• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.63-72
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• 2016

Closed-circuit television(CCTV) is a facility that forms the backbone of the ubiquitous services provided by the Integrated Management Center of the Ministry of Land, Infrastructure and Transport and the Integrated Control Center of the Ministry of the Interior. However, it is installed and managed according to different laws, as it is operated and managed by each department. Moreover, because there are no regulatory grounds for unified management of CCTV, each municipality responsible for the actual management manages it based on the individual standards of each department. Thus, the purpose of this study is to develop a standardized management form to establish an integrated management plan. The author inspected the existing situation by examining the legal system and public data and through hands-on worker interviews, and discovered the managed element by reviewing the specifications of the bidding system. The management form for integrated management comprises the above requirements along with the management histories and linkage of intelligent facilities. A uniform management form for integrated management containing specifications of the CCTVs installed by various departments is created, and is easily searched for facilities to check requirements for joint use. The result of this study can contribute to building the database of facility management system for integrated management of facilities at the integrated management center as well as for a detailed simulation of the selection of location of CCTV depending on the CCTV's specifications.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.15-31
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• 2009

The next generation IT environment is expected to be an open computing environment based on Grid computing technologies, which allow users to access to any type of computing resources through networks. The open computing environment has benefits in aspects of resource utilization, collaboration, flexibility and cost reduction. Due to the variation in performance of open computing resources, however, resource allocation simply based on users' budget and time constraints often fails to meet the Service Level Agreement(SLA). This paper proposes the Mean-Absolute Deviation(MAD) portfolio optimization approach, in which service brokers consider the uncertainty of performance of resources, and compose resource portfolios that minimize the uncertainty. In order to investigate the effect of this approach, we simulate an open computing environment with varying uncertainty levels, users' constraints, and brokers' optimization strategies. The simulation result concludes threefolds. First, the MAD portfolio optimization improves the success ratio of delivering the required performance to users. Second, the success ratio depends on the accuracy in predicting the variability of performance. Thirdly, the measured variability can also help service brokers expand their service to cost-critical users by discounting the access cost of open computing resources.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.57-65
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• 2010

This paper describes the method of efficient container number recognition in colored container image with number plate at ATC(Automatic Transfer Crane) in container terminal yard. At the Sinseondae terminal gate in Busan, the container number recognition system is installed by "intelligent port-logistics system technology development", that is government research and development project. It is the method that it sets up the tunnel structure inside camera on the gate and it recognizes the container number in order to recognize the export container cargo automatically. However, as the automation equipment is introduced to the container terminal and the unmanned of a task is gradually accomplished, the container number recognition system for the confirmation of the object of work is required at ATC in container terminal yard. Therefore, the container number recognition system fitted for it is necessary for ATC in container terminal yard in which there are many intrusive of the character recognition through image including a sunlight, rain, snow, shadow, and etc. unlike the gate. In this paper, hardware components of the camera, illumination, and sensor lamp were altered and software elements of an algorithm were changed. that is, the difference of the brightness of the surrounding environment, and etc. were regulated for recognize a container number. Through this, a shadow problem, and etc. that it is thickly below hung with the sunlight or the cargo equipment were solved and the recognition time was shortened and the recognition rate was raised.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.177-186
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• 2021

The 2015 revised curriculum is structured around the core competencies of the 21st century, this is in line with the world's flow of education, such as OECD Education 2030. A future practical leading model was studied to provide a variety of creative teaching and learning experiences to elementary and Secondary students using intelligent information technology to cultivate core competencies such as ICT and computing thinking. In order for this practical model to stably settle the school field, the training was planned and operated to strengthen the creative convergence education capacity required by the teachers at the unit school through various types of the training. In particular, a nationwide administrators training program was operated for three years, reflecting the new curriculum, teaching and learning methods, and evaluation that can lead to future convergence talent training. In this paper, the perception of creative convergence education was investigated and analyzed considering the influence that administrators may have on the school field. Based on this, through the three-year operation results of the training, it was intended to establish a new training method for stable access to future creative convergence education under the post-corona era's social issues.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.55-61
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• 2022

Currently police and fire departments use a Network/Wifi/GPS based emergency location positioning system established by mobile carriers to directly link with the device of the people who request the rescue to accurately position the expected location in the call area. However in the case of mountain rescue it is difficult to rescue the victim in golden time because the location of the search area cannot be limited when the victim is located in a radio shadow area of the mountain or the device power is off and this situation become worse if victim fail to report 911 by himself due to the injury. In this paper, we are expected to solve the previous problem by propose the mobile telecommunication forensic simulator consist of time series of cell information, human mobility model which include some general and specific features (age, gender, behavioral characteristics of victim, etc.) and intelligent infer system. The results of analysis appear in heatmap of polygons on the map based on the probability of the expected location information of the victim. With this technology we are expected to contribute to rapid and accurate lifesaving by reducing the search area of rescue team.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.365-385
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• 2022

The easy payment service is a payment and remittance service that uses a simple authentication method. As online transactions have increased due to COVID-19, the use of an easy payment service is increasing. At the same time, electronic financial industries such as Naver Pay, Kakao Pay, and Toss are diversifying the competition structure of the easy payment market; meanwhile overseas fintech companies PayPal and Alibaba have a unique market share in their own countries, while competition is intensifying in the domestic easy payment market, as there is no unique market share. In this study, the participants in the easy payment market were classified as electronic financial companies, mobile phone manufacturers, and financial companies, and a SWOT analysis was conducted on the representative services in each industry. The analysis examined the user reviews of Google Play Store via a topic modeling analysis, and it employed positive topics as strengths and negative topics as weaknesses. In addition, topic modeling was conducted by dividing news articles into political, economic, social, and technology (PEST) articles to derive the opportunities and threats to easy payment services. Through this research, we intend to confirm the service capabilities of easy payment companies and propose a service activation strategy that allows gaining the upper hand in the market.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.175-201
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• 2023

In the midst of going through a non-face-to-face society, most of human activities narrowed down to the platform, restrictions on external activities are bringing the internal scalability of digital technology. Metaverse is virtually shifting reality and increasing the possibility of utilization in various areas. However, researches linked to the educational effects of metaverse, especially students with disabilities, are still an unknown area that lacks exploration. This paper focuses on the fact that metaverse-education is widening educational fields that meets the various needs of disabled students to realize social good and inclusive education, and communication effects such as resolving barriers to interaction are prominent. As a research method, examining literature research papers linked to AR/VR, metaverse with communication skills, interviews, articles, and columns by experts, and policy suggestions and implications for the special education was conducted. Although the limitations of research are confirmed, significant results are found on inclusive education, which provides educational maximizing effects and realizing human rights through direct immersive experience reflecting the Cone of Experience Theory. Hopefully follow-up studies on meta-edu for disabled students will be carried out in the future, and various interdisciplinary discussions are needed to carefully observe inclusive policies and benefits so that the socially vulnerable are not excluded from technologies in ICT society.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.