• Tribology and Lubricants
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• v.10 no.4
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• pp.13-26
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• 1994

Effects of resin contents, number of carbonization, graphitization, sliding speed, and oxidation on friction and wear behavior of carbon/carbon composite materials were investigated. Friction and wear tests were carried out under various sliding conditions. An experimental setup was designed and built in the laboratory. Stainless steel disks were used as the counterface material. Friction coefficient, emperature, and wear factor were measured with a data acquisition system. Wear surfaces were observed by the scanning electron microscope. It has been shown that the average friction coefficient was increased with the sliding speed in the range of 1.43~6.10 m/s, but it as decreased in the range of 6.10~17.35 m/s. Specimens prepared by different numbers of carbonization. showed variations in friction coefficient and friction coefficient of the graphitized specimen was the highest. Friction coefficients depended on contribution of the plowing and adhesive components. As the number of carbonization was increased, wear factor was reduced. Wear factor of the graphitized specimens dropped further. In the case of graphitized specimens, sliding speed had a large influence on wear behavior. When the tribological experiments were conducted in nitrogen atmosphere, the wear factor was decreased to two thirds of the wear factor obtained in air. It is obvious that the difference was affected by oxidation. Results of friction and wear tests were applied to a neural network system based on the backpropagation algorithm. A neural network may be a valuable tool for prediction of tribological behavior of the carbon/carbon composite material if ample data are present.

• Genomics & Informatics
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• v.13 no.3
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• pp.76-80
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• 2015

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.201-206
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• 2021

The significance of high-quality development and green total factor productivity has attracted widespread attention and research, while few studies on green total factor productivity that considers the use of water resources have been conducted in the context of water shortages and water stress. In this study, the green total factor productivity of water use from 2005 to 2015 in mainland China is evaluated based on the global Malmquist-Luenberger productivity index. Results show that: (1) China's green total factor productivity of water use has been improving since 2005 with an annual global Malmquist-Luenberger productivity index of 1.0104. (2) At the regional level, the eastern zone in mainland China owns the highest green total factor productivity of water use, while that in the intermediate zone ranks last. (3) The green total factor productivity of water use in the southern region (1.0113) significantly higher than that in the northern region (1.0095), and also higher than the national average level in the same period. BPC index has been the most important incluencing factor of green total factor productivity of water use at both national level and regional level since 2011.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• International Journal of Highway Engineering
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• v.15 no.3
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• Journal of Navigation and Port Research
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• v.30 no.1 s.107
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• pp.45-51
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• 2006

This paper aims to analysis on evaluation factor for success of port innovative cluster. This paper is divided three factors such ac policy, source and operation In addition, three factors are divided into the twelve detail factors. From a total of 30 survey cases, 50 percent randomly selected as the training group and the other 50 percent as the validation group. cases in the training group were used in the development of the Kohonen Network The validation group was used to test the performance of this model. The major findings may be summarized as follows; The prediction accuracy rate is $73.33\%$ The weight of real root and detail factors is calculated by Kohonen Network At the result, success prediction group of port innovative cluster, this paper places the priority on the source factor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.191-204
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• 2010

There is a well-defined propagation model, named the random constant spread (RCS) model, which explains worms that spread their clones with a random scanning strategy. This model uses the number of infected hosts in a domain as a factor in the worms' propagation. However, there are difficulties in explaining the characteristics of new Internet worms because they have several considerable new features: the denial of service by network saturation, the utilization of a faster scanning strategy, a smaller size in the worm's propagation packet, and to cause maximum damage before human-mediated responses are possible. Therefore, more effective factors are required instead of the number of infected hosts. In this paper, the network bandwidth usage rate is found to be an effective factor that explains the propagations of the new Internet worms with the random scanning strategy. The analysis and simulation results are presented using this factor. The simulation results show that the scan rate is more sensitive than the propagation packet for detecting worms' propagations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.6 no.2
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• pp.19-27
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• 1988

In determining the horizontal positions, economic, speedy, and accurate analytical adjustment methods have studied and developed for a long time. From now on, the adjustment methods using both angles and distances are expected because the development of more precise instruments, E.D.M, and electronic total station provide us with more advantages than the conventional measurement system. The objective of this paper is to study the characteristics of triangulation, trilateration, and combination method due to change of the weight factor of angles, distances, azimuthes, and control point coordinates of combined geodetic network. The results of this study show that combined method is more accurate and effective than other methods in case of combined geodetic network as the other simple networks.