• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2006.05a
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• pp.70-73
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• 2006

In the present study, the flow-softening behavior occurring during high temperature deformation of AZ31 Mg alloy was investigated. Flow softening of AZ31 Mg alloy was attributed to (1) thermal softening by deformation heating and (2) microstructural softening by dynamic recrystallization. Artificial neural networks method was used to derive the accurate amounts of thermal softening by deformation heating. A series of mechanical tests (High temperature compression and load relaxation tests) was conducted at various temperatures ($250^{\circ}C{\sim}500^{\circ}C$) and strain rates ($10^{-4}/s{\sim}100/s$) to formulate the recrystallization kinetics and grain size relation. The effect of DRX kinetics on microstructure evolution (fraction of recrystallization) was evaluated by the unified SRX/DRX (static recrystallization/dynamic recrystallization) approaches

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.15-22
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• 2014

This paper presents the implementation of a control system of pellet boiler using wood pellet as carbon neutral material. The system also has the additional features to provide remote controlling and monitoring based on home networking technology through either public switched telephone networks or mobile communication networks. It consists of three kinds of sub-modules; a main controller provides basic and additional features such as a setting of temperature, a supplying of wood pellet, a controlling of ignition and fire-power, and a removing of soot. The second is temperature controller of individual rooms which is connected to the main controller through RS-485 links. And interface modules with PSTN and mobile networks can support remote controlling and monitoring the functions. The test results under the heating area of $172m^2$ show a thermal efficiency of 93.6%, a heating power of 20,640kcal/hr, and a fuel consumption of 5.54kg/hr. These results are superior to those of the conventional pellet boilers. In order to obtain the such high performance, we newly applied a 3-step ignition flow, a flame detection by $C_dS$ sensor, and a fire-power control by fine controlling of shutter to our pellet boiler.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.216-225
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• 1998

Although artificial neural network based on backpropagation algorithm is an excellent system simulator, it has still unsolved problems of its structure-decision and learning method. That is, we cannot find a general approach to decide the structure of the neural network and cannot train it satisfactorily because of the local optimum point which it frequently falls into. In addition, although there are many successful applications using backpropagation learning algorithm, there are few efforts to improve the learning algorithm itself. In this study, we suggest a general way to construct the hidden layer of the neural network using binary genetic algorithm and also propose the various learning methods by which the global minimum value of the teaming error can be obtained. A XOR problem and line heating problems are investigated as examples.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.70.2-70
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• 2001

This paper presents an application model for home automation control. In this work, we propose home application scenarios that are suitable for the home life style and design a control structure integrating various home automation functions, such as lightning, heating, cooling, security, fire protection, telemetering, entertainment and communication. State-of-the-art wired/wireless home networks such as Bluetooth, LonWorks, IEEE1394 and PLC(Power Line Communication) are included in the control structure.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Journal of the Korean Chemical Society
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• v.53 no.5
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• pp.553-559
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• 2009

One of zeolitic imidazolate framework materials (ZIF), ZIF-8, has been synthesized with microwave irradiation and conventional electric heating at $140{\sim}180^{\circ}C}$. ZIFs are porous crystalline materials and are similar to metal organic framework (MOF) materials because both ZIFs and MOFs are composed of both organic and metallic components. ZIFs are very stable and similar to zeolites because ZIFs have tetrahedral networks. ZIF-8, with a decreased crystal size, can be synthesized rapidly with microwave irradiation. The microwave synthesis of ZIF-8 is completed in 4 h at $140{^{\circ}C}$ and the reaction time is decreased by about 5 times compared with the conventional electric heating. The ZIF-8 obtained by microwave heating has larger surface area and micropore volume compared with the ZIF-8 synthesized with conventional electric heating. It can be confirmed that ZIF-8s show type-I adsorption isotherms, explaining the microporosity of the ZIF-8s. Based on FTIR and TGA results, it can be understood that the ZIF-8s have similar bonding and thermal characteristics irrespective of heating methods such as microwave and conventional heating.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.599-605
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• 2018

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

• Applied Biological Chemistry
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• v.34 no.4
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• pp.379-385
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• 1991

Morphological changes of starch granules from 12 different varieties of rice were examined by scanning electron microscopy during heating at 2.5% (w/v) concentration. Rice starch granules proceeded through a similar pattern of progressive morphological changes daring heating, regardless of variety. Rice starch granules began to swell radially in the initial stage of gelatinization and then undergo radial contraction and random tangential expansion to form complex structures in the latter stage of gelatinization temperature range. At higher temperatures, starch granules softened and melted into thin flat discs, and then stretched into thin filaments to form three-dimensional networks. These progressive morphological changes were reflected in the changes of swelling power, solubility and amylograph viscosity of starch. During the transition of melting or softening, swelling power, solubility and amylograph viscosity increased rapidly. The time of loss of granular structure of starch depended on gelatinization temperature range. The ratio of amylose to amylopectin was largely responsible fur the rate of melting or softening and the fineness of a three-dimensional filamentous network above the gelatinization temperature range. Therefore, both the gelatinization temperature range and amylose content of starch affect the rate of cooking, and amylose content of starch affects the final texture of cooked starch paste.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.10
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• pp.1157-1165
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• 2012

In this paper, we measure and analyze propagation characteristic of heating, ventilation, and air conditioning(HVAC) ducts within buildings for wireless networks. We analyze the duct structures, implement the feeders exciting propagating modes, and simulate the excitation characteristic. We measure the propagation characteristic of HAVC ducts at 2.45 GHz WiFi band and compare it with that of LOS and partitioned office environments. We propose the design method of wireless network using HVAC ducts based on our results.

• Journal of Powder Materials
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• v.26 no.5
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• pp.369-374
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• 2019

The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson's r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.