• Journal of Navigation and Port Research
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• v.36 no.1
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• pp.21-26
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• 2012

Recently, vessel hijacking by pirates has been a big issue around the world. For example, the hostages of VLCC "SAMHO Dream" were released for a large sum of ransom. On January 20, 2011 "SAMHO Jewelry" succeeded releasing all of the 21 crews on the vessel by attacking the pirates in international waters for the first time since the founding of the Naval Force. Furthermore, the "HANJIN Tianjin" crews evacuated to the Citadel promptly and were rescued by the navy. As hijacking of Korean vessels by pirates is increasing, various safety measures must be implemented. As a matter of fact, the standard for ship's facilities has been partially revised and setting up an evacuation shelter on all vessels sailing dangerous zone has been reinforced. This research aims to discuss crew Citadel installation on vessels intended for long haul. In addition, it will look at measures against potential gas flow in the event of pirate armed attacks and fire outbreak onboard a vessel. It will also assess the optimal number of crew Citadels theoretically. Lastly, the optimal number of shelters in the event of fire outbreak will be discussed based on an FDS simulation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.1
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• pp.119-127
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• 2014

The application of sulfur polymer emulsion (SPE) as an acrylate substitute for semi-rigid pavement grout was evaluated, and the performance improvement by employing PVA fibers were also evaluated. The result indicated that the filling ratio of semi-rigid pavement material decreased as the fiber content increased, but it was measured to be 92~94% in every mixing condition, which satisfies the target performance, 90%. The maximum Marshall stability value of semi-rigid pavement material was measured to be 25.4 kN, which is about 4.7 times higher than the Korean Standard required for semi-rigid pavement material, 5.0 kN. The dynamic stability evaluation of semi-rigid pavement material indicated that the resistance to deformation from the wheel tracking test was improved by an SPE substitution, and in every mixing condition, the deformation converged to a constant value after 45 minutes with the same dynamic stability of 31,500 times/mm. The strain at the flexural failure was about 0.53%, which shows superior rigidity to asphalt pavements. The examination of abrasion resistance and impact resistance showed that the loss ratio was 9.8~6.0% in every mixing condition, which indicates a good abrasion resistance. Also, when fiber content ratio was 0.3%, the impact resistance was 2.82 times higher compared to plain (i.e., when fibers were not added). In the limited range of this study, an SPE substitution ratio of 30% was found to be an optimal level considering the mechanical and durability performance. In addition, it is thought that semi-rigid pavement material with superior performance could be manufactured if fiber content ratio up to 0.3% is applied depending on the purpose of use.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.367-376
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• 2007

In this paper, we introduce an application-specific and adaptive power management technique for portable systems that support dynamic voltage scaling (DVS). We exploit both the idle time of multitasking systems running soft real-time tasks as well as memory- or CPU-bound code regions. Detailed power and execution time profiles guide an adaptive power manager (APM) that is linked to the operating system. A post-pass optimizer marks candidate regions for DVS by inserting calls to the APM. At runtime, the APM monitors the CPU's performance counters to dynamically determine the affinity of the each marked region. for each region, the APM computes the optimal voltage and frequency setting in terms of energy consumption and switches the CPU to that setting during the execution of the region. Idle time is exploited by monitoring system idle time and switching to the energy-wise most economical setting without prolonging execution. We show that our method is most effective for periodic workloads such as video or audio decoding. We have implemented our method in a multitasking operating system (Microsoft Windows CE) running on an Intel XScale-processor. We achieved up to 9% of total system power savings over the standard power management policy that puts the CPU in a low Power mode during idle periods.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.