• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1704-1709
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• 2014

In order to increase the performance of multi-core system processor architectures, the multi-thread branch predictor which speculatively fetches and allocates threads to each core should be highly accurate. In this paper, the perceptron based multi-thread branch predictor is proposed for the multi-core processor architectures. Using SPEC 2000 benchmarks as input, the trace-driven simulation has been performed for the 2 to 16-core architectures employing perceptron multi-thread branch predictor extensively. Its performance is compared with the architecture which utilizes the two-level adaptive multi-thread branch predictor.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.25-30
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• 2018

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.806-808
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• 2000

We propose a new Dynamic Multilayer Perceptorn(DMP) architecture for optimal model identification of complex and nonlinear system in this paper. The proposed DMP scheme is presented as the generic and advanced type based on the GMDH(Group Method of Data Handling) method for the limitation of GMDH under only two system input variables. It is worth stressing that the number of the layers and the nodes in each layer of the DMP are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. The experimental part of the study comes with representative nonlinear static system. Comparative analysis is included and shows that a new DMP can produce the model with higher accuracy than previous other works.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.287-289
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• 2005

This paper presents a hybrid neural network system which used a Self-Organizing Map and Multilayer Perceptron for the problem of Malay syllables speech recognition. The novel idea in this system is the usage of a two-dimension Self-organizing feature map as a sequential mapping function which transform the phonetic similarities or acoustic vector sequences of the speech frame into trajectories in a square matrix where elements take on binary values. This property simplifies the classification task. An MLP is then used to classify the trajectories that each syllable in the vocabulary corresponds to. The system performance was evaluated for recognition of 15 Malay common syllables. The overall performance of the recognizer showed to be 91.8%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1110-1118
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• 1995

Over the past few years, a wide variety of techniques have been developed which make a reliable recognition of speech signal. Multi-layer perceptron(MLP) which has excellent pattern recognition properties is one of the most versatile networks in the area of speech recognition. This paper describes an automatic speech recognition system which use both MLP and lexicon. In this system., the recognition is performed by a network search algorithm which matches words in lexicon to MLP output scores. We also suggest a recognition algorithm which incorperat durational information of each phone, whose performance is comparable to that of conventional continuous HMM(CHMM). Performance of the system is evaluated on the database of 26 vocabulary size from 9 speakers. The experimental results show that the proposed algorithm achieves error rate of 7.3% which is 5.3% lower rate than 12.6% of CHMM.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.135-139
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• 2022

The current-voltage modeling plays an important role in characterizing photovoltaic systems. A solar cell has a nonlinear characteristic with various parameters influenced by the external environments such as the irradiance and the temperature. In order to accurately predict current-voltage characteristics at low irradiance, the artificial neural networks are applied to effectively quantify nonlinear behaviors. In this paper, a multi-layer perceptron scheme that can make accurate predictions is employed to learn complex formulas for large amounts of continuous data. The simulated results of artificial neural networks model show the accuracy improvement by using MATLAB/Simulink.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.88-98
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• 2002

The multilayer perceptron (MLP) has several advantages against other pattern recognition methods, and is expected to be used as the learning and recognizing speakers of speaker verification system. But because of the low learning speed of the error backpropagation (EBP) algorithm that is used for the MLP learning, the MLP learning requires considerable time. Because the speaker verification system must provide verification services just after a speaker's enrollment, it is required to solve the problem. So, this paper tries to make short of time required to enroll speakers with the MLP based speaker verification system, using the method of improving the EBP learning speed and the method of reducing background speakers which adopts the cohort speakers method from the existing speaker verification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.305-313
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• 2021

The upcoming warfare will be network-centric warfare with the acquiring and sharing of information on the battlefield through the connection of the entire weapon system. Therefore, the amount of information generated increases, but the technology of evaluating the information is insufficient. Threat assessment is a technology that supports a quick decision, but the information has many uncertainties and is difficult to apply to an advanced battlefield. This paper proposes a threat assessment based on artificial intelligence while removing the target uncertainty. The artificial intelligence system used was a fuzzy inference system and a multi-layer perceptron. The target was classified by inputting the unique characteristics of the target into the fuzzy inference system, and the classified target information was input into the multi-layer perceptron to calculate the appropriate threat value. The validity of the proposed technique was verified with the threat value calculated by inputting the uncertain target to the trained artificial neural network.

• Computers and Concrete
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• v.15 no.5
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• pp.759-770
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• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2977-2979
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• 2000

A Fuzzy Hybrid-Multilayer Perceptron (FH-MLP) Structure is proposed in this paper. proposed FH-MLP is not a fixed architecture. that is to say. the number of layers and the number of nodes in each layer of FH-MLP can be generated to adapt to the changing environment. FH-MLP consists of two parts. one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules. and its fuzzy system operates with Gaussian or Triangular membership functions in premise part and constants or regression polynomial equation in consequence part. the other is polynomial nodes which several types of high-order polynomial such as linear. quadratic. and cubic form are used and is connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method. time series data for gas furnace process has been applied.