• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.614-618
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• 2016

As information on the internet and the data from smart devices are growing, the amount of stream data is also increasing in the real world. The stream data, which is a potentially large data, requires online learnable models and algorithms. In this paper, we propose a novel class of models: predictive convolutional neural networks to be able to perform online learning. These models are designed to deal with longer patterns as the layers become higher due to layering convolutional operations: detection and max-pooling on the time axis. As a preliminary check of the concept, we chose two-month gathered GPS data sequence as an observation sequence. On learning them with the proposed method, we compared the original sequence and the regenerated sequence from the abstract information of the models. The result shows that the models can encode long-range patterns, and can generate a raw observation sequence within a low error.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.99-104
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• 2022

Convolutional neural network (CNN) generally uses two-stream architecture RGB and optical flow stream for its action recognition function. RGB frames stream display appearance and optical flow stream interprets its action. However, the standard method of using optical flow is costly in its computational time and latency associated with increased action recognition. The purpose of the study was to evaluate a novel way to create a two sub-networks in neural networks. The optical flow sub-network was assigned as a teacher and the RGB frames as a student. In the training stage, the optical flow sub-network extracts features through the teacher sub-network and transmits the information to student sub-network for baseline training. In the test stage, only student sub-network was operational with decreased in latency without computing optical flow. Experimental results shows that our network fed only by RGB stream gets a competitive accuracy of 54.5% on HMDB51, which is 1.5 times better than that on R3D-18.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1152-1168
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• 2010

Recently, due to the prevalence of various mobile devices and wireless broadband networks, there has been a significant increase in interest and demand for multimedia streaming services such as the mobile IPTV. In such a wireless broadband network, transmitting a continuous stream of multimedia data is difficult to achieve due to mobile stations (MSs) movement. Providing Quality of Service (QoS) for multimedia video streaming applications requires the server and/or client to be network-aware and adaptive. Therefore, in order to deploy a mobile IPTV service in wireless broadband networks, offering users efficient wireless resource utilization and seamlessly offering user perceived QoS are important issues. In this paper, we propose a new adaptive streaming scheme, called MARC (Mobile-aware Adaptive Rate Control), which adjusts the quality of bit-stream and transmission rate of video streaming based on the wireless channel status and network status. The proposed scheme can control the rate of multimedia streaming to be suitable for the wireless channel status by using awareness information of the wireless channel quality and the mobile station location. The proposed scheme can provide a seamless multimedia playback service in wireless broadband networks in addition to improving the QoS of multimedia streaming services. The proposed MARC scheme alleviates the discontinuity of multimedia playback and allocates a suitable client buffer to the wireless broadband network. The simulation results demonstrate the effectiveness of our proposed scheme.

• Journal of Communications and Networks
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• v.12 no.3
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• pp.275-284
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• 2010

The current multihome-aware protocols (like stream control transmission protocol (SCTP) or parallel TCP for concurrent multipath data transfer (CMT) are not designed for high-capacity and large-latency networks; they often have performance problems transferring large data files over shared long-distance wide area networks. It has been shown that SCTP-CMT is more sensitive to receive buffer (rbuf) constraints, and this rbuf-blocking problem causes considerable throughput loss when multiple paths are used simultaneously. In this research paper, we demonstrate the weakness of SCTP-CMT rbuf constraints, and we then identify that rbuf-blocking problem in SCTP multihoming is mostly due to its loss-based nature for detecting network congestion. We present a simulation-based performance comparison of FAST TCP versus SCTP in high-speed networks for solving a number of throughput issues. This work proposes an end-to-end transport layer protocol (i.e., FAST TCP multihoming as a reliable, delaybased, multihome-aware, and selective ACK-based transport protocol), which can transfer data between a multihomed source and destination hosts through multiple paths simultaneously. Through extensive ns-2 simulations, we show that FAST TCP multihoming achieves the desired goals under a variety of network conditions. The experimental results and survey presented in this research also provide an insight on design decisions for the future high-speed multihomed transport layer protocols.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.4 s.19
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• pp.17-27
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• 2005

In this paper the watershed is divided by stream order law of Horton to estimate the runoff with stream order. We use the contour data to extract spatially distributed topographical information like stream channels and networks of sub-basins. A contour model is developed, validated, and adopted to estimate the effective stream order number for the runoff. The results show that the peak discharge which is divided into first river order was close to observed one. The contour model will provide effective informations to plan river works classified by sub-basins for river restoration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2922-2945
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• 2018

Achieving efficient authentication is a crucial issue for stream data commonly seen in content delivery, peer-to-peer, and multicast/broadcast networks. Stream authentication mechanisms need to be operated efficiently at both sender-side and receiver-side at the same time because of the properties of stream data such as real-time and delay-sensitivity. Until now, many stream authentication mechanisms have been proposed, but they are not efficient enough to be used in stream applications where the efficiency for sender and receiver sides are required simultaneously since most of them could achieve one of either sender-side and receiver-side efficiency. In this paper, we propose an efficient stream authentication mechanism, so called TIM, by integrating Trapdoor Hash Function and Merkle Hash Tree. Our construction can support efficient streaming data processing at both sender-side and receiver-side at the same time differently from previously proposed other schemes. Through theoretical and experimental analysis, we show that TIM can provide enhanced performance at both sender and receiver sides compared with existing mechanisms. Furthermore, TIM provides an important feature for streaming authentication, the resilience against transmission loss, since each data block can be verified with authentication information contained in itself.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.101-104
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• 2007

Efficient Query processing and optimization are critical for reducing network traffic and decreasing latency of query when accessing and manipulating sensor data of large-scale sensor networks. Currently it has been studied in sensor database projects. These works have mainly focused on in-network query processing for sensor networks and assumes homogeneous sensor networks, where each sensor network has same hardware and software configuration. In this paper, we present a framework for efficient query processing over heterogeneous sensor networks. Our proposed framework introduces query processing paradigm considering two heterogeneous characteristics of sensor networks: (1) data dissemination approach such as push, pull, and hybrid; (2) query processing capability of sensor networks if they may support in-network aggregation, spatial, periodic and conditional operators. Additionally, we propose multi-query optimization strategies supporting cross-translation between data acquisition query and data stream query to minimize total cost of multiple queries. It has been implemented in WSN middleware, COSMOS, developed by ETRI.

• Journal of the Korean Geographical Society
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• v.39 no.4
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• pp.633-642
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• 2004

This study tries to generalize the stream network by constructing rule-based modelling. A study on the map generalization tends to be concentrated on development of algorithms for modification of linear features and evaluations to the limited cartographic elements. Rule-based modelling can help to improve previous algorithms by application of generalization process with the results that analyzing mapping principles and spatial distribution patterns of geographical phenomena. Rule-based modelling can be applied to generalize various cartographic elements, and make an effective on multi-scaling mapping in the digital environments. In this research, nile-based modelling for stream network is composed of generalization rule, algorithm for centerline extraction and linear features. Before generalization, drainage pattern was analyzed by the connectivity with lake to minimize logical errors. As a result, 17 streams with centerline are extracted from 108 double-lined streams. Total length of stream networks is reduced as 17% in 1:25,000 scale, and as 29% in 1:50,000. Simoo algorithm, which is developed to generalize linear features, is compared to Douglas-Peucker(D-P) algorithm. D-P made linear features rough due to the increase of data point distance and widening of external angle. But in Simoo, linear features are smoothed with the decrease of scale.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2207-2216
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• 2012

Recently, wireless Internet environment with mobile phones and wireless sensor networks with severe resource restrictions have been actively studied. Moreover, an overall security issues are essential to build a reliable and secure sensor network. One of secure solution is to develop a fast cryptographic algorithm for data encryption. Therefore, we propose a 128-bit stream cipher, AA128 which has efficient implementation of software and hardware and is suitable for real-time applications such as wireless Internet environment with mobile phones, wireless sensor networks and Digital Right Management (DRM). AA128 is stream cipher which consists of 278-bit ASR and non-linear transformation. Non-linear transformation consists of Confusion Function, Nonlinear transformation(SF0 ~ SF3) and Whitening. We show that the proposed stream cipher AA128 is faster than AES and Salsa20, and it satisfies the appropriate security requirements. Our hardware simulation result indicates that the proposed cipher algorithm can satisfy the speed requirements of real-time processing applications.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.100-108
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• 2007

Currently, multimedia stream services are provided in various network environments such as wireless networks. It is crucial to address QoS problem for multimedia service in heterogeneous networks. This paper suggests the scheme to increase the efficiency for the transmission of multimedia data. We also propose the adaptive algorithm that switch between UDP and TCP based on the size of the transmitted multimedia data. In addition, it presents the method that duplicates the multimedia data for the efficiency of transmission. We designed the MPEG stream service and conducted the simulation to verify our adaptive MPEG stream service.