• Information Systems Review
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• v.23 no.3
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• pp.201-223
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• 2021

Smart home is an advanced Internet of Things (IoT) service that enhances the convenience of human daily life and improves the quality of life at home. Recently, with the emergence of smart home products and services to which artificial intelligence (AI) technology is applied, interest in smart home is increasing. To gain a competitive edge in the smart home market, companies are providing "personalized service" to users, which is a key service that can promote smart home use. This study investigates the factors affecting the value awareness of personalized service and intention to use smart home. This research focuses on four-dimensional motivated innovativeness (cognitive, functional, hedonic, and social innovativeness) and privacy risk awareness as key factors that influence the value awareness of personalized service of smart home. In particular, this study conducts a comparative analysis between the generation MZ (young people in late teens to 30s), who are showing socially differentiated characteristics, and the generation X and baby boomers in 40s to 50s or older. Based on the analysis results, this study derives the distinctive characteristics of generation MZ that are different from the older generation, and provides academic and practical implications for expanding the use of smart home services.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.199-226
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• 2024

In response to the era of transformation necessitating the introduction of Artificial Intelligence (AI) and digital technologies, educational innovation is undertaken with the implementation of AI digital textbooks in Mathematics, English, and Information subjects by 2025 in Korea. Within this context, this study analyzed the perceptions and needs of elementary school teachers regarding mathematics AI digital textbook. Based on a survey conducted in November 2023, involving 132 elementary school teachers across the country, the analysis revealed that the majority of elementary school teachers had a low perception of the introduction and need for mathematics AI digital textbooks. However, some recognized the potential for personalized learning and effective teaching support. Furthermore, among the core technologies of the AI digital textbook, teachers highly valued the necessity of learning diagnostics and teacher reconfiguration functions and had the most positive perception of their usefulness in math lessons, while their perception of interactivity was relatively low. These findings suggest the need for changing teachers' perceptions through professional development and information provision to ensure the successful adoption and use of mathematics AI digital textbooks. Specifically, providing concrete and practical ways to use the AI digital textbook, exploring alternatives to digital overload, and continuing development and research on core technologies.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.864-881
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• 2021

Purpose: This study aims is to provide a total care solution preventing disaster based on Big Data and AI technology and to service safety considered by individual situations and various risk characteristics. The purpose is to suggest a method that customized comprehensive index services to prevent and respond to safety accidents for calculating the living safety index that quantitatively represent individual safety levels in relation to daily life safety. Method: In this study, we use method of mixing AHP(Analysis Hierarchy Process) and Likert Scale that extracted from consensus formation model of the expert group. We organize evaluation items that can evaluate life safety prevention services into risk indicators, vulnerability indicators, and prevention indicators. And We made up AHP hierarchical structure according to the AHP decision methodology and proposed a method to calculate relative weights between evaluation criteria through pairwise comparison of each level item. In addition, in consideration of the expansion of life safety prevention services in the future, the Likert scale is used instead of the AHP pair comparison and the weights between individual services are calculated. Result: We obtain result that is weights for life safety prevention services and reflected them in the individual risk index calculated through the artificial intelligence prediction model of life safety prevention services, so the comprehensive index was calculated. Conclusion: In order to apply the implemented model, a test environment consisting of a life safety prevention service app and platform was built, and the efficacy of the function was evaluated based on the user scenario. Through this, the life safety index presented in this study was confirmed to support the golden time for diagnosis, response and prevention of safety risks by comprehensively indication the user's current safety level.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11B
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• pp.1346-1356
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• 2011

Transportation has been playing important role in our society by providing for people, freight, and information. However, it cuts its own throat by causing car accidents, traffic congestion, and air pollution. The main cause of these problems is a noticeable growth in the number of vehicles. The easiest way to mitigate these problems is to build new road infrastructures unless resources such as time, money, and space are limited. Therefore, there is a need to manage the existing road infrastructures effectively and safely. In this paper, we propose a road reservation scheme that provides fast and safe response for emergency vehicles using ubiquitous sensor network. Our idea is to allow emergency vehicle to reserve a road on a freeway for arriving to the scene of the accident quickly and safely. We evaluate the performance by three reservation method (No, Hop, and Full) to show that emergency vehicles such as ambulances, fire trucks, or police cars can rapidly and safely reach their destination. Simulation results show that the average speed of road reservation is about 1.09 ~ 1.20 times faster than that of non-reservation at various flow rates. However, road reservation should consider the speed of the emergency vehicle and the road density of the emergency vehicle processing direction, as a result of Hop Reservation and Full Reservation performance comparison analysis. We confirm that road reservation can guarantee safe driving of emergency vehicles without reducing their speed and help to mitigate traffic congestion.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.27-40
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• 2006

The traffic information is provided, which based on the volume of traffic, speed, occupancy collected through the currently operating Vehicle Detector System(VDS). In addition to the trend in utilization fold of traffic information is increasing gradually with the applied various fields and users. Missing data in Vehicle detector data means series of data transmitted to controller without specific property. The missing data does not have a data property, so excluded at the whole data Process Hence, increasing ratio of missing data in VDS data inflicts unreliable representation of actual traffic situation. This study presented the imputation process due out which applied the methodologies that utilized adjacent stations reference and historical data utilize about missing data. Applied imputation process methodologies to VDS data or SeoHaeAn/Kyongbu Expressway, currently operation VDS, after processes at missing data ratio of an option. Imputation process held presented to per lane-30seconds-period, and morning/afternoon/daily time scope ranges classified, and analyzed an error of imputed data preparing for actual data. The analysis results, an low error occurred relatively in the results of the imputation process way that utilized a historical data compare with adjacent stations reference methods.

• Communications of Mathematical Education
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• v.34 no.3
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• pp.215-234
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• 2020

With the advent of the AI, the need to use AI in the field of education is widely recognized. The purpose of this study is to shed light on how prospective mathematics teachers perceive the need for AI and the role of teachers in future mathematics education. As a result, with regard to teaching, prospective teachers recognized that the use of AI in school mathematics is a demand of a new era, that various types of lesson can be implemented, and that accurate knowledge and information can be delivered. On the other hand, they recognized that AI has limitations in having cognitive and emotional interactions with students. As for mathematics learning, the prospective teachers recognized that AI can provide individualized learning, be used for supplementary learning outside of school, and stimulate students' interest in learning. However, they also said that learning through AI could undermine students' ability to think on their own. With regard to assessment, the prospective teachers recognized that AI is objective, fair and can reduce teachers' workload, but they also said that AI has limitations in evaluating students' abilities in constructed-response items and in process-focused assessment. The roles of teachers that the prospective teachers think were to conduct a lesson, emotional interaction, unstructured assessment, and counseling, and those of AI were individualized learning, rote learning, structured assessment, and administrative works.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.169-169
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• 1999

최근 산업 플랜트의 공정제어 시스템은 복잡하고 대규모화되어 고장 발생시 경제적 손실과 위험성이 증폭되어 규정된 안정서와 신뢰성 확보가 필수적이라 할 수 있다. 고장검출 및 진단기법은 시스템의 신뢰성을 높이기 위한 효과적인 방안을 연구하는 것으로 현대에 들어서 많은 학자들의 관심을 끌고 있으며 실제 계통에 점차적으로 응용되고 있다. 현재까지 개발된 고장검출 및 진단기법은 사용된 프로세스 모델의 형태, 고장검출 진단 알고리즘에 따라 다양하게 분류 될 수 있으며 일반적으로 사용된 모델에 따라 크게 1) 정량적 모델에 근거한 해석적 기법, 2) 정성적 모델에 근거한 기법, 3) 지식기반 진단 기법으로 구분 할 수 있다. 이중 정량적 모델 기법은 대상계통의 수학적 모델에 근거하여 운전 데이터를 분석함으로서 고장검출 진단을 수행하는 해석적 기법으로서 근본적으로 계통의 정확한 수학적 모델을 요구하므로 불확실성을 포함한 계통 및 비선형성이 강한 계통등에는 적용이 곤란하다. 정성적 모델 및 지식기반 기법은 정량적 진단 기법과는 달리 대상 프로세스에 대한 수학적 모델 대신에 운전자의 경험과 프로세스 변수간의 상호 작용 및 고장의 전파과정, 고장원인과 증상과의 직접적인 관계에 대한 구조적 지식에 근거한 것으로 고장원인에 대한 계통의 동작을 추론 할 수 있으며, 상황 변화에 따른 영향을 예측할 수 있다. 본 논문에서는 정성적 모델 및 지식기반 기법에 근거한 고장검출 및 진단 기술을 화력 발전소 보일로 프로세스에 적용하여 정성적 시뮬레이션에 의한 설비의 고장을 조기에 발견하여 고장 파급으로 인한 발전 정지 및 설비의 손상 확대를 방지하고 고장 발생시 신속한 원인 규명 및 후속 조치관련 정보들을 운전원에게 제공할 목적으로 현재 전력원에서 개발중인 지능형 경보시스템에 대하여 기술하고자 한다.음과 같이 설명하였다. 서로 상반되는 것들이 다음과 같이 설명하였다. 서로 상반되는 것들이 부딛힘이 없이 공존하고 일상의 논리가 무시된다. 부정, 의심이 없고 확실한 것이 없다. 한 대상에 가졌던 생각이 다른 대상에 옮겨간다(displacement). 한 대상이 여러 대상이 갖고 있는 의미를 함축하고 있다(condensation). 시각적인 순서가 무시된다. 마음속의 생각과 외부의 실제적인 일을 구분하지 못한다. 시간 상의 순서가 있다가 없다가 한다. 차례로 일어나야 할 일이 동시에 한꺼번에 일어난다. 대상들이 서로 비슷해지고 동시에 있을 수 없는 대상들이 함께 나타난다. 사고의 정상적인 구조가 와해된다. Matte-Blance는 무의식에서는 여러 독립된 대상들간의 구분을 없애며, 주체와 객체를 하나로 보려는 대칭화(symmetrization)의 경향이 있기 때문에 이런 변화가 생긴다고 하였다. 또 대칭화가 진행되면 무한대의 느낌을 갖게 되어, 전지(moniscience), 전능(omnipotence), 무력감(impotence), 이상화(idealization)가 나타난다. 그러나 무의식에 대칭화만 있는 것은 아니며, 의식의 사고양식인 비대칭도 어느 정도 나타나며, 대칭화의 정도에 따라, 대상들이 잘 구분되어 있는 단계, 의식수준의 감정단계, 집단 내에서의 대칭화 단계, 집단간에서의 대칭화 단계, 구분이 없어지는 단계로 구분하였다.systems. We believe that this taxonomy is a significant contribution because it adds clarity, completeness, and "global perspective" to workflow architectural discussions. The vocabulary suggested here

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.588-594
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• 2015

Although GPS technology for location positioning system has been widely used, it is difficult to be used in intelligent transport systems, due to the large positioning error and limited area for receiving radio signals. Thanks to the rapid development of LED technology, LED lights become popular in many applications. Especially, visible light communications (VLC) has raised a lot of interests because of the simultaneous functioning of LED illumination and communication. Recent studies on positioning system using VLC mainly focused on indoor environments and still difficult to satisfy positioning accuracy and simple implementation simultaneously. In this paper, we propose a positioning system based on VLC using the coordinate information of LEDs installed on the road infrastructure. Extracting the LED signal, obtained through VLC, from the easily accessible camera image, it is possible to estimate the position of the car on the road. Simulation results show that the proposed scheme can achieve a high positioning accuracy of 1 m when large number of pixels is utilized and the distance from the LED light is close.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.79-92
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• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.