• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.87-94
• /
• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.3B
• /
• pp.212-218
• /
• 2012

An Internet worm is a self-replicating malware program which uses a computer network. As the network connectivity among computers increases, Internet worms have become widespread and are still big threats. There are many approaches to model the propagation of Internet worms such as Code Red, Nimda, and Slammer to get the insight of their behaviors and to devise possible defense methods to suppress worms' propagation activities. The influence of the network characteristics on the worm propagation has usually been modeled by medical epidemic model, named SI model, due to its simplicity and the similarity of propagation patterns. So far, SI model is still dominant and new variations of the SI model, called SI-style models, are being proposed for the modeling of new Internet worms. In this paper, we elaborate the problems of SI-style models and then propose a new accurate stochastic model using an occupancy problem.

• Journal of the Society of Disaster Information
• /
• v.13 no.3
• /
• pp.322-331
• /
• 2017

This study proposed subway construction information classification system based on civil engineering information classification system proposed by Korea Institute of Construction Technology. Also, Based on this criterion, This study established data modeling for NAS operating system Composed of construction information classification system - network - operation and presented an relational database integrated model. The data modeling method proposed in this study can be applied to other civil engineering facilities, so it can be operated as scientific NAS.

• Child Health Nursing Research
• /
• v.29 no.3
• /
• pp.182-194
• /
• 2023

Purpose: This study analyzed research trends in infant and toddler rearing behavior among family caregivers over a 10-year period (2010-2021). Methods: Text network analysis and topic modeling were employed on data collected from relevant papers, following the extraction and refinement of semantic morphemes. A semantic-centered network was constructed by extracting words from 2,613 English-language abstracts. Data analysis was performed using NetMiner 4.5.0. Results: Frequency analysis, degree centrality, and eigenvector centrality all revealed the terms ''scale," ''program," and ''education" among the top 10 keywords associated with infant and toddler rearing behaviors among family caregivers. The keywords extracted from the analysis were divided into two clusters through cohesion analysis. Additionally, they were classified into two topic groups using topic modeling: "program and evaluation" (64.37%) and "caregivers' role and competency in child development" (35.63%). Conclusion: The roles and competencies of family caregivers are essential for the development of infants and toddlers. Intervention programs and evaluations are necessary to improve rearing behaviors. Future research should determine the role of nurses in supporting family caregivers. Additionally, it should facilitate the development of nursing strategies and intervention programs to promote positive rearing practices.

• Journal of Information Technology Applications and Management
• /
• v.30 no.1
• /
• pp.53-70
• /
• 2023

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.11
• /
• pp.4016-4027
• /
• 2014

Understanding network latency is important for providing consistent and acceptable levels of services in network-based applications. However, due to the difficulty of estimating applications' network demands and the difficulty of network latency modeling the management of network resources has often been ignored. We expect that, since network latency repeats cycles of congested states, a systematic classification method for network status would be helpful to simplify issues in network resource managements. This paper presents a simple empirical method to classify network status with a real operational network. By observing oscillating behavior of end-to-end latency we determine networks' status in run time. Five typical network statuses are defined based on a long-term stability and a short-term burstiness. By investigating prediction accuracies of several simple numerical models we show the effectiveness of the network status classification. Experimental results show that around 80% reduction in prediction errors depending on network status.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.3
• /
• pp.107-118
• /
• 1998

Two-phase neuro-system identification method is presented. The 1$^{st}$-phase identification uses conventional neural network mapping for modeling an input-output system. The 2$^{nd}$ -phase modeling is also performed sequentially using the 1$^{st}$-phase modeling errors. In the 2$^{nd}$ a phase modeling, newly generated input signals, which are obtained by summing the 1st-phase modeling error and artificially generated uniform series, are utilized as system's I-O mapping elements. The 1$^{st}$-phase identification is interpreted as a “Real Model” system identification because it uses system's real data(i.e., observations and control inputs) while the 2$^{nd}$ -phase identification as a “Artificial Model” identification because of using artificial data. Experimental results are given to verify that the two-phase neuro-system identification could reduce the overall modeling errors.rrors.

• International Journal of Advanced Culture Technology
• /
• v.9 no.4
• /
• pp.385-391
• /
• 2021

3D modeling of the human body is an integral part of computer graphics. Among them, several studies have been conducted on hair modeling, but there are generally few studies that effectively implement hair and face modeling simultaneously. This study has the originality of providing users with customized face modeling and hair modeling that is different from previous studies. For realistic hair styling, We design and realize hair segmentation using FCN, and we select the most appropriate model through comparing PSPNet, DeepLab V3+, and MobileNet. In this study, we use the open dataset named Figaro1k. Through the analysis of iteration and epoch parameters, we reach the optimized values of them. In addition, we experiment external parameters about the location of the camera, the color of the lighting, and the presence or absence of accessories. And the environmental analysis factors of the avatar maker were set and solutions to problems derived during the analysis process were presented.

• Journal of the Korean Geographical Society
• /
• v.43 no.4
• /
• pp.526-543
• /
• 2008

Based on the social network analysis(SNA), this study examines the structural and spatial characteristics of the actor networks of the manufacturing industries for the elderly. In the field of economic geography, former researches on network have mainly focused on the network governance. However, this study focused on the social network analysis. Centrality indexes are used to analyze the topological structure of actor networks of firms and organizations. In order to investigate the spatial structure of actor networks, not only the regional distribution of actors but also the correlation between centrality index and distance are analyzed. Network matrixes among actors are transformed to network matrixes among regions using block modeling method to reveal the spatial characteristics of the actor networks. In spite of the importance of the Capital Region, networks in the non-Capital Region like Chungnam and Pusan were showed high network density. This suggested that some kinds of policy project operating in the non-Capital Region had the influence on this network in the initial stage of industry.

• Journal of the Korea Society for Simulation
• /
• v.15 no.1
• /
• pp.87-95
• /
• 2006

We need to check into when a security system is newly developed, we against cyber attack which is expected in real network. However it is impossible to check it under the environment of a large-scale distributive network. So it is need to simulate it under the virtual network environment. SSFNet is a event-driven simulator which can be represent a large-scale network. Unfortunately, it doesn't have the module to simulate security functions. In this paper, we added the IDS module to SSFNet. We implement the IDS module by modeling a key functions of Snort. In addition, we developed some useful functions using Java language which can manipulate easily a packet for network simulation. Finally, we performed the simulation to verify the function if our developed IDS and Packets Manipulation. The simulation shows that our expanded SSFNet can be used to further large-scale security system simulator.