• Title/Summary/Keyword: parameter evaluation simulation

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A Study on the Characteristics Analysis of LLC AC to DC High Frequency Resonant Converter capable of ZVZCS (ZVZCS가 가능한 LLC AC to DC 고주파 공진 컨버터의 특성 해석에 관한 연구)

  • Kim, Jong-Hae
    • Journal of IKEEE
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    • v.25 no.4
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    • pp.741-749
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    • 2021
  • This paper presents the current-fed type LLC AC to DC high frequency resonant converter capable of ZVZCS(Zero-Voltage and Zero-Current Switching). The current-fed type LLC AC to DC high frequency resonant converter proposed in this paper could operate not only in ZVS(Zero-Voltage Switching) operation by connecting the resonant capacitors(C1, C2) in parallel across the switching devices but also in ZCS(Zero-Current Switching) operation of the secondary diode. The ZVS and ZCS operations can reduce the turn-on loss of the switching devices and the turn-off loss of the secondary diodes, respectively. The circuit analysis of current-fed type LLC AC to DC high frequency resonant converter proposed in this paper is addressed generally by adopting the normalized parameters. The operating characteristics of proposed LLC AC to DC high frequency resonant converter were also evaluated by using the normalized control parameters such as the normalized control frequency(μ), the normalized load resistor(λ) and so on. Based on the characteristic values through the characteristics of evaluation, an example of the design method of proposed LLC AC to DC high frequency resonant converter is suggested, and the validity of the theoretical analysis is confirmed using the experimental results and PSIM simulation.

Application of rock mass index in the prediction of mine water inrush and grouting quantity

  • Zhao, Jinhai;Liu, Qi;Jiang, Changbao;Defeng, Wang
    • Geomechanics and Engineering
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    • v.30 no.6
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    • pp.503-515
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    • 2022
  • The permeability coefficient is an essential parameter for the study of seepage flow in fractured rock mass. This paper discusses the feasibility and application value of using readily available RQD (rock quality index) data to estimate mine water inflow and grouting quantity. Firstly, the influence of different fracture frequencies on permeability in a unit area was explored by combining numerical simulation and experiment, and the relationship between fracture frequencies and pressure and flow velocity at the monitoring point in fractured rock mass was obtained. Then, the stochastic function generation program was used to establish the flow analysis model in fractured rock mass to explore the relationship between flow velocity, pressure and analyze the universal law between fracture frequency and permeability. The concepts of fracture width and connectivity are introduced to modify the permeability calculation formula and grouting formula. Finally, based on the on-site grouting water control example, the rock mass quality index is used to estimate the mine water inflow and the grouting quantity. The results show that it is feasible to estimate the fracture frequency and then calculate the permeability coefficient by RQD. The relationship between fracture frequency and RQD is in accordance with exponential function, and the relationship between structure surface frequency and permeability is also in accordance with exponential function. The calculation results are in good agreement with the field monitoring results, which verifies the rationality of the calculation method. The relationship between the rock mass RQD index and the rock mass permeability established in this paper can be used to invert the mechanical parameters of the rock mass or to judge the permeability and safety of the rock mass by using the mechanical parameters of the rock mass, which is of great significance to the prediction of mine water inflow and the safety evaluation of water inrush disaster management.

Refinement of damage identification capability of neural network techniques in application to a suspension bridge

  • Wang, J.Y.;Ni, Y.Q.
    • Structural Monitoring and Maintenance
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    • v.2 no.1
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    • pp.77-93
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    • 2015
  • The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

Disease Recognition on Medical Images Using Neural Network (신경회로망에 의한 의료영상 질환인식)

  • Lee, Jun-Haeng;Lee, Heung-Man;Kim, Tae-Sik;Lee, Sang-Bock
    • Journal of the Korean Society of Radiology
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    • v.3 no.1
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    • pp.29-39
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    • 2009
  • In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.

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Evaluation of extreme rainfall estimation obtained from NSRP model based on the objective function with statistical third moment (통계적 3차 모멘트 기반의 목적함수를 이용한 NSRP 모형의 극치강우 재현능력 평가)

  • Cho, Hemie;Kim, Yong-Tak;Yu, Jae-Ung;Kwon, Hyun-Han
    • Journal of Korea Water Resources Association
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    • v.55 no.7
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    • pp.545-556
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    • 2022
  • It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

Impact of Sulfur Dioxide Impurity on Process Design of $CO_2$ Offshore Geological Storage: Evaluation of Physical Property Models and Optimization of Binary Parameter (이산화황 불순물이 이산화탄소 해양 지중저장 공정설계에 미치는 영향 평가: 상태량 모델의 비교 분석 및 이성분 매개변수 최적화)

  • Huh, Cheol;Kang, Seong-Gil;Cho, Mang-Ik
    • Journal of the Korean Society for Marine Environment & Energy
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    • v.13 no.3
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    • pp.187-197
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    • 2010
  • Carbon dioxide Capture and Storage(CCS) is regarded as one of the most promising options to response climate change. CCS is a three-stage process consisting of the capture of carbon dioxide($CO_2$), the transport of $CO_2$ to a storage location, and the long term isolation of $CO_2$ from the atmosphere for the purpose of carbon emission mitigation. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the $CO_2$ mixture captured from the power plants and steel making plants contains many impurities such as $N_2$, $O_2$, Ar, $H_2O$, $SO_2$, $H_2S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification, transport and injection processes. In order to design a reliable $CO_2$ marine geological storage system, it is necessary to analyze the impact of these impurities on the whole CCS process at initial design stage. The purpose of the present paper is to compare and analyse the relevant physical property models including BWRS, PR, PRBM, RKS and SRK equations of state, and NRTL-RK model which are crucial numerical process simulation tools. To evaluate the predictive accuracy of the equation of the state for $CO_2-SO_2$ mixture, we compared numerical calculation results with reference experimental data. In addition, optimum binary parameter to consider the interaction of $CO_2$ and $SO_2$ molecules was suggested based on the mean absolute percent error. In conclusion, we suggest the most reliable physical property model with optimized binary parameter in designing the $CO_2-SO_2$ mixture marine geological storage process.

Effect of Nitrogen Impurity on Process Design of $CO_2$ Marine Geological Storage: Evaluation of Equation of State and Optimization of Binary Parameter (질소 불순물이 이산화탄소 해양 지중저장 공정설계에 미치는 영향 평가: 상태방정식의 비교 분석 및 이성분 매개변수 최적화)

  • Huh, Cheol;Kang, Seong-Gil
    • Journal of the Korean Society for Marine Environment & Energy
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    • v.12 no.3
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    • pp.217-226
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    • 2009
  • Marine geological storage of $CO_2$ is regarded as one of the most promising options to response climate change. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources, to transport to the storage sites and to store $CO_2$ into the marine geological structure such as deep sea saline aquifer. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the captured $CO_2$ mixture contains many impurities such as $N_2$, $O_2$, Ar, $H_2O$, $SO_x$, $H_2S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification and transport processes. In order to design a reliable $CO_2$ marine geological storage system, it is necessary to perform numerical process simulation using thermodynamic equation of state. The purpose of the present paper is to compare and analyse the relevant equations of state including PR, PRBM, RKS and SRK equation of state for $CO_2-N_2$ mixture. To evaluate the predictive accuracy of the equation of the state, we compared numerical calculation results with reference experimental data. In addition, optimum binary parameter to consider the interaction of $CO_2$ and $N_2$ molecules was suggested based on the mean absolute percent error. In conclusion, we suggest the most reliable equation of state and relevant binary parameter in designing the $CO_2-N_2$ mixture marine geological storage process.

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Uncertainties Influencing the Collapse Capacity of Steel Moment-Resisting Frames (철골모멘트 골조의 붕괴성능에 영향을 미치는 불확실성 분석)

  • Shin, Dong-Hyeon;Kim, Hyung-Joon
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.28 no.4
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    • pp.351-359
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    • 2015
  • In order to exactly evaluate the seismic collapse capacity of a structure, probabilistic approach is required by considering uncertainties related to its structural properties and ground motion. Regardless of the types of uncertainties, they influence on the seismic response of a structures and their effects are required to be estimated. An incremental dynamic analysis(IDA) is useful to investigate uncertainty-propagation due to ground motion. In this study, a 3-story steel moment-resisting frame is selected for a prototype frame and analyzed using the IDA. The uncertainty-propagation is assessed with categorized parameters representing epistemic uncertainties, such as the seismic weight, the inherent damping, the yield strength, and the elastic modulus. To do this, the influence of the uncertainty-propagation to the seismic collapse capacity of the prototype frame is probabilistically evaluated using the incremental dynamic analyses based on the Monte-Carlo simulation sampling with the Latin hypercube method. Of various parameters related to epistemic uncertainty-propagation, the inherent damping is investigated to be the most influential parameter on the seismic collapse capacity of the prototype frame.

A Study on the Validity of the Prediction of Binaural Parameters by 5 Channel Microphone System (5채널 마이크로폰 시스템을 활용한 공간감 지표 예측의 타당성에 관한 연구)

  • Jang Jae-Hee;Oh Yang-Ki;Jeong Dae-Up;Jeong Hyok
    • The Journal of the Acoustical Society of Korea
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    • v.24 no.2
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    • pp.103-110
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    • 2005
  • Providing adequate amount of spatial impression for spaciousness) has been known to be one of the most important design considerations for the good acoustics of rooms for music. and the measurement, of room acoustics using parameters. such as LEF and IACC, forms an essential part of such evaluation. However. it is unavoidable to use different transducers (figure of eight microphones. head and torso) for the measurement of each parameter and it tends to make the measurement procedure complicated. The Present work tried to provide a simpler way to measure these binaural room acoustic parameters including monaural ones with a single measurement system using both spatial information collected through a 5-channel microphone and a trained neural network. A computer simulation program, CATT-Acoustic V7.2. which allowed us to obtain exactly the same spatial information as a 5-channel microphone was used. since it requires quite a large amount of data for practical training of a neural network. Since each reflection has different energy. delay and direction, energy should be integrated properly. the concept of ray tracing method was applied inversely in this work. Also applying weightings according to the delay times was considered in this work. Finally, predicted results were compared with the measured data md their correlations were analyzed and discussed.

Performance Evaluation and Offset Time Decision for Supporting Differential Multiple Services in Optical Burst Switched Networks (광 버스트 교환 망에서 차등적 다중 서비스 제공을 위한 offset 시간 결정 및 성능 평가)

  • So W.H.;im Y.C.K
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.41 no.1
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    • pp.1-12
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    • 2004
  • In this paper, we take advantage of the characteristics of optical burst switching (OBS) to support service-differentiation in optical networks. With the offset time between control packet and burst data, the proposed scheme uses different offset time of each service class. As contrasted with the Previous method, in which the high Priority service use only long offset time, it derives the burst loss rate as a QoS parameter in consideration of conservation law and given service-differential ratios and decides a reasonable offset time for this QoS finally Firstly proposed method classifies services into one of high or low class and is an algorithm deciding the offset time for supporting the required QoS of high class. In order to consider the multi-classes environment, we expand the analysis method of first algorithm and propose the second algorithm. It divides services into one of high or low group according to their burst loss rate and decides the offset time for high group, and lastly cumulates the offset time of each class. The proposed algorithms are evaluated through simulation. The result of simulation is compared with that of analysis to verify the proposed scheme.