• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2657-2664
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• 2012

Web-based learning receives much attention as an effective learning method because users can use the learning service at any time from any space. A learning management system(LMS) provides online educational environment among teachers and students, supporting various facilities to deliver educational contents. Since most of the existing LMSs support one-way or limited two-way teaching services among teachers and students, there are a lot of difficulties in performing collaboration among students and/or collaboration among teachers and students. In this paper, we describe the development of collaborative workspaces which provides effective collaborative educational environment on Moodle which is widely accepted as a typical LMS. Through the provided various types of collaborative workspaces, users can easily perform group activities, sharing educational with appropriate access control mechanism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.91-103
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• 2022

Purpose: After COVID-19, changes in the educational ecosystem take place and fire service academy education system have shifted from face-to-face into non fact-to-face. So, the educational effect of fire officials is decreased and the satisfaction level is also decreased. In this study, we want to examine the current status of non-face-to-face real-time remote education and supplement the problems to improve the educational methods, the educational environment, etc. Method: This study is an independent variable that affects non-face-to-face real-time remote education, consisting of education system environment, self-efficacy of computers, contents (education contents, structure, design, etc.), and proper interaction. A dependent variable was selected with satisfaction for non-face-to-face real-time remote education. In addition, it was selected and analyzed as an active property of learning motivation and learning attitude as control variables. Result: The better the content and the more active the learning motivation and the attitude toward learning, the higher the satisfaction of non-face-to-face real-time remote education, and the more active the learning motivation and the attitude toward learning, the more positive the computer self-efficacy and the satisfaction of learning Conclusion: In order to increase the satisfaction of non-face-to-face real-time education due to COVID-19, education designers or professors need to provide non-face-to-face education contents that can increase the aggressiveness of their learning motivation and learning attitude, and to increase the satisfaction of education for learners by increasing computer self-efficacy through pre-education of non-face-to-face education systems.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.428-442
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• 2022

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.506-515
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• 2000

In this paper a web-based real-time education system, which is able to support education through multimedia, is suggested for the expansion of learner's creative ability in the school. This system is designed so that it can support three things: 1) a real time interaction between interaction between instructors and learners, 2) individual learning through such an interaction, and 3) a coercive distribution of display by instructions for preventing the deviation of learners from learning. Also, the system, which UML is applied to, makers efficient interaction possible through the module for the real-time exchange and management of messages even in the multi-user environment. Through this system, not only the simulation by learners can be made for experiments and practices, but also questions and respondence can be supported on the procedure of experiments and the analysis of their results. This system is bulit on constructivism, and aimed at helping the learning progress and knowledge formation of learners.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.505-509
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• 2020

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.91-98
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• 2016

We propose a method to train a model that can predict the probability of a crime being committed. CCTV data by matching criminal events are required to train the crime prediction model. However, collecting CCTV data appropriate for training is difficult. Thus, we collected actual criminal records and converted them to an appropriate format using variables by considering a crime prediction environment and the availability of real-time data collection from CCTV. In addition, we identified new specific crime types according to the characteristics of criminal events and trained and tested the prediction model by applying neural network partial least squares for each crime type. Results show a level of predictive accuracy sufficiently significant to demonstrate the applicability of CCTV to real-time crime prediction.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.195-200
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• 2021

In recent years, Vietnam's economic growth rate has been attributed to the growth of many well-managed industries within Southeast Asia. Among them is the civil construction industry. Construction projects typically take a long time to complete and require a huge budget. Many socio-economic variables and factors affect total construction project costs due to market fluctuations. In recent years, crucial socioeconomic development indicators of construction reached a fairly high growth rate. Also, most infrastructure and construction projects have a high degree of complexity and uncertainty. This makes it challenging to predict the accurate project price. These challenges raise the need to recognize significant factors that influence the construction price index of civil buildings in Vietnam, both micro and macro. Therefore, this paper presents critical factors that affect the construction price index using the fuzzy extent analysis process in an uncertain environment. This proposed quantitative model is expected to reflect the uncertainty in the process of evaluating and ranking the influencing factors of the construction price index in Vietnam. The research results would also allow project stakeholders to be more informed of the factors affecting the construction price index in the context of Vietnam's civil construction industry. They also enable construction contractors to estimate project costs and bid rates better, enhancing their project and risk management performance.