• Journal of KIISE:Information Networking
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• v.31 no.4
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• pp.414-428
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• 2004

Many portable devices are coming to be commercially successful and provide useful services to mobile users. Mobile devices may request a variety of data types, including text and multimedia data, thanks to the rich content of the Internet. Different types of data and/or different classes of users may need to be treated with different qualities of service. The implementation of service differentiation in wireless networks is very difficult because of device mobility and wireless channel contention when the backoff algorithm is used to resolve contention. Modification of the t)mary exponential backoff algorithm is one possibility to allow the design of several classes of data traffic flows. We present a study of modifications to the backoff algorithm to support three classes of flows: sold, silver, and bronze. For example, the gold c]ass flows are the highest priority and should satisfy their required target bandwidth, whereas the silver class flows should receive reasonably high bandwidth compared to the bronze class flows. The mixture of the two different transport protocols, UDP and TCP, in ad hoc networks raises significant challenges when defining backoff algorithm modifications. Due to the different characteristics of UDP and TCP, different backoff algorithm modifications are applied to each class of packets from the two transport protocols. Nevertheless, we show by means of simulation that our approach of backoff algorithm modification clearly differentiates service between different flows of classes regardless of the type of transport protocol.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.10
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• pp.1145-1156
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• 2015

There is a shortage of mobile application systems readily applicable to the field of crop cultivation in relation to diseases and insect pests directly connected to the quality of crops. Most of system have been devoted to diseases and insect pests that would offer full predictions and basic information about diseases and insect pests currently. But for lack of the instant diagnostic functions seriously and the field of crop cultivation, we design and implement a crop cultivation field-oriented smart diseases and insect pests information retrieval system based on mobile for u-Farm. The proposed system had such advantages as providing information about diseases and insect pests in the field of crop cultivation and allowing the users to check the information with their smart-phones real-time based on the Lucene, a search library useful for the specialized analysis of images, and JSON data structure. And it was designed based on object-oriented modeling to increase its expandability and reusability. It was capable of search based on such image characteristic information as colors as well as the meta-information of crops and meta-information-based texts. The system was full of great merits including the implementation of u-Farm, the real-time check, and management of crop yields and diseases and insect pests by both the farmers and cultivation field managers.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.99-120
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• 2017

In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.519-527
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• 2011

The e-Learning program is a multimedia data program consisting of texts, images, animation, audio and video. The development of an e-Learning program requires time and is a complex process, requiring cooperation and open-communication between all parties involved, particularly in the event of a problem. This study will analyze the e-Learning contents development process from the Social Negotiation Perspective. An appropriate process for the development of the program and effective decision-making guidelines for those parties involved will be recommended. Participants' viewpoints regarding program development and guidelines were studied qualitatively, while the evaluation of developed content employed both qualitative and quantitative research. The study found the following results. First, the development of an e-Learning program requires a clear goal and purpose. Second, the target group must be clearly identified. Third, all parties involved must share in the development process and its outcomes. Fourth, the party requesting the program must allocate the appropriate time and budget for the development group. Finally, the project requires a strong, capable leadership for effective decision-making.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.699-707
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• 2020

With the development of deep learning technology, voice processing-related technology is applied to various areas, such as STT (Speech To Text), TTS (Text To Speech), ChatBOT, and intelligent personal assistant. In particular, the STT is a voice-based, relevant service that changes human languages to text, so it can be applied to various IT related services. Recently, many places, such as general private enterprises and public institutions, are attempting to introduce the relevant technology. On the other hand, in contrast to the general IT solution that can be evaluated quantitatively, the standard and methods of evaluating the accuracy of the STT engine are ambiguous, and they do not consider the characteristics of the Korean language. Therefore, it is difficult to apply the quantitative evaluation standard. This study aims to provide a guide to an evaluation of the STT engine conversion performance based on the characteristics of the Korean language, so that engine manufacturers can perform the STT conversion based on the characteristics of the Korean language, while the market could perform a more accurate evaluation. In the experiment, a 35% more accurate evaluation could be performed compared to the existing methods.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.183-187
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• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.27-41
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• 2016

Named entity recognition is to classify elements in text into predefined categories and used for various departments which receives natural language inputs. In this paper, we propose a method which can generate named entity training corpus automatically using knowledge bases. We apply two different methods to generate corpus depending on the knowledge bases. One of the methods attaches named entity labels to text data using Wikipedia. The other method crawls data from web and labels named entities to web text data using Freebase. We conduct two experiments to evaluate corpus quality and our proposed method for generating Named entity recognition corpus automatically. We extract sentences randomly from two corpus which called Wikipedia corpus and Web corpus then label them to validate both automatic labeled corpus. We also show the performance of named entity recognizer trained by corpus generated in our proposed method. The result shows that our proposed method adapts well with new corpus which reflects diverse sentence structures and the newest entities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1458-1467
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• 2010

Recently, imitation and infringement rights of an intellectual property are being recognized as impediments to nation's industrial growth. To prevent the huge loss which comes from theses impediments, many researchers are studying protection and efficient management of an intellectual property in various ways. Especially, the prediction of patent registration is very important part to protect and assert intellectual property rights. In this study, we propose the patent document analysis method by using text mining to predict whether the patent is registered or rejected. In the first instance, the proposed method builds the database by using the word frequencies of the rejected patent documents. And comparing the builded database with another patent documents draws the similarity value between each patent document and the database. In this study, we used k-means which is partitioning clustering algorithm to select criteria value of patent rejection. In result, we found conclusion that some patent which similar to rejected patent have strong possibility of rejection. We used U.S.A patent documents about bluetooth technology, solar battery technology and display technology for experiment data.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.550-566
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• 2017

Recent advances in wearable devices and quantified-self movement increase the number of personal informatics application that may cause an concern to health industry and user. In this light, the goal of this study is to identify more effective ways of design and evaluation of personal informatics application for self-tracking and delivering health information to users. For this goal, this study conducted areal-world study that processes such that user can assess, be aware of, and self-reflect on their data and behavior activity. In doing so, this study aims to determine the psychological effects of forms of health feedback (comparative vs. non-comparative) and presentation modes (text vs. image) on users' tendencies toward health conservation. Results from a between-subjects experiment revealed that health information in a comparative and textual format was more effective in encouraging health conservation in participants than identical information presented in a non-comparative and image format. In addition, participants' level of health consciousness emerged as a significant predictor. Through this analysis of quantitative data and inferences, this study make a number of contributions to the user affordance research and its methodology of health informatics study and designing personal informatics application that support user's behavior change in various contexts.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.387-398
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• 2002

As whole genome sequences of many organisms have been revealed by small-scale genome projects, the intensive research on individual genes and their functions has been performed. However on-memory algorithms are inefficient to analysis of whole genome sequences, since the size of individual whole genome is from several million base pairs to hundreds billion base pairs. In order to effectively manipulate the huge sequence data, it is necessary to use the indexed data structure for external memory. In this paper, we introduce a workbench system for analysis and visualization of whole genome sequence using string B-tree that is suitable for analysis of huge data. This system consists of two parts : analysis query part and visualization part. Query system supports various transactions such as sequence search, k-occurrence, and k-mer analysis. Visualization system helps biological scientist to easily understand whole structure and specificity by many kinds of visualization such as whole genome sequence, annotation, CGR (Chaos Game Representation), k-mer, and RWP (Random Walk Plot). One can find the relations among organisms, predict the genes in a genome, and research on the function of junk DNA using our workbench.