• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5446-5463
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• 2019

Parallel computing system components should be harmonized, and this harmonization is kept existent using synchronization time. Synchronization time affects the system in two ways. First, if we have too little synchronization time, some tasks face the problem of harmonization, as they need appropriate time to update and synchronize with the system. Second, if we allocate a large amount of time, stall system created. Random allocation of synchronization time for parallel systems slows down not only the booting time of the system but also the execution time of each application involved in the system. This paper presents a simulator used to test and allocate appropriate synchronization time for distributed and parallel heterogeneous systems. The simulator creates the parallel and heterogeneous system to be evaluated, and lets the user vary the synchronization time to optimize the booting time. NS3-cGEM5 simulator in this paper is formed by HLA-RTI federation integration of the two independent architecture and network simulators - NS3 and cGEM5. Therefore, nodes created on these simulators need synchronizations for harmonized system performance. We tested and allocated the appropriate synchronization time for our sample parallel system composed of one x86 server and three ARM clients.

• Journal of Computing Science and Engineering
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• v.4 no.4
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• pp.291-312
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• 2010

The last decades a large amount of research has been done in the genomics domain which has and is generating terabytes, if not exabytes, of information stored globally in a very fragmented way. Different databases use different ways of storing the same data, resulting in undesired redundancy and restrained information transfer. Adding to this, keeping the existing databases consistent and data integrity maintained is mainly left to human intervention which in turn is very costly, both in time and money as well as error prone. Identifying a fixed conceptual dictionary in the form of a conceptual model thus seems crucial. This paper presents an effort to integrate the mutational data from the established genomic data source HGMD into a conceptual model driven database HGDB, thereby providing useful lessons to improve the already existing conceptual model of the human genome.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.95-126
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• 2009

The present contribution addresses the parallelization of advanced simulation methods for structural reliability analysis, which have recently been developed for large-scale structures with a high number of uncertain parameters. In particular, the Line Sampling method and the Subset Simulation method are considered. The proposed parallel algorithms exploit the parallelism associated with the possibility to simultaneously perform independent FE analyses. For the Line Sampling method a parallelization scheme is proposed both for the actual sampling process, and for the statistical gradient estimation method used to identify the so-called important direction of the Line Sampling scheme. Two parallelization strategies are investigated for the Subset Simulation method: the first one consists in the embarrassingly parallel advancement of distinct Markov chains; in this case the speedup is bounded by the number of chains advanced simultaneously. The second parallel Subset Simulation algorithm utilizes the concept of speculative computing. Speedup measurements in context with the FE model of a multistory building (24,000 DOFs) show the reduction of the wall-clock time to a very viable amount (<10 minutes for Line Sampling and ${\approx}$ 1 hour for Subset Simulation). The measurements, conducted on clusters of multi-core nodes, also indicate a strong sensitivity of the parallel performance to the load level of the nodes, in terms of the number of simultaneously used cores. This performance degradation is related to memory bottlenecks during the modal analysis required during each FE analysis.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1087-1101
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• 2018

As cloud computing has become a widespread technology, malicious attackers can obtain the private information of users that has leaked from the service provider in the outsourced databases. To resolve the problem, it is necessary to encrypt the database prior to outsourcing it to the service provider. However, the most existing data encryption schemes cannot process a query without decrypting the encrypted databases. Moreover, because the amount of the data is large, it takes too much time to decrypt all the data. For this, Programmable Order-Preserving Secure Index Scheme (POPIS) was proposed to hide the original data while performing query processing without decryption. However, POPIS is weak to both order matching attacks and data count attacks. To overcome the limitations, we propose a group order-preserving data encryption scheme (GOPES) that can support efficient query processing over the encrypted data. Since GOPES can preserve the order of each data group by generating the signatures of the encrypted data, it can provide a high degree of data privacy protection. Finally, it is shown that GOPES is better than the existing POPIS, with respect to both order matching attacks and data count attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.321-344
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• 2017

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.48-52
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• 2007

As huge amount of literature including biological data is being generated after post genome era, it becomes difficult for researcher to find useful knowledge from the biological databases. Bio-text mining and related natural language processing technique are the key issues in the intelligent knowledge retrieval from the biological databases. We propose a bio-text mining technique for the biologists who find Knowledge from the huge literature. At first, web robot is used to extract and transform related literature from remote databases. To improve retrieval speed, we generate an inverted file for keywords in the literature. Then, text mining system is used for extracting given knowledge patterns and keywords. Finally, we construct a grid computing environment to guarantee processing speed in the text mining even for huge literature databases. In the real experiment for 10,000 bio-literatures, the system shows 95% precision and 98% recall.

• Journal of the Korean Society of Marine Environment & Safety
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• v.1 no.2
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• pp.69-82
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• 1995

This study is concerned with developing a heuristic algorithm for solving a class of ninlinear integer programs(NLIP). Exact algrithm for solving a NLIP either may not exist, or may take an unrealistically large amount of computing time. This study develops a new heuristic, the Excursion Algorithm(EA), for solving a class of NLIP's. It turns out that excursions over a bounded feasible and/or infeasible region is effective in alleviation the risks of being trapped at a lical optimum. The developed EA is applied to the redundancy optimization problems for improving the system safety, and is compared with other existing heuristic methods. We also include simulated annealing(SA) method in the comparision experiment due to ist populatrity for solving complex combinatorial problems. Computational results indicate that the proposed EA performs consistently better than the other in terms of solution quality, with moderate increase in computing time. Therefore, the proposed EA is believed to be an attractive alternative to other heuristic methods.

• Convergence Security Journal
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• v.7 no.4
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• pp.15-21
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• 2007

As the amount of flash memory being used in embedded device is increased and embedded devices become more important in many computing environments, embedded file system security becomes more important issue. Moreover embedded devices can be easily stolen or lost because of it's high portability. If the lost embedded device has very important information, there's no means to protect it except data encryption. For improving embedded devices' security this paper propose design and implementation of flash cryptographic file system. For this purpose YAFFS is used. By the modified YAFFS cryptographic file system, the security of embedded devices can be improved.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.3-13
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• 2006

Internet mail has become a common communication method to send and receive an amount of data due to the tremendous high speed Internet service increment. But in other respect, the risk and damage of Junk mail is growing rapidly and nowadays Junk mail delivery problem is becoming more serious, because this is used for an attack or propagation scheme of malicious code. It's a most dangerous dominant cause for computer system accident. This paper shows the Junk mail characteristic which is based on the analysis of mail log in reality and then shows the implementation of the FQDN (Fully Qualified Domain Name) check and Personalized classification system and evaluates its performance.