• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.959-961
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• 2003

In this paper, we describe implemented DMAC (Direct Memory Access Controller) architecture on Altera's $Excalibur^{TM}$ that includes industry-standard $ARM922T^{TM}$ 32-bit RISC processor core operating at 200 MHz. We implemented DMAC based on AMBA (Advanced Micro-controller Bus Architecture) AHB (Advanced Micro-performance Bus) interface. Implemented DMAC has 8-channel and can extend supportable channel count according to user application. We used round-robin method for priority selection. Implemented DMAC supports data transfer between Memory-to-Memory, Memory-to-Peripheral and Peripheral-to-Memory. The max transfer count is 1024 per a time and it can support byte, half-word and word transfer according to AHB protocol (HSIZE signals). We implemented with VHDL and functional verification using $ModelSim^{TM}$. Then, we synthesized using $LeonardoSpectrum^{TM}$ with Altera $Excalibur^{TM}$ library. We did FPGA P&R and targeting using $Quartus^{TM}$. We can use implemented DMAC module at any system that needs high speed and broad bandwidth data transfers.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.510-513
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• 2018

본 논문은 다양한 워드 임베딩 모델(word embedding model)들과 하이퍼 파라미터(hyper parameter)들을 조합하였을 때 특정 영역에 어떠한 성능을 보여주는지에 대한 연구이다. 3 가지의 워드 임베딩 모델인 Word2Vec, FastText, Glove의 차원(dimension)과 윈도우 사이즈(window size), 최소 횟수(min count)를 각기 달리하여 총 36개의 임베딩 벡터(embedding vector)를 만들었다. 각 임베딩 벡터를 Fast and Accurate Dependency Parser 모델에 적용하여 각 모들의 성능을 측정하였다. 모든 모델에서 차원이 높을수록 성능이 개선되었으며, FastText가 대부분의 경우에서 높은 성능을 내는 것을 알 수 있었다.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.31-36
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• 2019

A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.

• Information and Communications Magazine
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• v.25 no.12
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• pp.10-18
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• 2008

In this paper, we present a low-power design of H.264 codec based on dedicated hardware and software solution on EMP(ETRI Multi-core platform). The dedicated hardware scheme has reducing computation using motion estimation skip and reducing memory access for motion estimation. The design reduces data transfer load to 66% compared to conventional method. The gate count of H.264 encoder and the performance is about 455k and 43Mhz@30fps with D1(720x480) for H.264 encoder. The software solution is with ASIP(Application Specific Instruction Processor) that it is SIMD(Single Instruction Multiple Data), Dual Issue VLIW(Very Long Instruction Word) core, specified register file for SIMD, internal memory and data memory access for memory controller, 6 step pipeline, and 32 bits bus width. Performance and gate count is 400MHz@30fps with CIF(Common Intermediated format) and about 100k per core for H.264 decoder.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.65-74
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• 2022

In natural language processing, commonly used words such as prepositions are referred to as stop-words; they have no inherent meaning and are therefore ignored in indexing and retrieval tasks. The removal of stop-words from Arabic text has a significant impact in terms of reducing the size of a cor- pus text, which leads to an improvement in the effectiveness and performance of Arabic-language processing systems. This study investigated the effectiveness of applying a stop-word lists elimination with normalization as a preprocessing step. The idea was to merge statistical method with the linguistic method to attain the best efficacy, and comparing the effects of this two-pronged approach in reducing corpus size for Ara- bic natural language processing systems. Three stop-word lists were considered: an Arabic Text Lookup Stop-list, Frequency- based Stop-list using Zipf's law, and Combined Stop-list. An experiment was conducted using a selected file from the Arabic Newswire data set. In the experiment, the size of the cor- pus was compared after removing the words contained in each list. The results showed that the best reduction in size was achieved by using the Combined Stop-list with normalization, with a word count reduction of 452930 and a compression rate of 30%.

• Research in Mathematical Education
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• v.12 no.2
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• pp.127-142
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• 2008

The most important aim of mathematics education is to promote mathematical thinking. In the Hong Kong primary school, mathematical thinking is usually conducted through the use of formula and working on "application problem" or "word problems". However, there are many other ways that can promote mathematical thinking, and investigation on mathematical structure by using counting is one important source for promoting mathematical thinking for primary school children, as every children can count and hence a well designed question that can be solved by counting can enable children of different abilities to work together and obtain different results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.283-292
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• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.4875-4880
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• 2011

Individuals' language use has been hypothesized as a useful tool for the analysis of psychological aspects. This study examined relationships between language use and their subjective well-being, which consists of life satisfaction and feeling about life. For this, 126 college students wrote an essay and responded to the subjective well-being scale. Then we analyzed their writings through KLIWC (Korean Linguistic Inquiry and Word Count) and compared language use between the high and the low groups of subjective well-being. We also examined the relationships between KLIWC factors and the two sub-factors of subjective well being. The results shows that various individual factors of KLIWC reflect participants' subjective well-being and provids preliminary descriptive data on language use and subjective well-being.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.669-678
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• 2015

This study aims at investigating students' perceptions and peer feedback to practicum for teaching English in the English Department at a Korean university. The participants were forty-two students at an elective course, 'Method for teaching English', and the data comprised questionnaire, 12 teams of practicum, and 15 sets of PF. A 'Word Count System (WCS)' was adopted to analyze the data. The findings show that students regarded 'practicum' (52.4%) as more important than 'teacher's lectures' (42.8%), and most students (80%) applied more than 70% of lesson plans to their practicums. The practicum gave them experience of a teacher, development of confidence, recognition on their weaknesses and values of teaching. While the strengths shown in PF were mainly 'teaching methods and technique', 'use of multimedia', and 'teaching materials', the weaknesses were 'classroom interactions', 'teaching methods and techniques' and 'use of blackboard'. Overall praises were 1.8 times more than the matters which needed to be developed. The conclusion suggest that the students had their own insights toward teaching practices and how learners can be motivated.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose a new register file architecture called the Register File Extension for Multi-words or Long-word Operation (RFEMLO) to accelerate both symmetric and asymmetric cryptographic algorithms. Based on the idea that most of cryptographic algorithms heavily use multi-words or long-word operations, RFEMLO allows multiple contiguous registers to be specified as a single operand. Thus, a single instruction can specify a SIMD-style multi-word operation or a long-word operation. RFEMLO can be applied to general purpose processors by adding instruction set for multi-words or long-word operands and functional units for additional instruction set. To evaluate the performance of RFEMLO, we use Simplescalar/ARM 3.0 (with gcc 2.95.2) and run detailed simulations on various symmetric and asymmetric cryptographic algorithms. By applying RFEMLO, we could get maximum 62% and 70% reductions in the total instruction count of symmetric and asymmetric cryptographic algorithms respectively. Also, performance results show that a speedup of 1.4 to 2.6 can be obtained in symmetric cryptographic algorithms and a speedup of 2.5 to 3.3 can be obtained for asymmetric cryptographic algorithms when we apply RFEMLO to a processor with an in-order pipeline. We also found that RFEMLO can effectively improve the performance of these cryptographic algorithms with much less cost compared to issue-width increase available in Superscalar implementations. Moreover, the RFEMLO can also be applied to Superscalar processor, leading to additional 83% and 138% performance gain in symmetric and asymmetric cryptographic algorithms.