• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.40-48
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• 2015

This paper presents a processor that performs QR decomposition (QRD) as well as Lattice Reduction (LR) for multiple-input multiple-output (MIMO) systems. By sharing the operations commonly required in QRD and LR, the hardware complexity of the proposed processor is reduced significantly. In addition, the proposed processor is designed based on a multi-cycle architecture so as to reduce the hardware complexity. The proposed processor is implemented with 139k logic gates in a $0.18-{\mu}m$ CMOS process, and its latency is $5{\mu}s$ for $8{\times}8$ MIMO preprocessing both QRD and LR where the operating frequency is 117MHz.

• Proceedings of the CALSEC Conference
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• 1997.11a
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• pp.263-268
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• 1997

Functional Modeling methodology, IDEF0 is widely used for modeling, analysis and description of enterprise system. The limitation of modeling components restricts applicability and give rise to confusion about model. In this paper, we propose new method to extend IDEF0. The first is adding modeling components which are semantic representations. In addition to ICOMs, we add the time and cost component which is required to execute the function. The second is tracing mechanism. When we need some information, we drive the functions related with the information by reverse tracing of the function which produces the information as a output and input. Through the tracing, we find out the bottleneck process or high cost process. Finally, we suggest the final decomposition level. We call the final decomposed function into unit function which has only one output data. We can combine and reconstruct some of functions because an unit function is similar to ‘lego block’. To reach the integrated system, the main problem to be solved is the integration of information produced by different functional subsystem. This can be resolved when the creation of data must be dependent on only one function. Through view integration of function output, we can guarantee the integrity of data.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.45-54
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• 2023

Under combat simulation environment when inputting the detection performance data of the real system into the simulated object the given data affects the simulation analysis result. ACQUIRE-Target Task Performance Metric (TTPM)-Target Angular Size (TAS) model is used as a target acquisition model to simulate the detection ability of entities in the main combat simulation tool. This model estimates the decomposition curve of the object sensor and output the detection distance according to the target type. However, it is not easy to apply the performance of the new detection object that the user wants to input to the target acquisition model. Users want to input the detection distance into the target acquisition model, but the target acquisition model requires sensor decomposition curve data according to encounter conditions. In this paper, we propose a method of inversely deriving the sensor decomposition curve data of the target acquisition model by taking the detection distance to the target as an input. Here, the sensor decomposition curve data simultaneously satisfies each detection distance for three types of targets: personnel, ground vehicles, and aircraft. Finally, the detection distance of various reconnaissance equipment is applied to the detection object, and the detection effect according to the reconnaissance equipment is analyzed.

• Environmental and Resource Economics Review
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• v.24 no.1
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• pp.55-84
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• 2015

Korea is one of the few OECD countries having no binding Greenhouse gas (GHG) emissions reduction obligations under the Kyoto Protocol. Korea is going to enforce a powerful greenhouse gas emissions control to the industry from 2015. Current GHG reduction policies do not take into account the trade-off between economic growth and GHG mitigation, this approach will not be sustainable. Sectoral approach, considering industry by industry may be more eco-friend approach. This study verified the validity of the analysis results counted from whole procedure of energy input-output analysis and decomposition analysis to sector 'Organic basic chemical products' and 'Cement and concrete products'. Empirical test was performed using changes in energy consumption, production, process improvements and new facilities. Although the results showed unstable fluctuations from Divisia index decomposition analysis, it was verified that the entire procedure can provide a clue in understanding of the industry's energy and GHG footprint.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1452-1457
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• 2005

In the QR Decomposition-based (QRD) decoding class, the system performance is sensitive to the error propagation. Thus, it is critical to correctly decode the previous layers. One apprach to desensitize the error propagation is to propose the optimal decoding order of layers. In this wort we propose a new extended-list Soled QRD-based (SQRD) decoding approach. In the proposed decoding scheme, the solution of the few first layers is extended as the list of promising possible solutions. By doing so, the diversity of the lowest layer is increased. As a result, the system performance is less sensitive to the error propagation than its counterparts. The proposed approach is verified by the computer simulation results.

• The Journal of Society for e-Business Studies
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• v.2 no.2
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• pp.89-113
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• 1997

Modeling methodology has been widely used for analysis and design of a information system. Specially, under the CALS environments, modeling approach is more important because the enterprise functions are inter-related and information sharing speeds up the business. In this paper, we suggest functional requirements about modeling methodology for CALS by surveying the IDEF0 and ARIS. The former is FIPS 183 and the latter is basic methodology of SAP/R3 which is world-wide ERP system. The proposed functional requirements include all semantics of IDEF0 and adds some features. The first is adding modeling components which are semantic representations. In addition to ICOMs, we add the time and cost component which is required to execute the function. The second is tracing mechanism. When we need some information, we drive the functions related with the information by reverse tracing of the function which produces the information as a output and input. Through the tracing, we find out the bottleneck process or high cost process. This approach guarantees the integrity of data by designating the data ownership. Finally, we suggest the final decomposition level. We call the final decomposed function into unit function which has only one output data. We can combine and reconstruct some of functions such as 'lego block' combination.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• ETRI Journal
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• v.35 no.3
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• pp.371-377
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• 2013

An effective signal detection algorithm with low complexity is presented for multiple-input multiple-output orthogonal frequency division multiplexing systems. The proposed technique, QR-MLD, combines the conventional maximum likelihood detection (MLD) algorithm and the QR algorithm, resulting in much lower complexity compared to MLD. The proposed technique is compared with a similar algorithm, showing that the complexity of the proposed technique with T=1 is a 95% improvement over that of MLD, at the expense of about a 2-dB signal-to-noise-ratio (SNR) degradation for a bit error rate (BER) of $10^{-3}$. Additionally, with T=2, the proposed technique reduces the complexity by 73% for multiplications and 80% for additions and enhances the SNR performance about 1 dB for a BER of $10^{-3}$.

• 유체기계공업학회:학술대회논문집
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• 2002.12a
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• pp.346-353
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• 2002

The steam generator feedwater flow rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow rate in pressurized water reactors, may result in unnecessary plant power derating. The backpropagation network was used to generate models of signals for a pressurized water reactor. Multiple-input single-output heteroassociative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.267-274
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• 2017

In this study was investigated of possibility using the recorded micro tremor data on ground level as ambient vibration input excitation data for investigation and application Operational Modal Analysis (OMA) on the bench-scale earthquake simulator (The Quanser Shake Table) for model steel structures. As known OMA methods (such as EFDD, SSI and so on) are supposed to deal with the ambient responses. For this purpose, analytical and experimental modal analysis of a model steel structure for dynamic characteristics was evaluated. 3D Finite element model of the building was evaluated for the model steel structure based on the design drawing. Ambient excitation was provided by shake table from the recorded micro tremor ambient vibration data on ground level. Enhanced Frequency Domain Decomposition is used for the output only modal identification. From this study, best correlation is found between mode shapes. Natural frequencies and analytical frequencies in average (only) 2.8% are differences.