• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.227-230
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• 2009

This paper presents an architecture of various devices convergence for ubiquitous network. Integration of a variety of devices be can connect to every kind of device should not be constrained. We construct PLC to UPnP protocol architecture and UPnP Bridge Module for interconnecting Non-IP devices with heterogeneous network interfaces to UPnP devices on UPnP networks.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Communications of Mathematical Education
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• v.31 no.3
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• pp.241-255
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• 2017

The 4th industrial revolution is coming. In order to prepare for the new learning environment with it, we may need digital mathematics textbooks that fully utilize all possible technologies. So various attempts have been made in elementary and middle school mathematics education. However, despite the importance of higher mathematics, we haven't seen a best possible math digital textbooks yet in Korea. In this paper, we introduce our new model of interactive math digital textbook about Linear Algebra/ Calculus/ Differential Equations/ Statistics/ Engineering Math. Especially, this manuscript focuses on our experience of using digital contents and interactive labs for developing a new model for linear algebra digital textbook. We introduce our works on linear algebra digital textbooks which include pdf e-book, web contents, video clips of lectures, interactive lab. Using this linear algebra digital textbook, students can freely use any mobile devices to access diverse learning materials, lessons, and hands-on exercises without any limitations. Also, times saved in the computation, coding, and typing process can be used to have more discussions for deeper understanding of mathematical concepts. This type of linear algebra digital textbook, which contains all interactive free cyber-lab with codes and all lectures for each sections, can be considered as a new model for the next generation of math digital textbook.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.171-178
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• 2015

In these days, the practical training education is getting highlighted in IT curriculum. This study is for the Cloud computing based Virtual Desktop Service Plan of IT education and its efficient operation and management plan. With the implementation of a virtual lab environment system, the training environment which is customized by the curriculum is able to be provided. Also in the case of the limited system, the curriculum is able to be provided for each subject in advance. Therefore if the Cloud Training (or Practicing) Portal system for the multiple cyber universities is implemented according to this study's estimated scale and operation managing policies, the virtual training education service system could be provided in more efficient and more effective ways.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.77-84
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• 2004

This study proposed the agent system for educational information sharing using bookmark. In order to search and share the educational information effectively, we designed DAML+OIL-typed bookmark information. Proposed system in this study had the P2P type based on Client-Server type. We implemented the bookmark agent that has the intelligent characteristics, that is, automatic categorization of peers and documents, autonomous communication between agents using DAML, and delicate information searching using the ontology dictionary in Semantic Web environment. Hereafter, this study will contribute to activate sharing and searching educational information as well as proposed system will offer the important technologies for SCORM-based e-learning environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2304-2320
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• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.151-162
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• 2020

Education is one of the priority sectors specified in Tanzania, and it has committed to provide 11 years of compulsory free basic education for all from pre-primary to lower secondary level. Despite the Government's efforts to provide free basic education to all children, there are 2.0 million (23.2 per cent) out of 8.5 million children at the primary school age of 7-13, who are out of school in Tanzania. The ICT class should be offered as a regular class in all secondary schools in Tanzania, recommended by the ministry of education. However, many schools are struggling to implement this mandate. Most of schools offer the ICT class with theory without any real hardware. Some schools were given with computers but they were not maintained for operation. There is a huge task to make ICT education universal. Main issues include: remoteness (off-grid area), lack of ICT teachers, lack of resources such as hardware, infrastructure, and lack of practical lessons or projects to be used at schools. An innovative blended ICT/STEM education program is being conducted not only for Tanzanian public and private/international schools, but also for out-of-school adolescents through institutions, NGO centers, home visits and at the E3 Empower academy center. For effective STEM education to take place and remain sustainable, more practical curriculum, and close-up teacher support need to be accompanied concurrently. Practical, project-based simple coding lessons have been developed and employed that students experience true learning. The effectiveness of the curriculum has been demonstrated in various project centers, and it showed that students are showing new interests in exploring new discovery, even though this was a totally new area for them. It has been designed for an easy replication, thus students who learned can repeat the lessons themselves to other students. The ultimate purpose of this project is to have IT education offered as universally as possible throughout the whole Tanzania. Quality education for all children is a key for better future for all. Previously it was hoped that education with discipline will improve the active learning. But now more than ever, we believe that children have the ability to learn on their own with given proper STEM education tools, guidelines and environment. This gives promising hope to all of us, including those in the developing countries.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.623-634
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• 2015

This research aims to perform an in-depth investigation about meanings and essence of working as clinical research nurses in local general hospitals. In order to interpret and reveal the meanings of role experience, data were collected from objects of 7 participants for 3 months. Data were analyzed by ethnographic research tools of Spradley. Themes conducted from this study were 'new experience about social learning process' and 'joys and sorrows through study participants ', 'lack of specialized learning course in nursing curriculums' and 'roles of general research planner', 'one's own work space' and 'proactive work environment that is relaxing and filled with consideration for others', 'hardship of being temporary employees. Clinical research nurses have experienced expansion of roles through new social learning processes. Conclusively, this study will provide useful basic data to develop new curriculum about clinical research nursing for nursing students and to improve working conditions for clinical research nurses.e purpose of this study is to design and implement a sign language dictionary for the deaf to understand information communication terminologies. When the deafs who have difficulties in communication use the internet, they can get help from this dictionary in accessing various types of information and expressing their intension. In order for the deaf to utilize the internet as efficiently as ordinary people, they must understand information communication terminologies first.