• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.341-349
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• 2014

Dynamic adaptive streaming over HTTP (DASH) is the most recent and promising technology to support high quality streaming services. In dynamic adaptive streaming over HTTP (DASH), a client consecutively estimates the available network bandwidth and decides the transmission rate for the forthcoming video chunks to be downloaded. In this paper, we propose a novel rate adaptation algorithm called quality of experience QoE-enhanced adaptation algorithm over DASH (QAAD), which preserves the minimum buffer length to avoid interruption and minimizes the video quality changes during the playback. We implemented a DASH test bed and conducted extensive experiments. Experimental results demonstrate that under fluctuating network conditions, QAAD provides seamless streaming with stabilized video quality while the previous buffer-aware algorithm (i.e., QDASH[9]) frequently changes the video quality and undergoes the interruption.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.253-261
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• 2006

In spite of the popularity of P2P technology, a multimedia streaming using the P2P technology has been neglected. The reason for this is that the P2P multimedia streaming has suffered from several inherent problems especially poor bandwidth and unreliable connection among peers. We suggest a Multi-Peer Binding Protocol (MPBP) in this paper that provides a virtual single channel composed of multiple connections to several peers to ease these problems. The protocol enables applications to download data from multiple peers simultaneously, so they can achieve throughput improvement and reliable streaming. For this, the MPBP splits media files into small chunks and provides a mechanism for identifying and transmitting each chunk. Implemented MPBP engine focuses on handling an abrupt disconnection from data sending peers and the evaluation result shows the MPBP is able to handle it gracefully. The MPBP is also desisted to support various media types. To verify this, video and audio applications are implemented using the MPBP engine in this paper.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.25-33
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• 2013

P2P(Peer-to-Peer) architecture can reduce the network bandwidth and resource on the server since peers exchange data chunks with each other, while server-client architecture causes a lot of traffic on the server. Peers receive a data more reliably when the number of participating peer increases. Currently, P2P traffic has accounted for about 65% of the world's Internet traffic and diverse P2P streaming services have launched combining to video streaming technology. However, the requirements and data chunk delivery algorithms for mobile P2P streaming service should be investigated, since the existing P2P technologies have been developed and designed for the wired network. In particular, the bandwidth fluctuation caused by user mobility, wireless packet collisions, and packet losses brings about different problems on the mobile P2P streaming service compared to existing P2P streaming service. In this paper, we analyzed the problem of mobile P2P streaming services in the 802.11n wireless LAN environment through experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5B
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• pp.509-516
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• 2009

The Stream Control Transmission Protocol(SCTP) is a reliable transport layer protocol that provides several features. Multihoming is the one of the features and allows an association(SCTP's term for a connection) between two endpoints to use multiple paths. One of the paths, called a primary path, is used for initial data transmission and in the case of retransmission an alternate path is used. SCTP's current retransmission policy attempts to improve the chance of success by sending all retransmissions to an alternate destination address. However, SCTP's current retransmission policy has been shown to actually degrade performance in many circumstances. It is because that, due to Karn's algorithm, successful retransmissions on the alternate path cannot be used to update RTT(Round-Trip Time) estimation for the alternate path. In this paper we propose a scheme to avoid such performance degradation. We utilize 2bits which is not used in the flag field of DATA and SACK chunks to disambiguate original transmissions from retransmissions and to keep RTT and RTO(Retransmission Time-Out) values more accurate.

• Korean Journal of Food Science and Technology
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• v.30 no.4
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• pp.842-847
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• 1998

Ohmic thawing in combination with conventional water immersion thawing was investigated. Frozen meat chunks $(10{\times}10{\times}10{\;}cm)$ were immersed in a water reservoir $(12{\times}12{\times}12{\;}cm)$ which temperature was maintained at $10^{\circ}C{\;}or{\;}20^{\circ}C$, and were positioned between two stainless-steel electrodes $(10{\times}10{\;}cm)$ having no direct contact with the samples. Alternating current $(60{\;}V{\sim}210{\;}V)$ at various frequency $(60{\;}Hz{\sim}60{\;}kHz)$ was used to generate internal heat by the electrical resistance. When the frequency was fixed to 60Hz, thawing time was reduced as the voltage increased. Frequency changes gave no significant effect on thawing time. Ohmically-thawed samples treated with lower voltage showed lower drip loss and higher water holding capacity.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.9
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• pp.1174-1183
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• 2010

In goats fed on dry forage twice a day, an esophageal fistula was used to investigate the physiological factors present in the marked suppression of dry forage intake that occurs after 40 min of feeding. The animals used in this study were five large-type male esophageal- and ruminal-fistulated goats. Roughly crushed alfalfa hay cubes with any large remaining chunks removed were used as feed for this research. The study was conducted under both normal feeding conditions (NFC) and sham feeding conditions (SFC). In the NFC control, the esophageal fistulae were closed by plugs and the animals ate dry forage in the normal manner. In the SFC treatment, before starting the experiment the plugs for closing the esophageal fistula were removed and the cannulae for collecting boluses were fitted into the fistulae. Therefore, the esophageal boluses were removed via an esophageal fistula before they entered the rumen. In the NFC control, eating rates sharply decreased in the first 40 min of feeding and were subsequently maintained at low levels. However, eating rates in the SFC treatment remained high after 40 min of the feeding period had elapsed and the goats ate continuously during the 2 h feeding period. In comparison with the NFC control ($1,794{\pm}203.80\;g$/2 h), cumulative dry forage intake in the SFC treatment ($3,182{\pm}381.69\;g$/2 h) was 77.4% greater (p<0.05) upon conclusion of the 2 h feeding period. In the SFC treatment, cumulative bolus output ($6,804{\pm}469.92\;g$/2 h) was about twofold the cumulative dry forage intake due to cumulative salivary secretion volume ($3,622{\pm}104.13\;g$/2 h) upon conclusion of the 2 h feeding period. The result indicates that large amounts of secreted saliva during dry forage feeding act in conjunction with consumed feed to form the ruminal load responsible for ruminal distension. The increased plasma total protein concentrations were higher in the SFC treatment than in the NFC control. However, plasma and ruminal fluid osmolalities increased in the NFC control during and after feeding but were mostly unchanged in the SFC treatment. In comparison with the NFC control ($3,440{\pm}548.04\;g$/30 min), thirst level in the SFC treatment ($1,360{\pm}467.02\;g$/30 min) was 60.5% significantly less (p<0.05) upon conclusion of the 30 min drinking period. The results of the present study indicate that In the second hour of the 2 h feeding period, dry forage intake is regulated by factors produced when boluses enter the rumen.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.143-151
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• 2013

NAND flash-based SSDs (Solid State Drive) have advantages of fast input/output performance and low power consumption so that they could be widely used as storages on tablet, desktop PC, smart-phone, and server. But, SSD has the disadvantage of wear-leveling due to increase of the number of writes. In order to improve the lifespan of the SSD, a variety of data deduplication techniques have been introduced. General fixed-size splitting method allocates fixed size of chunk without considering locality of data so that it may execute unnecessary chunking and hash key generation, and variable-size splitting method occurs excessive operation since it compares data byte-by-byte for deduplication. This paper proposes adaptive chunking method based on application locality and file name locality of written data in SSD-based server storage. The proposed method split data into 4KB or 64KB chunks adaptively according to application locality and file name locality of duplicated data so that it can reduce the overhead of chunking and hash key generation and prevent duplicated data writing. The experimental results show that the proposed method can enhance write performance, reduce power consumption and operation time compared to existing variable-size splitting method and fixed size splitting method using 4KB.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.1-14
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• 2008

This paper is on query processing in the mobile devices where memory capacity is limited. In case that a query against a large volume of XML data is processed in such a mobile device, techniques of fragmenting the XML data into chunks and of streaming and processing them are required. Such techniques make it possible to process queries without materializing the XML data in its entirety. The previous schemes such as XFrag[4], XFPro[5], XFLab[6] are not scalable with respect to the increase of the size of the XML data because they lack proper memory management capability. After some information on XML fragments necessary for query processing is stored, it is not deleted even after it becomes of no use. As such, when the XML fragments are dynamically generated and infinitely streamed, there could be no guarantee of normal completion of query processing. In this paper, we address scalability of query processing over dynamic XML fragment stream, proposing techniques of deleting information on XML fragments accumulated during query processing in order to extend the previous schemes. The performance experiments through implementation showed that our extended schemes considerably outperformed the previous ones in memory efficiency and scalability with respect to the size of the XML data.

• Journal of Platform Technology
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• v.9 no.1
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• pp.15-22
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• 2021

In a smart manufacturing environment, more and more devices are connected to the Internet so that a large volume of data can be obtained during all phases of the product life cycle. The large-scale industries, companies and organizations that have more operational units scattered among the various geographical locations face a huge resource consumption because of their unorganized structure of sharing resources among themselves that directly affects the supply chain of the corresponding concerns. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. The manufacturing units include machinery that may be situated in different geological areas and process instances that are executed from different machinery data should be constantly managed by the super admin to coordinate the manufacturing process in the large-scale industries these environments make the manufacturing process a tedious work to maintain the efficiency of the production unit. The data from all these instances should be monitored to maintain the integrity of the manufacturing service system, all these data are computed in the cloud environment which leads to the latency in the performance of the smart manufacturing service system. Instead, validating data from the external device, we propose to validate the data at the front-end of each device. The validation process can be automated by script validation and then the processed data will be sent to the cloud processing and storing unit. Along with the end-device data validation we will implement the APM(Asset Performance Management) to enhance the productive functionality of the manufacturers. The manufacturing service system will be chunked into modules based on the functionalities of the machines and process instances corresponding to the time schedules of the respective machines. On breaking the whole system into chunks of modules and further divisions as required we can reduce the data loss or data mismatch due to the processing of data from the instances that may be down for maintenance or malfunction ties of the machinery. This will help the admin to trace the individual domains of the smart manufacturing service system that needs attention for error recovery among the various process instances from different machines that operate on the various conditions. This helps in reducing the latency, which in turn increases the efficiency of the whole system

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.435-442
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• 2018

Outlier detection means to detect data samples that deviate significantly from the distribution of normal data. Most outlier detection methods calculate an outlier score that indicates the extent to which a data sample is out of normal state and determine it to be an outlier when its outlier score is above a given threshold. However, since the range of an outlier score is different for each data and the outliers exist at a smaller ratio than the normal data, it is very difficult to determine the threshold value for an outlier score. Further, in an actual situation, it is not easy to acquire data including a sufficient amount of outliers available for learning. In this paper, we propose a clustering-based outlier detection method by constructing a model representing a normal data region using only normal data and performing binary classification of outliers and normal data for new data samples. Then, by dividing the given normal data into chunks, and constructing a clustering model for each chunk, we expand it to the ensemble method combining the decision by the models and apply it to the streaming data with dynamic changes. Experimental results using real data and artificial data show high performance of the proposed method.