• Proceedings of the Korea Electromagnetic Engineering Society Conference
• /
• 2000.11a
• /
• pp.99-103
• /
• 2000

본 논문에서는 FM Reflectometry를 이용하여 마이크로파 대역에서 다양한 전송선로의 분산특성을 측정하였다. 실험에 사용된 전송선로는 도파관에 금속봉이 주기적으로 위치한 구조로서 Gyro-TWT의 iinteractio circuit으로 사용되기 위해서 설계되었다. 도파관에 금속봉이 주기적으로 위치한 구조의 group velocity를 측정하기 위해서 FM Reflectometry와 기존의 Network Analyzer Time Domain 기능이 사용되어졌으며, phase velocity를 측정하기위해 서 short metal plate를 이용한 방법과 High Frequency Structure Simulator (HFSS)가 이용되었다 측정된 결과는 이론치, 시뮬레이션과 비교했으며 각각의 비교는 잘 일치함을 보여 FM Reflectometry기법이 분산특성 측정에 사용되어질 수 있음을 보였다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2002.05c
• /
• pp.247-250
• /
• 2002

SAW filters were fabricated on $LiNbO_3$ substrates to evaluate frequency response and properties of photolithography. In the both of etch and lift-off methods, lift off method was superior to etch method in fabrication process. Frequency response property was measured by network analyzer. From measurement of acoustic property, SAW propagation velocity was 3574.9m/sec for $LiNbO_3$ SAW filter.

• Computers and Concrete
• /
• v.27 no.1
• /
• pp.21-28
• /
• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• Advances in nano research
• /
• v.13 no.3
• /
• pp.297-312
• /
• 2022

Coughing and breath shortness are common symptoms of nano (small) cell lung cancer. Smoking is main factor in causing such cancers. The cancer cells form on the soft tissues of lung. Deformation behavior and wave vibration of lung affected when cancer cells exist. Therefore, in the current work, phase velocity behavior of the small cell lung cancer as a main part of the body via an exact size-dependent theory is presented. Regarding this problem, displacement fields of small cell lung cancer are obtained using first-order shear deformation theory with five parameters. Besides, the size-dependent small cell lung cancer is modeled via nonlocal stress/strain gradient theory (NSGT). An analytical method is applied for solving the governing equations of the small cell lung cancer structure. The novelty of the current study is the consideration of the five-parameter of displacement for curved panel, and porosity as well as NSGT are employed and solved using the analytical method. For more verification, the outcomes of this reports are compared with the predictions of deep neural network (DNN) with adaptive optimization method. A thorough parametric investigation is conducted on the effect of NSGT parameters, porosity and geometry on the phase velocity behavior of the small cell lung cancer structure.

• Proceedings of the IEEK Conference
• /
• 2004.06a
• /
• pp.119-122
• /
• 2004

This mobile oriented Ad-hoc network yet steps on development level, related issues and standardization are progressing. This paper presented ad-hoc network's overview and overall property, followed by routing scheme that must be setup for communication between each nodes. Among several routing scheme proposed for standardization, ZRP that is foaled by this paper, is hybrid type algorithm, and only includes advantages of others This paper presented running process and implementation method, result that is made from network simulation, shows analysis of each parameter values's change. Each parameters changed with many differences according to Zone radius and communication node's location from simualtion that was performed changing a number of nodes and a velocity of nodes.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.11
• /
• pp.930-935
• /
• 2005

In this paper, position tracking control of an autonomous helicopter is presented. Combining an LQR method and a proportional control forms a simple PD control. Since LQR control gains are set for the velocity control of the helicopter, a position tracking error occurs. To minimize a position tracking error, neural network is introduced. Specially, in the frame of the reference compensation technique for teaming neural network compensator, a position tracking error of an autonomous helicopter can be compensated by neural network installed in the remotely located ground station. Considering time delay between an auto-helicopter and the ground station, simulation studies have been conducted. Simulation results show that the LQR with neural network performs better than that of LQR itself.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1008-1013
• /
• 2005

In this paper, position tracking control of an autonomous helicopter is presented. Velocity is controlled by using an optimal state controller LQR. A position control loop is added to form a PD controller. To minimize a position tracking error, neural network is introduced. The reference compensation technique as a neural network control structure is used, and a position tracking error of an autonomous helicopter is compensated by neural network installed in the remotely located ground station. Considering time delays between an autonomous helicopter and the ground station, simulation studies have been conducted. Simulation results show that the LQR with neural network compensation performs better than that of the LQR itself.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.321-322
• /
• 2008

CSS(Chirp Spread Spectrum) technology adopt SDS-TWR algorithms of TOA(Time of Arrival) using velocity of specific medium and ToF(Time of Flight) to measure a distance, but this method always has a regular error on distance regardless of a real distance, as a result, in far distance, it decrease a error on distance relatively, but in near distance, it increase a error on distance relatively. in this paper, we propose and test new method measuring a distance more precisely in near distance using CSS and RSSI

• 한국지구물리탐사학회:학술대회논문집
• /
• 2003.11a
• /
• pp.252-254
• /
• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.8
• /
• pp.471-481
• /
• 2003

The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.