• Journal of Korean Society of Neurocognitive Rehabilitation
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• v.10 no.2
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• pp.35-44
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• 2018

This study was conducted to explore the effects of semi-structured $DIRFloortime^{(R)}$ treatment on the enhancement of language comprehension and information processing of children with high-performance autistic spectrum disabilities. We measured the general characteristics of the test subjects, which are level of autism, total intelligence, language comprehension and information processing indicators. The intervention method used was a semi-structured $DIRFloortime^{(R)}$ therapy using board game intervention program after revising and supplementing the expert content validity. A pre/post-test for a group was designed as a similar experiment and the pre/post test was initiated with the t certification at .05 of significance level. After initiating the program, the post test has shown that the language comprehension indicators showed statistically significant levels of difference (p<.001) and the information processing indicator also had a statistically significant effect (p<.001). There was a statistically significant difference (p<.001) in the level of verbal comprehension index after the program implementation, and statistically significant differences in the information processing index (p<.001). The semi-structured $DIRFloortime^{(R)}$ treatment using boardgames for enhancing the language comprehension and information processing indicators of children with high performance autistic spectrum disorder had a significant effect.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.54-60
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• 2018

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

• International Journal of Contents
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• v.10 no.4
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• pp.30-37
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• 2014

The current study was conducted to examine the temporal and spatial activation sequences related to morphosyntactic, semantic and orthographic-lexical sentences, focusing on the morphological-orthographic and lexical-semantic deviation processes in Korean language processing. The Event-related Potentials (ERPs) of 15 healthy students were adopted to explore the processing of head-final critical words in a sentential plausibility task. Specifically, it was examined whether the ERP-pattern to orthographic-lexical violation might show linear precedence over other processes, or the presence of additivity across combined processing components. For the morphosyntactic violation, fronto-central LAN followed by P600 was found, while semantic violation elicited N400, as expected. Activation of P600 was distributed in the left frontal and central sites, while N400 appeared even in frontal sites other than the centro-parietal areas. Most importantly, the orthographic-lexical violation process revealed by earlier N2 with fronto-central activity was shown to be complexes of morphological and semantic functions from the same critical word. The present study suggests that there is a linear precedence over the morphological deviation and its lexical semantic processing based on the immediate possibility of lexical information, followed by sentential semantics. Finally, late syntactic integration processes were completed, showing different topographic activation in order of importance of ongoing sentential information.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.74-80
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• 2002

There are many researches on post-processing methods for the Korean continuous speech recognition enhancement using natural language processing techniques. It is very difficult to use a formal morphological analyzer for improving the speech recognition because the analysis technique of natural language processing is mainly for formal written languages. In this paper, we propose a speech recognition enhancement model using syntactic unit of morphemes. This approach uses the functional word level longest match which dose not consider spacing words. We describe the post-processing mechanism for the improving speech recognition by using proposed model which uses the relationship of phonological structure information between predicates md auxiliary predicates or bound nouns that are frequently occurred in Korean sentences.

• Annual Conference on Human and Language Technology
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• 2002.10e
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• pp.103-107
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• 2002

데이터 기반 기계번역의 성공여부는 대량의 데이터를 단기간에 구축하는 방법과, 또 구축된 데이터에 대한 효과적인 관리 방법이 좌우한다고 할 수 있다. 대표적인 데이터 기반 기계번역 방법론인 예제 기반 기계번역 방식이나 패턴 기반 기계번역 방식에서는 최소한의 학습 내지는 학습과정 없이 데이터를 구축하는 데에 연구가 중점적으로 이루어져왔으나, 데이터의 관리 문제에 대해서는 많은 연구가 이루어지지 못하였다. 그러나 데이터의 확장 못지않게 데이터의 효율적인 관리도 데이터 기반 기계번역 시스템의 개발에서 매우 중요하다. 이 논문에서는 사/피동 링크 등을 이용하여 사전을 재구성하는 것이 데이터의 일관성과 관리성을 향상시키고, 이론적인 면에서는 정보 기술상의 잉여성을 줄인다는 점을 보인다. 또한 이러한 정보에 기반하여 기구축된 동사구 패턴으로부터 대역어 정보를 이용하여 새로운 패턴을 만들어내는 방법론도 제시한다.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.603-614
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• 1999

As multimedia application services on high-speed information network have been rapidly developed, the need for the video information management system that provides an efficient way for users to retrieve video data is growing. In this paper, we propose a video data model that integrates free annotations, image features, and spatial-temporal features for video purpose of improving content-based retrieval of video data. The proposed video data model can act as a generic video data model for multimedia applications, and support free annotations, image features, spatial-temporal features, and structure information of video data within the same framework. We also propose the video query language for efficiently providing query specification to access video clips in the video data. It can formalize various kinds of queries based on the video contents. Finally we design and implement the query processing engine for efficient video data retrieval on the proposed metadata model and the proposed video query language.

• Current Optics and Photonics
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• v.2 no.5
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• pp.453-459
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• 2018

We have developed a high-performance signal-processing and image-rendering heterogeneous computation system for optical coherence tomography (OCT) on mobile processor. In this paper, we reveal it by demonstrating real-time OCT image processing using a Snapdragon 800 mobile processor, with the introduction of a heterogeneous image visualization architecture (HIVA) to accelerate the signal-processing and image-visualization procedures. HIVA has been designed to maximize the computational performances of a mobile processor by using a native language compiler, which targets mobile processor, to directly access mobile-processor computing resources and the open computing language (OpenCL) for heterogeneous computation. The developed mobile image processing platform requires only 25 ms to produce an OCT image from $512{\times}1024$ OCT data. This is 617 times faster than the naïve approach without HIVA, which requires more than 15 s. The developed platform can produce 40 OCT images per second, to facilitate real-time mobile OCT image visualization. We believe this study would facilitate the development of portable diagnostic image visualization with medical imaging modality, which requires computationally expensive procedures, using a mobile processor.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.223-229
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• 2022

LinkedIn is one of the most job hunting and career-growing applications in the world. There are a lot of opportunities and jobs available on LinkedIn. According to statistics, LinkedIn has 738M+ members. 14M+ open jobs on LinkedIn and 55M+ Companies listed on this mega-connected application. A lot of vacancies are available daily. LinkedIn data has been used for the research work carried out in this paper. This in turn can significantly tackle the challenges faced by LinkedIn and other job posting applications to improve the levels of jobs available in the industry. This research introduces Text Processing in natural language processing on datasets of LinkedIn which aims to find out the jobs that appear most in a month or/and year. Therefore, the large data became renewed into the required or needful source. This study thus uses Multinomial Naïve Bayes and Linear Support Vector Machine learning algorithms for text classification and developed a trained multilingual dataset. The results indicate the most needed job vacancies in any field. This will help students, job seekers, and entrepreneurs with their career decisions

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.322-327
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• 2017

The classification problem in the field of Natural Language Processing has been studied for a long time. Continuing forward with our previous research, which classifies large-scale text using Convolutional Neural Networks (CNN), we implemented Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM) and Gated Recurrent Units (GRU). The experiment's result revealed that the performance of classification algorithms was Multinomial Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows: First, the result of CNN was better than LSTM. Therefore, the text classification problem might be related more to feature extraction problem than to natural language understanding problems. Second, judging from the results the GRU showed better performance in feature extraction than LSTM. Finally, the result that the GRU was better than CNN implies that text classification algorithms should consider feature extraction and sequential information. We presented the results of fine-tuning in deep neural networks to provide some intuition regard natural language processing to future researchers.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.3
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• pp.375-380
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• 2015

This paper is studied the Korean language to optimize the Hangul character coding for information processing in information terminal device and transmission in network. The paper analyzed Hangul character in Korean language and use frequency of each character. The paper also compared the analysis result to Hangul characters which are coded in standard in Korean character and Unicode. This study referred "Modern Korean Use Frequency Rate Survey Result" issued by The National Institute of the Korean Language. There are total 58,437 Korean words in the report. As a result of this paper, the Korean word 58,437ea are consisted of Hangul character total 1,540ea. The highest use frequency character is "다" and its use frequency to total use frequency rate is 15%. The lowest use character is "휫"and its use frequency to total use frequency rate is 0.00003%. The number of analyzed Hangul character 1,540 is less 7.2 times and 1.5 times than Korean and Unicode standard respectively.