• Journal of Digital Convergence
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• v.16 no.11
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• pp.329-334
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• 2018

Web services are standardized software technologies that enable interoperability of operating systems and programming languages through networks and related standards. Web services are distributed computing services that provide and discover services making it possible to access various services. Since the search method of web service considers only the functional aspect, it has a limitation on user-oriented search when selecting a service. In order to solve these problems, this study proposes an automatic IoT information generation tool, and provides IoT extension information when searching a web service, thereby improving the problem so that a suitable service can be selected for a user. Automatic IoT extension information generation tool proposed in this study collects and stores various information generated in the process of sensing, networking, and information processing by collaborating autonomously in a distributed environment of user, object, and service. The proposed method supports the service search suitable for the user by providing the information generated by the user as extended information when searching the web service. The proposed method can be applied to the 4th industry sector to provide a customized service that meets various environment requirements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.87-92
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• 2021

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.231-247
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• 2020

As artificial intelligence (AI) has emerged as a promising future technology among the fourth industrial revolution, we are trying to apply artificial intelligence technology across all area of society, including libraries. This study investigated the effects, issues, and implications of artificial intelligence on university library services. As a research method, in-depth interviews were conducted with IT experts of university libraries in North America, and conclusions and discussion were drawn from interview results and related documents. Research results revealed that university libraries in North America were trying to build an infrastructure that facilitates information access and retrieval based on artificial intelligence systems and to provide new services in collaboration with AI research institutes in universities. This study raised issues regarding the expansion of the role of libraries and librarians, privacy, and data quality. It was also discussed that the need for re-education of university librarians to become software engineers who play a role in disseminating knowledge. In addition, this study suggested the investment for the establishment of the information system and an artificial intelligence research center in the library. The study discussed limitations of research due to changes in the research environment and suggestions for future research.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.221-228
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• 2022

With the application of artificial intelligence technology, robots can provide efficient services in real life. Unlike industrial manipulators that do simple repetitive work, this study presented design methods of 6 degree of freedom robot arm and intelligent object search and movement methods for use alone or in collaboration with no place restrictions in the service robot field and verified performance. Using a depth camera and deep learning in the ROS environment of the embedded board included in the robot arm, the robot arm detects objects and moves to the object area through inverse kinematics analysis. In addition, when contacting an object, it was possible to accurately hold and move the object through the analysis of the force sensor value. To verify the performance of the manufactured robot arm, experiments were conducted on accurate positioning of objects through deep learning and image processing, motor control, and object separation, and finally robot arm was tested to separate various cups commonly used in cafes to check whether they actually operate.

• Journal of Creative Information Culture
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• v.5 no.2
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• pp.183-191
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• 2019

The purpose of this paper is to develop an unplugged education program that develops the 4C (Creativity, Critical thinking, Communication ability, Collaboration) and CT (Computational Thinking) competencies required in modern society. This study discovered "Fine Dust Robot" as a theme suitable for the unplugged education program, and designed the Unplugged 4-hour education program which can develop 4C and CT competencies. The first stage motivates learning, and the second and third stages develop unplugged activity to develop CT. In the fourth stage, the algorithms created through unplugged activities were programmed through the natural language instruction card and produced the output. We developed educational materials that can be utilized in the unplugged education program. Finally, education programs were conducted for elementary school students, and pre- and post-tests of computational thinking were conducted for general students and gifted students. Educational effective was found in both groups.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.619-624
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• 2023

As the importance of digital competency education is highlighted, this study is a case study on block coding classes for elementary school students during vacation for the purpose of bridging the information education gap among students. The purpose of this study is to design and operate a play-centered block coding class program and find out if it is effective in improving students' interest. As a result of completing the teaching plan through the second consultation and revision, running the class, and analyzing the change in learning interest of the students through the t-test, the play-oriented block coding class designed in this study was effective in improving students' interest. In addition, it was possible to discover interesting elements such as student-led learning process and immersion through realistic play activities, friendship, collaboration, and communication through group activities. This study is significant in suggesting a plan to increase learning interest for students who are new to coding.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.37-43
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• 2023

In today's business landscape, collaboration and interoperability are crucial for organizational success and profitability. However, integrating operations across multiple organizations is challenging due to differing roles and policies in Identity and Access Management (IAM). User-centric identity (UCI) adopts a personalized approach to digital identity management, centering on the end-user for authentication and access control. It provides a decentralized system that ensures secure and customized access for each user. UCI aims to address complex security challenges by aligning access privileges with individual user requirements. This research delves into UCI's ability to streamline resource access amidst conflicting IAM roles and protocols across various organizations. The study presents a UCI-based multi-domain access control (MDAC) framework, which encompasses an ontology, a unified method for articulating access roles and policies across domains, and software services melding with UCI infrastructure. The goal is to enhance organizational resource management and decision-making by offering clear guidelines on access roles and policy management across diverse domains, ultimately boosting companies' return on investment.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.151-160
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• 2015

Internet-of-Things(IoT) is the computing environment to provide valuable services by interacting with multiple devices, where diverse devices are connected within the existing Internet infrastructure and acquire context information by sensing. As the concern of IoT has been increased recently, most of the industries develop many IoT devices. And, many people are focused on the IoT application that is utilizing different technologies, which are sensor network, communication technologies, and software engineering. Developing on-ground IoT application is especially even more active in progress depending on increasing of on-ground IoT devices because it is possible for them to access dangerous and inaccessible situation. However, There are a few studies related IoT. Moreover, since on-ground IoT application, which is different from typical software application, has to consider device's characteristics, communication, and surround condition, it reveal challenges, decreasing reliability. Therefore, in this paper, we analyze reliability challenges related to maturity and fault tolerance, one of reliability attributes, occurring in developing on-ground IoT applications and suggest the effective solutions to resolve the challenges. To verify proposed the challenges and solutions, we show result that is applying the solutions to applications. By presenting the case study, we evaluate the effectiveness of applying the solutions to the application.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.337-350
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• 2014

Internet-of-Things(IoT) is the computing paradigm converged with different technologies, where diverse devices are connected via the wireless network, acquire environmental information from their equipped sensors, and are actuated. IoT applications provide smart services to users by interacting with multiple devices connected to the network. IoT devices provide the simple set of the information and also offer smart services by collaborating with other devices. That is, IoT applications always interact with IoT devices which are becoming very popular at a fast pace. However, due to this fact, developing IoT application results in unconventional technical challenges which have not been observed in typical software applications. Moreover, since IoT computing has its own characteristics which are distinguished from other former paradigms such as embedded computing and mobile computing, IoT applications also reveal their own technical challenges. Therefore, we analyze technical challenges occurring in developing IoT applications and present effective solutions to overcome the challenges. To verify identified issues and presented solutions, we present the result of performing a case study of developing an IoT application. Through the case study, we verify how the unconventional technical issues are raised in a real domain and analyze effectiveness of applying the solutions to the application.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.