• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.3
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• pp.355-373
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• 2017

Tunnelling in urban areas, it is essential to understand existing structure-tunnel interactive behavior. Serviced structures in the city are supported by pile foundation, since they are certainly effected due to tunnelling. In this research, thus, pile load distribution and ground behavior due to tunnelling below grouped pile were investigated using laboratory model test. Grouped pile foundations were considered as 2, 3 row pile and offsets (between pile tip and tunnel crown: 0.5D, 1.0D and 1.5D for generalization to tunnel diameter, D means tunnel diameter). Soil in the tank for laboratory model test was formed by loose sand (relative density: Dr = 30%) and strain gauges were attached to the pile inner shaft to estimate distribution of axial force. Also, settlements of grouped pile and adjacent ground surface depending on the offsets were measured by LVDT and dial gauge, respectively. Tunnelling-induced deformation of underground was measured by close range photogrammetric technique. Numerical analysis was conducted to analyze and compare with results from laboratory model test and close range photogrammetry. For expression of tunnel excavation, the concept of volume loss was applied in this study, it was 1.5%. As a result from this study, far offset, the smaller reduction of pile axial load and was appeared trend of settlement was similar among them. Particulary, ratio of pile load and settlement reduction were larger when the offset is from 0.5D to 1.0D than from 1.0D to 1.5D.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.203-203
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• 2016

The linkage between climate change and water security, i.e., the response of water resource to the future climate change, have been of great concern to both scientific community and policy makers. In this study, the impact of future climate on water resources in Yellow River Basin in North of China has been investigated using the Coupled Land surface and Hydrology Model System (CLHMS) and IPCC AR5 projected future climate change in the basin. Firstly, the performances of 14 IPCC AR5 models in reproducing the observed precipitation and temperature in China, especially in North of China, have been evaluated, and it's suggested most climate models do show systematic bias compared with the observation, however, CNRM-CM5、HadCM5 and IPSL-CM5 model are generally the best models among those 14 models. Taking the daily projection results from the CNRM-CM5, along with the bias-correction technique, the response of water resources in Yellow river basin to the future climate change in different emission scenarios have been investigated. All the simulation results indicate a reduction in water resources. The current situation of water shortage since 1980s will keep continue, the water resources reduction varies between 28 and 23% for RCP 2.6 and 4.5 scenarios. RCP 8.5 scenario simulation shows a decrease of water resources in the early and mid 21th century, but after 2080, with the increase of rainfall, the extreme flood events tends to increase.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1527-1539
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• 2015

In this paper the hardware implementation of the direct torque control based on the fuzzy logic technique of induction motor on the Field-Programmable Gate Array (FPGA) is presented. Due to its complexity, the fuzzy logic technique implemented on a digital system like the DSP (Digital Signal Processor) and microcontroller is characterized by a calculating delay. This delay is due to the processing speed which depends on the system complexity. The limitation of these solutions is inevitable. To solve this problem, an alternative digital solution is used, based on the FPGA, which is characterized by a fast processing speed, to take the advantage of the performances of the fuzzy logic technique in spite of its complex computation. The Conventional Direct Torque Control (CDTC) of the induction machine faces problems, like the high stator flux, electromagnetic torque ripples, and stator current distortions. To overcome the CDTC problems many methods are used such as the space vector modulation which is sensitive to the parameters variations of the machine, the increase in the switches inverter number which increases the cost of the inverter, and the artificial intelligence. In this paper an intelligent technique based on the fuzzy logic is used because it is allows controlling the systems without knowing the mathematical model. Also, we use a new method based on the Xilinx system generator for the hardware implementation of Direct Torque Fuzzy Control (DTFC) on the FPGA. The simulation results of the DTFC are compared to those of the CDTC. The comparison results illustrate the reduction in the torque and stator flux ripples of the DTFC and show the Xilinx Virtex V FPGA performances in terms of execution time.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.1
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• pp.83-91
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• 2000

This paper presents a new technique to reduce a large interconnect circuit with tens of thousands of elements into the one that is small enough to be analyzed by circuit simulators such as SPICE. This technique takes a fundamentally different approach form the conventional methods based on the interconnect circuit structure analysis and several rules based on the Elmore time constant. The time moments are computed form the circuit consisting of the interconnect circuit and the CMOS gate driver model computed by the AWE technique. Then, the equivalent RC circuit is synthesized from those moments. The characteristics of the driving CMOS gate can be reflected with the high degree of accuracy and the size of the compressed circuit is determined by the number of output nodes regardless of the size of the original interconnect circuits. This technique has been implemented in C language, applied to several interconnect circuits driven by a 0.5${\mu}m$ CMOS gate and the equivalent RC circuits with more than 99% reduction ratio and accuracy with 1 ~ 10% error in therms of propagation delays were obtained.

• The Journal of Advanced Prosthodontics
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• v.8 no.6
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• pp.439-448
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• 2016

PURPOSE. The purposes of this study are to evaluate the internal and marginal adaptation of two widely used CAD/CAM systems and to study the effect of porcelain press veneering process on the prosthesis adaptation. MATERIALS AND METHODS. Molar of a lower jaw typodont resin model was prepared by adjusting a 1.0 mm circumferential chamfer, an occlusal reduction of 2.0 mm, and a $5^{\circ}$ convergence angle and was duplicated as an abrasion-resistant master die. The monolithic crowns and copings were fabricated with two different CAD/CAM system-Ceramil and Zirkonzahn systems. Two kinds of non-destructive analysis methods are used in this study. First, weight technique was used to determine the overall fitting accuracy. And, to evaluate internal and marginal fit of specific part, replica technique procedures were performed. RESULTS. The silicone weight for the cement space of monolithic crowns and copings manufactured with Ceramil system was significantly higher than that from Zirkonzahn system. This gap might cause the differences in the silicone weight because the prostheses were manufactured according to the recommendation of each system. Marginal discrepancies of copings made with Ceramil system were between 106 and $117{\mu}m$ and those from Zirkonzahn system were between 111 and $115{\mu}m$. Marginal discrepancies of copings made with Ceramil system were between 101 and $131{\mu}m$ and those from Zirkonzahn system were between 116 and $131{\mu}m$. CONCLUSION. Marginal discrepancy was relatively lower in Ceramil system and internal gap was smaller in Zirkonzahn system. There were significant differences in the internal gap of monolithic crown and coping among the 2 CAD/CAM systems. Marginal discrepancy produced from the 2 CAD/CAM systems were within a reported clinically acceptable range of marginal discrepancy.

• Analytical Science and Technology
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• v.27 no.1
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• pp.41-59
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• 2014

The aims of this work were the identification and the classification of fresh lubricants and used engine oils of vehicles for the application in forensic science field-80 kinds of fresh lubricants were purchased and 86 kinds of used engine oils were sampled from 24 kinds of diesel and gasoline vehicles with different driving conditions. The sample of lubricants and used engine oils were analyzed by GC/MS. The Bayesian model technique was developed for classification or identification. Both the wavelet fitting and the principal component analysis (PCA) techniques as a data dimension reduction were applied. In fresh lubricants classification, the rates of matching by Bayesian model technique with wavelet fitting and PCA were 97.5% and 96.7%, respectively. The Bayesian model technique with wavelet fitting was better to classify lubricants than it with PCA based on dimension reduction. And we selected the Bayesian model technique with wavelet fitting for classification of lubricants. The other experiment was the analysis of used engine oils which were collected from vehicles with the several mileage up to 5,000 km after replacing engine oil. The eighty six kinds of used engine oil sample with the mileage were collected. In vehicle classification (total 24 classes), the rate of matching by Bayesian model with wavelet fitting was 86.4%. However, in the vehicle's fuel type classification (whether it is gasoline vehicle or diesel vehicle, only total 2 classes), the rate of matching was 99.6%. In the used engine oil brands classification (total 6 classes), the rate of matching was 97.3%.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.603-611
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• 2003

This paper proposes an effective feature compensation scheme based on speech model for achieving robust speech recognition. Conventional model-based method requires off-line training with noisy speech database and is not suitable for online adaptation. In the proposed scheme, we can relax the off-line training with noisy speech database by employing the parallel model combination technique for estimation of correction factors. Applying the model combination process over to the mixture model alone as opposed to entire HMM makes the online model combination possible. Exploiting the availability of noise model from off-line sources, we accomplish the online adaptation via MAP (Maximum A Posteriori) estimation. In addition, the online channel estimation procedure is induced within the proposed framework. For more efficient implementation, we propose a selective model combination which leads to reduction or the computational complexities. The representative experimental results indicate that the suggested algorithm is effective in realizing robust speech recognition under the combined adverse conditions of additive background noise and channel distortion.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2948-2957
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• 2024

High-order harmonic techniques can be used to recreate neutron flux distributions in reactor cores using the neutron diffusion equation. However, traditional source iteration and source correction iteration techniques have sluggish convergence rates and protracted calculation periods. The correctness of the implicitly restarted Arnoldi method (IRAM) in resolving the eigenvalue problems of the one-dimensional and two-dimensional neutron diffusion equations was confirmed by computing the benchmark problems SLAB_1D_1G and two-dimensional steady-state TWIGL using IRAM. By integrating Galerkin projection with Proper Orthogonal Decomposition (POD) techniques, a POD-Galerkin reduced-order model was developed and the IRAM model was used as the full-order model. For 14 macroscopic cross-section values, the TWIGL benchmark problem was perturbed within a 20% range. We extracted 100 sample points using the Latin hypercube sampling method, and 70% of the samples were used as the testing set to assess the performance of the reduced-order model The remaining 30% were utilized as the training set to develop the reduced-order model, which was employed to rebuild the TWIGL benchmark problem. The reduced-order model demonstrates good flexibility and can efficiently and accurately forecast the effective multiplication factor and neutron flux distribution in the core. The reduced-order model predicts k_eff and neutron flux distribution with a high degree of agreement compared to the full-order model. Additionally, the reduced-order model's computation time is only 10.18% of that required by the full-order model.The neutron flux distribution of the steady-state TWIGL benchmark was recreated using the reduced-order model. The obtained results indicate that the reduced-order model can accurately predict the k_eff and neutron flux distribution of the steady-state TWIGL benchmark.Overall, the proposed technique not only has the potential to accurately project neutron flux distributions in transient settings, but is also relevant for reconstructing neutron flux distributions in steady-state conditions; thus, its applicability is bound to increase in the future.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.597-607
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• 2011

Value at Risk(VaR) is being widely used as a simple tool for measuring financial risk. Although VaR has a few weak points, it is used as a basic risk measure due to its simplicity and easiness of understanding. However, it becomes very difficult to estimate the volatility of the portfolio (essential to compute its VaR) when the number of assets in the portfolio is large. In this case, we can consider the application of a dimension reduction technique; however, the ordinary factor analysis cannot be applied directly to financial data due to autocorrelation. In this paper, we suggest a dimension reduction method that uses the time-series factor analysis and DCC(Dynamic Conditional Correlation) GARCH model. We also compare the method using time-series factor analysis with the existing method using ordinary factor analysis by backtesting the VaR of real data from the Korean stock market.