• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.235-243
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• 2019

Universities are opening related majors and subjects to nurture the problem-solving fusion that businesses want. The time has come when rapid technological. On this thesis, we analyzed three years (2017-2019) of survey result of Women University students in order to figuring out and dealing with the change in 4th industrial revolution and intellectual information technology. It turns out that 1) there was an increase of interest in 4th industrial revolution from 59% in 2017 to 80% in 2019, 2) IoT, ICT, Artificial Intelligence, and Education Research System became top priority in technical strategy, 3)the prime keyword is AI, robot, job, 4)the expectation on increasing of the opportunity and the number of jobs in science technology field was 50%, 5)the importance of universities and companies was 50%, 80% each, 6) the information needed for science technology were educational discipline, change in future science, prospective future information in order, and 7)the most needed education were education on creativity, coding, cross-subject, engineering in order. In the era of the fourth industrial revolution, it is essential to expand the SW manpower base in various fields. University education, which should provide connectivity for super-fusion, should provide curriculum optimized for industrial demands such as, fusion and connected education, creative thinking, self-directed problem solving and etc.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.75-81
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• 2021

In the new era of the 4th industrial revolution, new jobs using the latest technologies such as big data, cloud, IoT, and AI are increasing. In the field of various industries, talents who can converge IT and industrial fields are needed, but such convergence-type talents are insufficient. This study analyzed the effects of IT convergence startup education on the learning effect and educational performance of trainees preparing for re-employment. A survey was conducted with 160 trainees preparing for reemployment. Frequency analysis, reliability analysis, correlation analysis, and multiple regression analysis were performed using the analysis tool SPSS 22.0 program. As a result of the study, first, in the IT convergence startup education of trainees who are preparing for re-employment, it was found that the sub-factors such as education content, instructor, and member satisfaction had a positive effect on the learning effect. Second, in the IT convergence start-up education for employment trainees, it was found that the sub-factors such as education contents, instructors, and the satisfaction of learning members had a significant effect on educational performance. It is expected that this study will serve as a basic data for preparing a start-up support system to revitalize start-ups in the IT convergence field.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.9-14
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• 2021

This paper discovers the energy loss factors through the insulation diagnosis of houses or buildings, and proposes directions for energy efficiency improvement. The energy efficiency factor of a building consists of insulation diagnosis, thermal bridge diagnosis, window diagnosis, airtight diagnosis, and equipment diagnosis. Among the residents and facilities in the energy welfare blind spot, an energy efficiency diagnosis was conducted for one senior citizen building located in Naju-si, Jeollanam-do, and energy efficiency diagnosis was conducted after insulation was installed. Energy measurement, diagnosis and analysis were performed using the IoT-based integrated wired/wireless energy diagnosis platform, Energy Finder. As a result of comparison, an overall energy saving rate of 16.38% was achieved. Annual heating energy consumption per unit area decreased from 333.51kWh before construction to 277.35kWh after construction, and annual cooling energy consumption per unit area decreased from 5.51kWh before construction to 5.22kWh after construction. The annual primary energy consumption per unit area decreased from 464.52kWh before construction to 403.69kWh after construction, and the annual energy cost was reduced from 3,063,307.14 won before construction to 2,641,072.49 won after construction. The additional improvement work is needed on the standards affecting energy efficiency other than insulation.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.242-264
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• 2019

This study focuses on considering the factors affecting the user intention to adopt an intelligent service - A.I. speaker services. Currently there can be a considerable difference between the expectation and the realized diffusion of IT-based intelligent services. This study aims to find out this gap based on the idea of diver previous researches including TAM and UTAUT studies and to identify the direct and indirect effects of diverse factors such as security issues, perceived time pressure, service innovativeness, and the experience of these IT-based intelligent services. And this study considers the expected impact of perceived time pressure factor on the user acceptance of A.I. speaker services. In analysis results, not only the traditional factors such as the perceived usefulness and the hedonic/utilitarian motives but also the perceived time pressure, the perceived security issues, and the experience of the services should be considered as meaningful factors to affect the users adopting A.I. speaker services.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.268-278
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• 2021

Technologies that are leading the fourth industrial revolution, such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cyber-physical systems (CPS) are developing and generalizing. The demand to improve productivity, economy, safety, etc., is spreading in various industrial fields by applying these technologies. Digital twins are attracting attention as an important technology trend to meet demands and is one of the top 10 tasks of the Korean version of the New Deal. In this study, papers, magazines, reports, and other literature were searched using Google. In order to investigate the contribution or role of geospatial information in the digital twin application, the definition of a digital twin, we investigated technology trends of domestic and foreign companies; the components of digital twins required in manufacturing, plants, and smart cities; and the core techniques for driving a digital twin. In addition, the contributing contents of geospatial information were summarized by searching for a sentence or word linked between geospatial-related keywords (i.e., Geospatial Information, Geospatial data, Location, Map, and Geodata and Digital Twin). As a result of the survey, Geospatial information is not only providing a role as a medium connecting objects, things, people, processes, data, and products, but also providing reliable decision-making support, linkage fusion, location information provision, and frameworks. It was found that it can contribute to maximizing the value of utilization of digital twins.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.27-35
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• 2021

With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.5-33
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• 2021

This Study seeks for a plan for promotion of smartification of digital services for improving convenience in use and user services of public libraries in smart digital environment. Thus, in this Study, a survey on awareness of a plan for revitalization of digital data and smart libraries has been conducted for the persons in charge of digital data and librarians from public libraries. The result of this Survey are as follows: first, the introduction of smart libraries was effective by first implementing them in small and medium-sized cities with high interest in in information technology, and spreading them to public libraries in metropolitan cities and special autonomous cities; second, it is analyzed that the essential factor of success in introduction of smart libraries is the contents free from the terminals and the upgrade of computer equipment of users available for the use of these services. Terminals are to be individually utilized by smartphone users but it is necessary for upgrade and introduction of 5G which can secure the mobility of users including open type Wi-Fi; third, it is discovered that the information technology the applicability of which is expected to be easy while introducing smart libraries is RFID, which has been already generalized, and bigtata technology. The introduction of IoT technology in which the stakeholders of public libraries in metropolitan cities and special self-governing cities must be considered first.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.318-331
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• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.41-49
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• 2022

The Internet Engineering Task Force (IETF) has standardized RPL (IPv6 Routing Protocol for Low-power Lossy Network) as a routing protocol for Low Power and Lossy Networks (LLNs), a low power loss network environment. RPL creates a route through an Objective Function (OF) suitable for the service required by LLNs and builds a Destination Oriented Directed Acyclic Graph (DODAG). Existing studies check the residual energy of each node and select a parent with the highest residual energy to build a DODAG, but the energy exhaustion of the parent can not avoid the network disconnection of the children nodes. Therefore, this paper proposes EC-RPL (Enhanced Connectivity-RPL), in which ta node leaves DODAG in advance when the remaining energy of the node falls below the specified energy threshold. The proposed protocol is implemented in Contiki, an open-source IoT operating system, and its performance is evaluated in Cooja simulator, and the number of control messages is compared using Foren6. Experimental results show that EC-RPL has 6.9% lower latency and 5.8% fewer control messages than the existing RPL, and the packet delivery rate is 1.7% higher.