• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5550-5558
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• 2015

Recently, fuel cell with high energy efficiency and low CO2 emission is energetically interconnected with power system. Especially, FCGS(Fuel Cell Generation System) which usually operates at high temperature, is being developed and installed in the form of large scale system. However, it is reported that power system disturbances related to surge, harmonic and EMI have caused several problems such as malfunction of protection device and damage of control device in the large scale FCGS. In order to solve these problems, this paper presents a modeling of operation characteristics of FCGS by PSCAD/EMTDC, ETAP, P-SIM software. And also, this paper proposes countermeasure algorithms to prevent power system disturbances. From the simulation results, it is confirmed that the proposed algorithm is useful method for the stable operation of large scale FCGS.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.535-542
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• 2013

Since case-based reasoning(CBR) has many advantages, it has been used for supporting decision making in various areas including medical checkup, production planning, customer classification, and so on. However, there are several factors to be set by heuristics when designing effective CBR systems. Among these factors, this study addresses the issue of selecting appropriate neighbors in case retrieval step. As the criterion for selecting appropriate neighbors, conventional studies have used the preset number of neighbors to combine(i.e. k of k-nearest neighbor), or the relative portion of the maximum similarity. However, this study proposes to use the absolute similarity threshold varying from 0 to 1, as the criterion for selecting appropriate neighbors to combine. In this case, too small similarity threshold value may make the model rarely produce the solution. To avoid this, we propose to adopt the coverage, which implies the ratio of the cases in which solutions are produced over the total number of the training cases, and to set it as the constraint when optimizing the similarity threshold. To validate the usefulness of the proposed model, we applied it to a real-world target marketing case of an online shopping mall in Korea. As a result, we found that the proposed model might significantly improve the performance of CBR.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.705-713
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• 2021

Because the cage assembly serves as the launch platform, an accurate aim is essential to ensure shooting accuracy for the target. On the other hand, the abnormal rotation of the cage due to the directional errors of the K-MLRS has continuously caused quality problems. The quality problem of weapon systems may have a negative impact on the military's power loss. In this study, improvement plans were derived by examining the defects and analyzing the directional errors of the K-MLRS launcher. In addition, all possible causes of directional errors were derived from the flow diagram for cage directionality. Based on the results, the defense design through the software program was intended to prevent the loss of direction. Through this study, the signal error of the resolver was improved by preventing unspecific signals in the data. Furthermore, the directional judgment method was improved to minimize the impact of data distortion. Lastly, directional storage and verification methods were improved so that data for the cage rotation direction would not be affected by errors. For the design improvement method, the reliability was verified through the system applicability. This study is expected to be a reference for failure analysis and design for similar weapon systems in the future.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.507-518
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• 2021

Magnetic resonance imaging (MRI) is a key technology that has been seeing increasing use in studying the structural and functional innerworkings of the brain. Analyzing the variability of brain connectome through tractography analysis has been used to increase our understanding of disease pathology in humans. However, there lacks standardization of analysis methods for small animals such as mice, and lacks scientific consensus in regard to accurate preprocessing strategies and atlas-based neuroinformatics for images. In addition, it is difficult to acquire high resolution images for mice due to how significantly smaller a mouse brain is compared to that of humans. In this study, we present an Allen Mouse Brain Atlas-based image data analysis pipeline for structural connectivity analysis involving structural region segmentation using mouse brain structural images and diffusion tensor images. Each analysis method enabled the analysis of mouse brain image data using reliable software that has already been verified with human and mouse image data. In addition, the pipeline presented in this study is optimized for users to efficiently process data by organizing functions necessary for mouse tractography among complex analysis processes and various functions.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.173-178
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• 2023

As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.

• Journal of the Korean earth science society
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• v.23 no.6
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• pp.461-473
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• 2002

Concept mapping is a device for representing the conceptual structure of a subject discipline in a two dimensional form which is analogous to a road map. In the teaching and learning of earth science, each concept depends on its relationships to many others for meaning. Using concept mapping in teaching helps teachers and students to be more aware of the key concepts and relationships among them. The purpose of this study is to investigate the effect of the use of concept mapping on science achievement and the scientific attitude in ocean units of earth science. The results of this study are as follows; first, the science achievement of a group of concept mapping teaching is significantly higher than that of the group of traditional teaching. Also, when the achievement levels are compared among different cognitive ability groups, the effect is more significant in mid or lower level student groups than in high level groups. The use of concept mapping is more effective when the concepts have a distinct concept hierarchy. Second, the scores of the test of ‘attitude toward scientific inquiry’ and ‘application of scientific attitude’ of the group of concept mapping teaching are significantly higher than those of the group of traditional teaching, whereas the scores of the test of ‘interest in science learning’ of concept mapping teaching is not different from those of group of traditional teaching. Third, the survey on the use of concept mapping shows a positive response across the tested groups. The use of concept mapping is more beneficial in fostering the comprehension of the topic. A concept map of student's own construction facilitates the assessment of learning, thus promising the usefulness of concept mapping as a means of evaluation. In regard to retention aspect, concept mapping is considered to be more effective in confirming and remembering the topic, while less effective in the aspects of activity and interest. In conclusion, the use of concept maps makes learning an active meaningful process and improves student's academic achievement and scientific attitude. If the concept mapping is more effectively as an active teaching strategy, more meaningful learning will be attained.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.825-832
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• 2012

GPS signal delay that caused by dry gases and water vapor in troposphere is a main error source of GPS point positioning and it must be eliminated for precise point positioning. In this paper, we implemented to generate tropospheric delay grid map over the Korean Peninsula based on post-processing method by using the GPS permanent station network in order to determine the availability of tropospheric delay generation algorithm. GIPSY 5.0 was used for GPS data process and nationwide AWS observation network was used to calculate the amount of dry delay and wet delay separately. As the result of grid map's accuracy analysis, the RMSE between grid map data and GPS site data was 0.7mm in ZHD, 7.6mm in ZWD and 8.5mm in ZTD. After grid map accuracy analysis, we applied the calculated tropospheric delay grid map to single frequency relative positioning algorithm and analyzed the positioning accuracy enhancement. As the result, positioning accuracy was improved up to 36% in case of relative positioning of Suwon(SUWN) and Mokpo(MKPO), that the baseline distance is about 297km.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.7
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• pp.118-127
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• 2010

Current TCP/IP-based Internet architecture has been used for over 30 years, however it will confront with fundamental problems due to new protocol extension limitation since communication environments will change drastically and various user requirements will be emerging in near future. To solve these problems, major countries have started Future Internet researches based on clean slate approach and they will deploy large-scale testbed to experiment and verify new functions. OpenFlow switch technology has been proposed as a new experimental technology for independent protocol that can utilized the legacy network devices and does not interfere with the production Internet traffic. Korea also started Future Internet testbed project called FIRST and OpenFlow switch with NetFPGA card will be used to deploy this testbed. To interconnect distributed testbed using OpenFlow switches, logical tunnel should be established by encapsulating MAC frame inside a unicast IP packet between OpenFlow switches because OpenFlow switches are not directly connected. In this paper, we have implemented a NetFPGA-based that performs MAC in IP tunneling between various OpenFlow switch sites implemented in domestic research network KOREN. The performance evaluation shows that the NetFPGA-based capsulator reveals better performance than the software-based tunneling and it can be utilized as a testbed for experimentation of Future Internet technologies.