• Journal of Broadcast Engineering
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• v.24 no.7
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• pp.1246-1258
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• 2019

Since light field (LF) image has huge data volume, it requires high-performance compression technique for efficient transmission and storage of its data. Camera users may like to represent parts of image at different levels of focus at their choice anytime. To address this refocusing functionality, in this paper, we first render a focal stack consisting of multi-focus images, then compress it instead of original LF data. The proposed method has advantage of minimizing the amount of LF data to realize the targeted refocusing applications. Our experiment results show that the proposed method outperforms the state-of-the-art for LF image compression method.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.6
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• pp.11-22
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• 1996

We address an issue of channel sharing among users by using a random assignment-transmitter-based (RA-T) spread-spectrum transmission scheme which permits the contention mode only in the transmission of a header while avoiding collision during the data packet transmission. Once the header being successfully received, the data packet is ready for reception by switching to one of programmable matched-filters. But the receoption may be blocked due to limited number of matched-filters so that this effect is taken into account in our analysis. For realistic analysis, we integrate detection performance at the physical level with channel activity at the link level through a markov chain model. We also consider an acknowledgement scheme to notify whether the header is correctly detcted and the data packet can be processed continuously, which aims at reducing the interference caused unwanted data transmission. It is shown that receiver complexity can be greatly reduced by choosing a proper number of RA codes at the cost of only a little throughput degradation.

• Journal of Information Science Theory and Practice
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• v.1 no.1
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• pp.69-82
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• 2013

Although MAchine Readable Cataloging (MARC) and Functional Requirements for Bibliographic Records (FRBR) are currently the most broadly used bibliographic structures for generating bibliographic data in the library community, each has its own weaknesses in describing information resources in diverse media. If the MARC format could be implemented in a structure that reflects the multi-layered characteristics of FRBR, its use could address current problems and limitations in resource description. The purpose of this research is to propose an alternative approach that can integrate the heterogeneous bibliographic structures of MARC and FRBR through the applications of facet and facet analysis. The proposed faceted data model is expected to function as a conceptual structure that can mediate between MARC data elements and FRBR attributes in order to utilize these structures in a more reliable and comprehensive way.

• The Journal of Information Systems
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• v.14 no.3
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• pp.53-58
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• 2005

In this study, we will employ a multi-agent for the search and extraction of data in a distributed environment. We will use an Integrator Agent in the proposed model on the Hierarchical Clustering and Association Rule Discovery(HCARD). The HCARD will address the inadequacy of other data mining tools in processing performance and efficiency when use for knowledge discovery. The Integrator Agent was developed based on CORBA architecture for search and extraction of data from heterogeneous servers in the distributed environment. Our experiment shows that the HCARD generated essential association rules which can be practically explained for decision making purposes. Shorter processing time had been noted in computing for clusters using the HCARD and implying ideal processing period than computing the rules without HCARD.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.675-689
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• 2021

Nowadays, cloud computing is being adopted for more organizations. However, since cloud computing has a virtualized, volatile, scalable and multi-tenancy distributed nature, it is challenging task to perform attack detection in the cloud following conventional processes. This work proposes a solution which aims to collect web server logs by using Flume and filter them through Spark Streaming in order to only consider suspicious data or data related to denial-of-service attacks and reduce the data that will be stored in Hadoop Distributed File System for posterior analysis with the frequent pattern (FP)-Growth algorithm. With the proposed system, we can address some of the difficulties in security for cloud environment, facilitating the data collection, reducing detection time and consequently enabling an almost real-time attack detection.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.97-108
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• 2022

The video-assisted human action recognition [1] field is one of the most active ones in computer vision research. Since the depth data [2] obtained by Kinect cameras has more benefits than traditional RGB data, research on human action detection has recently increased because of the Kinect camera. We conducted a systematic study of strategies for recognizing human activity based on deep data in this article. All methods are grouped into deep map tactics and skeleton tactics. A comparison of some of the more traditional strategies is also covered. We then examined the specifics of different depth behavior databases and provided a straightforward distinction between them. We address the advantages and disadvantages of depth and skeleton-based techniques in this discussion.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.287-288
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• 2009

Multi-query optimization (MQO) is a critical research issue in the real-time data stream management system (DSMS). We propose to address this problem in the ubiquitous GIS (u-GIS) environment, focusing on grouping 'similar' spatio-temporal queries incrementally into N clusters so that they can be processed virtually as N queries. By minimizing N, the overlaps in the data requirements of the raw queries can be avoided, which implies the reducing of the total disk I/O cost. In this paper, we define the spatio-temporal query clustering problem and give a data cubing approach (Q-cube), which is expected to be implemented in the cloud computing paradigm.

• Smart Media Journal
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• v.11 no.11
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.17-24
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• 2023

Given the surge in the elderly population, and increasing in dementia cases, there is a growing interest in digital therapies that facilitate steady remote treatment. However, in the cognitive assessment of digital therapies through clinical trials, the absence of log data as an essential evaluation factor is a significant issue. To address this, we propose a solution of utilizing weighted derived variables based on high-importance variables' accuracy in log data utilization as an indirect cognitive assessment factor for digital therapies. We have validated the effectiveness of this approach using machine learning techniques such as XGBoost, LGBM, and CatBoost. Thus, we suggest the use of log data as a rapid and indirect cognitive evaluation factor for digital therapy users.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.29 no.3
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• pp.163-174
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• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.