• Journal of Digital Contents Society
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• v.10 no.4
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• pp.485-492
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• 2009

In these days mobile handsets have come to be used at almost every user. The mobility of mobile devices and the performance improvement of the mobile networks have made this trend possible. As a great variety of mobile applications are published, the necessity of running large-scale mobile applications becomes greater than before. To accomplish this, the existing researchers have developed mobile cluster computing libraries like Mobile-JPVM. In this paper, we implement a compute-intensive Animated GIF generating application and its cell phone viewer software using Mobile-JPVM library. We find out by the real execution of our softwares on the KTF handsets that they can sufficiently run on cellular phones. Our Animated GIF generator and its viewer are going to be commercially used for the mobile fashion advertisement systems.

• Journal of Internet Computing and Services
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• v.13 no.6
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• pp.1-8
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• 2012

Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering and is adequate to cluster so many documents rapidly and easily. It has disadvantage that the random initial centers cause different result. So, the better choice is to place them as far away as possible from each other. We propose a new method of selecting initial centers in K-Means clustering. This method uses triangle height for initial centers of clusters. After that, the centers are distributed evenly and that result is more accurate than initial cluster centers selected random. It is time-consuming, but can reduce total clustering time by minimizing the number of allocation and recalculation. We can reduce the time spent on total clustering. Compared with the standard algorithm, average consuming time is reduced 38.4%.

• Journal of The Korean Astronomical Society
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• v.49 no.4
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• pp.157-162
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• 2016

High resolution reconstruction technology is developed to help enhance the spatial resolution of observational images for ground-based solar telescopes, such as speckle masking. Near real-time reconstruction performance is achieved on a high performance cluster using the Message Passing Interface (MPI). However, much time is spent in reconstructing solar subimages in such a speckle reconstruction. We design and implement a novel parallel method for speckle masking reconstruction of solar subimage on a shared memory machine using the OpenMP. Real tests are performed to verify the correctness of our codes. We present the details of several parallel reconstruction steps. The parallel implementation between various modules shows a great speed increase as compared to single thread serial implementation, and a speedup of about 2.5 is achieved in one subimage reconstruction. The timing result for reconstructing one subimage with 256×256 pixels shows a clear advantage with greater number of threads. This novel parallel method can be valuable in real-time reconstruction of solar images, especially after porting to a high performance cluster.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.9-13
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• 2014

The performance of large scale software applications has been automatically increasing for last few decades under the influence of Moore's law - the number of transistors on a microprocessor roughly doubled every eighteen months. However, on-chip transistors limitations and heating issues led to the emergence of multicore processors. The energy efficient ARM based System-on-Chip (SoC) processors are being considered for future high performance computing systems. In this paper, we present a case study of two widely used parallel programming models i.e. MPI and MapReduce on distributed memory cluster of ARM SoC development boards. The case study application, Black-Scholes option pricing equation, was parallelized and evaluated in terms of power consumption and throughput. The results show that the Hadoop implementation has low instantaneous power consumption that of MPI, but MPI outperforms Hadoop implementation by a factor of 1.46 in terms of total power consumption to execution time ratio.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.59-69
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• 2010

With the growing number of web services, there arise issues of finding suitable services. But, the traditional keyword search method is insufficient for two reasons: (1) this does not capture the underlying semantics of web services. (2) this does not suffice for accurately specifying users' information needs. In order to overcome limitations of this keyword search method, we propose a novel syntactic analysis and ontology learning method. The syntactic analysis method gives us a breadth of coverage for common terms, while the ontology learning method gives a depth of coverage by providing relationships. By combining these two methods, we hope to improve both the recall and the precision. We describe an experimental study on a collection of 508 web services that shows the high recall and precision of our method.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.1-7
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• 2014

In this paper, we propose a virtual machine(VM) remapping scheme that reduces VM relocation time on a cloud cluster. The proposed scheme finds VMs that should be migrated in sequence from a given VM map, and exchanges destinations of some VMs among them to reduce the VM relocation time. The VMs, the destinations of which will be exchanged, are chosen based on the amount of physical machine's available resources and migration completion time. The exchange of destinations is repeated until the VM relocation time cannot be shortened any further. Through a simulation, we show that the proposed scheme reduces VM relocation time by 42.7% in maximum.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.401-415
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• 2005

Owing to the improved wireless communication technologies, it is possible to provide streaming service of multimedia with PDAs and mobile phones in addition to desktop PCs. Since mobile client devices have low computing power and low network bandwidth due to wireless network, the transcoding technology to adapt media for mobile client devices considering their characteristics is necessary. Transcoding servers transcode the source media to the target media within corresponding grades and provide QoS in real-time. In particular, an effective load balancing policy for transcoding servers is inevitable to support QoS for large scale mobile users. In this paper, the resource weighted load distribution policy is proposed for a fair load balance and a more scalable performance in cluster-based transcoding servers. Our proposed policy is based on the resource weighted table and number of maximum supported users, which are pre-computed for each pre-defined grade. We implement the proposed policy on cluster-based transcoding servers and evaluate its fair load distribution and scalable performance with the number of transcoding servers.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.76-79
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• 2008

In this work wood crib burning behaviors have been simulated by using the FDS(Fire Dynamic Simulator) program. Wood cribs are regularly stacked arrays of wood sticks, and available for the performance rating of fire-extinguishers. On the basis of an angle iron supporter 26 layers of wood sticks have been stacked up. Each layer consists of 5 or 6 wood sticks which are placed in parallel, with a constant distance, and in alternating rows. They are laid between the horizontally adjacent sticks at the before last layer. The wood crib is ignited instantaneously by an amount of burning gasoline below. A comprehensive simulation of such a practical sophisticated combustion is still too difficult to realize with any currently available computer, although the performance of modern processors is getting better everyday. We could carry it out here through parallel computing on the HPC(High Performance Computing) cluster as the feasible alternative. At last the validation has been executed by means of temperature distribution data measured by the thermal video camera.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.