• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.499-506
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• 2004

The Unscented Kalman Filter is known to outperform the Extended Kalman Filter for the nonlinear state estimation with a significance advantage that it does not require the computation of Jacobian but EKF has a competitive advantage to the UKF on the performance time. We compare both algorithms on training the artificial neural network. The validation data set is used to estimate parameters which are supposed to result in better fitting for the test data set. Experimental results are presented which indicate the performance of both algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.208-218
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• 2006

In this paper, we introduce the creation methods of attack detection model using data mining technologies that can classify the latest attack types, and can detect the modification of existing attacks as well as the novel attacks. Also, we evaluate comparatively these attack detection models in the view of detection accuracy and detection time. As the important factors for creating detection models, there are data, attribute, and detection algorithm. Thus, we used NetFlow data gathered at the real network, and KDD Cup 1999 data for the experiment in large quantities. And for attribute selection, we used a heuristic method and a theoretical method using decision tree algorithm. We evaluate comparatively detection models using a single supervised/unsupervised data mining approach and a combined supervised data mining approach. As a result, although a combined supervised data mining approach required more modeling time, it had better detection rate. All models using data mining techniques could detect the attacks within 1 second, thus these approaches could prove the real-time detection. Also, our experimental results for anomaly detection showed that our approaches provided the detection possibility for novel attack, and especially SOM model provided the additional information about existing attack that is similar to novel attack.

• Journal of Environmental Science International
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• v.19 no.6
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• pp.689-701
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• 2010

The effects of high-resolution wind profiler (HWP) data on the wind distributions were evaluated in two different coastal areas during the study period (23-26 August, 2007), indicating weak-gradient flows. The analysis was performed using the Weather Research and Forecasting (WRF) model coupled with a three-dimensional variational (3DVAR) data assimilation system. For the comparison purpose, two coastal regions were selected as: a southwestern coastal (SWC) region characterized by a complex shoreline and a eastern coastal (EC) region surrounding a simple coastline and high mountains. The influence of data assimilation using the HWP data on the wind distributions in the SWC region was moderately higher than that of the EC region. In comparison between the wind speed and direction in the two coastal areas, the application of the HWP data contributed to improvement of the wind direction distribution in the SWC region and the wind strength in the EC region, respectively. This study suggests that the application of the HWP data exerts a large impact on the change in wind distributions over the sea and thus can contribute to the solution to lack of satellite and buoy data with their observational uncertainty.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.93-105
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• 2022

This study investigated the aspects of how three preservice elementary school teachers conducted the data-text transforming discourses in their science simulation teaching and how their epistemological conversations worked for learners' construction of scientific knowledge. Three preservice teachers, who had presented simulation teaching on the seasonal change of constellations, participated in the study. The results revealed that one preservice teacher, who had implemented the transforming discourses of data-to-evidence and model-to-explanation, appeared to facilitate learners' knowledge construction. The other two preservice teachers had difficulty helping learners construct science knowledge due to their lack of transforming discourses. What we should consider for improving preservice elementary school teachers' teaching competencies was discussed based on a detailed comparison of three cases of preservice teachers' data-text transforming.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.491-493
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• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.

• International journal of advanced smart convergence
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• v.3 no.1
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• pp.20-24
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• 2014

To compare body signal, was designed the F-B function system on the body movement for the comfortable state. To detect subject of the normal state, was decided on the base of physical signal in the body movement. There are to detect the condition of Vision, Vestibular, Somatosensory and CNS. Vision condition was verified a variation of greater average (Vi-${\Phi}_{AVG-AVG}$) was presented slightly greater at $17.424{\pm}9.65$ unit. Vestibular condition was identified a variation of slightly greater average (Ve-${\Phi}_{AVG-AVG}$) was presented at $9.068{\pm}1.478$ unit. Somatosensory condition was checked a variation of smaller average (So-${\Phi}_{AVG-AVG}$) was presented slightly smaller at $2.79{\pm}0.419$ unit. CNS condition was confirmed a variation of diminutive smaller average (C-${\Phi}_{AVG-AVG}$) was presented slightly larger at $0.557{\pm}0.153$ unit. As the model depends on the F-B function system of body movement, average values of these perturbation were computed F-B function comparison data. These systems will be to infer a data algorithm and a data signal processing system for the evaluation of the stability.

• Genomics & Informatics
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• v.12 no.1
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• pp.21-34
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• 2014

A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.

• Environmental and Resource Economics Review
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• v.9 no.2
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• pp.349-371
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• 2000

Data Envelopment Analysis model is a linear programming based technique for measuring the relative performance of organizational units where the presence of multiple inputs and outputs makes comparison difficult. A common measure for relative efficiency is weighted sum of outputs divided by weighted sum of inputs. DEA model allows each unit to adopt a set of weight that shows it in the most favorable light in comparison to the other unit. In this paper, we present the mathematical background and characteristics of DEA model, and give a short case study where we apply the DEA model to evaluate the relative efficiencies of 51 global electricity companies. The technical efficiency and scale efficiency are also to be investigated. Generating capacity and the number of employees are used for input data, and revenue, net profit and electricity sales are used for output data. We find that the companies with 100% relative efficiency are only 9 among 51 electricity companies. And the technical and scale efficiency of KEPCO is 98.7% and 78.89%, respectively. This means that the inefficiency of KEPCO is caused by the scale inefficiency. The analysis shows that the employees should be decreased by 15% at minimum to get the 100% efficiency. The result suggests that KEPCO needs the structural reform to improve the efficiency.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.10-16
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• 2009

This paper is concerned with Korean students' attitudes towards robots, presenting two survey studies. The first study was concerned with a group of college students, taking the perspective of international comparison. Data were collected by administering an online survey, where 106 volunteer students had participated. In the survey, the Negative Attitude towards Robot Scale(NARS) was adopted to compare the Korean students' scores with those of multi-national groups (U.S.A, Germany, Netherland, Japan, Mexico, and China) who responded to the same scale in Bartneck et al.'s research. The analysis of the data reveals that Korean students tend to be more concerned about social impacts that robots might bring to future society and are very conscious about the uncertain influences of robots on human life. The second study investigated factors that may affect K-12 students' attitudes towards robots, with survey data garnered from 298 elementary, middle, and high school students. The data were analyzed by the method of multiple regression analysis to test the hypothesis that a student's gender, age, the extent of interest in robots, and the extent of experiences with robots may influence his or her attitude towards robots. The hypothesis was partially supported in that variables of a student's gender, age, and the extent of interest in robots were statistically significant with regard to the attitude variable. Given the results, this paper suggests three points of discussions to better understand Korean students' attitudes towards robots: social and cultural context, individual differences, and theory of mind.

• ETRI Journal
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• v.39 no.1
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• pp.124-134
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• 2017

Data races are one of the most difficult types of bugs in concurrent multithreaded systems. It requires significant time and cost to accurately detect bugs in complex large-scale programs. Although many race detection techniques have been proposed by various researchers, none of them are effective in all aspects. In this paper, we compare the performance of five recent dynamic race detection techniques: FastTrack, Acculock, Multilock-HB, SimpleLock+, and causally precedes (CP) detection. We experimentally demonstrate the strengths and weaknesses of these dynamic race detection techniques in terms of their detection capability, running time, and runtime overhead using 20 benchmark programs with different characteristics. The comparison results show that the detection capability of CP detection does not differ from that of FastTrack, and that SimpleLock+ generates the lowest overhead among the hybrid detection techniques (Acculock, SimpleLock+, and Multilock-HB) for all benchmark programs. SimpleLock+ is 1.2 times slower than FastTrack on average, but misses one true data race reported from Mutilock-HB on the large-scale benchmark programs.