• Smart Media Journal
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• v.10 no.2
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• pp.61-69
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• 2021

Wayfinding is a 'Process solving problem to find destination', and it is important to select spatial data for optimal way. Recently, due to the complexity of space and the expansion of the medium of the wayfinding service, it is necessary to the approach on the information design for them. Therefore, the purpose of this study is to propose a meta guideline on the information design for the design of orientation sign, which is an important cognitive clue in the wayfinding. It was conducted in 3 stages, First, a design process was proposed and design elements were derived for each step by literature research related to information and sign design, and analysis of manual for signage design. Second, a meta guideline for information organization and visualization in the three-stage design process was proposed by FGI and analysis. Third, the meta guideline was applied to the sign design on an area for user evaluation to inspect the applicability of the meta guideline. Through the user questionnaire, the possibility of applying the guideline for visualization of directions and spaces, information hierarchy according to spatial characteristics and information priority was identified. It is meaningful in that necessary element for signage design are systematized centering on the process and that it presents a macroscopic methodology according to spatial characteristics.

• Smart Media Journal
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• v.8 no.3
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• pp.23-30
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• 2019

With the popularization of the Internet and smartphone uses, people in the modern era are living in a multi-channel environment in which they access the information system freely through various methods and media. In the process of utilizing such services, users must authenticate themselves, the typical of which is ID & password authentication. It is considered the most convenient method as it can be authenticated only through the keyboard after remembering its own credentials. On the other hand, modern web services only allow passwords to be set with high complexity by different combinations. Passwords consisting of these complex strings also increase proportionally, since the more services users want to use, the more user authentication information they need to remember is recommended periodically to prevent personal information leakage. It is difficult for the blind, the disabled, or the elderly to remember the authentication information of users with such high entropy values and to use it through keyboard input. Therefore, this paper proposes a user authentication method using Google Assistant, MFCC and DTW algorithms and speaker authentication to provide the handicapped users with an easy user authentication method in the login process.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.1
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• pp.5-27
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• 2019

This study analyzed the effects of comic book reading program on Korean proficiency and acculturation of youth with immigration background, by conducting a six-month reading program with five teenagers with immigration background. Ten comic books were selected from published by School Library Journal, based on the themes - that are related to the lives of youth with immigration background - and interests of participating teens. According to the literacy skills test conducted before and after the reading program, the participating teens' Korean proficiency has generally improved, particularly in the areas of interpretation and vocabulary. In terms of writing, grammatically incorrect sentences, phrases, and expressions have declined. Most participants showed stable adjustment to Korean culture, but one participant who felt still insecure of her ethnic identity deeply empathized with one of the characters of the books, and shared the difficulties of living as an outsider of a society. The participants of this research learned or rediscovered the joy of reading through this comic book reading program; at the end of the program, many of them expanded their interest in reading novels, books without any illustrations.

• Smart Media Journal
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• v.10 no.1
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• pp.16-24
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• 2021

The exiting word embedding methodology such as word2vec represents words, which only occur in the raw training corpus, as a fixed-length vector into a continuous vector space, so when mapping the words incorporated in the raw training corpus into a fixed-length vector in morphologically rich language, out-of-vocabulary (OOV) problem often happens. Even for sentence embedding, when representing the meaning of a sentence as a fixed-length vector by synthesizing word vectors constituting a sentence, OOV words make it challenging to meaningfully represent a sentence into a fixed-length vector. In particular, since the agglutinative language, the Korean has a morphological characteristic to integrate lexical morpheme and grammatical morpheme, handling OOV words is an important factor in improving performance. In this paper, we propose parallel Tri-LSTM sentence embedding that is robust to the OOV problem by extending utilizing the morphological information of words into sentence-level. As a result of the sentiment analysis task with corpus in Korean, we empirically found that the character unit is better than the morpheme unit as an embedding unit for Korean sentence embedding. We achieved 86.17% accuracy on the sentiment analysis task with the parallel bidirectional Tri-LSTM sentence encoder.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• Smart Media Journal
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• v.9 no.4
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• pp.26-35
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• 2020

Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.161-180
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• 2022

This study aims to improve the accessibility of websites that can search for online e-journals and check the original text, and the accessibility of web contents in the form of article papers. In order to publish online e-journals in Korea, article contribution management system is used, and services are provided through public or private academic DB companies. There was no content related to accessibility in the publishing and editing stage of online e-journals. In the case of foreign countries, objective to comply with Level AA of WCAG 2.1 to improve accessibility of websites and web content. In addition, the level of accessibility of academic journals is guided through VPAT. In order to improve access to web content in online journals, Accessibility matters are added to the academic society's editorial and publication regulations. Accessibility education should be provided to journal editors. Accessibility checklists should be developed and researchers should verify themselves. To improve the accessibility of online e-journals to websites, For equal use, various convenience functions should be provided when using the website. It guides the accessibility function to the article contribution management system. Each academic and academic DB company should be required to submit a Korean VPAT.

• Smart Media Journal
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• v.12 no.2
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• pp.56-65
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• 2023

In broadcasts such as news and coverage programs, voice is modulated to protect the identity of the informant. Adjusting the pitch is commonly used voice modulation method, which allows easy voice restoration to the original voice by adjusting the pitch. Therefore, since broadcast voice modulation methods cannot properly protect the identity of the speaker and are vulnerable to security, a new voice modulation method is needed to replace them. In this paper, using the Lightweight speech de-identification model as the evaluation target model, we compare speech de-identification performance with broadcast voice modulation method using pitch modulation. Among the six modulation methods in the Lightweight speech de-identification model, we experimented on the de-identification performance of Korean speech as a human test and EER(Equal Error Rate) test compared with broadcast voice modulation using three modulation methods: McAdams, Resampling, and Vocal Tract Length Normalization(VTLN). Experimental results show VTLN modulation methods performed higher de-identification performance in both human tests and EER tests. As a result, the modulation methods of the Lightweight model for Korean speech has sufficient de-identification performance and will be able to replace the security-weak broadcast voice modulation.

• Smart Media Journal
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• v.11 no.11
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• pp.49-62
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• 2022

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the "Big Data" environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.

• Smart Media Journal
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• v.11 no.2
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• pp.77-83
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• 2022

The neuromorphic architecture uses a spiking neural network (SNN) model to derive more accurate results as more spike values are accumulated through inference experiments. When the inference result converges to a specific value, even if the inference experiment is further performed, the change in the result is smaller and power consumption may increase. In particular, in an AI-based IoT environment, power consumption can be a big problem. Therefore, in this paper, we propose a technique to reduce the power consumption of AI-based IoT by reducing the inference time by adjusting the inference image exposure time in the neuromorphic architecture environment. The proposed technique calculates the next inferred image exposure time by reflecting the change in inference accuracy. In addition, the rate of reflection of the change in inference accuracy can be adjusted with a coefficient value, and an optimal coefficient value is found through a comparison experiment of various coefficient values. In the proposed technique, the inference image exposure time corresponding to the target accuracy is greater than that of the linear technique, but the overall power consumption is less than that of the linear technique. As a result of measuring and evaluating the performance of the proposed method, it is confirmed that the inference experiment applying the proposed method can reduce the final exposure time by about 90% compared to the inference experiment applying the linear method.