• The Journal of the Acoustical Society of Korea
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• v.22 no.2E
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• pp.49-55
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• 2003

The modified neural net pattern recognition equations were attempted to apply to speech recognition. The proposed method has a dynamic process of self-organization that has been proved to be successful in recognizing a depth perception in stereoscopic vision. This study has shown that the process has also been useful in recognizing human speech. In the processing, input vocal signals are first compared with standard models to measure similarities that are then given to a process of self-organization in neural net equations. The competitive and cooperative processes are conducted among neighboring input similarities, so that only one winner neuron is finally detected. In a comparative study, it showed that the proposed neural networks outperformed the conventional HMM speech recognizer under the same conditions.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1194-1198
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• 2005

A computer vision system was designed and validated to recognize an individual Holstein cattle by processing images of their body patterns. This system involves image capture, image pre-processing, algorithm processing, and an artificial neural network recognition algorithm. Optimum management of individuals is one of the most important factors in keeping cattle healthy and productive. In this study, an image-processing system was used to recognize individual Holstein cattle by identifying the body-pattern images captured by a charge-coupled device (CCD). A recognition system was developed and applied to acquire images of 49 cattles. The pixel values of the body images were transformed into input data comprising binary signals for the neural network. Images of the 49 cattle were analyzed to learn input layer elements, and ten cattles were used to verify the output layer elements in the neural network by using an individual recognition program. The system proved to be reliable for the individual recognition of cattles in natural light.

• Analytical Science and Technology
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• v.14 no.4
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• pp.311-316
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• 2001

We have investigated the trace amount in an oriental medicine in oder to determine the geographical origin by using inductively coupled plasma-atomic emission spectrometry(ICP-AES) and chemometric anlysis with principal component analysis(PCA) and pattern recognition. Astragali Radix from several agricultural places in Korea was selected as an example of the oriental medicine and analyzed by ICP-AES. The dried Astragali Radix sample was treated with $HNO_3$ and $H_2O_2$, then digested using microwave oven. Elements such as Mg, Al, K, Ca, Ti, Mn, Fe, Cu, Zn, and Ba with different concentrations were found an used for the identification of the origin of agriculture places. Especially, the concentration of Al, Fe, Zn and Ti were employed to investigate the relationship between. Astragali Radix and the agricultural places by PCA and pattern recognition. We have made a program that is based on chemometrics in analytical spectroscopy. The results of the chemometrics analysis indicated that a distinction among Yechon and Chechon, Chungson, Kurye and Chinese Astragali Radix could be made. We believe that principal component analysis(PCA) and pattern recognition is a valuable tool to identify the origin of Astragali Radix in terms of the agricultural place.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.1-12
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• 1996

We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

• Journal of Multimedia Information System
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• v.3 no.4
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.27-37
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• 1993

In unmanned machining, One of the most essential issue is the tool management system which includes controlling. identification, presetting and monitoring of cutting tools. Especially the monitoring of tool wear and fracture may be the heart of the system. In this study a computer vision based tool monitoring system is developed. Also an algorithm which can determine the tool condition using this system is presented. In order to enhance practical adaptability the vision system through which two modes of images are taken is located over the rake face of a tool insert. And they are analysed quantitatively and qualitatively with image processing technique. In fact the morphologies of tool fracture or wear are occurred so variously that it is difficult to predict them. For the purpose of this problem the pattern recognition is introduced to classify the modes of the tool such as fracture, crater, chipping and flank wear. The experimental results performed in the CNC turning machine have proved the effectiveness of the proposed system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.58-65
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• 1992

In this paper new back propagation algorithm which adds neurons to hidden layer is proposed. this proposed algorithm is applied to the pattern recognition of written number coupled with back propagation algorithm through omitting redundant learning. Learning rate and recognition rate of the proposed algorithm are compared with those of the conventional back propagation algorithm and the back propagation through omitting redundant learning. The learning rate of proposed algorithm is 4 times as fast as the conventional back propagation algorithm and 2 times as fast as the back propagation through omitting redundant learning. The recognition rate is 96.2% in case of the conventional back propagation algorithm, 96.5% in case of the back propagation through omitting redundant learning and 97.4% in the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1E
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• pp.64-70
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• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. But those models suffer from poor discrimination between acoustically similar words. In this paper we propose an discriminative training algorithm for predictive neural network models. This algorithm is derived from GPD (Generalized Probabilistic Descent) algorithm coupled with MCEF(Minimum Classification Error Formulation). It allows direct minimization of a recognition error rate. Evaluation of our training algoritym on ten Korean digits shows its effectiveness by 30% reduction of recognition error.

• Bulletin of the Korean Chemical Society
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• v.35 no.7
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• pp.1956-1964
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• 2014

To provide the scientific corroboration of the traditional uses of Akebia quinata (Thunb.) Decne., a detailed analytical examination of A. quinata stems was carried out using a reversed-phase high performance liquid chromatography (RP-HPLC) method coupled to photodiode array detector (PDA) for the simultaneous determination of four phenolic substances; cuneataside D (1), 2-(3,4-dihydroxyphenyl)ethyl-O-${\beta}$-D-glucopyranoside (2), 3-caffeoylquinic acid (3) and calceolarioside B (4). Particular attention was focused on the main compound, 3-caffeoylquinic acid (3), which has a range of biological functions. In addition, 2-(3,4-dihydroxyphenyl)ethyl-O-${\beta}$-D-glucopyranoside (2) was considered as a discernible marker of A. quinata from its easy confuse plants. The contents of compounds 2 and 3 ranged from 0.72 to 2.68 mg/g and from 1.66 to 5.64 mg/g, respectively. The validation data indicated that this HPLC/PDA assay was used successfully to quantify the four phenolic compounds in A. quinata from different locations using relatively simple conditions and procedures. The pattern-recognition analysis data from 53 samples classified them into two groups, allowing discrimination between A. quinata and comparable herbs. The results suggest that the established HPLC/PDA method is suitable for quantitation and pattern-recognition analyses for a quality evaluation of this medicinal herb.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.