• Journal of the Korean Society of Fisheries and Ocean Technology
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• 제29권3호
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• pp.183-190
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• 1993

A cooperative Korea-Japan investigation for the demersal fisheries resources of the East China Sea carried out by using the training ship Oshoro Maru belong to Hok-kaido University, Japan, during 1-8 November, 1991. The research vessel sampled 15 stations with demersal trawls on the East China Sea, and 1,364 nautical miles of track line were surveyed hydroacoustically. The hydroacoustic observations were taken with a scientific echo sounder operating at two frequencies of 25 kHz and 100 kHz, and a microcomputer-based echo integrator. Fish samples were collected by demersal trawling, and temperature, salinity and dissolved oxygen were measured with a CTD system. The target strength of fish school was estimated from the relationship between mean scattering strength and catches caught by demersal trawling. The results obtained can be summarized as follows: 1. The mean backscattering strength for 15 layers occupied by demersal trawls at 25 kHz ranged from -70.4 dB to -59.1 dB. Then the catch per one hour ranged from 8.2 to 587.5 kg/hour. 2. The mean backscattering strength for the entire layer between transducer and seabed in the survey area of the East China Sea at 25 kHz and 100 kHz were -68.0 dB and -73.1 dB, respectively. 3. The mean fish-school target strength per one kilogram at 25 kHz and 100 kHz were -28.3 dB/kg, and -30.4 dB/kg, respectively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• 제25권4호
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Water Engineering Research
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• 제6권1호
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• pp.17-30
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• 2005

The bed evolution of the stretch of the River Rhine between km-812.5 and km-821.5 is characterised by general bed degradation as a result of the river training works and dredging activities of the last two centuries. The degradation of the river bed affects the water levels, and so the navigation conditions. To combat the erosion of the river bed with the aim to keep up the shipping traffic and to avoid the ecological system damages due to water level reductions, sand-gravel-mixtures were added to the river (so called artificial grain feeding activities). This paper presents the results of an application of a graded sediment transport model in order to study morpholodynamical characteristics due to artificial grain feeding activities in the river stretch. The finite element code TELEMAC2D was used for flow calculation by solving the 2D shallow water equation on non-structured grids. The sediment transport module SISYPHE has been developed for graded sediment transport using a multiple layer model. The needs to apply such graded sediment transport approaches to study morphological processes in the domain are discussed. The calculations have been carried out for the case of middle water flow and different size-fraction distributions. The results show that the grain feeding process could be well simulated by the model.

• Transactions of Materials Processing
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• 제4권3호
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• pp.214-221
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• 1995

In the paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in the neural network. The architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of a neural network, an optimal billet is determined by applying the nonlinear mathematical relationship between the aspect ratios in the initial billet and the final products. The amount of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet aspect ratios and those of the unfilled volumes. After learning, the system is able to predict the filling regions which are exactly the same or slightly different to the results of finite element simulation. This new method is applied to find the optimal billet size for the plane strain rib-web product in cold forging. This would reduce the number of finite element simulation for determining the optimal billet size of forging product, further it is usefully adapted to physical modeling for the forging design.

• ETRI Journal
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• 제43권5호
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• pp.941-949
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• 2021

In this study, a spatial spectrum method is proposed to cope with the pilot spoofing attack (PSA) problem by exploiting the of uplink-downlink channel reciprocity in time-division-duplex multiple-input multiple-output systems. First, the spoofing attack in the uplink stage is detected by a threshold derived from the predefined false alarm based on the estimated spatial spectrum. When the PSA occurs, the transmitter (That is Alice) can detect either one or two spatial spectrum peaks. Then, the legitimate user (That is Bob) and Eve are recognized in the downlink stage via the channel reciprocity property based on the difference between the spatial spectra if PSA occurs. This way, the presence of Eve and the direction of arrival of Eve and Bob can be identified at the transmitter end. Because noise is suppressed by a spatial spectrum, the detection performance is reliable even for low signal-noise ratios and a short training length. Consequently, Bob can use beamforming to transmit secure information during the data transmission stage. Theoretical analysis and numerical simulations are performed to evaluate the performance of the proposed scheme compared with conventional methods.

• Journal of information and communication convergence engineering
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• 제22권2호
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• pp.145-152
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• 2024

Closed quotient (CQ) represents the time ratio for which the vocal folds remain in contact during voice production. Because analyzing CQ values serves as an important reference point in vocal training for professional singers, these values have been measured mechanically or electrically by either inverse filtering of airflows captured by a circumferentially vented mask or post-processing of electroglottography waveforms. In this study, we introduced a novel algorithm to predict the CQ values only from audio signals. This has eliminated the need for mechanical or electrical measurement techniques. Our algorithm is based on a gated recurrent unit (GRU)-type neural network. To enhance the efficiency, we pre-processed an audio signal using the pitch feature extraction algorithm. Then, GRU-type neural networks were employed to extract the features. This was followed by a dense layer for the final prediction. The Results section reports the mean square error between the predicted and real CQ. It shows the capability of the proposed algorithm to predict CQ values.

• Applied Microscopy
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• 제39권4호
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• pp.365-371
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• 2009

This study was investigated to observe morphological changes of two pre-astronauts' hair, male and female by electron microscopy and to analyze its tensile strength by using rheometer. The surface of those two pre-astronauts' hair, which were very rough and irregular, contained separated scales and destroyed remnants of cuticular cells. Also, there were many holes on the cytoplasm of the cuticular cells which forms the cuticle layer. The destruction begins when the endocuticle where the holes form gets destroyed. And then, The tensile strength of female pre-astronaut's hair was 14.60 mm which is 10% reduced, compared to that of the normal healthy hair. Thus, this result thought to be due to the prolonged change of the biorhythm and psychological instability of the pre-astronauts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1477-1491
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• 2017

Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.

• The Journal of the Korea institute of electronic communication sciences
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• 제6권2호
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• pp.237-242
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• 2011

This paper proposes a detection algorithm for each section which detects the voiced section, unvoiced section, and the silence section at each frame using a multi-layer perceptron neural network. First, a power spectrum and FFT (fast Fourier transform) coefficients obtained by FFT are used as the input to the neural network for each frame, then the neural network is trained using these power spectrum and FFT coefficients. In this experiment, the performance of the proposed algorithm for detection of the voiced section, unvoiced section, and silence section was evaluated based on the detection rates using various speeches, which are degraded by white noise and used as the input data of the neural network. In this experiment, the detection rates were 92% or more for such speech and white noise when training data and evaluation data were the different.

• Journal of the Korean Geotechnical Society
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• 제19권6호
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• pp.343-350
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• 2003

The range of applications of artificial neural networks(Am) in many branches of geotechnical engineering is growing rapidly. This study was undertaken to develop an analysis model representing ultrasonically enhanced soil flushing by the use of ANN. Input data for the model-development were obtained by laboratory study, and used for training and verification. Analyses involved various ranges of momentum, loaming rate, activation function, hidden layer, and nodes. Results of the analyses were used to obtain the optimum conditions for establishing and verifying the model. The coefficient of correlation between the measured and the predicted data using the developed model was relatively high. It shows potential application of ANN to ultrasonically enhanced soil flushing which is not easy to build up a mathematical model.