• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.21-26
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• 2006

This paper focuses on modeling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.133-144
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• 2009

This paper presents a method for constructing and using transliteration rules which are based on Phonics, an instructional method for speaking and writing English letters. Conventional approaches to automatic transliteration often focused on statistical methods. However, the construction or the collection of correct transliteration examples is always the bottleneck of the statistical transliteration model. Also, in practical domains where the collection of such data is very difficult, such as education and tourism, it is reasonable to build a system without much qualified data. Furthermore, compared with Korean orthography of borrowed foreign words, the proposed approach is much easier to construct, and can generate more refined rules. The experimentation result shows that the proposed approach can improve the performance of a statistical-based transliteration system.

• Journal of Korean Academy of Nursing
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• v.31 no.6
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• pp.1055-1066
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• 2001

The Purpose of this study is to understand the meaning and the essence of suffering as viewpoint and to find the meaning and structure of the experience from encounters with patients in their terminal stages of illness. Method: A descriptive design based on the phenomenological approach model developed by Collaizzi was used. The period of data collection was from August to November of 2000. Data collection was conducted by open-ended and audio-taped interviews. The participants were nine female nurses who were willing to take part in this study. Results: Results of this study were classified into five main categories. The main category clusters were "difficulty in experiencing suffering," "professional challenges to expert nurses," "formation of empathic relationships," "expanding consciousness through suffering," and "alleviation of the patient's suffering." Conclusion: In conclusion, the implications for providing nursing care to end-stage patients in the throes of suffering is both rewarding and stressful. However, sharing these research results may help other nurses discover and experience deeper meanings in their own practice and careers.deeper meanings in their own practice and careers.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.309-318
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• 2023

Recently, each country has been moving to introduce an LCA (Life Cycle Assessment) to regulate greenhouse gas emissions. The LCA is a mean of measuring and evaluating greenhouse gas emissions generated over the entire life cycle of a vehicle. Reliable data for each electric vehicle component is needed to increase the reliability of the LCA results. To this end, studies on life cycle evaluation models using blockchain technology have been conducted. However, in the existing model, key product information is exposed to other participants. And each time parts data information is updated, it must be recorded in the blockchain ledger in the form of a transaction, which is inefficient. In this paper, we proposed a DID(Decentralized Identity)-based data collection model for LCA to collect vehicle component data and verify its validity effectively. The proposed model increases the reliability of the LCA by ensuring the validity and integrity of the collected data and verifying the source of the data. The proposed model guarantees the validity and integrity of collected data. As only user authentication information is shared on the blockchain ledger, the model prevents indiscriminate exposure of data and efficiently verifies and updates the source of data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.33-45
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• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.95-117
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• 2005

This research Investigates the determinants of customer Intention to participate In Internet auction. Based on technology acceptance and trust related studies, our research proposes a theoretical model consisting of factors such as perceived usefulness, perceived ease of use, institution based trust, beliefs on sellers, trusting beliefs, and participation Intention. For examining the relationships implied by the research model, a field study using a survey methodology for data collection was conducted. The data were analyzed using AMOS based on the structural equation modeling, a second-generation multivariate technique, which has gained distinct advantages over other technique. After some model modification according to model generating strategy approach, this study shows that trusting beliefs have significant effects on the participating intention in Internet auction site. In conclusion, Implications are discussed along with limitations and further research direction.

• Journal of KIISE
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• v.42 no.3
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• pp.399-407
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• 2015

Wireless sensor networks are used for data collection and processing from the surrounding environment for various applications. Since wireless sensor nodes operate on low computing power, restrictive battery capacity, and low network bandwidth, their architecture model has greatly affected the performance of applications. If applications have high computation complexity or require the real-time processing, the centralized architecture in wireless sensor networks have a delay in data processing. Otherwise, if applications only performed simple data collection for long period, the distributed architecture wasted battery energy in wireless sensors. In this paper, the energy consumption and processing delay were analyzed in centralized and distributed sensor networks. In addition, we proposed a new hybrid architecture for wireless sensor networks. According to the characteristic of applications, the proposed method had the optimal number of wireless sensors in wireless sensor networks.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.233-244
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• 2023

Although artificial intelligence and machine learning technologies have been used in various fields, problems with personal information protection have arisen based on centralized data collection and processing. Federated learning has been proposed to solve this problem. Federated learning is a process in which clients who own data in a distributed data environment learn a model using their own data and collectively create an artificial intelligence model by centrally collecting learning results. Unlike the centralized method, Federated learning has the advantage of not having to send the client's data to the central server. In this paper, we quantitatively present the performance improvement when federated learning is applied using the building change detection learning data. As a result, it has been confirmed that the performance when federated learning was applied was about 29% higher on average than the performance when it was not applied. As a future work, we plan to propose a method that can effectively reduce the number of federated learning rounds to improve the convergence time of federated learning.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.6
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• pp.474-483
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• 2003

In this study, the dynamic characteristics of a catenary system and pantograph supplying electrical power to high-speed trains are investigated. One of the most important issues accompanied by increasing the speed of high-speed rail is stabilization of current collection. To stabilize current collection, it is necessary the contact force between the catenary and the pantograph to be kept continuous without loss of contact. The analytical model of a catenary and a pantograph is constructed to simulate the behavior of an actual system. The analysis of the catenary based on the Finite Element Method (FEM) is performed to develop a catenary model suitable for high speed operation. The reliability of the models is verified by the comparison of the excitation test with Fast Fourier Transform (FFT) data of the actual system. The static deflection of the catenary, stiffness variation in contact lines, dynamic response of the catenary undergoing constant moving load, contact force, and each state of the pantograph model were calculated. It is confirmed that a catenary and pantograph model are necessary for studying the dynamic behavior of the pantograph system.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.189-194
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• 2020

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.