• The Transactions of the Korea Information Processing Society
• /
• v.7 no.9
• /
• pp.2969-2979
• /
• 2000

A mobile host has failure causes such as failure of the mobile host, disconnection of the mobile host, and wireless link failure that have not been seen in traditional computing environments. So far there have been few studies on fault tolerance of a mobile host in mobile computing environments. The Lazy scheme, a failure recovery technique of the mobile host, is a cost-effective one. However, this scheme has a defect that the mobile host cannot be recovered from failure of the base station with acheckpoint of the mobile host. In this paper, we propose and evaluate the Redundant Lazy scheme for performance improvement of the Lazy scheme.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.1
• /
• pp.216-224
• /
• 1999

In the paper we propose two recovery methods by adopting a rollback and/or roll-forward technique (S) to recover TMR failures in a TMR (structured ) system that is the simplest spatial redundancy. This technique is apparently effective to recovering TMR failures primarily caused by transient faults. The proposed policies carry out few reconfigurations at the cost of (minimal) time-overhead needed for those time-redundant schemes. The optimal checkpoint-interval vectors are derived for both methods through the likelihoods of all (possible) states of the system as well as the total execution-time. Consequently the effectiveness of our proposed policies is validated through certain numerical examples and simulations.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.3
• /
• pp.173-180
• /
• 2004

In this paper we provide a new image restoration method based on the multiscale regularization in the redundant wavelet transform domain. The proposed method uses the redundant wavelet transform to decompose the single-scale image restoration problem to multiscale ones and applies scale dependent regularization to the decomposed restoration problems. The proposed method recovers sharp edges by applying rather less regularization to wavelet related restorations, while suppressing the resulting noise magnification by the wavelet shrinkage algorithm. The improved performance of the proposed method over more traditional Wiener filtering is shown through numerical experiments.

• The KIPS Transactions:PartD
• /
• v.17D no.5
• /
• pp.353-358
• /
• 2010

Unlike the traditional databases, queries on XML streams are restricted to a real time processing and memory usage. In this paper, a robust labeling scheme is proposed, which quickly identifies structural relationship between XML fragments. The proposed labeling scheme provides an effective query processing by removing many redundant operations and minimizing the number of fragments being processed. In experimental results, the proposed labeling scheme efficiently processes query processing and optimizes memory usage.

• Science of Emotion and Sensibility
• /
• v.13 no.3
• /
• pp.475-484
• /
• 2010

This study examined the influence of the SOA(stimulus onset asynchrony) between visual and auditory stimuli on the integration phenomenon of audio-visual senses. Within the stimulus integration phenomenon, the redundant target effect (the faster and more accurate response to the target stimulus when the target stimulus is presented with more than two modalities) and the visual dominance effect (the faster and more accurate response to a visual stimulus compared to an auditory stimulus) were examined as we composed a visual and auditory unimodal target condition and a multimodal target condition and then observed the response time and accuracy. Consequently, despite the change between visual and auditory stimuli SOA, there was no redundant target effect present. The auditory dominance effect appeared when the SOA between the two stimuli was over 100ms. Theses results imply that the redundant target effect is continuously maintained even when the SOA between two modal stimuli is altered, and also suggests that the behavioral results of superior information processing can only be deducted when the time difference between the onset of the auditory stimuli and the visual stimuli is approximately over 100ms.

• Journal of KIISE
• /
• v.42 no.8
• /
• pp.1010-1021
• /
• 2015

Big data systems are widely used to collect large-scale log data, so it is very important for these systems to operate with a high level of performance. However, the current Hadoop-based big data system architecture has a problem in that its performance is low as a result of redundant processing. This paper solves this problem by improving the design of the Hadoop system architecture. The proposed architecture uses the batch-based data collection of the existing architecture in combination with a single processing method. A high level of performance can be achieved by analyzing the collected data directly in memory to avoid redundant processing. The proposed architecture guarantees system expandability, which is an advantage of using the Hadoop architecture. This paper confirms that the proposed architecture is approximately 30% to 35% faster in analyzing and processing data than existing architectures and that it is also extendable.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.874-877
• /
• 2008

Nowadays the existing data processing systems can only support some simple query for sensor network. It is increasingly important to process the vast data streams in sensor network, and achieve effective acknowledges for users. In this paper, we propose a holistic distributed k-means algorithm for sensor network. In order to verify the effectiveness of this method, we compare it with central k-means algorithm to process the data streams in sensor network. From the evaluation experiments, we can verify that the proposed algorithm is highly capable of processing vast data stream with less computation time. This algorithm prefers to cluster the data streams at the distributed nodes, and therefore it largely reduces redundant data communications compared to the central processing algorithm.

• Journal of the Institute of Convergence Signal Processing
• /
• v.15 no.3
• /
• pp.104-111
• /
• 2014

In this paper, we deal with robotic automation for assembling car brake modules. A car brake module is comprises of a torque member, two brake pads, and two pad liners. In the assembly process, brake pads and pad liners are needed to be inserted in a torque member. If we use a typical robotic hand for the assembly, task time takes too long. So, we propose two methods. The first method is to use an end effector that has five grippers capable of gripping five assembly parts. In the first method we attached the implemented end effector to a conventional 6 degrees of freedom industrial manipulator and performed the bake module assembly task. Experimental results show that the task time is remarkably reduced. The brake module assembly task needs the robot to change its orientation frequently, so, in the second method, we added one degree of freedom to the end effector that is used in the first method. By attaching it to a conventional 6 degrees of freedom industrial manipulator, we composed a 7 degrees of freedom redundant manipulator. A redundant manipulator has the advantage of flexible manipulation so the robot can change its orientation easily and can perform assembly task very fast. Experimental results show that the second method dramatically reduce whole task time for brake module assembly.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.816-819
• /
• 2017

Current graph mining algorithms suffers from performance issues when querying patterns are in increasingly massive network graphs. However, from our observation most data graphs inherently contains recurring semantic subgraphs/substructures. Most graph mining algorithms treat them as independent subgraphs and perform computations on them redundantly, which result in performance degradation when processing massive graphs. In this paper, we propose an algorithm which exploits these inherent recurring subgraphs/substructures to reduce graph sizes so that redundant computations performed by the traditional graph mining algorithms are reduced. Experimental results show that our graph compression approach achieve up to 69% reduction in graph sizes over the real datasets. Moreover, required time to construct the compressed graphs is also reasonably reduced.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.2
• /
• pp.25-32
• /
• 2016

In this paper, we design a middleware for context-awareness which provides compound contexts from diverse sensors on a mobile device. Until now, most of context-aware application developers have taken responsibility for context processing from sensing data. Such application-level context processing causes heavily redundant data processing and leads to significant resource waste in energy as well as computing. In the proposed scheme, we define primitive and compound context map which consists of relavant sensors and features. Based on the context definition, each application demands a context of interest to the middleware, and thus similar context-aware applications inherently share context information and procesing within the middleware. We show that the proposed scheme significantly reduces the resource amounts of cpu, memory, and battery, and that the performance gain gets much more when multiple applications which need similar contexts are running.