• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.163-171
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• 2020

The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

• Computers and Concrete
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• v.4 no.6
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• pp.477-497
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• 2007

The ultrasonic pulse velocity method has been widely used to evaluate the quality of concrete and assess the structural integrity of concrete structures. But its use for predicting strength is still limited since there are many variables affecting the relationship between strength and pulse velocity of concrete. This study is focused on establishing a complicated correlation between known input data, such as pulse velocity and mixture proportions of concrete, and a certain output (compressive strength of concrete) using artificial neural networks (ANN). In addition, the results predicted by the developed multilayer perceptrons (MLP) networks are compared with those by conventional regression analysis. The result shows that the correlation between pulse velocity and compressive strength of concrete at various ages can be well established by using ANN and the accuracy of the estimates depends on the quality of the information used to train the network. Moreover, compared with the conventional approach, the proposed method gives a better prediction, both in terms of coefficients of determination and root-mean-square error.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.25-30
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• 2005

In this paper, we analyzed and compared the performances of modular FFMLP(feedforward multilayer perceptron) and SVUT(Support Vector Machine) for the large-set classification problems. Overall, SVM dominated modular FFMLP in the correct recognition rate and other aspects Additionally, the recognition rate of SVM degraded more slowly than neural network as the number of classes increases. The trend of the recognition rates depending on the rejection rate has been analyzed. The parameter set of SVM(kernel functions and related variables) has been identified for the large-set classification problems.

• Proceedings of the KSRS Conference
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• v.1
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.

• ETRI Journal
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• v.17 no.1
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• Korea Journal of Hospital Management
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• v.8 no.1
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• pp.81-94
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• 2003

This paper aims at developing a prediction model and analyzing a sensitivity for the outpatient's overall satisfaction on utilizing hospital services by using data mining techniques within the context of customer satisfaction. From a total of 900 outpatient cases, 80 percent were randomly selected as the training group and the other 20 percent as the validation group. Cases in the training group were used in the development of the CHAID and Neural Networks. The validation group was used to test the performance of these models. The major findings may be summarized as follows: the CHAID provided six useful predictors - satisfaction with treatment level, satisfaction with healthcare facilities and equipments, satisfaction with registration service, awareness of hospital reputation, satisfaction with staffs courtesy and responsiveness, and satisfaction with nurses kindness. The prediction accuracy rates based on MLP (77.90%) is superior to RBF (76.80%).

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.33-36
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• 2006

As the computing environment is rapidly improved, the interests of CFD are gradually focused on large-scale computation over complex geometry. Keeping pace with the trend, essential computational tools to obtain solutions of complex aerospace flow analysis and design problems are examined. An accurate and efficient flow analysis and design codes for large-scale aerospace problem are presented in this work. With regard to original numerical schemes for flow analysis, high-fidelity flux schemes such as RoeM, AUSMPW+ and higher order interpolation schemes such as MLP (Multi-dimensional Limiting Process) are presented. Concerning the grid representation method, a general-purpose basis code which can handle multi-block system and overset grid system simultaneously is constructed. In respect to design optimization, the importance of turbulent sensitivity is investigated. And design tools to predict highly turbulent flows and its sensitivity accurately by fully differentiating turbulent transport equations are presented. Especially, a new sensitivity analysis treatment and geometric representation method to resolve the basic flow characteristics are presented. Exploiting these tools, the capability of the proposed approach to handle complex aerospace simulation and design problems is tested by computing several flow analysis and design problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.4
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• pp.6-15
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• 2002

A neural network disturbance observer for a robotic manipulator is derived in this paper. The neural network used as the disturbance observer is a feedforward MLP(multiple-layered perceptron) network. The uniform ultimate boundness(UUB) of the proposed neural disturbance observer and the control error within a sufficiently small compact set is guaranteed. This neural disturbance observer method overcomes the disadvantages of the existing adaptive control methods which require the tedious analysis of the regressor matrix of the given manipulator. The effectiveness of the proposed neural disturbance observer is demonstrated by the application to the three-link robotic manipulator.