• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.1-6
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• 2002

Hopfield introduced the neural network for linear program with linear constraints. In this paper, Hopfield neural network has been generalized to solve the optimization problems including nonlinear constraints. Also, it has been discussed the methods hew to reconcile optimization problem with neural networks and how to implement the circuits.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.620-623
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.368-371
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• 1999

In this paper, we propose a neural network processor architecture with on-chip learning and with reconfigurability according to the data dependencies of the algorithm applied. For the neural network model applied, the proposed architecture can be configured into either SIMD or SRA(Systolic Ring Array) without my changing of on-chip configuration so as to obtain a high throughput. However, changing of system configuration can be controlled by user program. To process activation function, which needs amount of cycles to get its value, we design it by using PWL(Piece-Wise Linear) function approximation method. This unit has only single latency and the processing ability of non-linear function such as sigmoid gaussian function etc. And we verified the processing mechanism with EBP(Error Back-Propagation) model.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

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

• Structural Engineering and Mechanics
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• v.33 no.1
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• pp.15-30
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• 2009

In this study, the nonlinear vibrations of stepped beams having different boundary conditions were investigated. The equations of motions were obtained by using Hamilton's principle and made non dimensional. The stretching effect induced non-linear terms to the equations. Natural frequencies are calculated for different boundary conditions, stepped ratios and stepped locations by Newton-Raphson Method. The corresponding nonlinear correction coefficients are also calculated for the fundamental mode. At the second part, an alternative method is produced for the analysis. The calculated natural frequencies and nonlinear corrections are used for training an artificial neural network (ANN) program which has a multi-layer, feed-forward, back-propagation algorithm. The results of the algorithm produce errors less than 2.5% for linear case and 10.12% for nonlinear case. The errors are much lower for most cases except clamped-clamped end condition. By employing the ANN algorithm, the natural frequencies and nonlinear corrections are easily calculated by little errors, and the computational time is drastically reduced compared with the conventional numerical techniques.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.77-84
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• 2001

Since Hopfield introduced the neural network for liner programming problems many papers have been published about it and some of them are about nonlinear programming problems Therefore nonlinear, cost function problem has been solved however nonlinear constraints problem has not been solved In this paper I have proposed the general nonlinear programming neural networks which minimize cost function with nonlinear constraints.

• Journal of the Korean Society for Nondestructive Testing
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• v.15 no.4
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• pp.520-530
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• 1996

In this study, the artificial defects in rotary compressor are classified using pattern recognition of acoustic emission signal. For this purpose the computer program is developed. The neural network classifier is compared with the statistical classifier such as the linear discriminant function classifier and empirical Bayesian classifier. It is concluded that the former is better. It is possible to acquire the recognition rate of above 99% by neural network classifier.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.87-99
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• 2024

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.80-87
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• 2006

Recently, the distribution and using of the digital multimedia contents are easy by developing the internet application program and related technology. However, the digital signal is easily duplicated and the duplicates have the same quality compare with original digital signal. To solve this problem, there is the multimedia fingerprint which is studied for the protection of copyright. Fingerprinting scheme is a techniques which supports copyright protection to track redistributors of electronic inform on using cryptographic techniques. Only regular user can know the inserted fingerprint data in fingerprinting schemes differ from a symmetric/asymmetric scheme and the scheme guarantee an anonymous before recontributed data. In this paper, we present a new scheme which is the detection of colluded multimedia fingerprint by neural network. This proposed scheme is consists of the anti-collusion code generation and the neural network for the error correction. Anti-collusion code based on BIBD(Balanced Incomplete Block Design) was made 100% collusion code detection rate about the average linear collusion attack, and the hopfield neural network using (n,k)code designing for the error bits correction confirmed that can correct error within 2bits.