• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.4
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• pp.406-412
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• 2009

Flight simulation model for helicopter simulator is one of the most important models which affect flight performance and handling quality. It is typical to develop the model based on the raw data and models from the helicopter designers/manufacturers. The approaches in this study were to develop the basic model based on the available resources regarding helicopter operation/maintenance and to tune and validate it based on the flight test results. The basic model was developed with maintenance manuals, flight manuals, analyses, measurements, papers and so on considering that KA-32T data could not be obtained from the manufacturer. The flight test for KA-32T was performed and the reference data for the simulation validation tests were acquired. The flight simulation model was validated to have the fidelity compatible with level C of FAA AC120-63 after comparison and tuning with flight test results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.80-87
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• 2005

T-50 supersonic jet trainer aircraft using digital flight-by-wire flight control system receives aircraft flight conditions such as altitude, VCAS(Calibrated Airspeed) and Angle of Attack from IMFP(Integrated Multi-Function Probe). IMFP sensors information have triplex structure using three IMFP sensors. Air-data selection logic is mid-value selection in three information from three IMFP sensors in order to have more reliability. From supersonic flight test at high altitude, air-data information is dropped simultaneously because of supersonic shock wave effect. This error information may affect to aircraft stability and safety in supersonic area at low altitude. This paper propose that sensitivity analysis and HQS(Handling Quality Simulator) pilot simulation in order to analyze flight stability and controllability in supersonic area at low altitude when these information is applied to flight control law.

• Earthquakes and Structures
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• v.12 no.6
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• pp.689-697
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• 2017

Nowadays, there are a great number of various structures that have been retrofitted by using different FRP Composites. Due to this, more researches need to be conducted to know more the characteristics of these structures, not only that but also a comparison among them before and after the retrofitting is needed. In this research, a model steel structure is tested using a bench-scale earthquake simulator on the shake table, using recorded micro tremor data, in order to get the dynamic behaviors. Beams of the model steel structure are then retrofitted by using CFRP composite, and then tested on the Quanser shake table by using the recorded micro tremor data. At this stage, it is needed to evaluate the dynamic behaviors of the retrofitted model steel structure. Various types of methods of OMA, such as EFDD, SSI, etc. are used to take action in the ambient responses. Having a purpose to learn more about the effects of FRP composite, experimental model analysis of both types (retrofitted and no-retrofitted models) is conducted to evaluate their dynamic behaviors. There is a provision of ambient excitation to the shake table by using recorded micro tremor ambient vibration data on ground level. Furthermore, the Enhanced Frequency Domain decomposition is used through output-only modal identification. At the end of this study, moderate correlation is obtained between mode shapes, periods and damping ratios. The aim of this research is to show and determine the effects of CFRP Composite implementation on structural responses of the model steel structure, in terms of changing its dynamical behaviors. The frequencies for model steel structure and the retrofitted model steel structure are shown to be 34.43% in average difference. Finally, it is shown that, in order to evaluate the period and rigidity of retrofitted structures, OMA might be used.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.9
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• pp.548-556
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• 2014

To cope with recently increasing demand for data traffics, heterogeneous networks have been actively studied, where small cells are deployed within a macro cell coverage with the same frequency band. To mitigate the interference from the macro cell to small cells, an enhanced Inter-cell Interference Coordination (eICIC) technique has been proposed, where ABS (Almost Blank Subframe) is used in time domain. However, there is a waste of resource since no data is transmitted in a macro-cell in ABS. In this paper, we propose a new interference management method by using a 3D sector beam based on Active Antenna System (AAS), where Genetic Algorithm (GA) is applied to reduce the antenna gain toward a small-cell. With the proposed scheme, the macro-cell and small cells can transmit data at the same time with the AAS antenna pattern generating reduced interference to small cells. The performance of the proposed scheme is evaluated by using an LTE-Advanced system level simulator.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.1
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• pp.46-56
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• 1991

In this paper, 16word X 8bit Content Addressable and Reentrant Memory(CARM) is described. This device has 4 operation modes(read, write, match, reentrant). The read and write operation of CARM is like that of static RAM, CARM has the reentrant mode operation where the on chip garbage collection is accomplished conditionally. Thus function can be used for high speed matching unit of dynamic data flow computer. And CARM also can encode matching address sequentially according to therir priority. CARM consists of 8 blocks(CAM cell, Sequential Address Encoder(S.A.E). Reentrant operation. Read/Write control circuit, Data/Mask Register, Sense Amplifier, Encoder. Decoder). Designed DARM can be used in data flow computer, pattern, inspection, table look-up, image processing. The simulation is performed using the QUICKSIM logic simulator and Pspice circuit simulator. Having hierarchical structure, the layout was done using the 3{\;}\mu\textrm{m} n well CMOS technology of the ETRI design rule.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.72-81
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• 2014

Recently, because of irregular power demand, we have suffered from an electric power shortage. The necessity of the adoption of smart grid which makes effective supply of power by using the two-way communication across the grid between the customers and electric energy providers is growing more and more. If smart grid set up in our country, the third-parties which provide services to customer using the information acquired from smart grid, might be revved up. In this paper, we suggest a methodology how classify the types of family by analysing an power consumption pattern using data mining technique. To make a classifier for categorizing the household types, we need power consumption data and their family type. However, it is hard to get both of them. Therefore we develop the simulator that generates power consumption patterns of the household and classify the types of family. Also, we present a potential for application services such as customized services for a specific family or goods marketing.

• Korean Journal of Oriental Medicine
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• v.17 no.3
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• pp.77-85
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• 2011

Objectives : The purpose of this study was to develop a simulator which can analyze the anti-inflammatory effects of medical herbs based on e-cell concerning p38 MAPK signal pathway. Methods : We collected data concerning medical herbs with anti-inflammatory effects and the active compounds to provide as a fundamental databse and to validate the newly developed algorithm. At this time, we used the target database as pubmed and gathered the data by data mining tool, pathway studio. Also we have developed the web-based search system for confirming database related to anti-inflammation. We researched the mechanism of actions of proteins in p38 MAPK signal pathway when active compound has been inserted into the network. We reduced total network into TAK-MKK3-p38 and made the two types of mathematical model about active compounds' interaction. Results & Conclusion : We constructed the database which have 69 cases of medical herbs, 71 cases of active compounds, about 8,000 cases of URL(Uniform Resource Locator) related to papers and reports. We designed the ordinary differential equations for response of TAK, MKK3, p38 in e-cell's cytosol and nucleus. We used this formular as measure whether an active compound of medicinal plants which is inputted by an user would have an anti-inflammation effects. We developed the visualization program which could show the change of concentration over time.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.789-798
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• 2021

In this study, for the purpose of collecting and analyzing target-specific RCS data of target signals simulator for verification/improvement of radar system performance, VHF band monostatic/bistatic RCS of civil aircraft(B-747, B-737) and fighter(F-16) models were analyzed by EM simulation tool. In order to reduce the RCS analysis time, the analysis time and RCS data were compared and cross-verified. Also, the analysis range was selected by examining the interpolation error according to the analysis angle resolution. The RCS data obtained for each model were analyzed separately by the incident/reflection elevation angle and frequency. The RCS characteristics according to the shape of the aircraft and the incident/reflection azimuth angle were described. Finally, the statistical RCS distribution value of each model is presented through RCS distribution histogram analysis. In the future, the RCS database obtained by this study will be used for the target signals simulator of the VHF band radar system.

• Journal of IKEEE
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• v.25 no.1
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• pp.180-192
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• 2021

One widely used algorithm for image recognition and pattern detection is the convolution neural network (CNN). To efficiently handle convolution operations, which account for the majority of computations in the CNN, we use hardware accelerators to improve the performance of CNN applications. In using these hardware accelerators, the CNN fetches data from the off-chip DRAM, as the massive computational volume of data makes it difficult to derive performance improvements only from memory inside the hardware accelerator. In other words, data communication between off-chip DRAM and memory inside the accelerator has a significant impact on the performance of CNN applications. In this paper, a simulator for the CNN is developed to analyze the main memory or DRAM access with respect to the size of the on-chip memory or global buffer inside the CNN accelerator. For AlexNet, one of the CNN architectures, when simulated with increasing the size of the global buffer, we found that the global buffer of size larger than 100kB has 0.8x as low a DRAM access count as the global buffer of size smaller than 100kB.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.111-116
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• 2022

In this study, the applicability of machine learning for the development of a simulator for general X-ray examination education is evaluated. To this end, k-nearest neighbor(kNN), support vector machine(SVM) and neural network(NN) classification models are analyzed to present the most suitable model by analyzing the results. Image data was obtained by taking 100 photos each corresponding to Posterior anterior(PA), Posterior anterior oblique(Obl), Lateral(Lat), Fan lateral(Fan lat). 70% of the acquired 400 image data were used as training sets for learning machine learning models and 30% were used as test sets for evaluation. and prediction model was constructed for right-handed PA, Obl, Lat, Fan lat image classification. Based on the data set, after constructing the classification model using the kNN, SVM, and NN models, each model was compared through an error matrix. As a result of the evaluation, the accuracy of kNN was 0.967 area under curve(AUC) was 0.993, and the accuracy of SVM was 0.992 AUC was 1.000. The accuracy of NN was 0.992 and AUC was 0.999, which was slightly lower in kNN, but all three models recorded high accuracy and AUC. In this study, right-handed PA, Obl, Lat, Fan lat images were classified and predicted using the machine learning classification models, kNN, SVM, and NN models. The prediction showed that SVM and NN were the same at 0.992, and AUC was similar at 1.000 and 0.999, indicating that both models showed high predictive power and were applicable to educational simulators.