• Journal of the Korean Society for Railway
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• v.11 no.6
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• pp.550-561
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• 2008

The purpose of this paper is to propose a new transit assignment model for intercity rail networks. The characteristics of intercity rail are different from that of public transit in urban area. Line selection probability on route section is introduced to include the characteristics of intercity rail into transit assignment model. Network expansion is more simplified by a assumption line selection probability is externally given. The generalized cost is used to decide the volume of each transit line in most of existing transit assignment models. But, many variables have influence on the volume of each line such as time schedule of transit lines, inter-station distance, passengers' income, seasonal variation of demand and regional characteristics. The influence of these variables can be considered to decide the volume of each line by introducing line selection probability on route section. The tests on a small scale network show that the model proposed in this paper is superior to existing models for predicting intercity rail demand. Proposed model is suitable to consider the complicated fare structure of intercity rail and to draw inter-station demand directly as a result of assignment procedure.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.19-29
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• 2019

This is an empirical study that examines the effect of the self-sufficiency case management, on the emotional self-sufficiency of the self-sufficiency program participants. To this end, it created self-sufficiency case management evaluation scale utilizing confirmatory factor analysis. Then a face-to-face questionnaire survey was conducted for 142 self-sufficiency program participants in Chuncheon local self-sufficiency center. The results show that the self-sufficiency case management, more specifically, the social support network components significantly affect the emotional self-sufficiency. The findings of this study not only provide empirical evidence of the effectiveness of self-sufficiency case management but raises the need for the expansion of social network type of self-sufficiency case management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.316-319
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• 2014

People's interests in coffee are increasing with the expansion of coffee market. In this trend, people's taste becomes more luxurious and coffee bean's quality is considered to be very important. Currently, bean defects are mainly detected by experienced specialists. In this paper, a detection system of bean defects using machine learning is presented. This system concentrates on detecting two main defect types : bean's shape and insect damage. Convolutional Neural Networks are used for machine learning. The neural networks are comprised of two neural networks. The first neural network detects defects in the bean's shape, and the second one detects the bean's insect damage. The development of this system could be a starting point for automated coffee bean defects detection. Later, further research is needed to detect other bean defect types.

• Journal of Industrial Convergence
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• v.18 no.6
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• pp.147-154
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• 2020

It is difficult to educate the overall operation of public and private blockchains with different characteristics. Recently, most education for blockchain is targeted at public blockchains such as Bitcoin and Ethereum. However, in an actual business environment, a private blockchain such as HyperLedger Fabric is used because access to corporate data is controlled through user authentication. In the case of HLF-based education, it is necessary to understand various components that are not in the public blockchain, such as peers, orderers, and channels. In this paper, a lab framework for HLF is designed for an efficient and systematic understanding of the functions and operations. The framework consists of HLF network, chaincode, and decentralized software control functions. Through the framework, the network configuration, distribution and activation of chaincode, and dApp execution process were checked step by step, and it was very easy to understand the overall flow for blockchain services. In addition, it is expected that a systematic understanding of the overall flow will be possible even in future network expansion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.562-570
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• 2022

The supply expansion of 5G services and personal smart devices has caused the sharp increase of data traffic and the demand of various services. Again, these facts have resulted in the huge demand of network bandwidth. However, existing network technologies using electronic signal have reached the limit to accommodate the demand. Therefore, in order to accept this request, optical internet has been studied actively. However, optical internet still has a lot of problems to solve, and among these barriers a very urgent issue is to develop QoS technologies. Hence, in order to achieve service differentiation between classes in optical internet, especially in OBS network, a new QoS method automatically tuning the size of data bursts is proposed in this article. Especially, the algorithm suggested in this article is based on fiber delay line.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Convergence Security Journal
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• v.22 no.2
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• pp.9-19
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• 2022

As the expansion of digital transformation, we are more exposed to the threat of cyber attacks, and many institution or company is operating a signature-based intrusion prevention system at the forefront of the network to prevent the inflow of attacks. However, in order to provide appropriate services to the related ICT system, strict blocking rules cannot be applied, causing many false events and lowering operational efficiency. Therefore, many research projects using artificial intelligence are being performed to improve attack detection accuracy. Most researches were performed using a specific research data set which cannot be seen in real network, so it was impossible to use in the actual system. In this paper, we propose a technique for classifying major attack keywords in the security event log collected from the actual system, assigning a weight to each key keyword, and then performing a similarity check using TF-IDF to determine whether an actual attack has occurred.

• Korea Trade Review
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• v.48 no.2
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• pp.27-43
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• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• Journal of the Korean Institute of Gas
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• v.28 no.1
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• pp.1-10
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• 2024

In this study, we constructed a process simulation model for an agricultural by-products based Solid Oxide Fuel Cell (SOFC) combined heat and power generation system as part of the introduction of technology for energy self-sufficiency in the agricultural sector. The aim was to reduce the burden of increasing fuel and electricity consumption due to rapid fluctuations in international oil prices and the expansion of smart farming in domestic farms, while contributing to the national greenhouse gas reduction goals. Based on the experimental results of 0.3 ton/day torrefied agricultural by-product gasification experiment, a model for an agricultural by-product-based SOFC cogeneration system was constructed, and optimization of the heat exchange network was conducted for SOFC capacities ranging from 4 to 20 kW. The results indicated that an 8 kW agricultural by-product-based SOFC cogeneration system was optimal under the current system conditions. It is anticipated that these research findings can serve as foundational data for future commercial facility design.