• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.184-189
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• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• Proceedings of the KIEE Conference
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• 2001.11a
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• pp.140-145
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• 2001

In order to apply for silane crosslinking process to PTC products, especially. self-regulating heater, silane crosslinked samples were compared with radiation crosslinked sample in terms of PTC characteristic and PTC stability. Silane crosslinked samples have lower PTC intensity than radiation crosslinked sample. It can be explained that multiple networks of silane crosslink restrict the movement of molecules in the composite as the sample is heating. As a result of heat cycles at $140^{\circ}C$, the changes of volume resistivity and PTC intensity for radiation crosslinked sample were higher than those of silane crosslinked samples. Whereas, in the case of heat cycles at $85^{\circ}C$, which is limiting temperature for self-regulating heater, both silane and radiation crosslinked samples show stable results without pronounce changes of resistivity up to five cycles.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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• pp.1211-1217
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• 2001

This paper presnts a controller for the heat capacity of thermal storage systems using off-peak electricity which is composed of an identifier using neural networks and a storage time adjuster in order to store exactly the required thermal energy without loss. Since thermal storage systems have nonlinear characteristics and large time constant, even if we predict the heating load accurately, it is very difficult to store exactly the required thermal energy. Thus, in the neural network for the identifier, the adaptive learning rate for high learning speed and bit inputs based on state changes of thermal storage power source are used. Also a hardware for the controller using a microprocessor is developed. The performance of the proposed controller is shown by experiment.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.604-607
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• 2006

Self-heating 키 분배 기법은 불안정한 네트워크 환경에서 그룹 키를 설정할 수 있게 하며, 그룹을 가입하거나 탈퇴하는 멤버 노드들에 의한 공모 공격에 대한 안전성으로 인하여, 센서 네트워크 환경에 적합한 방식이다. 하지만 기존에 제안된 Self-healing 키 분배 기법들은 브로드캐스트 되는 메시지의 통신량과 그룹 멤버의 그룹 키 복원을 위한 정보 저장량 측면에서 비효율적인 문제가 있다. 본 논문에서는 슬라이딩 윈도우(Sliding Window) 개념을 도입함으로써 향상된 Self-healing 키 분배 기법을 제안하여, 브로드캐스트 되는 메시지의 크기를 줄이고 멤버 노드 단위의 메모리에 대한 효율성을 향상시킨다.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.2066-2073
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• 2021

The use of nuclear reactors is a large studied possible solution for thermochemical water splitting cycles. Nevertheless, there are several problems that have to be solved. One of them is to increase the efficiency of the cycles. Hence, in this paper, a thermal energy optimization of a Sulfur-Iodine nuclear hydrogen production cycle was performed by means a heuristic method with the aim of minimizing the energy targets of the heat exchanger network at different minimum temperature differences. With this method, four different heat exchanger networks are proposed. A reduction of the energy requirements for cooling ranges between 58.9-59.8% and 52.6-53.3% heating, compared to the reference design with no heat exchanger network. With this reduction, the thermal efficiency of the cycle increased in about 10% in average compared to the reference efficiency. This improves the use of thermal energy of the cycle.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.135-135
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• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• ETRI Journal
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• v.34 no.5
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• pp.713-718
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• 2012

Sensor nodes in ubiquitous sensor networks require autonomous replacement of deteriorated gas sensors with reserved sensors, which has led us to develop an encapsulation technique to avoid poisoning the reserved sensors and an autonomous activation technique to replace a deteriorated sensor with a reserved sensor. Encapsulations of $In_2O_3$ nanoparticles with poly(ethylene-co-vinyl alcohol) (EVOH) or polyvinylidene difluoride (PVDF) as gas barrier layers are reported. The EVOH or PVDF films are used for an encapsulation of $In_2O_3$ as a sensing material and are effective in blocking $In_2O_3$ from contacting formaldehyde (HCHO) gas. The activation process of $In_2O_3$ by removing the EVOH through heating is effective. However, the thermal decomposition of the PVDF affects the property of the $In_2O_3$ in terms of the gas reactivity. The response of the sensor to HCHO gas after removing the EVOH is 26%, which is not significantly different with the response of 28% in a reference sample that was not treated at all. We believe that the selection of gas barrier materials for the encapsulation and activation of $In_2O_3$ should be considered because of the ill effect the byproduct of thermal decomposition has on the sensing materials and other thermal properties of the barrier materials.

• Corrosion Science and Technology
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• v.23 no.2
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• pp.83-92
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• 2024

A hot-dip simulator was utilized to replicate abnormal coating buildup observed during line stops at galvanize lines, assessing the influence of processing conditions on buildup (up to 14 mm/side). Steel samples from 19 coils (comprising IF, BH, LCAK, HSLA, DP600-DP1180, Si: 0.006 - 0.8 wt%, P: 0.009 - 0.045 wt%) were examined to explore the phenomenon of heavy coating growth. It was discovered that heavy coating buildup (~3 mm/h) and rapid strip dissolution (~0.17 mm/h) in a GA or GI pot can manifest with specific combinations of steel chemistry and processing conditions. The results reveal the formation of a unique coating microstructure, characterized by a blend of bulky Zeta crystals and free Zn pockets/networks due to the "Sandlin" growth mechanism. Key variables contributing to abnormal coating growth include steel Si content, anneal temperature, dew point in heating and soaking furnaces, Zn pot temperature, Zn bath Al%, and cold-rolling reduction%. At ArcelorMittal Dofasco galvanize lines, an automatic online warning system for operators and special scheduling for incoming Si-bearing steels have been implemented, effectively preventing further heavy buildup occurrences.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.588-595
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• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.179-184
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• 2017

The cage of a tapered roller bearing keeps the gap between the rollers, which prevents friction, wear and suppresses heating. The material of the cage is changing from metal to plastic for lightening the weight. If the cage is severely deformed due to resonance, the roller may not be able to roll and even get off the cage. In this paper, the dynamic characteristics of the cage is analyzed according to the cage material. Under the assumption that a train runs at the highest speed, frequency harmonics of that speed is calculated, and the comparative analysis is carried out in order to select the optimum thickness of the cage, which is easy to change among the cage design variables for avoiding the resonance.