• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.05a
• /
• pp.111-114
• /
• 2003

It has well known that MUSIC and ESPRIT algorithms estimate angle of arrival(AOA) with high resolution by eigenvalue decomposition of the covariance matrix which were obtained from the array antennas. In the case that 2-D large-sized array antenna is required, however, one of the disadvantages of MUSIC and ESPRIT is that they are computationally ineffective, and then they are difficult to implement in real time. To alleviate the computational complexity, several method using neural model have been study. For multiple signals, those methods require huge training data prior to AOA estimation. This paper proposes the algorithm for AOA estimation by interconnected hopfield neural model. Computer simulations show the validity of the proposed algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2012.06a
• /
• pp.107-109
• /
• 2012

This research studies on Bridge Resource Management (BRM )improvement method base on BRM questionnaire result which was conducted and participated by the mariners and cadets who finished the basic onboard training by STCW 2010 (The Convention on Standards of training, Certification and Watch-keeping for Seafarers) Code A. Through questionnaire result, this paper collected opinions and analyzed data above all of survey items, such as: Time, education times, education material etc relate on theory, practice, simulation, ${\ldots}$to suggest efficient BRM program.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.32 no.4
• /
• pp.24-33
• /
• 2004

In this paper, the helicopter flight control system using online adaptive neural networks which have the universal function approximation property is considered. It is not compensation for modeling errors but approximation two functions required for feedback linearization control action from input/output of the system. To guarantee the tracking performance and the stability of the closed loop system replaced two nonlinear functions by two neural networks, weight update laws are provided by Lyapunov function and the simulation results in low speed flight mode verified the performance of the control system with the neural networks.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.12
• /
• pp.6484-6487
• /
• 2013

This paper reports the direct controller for nonlinear plants using a neural network. The controller was composed of an approximate controller and a neural network auxiliary controller. The approximate controller provides rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not place too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network was trained and the system showed stable performance for the inputs it has been trained for. The simulation results showed that it was quite effective and could realize satisfactory control of the nonlinear system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.7
• /
• pp.1513-1517
• /
• 2005

In this paper, I make use of a Multi-Channel skin color model with Hue, Cb, Cg using Red, Blue, Green channel altogether which remove bight component as being consider the characteristics of skin color to do modeling more effective to a facial skin color for extracting a facial area. 1 used efficient HOLA(Higher order local autocorrelation function) using 26 feature vectors to obtain both feature vectors of a facial area and the edge image extraction using Harr wavelet in image which split a facial area. Calculated feature vectors are used of date for the facial recognition through learning of neural network It demonstrate improvement in both the recognition rate and speed by proposed algorithm through simulation.

• Korean Journal of Cognitive Science
• /
• v.6 no.3
• /
• pp.25-40
• /
• 1995

This paper proposes a generating method of fuzzy rules by genetic and descent method (GAGDM),and its applied to classification problems.The number of inference rules and the shapes of membership function in the antecedent part are detemined by applying the genetic algorithm,and the real numbers of the consequent parts are derived by using the descent method.The aim of the proposed method is to generation a minmun set of fuzzy rules that can correctly classify all training patterns,and fiteness function of GA defined by the aim of th proposed method.Finally,in order to demonstrate the effectiveness of the present method,simulation results are shown.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.4
• /
• pp.335-341
• /
• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.2
• /
• pp.149-158
• /
• 1996

In this paper we describe designing and implementing a digital neural chip and a parallel neural machine for simulating large scale neural netsorks. The chip is a single-chip multiprocessor which has four digiral neural processors (DNP-II) of the same architecture. Each DNP-II has program memory and data memory, and the chip operates in MIMD (multi-instruction, multi-data) parallel processor. The DNP-II has the instruction set tailored to neural computation. Which can be sed to effectively simulate various neural network models including on-chip learning. The DNP-II facilitates four-way data-driven communication supporting the extensibility of parallel systems. The parallel neural machine consists of a host computer, processor boards, a buffer board and an interface board. Each processor board consists of 8*8 array of DNP-II(equivalently 2*2 neural chips). Each processor board acn be built including linear array, 2-D mesh and 2-D torus. This flexibility supports efficiency of mapping from neural network models into parallel strucgure. The neural system accomplishes the performance of maximum 40 GCPS(giga connection per second) with 16 processor boards.

• The Korean Journal of Emergency Medical Services
• /
• v.17 no.2
• /
• pp.77-87
• /
• 2013

Purpose : The purpose of this study was to develop a scenario and to evaluate the students by simulation-based learning of acute abdominal pain case in an emergency unit. The expert group of simulation developed the scenario based on actual abdominal pain by medical treatment records. Methods : Scenario was developed to evaluate the simMan3G simulation-based learning. The scenario was used in 2013 with ten groups of fourth grade 50 nursing students who voluntarily participated in the simulation class. Results : The nursing students were able to express nursing knowledge, learning attitude and self-efficacy. The simulation-based scenario proved to be very effective to students' skill training. The performance of nursing practice through simulation class made the nursing students more confident with patient care. Conclusion : Simulation-based learning was found to be the most effective curriculum to the nursing students and made the students satisfied and confident. So the simulation-based learning would be helpful to other students including paramedic students and medical school students.

• Journal of Korea Game Society
• /
• v.20 no.1
• /
• pp.133-142
• /
• 2020

Digital educational game market is growing as it expands to smart platforms such as smart phones and tablet PCs. As games have become digital in the classroom evolving to two-ways. In this paper, it designed that enables elementary students to acquire musical knowledge related to musicians through digital board games. By collecting achievement cards of musicians associated with. Elementary students repeatedly gain relevant musical knowledge in the end. The developed game was simulated using Petri net to check the gameplay flow.