• Journal of Software Engineering Society
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• v.26 no.2
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• pp.45-52
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• 2013

A cloud computing service provider must assure Service Level Agreement (SLA) to provide reliable and consistent quality of service to a user. The SLA is a contract between the user and the service provider that connects to assure constant level such as availability to target provided service. The cloud computing is classified into IaaS, PaaS, and SaaS according to IT resources of the various cloud service. The existing SLA is difficult to reflect quality factors of service because it only considers factors about the physical Network environment. In this paper, we suggest the UDDI-based interchange process with the architecture and the specification language having a XML schema for the SLA specification. The quality requirements of SaaS are defined by a proposed specification language in the cloud environment. It is stored in the repository of a quality specification and exchanged on during the service binding time based on the exchange architecture.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.299-304
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• 2021

Existing human activity recognition systems detect activities through devices such as wearable sensors and cameras. However, these methods require additional devices and costs, especially for cameras, which cause privacy issue. Using WiFi signals that are already installed can solve this problem. In this paper, we propose a CNN-based human activity recognition system using channel state information of WiFi signals, and present results of designing and implementing accelerated hardware structures. The system defined four possible behaviors during studying in indoor environments, and classified the channel state information of WiFi using convolutional neural network (CNN), showing and average accuracy of 91.86%. In addition, for acceleration, we present the results of an accelerated hardware structure design for fully connected layer with the highest computation volume on CNN classifiers. As a result of performance evaluation on FPGA device, it showed 4.28 times faster calculation time than software-based system.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.633-641
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• 2009

In this paper, we proposed a new pattern classifier which can be incrementally learned, be added new class in learning time, and handle with analog data. Proposed pattern classifier has hierarchical structure and the classification rate is improved by using different metric for each levels. Proposed model is based on the Gaussian ARTMAP which is an artificial neural network model for the pattern classification. We hierarchically constructed the Gaussian ARTMAP and proposed the Principal Component Emphasis(P.C.E) method to be learned different features in each levels. And we defined new metric based on the P.C.E. P.C.E is a method that discards dimensions whose variation are small, that represents common attributes in the class. And remains dimensions whose variation are large. In the learning process, if input pattern is misclassified, P.C.E are performed and the modified pattern is learned in sub network. Experimental results indicate that Hierarchical Gaussian ARTMAP yield better classification result than the other pattern recognition algorithms on variable data set including real applicable problem.

• Smart Media Journal
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• v.4 no.2
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• pp.55-61
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• 2015

A bandwidth-enhanced ultra-wide band (UWB) CMOS balun-LNA is implemented as a part of a software defined radio (SDR) receiver which supports multi-band and multi-standard. The proposed balun-LNA is composed of a single-to-differential converter, a differential-to-single voltage summer with inductive shunt peaking, a negative feedback network, and a differential output buffer with composite common-drain (CD) and common-source (CS) amplifiers. By feeding the single-ended output of the voltage summer to the input of the LNA through a feedback network, a wideband balun-LNA exploiting negative feedback is implemented. By adopting a source follower-based inductive shunt peaking, the proposed balun-LNA achieves a wider gain bandwidth. Two LNA design examples are presented to demonstrate the usefulness of the proposed approach. The LNA I adopts the CS amplifier with a common gate common source (CGCS) balun load as the S-to-D converter for high gain and low noise figure (NF) and the LNA II uses the differential amplifier with the ac-grounded second input terminal as the S-to-D converter for high second-order input-referred intercept point (IIP2). The 3 dB gain bandwidth of the proposed balun-LNA (LNA I) is above 5 GHz and the NF is below 4 dB from 100 MHz to 5 GHz. An average power gain of 18 dB and an IIP3 of -8 ~ -2 dBm are obtained. In simulation, IIP2 of the LNA II is at least 5 dB higher than that of the LNA I with same power consumption.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.19-24
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• 2018

The IP Multimedia Subsystem(IMS) is an architectural framework for delivering IP multimedia services to mobile users. In order to guarantee the reliability and Quality of Service(QoS) of a variety of multimedia services, we need a new evolutionary approach that maintains the IMS based signaling platform which can perform the processing of flow through distributed controllers. Software Defined Network(SDN) is an architecture purporting to be distributed, dynamic, cost-effectives as well as adapting and seeking to be suitable for the high-bandwidth, dynamic nature of today's applications. It requires some methods for the control plane to communication with the data plane. One of such mechanisms is OpenFlow which is a prominent standard protocol and interface that is responsible for managing the network resources by using the remote SDN controller. In this paper, we propose a straightforward approach for integrating SDN technology together with the IMS architecture. Therefore we propose and construct a combined architecture model that performs flow processing using OpenFlow via the IMS based signaling platform, which maintains the existing telecom call service. Additionally, we describe some relevant experimentation results from the proposed architecture.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.347-349
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• 2009

Air quality of indoor space environment is affected by various pollutants like as particles and chemical stuffs. The indoor air pollution affects directly the human respiration organs to cause consequently unpleasant mental status. The $CO_2$ concentration level is one of the harmful components of air pollutants. Major factor to increase the $CO_2$ concentration level is the people's breath amount in indoor. The car exhaust gas diffused from the around road also has strong affect on $CO_2$ concentration. There are some other reasons to affect the $CO_2$ concentration change, such as, real-time change of the population movement, closeness to the indoor air flow inlet window and changes in road car traffic amount. A remote monitoring system to measure environmental indoor air pollution concerning on the $CO_2$ concentration was studied and installed realized set-up model. Zigbee network configuration was applied for this system and the $CO_2$ concentration data were collected through USN network. A software program was developed to assure systematic analysis and to display real-time data on web pages. For the experimental test various condition was set up, like as, window opening, stopping air condition operation and adjusting fan heater work, etc. The analysis result showed the relation of various environmental conditions to $CO_2$ concentration changes. The causes to increase $CO_2$ concentration were experimentally defined as windows closing, the stopping air condition system, fan heater operation. To keep the $CO_2$ concentration under the legally required ppm level in public access indoor space, the developed remote measurement system will be usefully applied.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1545-1554
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• 2018

In order to support the mobility of the wireless devices, at least two APs (Access Points) must be arranged in a single AP area to maintain communication area. In the WLAN (Wireless LAN) environment, seamless handover is one of the most important issues in terms of effective utilization of wireless networks and maximization of services for users. On the other hand, SDN (Software-Defined Networking), which is emerging rapidly in recent years, is revolutionizing network management in terms of flexibility, fine control, and convenience. SDN originally reduces latency time or increases network robustness by real-time flow table control reducing or bypassing paths between switches in LAN-based data centers. In this study, we apply OpenFlow, a SDN platform focused on wired LAN, to a dense WLAN environment using legacy APs to implement and evaluate seamless handover for streaming services of digital contents.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.65-74
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• 2014

IEEE 802.11n standard has introduced the new schemes in MAC and PHY layers to improve network throughput. Frame aggregation and Block ACK are mainly defined to increase the efficiency of the MAC layer. There exists still problem in IEEE 802.11n. When block ACK request and/or response frames are missing or received in error, the sender does not know the status (success/failure) of each frame in the aggregated large frame and retransmits all the frames. This can cause a lower network performance. To solve this problem, we propose a new effective scheme, called reduced retransmission of MPDUs (RRM) scheme. In the proposed scheme, when a sender does not receive a block ACK response frame, it just transmits a next data frame and requests a block ACK. Therefore, it can retransmits the erroneous frames. Performance of the proposed scheme is investigated by simulation. Our results show that the proposed scheme is very effective and improves the performance under a wide range of channel error conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.69-75
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• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.

• KNOM Review
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• v.23 no.1
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• pp.10-17
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• 2020

As the threat of Distributed Denial-of-Service attacks that exploit weakly secure IoT devices has spread, research on source-side Denial-of-Service attack detection is being activated to quickly detect the attack and the location of attacker. In addition, a collaborative source-side attack detection technique that shares detection results of source-side networks located at individual sites is also being activated to overcome regional limitations of source-side detection. In this paper, we evaluate the performance of a collaborative source-side DDoS attack detection using statistical weights. The statistical weight is calculated based on the detection rate and false positive rate corresponding to the time zone of the individual source-side network. By calculating weighted sum of the source-side DoS attack detection results from various sites, the proposed method determines whether a DDoS attack happens. As a result of the experiment based on actual DNS request to traffic, it was confirmed that the proposed technique reduces false positive rate 2% while maintaining a high attack detection rate.