• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.738-750
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• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.259-262
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• 2022

정보화 시대 스마트폰이 대중화되고 실시간 인터넷 사용이 가능해짐에 따라, 본인을 식별하기 위한 사용자 인증이 필수적으로 요구된다. 대표적인 사용자 인증 기술로는 아이디와 비밀번호를 이용한 비밀번호 인증이 있지만, 키보드로부터 입력받는 이러한 인증 정보는 시각 장애인이나 손 사용이 불편한 사람, 고령층과 같은 사람들이 많은 서비스로부터 요구되는 아이디와 비밀번호를 기억하고 입력하기에는 불편함이 따를 뿐만 아니라, 키로거와 같은 공격에 노출되는 문제점이 존재한다. 이러한 문제점을 해결하기 위하여, 자신의 신체의 특징을 활용하는 생체 인증이 대두되고 있으며, 그중 목소리로 사용자를 인증한다면, 효과적으로 비밀번호 인증의 한계점을 극복할 수 있다. 이러한 화자 인식 기술은 KT의 기가 지니와 같은 음성 인식 기술에서 활용되고 있지만, 목소리는 위조 및 변조가 비교적 쉽기에 지문이나 홍채 등을 활용하는 인증 방식보다 정확도가 낮고 음성 인식 오류 또한 높다는 한계점이 존재한다. 상기 목소리를 활용한 사용자 인증 기술인 화자 인식 기술을 활용하기 위하여, 사용자 목소리를 학습시켰으며, 목소리의 주파수를 추출하는 MFCC 알고리즘을 이용해 테스트 목소리와 정확도를 측정하였다. 그리고 악의적인 공격자가 사용자 목소리를 흉내 내는 경우나 사용자 목소리를 마이크로 녹음하는 등의 방법으로 획득하였을 경우에는 높은 확률로 인증의 우회가 가능한 것을 검증하였다. 이에 따라, 더욱 효과적으로 화자 인식의 정확도를 향상시키기 위하여, 본 논문에서는 목소리에 잡음을 섞는 방법으로 화자를 인식하는 방안을 제안한다. 제안하는 방안은 잡음이 정확도에 매우 민감하게 반영되기 때문에, 기존의 인증 우회 방법을 무력화하고, 더욱 효과적으로 목소리를 활용한 화자 인식 기술을 제공할 것으로 사료된다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.4
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• pp.207-234
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• 2023

Since the advent of the Fourth Industrial Revolution, related studies have been conducted in various fields including industrial fields. In this study, to analyze domestic research trends on the Fourth Industrial Revolution, a keyword analysis and topic modeling analysis based on the LDA algorithm were conducted on 2,115 papers included in the KCI from January 2016 to August 2023. As a result of this study, first, the journals in which more than 30 academic papers related to the Fourth Industrial Revolution were published were digital convergence research, humanities society 21, e-business research, and learner-centered subject education research. Second, as a result of the topic modeling analysis, seven topics were selected: "human and artificial intelligence," "data and personal information management," "curriculum change and innovation," "corporate change and innovation," "education change and jobs," "culture and arts and content," and "information and corporate policies and responses." Third, common research topics related to the Fourth Industrial Revolution are "change in the curriculum," "human and artificial intelligence," and "culture arts and content," and common keywords include "company," "information," "protection," "smart," and "system." Fourth, in the first half of the research period (2016-2019), topics in the field of education appeared at the top, but in the second half (2020-2023), topics related to corporate, smart, digital, and service innovation appeared at the top. Fifth, research topics tended to become more specific or subdivided in the second half of the study. This trend is interpreted as a result of socioeconomic changes that occur as core technologies in the fourth industrial revolution are applied and utilized in various industrial fields after the corona pandemic. The results of this study are expected to provide useful information for identifying research trends in the field of the Fourth Industrial Revolution, establishing strategies, and subsequent research.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.2
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• pp.203-221
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• 2020

This study closely analyzed changes in the educational environment and changes in the needs of children's and young people's reading culture programs, which are directly beneficiaries of the promotion of reading culture as they enter the fourth industrial revolution. It also comprehensively evaluated the reading culture promotion project for children and adolescents promoted by the National Children and Youth Library and proposed a reading culture promotion project that meets the needs of the smart era. This study investigated the cases of various domestic and foreign reading culture promotion projects to divulge trends. The authors invited experts from public libraries and school libraries with experience of the reading culture promotion projects and performed Focus Group Interviews (FGI). The authors evaluated individual reading culture program based on the PDCA method (Plan, Do, Check, Act). Based on the data obtained through case studies and expert evaluations, the development plan of reading culture promotion project and the strategy of promoting new projects to be pursued in the National Children and Youth Library were presented. By gathering the results of the research, 'Interactive e-book making platform production / distribution business', 'Game-type reading program production / distribution business', 'Habruta reading culture dissemination project using backward learning method', 'Youth coding branding "Teen-Start -Up"' were proposed as new services.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.