPW patterns has performed with an ADS software tool of Hewlett-Packard Corp. Inductance and capacitance circuits of a Colpitts oscillator was interconnected to a MESFET with CPW bend structures of including the input and output impedance matching circuits of the active transistor. Circuit parameters for impedance matching were determined through the network conversion to the equivalent length of CPW transmission lines by using T-network 1 $\pi$-network conversion circuit. A Colpitts oscillator was fabricated on the substrate of a area of 8.5mm x 17.4mm with a MESFET of Fujitsu FMM5704X and CPW series short stubs. The design suggested the possibility of realizing oscillators on a planar surface for the wireless system of tansmitter and receiver modules in the frequency range of 30GHz

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3B
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• pp.200-208
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• 2003

In this paper, we propose an enhanced p-cycles (preconfigured protection cycles) scheme for fast restoration in WDM (Wavelength Division Multiplexing) optical mesh network with limited wavelength conversion for fast restoration. We enhanced the p-cycles to accommodate uni-directional connections to be used in uni-directional multicasting or asymmetric broadband multimedia communications with bi-directional connectivity. We applied it to WDM network with limited wavelength conversion and analyzed the result. The analysis results show that the enhanced p-cycle algorithm provides better performance in WDM optical networks with limited wavelength converter.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.73-78
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• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.71-79
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• 2017

In this paper, we implemented the exchange rate forecasting neural network using heterogeneous computing. Exchange rate forecasting requires a large amount of data. We used a neural network that could leverage this data accordingly. Neural networks are largely divided into two processes: learning and verification. Learning took advantage of the CPU. For verification, RTL written in Verilog HDL was run on FPGA. The structure of the neural network has four input neurons, four hidden neurons, and one output neuron. The input neurons used the US $ 1, Japanese 100 Yen, EU 1 Euro, and UK £ 1. The input neurons predicted a Canadian dollar value of $ 1. The order of predicting the exchange rate is input, normalization, fixed-point conversion, neural network forward, floating-point conversion, denormalization, and outputting. As a result of forecasting the exchange rate in November 2016, there was an error amount between 0.9 won and 9.13 won. If we increase the number of neurons by adding data other than the exchange rate, it is expected that more precise exchange rate prediction will be possible.