• Health Policy and Management
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• v.10 no.1
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• pp.126-147
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• 2000

This paper empirically explores the nature of the medical service industry and its various propagation effects on the economy in the input-output model, as revealed by a comparative analysis between Korea and Japan. The main findings of the paper are as follows; First, the growth of medical industry induces above-average effect on employment. Second, the industry is of the characteristics of weak both backward and forward linkage effects implying a 'final demand dependency industry'. When compared with public service sectors, however, the medical services industry shows stronger backward linkage effect than those sectors. Furthermore, it has strong repercussion effects on the goods industries. Third, in order to produce per unit of services, the medical services industry of Korea uses relatively more drugs and medical devices than that of Japan. In general, it has been shown that production structure of medical service industry in Korea is 'hardware-oriented' one; on the other hand, 'software-oriented' in Japan which means that, as intermediate inputs, outsourcing and informatization has been used than those of Korea. From the findings of the paper it could be emphasized that the medical organizations in Korea should put more efforts on shifting the current hardware-oriented production structure to strengthen core competence by enhancing productivity and by outsourcing to improve efficiency of production process. However, the medical organizations in Korea would not have enough incentives for high value-added production structure because they enjoy high operating surplus. Therefore, it would be necessary that government policy should be taken into account of these environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.2913-2918
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• 2011

This paper proposed the friction model using Preisach algorithm with neural network based on experimental results. In order to apply the neural network algorithm, the back propagation update rule was used and the updated weighting factor of neural network was applied to distribute function of Preisach model. In order to implement the proposed algorithm, the LabView software was used to apply to the precision control of mechanical system. The evaluation of the proposed friction model was executed through experiments.

• Advances in nano research
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• v.7 no.6
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• pp.431-442
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• 2019

Vibration analysis of carbon nanotubes (CNTs) is very essential field owing to their many promising applications in tiny instruments. In current study, the Eringen's nonlocal elasticity theory with clamped-clamped and clamped-free end conditions is utilized for the vibration analysis of armchair and zigzag SWCNTs. The Fourier method is utilized to solve the ordinary differential equation. The motion equation for this system is developed using a novel wave propagation method. Complex exponential functions have been used and the axial model depends on BCs that has been described at the edges of CNTs. The behavior of different nonlocal parameters is considered to find the vibrational frequency of SWCNTs. It is exhibited that the effect of frequencies against aspect ratio by varying the bending rigidity. It has been investigated that by increasing the nonlocal parameter decreases the frequencies and on increasing the aspect ratio increases the frequencies for both the tubes. To generate the fundamental natural frequencies of SWCNTs, computer software MATLAB engaged. The numerical results are validated with existing open text. Since the percentage of error is negligible, the model has been concluded as valid.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.288-294
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• 2021

There are various ways to be infected with malicious code due to the increase in Internet use, such as the web, affiliate programs, P2P, illegal software, DNS alteration of routers, word processor vulnerabilities, spam mail, and storage media. In addition, malicious codes are produced more easily than before through automatic generation programs due to evasion technology according to the advancement of production technology. In the past, the propagation speed of malicious code was slow, the infection route was limited, and the propagation technology had a simple structure, so there was enough time to study countermeasures. However, current malicious codes have become very intelligent by absorbing technologies such as concealment technology and self-transformation, causing problems such as distributed denial of service attacks (DDoS), spam sending and personal information theft. The existing malware detection technique, which is a signature detection technique, cannot respond when it encounters a malicious code whose attack pattern has been changed or a new type of malicious code. In addition, it is difficult to perform static analysis on malicious code to which code obfuscation, encryption, and packing techniques are applied to make malicious code analysis difficult. Therefore, in this paper, a method to detect malicious code through dynamic analysis and static analysis using Trojan-type Downloader/Dropper malicious code was showed, and suggested to malicious code detection and countermeasures.

• Smart Structures and Systems
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• v.26 no.5
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• pp.657-666
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• 2020

A rotational pulse-echo ultrasonic propagation imager that can inspect cylindrical specimens for material nondestructive evaluations is proposed herein. In this system, a laser-generated ultrasonic bulk wave is used for inspection, which enables a clear visualization of subsurface defects with a precise reproduction of the damage shape and size. The ultrasonic waves are generated by a Q-switched laser that impinges on the outer surface of the specimen walls. The generated waves travel through the walls and their echo is detected by a Laser Doppler Vibrometer (LDV) at the same point. To obtain the optimal Signal-to-Noise Ratio (SNR) of the measured signal, the LDV requires the sensed surface to be at a right angle to the laser beam and at a predefined constant standoff distance from the laser head. For flat specimens, these constraints can be easily satisfied by performing a raster scan using a dual-axis linear stage. However, this arrangement cannot be used for cylindrical specimens owing to their curved nature. To inspect the cylindrical specimens, a circular scan technology is newly proposed for pulse-echo laser ultrasound. A rotational stage is coupled with a single-axis linear stage to inspect the desired area of the specimen. This system arrangement ensures that the standoff distance and beam incidence angle are maintained while the cylindrical specimen is being inspected. This enables the inspection of a curved specimen while maintaining the optimal SNR. The measurement result is displayed in parallel with the on-going inspection. The inspection data used in scanning are mapped from rotational coordinates to linear coordinates for visualization and post-processing of results. A graphical user interface software is implemented in C++ using a QT framework and controls all the individual blocks of the system and implements the necessary image processing, scan calculations, data acquisition, signal processing and result visualization.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.603-611
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• 2001

This paper proposes a novel fast layer-by-layer algorithm that has better generalization capability. In the proposed algorithm, the weights of the hidden layer are updated by the target vector of the hidden layer obtained by least squares method. The proposed algorithm improves the learning speed that can occur due to the small magnitude of the gradient vector in the hidden layer. This algorithm was tested in a handwritten digits recognition problem. The learning speed of the proposed algorithm was faster than those of error back propagation algorithm and modified error function algorithm, and similar to those of Ooyen's method and layer-by-layer algorithm. Moreover, the simulation results showed that the proposed algorithm had the best generalization capability among them regardless of the number of hidden nodes. The proposed algorithm has the advantages of the learning speed of layer-by-layer algorithm and the generalization capability of error back propagation algorithm and modified error function algorithm.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.41-49
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• 2005

In this Paper, we Propose an automatic lung registration technique using local distance propagation for correcting the difference between two temporal images by a patient's movement in abdomen CT image obtained from the same patient to be taken at different time. The proposed method is composed of three steps. First, lung boundaries of two temporal volumes are extracted, and optimal bounding volumes including a lung are initially registered. Second, 3D distance map is generated from lung boundaries in the initially taken volume data by local distance propagation. Third, two images are registered where the distance between two surfaces is minimized by selective distance measure. In the experiment, we evaluate a speed and robustness using three patients' data by comparing chamfer-matching registration. Our proposed method shows that two volumes can be registered at optimal location rapidly. and robustly using selective distance measure on locally propagated 3D distance map.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.211-224
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• 1996

In the research area of estimation of the software development efforts, a number of researches have been accomplished in order to control the costs and to make software more competitive. However, most of them were restricted to the functional algorithm models or the statistic models. Moreover, since they are dealing with the cases of foreign countries, the results are hard to apply directly to the domestic environment for the efficient project management because of lack of accuracy, fitness, flexibility and portability. Therefore, it is appropriate to suggest and propose a new approach supported by artificial neural network which is composed of back propagation and feel-forward algorithms to improve the exactness of the efforts estimation and to advance practical uses. In this study, the artificial neural network approach is used to model the software cost estimation and the results are compared with the revised COCOMO and the multiregression model in order to validate the superiority of the model.

• Computers and Concrete
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• v.6 no.3
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• pp.253-268
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• 2009

This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.

• ETRI Journal
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• v.42 no.1
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.