• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.17-22
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• 2022

With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.

• Journal of Korean Academy of Nursing
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• v.52 no.3
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• pp.291-307
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• 2022

Purpose: The aims of study were to identify the main keywords, the network structure, and the main topics of press articles related to nurses that have appeared in media reports. Methods: Data were media articles related to the topic "nurse" reported in 16 central media within a one-year period spanning July 1, 2019 to June 30, 2020. Data were collected from the Big Kinds database. A total of 7,800 articles were searched, and 1,038 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: The number of media reports related to nurses increased by 3.86 times after the novel coronavirus (COVID-19) outbreak compared to prior. Pre- and post-COVID-19 network characteristics were density 0.002, 0.001; average degree 4.63, 4.92; and average distance 4.25, 4.01, respectively. Four topics were derived before and after the COVID-19 outbreak, respectively. Pre-COVID-19 example topics are "a nurse who committed suicide because she could not withstand the Taewoom at work" and "a nurse as a perpetrator of a newborn abuse case," while post-COVID-19 examples are "a nurse as a victim of COVID-19," "a nurse working with the support of the people," and "a nurse as a top contributor and a warrior to protect from COVID-19." Conclusion: Topic modeling shows that topics become more positive after the COVID-19 outbreak. Individual nurses and nursing organizations should continuously monitor and conduct further research on nurses' image.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.455-464
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• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.331-334
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• 2021

기존의 택배 배송시스템은 수령인 본인이 아니더라도 주소, 전화번호와 같은 개인정보를 쉽게 확인할 수 있다. 코로나 19로 인해 언택트(Untact) 주문 및 배달, 배송 서비스가 급격히 늘어나면서 택배 배송 관련 사업은 거대한 시장으로 성장하고 있으며 이와 함께 노출된 개인정보가 범죄에 악용될 수 있다는 우려도 증가하고 있다. 더불어 여러 택배 및 배송물이 도착했을 때, 수신자는 택배 상자를 뜯지 않고 배송물의 오도착 여부를 확인하기 어려우며 원하는 물품이 담긴 택배가 정확히 어떤 것인지 알기 힘들다.본 프로젝트에서는 다단계 인증이 가능한 QR code를 활용해 송수신자의 주소, 제품 종류, 명칭 등을 포함한 여러 정보를 배송기사, 수령인 등에 따라 선택적으로 접근한다. 같은 QR code를 스캔하더라도 수령인의 경우 모든 정보를 확인할 수 있고, 배달원은 일부의 정보를 확인할 수 있지만, 권한이 없는 사람은 어떠한 정보도 확인할 수 없다. 기존의 택배 배송시스템처럼 정보를 맨눈으로 인식할 수도 없으므로 개인정보 노출의 한계를 극복할 수 있다. 이때 송장 정보는 텍스트 형태뿐 아니라 주문한 내용물의 종류 및 모양 등을 그대로 구현한 AR(augmented reality) 형태로도 확인할 수 있어 포장된 상태 그대로 배송물의 오도착 여부를 확인하거나 다량의 택배를 보다 효율적으로 구분할 수 있다. 이처럼 같은 QR code로 서로 다른 정보를 제공하는 SQRC(Security/Secure QR code)의 원리를 이용해 정보를 안전하게 보호하는 것에 그치지 않고, 비디오나 이미지와 같은 멀티미디어 서비스를 추가로 제공해 실감 미디어의 적용 범위를 넓힐 수 있다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.5
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• pp.59-68
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• 2015

Since interdisciplinary convergence could beyond the borders of each disciplines, it is able to create new and meaningful knowledge through collaborative research between different study areas. Especially, in recent years, the Digital Humanities has attracted the attention as the convergence form of the Humanities and ICT. From a research methodology perspective, the Digital Humanities is a tool that can be used as a convergence system for various information utilization such as storage, retrieve, share, and spread. In view of Information system, Digital Humanities has been constructed and used in a variety of systems. Among them, studies related to information visualization for the Digital Humanities have been actively conducted. To visualize data or information, various types such as images, multimedia, interface, and etc could be applied. In this paper, we analyze the cases of various information visualization in digital humanities systems, and propose a method to utilize them in the Humanities and Area Studies.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.210-211
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• 2020

COVID-19으로 인해 국민들에게 전달되는 재난정보의 양상이 서서히 변화하고 있다. 이는 정보통신의 발전 양상과도 매우 깊은 관계를 가지고 있다고 볼 수 있다. 이전까지의 정부 및 지자체에서 제공되는 재난정보에 대한 형태는 문자와 음성으로만 제공하므로써 고령자와 외국인과 같은 재난 약자에게 명확한 상황인지를 하기에 어려움이 있었다. 이를 해결하기 위한 전방위적인 노력을 하고 있으며, 보다 정확하고 보다 다양한 정보를 제공하고자 관련 연구를 수행하고 있다. 이는 급속도로 발전하는 정보통신 매체(UHD 및 5G, 오픈스크린 등)를 기반으로 국민들로 하여금 신속.정확한 재난상황인지를 가능케 할 수 있다. 이로 인한 재난경보 관련 최근 이슈는 '내 위치 맞춤형 정보'와 '다매체 정보'가 아닐까 싶다. 정보통신 매체가 발달함에 따라 제공되는 재난경보의 범위가 내 위치를 기준으로 좁아지며, 시각적으로 직관적인 콘텐츠를 제공할 수 있다. 이는 각 매체의 고유 정보를 통해 위치가 확인 가능하면서 해당 지역에 맞는 정보만 선택적으로 취함으로써 불필요한 정보를 제공하지 않게 된다. 본 연구를 통해 이러한 부분을 해결하기 위해 TTA에서 표준으로 제정된 CAP (Common Alerting Protocol)을 활용하였으며, 'Area' 항목에 지역코드(전국~읍면동)를 함께 포함함으로써 가능해졌다. 또한 CAP을 활용함에 따라 텍스트부터 음성, 이미지, 웹 콘텐츠까지 최신의 영상 매체에 적용 가능한 재난정보 콘텐츠를 제공 가능해졌으며, 특히 UHD 및 5G, 오픈스크린과 같은 통신 네트워크 기반 영상 매체에 적합한 멀티미디어 재난정보 콘텐츠를 제공할 수 있다. 제공된 콘텐츠에는 각종 관련 정보를 확인 가능하도록 링크를 제공하여 필요에 따라 보다 자세한 재난정보를 확인할 수 있다. 이를 기반으로 재난경보에 대한 다변화를 통해 나에게 꼭 필요한 정보가 제공될 수 있도록 발령 체계 개편이 필요하다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.187-195
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• 2023

This study propose to develop artificial intelligence liberal arts courses for college students in the humanities and social sciences majors using the entry artificial intelligence model. A group of experts in computer, artificial intelligence, and pedagogy was formed, and the final artificial intelligence liberal arts course was developed using previous research analysis and Delphi techniques. As a result of the study, the educational topics were largely composed of four categories: image classification, image recognition, text classification, and sound classification. The training consisted of 1) Understanding the principles of artificial intelligence, 2) Practice using the entry artificial intelligence model, 3) Identifying the Ethical Impact, and 4) Based on learned, team idea meeting to solve real-life problems. Through this course, understanding the principles of the core technology of artificial intelligence can be directly implemented through the entry artificial intelligence model, and furthermore, based on the experience of solving various real-life problems with artificial intelligence, and it can be expected to contribute positively to understanding technology, exploring the ethics needed in the artificial intelligence era.

• Smart Media Journal
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• v.12 no.9
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• pp.81-88
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• 2023

Recently, communication through online is increasing due to the spread of non-face-to-face services due to COVID-19. In non-face-to-face situations, the other person's opinions and emotions are recognized through modalities such as text, speech, and images. Currently, research on multimodal emotion recognition that combines various modalities is actively underway. Among them, emotion recognition using speech data is attracting attention as a means of understanding emotions through sound and language information, but most of the time, emotions are recognized using a single speech feature value. However, because a variety of emotions exist in a complex manner in a conversation, a method for recognizing multiple emotions is needed. Therefore, in this paper, we propose a multi-emotion regression model that extracts feature vectors after preprocessing speech data to recognize complex, inherent emotions and takes into account the passage of time.