• International Commerce and Information Review
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• v.14 no.4
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• pp.89-107
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• 2012

Recently, the successful appointment of the general directorate of GCF (Green Climate Fund) in Songdo of Korea made a great history for the golden triangle with GGGI (global Green Growth Institute) and GTC (Green Technology Center). Now, Korea became the Mecca for the global green growth and it gave a great opportunity foe the Korea to lead the global economy in the future. However, to successfully manage the GCF, the Korean government should show their willingness as well as the readiness for the green prowth and green productivity. It is really hard for the Korea, since it takes the second rank for the growth rate of carbon dioxide emission in the world. To overcome this shameful status, it should make the best effort to promote the green productivity, especially in a field of logistics industry, because it takes 21% of global CO2 emission, the second largest portion. The research aims to systematically introduce the Global Malmquist-Luenberger Index (GML) and to evaluate the logistics industry of Korea based on the GML approach. It concludes the innovative technology is utmost important to improve the green productivity of the logistics industry and thus the Korean government should make more aggressive role to fill this missing link in the innovation network.

• Archives of design research
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• v.17 no.3
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• pp.81-90
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• 2004

The reason of the appearance of Internet is regarded as a milestone since we have shared information globally in a mutual way. The important thing on this point is what contents we choose for ourselves. The Internet could be meaningless unless we use it in a certain way, which ultimately means that the Internet has to deliver something valuable to us humans. Therefore, we have looked at how we can deliver and share humanity and emotion through the Internet, also how we can instill vital power into our real life, through the Internet. Fortunately, the current study must essentially be ongoing due to its nature with perhaps a multidisciplinary team brainstorming ideas. The reason for that is that not only could we find new business models for companies, but also find out new ways to appease the human mind in the modern age. In addition, as consumers needs become more specialized and diversified, companies are expected to face up to fierce competition with the help of innovative ideas. The ever-intensifying competition requires companies to cultivate new strategic tools in order to have new, powerful and sustainable comparative advantages. The goal of this research will be to explore ways of finding a new approach. Specifically, this research is about how to use the EVC(empathetic virtual character), which, this researcher believes, can deliver emotional benefits so as to make e-branding successful. According to reports, it has been proven that this new concept including the EVC can result in tremendous success. So the goal of this research is to explore the current situation and to anticipate the future concerning virtual characters.

• Journal of KIISE
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• v.42 no.5
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• pp.591-600
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• 2015

SLA (Service Level Agreement) is an essential factor that must be guaranteed to provide a reliable and consistent service to user in cloud computing environment. Especially, a contract between user and service provider with SLA is important in an environment using a cloud service brokerage. The cloud computing is classified into IaaS, PaaS, and SaaS according to IT resources of the various cloud service. The existing SLA is difficult to reflect the quality factors of service, because it only considers factors about the physical Network environment and have no methodological approach. In this paper, we suggested a method to specify the quality characteristics of software and proposed a mechanism and structure that can exchange SLA specification between the service provider and consumer. We defined a meta-model for the SLA specification in the SaaS level, and quality requirements of the SaaS were described by the proposed specification language. Through case studies, we verified proposed specification language that can present a variety of software quality factors. By using the UDDI-based mediation process and architecture to interchange this specification, it is stored in the repository of quality specifications and exchanged during service binding time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.291-298
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• 2021

In this study, the future of countermobility capability is presented by analyzing the status of the countermobility obstacles focusing on the history of landmines and munitions. The conventional landmine was forbidden globally by the CCW and Ottawa Treaty because it caused civilian damage after the war. Because the inhumanity of those mines had been acknowledged, shatterable mines with a self-destruct (SD) function and M93 "HORNET" anti-tank munition with enhanced sensors have been fielded. In 2016, the Obama administration announced a policy that banned all antipersonnel landmines, leaving a considerable gap in the countermobility capability. To deal with these problems, the developments of "SAVO" and the SLEP program of Volcano mines were conducted. In the sense of a long-term approach, the countermobility obstacles, including mines, were chosen as fundamental forces for Multi-Domain Operations and were improved to Terrain Shaping Obstacles (TSO). TSO has improved sensors and mobility kill capabilities and features an enhanced remote control over each munition on the battlefield through a network established with satellite communication. The combined arms countermobility might be fully capable until 2050 if the TSO program can be completed successfully.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.69-79
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• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.7-14
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• 2010

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.123-129
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• 2009

Ubiquitous home is emerging as the future digital home environments that provide various ubiquitous home services like u-Life, u-Health, etc. It is composed of some home appliances and sensors which are connected through wired/wireless network. Ubiquitous home services become aware of user's context with the information gathered from sensors and make home appliances adapt to the current home situation for maximizing user convenience. In these context-aware home environments, it is the one of significant research topics to predict user behaviors in order to proactively control the home environment. In this paper, we propose Multi-Level prediction algorithm for context-aware services in ubiquitous home environment. The algorithm has two phases, prediction and execution. In the first prediction phase, the next location of user is predicted using tree algorithm with information on users, time, location, devices. In the second execution phase, our table matching method decides home appliances to run according to the prediction, device's location, and user requirement. Since usually home appliances operate together rather than separately, our approach introduces the concept of mode service, so that it is possible to control multiple devices as well as a single one. We also devised some scenarios for the conceptual verification and validated our algorithm through simulations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.531-534
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• 2014

DLNA is an established industry standard which supports contents sharing among smart devices in home wired- and wireless-network environment and is well known in Korea as Allshare or Smartshare. The DLNA standard is implemented as built-in services in most of Android smart phones and tablets. In addition to the handheld devices, DLNA service can also be employed in speakers, printers, and so on. However, users have reported many interoperability issues between DLNA devices. Developers typically identify causes by analyzing the packet exchange information between devices. However, this approach costs them to put additional effort to filter relevant packets, to reconstruct packet exchange history and the protocol flow. Consequently, it ends up with increased development time. In this paper, we demonstrate a technique to automatically analyze and visualize the packet exchange history. We modified a router firmware to capture and store packets exchanged between DLNA devices, and then analyze and visualize the stored packet exchange history for developers. We believe that visualized packet exchange history can help developers to test the interoperability between DLNA devices with less effort, and ultimately to improve the productivity of developers.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.