• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2067-2075
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• 2017

Lately big data processing is considered as an emerging issue. As a huge amount of data is generated, data processing capability is getting important. In processing big data, both Hadoop distributed file system and unstructured date processing-based NoSQL data store are getting a lot of attention. However, there still exists problems and inconvenience to use NoSQL. In case of low volume data, MapReduce of NoSQL normally consumes unnecessary processing time and requires relatively much more data retrieval time than RDBMS. In order to address the NoSQL problem, in this paper, an interworking scheme between NoSQL and the conventional RDBMS is proposed. The developed auto-mapping scheme enables to choose an appropriate database (NoSQL or RDBMS) depending on the amount of data, which results in fast search time. The experimental results for a specific data set shows that the database interworking scheme reduces data searching time by 35% at the maximum.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.369-372
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• 2019

세계 주요 자동차 회사들은 효율적인 차량용 소프트웨어 개발을 위해 AUTOSAR 표준을 필수로 적용하고 있다. AUTOSAR 기반 소프트웨어의 기능은 러너블(runnable) 단위로 구현되며 이는 태스크에 매핑되어 동작하는데, 러너블-태스크 매핑은 시스템 오버헤드 발생과 러너블의 실제 수행 시점에 크게 영향을 미치므로 시스템 성능 측면에서 매우 중요한 작업이다. 본 논문에서는 자동차의 제어를 보조하는 타겟 응용에 대하여 최적의 성능을 보이는 러너블-태스크 매핑을 찾고자 기존 연구에서 제안된 6개의 매핑 규칙을 적용하며, 기존 규칙의 한계점을 개선한 매핑 규칙을 제안하여 추가로 적용한다. Infineon 사의 AURIX 보드와 ETAS 사의 AUTOSAR 플랫폼 상에 타겟 응용을 구현하여 실험한 결과, 기존 매핑 규칙에 비해 개선된 규칙을 적용하였을 때 종단 간 응답시간이 21.23% 단축되었다.

• Information Systems Review
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• v.25 no.1
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• pp.93-110
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• 2023

Recently, attempts have been made to convert unstructured text into vectors and to analyze vast amounts of natural language for various purposes. In particular, the demand for analyzing texts in specialized domains is rapidly increasing. Therefore, studies are being conducted to analyze specialized and general-purpose documents simultaneously. To analyze specific terms with general terms, it is necessary to align the embedding space of the specific terms with the embedding space of the general terms. So far, attempts have been made to align the embedding of specific terms into the embedding space of general terms through a transformation matrix or mapping function. However, the linear transformation based on the transformation matrix showed a limitation in that it only works well in a local range. To overcome this limitation, various types of nonlinear vector alignment methods have been recently proposed. We propose a vector alignment model that matches the embedding space of specific terms to the embedding space of general terms through end-to-end learning that simultaneously learns the autoencoder and regression model. As a result of experiments with R&D documents in the "Healthcare" field, we confirmed the proposed methodology showed superior performance in terms of accuracy compared to the traditional model.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.349-349
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• 2020

기후변화로 인한 돌발홍수와 같은 집중적인 강우현상은 노후화된 제방의 안정성 저하 및 붕괴 등을 야기시킨다. 향후 홍수량이 증가함에 따라 하천의 통수면적이 부족하여 침수 및 범람의 위험성이 증가할 것으로 생각된다. 계획규모 이상의 홍수가 발생하여 홍수위가 제방고보다 높을 때 월류에 의한 제방붕괴로 이어지며, 이러한 월류에 의한 제방붕괴는 가장 전형적인 것이다. 지금까지 월류에 의한 제방붕괴에 관한 연구는 연구자의 다양한 관점 및 방법을 통해 진행되고 있다. 실제 제방붕괴를 관측하는 것은 불가능하므로 기존의 소규모 수리실험 및 모델링을 통한 제방붕괴 메커니즘 분석에는 사실상 한계가 있다. 이러한 점에서 실규모 수리실험을 통한 월류에 의한 제방붕괴 메커니즘을 3차원으로 분석할 필요가 있다. 본 연구에서는 드론 영상을 이용하여 제방붕괴 메커니즘 분석 연구를 수행하였다. 제방은 시간의 흐름에 따라 붕괴양상이 발전한다는 점 등에서 매우 복잡한 물리적 특성이 있다. 드론의 오토촬영 기법을 통한 제방이 붕괴되는 순간을 촬영하기는 쉽지 않기 때문에 셔터스피드촬영 기법을 적용하였다. 특히, 짧은 시간에 변화되는 제방의 붕괴양상을 구체적으로 표현하기 위해 두 대의 드론을 횡·종 방향으로 동시에 비행하여 분석 시 3차원 입체감을 최대화하였다. 이후 횡·종 방향에서 동 시간대 수집된 드론 이미지를 분류하여 PIX4D 매핑 기법을 활용한 최소 정합을 통하여 드론을 활용한 제방붕괴 메커니즘 분석의 활용 가능성을 제시하였다. 향후 스마트 시대의 물산업 경쟁력을 제고함에 있어, 폭이 좁은 하천에 효율적이며 고해상도 시공간 자료를 확보할 수 있는 드론을 활용한 스마트 하천재해 예측 및 관리기술 개발을 통한 하천 원격탐사의 경쟁력을 확보하는 것이 중요하다고 사료된다.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.560-569
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• 2011

In this paper, we propose a text processing method for Hindi characters, Devanagari scripts, in the Android. The key points of the text processing are to device automata, which define the combining rules of alphabets into a set of syllables, and to implement a font rendering engine, which retrieves and displays the glyph images corresponding to specific characters. In general, an automaton depends on the type and the number of characters. For the soft-keyboard, we designed the automata with 14 consonants and 34 vowels based on Unicode. Finally, a combined syllable is converted into a glyph index using the mapping table, used as a handle to load its glyph image. According to the multi-lingual framework of Freetype font engine, Dvanagari scripts can be supported in the system level by appending the implementation of our method to the font engine as the Hindi module. The proposed method is verified through a simple message system.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.752-757
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• 2000

The recursive formulae, which can self-reproduce the state transition graphs, of one-dimensional cellular automata rule 15 with two states (0 and 1) and four different boundary conditions were founded by categorical access. The categorical access makes the evolution process for cellular automata be expressed easily since it enables the mapping of automata between different domains.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.