• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.387-394
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• 1996

This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonlinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonlinear Left Ventricular Assist Device.

• Journal of Information Technology Applications and Management
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• v.22 no.4
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• pp.159-179
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• 2015

In 2005, Korean government created the Internet Personal Identification Number (i-PIN) for a new online personal identification system to protect citizens' personal information against criminal abuse. However, i-PINs have not been widely in use over a decade. Although many people joined the i-PIN service, its actual use rate has been low. This study intends to identify the factors necessary to boost the continuous use of i-PINs. It was shown that government support and perceived security had a positive effect on the perceived ease of use and perceived usefulness of the i-PIN, respectively. Perceived security also directly affected the continuous use intention of the i-PIN. The perceived ease of use significantly affected the perceived usefulness, but it did not affect the intention to continuously use the i-PIN. The factor that had the most significant influence on the continuous use intention of the i-PIN was perceived usefulness. To increase the i-PIN use rate, Korean government must reduce users' anxiety through strict security functions, and must attempt to help people use the i-PIN easily.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.203-207
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• 2006

Nowadays, Cars are continuing to grow at an alarming rate but they also cause many problems such as traffic accident, pollutions and so on. One of the most effective methods that prevent traffic accidents is the use of traffic monitoring systems, which are already widely used in many countries. The monitoring system is beginning to be used in domestic recently. An intelligent monitoring system generates photo images of cars as well as identifies cars by recognizing their plates. That is, the system automatically recognizes characters of vehicle plates. An automatic vehicle plate recognition consists of two main module: a vehicle plate locating module and a vehicle plate number identification module. We study for a vehicle plate number identification module in this paper. We use image preprocessing, feature extraction, multi-layer neural networks for recognizing characters of vehicle plates and we present a feature-comparison method for improving the performance of vehicle plate number identification module. In the experiment on identifying vehicle plate number, 300 images taken from various scenes were used. Of which, 8 images have been failed to identify vehicle plate number and the overall rate of success for our vehicle plate recognition algorithm is 98%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.205-211
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• 2021

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

• Parasites, Hosts and Diseases
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• v.60 no.4
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• pp.273-279
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• 2022

Laelapinae mites are involved in transmission of microbial diseases between wildlife and humans, with an impact on public health. In this study, 5 mite members in the subfamily Laelapinae (laelapin mites; LM) were morphologically identified by light microscopy, and the phylogenetic relationship of LM was analyzed in combination with the sequence information of part of the LM cytochrome oxidase subunit I (cox1) gene. The morphological identification revealed that 5 mites belonged to the genera Laelaps and Haemolaelaps, respectively. Sequence analysis showed that the ratio of nonsynonymous mutation rate to synonymous mutation rate of LM was less than 1, indicating that the LM cox1 gene had undergone purifying selection. Phylogenetic analysis showed that the Laelapinae is a monophyletic group. The genera Haemolaelaps and Hyperlaelaps did not separated into distinct clades but clustered together with species of the genus Laelaps. Our morphological and molecular analyses to describe the phylogenetic relationships among different genera and species of Laelapinae provide a reference for the improvement and revision of the LM taxonomy system.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.5
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• pp.690-698
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• 1990

Among the target identification algorithms presented up to now, the NNR algorithm utilize multifrequency and single observation method. But it is generally known that its implementation is extremely difficult. In this paper a new identification algorithm using the single frequency and cumulative observation method was proposed. The simulation result shows that the proposed algorithm with 31 observation and accumulation is more effective and realizable than the NNR with 3 freqnencies when the noise level n is below 0.3.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.136.1-136
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• 2003

Bacterial grain rot by Burkholderia gluae cause severe damage in seedling and grain of rice after heading season. This seed-borne pathogen play a role as first infection agent that could be cause disease following cropping season. Until now the direct isolation of the bacteria has some trouble by interference of other bacteria existed inside seed. This study established convenient identification method as simple isolation with KB medium from seed showing symptom and using PCR identification. By this isolation method, B. glumae was isolated from 40 to 50％ in brown rice and inner hull, however, there were saprophytic bacteria and fungi outer hull. In PCR identification with Ogf4 and Ogr3 primer to these 25 isolates, the amplified products were presented in all of the collections but not in 10 saprophytic germs. The isolation rate was constant to 3 months stored seeds. This result provide a rapid and convenient isolation and identification of B. glumae.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1633-1642
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• 2000

Like many other mechanical dynamic systems, flexible manipulator systems experience stiction or sticking friction, which may cause input-dependent instabilities. Manipulator performance can be enha nced by identifying friction but it is hard and expensive to measure friction by direct and precise sensing of contact displacements and forces. This study addresses the problem of identifying flexible manipulator joint friction. A dynamic model of a two-link flexible manipulator based upon finite element and Lagrange's method is constructed. The dynamic model includes the effects of joint compliances and actuator dynamics. Friction is also incorporated in the dynamic model to account for stick-slip at the joints. Next, the friction parameters are to be determined. The identification problem is posed as an optimization problem to be solved using nonlinear programming methods. A genetic algorithm is used to increase the convergence rate and the chances of finding the global optimum. The identified friction parameters are experimentally verified and it is expected that the identification technique is applicable to a system parameter identification problem associated with a wide class of nonlinear systems.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.49 no.2
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• pp.224-233
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• 2016

The objective of this study was to develop an artificial neural network (ANN) model for the acoustic identification of commercially important fish species in Korea. A broadband echo acquisition and processing system operating over the frequency range of 85-225 kHz was used to collect and process species-specific, time-frequency feature images from six fish species: black rockfish Sebastes schlegeli, black scraper Thamnaconus modesutus [K], chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, konoshiro gizzard shad Konosirus punctatus and large yellow croaker Larimichthys crocea. An ANN classifier was developed to identify fish species acoustically on the basis of only 100 dimension time-frequency features extracted by the principal components analysis (PCA). The overall mean identification rate for the six fish species was 88.5%, with individual identification rates of 76.6% for black rockfish, 82.8% for black scraper, 93.8% for chub mackerel, 90.6% for goldeye rockfish, 96.9% for konoshiro gizzard shad and 90.6% for large yellow croaker, respectively. These results demonstrate that individual live fish in well-controlled environments can be identified accurately by the proposed ANN model.