• Ocean Systems Engineering
• /
• v.1 no.1
• /
• pp.1-15
• /
• 2011

Offshore structures are subjected to wind loads, wind generated wave excitations, and current forces. In this paper we focus on the wind generated wave excitations as the main source for the external forces on the structure. The main objective of the paper is to provide a tool for using deck acceleration measurements to predict the value of the force and moment acting on the offshore structure foundation. A change in these values can be used as an indicator of the health of the foundation. Two methods of analysis are used to determine the relationship between the force and moment acting on the foundation and deck acceleration. The first approach uses neural networks while the other uses a Fokker-Planck formulation. The Fokker-Plank approach was used to relate the variance of the excitation to the variance of the deck acceleration. The total virtual mass of the equivalent SDOF of the structure was also determined at different deck masses.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.8
• /
• pp.1454-1463
• /
• 2008

Blind source separation by independent component analysis (ICA) has applied in signal processing, telecommunication, and image processing to recover unknown original source signals from mutually independent observation signals. Neural networks are learned to estimate the original signals by unsupervised learning algorithm. Because the outputs of the neural networks which yield original source signals are mutually independent, then mutual information is zero. This is equivalent to minimizing the Kullback-Leibler convergence between probability density function and the corresponding factorial distribution of the output in neural networks. In this paper, we present a learning algorithm using information theory and higher order statistics to solve problem of blind source separation. For computer simulation two deterministic signals and a Gaussian noise are used as original source signals. We also test the proposed algorithm by applying it to several discrete images.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.16 no.2
• /
• pp.115-124
• /
• 2003

In this paper, the effects of vehicle loads on flat slab system are investigated on the basis of the previous studies for beam-gilder parking structural system. The influence surfaces of flat slab for a typical design section are constructed lot the purpose of obtaining maximum member forces under vehicle loads. In addition, the equivalent vehicle load factors for flat slab parking structures are suggested using artificial neural network. The network responses we compared with the results obtained by numerical analyses to verify the validation of Levenberg-Marquardt algorithm adopted as training method in this Paper. Many parameter studies for the flat slab structural system show dominant vehicle load effects at the center positive moments in both column and middle strips, like the beam-girder parking structural system.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.9
• /
• pp.2259-2268
• /
• 1997

Multistage interconnection networks(MINs) provide a high-bandwidth communication between processors and/or memory modules in a cost-effective way. In this paper, we propose a class of multipath MINs, called the Augmented Modified Delta(AMD) network, and analyze its performance and reliability. The salient features of the AMD network include fault-tolerant capability, modular structure, and high performance, which are essential for real-time parallel/distributed processing environments. The class of the AMD network retains well-known characteristics of the Kappa network, but it's design procedure is more systematic. Like Delta networks, all the AMD networks are topologically equivalent with each other.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.156-161
• /
• 2003

It is an interest problem to predict substance distributions in three-dimensional space. Recently, a research field as Geostatistics is advanced. It is a kind of inter- or extrapolation mathematically. Some useful means for the inter- and extrapolation are known, in which slide window method with neural networks is hopeful one. We propose multi-dimensional extrapolation using multi-layer neural networks and the slide-window method. The multi-dimensional extrapolation is not similar to one-dimension. It has plural algorithms. We researched line predictors and local-plain predictors I two-dimensional space. The both predictors are equivalent; however, in multi-dimensional extrapolation, it is very important to find the direction of predictions. Especially, since the slide window method requires information to predict the future in sampling data, if they are not ordered appropriately in the direction, the predictor cannot operate. We tested the extrapolation for typical two-dimensional functions, and found an excellent character of slide-window method based on local-plain. By using the method, we can extrapolate the function until twice-outer regions of the definitions.

• IEIE Transactions on Smart Processing and Computing
• /
• v.2 no.3
• /
• pp.160-167
• /
• 2013

Resource allocation based on quantized feedback plays a critical role in wireless mesh networks with a time division multiple access (TDMA) physical layer. In this study, a resource allocation problem was formulated based on quantized feedback for TDMA wireless mesh networks that minimize the total transmission power. Three steps were taken to solve the optimization problem. In the first step, the codebook of the power, rate and equivalent channel quantization threshold was designed. In the second step, the timeslot allocation criterion was deduced using the primal-dual method. In the third step, a resource allocation scheme was developed based on quantized feedback using the stochastic optimization tool. The simulation results show that the proposed scheme not only reduces the total transmission power, but also has the advantage of quantized feedback.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.37 no.3
• /
• pp.72-83
• /
• 2000

This paper presents the implementation of the position control system for 3 phase induction motor using reaching mode controller and neural networks. The reaching mode controller is used to bring the position error and speed error trajectories toward the sliding surface and to train neural networks at the first time. The structure of the reaching mode controller consists of the switch function of sliding surface. And feedforward neural networks approximates the equivalent control input using the reference speed and reference position and actual speed and actual position measured form an encoder and, are tuned on-line. The reaching mode controller and neural networks are applied to the position control system for 3 phase induction motor and, are compared with a PI controller through computer simulation and experiment respectively. The results are illustrated that the output of reaching mode controller is decreased and feedforward neural networks take charge of the main part for the control action, and the proposed controllers show better performance than the PI controller in abrupt load variation and the precise control is possible because the steady state error can be minimized by training neural networks.

• Journal of Computing Science and Engineering
• /
• v.8 no.3
• /
• pp.129-136
• /
• 2014

This paper presents the equivalent circuit modelling and eigenfrequency analysis of a wideband robust capacitive radio frequency (RF) microelectromechanical system (MEMS) switch that was designed using Poly-Si and Au layer membrane for highly reliable switching operation. The circuit characterization includes the extraction of resistance, inductance, on and off state capacitance, and Q-factor. The first six eigenfrequencies are analyzed using a finite element modeler, and the equivalent modes are demonstrated. The switch is optimized for millimeter wave frequencies, which indicate excellent RF performance with isolation of more than 55 dB and a low insertion loss of 0.1 dB in the V-band. The designed switch actuates at 13.2 V. The R, L, C and Q-factor are simulated using Y-matrix data over a frequency sweep of 20-100 GHz. The proposed switch has various applications in satellite communication networks and can also be used for devices that will incorporate the upcoming IEEE Wi-Fi 802.11ad protocol.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.9
• /
• pp.896-902
• /
• 2012

For most applications of the vibration energy harvesting technology as in wireless sensor networks for smart buildings and plants, the evaluation of DC output performance of vibration energy harvesters is typically required. However, there is no dedicated algorithm for the evaluation. The lack of a dedicated algorithm results from difficulties in the direct incorporation of nonlinear rectifying and regulating circuitry into finite element models of piezoelectric vibration energy harvesters. In this study, we develop a dedicated algorithm and present software based on it for the evaluation of not only AC but also DC electrical quantities. Here, an equivalent electrical circuit model is employed. The COMSOL multiphysics simulation tool is adopted for extracting equivalent electrical circuit parameters of a piezoelectric vibration energy harvester and MATLAB is used to make a graphical user interface. The AC voltage and power outputs calculated by the proposed algorithm under various conditions are compared with those by a traditional finite element analysis. The DC output voltage and power through a rectifier are obtained for varying values of smoothing capacitance and external resistance.

• Journal of information and communication convergence engineering
• /
• v.9 no.6
• /
• pp.743-746
• /
• 2011

An implementation for surface EMG network analysis and vertical control system of robotic arm is presented in this paper. The transmembranes are simulated by equivalent circuit and cable equation for propagation to be converted to circuit networks. The implementation is realized to be derived from the detecting EMG signal from 3 electrodes, and EMG transmembrane signals of human arm muscles are detected by several surface electrodes, high performance amplifier and filtering, converting analog to digital data and driving a servomotor for spontaneous robotic arm. The system is experimented by monitoring multiple steps vertical control angles corresponding to biceps muscle movement. The experimental results are that the vertical moving control level is measured to around 2 degrees and mean error ranges are lower 5%.