• KIISE Transactions on Computing Practices
• /
• v.20 no.11
• /
• pp.604-609
• /
• 2014

Decoding processes in portable media players have a high computational cost, resulting in high power consumption by the CPU. If decoding computations are reduced, the power consumed by the CPU is also be reduced, but such a choice generally results in a degradation of the video quality for the users, so it is essential to address this tradeoff. We proposed a new CPU power management scheme that can make use of the scalability property available in the H.164/SVC standard. We first proposed a new video quality model that makes use of a video quality metric(VQM) in order to efficiently take into account the different quantization factors in the SVC. We then propose a new dynamic voltage scaling(DVS) scheme that can selectively combine the previous decoding times and frame sizes in order to accurately predict the next decoding time. We then implemented a scheme on a commercial smartphone and performed a user test in order to examine how users react to the VQM difference. Real measurements show that the proposed scheme uses up to 34% fewer energy than the Linux DVFS governor, and user tests confirm that the degradation in the quality is quite tolerable.

• Journal of KIISE:Information Networking
• /
• v.29 no.2
• /
• pp.188-196
• /
• 2002

As networked virtual environment (NVE) scales in terms of users and network latency, a key aspect to consider is scalability for interactive performance because a large number of objects likely impose heavy burden especially on the network and computational resources. To improve the scalability, various relevance-filtering mechanism have been proposed. However, the existing filtering mechanism do not scale well in terms of interactive performance as the number of users increase and crowds in a specific place. In this paper, we propose a new scalable filtering scheme which reduces the number of messages by dynamically grouping users based on their interests and distance. Within a group, members communicate with each other with high fidelity. However, a representative sends up-to-dated group information of members with low transmission frequency when they are not of immediate interest but are still within the interest area. The representative is elected from members of the group in distributed manner. The proposed scheme enhances the interactive performance scalability of large-scale NVE systems as much as 18% compared with the existing approach.

• The Korean Journal of Applied Statistics
• /
• v.29 no.6
• /
• pp.999-1005
• /
• 2016

The advent of big data brings the opportunity to answer many open scientic questions but also presents some interesting challenges. Main features of contemporary datasets are the high dimensionality and massive sample size. In this paper, we give an overview of major challenges caused by these two features: (1) noise accumulation and spurious correlations in high dimensional data; (ii) computational scalability for massive data. We also provide applications of big data in various fields including forecast of disasters, digital humanities and sabermetrics.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.21 no.3
• /
• pp.125-142
• /
• 2017

In this paper, development of the three-dimensional structural analysis is performed by applying FETI-local method. In the FETI-local method, the penalty term is added as a preconditioner. The OPT-DKT shell element is used in the present structural analysis. Newmark-${\beta}$ method is employed to conduct the dynamic analysis. The three-dimensional FETI-local static structural analysis is conducted. The contour and the displacement of the results are compared following the different number of sub-domains. The computational time and memory usage are compared with respect to the number of CPUs used. The three-dimensional dynamic structural analysis is conducted while applying FETI-local method. The present results show appropriate scalability in terms of the computational time and memory usage. It is expected to improve the computational efficiency by combining the advantages of the original FETI method, i.e., FETI-mixed using the mixed local-global Lagrange multiplier.

• Clinical and Experimental Pediatrics
• /
• v.65 no.5
• /
• pp.239-249
• /
• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Information Systems Review
• /
• v.16 no.1
• /
• pp.37-50
• /
• 2014

Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. In order to overcome this problem, some previous researches suggest clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new hybrid case-based reasoning method which dynamically composing a searching pool for each target case. This method is applied to diagnose for the heart disease dataset. The results show that the suggested hybrid method produces statistically the same level of predictive performances with using significantly less computational cost than the CBR method and also outperforms the basic clustering-CBR (C-CBR) method.

• IEIE Transactions on Smart Processing and Computing
• /
• v.2 no.6
• /
• pp.345-349
• /
• 2013

This paper proposes a simplified generalized residual prediction (GRP) that reduces the computational complexity of spatial scalability in scalable high efficiency video coding (SHVC). GRP is a coding tool to improve the inter prediction by adding a residual signal to the inter predictor. The residual signal was created by carrying out motion compensation (MC) of both the enhancement layer (EL) and up-sampled reference layer (RL) with the motion vector (MV) of the EL. In the MC process, interpolation of the EL and the up-sampled RL are required when the MV of the EL has sub-pel accuracy. Because the up-sampled RL has few high frequency components, interpolation of the up-sampled RL does not give significantly new information. Therefore, the proposed method reduces the computational complexity of the GRP by skipping the interpolation of the up-sampled RL. The experiment on SHVC software (SHM-2.0) showed that the proposed method reduces the decoding time by 10 % compared to conventional GRP. The BD-rate loss of the proposed method was as low as 1.0% on the top of SHM-2.0.

• Publications of The Korean Astronomical Society
• /
• v.19 no.1
• /
• pp.77-81
• /
• 2004

A high performance computing cluster for astronomical computations has been built at Korea Astronomy Observatory. The 64 node cluster interconnected with Gigabit Ethernet is composed of 128 Intel Xeon processors, 160 GB memory, 6 TB global storage space, and an LTO (Linear Tape-Open) tape library. The cluster was installed and has been managed with the Open Source Cluster Application Resource (OSCAR) framework. Its performance for parallel computations was measured with a three-dimensional hydrodynamic code and showed quite a good scalability as the number of computational cells increases. The cluster has already been utilized for several computational research projects, some of which resulted in a few publications, even though its full operation time is less than one year. As a major resource of the $K^*Grid$ testbed, the cluster has been used for Grid computations, too.

• Korean Journal of Computational Design and Engineering
• /
• v.10 no.2
• /
• pp.143-150
• /
• 2005

A design framework to employ the multidisciplinary design optimization technologies on a computer system has been developed and is named as the Extensible Multidisciplinary Design Integration and Optimization System (EMDIOS). The framework can not only effectively solve complex system design problems but also conveniently handle MDO problems. Since the EMDIOS exploits both state-of-the-art of computing capabilities and sophisticated optimization techniques, it can overcome many scalability and complexity problems. It can make users who are not even familiar with the optimization technology use EMDIOS easily to solve their design problems. The client of EMDIOS provides a front end for engineers to communicate the EMDIOS engine and the server controls and manages various resources luck as scheduler, analysis codes, and user interfaces. EMDIOS client supports data monitoring, design problem definition, request for analyses and other user tasks. Three main components of the EMDIOS are the Engineering Design Object Model which is a basic idea to construct EMDIOS, EMDIOS Language (EMDIO-L) which is a script language representing design problems, and visual modeling tools which can help engineers define design problems using graphical user interface. Several example problems are solved and EMDIOS has shown various capabilities such as ease of use, process integration, and optimization monitoring.

• Journal of Internet Computing and Services
• /
• v.8 no.4
• /
• pp.81-91
• /
• 2007

Binding update for the routing optimization in MIPv6 can make the involved nodes vulnerable to various attacks. Therefore, secure binding update becomes an important research issue in MIPv6, and several protocols have been proposed for this purpose. In this paper, we compare several existing binding update protocols such as RR, SUCV and OMIPv6 and analyze the vulnerability of nodes to the possible attacks and drawbacks of address management and scalability and overhead of encryption operations. Then, we suggest the design requirements for the secure binding update and propose an advanced protocol based on the design principle. Through the analysis, we show that our protocol can achieve a higher level of security against the various attacks and enable better management of address, provide the location privacy and reduce the computational overhead of mobile nodes with constraint computational power.