• Physical Therapy Korea
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• v.25 no.1
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• pp.53-61
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• 2018

Background: The process of language translation, adaptation, and cross-cultural validation of tools for use in multiple countries requires the adoption of well-established, comprehensive, and rigorous methodological approaches. Back translation, which is the most recommended method, permits the detection of errors in the translation and the identification of words or phrases that cannot be accurately or literally translated. Objects: The aim of this study was to verify the content validity of a Korean version of a Balance Evaluation Systems test (BESTest) by using a back-translation method. Methods: This research was conducted in six steps: 1) translation of the BESTest into Korean, 2) evaluation of the translation conformity of Korean-translated BESTest, 3) evaluation of the degree of translation comprehension, 4) back translation of Korean BESTest, 5) evaluation of the technical and conceptual equivalence, and 6) completion of the Korean version of BESTest by the translation verification committee. Results: In this study, Korean version of the BESTest achieved a rating of more than 3 (moderate) for translation comprehension, and technical equivalence and conceptual equivalence of back translation were evaluated as 3 (moderate) or more. Conclusion: The Korean version of the BESTest has proven content validity and is an appropriate tool to measure balance function.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.75-86
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• 2006

Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the translation accuracy of machine translation systems. In this paper, we present a new approach to select Korean target word for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean local context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. The experiment showed promising results for diverse sentences from web documents.

• Journal of Science and Technology Studies
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• v.13 no.2
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• pp.137-172
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• 2013

Globally, awareness about obesity is increasing rapidly. In Korea, obesity is recognized as a disease and steps are being taken to treat it. From the health governance point of view, such standardized measures amplify the risk of obesity and thus play an important part in the prevention of the disease. In this context, various obesity treatments act as a medium for the problem-solving process. In recent years, obesity surgery has been viewed as a rational solution to the problem of obesity. In the context of standardization of treatment, Callon's "Process of Translation" in STS theories highlights the importance of the central actor (Obligatory Passage Point; OPP). However, in the case of obesity, it is difficult to identify a single OPP to project different perspectives of an actor's needs. "Lapband surgery" often acts as a "boundary object" in this context. This article assesses this absence of central actors in the process of problem solving through a case study of adoption of Lapband surgery in Korea. Further, we attempt to suggest an analytical framework with a boundary object and multiple translation concepts to aid solving the problem of obesity.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.313-326
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• 2020

Deep Learning is the most important key to the development of Artificial Intelligence (AI). There are several distinguishable architectures of neural networks such as MLP, CNN, and RNN. Among them, we try to understand one of the main architectures called Recurrent Neural Network (RNN) that differs from other networks in handling sequential data, including time series and texts. As one of the main tasks recently in Natural Language Processing (NLP), we consider Neural Machine Translation (NMT) using RNNs. We also summarize fundamental structures of the recurrent networks, and some topics of representing natural words to reasonable numeric vectors. We organize topics to understand estimation procedures from representing input source sequences to predict target translated sequences. In addition, we apply multiple translation models with Gated Recurrent Unites (GRUs) in Keras on English-Korean sentences that contain about 26,000 pairwise sequences in total from two different corpora, colloquialism and news. We verified some crucial factors that influence the quality of training. We found that loss decreases with more recurrent dimensions and using bidirectional RNN in the encoder when dealing with short sequences. We also computed BLEU scores which are the main measures of the translation performance, and compared them with the score from Google Translate using the same test sentences. We sum up some difficulties when training a proper translation model as well as dealing with Korean language. The use of Keras in Python for overall tasks from processing raw texts to evaluating the translation model also allows us to include some useful functions and vocabulary libraries as well.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1834-1843
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• 2000

In this paper, we propose a modular neural network based classification method for handwritten numerals utilizing rotated and translated images of an input image. The whole numeral pattern space is divided into smaller spaces which overlap each other and form multiple clusters. On these multiple clusters, multiple multilayer perceptrons (MLP) neural networks, specialized in those clusters, are constructed. Thus, each MLP acts as an expert network on the corresponding cluster. An MLP is also used as a gating network functioning as a mediator among the multiple MLPs. In the learning phase, an input numeral image is dithered by tow geometric operations of translation and rotation so that new numeral images similar to original one are generated. In the recognition phase, we utilize not only input numeral image, but also nearly generated images through the rotation and the translation of the original image. Thus, multiple output values for those generated images were combined to make class decision by various combination methods. The experimental results confirm the validity of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.799-802
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• 2017

We attempt to combines concepts of Morphological Operator(MO) and Convolutional Neural Networks(CNN) to improve image-to-image translation. To do this, we propose an operation that approximates morphological operations. Also we propose S-Convolution, an operation that extends the operation to use multiple filters like CNN. The experiment result shows that it can learn MO with big filter using multiple S-convolution layer of small filter. To validate effectiveness of the proposed layer in image-to-image translation we experiment with GAN with S-convolution applied. The result showed that GAN with S-convolution can achieve distinct result from that of GAN with CNN.

• Molecules and Cells
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• v.42 no.6
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• pp.495-500
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• 2019

Brain cytoplasmic 200 RNA (BC200 RNA), originally identified as a neuron-specific non-coding RNA, is also observed in various cancer cells that originate from non-neural cells. Studies have revealed diverse functions of BC200 RNA in cancer cells. Accordingly, we hypothesized that BC200 RNA might be modified in cancer cells to generate cancerous BC200 RNA responsible for its cancer-specific functions. Here, we report that BC200 RNA sequences are highly heterogeneous in cancer cells by virtue of multiple adenine nucleotide insertions in the internal A-rich region. The insertion of adenine nucleotides enhances BC200 RNA-mediated translation inhibition, possibly by increasing the binding affinity of BC200 RNA for eIF4A (eukaryotic translation initiation factor 4A).

• Cross-Cultural Studies
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• v.24
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• pp.147-156
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• 2011

A brief consultation of English Arabic dictionaries and encyclopedias shows that there is no one single standard Arabic translation of the English concept 'democracy'. Arab authors use, instead, a series of multiple terms that need clarification if the first term is to be clear. In many cases, they tend to localize the term into Arabic using various orthographic forms; at other times, they run a rather lengthy analysis to elucidate the concept that seems to be an essentially contested term. This paper aims to inquire into the reasons for the confusion and inconsistency in the translation of the concept 'democracy', as well as the underlying arguments for advocating the localization rather than translation of this political concept. This will be followed by a discussion of the implications of this study for lexicographers and translators. Given the fact that ideology is of non-Arabic origin, English perceptions of this fluid concept might help account for its lack of clarity in Arabic translations since Arabic is highly influenced by English in various spheres of life. It would thus be wise first to check the perceptivity of English authors of the concept. To better serve the purpose of this study, the author distinguishes here between 'translation' and so-called 'localization'. The term 'translation' is concerned with finding an existing term in the target language with an equivalent meaning for a foreign word, whereas localization involves taking the foreign term and making it linguistically and culturally appropriate to the target language, by subjecting it to the morphological and syntactic rules of Arabic to be used as if it were originally Arabic.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.21-30
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• 2009

Fusion flash memory such as OneNAND$^{TM}$ is popular as a ubiquitous storage device for embedded systems because it has advantages of NAND and NOR flash memory that it can support large capacity, fast read/write performance and XIP(eXecute-In-Place). Besides, OneNAND$^{TM}$ provides not only advantages of hybrid structure but also multi-block erase function that improves slow erase performance by erasing the multiple blocks simultaneously. But traditional NAND Flash Translation Layer may not fully support it because the garbage collection of traditional FTL only considers a few block as victim block and erases them. In this paper, we propose an Erase Group Flash Translation Layer for improving multi-block erase function. EGFTL uses a superblock scheme for enhancing garbage collection performance and invalid block management to erase multiple blocks simultaneously. Also, it uses clustered hash table to improve the address translation performance of the superblock scheme. The experimental results show that the garbage collection performance of EGFTL is 30% higher than those of traditional FTLs, and the address translation performance of EGFTL is 5% higher than that of Superblock scheme.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.71-76
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• 2006

To obtain a correctly translated sentence in a machine translation system, we must select target words that not only reflect an appropriate meaning in a source sentence but also make a fluent sentence in a target language. This paper points out that a source language word has various senses and each sense can be mapped into multiple target words, and proposes a new translation disambiguation method based on this 'word-to-sense and sense-to-word' relationship. In my method target words are chosen through disambiguation of a source word sense and selection of a target word. Most of translation disambiguation methods are based on a 'word-to-word' relationship that means they translate a source word directly into a target wort so they require complicate knowledge sources that directly link a source words to target words, which are hard to obtain like bilingual aligned corpora. By combining two sub-problems for each language, knowledge for translation disambiguation can be automatically extracted from knowledge sources for each language that are easy to obtain. In addition, disambiguation results satisfy both fidelity and intelligibility because selected target words have correct meaning and generate naturally composed target sentences.