• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.393-396
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• 2022

본 연구는 국방과학기술 분야의 특허 및 논문 실적을 이용하여 통계기반 기계학습 모델 4 종을 학습하고, 실제 분석 대상기관의 데이터 입력결과를 분석하여 실용성에 대한 한계점 분석을 목적으로 한다. 기존 연구에서는 특허분류코드를 기준으로 분류하여 특수 목적으로 활용하거나 세부 연구 범위 내 연구 주제탐색 및 특징연구 등 미시적인 관점에서의 상세연구 활용 목적인 반면, 본 연구는 거시적인 관점에서 연구의 전체적인 흐름과 경향성 파악을 목적으로 한다. 이에 ICT 기술 138 종의 특허 및 논문 30,965 건과 국방과학기술 192 종의 특허 및 논문 23,406 건을 학습데이터로 각 모델을 학습하였다. 비교한 통계기반 학습모델은 Support Vector Machines, Decision Tree, Naive Bayes, XGBoost 모델이다. 학습데이터에 대한 학습검증 단계에서는 최대 99.4%의 성능을 보였다. 다만, 실제 분석대상기관의 특허 및 논문 12,824 건으로 입력분석한 결과, 모델별 편향성 문제, 데이터 전처리 이슈, 다중클래스 및 다중레이블 문제를 확인, 도출한 문제에 대한 해결방안을 제시하고 추가 연구의 방향성을 제시한다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.11-19
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• 2023

In this paper, we propose the methods of invoice document structure recognition and of making a spreadsheet electronic document. The texts and block location information of word blocks are recognized by an optical character recognition engine through deep learning. The word blocks on the same row and same column are found through their coordinates. The document area is divided through arrangement information of the word blocks. The character recognition result is inputted in the spreadsheet based on the document structure. In simulation result, the item placement through the proposed method shows an average accuracy of 92.30%.

• Asian Journal of Business Environment
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• v.12 no.1
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• pp.1-9
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• 2022

Purpose: This study measures the effect of Information Technology (IT) on both cost and profit efficiency of State-owned Commercial Banks (SOCBs) in Bangladesh. Research design, data and methodology: Yearly Non-IT and IT data are collected from the annual report of SOCBs of Bangladesh from 2008 to 2017. Variable Return to Scale (VRS) cost Data Envelopment Analysis (DEA) and Profit DEA are employed to measure the efficiency of SOCBs and Ordinary Least Square (OLS) is used to investigate the impacts of ICT components on operating cost and profit efficiency for SOCBs. Results: The average cost efficiency (74.4%) was noticed higher than the average profit efficiency (20.6%) for SOCBs. SOCBs were more affordable and less profitable for both cost and profit efficiency. Rupali bank was the most cost efficient while Sonali bank was the most profit efficient. IT Investment and IT personnel expenses were positively significant for cost efficiency. IT income, IT personnel, IT personnel expenses, ATM expenses, and Credit card expenses were negatively significant for profit efficiency. Conclusion: The further studies can combine DEA with machine learning algorithms to study the impact of IT on banks' performances. The results could aid government to remove the hindrance of progress in Bangladesh.

• Educational Technology International
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• v.17 no.1
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• pp.87-116
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• 2016

In this research, we designed a teacher professional development (PD) program where a small group of mathematics teachers could share, reflect on, and discuss their pedagogical knowledge and practices of ICT-integrated lessons, using a video annotation tool called DIVER. The main purposes of this paper are both micro and macro: to examine how the teachers were engaged in the meaning-making process in a video-based PD (micro); and to derive implications about how to design effective video-based teacher PD programs toward a teacher community of practices (macro). To examine teachers' meaning-making in the PD sessions, discourse data from a series of 10 meetings was segmented into idea units and coded to identify discourse patterns, focusing on (a) participation levels, (b) conversation topics, and (c) conversation depth. Regarding the affordance of DIVER, discourse patterns of two meetings, before and after individual annotation with DIVER were compared through qualitative vignette analysis. Overall, we found that the teacher discourse shifted the focus from surface features to deeper pedagogical issues as the PD sessions progressed. In particular, the annotation function in DIVER afforded the teachers to exercise descriptive analyses of video clips in a flexible manner, thereby helping them cognitively prepared to take interpretative and evaluative stances in face-to-face discussions with colleagues. In conclusion, deriving from our research experiences, we discuss the possibilities and challenges of designing video-based teacher PD in a school context.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.163-169
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• 2024

India is a developing nation and has come with comprehensive way in modernizing its reducing poverty, economy and rising living standards for an outsized fragment of its residents. The STEM (Science, Technology, Engineering, and Mathematics) education plays an important role in it. STEM is an educational curriculum that emphasis on the subjects of "science, technology, engineering, and mathematics". In traditional education scenario, these subjects are taught independently, but according to the educational philosophy of STEM that teaches these subjects together in project-based lessons. STEM helps the students in his holistic development. Youth unemployment is the biggest concern due to lack of adequate skills. There is a huge skill gap behind jobless engineers and the question arises how we can prepare engineers for a better tomorrow? Now a day's Industry 4.0 is a new fourth industrial revolution which is an intelligent networking of machines and processes for industry through ICT. It is based upon the usage of cyber-physical systems and Internet of Things (IoT). Industrial revolution does not influence only production but also educational system as well. IoT in academics is a new revolution to the Internet technology, which introduced "Smartness" in the entire IT infrastructure. To improve socio-economic status of the India students must equipped with 21st century digital skills and Universities, colleges must provide individual learning kits to their students which can help them in enhancing their productivity and learning outcomes. The major goal of this paper is to present a low cost, effective learning mechanism for STEM implementation using Raspberry Pi 3+ model (Single board computer) and Node Red open source visual programming tool which is developed by IBM for wiring hardware devices together. These tools are broadly used to provide hands on experience on IoT fundamentals during teaching and learning. This paper elaborates the appropriateness and the practicality of these concepts via an example by implementing a user interface (UI) and Dashboard in Node-RED where dashboard palette is used for demonstration with switch, slider, gauge and Raspberry pi palette is used to connect with GPIO pins present on Raspberry pi board. An LED light is connected with a GPIO pin as an output pin. In this experiment, it is shown that the Node-Red dashboard is accessing on Raspberry pi and via Smartphone as well. In the final step results are shown in an elaborate manner. Conversely, inadequate Programming skills in students are the biggest challenge because without good programming skills there would be no pioneers in engineering, robotics and other areas. Coding plays an important role to increase the level of knowledge on a wide scale and to encourage the interest of students in coding. Today Python language which is Open source and most demanding languages in the industry in order to know data science and algorithms, understanding computer science would not be possible without science, technology, engineering and math. In this paper a small experiment is also done with an LED light via writing source code in python. These tiny experiments are really helpful to encourage the students and give play way to learn these advance technologies. The cost estimation is presented in tabular form for per learning kit provided to the students for Hands on experiments. Some Popular In addition, some Open source tools for experimenting with IoT Technology are described. Students can enrich their knowledge by doing lots of experiments with these freely available software's and this low cost hardware in labs or learning kits provided to them.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.147-155
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• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.

• Journal of The Korean Association of Information Education
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• v.16 no.4
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• pp.383-395
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• 2012

This study investigated the recognition of teachers on the introduction and use of SMART education, which is an ICT-based customized learning method. Through the investigation, the study was to empirically examine the relationship between the use intention of SMART education and its influential factors, and analyze the difference in the use intention of SMART education by teachers, focusing on the experience of SMART education. For analysis purpose, a structural equation model, which was expanded from the theory of reasoned action, was presented. In addition, the difference in path coefficient, which affects the use intention of SMART education, was compared based on the experience with or without SMART education. The results showed that teacher efficacy in the teacher group without SMART education experience was more negatively significant in class burden. In the teacher group with SMART education experience, it was found that the attitude toward SMART education was more significant in use intention; organizational citizenship behavior was more significant in use intention; teacher efficacy was more significant in the attitude toward SMART education and; teacher efficacy was more significant in organizational citizenship behavior. No significant difference was found in the path coefficient among the groups in other hypotheses. Through these results, the factors for introducing and promoting SMART education and its invigoration measures were presented.

• Journal of The Korean Association of Information Education
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• v.10 no.2
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• pp.191-200
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• 2006

Our research team has developed diagnosis papers which diagnose reading ability of students, reading materials and guidance of reading method which are proper for reading ability and various appreciation expression activities in the leveled reading supporting system applying the multimedia techniques, which is developed as a previous research for reading education that considers the reading ability and level of elementary school students. However some difficulties have been raised in activities after reading proper for level because we suggested them all the same regardless of level though we proposed the reading methods and reading materials by level, We tried to develop the leveled activities post-reading applying multimedia techniques which are proper for reading ability of students and exam the effects of leveled reading supporting system considering the subordinate goal of 7th Korean curriculum of elementary school and the ICT standard of the Education Ministry on this thesis for promoting an interest in reading of students. The leveled activities after reading which are applying multimedia techniques could be guided and applied on the web, and helped develop the reading ability and self-leading learning ability by raising the interest and concern of students in the activities after reading because the multimedia elements, instead of existing text, were added to the expression.