• Journal of Korea Society of Industrial Information Systems
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• v.23 no.4
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• pp.81-92
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• 2018

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.4
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• pp.311-321
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• 2020

With the recent breakthrough in technology for micro-satellite, small satellite, and mega constellation missions, and the cost of accessing space from recycled space launch vehicles sharply lowered. Thus, space development is set to make a transition to a new space age that is different from before. Accordingly, the problem of worsening space environment conditions due to the increase in space debris, an inevitable adjunct to active space development, is also emerging as an international concern. In this paper, the contents and trends of international organizations for the protection of the space environment and space debris mitigation guidelines of each country were reviewed and forecast the future movement of the international community. In addition, it is suggested example of guidelines that fit the reality of Korea and how to apply them.

• Clean Technology
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• v.21 no.4
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• pp.209-216
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• 2015

Indium-Tin-Oxide (ITO) is a material that is widely used for transparent conductive electrodes (TCEs). Indium (In), chief element of the ITO, is expected to be depleted in the near future owing to its high cost and limited reserves. To overcome the issue, ITO has to be retained by recycling redundant ITO targets after manufacturing processes. In this article, we proposed an efficient recycling way of the redundant ITO targets with investigation of the current recycling tendencies in domestic and foreign countries. As a result, it was revealed that only In is recycled from the redundant targets in domestic and Japan. As well, fabrication of TCEs is being researched with ITO nanoparticles solutions. However, since the TCEs fabricated with ITO target is superior to those with other materials, it is thought that establishment of regeneration technology of ITO itself is demanded for an efficient recycling and fabrication of ITO target.

• Korean Journal of Community Nutrition
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• v.8 no.5
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• pp.744-754
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• 2003

At present, health care industries throughout the world are struggling with the challenges to set up financial structures as cost-effective ways and means of satisfying customer needs for health care services. Many hospitals consign foodservice management to foodservice companies for the purpose of efficiency. The companies taking charge of hospital foodservice are also striving to gain an advantage over keen competitions. This study applied Quality Function Deployment(QFD) to one hospital (which will be shown as $\ulcorner$A hospital$\lrcorner$ below) managed by a contract foodservice company for the purpose of strategy planning to provide sustainable competitive advantage. First of all, this study scanned internal and external environment of $\ulcorner$A hospital$\lrcorner$ by means of a Quality Measurement Tool and a fieldwork study. With the result of environment scanning, this study elicited 20 strategies through SWOT analysis, which were categorized by 4 perspectives such as financial, customer, internal process, learning and growth perspectives. Finally, the priorities of 20 strategies were extracted from QFD methodology. According to the results obtained by applying QFD to $\ulcorner$A hospital$\lrcorner$'s foodservice, the strategies which $\ulcorner$A hospital$\lrcorner$ foodservice was obliged to introduce and implement were : the specialization of Children's hospital foodservice, scientific foodservice management through the standardization of foodservice operations, the maintenance of sanitary quality through sanitary system, the remodeling of facilities, the introduction of new equipment, the prompt and accurate response to customer needs, the development of appropriate patient menus, the provision of competitively priced meals for patient selection, the development of a demand forecast model by considering the characteristics of a children's hospital, improvement of productivity and the reduction of labor costs through the employment of experienced employees based on their seniority.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.195-202
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• 2009

As the generation and application of spatial information is gradually expanded not only in traditional surveying fields but also a CNS and an ITS recently. The Accuracy and the newest of data grow to be an important element. But digital map is updated with system based tile. So, it is hard to get the newest of data and to be satisfied with user requirements. In this study, management system is developed to manage feature change efficiently using bid informations from NaraJangter which service the bid informations. A construction works with change possibility of feature from bid informations are classified and are made DB. And the DB is used as the feature change forecast informations. Also, It is converted from bid information of text form to positioning informations connected to spatial information data. If this system is made successfully, this system contributes to reduce the cost for the update of digital map and to take the newest date of spatial informations.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.127-129
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• 2007

Since the competitive market environment was introduced into the electric power industry, the structure of the industry has been changing from vertically integrated system to functionally unbundled and decentralized system composed of multiple (decision-making) market participants. So the market participants such as Gencos or LSE (load serving entity) need to forecast the market clearing price and thus build their offer or bidding strategies. Not just these market players but also a market operator is required to perform market analysis and ensure simulation capability to manage and monitor the competitive electricity market. For fulfilling the demand for market simulation, many global venders like GE, Henwood, Drayton Analytics, CRA, etc. have developed and provided electricity market simulators. Most of these simulators are based on the optimization formulation which has been used mainly for the least cost resource planning in the centralized power system planning and operation. From this standpoint, it seems somehow inevitable to face many challenges on modeling competitive market based on the method of traditional market simulators. In this paper, we propose a kind of new method, which is MAS based market simulation. The agent based model has already been introduced in EMCAS, one of commercial market simulators, but there may be various ways of modeling agent. This paper, in particular, seeks to introduce an model for MAS based market simulator.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.41-47
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• 2014

In this paper, we implemented a CUDA Fortran parallel program to run the CFD_NIMR model on GP-GPU's, which simulates air diffusion on urban terrains. A GP-GPU is graphic processing unit in the form of a PCI card, and a general calculation accelerator to perform a large amount of high speed calculations with low cost and electric power. The GP-GPU gives performance enhancement of speed by 15 times to compare the Nvidia Tesla C1060 GPU with Intel XEON 2.0 GHz CPU. In addition, the program on a GP-GPU shows efficient performance compared to an MPI parallel program on multiple CPU's. It is expected that a proposed programming method on the GP-GPU parallel program can be used for numerical models with a similar structure.

• Resources Recycling
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• v.29 no.4
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• pp.45-50
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• 2020

Producer and distributor of electrical and electronic equipment may directly collect waste electrical and electronic equipment that falls under the class to which the equipment they distributed belongs, or may join KERC(Korea Electronic Recycling Cooperative) and have KERC fulfill the duty to collect on behalf. In this study, the system of calculating the financial contributions is reviewed, and then the defined problems and improvement plan are proposed. First, the standard operation and time should be set for collection and transportation costs, taking into account the operation by collection type. Second, since there is a difference in the screening method of the recycling center, the standard cost for the allocation factor should be set by reflecting the difference in these methods and the characteristics of the product line being processed. Third, it is necessary to secure a budget of sufficient size by determining the median or average value rather than the minimum value in the forecast model for visit collection. This study is suggesting in that it examines the problems of the allotted contributions paid by the mutual aid members to KERC and suggests ways to improve them.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.611-619
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• 2008

2.5D Mapping system that embody in this paper can make new design by doing draping to live various texture and model picture image of fashion clothes by pattern, and can confirm clothes work to simulation without producing direction sample or product directly. Also, the system can support function that can forecast fabric design and state of end article exactly, and the system can bring competitive power elevation of fashion industry and cost-cutting effect by doing draping using database of fabric and model picture image. 2.5D Mapping system composed and embodied by mesh warp algorithm module, light and shade extraction and application module, mapping path extraction module, mesh creation and transformation module, and 2.5D mapping module for more natural draping. Future work plans to study 3D fashion design system that graft together 3D clothes technology and 3D human body embodiment technology to do based on embodiment technology of 2.5D mapping system and overcomes expression limit of 2.5D mapping technology.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.