• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.1
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• pp.1-10
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• 1984

The time division multiplexing PCM encoder which is constructed for an airborne telemetering system is investigated. Selected by program switch, the PCM encoder has 0~64 words/framd($\pm$5V full scale) of allowable analog input channels, 0~30bits(5V$\pm$1V or 0V$\pm$1V dc) of discrete channels, 70 and 140K bits/sec of bit rate and 8~12bits/word of resolution. And filtered output PCM code is NRZ-L and Bi-S through the 5 pole Bessel LPF(f=100kHz), and the maximum accuracy of PCM encoder is $\pm$0.2% of its full scale.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.1
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• pp.42-47
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• 2020

In OFDM-based very high-speed communication systems, FFT/IFFT processor should have several properties of low-area and low-power consumption as well as high throughput and low processing latency. Thus, radix-2k MDF (multipath delay feedback) architectures by adopting pipeline and parallel processing are suitable. In MDF architecture, the feedback memory which increases in proportion to the input signal word-length has a large area and power consumption. This paper presents a feedback memory size reduction method of radix-22 MDF IFFT processor for OFDM applications. The proposed method focuses on reducing the feedback memory size in the first two stages of MDF architectures since the first two stages occupy about 75% of the total feedback memory. In OFDM transmissions, IFFT input signals are composed of modulated data and pilot, null signals. In order to reduce the IFFT input word-length, the integer mapping which generates mapped data composed of two signed integer corresponding to modulated data and pilot/null signals is proposed. By simulation, it is shown that the proposed method has achieved a feedback memory reduction up to 39% compared to conventional approach.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.1081-1087
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• 2008

This paper describes limitations and solutions of the existing processor-monitoring concept for a military supersonics aircraft Flight Control Computer (FLCC) equipped with modern architecture processor to perform the system integration test. Safecritical FLCC integration test, which requires automatic test for thousands of test cases and real-time input/output test condition generation, depends on the processor-monitoring device called Processor Interface (PI). The PI, which relies upon on the FLCC processor's external address and data-bus data, has some limitations due to multi-fetching capability of the modern sophisticated military processors, like C6000's VLIW (Very-Long Instruction Word) architecture and PowerPC's Superscalar architecture. Several techniques for limitations were developed and proper monitoring approach was presented for modem processor-adopted FLCC system integration test.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1311-1314
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• 2003

In this paper, we propose a method to measure document similarity. First, we have exploited single-term method that extracts nouns by using a lexical analyzer as a preprocessing step to match one index to one noun. In spite of irrelevance between documents, possibility of increasing document similarity is high with this method. For this reason, a term-phrase method has been reported. This method constructs co-occurrence between two words as an index to measure document similarity. In this paper, we tried another method that combine these two methods to compensate the problems in these two methods. Six types of features are extracted from two input documents, and they are fed into a neural network to calculate the final value of document similarity. Reliability of our method has been proved by an experiment of document retrieval.

• MALSORI
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• no.56
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• pp.207-223
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• 2005

The goal of language model adaptation is to improve the background language model with a relatively small adaptation corpus. This study presents a language model adaptation technique where additional text data for the adaptation do not exist. We propose the information retrieval (IR) technique with N-gram language modeling to collect the adaptation corpus from baseline text data. We also propose to use a dynamic language model interpolation coefficient to combine the background language model and the adapted language model. The interpolation coefficient is estimated from the word hypotheses obtained by segmenting the input speech data reserved for held-out validation data. This allows the final adapted model to improve the performance of the background model consistently The proposed approach reduces the word error rate by $13.6\%$ relative to baseline 4-gram for two-hour broadcast news speech recognition.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2006.06a
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• pp.67-71
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• 2006

현재까지 제안된 자동 띄어쓰기 교정 모델들은 그 중의 대다수가 입력 문장에서 공백을 제거한 후에 교정 작업을 수행한다. 이러한 교정 방식은 입력 문장의 띄어쓰기가 잘 되어 있는 경우에 입력 문장보다 좋지 못한 교정 문장을 생성하는 경우가 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여 입력 문장의 띄어쓰기를 고려한 자동 띄어쓰기 교정모델을 제안한다. 이 모델은 입력 문장의 음절단위 띄어쓰기 오류가 5%일 때 약 8%의 성능 향상을 보였으며, 10%의 오류가 존재할 때 약 5%의 성능 향상을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1770-1782
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• 2011

A brain-computer interface (BCI) is a communication system that translates brain activity into commands for computers or other devices. In other words, BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways consisting of nerves and muscles. This is particularly useful for facilitating communication for people suffering from paralysis. Due to the low bit rate, it takes much more time to translate brain activity into commands. Especially it takes much time to input characters by using BCI-based typewriters. In this paper, we propose a brain-operated typewriter which is accelerated by a language prediction model. The proposed system uses three kinds of strategies to improve the entry speed: word completion, next-syllable prediction, and next word prediction. We found that the entry speed of BCI-based typewriter improved about twice as much through our demonstration which utilized the language prediction model.

• Journal of the Korea Society for Simulation
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• v.11 no.3
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• pp.35-48
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• 2002

In the work presented here, we describe a method to extract TrueType features for supporting letter recognition. Even if variously existing document processing techniques have been challenged, almost few methods are capable of recognize a letter associated with its TrueType features supporting OCR free, which boost up fast processing time for image text retrieval. By reviewing the mechanism generating digital fonts and birth of TrueType, we realize that each TrueType is drawn by its contour of the glyph table. Hence, we are capable of deriving the segment with density for a letter with a specific TrueType, defined by the number of occurrence over a segment width. A certain number of occurrence appears frequently often due to the fixed segment width. We utilize letter recognition by comparing TrueType feature library of a letter with that from input word images. Experiments have been carried out to justify robustness of the proposed method showing acceptable results.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1378-1391
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• 2019

In recent years, automatic document summarization has been widely studied in the field of natural language processing thanks to the remarkable developments made using deep learning models. To decode a word, existing models for abstractive summarization usually represent the context of a document using the weighted hidden states of each input word when they decode it. Because the weights change at each decoding step, these weights reflect only the local context of a document. Therefore, it is difficult to generate a summary that reflects the overall context of a document. To solve this problem, we introduce the notion of a general context and propose a model for summarization based on it. The general context reflects overall context of the document that is independent of each decoding step. Experimental results using the CNN/Daily Mail dataset show that the proposed model outperforms existing models.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.83-87
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• 2021

By the increase of internet usage, communicating online became an everyday thing. Thereby various people have experienced profanity by anonymous users. Nowadays lots of studies tried to solve this problem using artificial intelligence, but most of the solutions were for non-real time situations. In this paper, we propose a Telegram plugin that detects swear words using word2vec, and an algorithm to find the target of the sentence. We vectorized the input sentence to find connections with other similar words, then inputted the value to the pre-trained CNN (Convolutional Neural Network) model to detect any swears. For target recognition we proposed a sequential algorithm based on KoNLPY.