• 한국컴퓨터정보학회논문지
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• 제12권5호
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• pp.321-326
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• 2007

냉동 컨테이너의 내용물들은 적정 온도를 유지하지 못하면 화물의 파손이 발생할 수 있다. 현재 냉동 컨테이너는 전담 관리원이 주기적이고 수동적으로 컨테이너의 내부 온도를 감시한다. 그래서 컨테이너 내부 온도를 실시간으로 감시할 수 없기 때문에 화물에서 문제가 발생하면 즉각적으로 대응하지 못한다. 본 논문은 무선 센서 네트워크를 이용하여 냉동 컨테이너에 센서 노드를 탑재하여 컨테이너 내부 온도를 실시간으로 감시하는 시스템을 제안한다. 무선 센서 네트워크는 독립된 센서들을 물리적 공간에 배치하여 주위의 온도, 빛 가속도 등의 정보를 감지하여, 무선으로 전송할 수 있는 기술이다. 제안된 시스템은 냉동 컨테이너의 상태를 실시간으로 감시하기 때문에 컨테이너의 적정 온도를 유지함으로써 화물을 효율적으로 관리할 수 있다.

• Advances in nano research
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• 제12권1호
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• pp.81-99
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• 2022

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE. The outcome reveals that MEE layer, temperature change, GNP weight function, and GNP distribution patterns GNP weight function have significant influence on the critical temperature and voltage of cylindrical shell made from GNP nanocomposites core with MEE face sheet on outer of the shell.

• Journal of Communications and Networks
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• 제11권2호
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• pp.148-156
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• 2009

The fixed spectrum assignment policy in today's wireless networks leads to inefficient spectrum usage. Cognitive radio network is a new communication paradigm that enables the unlicensed users to opportunistically use the spatio-temporally unoccupied portions of the spectrum, and hence realizing a dynamic spectrum access (DSA) methodology. Interference temperature model proposed by Federal Communications Commission (FCC) permits the unlicensed users to utilize the licensed frequencies simultaneously with the primary users provided that they adhere to the interference temperature constraints. In this paper, we formulate two NP-hard optimal scheduling methods that meet the interference temperature constraints for cognitive radio networks. The first one maximizes the network throughput, whereas the second one minimizes the scheduling delay. Furthermore, we also propose suboptimal schedulers with linear complexity, referred to as maximum frequency selection (MFS) and probabilistic frequency selection (PFS). We simulate the throughput and delay performance of the optimal as well as the suboptimal schedulers for varying number of cognitive nodes, number of primary neighbors for each cognitive node, and interference temperature limits for the frequencies. We also evaluate the performance of our proposed schedulers under both additive white gaussian noise (AWGN) channels and Gilbert-Elliot fading channels.

• 대한기계학회논문집B
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• 제21권3호
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• pp.457-472
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• 1997

We studied the temperature distribution and heat transfer characteristics of TEFC induction motor with thermal network program for more efficient design and better cooling performance of it. We knew the characteristics and the windage loss of outer cooling fan from fan test experiments. Frame axial and peripheral heat transfer coefficients and endwinding heat transfer coefficient were measured by various model experiments and then, compared with other experimental results. Frame was the main heat transfer surface, load-side and fan-side surface were not thermally symmetric from the heat flux distribution analysis. Steady and unsteady temperature distributions were measured by real motor experiments. From the results, we knew that rotor surface temperature was higher than coil temperature and the hottest spot in the coil was loadside endwinding outside surface. We compared the simulation results with those of real motor test and the two results showed a good agreement.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.83-86
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• 2001

In injection molding, short shot is one of the frequent and fatal defects. Experts of injection molding usually adjust process conditions such as injection time, mold temperature, and melt temperature because it is the most economic way in time and cost. However it is a difficult task to find appropriate process conditions for troubleshooting of short shot as injection molding process is a highly nonlinear system and process conditions are coupled. In this paper, a fuzzy neural network(FNN) has been applied to injection molding process to shorten troubleshooting time of short shot. Based on melt temperature and fill time, a reasonable initial mold temperature is recommended by the FNN, and then the mold temperature is inputted to injection molding process. Depending on injection molding result, specifically the insufficient quantity of an injection molded part, an appropriate mold temperature is recommend repeatedly through the FNN.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2003년도 하계학술대회 논문집
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• pp.112-112
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• 2003

• 한국정밀공학회지
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• 제19권2호
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• pp.114-125
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• 2002

As semi-solid forging (SSF) is compared with conventional easting such as gravity die-easting and squeeze casting, the product without inner defects can be obtained from semi-solid forming and globular microstructure as well. Generally speaking. SSF consists of reheating, forging, ejecting precesses. In the reheating process, the materials are heated up to the temperature between the solidus and liquidus line at which the materials exists in the form of liquid-solid mixture. The process variables such as reheating time, reheating temperature, reheating holding time, and induction heating power have much effect on the quality of the reheated billets. It is difficult to consider all the variables at the same time when predicting the quality. In this paper, Taguchi method, regression analysis and neural network were applied to analyze the relationship between processing conditions and solid fraction. A356 alloy was used for the present study, and the learning data were extracted by the reheating experiments. Results by neural network were on good agreement with those by experiment. Polynominal regression analysis was formulated by using the test data from neural network. Optimum processing condition was calculated to minimize the grain size, solid fraction standard deviation, otherwise, to maximize the specimen temperature average. In this time, discussion is liven about reheating process of row material and results are presented with regard to accurate process variables for proper solid fraction, specimen temperature and grain size.

• 한국전기전자재료학회논문지
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• 제36권6호
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• pp.603-608
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• 2023

Carbon black with high purity and excellent conductivity is used as a conductive filler in the semiconductive compound for EHV (Extra High Voltage) power cables of 345 kV or higher. When carbon black and CNT (carbon nanotube) are applied together as a conductive filler of a semiconductive compound, stable electrical properties of the semiconductive compound can be maintained even though the amount of conductive filler is significantly reduced. In EHV power cables, since the semi-conductive layer is close to the conductor, stable electrical characteristics are required even under high-temperature conditions caused by heat generated from the conductor. In this study, the theoretical principle that a semiconductive compound applied with carbon black and CNT can maintain excellent electrical properties even under high-temperature conditions was studied. Basically, the conductive fillers dispersed in the matrix form an electrical network. The base polymer and the matrix of the composite, expands by heat under high temperature conditions. Because of this, the electrical network connected by the conductive fillers is weakened. In particular, since the conductive filler has high thermal conductivity, the semiconductive compound causes more thermal expansion. Therefore, the effect of CNT as a conductive filler on the thermal conductivity, thermal expansion coefficient, and volume resistivity of the semiconductive compound was studied. From this result, thermal expansion and composition of the electrical network under high temperature conditions are explained.

• 제어로봇시스템학회논문지
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• 제12권6호
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• pp.539-546
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• 2006

The Air Quality Sensor(AQS), located near the fresh air inlet, serves to reduce the amount of pollution entering the vehicle cabin through the HVAC(heating, ventilating, and air conditioning) system by sending a signal to close the fresh air inlet door/ventilation flap when the vehicle enters a high pollution area. The sensor module which includes two independent sensing elements for responding to diesel and gasoline exhaust gases, and temperature sensor and humidity sensor was designed for intelligent AQS in automobile. With this sensor module, AVR microcontroller was designed with back propagation neural network to a powerful gas/vapor pattern recognition when the motor vehicles pass a pollution area. Momentum back propagation algorithm was used in this study instead of normal backpropagation to reduce the teaming time of neural network. The signal from neural network was modified to control the inlet of automobile and display the result or alarm the situation in this study. One chip microcontroller, ATmega 128L(ATmega Ltd., USA) was used for the control and display. And our developed system can intelligently reduce the malfunction of AQS from the dampness of air or dense fog with the backpropagation neural network and the input sensor module with four sensing elements such as reducing gas sensing element, oxidizing gas sensing element, temperature sensing element and humidity sensing element.

• 대한건축학회논문집:구조계
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• 제36권3호
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• pp.169-176
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• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.