• Advances in environmental research
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• v.5 no.3
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.77-87
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• 2023

Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.855-863
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• 2024

The use of Deep Neural Network (DNN) is gradually increasing in various fields due to their high efficiency in data analysis and prediction. However, as the use of deep neural networks becomes more frequent, the security threats associated with them are also increasing. In particular, if a fault occurs in the forward propagation process and activation function that can directly affect the prediction of deep neural network, it can have a fatal damage on the prediction accuracy of the model. In this paper, we performed some fault injection attacks on the forward propagation process of each layer except the input layer in a deep neural network and the Softmax function used in the output layer, and analyzed the experimental results. As a result of fault injection on the MNIST dataset using a glitch clock, we confirmed that faut injection on into the iteration statements can conduct deterministic misclassification depending on the network parameters.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.238-244
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• 2008

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing. This paper presents gamma neural network(GAM) to improve the dynamics of multilayer network. The GAM network uses the gamma memory kernel in the hidden layer of feedforword multilayer network. The GAM network is evaluated in linear and nonlinear system identification, and compared with feedforword(FNN) and recurrent neural networks(RNN) for the relative comparison of its performance. Experimental results show that the GAM network performs better with respect to the convergence and accuracy, indicating that it can be a more effective network than conventional multilayer networks in system identification.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.31-37
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• 2017

This study was initiated with the aim of suggesting a further step in the program of literary therapy by revealing the mechanism by which the body heals through the discharge of neural network codes. Sentence is encoded as neural signals in our body as it is being read. If the neural networks in the human body are activated and created, the code in which the neural networks are encoded is a code composed of sentences. That is, Sentence is a code. And if the Sentence connects to the human body again and activates the human neural networks, it can be said that Sentence is encoded. At this time, the relation of "neural network codes = Sentence codes" is established. In other words, human narrative and literary narratives are the mediums that convey the same kinds of neural network codes. Cho Ji-Hoon's Poem "White Night" draws sadness through the path of loneliness in 1strophe. Through the Sentence of Loneliness, it activates neural network codes of sadness. 2strophe for the 'pure white snow' is the encoding of the Sentence. In 3strophe, the sentence for 'sadness' is encoded. This flow causes a healing mechanism in this Poem, because the neural network codes about the loneliness, sadness, and eyes of the human body are passed to the other. Here, the other is "White Night". In the future, it is expected that more effective healing results will be obtained if a literary therapy program on the encoding of the sentence of Cho Ji-Hoon's Poem is performed in the future.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.263-273
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• 2008

For the discrete version of an artificial neural network of two neurons with piecewise constant argument, we obtain some sufficient conditions that every solution is either periodic or convergent.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1125-1128
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• 1989

In this paper, the inverse model of a servo system is realized in a PDP-type neural network. The neural network learns the mapping between the input and output of the servo system. Some simulation results show the effectiveness of this inverse model obtained here.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.815-821
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• 2005

Among the various artificial neural networks the backpropagation network (BPN) has become a standard one. One of the components in a neural network is an activating function or a transfer function of which a representative function is a sigmoid. We have discovered that by updating the slope parameter of a sigmoid function simultaneous with the weights could improve performance of a BPN.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.394-394
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• 2000

This Paper proposes a new nonlinear partial least square method that extends the linear PLS. Proposed nonlinear PLS uses self-organizing feature map as PLS outer relation and multilayer neural network as PLS inner regression method.