• Journal of Energy Engineering
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• v.17 no.2
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• pp.107-115
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• 2008

The numerical analysis of energy and material balance, and plant performance has been carried out when applying the heavy residual oil instead of heavy oil to the existing heavy oil power station. The performance analysis model has been constructed for A heavy oil power station in Korea, and the modeling results were compared with the design data in order to ensure the validity of the model, and further compared with the plant operation data. With the heavy residual oil, the simulation gave 315 MW in power output, which is higher than that of the heavy oil combustion, but the plant efficiency turned out to be lower. The sensitivity analysis of heat rate for the changes in cooling water and ambient temperature, flue gas recirculation and power output has provided valuable information for the optimal operation of the power station.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.145-156
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• 2024

The mechanical properties of the concrete-frozen soil interface play a significant role in the stability and service performance of construction projects in cold regions. Current research mainly focuses on the precast concrete-frozen soil interface, with limited consideration for the more realistic cast-in-place concrete-frozen soil interface. The two construction methods result in completely different contact surface morphologies and exhibit significant differences in mechanical properties. Therefore, this study selects silty clay as the research object and conducts direct shear tests on the concrete-frozen soil interface under conditions of initial water content ranging from 12% to 24%, normal stress from 50 kPa to 300 kPa, and freezing temperature of -3℃. The results indicate that (1) both interface shear stress-displacement curves can be divided into three stages: rapid growth of shear stress, softening of shear stress after peak, and residual stability; (2) the peak strength of both interfaces increases initially and then decreases with an increase in water content, while residual strength is relatively less affected by water content; (3) peak strength and residual strength are linearly positively correlated with normal stress, and the strength of ice bonding is less affected by normal stress; (4) the mechanical properties of the cast-in-place concrete-frozen soil interface are significantly better than those of the precast concrete-frozen soil interface. However, when the water content is high, the former's mechanical performance deteriorates much more than the latter, leading to severe strength loss. Therefore, in practical engineering, cast-in-place concrete construction is preferred in cases of higher negative temperatures and lower water content, while precast concrete construction is considered in cases of lower negative temperatures and higher water content. This study provides reference for the construction of frozen soil-structure interface in cold regions and basic data support for improving the stability and service performance of cold region engineering.

• Journal of the Korea Concrete Institute
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• v.26 no.4
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• pp.555-562
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• 2014

To perform performance-based seismic design of buildings, it is necessary clear goal for usage and stability after an earthquake. To clear this goal, it requires a review of the constituent material of the building and, in particular, a member used as an indicator of the residual strain is useful. There are more usage of prestressed concrete because of prestressing steel witch has characteristics of the origin-oriented. In this study, the goal is estimating of residual strain on the prestressed concrete beam member. The expression for angle of deformed prestressed concrete beam member was obtained from using of curvature on the critical section and the equivalent plastic hinge length based on 'equivalent plastic hinge length method'. Considering the balance of strength and deformation conditions, suitable analysis values were derived from 'split Element Method'. Through various parametric studies, various factors affecting the residual strain were decided. Based on the results of this study, it is expected many researches will be proceed in the future.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.35-43
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• 1999

In this paper, a new robust adaptive algorithm and a post-processing method are proposed to improve the performance of AEC without computational burden. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. To reduce the residual echoes, a new post-processing method, which is co-operated with the proposed noise-robust adaptive algorithm, is proposed in this paper. The method is based on the correlation of the desired signal and the estimation error signal. The residual echoes are attenuated as proportional to the correlation normalized with the power of desired signals. The normalized correlation plays a role as Wiener filter for residual echoes. In the double-talk situation, the estimation error signals, that are residual echoes, dominantly include the near-end speaker's speech and the normalized correlation closes to 1. Therefore, the near-end speaker's speech can be transmitted without being attenuated. When the desired signals consists of only the acoustic echoes, the residual echoes are mostly attenuated and canceled by the proposed post-processor. The computation of AEC using the proposed post-processor is comparable to NLMS algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.35-41
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• 2017

In this paper, we analyze the performance of the recently introduced Hint-knowledge distillation (KD) training approach based on the teacher-student framework for knowledge distillation and knowledge transfer. As a deep neural network (DNN) considered in this paper, the deep residual network (ResNet), which is currently regarded as the latest DNN, is used for the teacher-student framework. Therefore, when implementing the Hint-KD training, we investigate the impact on the weight of KD information based on the soften factor in terms of classification accuracy using the widely used open deep learning frameworks, Caffe. As a results, it can be seen that the recognition accuracy of the student model is improved when the fixed value of the KD information is maintained rather than the gradual decrease of the KD information during training.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.455-464
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• 2020

Reinforcement corrosion is one of the major problems in the durability of reinforced concrete structures exposed to aggressive environments. Deterioration caused by reinforcement corrosion reduces the durability and the safety margin of concrete structures, causing excessive costs in managing these structures safely. This paper aims to investigate the effects of reinforcement corrosion on the load bearing capacity deterioration of the corroded reinforced concrete structures. A new analytical method is proposed to predict the crack growth of cover concrete and evaluate the residual strength of concrete structures with corroded reinforcement failing in bond. The structural performance indicators, such as concrete crack growth and flexural strength deterioration rate, are assumed to be a stochastic process for lifetime distribution modelling of structural performance deterioration over time during the life cycle. The Weibull life evolution model is employed for analysing lifetime reliability and estimating remaining useful life of the corroded concrete structures. The results for the worked example show that the proposed approach can provide a reliable method for lifetime performance assessment of the corroded reinforced concrete structures.

• Speech Sciences
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• v.10 no.3
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• pp.263-277
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• 2003

One of major problems in speech recognition is performance degradation due to the mismatch between the training and test environments. Recently, Stereo-based Piecewise LInear Compensation for Environments (SPLICE), which is frame-based bias removal algorithm for cepstral enhancement using stereo training data and noisy speech model as a mixture of Gaussians, was proposed and showed good performance in noisy environments. In this paper, we propose several methods to improve the conventional SPLICE. First we apply Cepstral Mean Subtraction (CMS) as a preprocessor to SPLICE, instead of applying it as a postprocessor. Secondly, to compensate residual distortion after SPLICE processing, two-stage SPLICE is proposed. Thirdly we employ phonetic information for training SPLICE model. According to experiments on the Aurora 2 database, proposed method outperformed the conventional SPLICE and we achieved a 50% decrease in word error rate over the Aurora baseline system.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.1-7
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• 2018

The image-to-image translation is one of the deep learning applications using image data. In this paper, we aim at improving the performance of object transfiguration which transforms a specific object in an image into another specific object. For object transfiguration, it is required to transform only the target object and maintain background images. In the existing results, however, it is observed that other parts in the image are also transformed. In this paper, we have focused on the structure of artificial neural networks that are frequently used in the existing methods and have improved the performance by adding constraints to the exiting structure. We also propose the advanced structure that combines the existing structures to maintain their advantages and complement their drawbacks. The effectiveness of the proposed methods are shown in experimental results.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.3032-3037
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• 2008

Variable Valve Timing (VVT) system can be used to improve fuel economy, performance and emissions. This study is identified the effect of VVT in terms of wide open throttle torque, Residual gas fraction, volume efficiency. Engine cycle simulations are performed on 2.0L DOHC in-line 4-cylinder SI engine by using WAVE of Ricardo. Results of the simulations had good agreement with WOT torque experimental data, and helped to predict the tendency of performance as the valve timings change. WOT torque was higher when intake valves were closed early for low rpm and late for high rpm.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.