• 한국인터넷방송통신학회논문지
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• 제8권5호
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• pp.51-56
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• 2008

본 논문에서는 이동 무선 통신에 적합한 인쇄형 평면(printed planar) 안테나를 제안한다. 인쇄형 평면 안테나는 간단성, 낮은 가격, 가벼움 등으로 제작이 간단하기 때문에 통신 시스템에 널리 사용된다. 기존의 페치 안테나는 이동 수신자에게 적용하기 위해서는 너무 큰 표면의 사용이 요구된다. 인쇄형 평면 안테나를 사용하여 사이즈를 줄일지라도 상당한 크기의 대역폭이 요구된다. 이러한 단점들을 해결하기 위해서는 기판의 두께는 커야하며, 기판의 유전 상수는 작아야 한다. 이러한 문제점을 해결하기 위해서 본 연구에서는 간단한 형태의 인쇄형 평면 안테나를 제안하고 안테나 크기와 동작 주파수에 의존하는 최적의 입력 임피던스를 보여준다. 제안된 인쇄형 평면 안테나의 성능평가는 프로토타입 안테나 모델을 사용하여 분석한다.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• 소성∙가공
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• 제19권7호
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• pp.411-415
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• 2010

A sandwich panel which is comprised of truss cores faced with solid face sheets is lightweight and multi-functional. So it is widely used to not only structural material but also heat transfer media in transportation field such as airplane, train and vessel. There are various core topologies such as pyramidal and tetrahedral truss, square honeycombs and kagome truss. The study focused on analytical approach to optimize compression and shear quality of the unit cell of PCM with pyramidal configuration. With various unit cell models which have the same core weight per unit area but different truss member angle, analytical solution for effective stress ($\bar{\sigma},\bar{\tau}$), peak stress ($\bar{\sigma}_{peak},\bar{\tau}_{peak}$) by yielding and buckling, relative density ($\bar{\rho}_c$) and effective stiffness ($\bar{E},\bar{G}$) have been computed and compared each other. With this approach, the most optimal core configuration was predicted. The result has become the efficient guidelines for the design of PCM core structure.

• 한국식품영양학회지
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• 제31권1호
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• pp.109-116
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• 2018

Onion vinegar, which has an undesirable flavor and taste formed through alcohol and acetic acid fermentation, possesses additives that can improve sensory quality. Thus, the objective of this study was to present an optimized blending ratio using response surface methods for an onion vinegar beverage by adding Omija extracts. This study was performed to formulate an Omija-onion vinegar beverage (OOVB) and investigate its antioxidant properties and antimicrobiological effects. The experimental design was conducted using an optimal mixture model of response surface methodology which generated eighteen experimental trials with overall acceptance as the responses. According to the statistical analyses, OOVB demonstrated a ratio containing onion vinegar, water, brown sugar, apple extracts and Omija extracts of 10, 72.3, 4.4, 12.2 and 1.1 (weight ratio), respectively. The OOVB revealed desirable nutrition values (phenolics compounds 19.3 mg/100 g, total flavonoids 3.1 mg/100 g, quercetin 1.9 mg/100). The OOVB displayed antibacterial effects in Gram negative Enterobacter aerogenes, Escherichia coli, Salmonella typhimurium and Gram positive Staphylococcus aureus. The findings revealed that OOVB was 18% in DPPH radical inhibitionand 11% in superoxide dismutase-like activity thus, OOVB has nutritional value and good quality as well as potential biological activities for functional beverages.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 전자공학회논문지C
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• 제35C권3호
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• 한국정밀공학회지
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• 제20권6호
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• pp.113-122
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• 2003

Topology optimization is begun with layout optimization that is attributed to Rozvany and Prager of the 1960's. They claimed that structure was transformed into truss connecting all the nodes of finite element and optimized by control of its sectional modulus. But, this method is partial topology optimization. General layout optimal design appliable to continum structure was proposed by Bendsoe and Kikuchi in 1988. Topology optimization expresses material stiffness of structure into function of arbitrary variable. If this variable is 1, material exists but if this variable is 0, material doesn't exist. Therefore, topology optimization searches the distribution function of material stiffness for structure. There are a few researchs for simple engineering problem such as topology optimization of square plane structure or truss structure. So, This study applied to topology optimization of table liner for vertical roller mill that is the largest scale in the world. After table liner decreased by 20% of original weight, the structure analysis for first optimized model was performed.

• 대한산업공학회지
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• 제39권6호
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• pp.554-561
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• 2013

As a special topic of the vehicle routing problems (VRP), VRPSDP extends the vehicle routing problem as considering simultaneous pickup and delivery for goods. The past studies have mainly dealt with a only weight constraint of a loading capacity for heterogeneous products. However. this study suggests VRPSDP considering separate loading area according to characteristics of loading species. The objective is to design a set of minimum distance routes for the vehicle routing assignment with independent capacity for heterogeneous species. And then we present a another HVRPSDP model which is easy to utilizes in a unique circumstance that is a guarantee of executing a task simultaneously from the various areas under restricted time and raising an application of vehicles that returns at the depot for the next mission like the military group. The optimal results of the suggested mathematical models are solved by the ILOG CPLEX software ver. 12.4 that is provided by IBM company.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 로봇학회논문지
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• 제14권4호
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• pp.270-277
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• 2019

Inspired by small insects, which perform rapid and stable locomotion based on body softness and tripod gait, various milli-scale six-legged crawling robots were developed to move rapidly in harsh environment. In particular, cockroach's leg compliance was resembled to enhance the locomotion performance of the crawling robots. In this paper, we investigated the effects of changing leg compliance for the locomotion performance of the small light weight legged crawling robot under various payload condition. First, we developed robust milli-scale six-leg crawling robot which actuated by one motor and fabricated in SCM method with light and soft material. Using this robot platform, we measured the running velocity of the robot depending on the leg stiffness and payload. In result, there was optimal range of the leg stiffness enhancing the locomotion ability at each payload condition in the experiment. It suggests that the performance of the crawling robot can be improved by adjusting stiffness of the legs in given payload condition.