• Structural Engineering and Mechanics
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• v.38 no.6
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• pp.767-802
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• 2011

Developers of new finite elements or nonlinear solution techniques rely on discriminative benchmark tests drawn from the literature to assess the advantages and drawbacks of new formulations. Buckling benchmark tests provide a rigorous evaluation of finite elements applied to thin structures, and a complete and detailed set of reference results would therefore prove very useful in carrying out such evaluations. Results are usually presented in the form of load-deflection curves that developers must reconstruct by extracting the points, a procedure which is often tedious and inaccurate. Moreover the curves are usually given without accompanying information such as the calculation time or number of iterations it took for the model to converge, even though this type of data is equally important in practice. This paper presents ten different limit-point buckling benchmark tests, and provides for each one the reference load-deflection curve, all the points necessary to recreate the curve in tabulated form, analysis data such as calculation time, number of iterations and increments, and all of the inputs used to obtain these results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.53-62
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• 2010

This paper introduces a classification algorithm which can be applied to a learning problem with incomplete data sets, missing variable values or a class value. This algorithm uses a data expansion method which utilizes weighted values and probability techniques. It operates by extending a classifier which are considered to be in the optimal projection plane based on Fisher's formula. To do this, some equations are derived from the procedure to be applied to the data expansion. To evaluate the performance of the proposed algorithm, results of different measurements are iteratively compared by choosing one variable in the data set and then modifying the rate of missing and non-missing values in this selected variable. And objective evaluation of data sets can be achieved by comparing, the result of a data set with non-missing variable with that of C4.5 which is a known knowledge acquisition tool in machine learning.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.18 no.2
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• pp.39-59
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• 2007

With the rapid spread of the practice of collecting and treating data by using a data base system, it's increasingly more critical to approach data sets in the same manner as general records in the collection, evaluation, preservation, and utilization process. Despite the importance, however, the interest level in data sets in Korea's records management is very low. In order to suggest basic items to regard data sets as records and manage them systematically, this study examined the descriptive elements of data set records. descriptive elements of data set records were suggested by comparing and analyzing those ones adopted by the agencies that regarded data sets as records and provided the concerned service as well as the descriptive rules of electronic records set by the advanced nations in records management based on the descriptive areas of ISAD(G).

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.379-381
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• 2008

TETRA(Terrestrial Trunked Radio) is a digital trunked radio standard developed by the ETSI(European Telecommunications Standards Institute). Currently, TETRA was set Digital TRS in electric power If wireless backbone network. In this time, we use many company's TETRA modem. So, TETRA modem performance evaluation is very important. TETRA modem use two type of Data transfer mode. One is Packet Data using UDP/IP. and the other is SDS(Short Data Service). In this paper, We generate Packet Data using Traffic Generator module. Packet Data transfer 1000 times each 10 bytes to 400 bytes. We analyze transmission delay time, success rate and standard deviation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.869-876
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• 2021

In the field of music, various AI composition methods using machine learning have recently been attempted. However, most of this research has been centered on Western music, and little research has been done on Korean traditional music. Therefore, in this paper, we will create a data set of Korean traditional music, create a melody using three algorithms based on the data set, and compare the results. Three models were selected based on the similarity between language and music, LSTM, Music Transformer and Self Attention. Using each of the three models, a melody generator was modeled and trained to generate melodies. As a result of user evaluation, the Self Attention method showed higher preference than the other methods. Data set is very important in AI composition. For this, a Korean traditional music data set was created, and AI composition was attempted with various algorithms, and this is expected to be helpful in future research on AI composition for Korean traditional music.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.29-37
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• 2024

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1726-1732
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers - masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the used pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with the age of the user profile. The performance of the proposed scheme is evaluated by using a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed scheme that considers the age of user profiles.

• Journal of Communications and Networks
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• v.15 no.6
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• pp.614-634
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• 2013

The proliferation of real-time multimedia services requires huge amounts of data transactions demanding strict quality-of-service (QoS) guarantees. Multicast transmission is a promising technique because of its efficient network resource utilization. However, high head-of-line (HOL) blocking probability and lack of service-specific QoS control should be addressed for practical implementations of multicast networks. In this paper, a topology aware fanout set division (TAFD) scheme is proposed to resolve these problems. The proposed scheme is composed of two techniques that reduce HOL blocking probability and expedite packet delivery for large-delay branches regarding multicast tree topology. Since management of global topology information is not necessary, scalability of the proposed scheme is guaranteed. Mathematical analysis investigates effects of the proposed scheme and derives optimal operational parameters. The evaluation results show that the TAFD scheme achieves significant delay reduction and satisfies required delay bounds on various multicast networks.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.468-475
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• 2018

The current system of making course evaluations mandatory in universities in Korea in order to supplement low participation levels is a major hindering factor for the validity of the evaluation results due to students' insincere responses. This study points out the problems in course evaluations that are in the form of surveys and instead proposes a sample course evaluation system that utilizes a sample mean to deduce a population mean, attempting to prove through simulation that this is statistically significant. Thus, small, medium, and large scale courses as well as cyber courses that were available in S University in the fall 2016 semester were set as the population, and each course's evaluation average grade was set as the population mean. Furthermore, we used the average grade and standard deviation of the same courses from the previous year in order to decide the sample number, the reliability level was set as 95%, and the margin of error was set as ${\pm}0.25$. As a result of carrying out a simulation using the data analysis tool R, all the courses (small scale, medium scale, large scale, and cyber) showed to have a reliability level close to 95% including the population mean, and consequently practicality of the representative sample course evaluation system was proven.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.3
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• pp.205-212
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• 2008

This paper describes the application of an inverse building model to a calibrated forward building model using EnergyPlus program. Typically, inverse models are trained using measured data. However, in this study, an inverse building model was trained using data generated by an EnergyPlus model for an actual office building. The EnergyPlus model was calibrated using field data for the building. A training data set for a month of July was generated from the EnergyPlus model to train the inverse model. Cooling load prediction of the trained inverse model was tested using another data set from the EnergyPlus model for a month of August. Predicted cooling loads showed good agreement with cooling loads from the EnergyPlus model with root-mean square errors of 4.11%. In addition, different control strategies with dynamic cooling setpoint variation were simulated using the inverse model. Peak cooling loads and daily cooling loads were compared for the dynamic simulation.