• Journal of Digital Convergence
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• v.9 no.5
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• pp.225-233
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• 2011

Internet telephony is an Internet service which supports voice telephone using VoIP technology on the IP-based Internet. It has some advantages in that voice telephone services can be accompanied with multimedia services such as video communication and messaging services. Recently, the introduction of smart phones has led to a growth in social networking services and thus, the research and development of Internet telephony has been actively progressed and has the potential to become a replacement for the telephone service that is currently being used. In this paper we designed and implemented a recording system which records voice data of SIP-based Internet telephone's voice calls. It is developed on the linux system and has some features such as audio mixing of two in/out voice channels, live packet sniffing, and the ability to transfer mixed audio files to the log file server. These functions are implemented using various open source softwares. Afterwards, this VoIP recording system will be applied as a base technology to advanced services like a VoIP-based call center system.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.313-318
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• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.223-230
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• 2017

In the current wireless Internet environment, using a mobile device to quickly access a web site is closely related to measuring the performance of a website. When accessing a website, the user has a long time to access the website and has no access to the website.In this case, the performance of the web site should be improved by measuring and analyzing the performance of the connection delay due to a problem of the web site.Among the performance measurement factors of Web sites, Web page loading time is a very important factor for a successful service business in the situation where most of e-commerce business is being developed as a web-based service.An open source tool was analyzed to analyze the performance of the e-commerce web page to present problems, software optimization methods and hardware optimization methods. Applying two optimization methods to suit the environment will enable stable and e-commerce websites.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.313-318
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• 2016

Recently, the smart farm development using a PC and smart phone to manag the farm for improving competitiveness is in progress. In the smart farm, by using the various ICT technology including RFID, Wi-Fi, ZigBee, Wireless LAN, and etc., the growing environment of the crop and animals can be managed with the remote. By using the network including not only the TCP/IP based wired network but also ZigBee, Wireless LAN, and etc., each of the devices installed in the smart farm transmits the growing environment data to the server. So, smart farms have information and network security vulnerability. Therefore, we propose the method that analyzes the security vulnerability which can begenerated in the smart farm and user authentication method.

• Journal of Industrial Convergence
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• v.18 no.1
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• pp.79-85
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• 2020

Recently, the RPAS(Remote Piloted Aircraft System), by remote control and autonomous navigation, has been increasing in interest and utilization in various industries and public organizations along with delivery drones, fire drones, ambulances, agricultural drones, and others. The problems of the stability of unmanned drones, which can be self-controlled, are also the biggest challenge to be solved along the development of the drone industry. drones should be able to fly in the specified path the autonomous flight control system sets, and perform automatically an accurate landing at the destination. This study proposes a technique to check arrival by landing point images and control landing at the correct point, compensating for errors in location data of the drone sensors and GPS. Receiving from the Google Map API and learning from the destination video, taking images of the landing point with a drone equipped with a NAVIO2 and Raspberry Pi, camera, sending them to the server, adjusting the location of the drone in line with threshold, Drones can automatically land at the landing point.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.747-751
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• 2017

The log has variable informations that are important and necessary to manage a network when accessed to network servers. These informations are used to reduce a cost and efficient manage a network through the meaningful prediction information extraction from the amount of user access. And, the network manager can instantly monitor the status of CPU, memory, disk usage ratio on network using the SNMP. In this paper, firstly, we have accumulated and analysed the 6 network logs and extracted the informations that used to predict the amount of user access. And then, we experimented the prediction simulation with the time series analysis such as moving average method and exponential smoothing. Secondly, we have simulated the usage ration of CPU, memory, and disk using Xian SNMP simulator and extracted the OID for the time series prediction of CPU, memory, and disk usage ration. And then, we presented the visual result of the variable experiments through the Excel and R programming language.

• The Journal of Society for e-Business Studies
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• v.13 no.2
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• pp.149-163
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• 2008

In a ubiquitous sensor network (USN) with several nodes, real-time data processing is one of important factors. In order to process data appropriately, all the nodes should transmit sensor data in time and the transmission between nodes and their server should be managed very systematically. For the purpose of systematic management of transmission in a USN, this paper proposes a system for analyzing transmission time of sensor data. To implement the proposed system, an analyzing process of data transmission time, an analyzing method of clock drift, a collecting method of data send/receive times, and calculating formulas of data transmission duration are proposed. According to the proposed process and methods, this paper presents a system for monitoring and analyzing data transmission duration, and it also shows the results of a sample case.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.44-49
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• 2016

Recently, data has been growing dramatically along with the growth of social media and digital devices. A distributed memory processing system has been used to efficiently process large amounts of data. However, if a load is concentrated in a certain node in distributed environments, a node performance significantly degrades. In this paper, we propose a load balancing scheme to distribute load in a distributed memory environment. The proposed scheme replicates hot data to multiple nodes for managing a node's load and migrates the data by considering the load of the nodes when nodes are added or removed. The client reduces the number of accesses to the central server by directly accessing the data node through the metadata information of the hot data. In order to show the superiority of the proposed scheme, we compare it with the existing load balancing scheme through performance evaluation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.161-168
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• 2018

High blood pressure is main today's adult disease and existing blood pressure gauge is not possible for real-time blood pressure measurement and remote monitoring. But real-time blood pressure monitoring u-healthcare system makes effect health management. In my paper, for monitoring real-time blood pressure, an architecture of real-time blood pressure monitoring system which consisted of wrist type-blood pressure measurement, smart-phone and u-healthcare server is presented. And the analog circuit architecture which is major core function for pulse wave detection and digital hardware architecture for wrist type-blood pressure measurement is presented. Also for software development to operate this hardware system, UML analysis method and flowcharts and screen design for this software design are showed. Therefore such design method in my paper is expected to be useful for real-time blood pressure monitoring u-healthcare system implementation.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.189-194
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• 2020

Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.