• Journal of Information Processing Systems
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• v.8 no.1
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• pp.1-20
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• 2012

The most important criterion for achieving the maximum performance in a wireless mesh network (WMN) is to limit the interference within the network. For this purpose, especially in a multi-radio network, the best option is to use non-overlapping channels among different radios within the same interference range. Previous works that have considered non-overlapping channels in IEEE 802.11a as the basis for performance optimization, have considered the link quality across all channels to be uniform. In this paper, we present a measurement-based study of link quality across all channels in an IEEE 802.11a-based indoor WMN test bed. Our results show that the generalized assumption of uniform performance across all channels does not hold good in practice for an indoor environment and signal quality depends on the geometry around the mesh routers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.6
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• pp.533-544
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• 1991

In this paper, the performance of bridges used to interconnect LAN to FD야 backbone networks as well as the performance of MAC protocols for FD야 backbone networks were thoroughly analyzed, The exhaustive service discipline and three ource models were applied to analyze the mean waiting time of the system. the performance is evaluated in terms of the service rate of bridge, total load of backbone. medium length of back bone, value of T and station latency. The result of analysis show that in general , processing delay of the system is mainly determined by bridge delays. But when processing time of bridge mereases, processing delays of the system are primarily determined by MAC protocols. Therefore, speed-up of processing time of bridge is necessary to efficiently use the high speed backbone networks.

• Journal of Communications and Networks
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• v.11 no.3
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• pp.306-317
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• 2009

We consider the problem of architecting a reliable content delivery system across an overlay network using TCP connections as the transport primitive. We first argue that natural designs based on store-and-forward principles that tightly couple TCP connections at intermediate end-systems impose fundamental performance limitations, such as dragging down all transfer rates in the system to the rate of the slowest receiver. In contrast, the ROMA architecture we propose incorporates the use of loosely coupled TCP connections together with fast forward error correction techniques to deliver a scalable solution that better accommodates a set of heterogeneous receivers. The methods we develop establish chains of TCP connections, whose expected performance we analyze through equation-based methods. We validate our analytical findings and evaluate the performance of our ROMA architecture using a prototype implementation via extensive Internet experimentation across the PlanetLab distributed testbed.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.291-291
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• 2002

DGPS(Differential Global Positioning System) and RTK(RealTime Kinematic) is in one of today's most widely used surveying techniques. But It's use is restricted by the distance between reference station and rover station and it is difficult to process data in realtime by it's own orgnizational limitation in precise measurement of positioning. To meet these new demands, In This paper, new DGPS and RTK correction data services through Internet and PSTN(Public Switched Telephony Network) have been proposed. For this purpose, we implemented performance a DGPS and RTK error correction data transmission system for long-distance using the internet and PSTN network which allows a mobile user to increase the distance at which the rover receiver is located from the reference in realtime. and we analyzed and compared DGPS and RTK performance by experiments through the Internet and PSTN network with the distance and the time.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1259-1276
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of Internet of Things (IoT) technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatialtemporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.97-104
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• 2019

Distributed mobility management (DMM) does not use a centralized device. Its mobility functions are distributed among routers; therefore, the mobility services are not limited to the performance and reliability of specific mobility management equipment. The DMM scheme has been studied as a partially distributed architecture, which distributes only a packet delivery domain in combination with the software defined network (SDN) technology that separates the packet delivery and control areas. Particularly, a separated control area is advantageous in introducing a new service, thereby optimizing the network by recognizing the entire network situation and taking an optimal decision. The SDN-based mobility management scheme is studied as a method to optimize the packet delivery path whenever a mobile node moves; however, it results in excessive signaling processing cost. To reduce the high signaling cost, we propose a hybrid distributed mobility management method and analyze its performance mathematically.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.87-93
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• 2019

These days, as interest in the recommendation system with deep learning is increasing, a number of related studies to develop a performance for collaborative filtering through autoencoder, a state-of-the-art deep learning neural network architecture has advanced considerably. The purpose of this study is to propose autoencoder which is used by the recommendation system to predict ratings, and we added more hidden layers to the original architecture of autoencoder so that we implemented deep autoencoder with 3 to 5 hidden layers for much deeper architecture. In this paper, therefore we make a comparison between the performance of them. In this research, we use 2-dimensional arrays and 3-dimensional tensor as the input dataset. As a result, we found a correlation between matrix entry of the 3-dimensional dataset such as item-time and user-time and also figured out that deep autoencoder with extra hidden layers generalized even better performance than autoencoder.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.222-229
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• 2020

Recent multi-core processors for smart devices use per-core dynamic voltage and frequency scaling (DVFS) that enables independent voltage and frequency control of cores. However, because the conventional task scheduler was originally designed for per-core DVFS disabled processors, it cannot effectively utilize the per-core DVFS and simply allocates tasks evenly across all cores to core utilization with the same CPU frequency. Hence, we propose a novel task scheduler to effectively utilize percore DVFS, which enables each core to have the appropriate frequency, thereby improving performance and decreasing energy consumption. The proposed scheduler classifies applications into two types, based on performance-sensitivity and allows a performance-sensitive application to have a dedicated core, which maximizes core utilization. The experimental evaluations with a real off-the-shelf smart device showed that the proposed task scheduler reduced 13.6% of CPU energy (up to 28.3%) and 3.4% of execution time (up to 24.5%) on average, as compared to the conventional task scheduler.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.258-264
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• 2004

WLAN technologies enable high-speed data transmission in a small space with relatively low cost and power. There are two main standardizations regarding WLAN; namely, IEEE802.11a and ETSI BRAN. Two standards use a radio frequency of 5 ㎓ band and employs OFDM as a transmission mode. OFDM is known to provide high-frequency efficiency as well as high-speed data transmission. In this paper, We use SPW simulation tool and implement the HIPERLAN/2 system. Based on the HIPERLAN/2 system in the SPW platform, the system performance is analyzed and compared with turbo codes in place of a convolutional coder of the HIPERLAN/2 WLAN system. The simulation results show that the performance with the turbo codes (rate 1/3 and 1/4) at a data rate of 12 Mbps shows 3 ㏈ gain over the system with the convolutional codes. At a data rate of 36 Mbps, the performance with the turbo codes is further improved by approximately 4 ㏈.