• Structural Monitoring and Maintenance
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• v.3 no.2
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• pp.129-144
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• 2016

We present a methodology to validate with monitoring data finite element models of existing concrete dams: numerical analyses are performed to assess the structural response under the effects of seasonal loading conditions, represented by hydrostatic pressure on the upstream-downstream dam surfaces and thermal variations as recorded by a thermometers network. We show that the stiffness effect of the rock foundation and the surface degradation of concrete due to aging are crucial aspects to be accounted for a correct interpretation of the real behavior. This work summarizes some general procedures developed by this research group at Politecnico di Milano on traditional static monitoring systems and two significant case studies: a buttress gravity and an arch-gravity dam.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.263-271
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• 2014

Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.18 no.3
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• pp.1-6
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• 2022

Ground source heat pump (GSHP) system is highly efficient and environment-friendly and supplies heating, cooling and hot water to buildings. For an optimal design of the GSHP system, the ground thermal properties should be determined to estimate the heat exchange rate between ground and borehole heat exchangers (BHE) and the system performance during long-term operating periods. However, the process increases the initial cost and construction period, which causes the system to be hindered in distribution. On the other hand, much research has been applied to the artificial neural network (ANN) to solve problems based on data efficiently and stably. This research proposes the predictive performance model utilizing ANN considering local characteristics and weather data for the predictive performance model. The ANN model predicts the entering water temperature (EWT) from the GHEs to the heat pump for the modular GHEs, which were developed to reduce the cost and spatial disadvantages of the vertical-type GHEs. As a result, the temperature error between the data and predicted results was 3.52%. The proposed approach was validated to predict the system performance and EWT of the GSHP system.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.841-852
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• 2023

Gas turbines, which are used as generators for frequency regulation of the domestic power system, are increasing in use due to the carbon-neutral policy, quick startup and shutdown, and high thermal efficiency. Since the gas turbine rotates the turbine using high-temperature flame, the turbine inlet temperature is acting as a key factor determining the performance and lifespan of the device. However, since the inlet temperature cannot be directly measured, the temperature calculated by the manufacturer is used or the temperature predicted based on field experience is applied, which makes it difficult to operate and maintain the gas turbine in a stable manner. In this study, we present a model that can predict the inlet temperature of a reheat gas turbine based on Deep Neural Network (DNN), which is widely used in artificial neural networks, and verify the performance of the proposed DNN based on actual data.

• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1689-1695
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• 2019

This study suggests a simple economic model to analyze electricity grid that consists of different power sources. The substitutability of renewable energy for nuclear power in Korean electricity transmission network is investigated by suggested model. The monthly data from January 2006 to December 2013 reported by Electricity Power Statistics Information System (EPSIS) of Korea Power EXchange (KPX) are used. To estimate the elasticities of substitution among four power sources (i.e. coal, natural gas, nuclear power, and renewable energy), this paper uses the trans-log cost function model on which local concavity restrictions are imposed. The estimated Hicks-Allen and Morishima elasticity of substitution shows that renewable electricity and nuclear power are complementary. The results also evidenced that renewable electricity and fossil fueled thermal power generation are substitutes.

• Current Optics and Photonics
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• v.6 no.5
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• pp.479-488
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• 2022

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality β can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality β value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and certain pumping light distributions.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.10
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• pp.823-831
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• 2007

This paper presents the experimental and simulation study of a loop heat pipe (LHP) that can be applied to present electronics, space missions and thermal control systems. The present experimental study was carried out employing sintered alumina ceramic wick ($d=2.96\;{\mu}m$, ${\phi}=0.61$). High purity R-134a, R-22 and water were also used as alternative working fluids in addition to ammonia. The experimental study showed that the maximum heat transfer performance for the test LHP in the vertical top heating mode was over 100 Watts when ammonia was used as the working fluid. The simulation results have been compared with the experimental results to validate a simulation model based on the thermal resistance network that was developed to evaluate the performance of LHPs, focusing on their prospective applications in electronics. The simulation model is based on the loop overall energy, mass, and momentum balance. The simulation program can predict the effects of various parameters which affect the performance of LHP within 5% compared with the experimental results.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.29-39
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• 2019

In this study, we introduce a model that satisfies energy efficiency and economical efficiency by introducing and demonstrating cogeneration generators in industrial complexes using various actual data collected at the site. The proposed model is composed of three scenarios, ie, full - time operation, scenario operated according to demand, and a fusion type. In this study, the power generation profit and surplus thermal energy are measured according to the operation of the generator, and the thermal energy is traded according to the demand of the customer to calculate the profit and loss including the heat and evaluate the economic efficiency. As a result of the study, it is relatively profitable to reduce the generation of the generator under the condition that the electricity rate is low and the gas rate is high, while the basic charge is not increased. On the contrary, if the electricity rate is high and the gas rate is low, The more you start up, the more profit you can see. These results show that even a cogeneration power plant with a low economic efficiency due to a low "spark spread" has sufficient economic value if it can sell more than a certain amount of heat energy from a nearby customer and adjust the applied power through peak management.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2E
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• pp.98-104
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• 2002

The present study describes a combination method to recognize the human affective states such as anger, happiness, sadness, or surprise. For this, we extracted emotional features from voice signals and facial expressions, and then trained them to recognize emotional states using hidden Markov model (HMM) and neural network (NN). For voices, we used prosodic parameters such as pitch signals, energy, and their derivatives, which were then trained by HMM for recognition. For facial expressions, on the other hands, we used feature parameters extracted from thermal and visible images, and these feature parameters were then trained by NN for recognition. The recognition rates for the combined parameters obtained from voice and facial expressions showed better performance than any of two isolated sets of parameters. The simulation results were also compared with human questionnaire results.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.7
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• pp.414-420
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• 2003

The domestic needs for larger capability of power sources are increasing to cope with the expanding power load which results from the industrial developments & the progressed life style. In summer, the peak load is mainly due to the non-industrial reasons such as air-conditioners and other cooling equipments. To cover the concentrated peak load in stable, the power transmission lines should be more constructed and efficiently operated. The ampacity design of the underground cable system is generally following international standards such as IEC287, IEC60853 and JCS168 which regards the shape of 100% daily full power loads. It is not so efficient to neglect the real shapes of load curves generally below 60~70% of full load. The dynamic (real time) rating system tends to be used with the measured thermal parameters which make it possible to calculate the maximum ampacity within required periods. In this paper, the CTM(Conductor Temperature Monitoring) which is the base of dynamic rating systems for tunnel environment is proposed by a design of lumped thermal network ($\pi$-type thermal model) and distribution temperature sensor attached configuration, including the estimation results of its performances by load cycle test on 345kV single phase XLPE cable.