• Structural Engineering and Mechanics
• /
• v.80 no.5
• /
• pp.539-551
• /
• 2021

With the continuous increase of computational capacity, more and more complex nonlinear elastoplastic constitutive models were developed to study the mechanical behavior of elastoplastic materials. These constitutive models generally contain a large amount of physical and phenomenological parameters, which often require a large amount of computational costs to determine. In this paper, an inverse parameter determination method is proposed to identify the constitutive parameters of elastoplastic materials, with the consideration of both strain rate effect and temperature effect. To carry out an efficient design, a hybrid optimization algorithm that combines the genetic algorithm and the Nelder-Mead simplex algorithm is proposed and developed. The proposed inverse method was employed to determine the parameters for an elasto-viscoplastic constitutive model and Johnson-cook model, which demonstrates the capability of this method in considering strain rate and temperature effect, simultaneously. This hybrid optimization algorithm shows a better accuracy and efficiency than using a single algorithm. Finally, the predictability analysis using partial experimental data is completed to further demonstrate the feasibility of the proposed method.

• International conference on construction engineering and project management
• /
• 2005.10a
• /
• pp.791-795
• /
• 2005

A simulation model is proposed to evaluate the effect of correlations between activity durations on the overall project duration. The proposed model incorporates NORTA, a recent developed statistical method, into the simulation process to allow arbitrarily specified marginal distributions for activity durations and any desired correlation structure. The generality is of practical value when systematic data is not available and planners have to rely on arbitrary experts' estimation, which may involve a mixed situation when some activity durations are continuously distributed whereas others are discrete outcomes. The proposed model is validated by showing that the correlation coefficients of the simulation results are close to the originally specified ones. The simulation results are compared to two conventional approaches: PERT and simulation without correlation. The comparisons illustrate that the proposed model can provide important management information, which would otherwise be distorted due to the neglect of the correlations between activity durations.

• The Journal of the Acoustical Society of Korea
• /
• v.19 no.5
• /
• pp.20-26
• /
• 2000

In speaker verification system the HMM(hidden Markov model) parameter updating using small amount of data and the priori threshold decision are crucial factor for dealing with long-term variability in people voices. In the paper we present the speaker model updating technique which can be adaptable to the session-to-intra speaker variability and the priori threshold determining technique. The proposed technique decreases verification error rates which the session-to-session intra-speaker variability can bring by adapting new speech data to speaker model parameter through Baum Welch re-estimation. And in this study the proposed priori threshold determining technique is decided by a hybrid score measurement which combines the world model based technique and the cohen model based technique together. The results show that the proposed technique can lead a better performance and the difference of performance is small between the posteriori threshold decision based approach and the proposed priori threshold decision based approach.

• Current Photovoltaic Research
• /
• v.11 no.1
• /
• pp.27-33
• /
• 2023

In this paper, a machine learning model by using a regression algorithm is proposed to estimate the power generation performance of the BIPV system. The physical model formula for estimating the generation performance and the proposed model were compared and analyzed. For the physical model formula, simple efficiency model, temperature correction model, and regressive physics model for changing an irradiance were used. As a result, when comparing the regressive physics model for changing an irradiance and the proposed model with the actual generation measured data, the respective RMSE values are 0.1497 kW, 0.0451 kW and the accuracy values are 86.44%, and 96.56%. Therefore, the proposed model implemented in this experiment can be useful in estimating power generation.

• Phonetics and Speech Sciences
• /
• v.2 no.3
• /
• pp.141-148
• /
• 2010

In this paper, voice activity detection (VAD) for dual-channel noisy speech recognition is proposed in which spatial cues are employed. In the proposed method, a probability model for speech presence/absence is constructed using spatial cues obtained from dual-channel input signal, and a speech activity interval is detected through this probability model. In particular, spatial cues are composed of interaural time differences and interaural level differences of dual-channel speech signals, and the probability model for speech presence/absence is based on a Gaussian kernel density. In order to evaluate the performance of the proposed VAD method, speech recognition is performed for speech segments that only include speech intervals detected by the proposed VAD method. The performance of the proposed method is compared with those of several methods such as an SNR-based method, a direction of arrival (DOA) based method, and a phase vector based method. It is shown from the speech recognition experiments that the proposed method outperforms conventional methods by providing relative word error rates reductions of 11.68%, 41.92%, and 10.15% compared with SNR-based, DOA-based, and phase vector based method, respectively.

• Journal of Power Electronics
• /
• v.18 no.1
• /
• pp.1-10
• /
• 2018

Recently, the nine-switch inverter has been proposed as a dual output inverter. To date, studies on the control strategies for NSIs have been mostly combined with their application. However, in this paper, a mathematical model and control strategy for nine-switch inverters has been proposed in view of the topology. A switching function model and equivalent circuit model of a nine-switch inverter have been built in ${\alpha}{\beta}$ coordinates. Then, a novel current observer with an improved integrator is proposed based on the switching function model, and a direct power control strategy is proposed. No current sensors are used in the proposed strategy, and only two voltage sensors are employed. The performance of the proposed control method is verified by simulation and experimental results.

• Journal of Positioning, Navigation, and Timing
• /
• v.8 no.2
• /
• pp.59-68
• /
• 2019

This paper proposed a method to estimate the vertical delay from the slant delay, which can improve accuracy of the ionospheric correction of SBAS. Proposed method used Chapman profile which is a model for the vertical electron density distribution of the ionosphere. In the proposed method, we assumed that parameters of Chapman profile are given and the vertical ionospheric can be modeled with linear function. We also divided ionosphere into multi-layer. For the verification, we converted slant ionospheric delays to vertical ionospheric delays by using the proposed method and generated the ionospheric correction of SBAS with vertical delays. We used International Reference Ionosphere (IRI) model for the simulation to verification. As a result, the accuracy of ionospheric correction from proposed method has been improved for 17.3% in daytime, 10.2% in evening, 2.1% in nighttime, compared with correction from thin shell model. Finally, we verified the method in the SBAS user domain, by comparing slant ionospheric delays of users. Using the proposed method, root mean square value of slant delay error decreased for 23.6% and max error value decreased for 27.2%.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.32-35
• /
• 1996

In this paper, it is shown that the dominant errors of baro-altimeters can be characterized by bias and scale factor errors. Also an optimal filter for estimating both bias and scale factor is derived based on the concept of model transition. The optimal filter is, however, not realizable because the model transition hypotheses increase exponentially. Therefore a realizable suboptimal filter using the interacting multiple model(IMM) technique is proposed. Computer simulation results show that the estimation errors of the proposed filter are smaller than those of the conventional least squares algorithm with a forgetting factor when both the bias and the scale factor are varying.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.3
• /
• pp.613-618
• /
• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• ETRI Journal
• /
• v.29 no.4
• /
• pp.527-529
• /
• 2007

In this paper, a decision-tree-based Markov model for phrase break prediction is proposed. The model takes advantage of the non-homogeneous-features-based classification ability of decision tree and temporal break sequence modeling based on the Markov process. For this experiment, a text corpus tagged with parts-of-speech and three break strength levels is prepared and evaluated. The complex feature set, textual conditions, and prior knowledge are utilized; and chunking rules are applied to the search results. The proposed model shows an error reduction rate of about 11.6% compared to the conventional classification model.