• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Journal of Digital Convergence
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• v.6 no.4
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• pp.137-145
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• 2008

Embedded industry has emerged as one of the important industry for u-IT based application service. Technology development related to embedded hardware and software provides the base for the development of U-IT convergence industry. This study intends to suggest a research model to investigate the influence of industry, organization, technology factors on technology performance in the embedded companies. This study adopted research variables such as global network, local network, industry intensity, and technology capability as independent variables, with technology performance as a dependent variable from the existing literatures. The purpose of this study is to analyze the influence of industrial and organizational factors on technology performance of embedded companies SCM systems, including the moderating effect of government support. 94 companies data were collected by survey. The result of this empirical study is summarized as follows. First, local network and technology capability are the important determinants to influence technology performance of embedded companies in direct effect model. Second, global network and industry intensity has more positive influence on technology performance of embedded companies through a moderating variable of government support.

• Convergence Security Journal
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• v.3 no.3
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• pp.25-33
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• 2003

With the radical improvement of wire and wireless communication technologies, home network which interconnects various home appliances is approaching ripening stage. Digitalization of the home environment will break down the boundaries of information, communications and broadcasting, and enable us to realize many breakthroughs on the home front and connect to our home. In order to enable users to access securely to their home network, we first construct secure home network model which can authorize users using their permission policy. In this paper, we examine various security technique used in home network and propose home network security scheme which can service constantly.

• Convergence Security Journal
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• v.10 no.2
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• pp.1-9
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• 2010

Snort is an open source network intrusion prevention and detection system (IDS/IPS) developed by Sourcefire. Combining the benefits of signature, protocol and anomaly-based inspection, Snort is the most widely deployed IDS/IPS technology worldwide. With millions of downloads and approximately 300,000 registered users. Snort identifies network indicators by inspecting network packets in transmission. A process on a host's machine usually generates these network indicators. This means whatever the snort signature matches the packet, that same signature must be in memory for some period (possibly micro seconds) of time. Finally, investigate some security issues that you should consider when running a Snort system. Paper coverage includes: How an IDS Works, Where Snort fits, Snort system requirements, Exploring Snort's features, Using Snort on your network, Snort and your network architecture, security considerations with snort under digital forensic windows environment.

• Convergence Security Journal
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• v.5 no.3
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• pp.1-7
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• 2005

Today, network is a fragile state in many threat attacks. Especially, the company serviced like internet or e-commerce is exposed to danger and targeted of attacker Therefore, it is realistic that the company use the security solution. It exist various security solution in our school network. For example, Firewall, IDS, VirusWall, VPN, etc. The administrator must manage various security solution. But it is inefficient. Therefore, we need the Management System to controll every security solution. In this paper, we deal with basic contents of security solution to manage the ESM and merits and demerits when use it. Also we suggest method that the Administrator can manage his network more efficiently and systematically by using the ESM in our school network.

• Convergence Security Journal
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• v.12 no.4
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• pp.49-55
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• 2012

In this paper, we consider the limit of the previous works for ad-hoc network, then propose a dynamic routing scheme which employs a 2-tier hierarchical structure. We adopt the advantages of proactive and reactive routing scheme for efficient network management. We define this method as NSDR(New Secure Dynamic Routing) scheme. We also propose a trustworthy authentication and key management for the proposed ad-hoc network. We currently study the possibility that ad-hoc networks can provide a service such as key management and authentication for the next generation mobile network.

• Convergence Security Journal
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• v.17 no.3
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• pp.3-9
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• 2017

NFV is a technology that allows network services to be controlled and managed in software by separating various net work functions (NFs) from hardware devices in dedicated network equipment and implementing them in a high-performance general-purpose server. Therefore, standardized virtualization of network functions is one of the most important factors. However, until the introduction of NFV to provide commercial services, there are many technical issues to be solved such as guaranteeing performance, stability, support for multi-vendor environment, ensuring perfect interoperability, and linking existing virtual and non-virtual resources. In this paper, we propose a method to provide an end-to-end network virtualization service based on OSM R2 in KREONET.

• The Journal of the Korea Contents Association
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• v.8 no.4
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• pp.71-78
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• 2008

We propose the scheme that supports continuously service when a user tries to contact new target network in broadband convergence network even though the network resource for the service is not enough. The proposed scheme transforms original service capacity into suitable QoS for the target network using the scalable service techniques in the oncoding/decoding and supports handover for the user. We analyzed the handover blocking probability of two schemes using queueing system to show the improvement of performance by the proposed scheme. Subsequently, we showed that the handover blocking probability in the proposed scheme is lower than that in the existing scheme.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.201-209
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• 2017

This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.