• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.967-973
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• 2020

As the implementation of export controls is spreading, the importance of classifying strategic items is increasing, but Korean export companies that are new to export controls are not able to understand the concept of strategic items, and it is difficult to classifying strategic items due to various criteria for controlling strategic items. In this paper, we propose a method that can easily approach the process of classification by lowering the barrier to entry for users who are new to export controls or users who are using classification of strategic items. If the user can confirm the decision result by providing a manual or a catalog for the procedure of classifying strategic items, it will be more convenient and easy to approach the method and procedure for classfying strategic items. In order to achieve the purpose of this study, it utilizes deep learning, which are being studied in image recognition and classification, and OCR(optical character reader) technology. And through the research and development of the support tool, we provide information that is helpful for the classification of strategic items to our companies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.44-49
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• 2021

Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.591-599
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• 2020

Deep learning is widely utilized to classify or recognize objects of real-world. An abundance of data is trained on high-performance computers and a trained model is generated, and then the model is loaded in an inferencer. The inferencer is used in various environments, so that it may cause unrecognized objects or low-accuracy objects. To solve this problem, real-world objects are collected and they are trained periodically. However, not only is it difficult to immediately improve the recognition rate, but is not easy to learn an inferencer on embedded devices. We propose a real-time handwriting recognizer based on partial learning on embedded devices. The recognizer provides a training environment which partially learn on embedded devices at every user request, and its trained model is updated in real time. As this can improve intelligence of the recognizer automatically, recognition rate of unrecognized handwriting increases. We experimentally prove that learning and reasoning are possible for 22 numbers and letters on RK3399 devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.57-64
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• 2020

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.41-49
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• 2021

3D printing techniques have been recently adopted in the construction industry. It mainly utilizes additive manufacturing which is the fabrication process depositing successive layers of materials without any formworks. Conventional cementitious materials may not be directly applicable to 3D printing because 3D printable cementitious materials is required to satisfy such characteristics as pumpability, extrudability, and buildability in a fresh state. This study aimed to investigate rheological properties and required performances of 3D printable cementitious materials, by reviewing existing studies. Test methods and equipments, evaluation results and characteristics of mixture additives were compared. Based on reviews of existing studies, this study indicates that the viscosity is mainly relevant to the pumpability of 3D printable materials whereas the yield stress and thixotropy are important in securing buildability of the materials.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.410-418
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• 2021

In a program that runs an engineering education certification, program outcomes refer to the knowledge, skills, and attitudes a student must have until graduation. In general, capstone design is used as a tool for evaluating program outcomes. This paper applies the intraclass correlation coefficient (ICC) to measure the raters' reliability in assessing program outcomes. Several raters evaluate program outcomes, and the result is used to obtain the raters' ICC. ICC measures the reliability of ratings or measurements for clusters - data that has been collected as groups or sorted into groups. If the ICC is close to 1, it means that the reliability among the raters is high. We evaluated the proposed method's usefulness through case analysis. As a method for assessing an evaluation tool's objectivity, multiple raters measure the same evaluation tool. As a result, we measured the ICC values for all POs, and analyzed the cause for the low measured POs. We applied this method to evaluate program outcomes of the Department of Computer Engineering in the past two years. As a result, we derived guidelines for improvement and program outcomes.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.255-277
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• 2021

This study aims to analyze the current status of continuing education for academic librarians based on the records of education. For this purpose, the study investigated the academic librarian's preferences for the topics of continuing education in terms of regional and monthly differences using the 30,404 education records in university and college library statistics of Korean Education and Research Information Service (KERIS). The results shows that 'academic database system' and 'cataloging' were the most preferred topics, and there were topical differences between the academic librarians in university and college.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.109-116
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• 2021

In this paper, we propose a non-intrusive load monitoring(NILM) system which can find the power of each home appliance from the aggregated total power as the activation in the trading market of the distributed resource and the increasing importance of energy management. We transform the amount of appliances' power into a power on-off state by preprocessing. We use LSTM as a model for predicting states based on these data. Accuracy is measured by comparing predicted states with real ones after postprocessing. In this paper, the accuracy is measured with the different number of electronic products, data postprocessing method, and Time step size. When the number of electronic products is 6, the data postprocessing method using the Round function is used, and Time step size is set to 6, the maximum accuracy can be obtained.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.4
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• pp.1-9
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• 2021

This study was to investigate the types of emergencies transported by the Southwestern Coast Guard, the need for telemedicine guidance, and the perception and attitude of smart glasses as a communication method targeting 31 coast guards. A relatively high frequency and training requirement were confirmed for bleeding, abrasion, and abdominal pain. The demand for telemedicine guidance on medication and triage was higher, and the perceived usefulness and attitude scores for the use of smart glasses were 3.76±0.61 and 3.64±0.45, respectively. A moderate correlation between perceived usefulness and attitude toward smart glasses was confirmed (r=.630, p<.01). With the development of technology, it is time to actively introduce new devices such as smart glasses.

• Informatization Policy
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• v.29 no.2
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• pp.3-36
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• 2022

Most research in artificial intelligence (AI) has focused on the development of new algorithms. But as artificial intelligence has been spreading over many applications and gaining more attention from managers in the organization, academia has begun to understand the necessity of developing new artificial intelligence theories related to AI management. We reviewed recent studies in the field from 2015, and further analysis has been done for 785 studies chosen based on citation numbers of over 20. The results show that most studies have still been in the prototyping application phase of artificial intelligence across different industries. We conclude our study by calling for more research in the application of artificial intelligence in terms of organizational structures and project and risk management.