• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3683-3703
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• 2018

Wireless sensor networks (WSNs) consist of set of sensor nodes. These sensor nodes are deployed in unattended area which are able to sense, process and transmit data to the base station (BS). One of the primary issues of WSN is energy efficiency. In many existing clustering approaches, initial centroids of cluster heads (CHs) are chosen randomly and they form unbalanced clusters, results more energy consumption. In this paper, an energy efficient clustering protocol to prevent unbalanced clusters based on firefly and midpoint algorithms called EEC-FM has been proposed, where midpoint algorithm is used for initial centroid of CHs selection and firefly is used for cluster formation. Using residual energy and Euclidean distance as the parameters for appropriate cluster formation of the proposed approach produces balanced clusters to eventually balance the load of CHs and improve the network lifetime. Simulation result shows that the proposed method outperforms LEACH-B, BPK-means, Park's approach, Mk-means, and EECPK-means with respect to balancing of clusters, energy efficiency and network lifetime parameters. Simulation result also demonstrate that the proposed approach, EEC-FM protocol is 45% better than LEACH-B, 17.8% better than BPK-means protocol, 12.5% better than Park's approach, 9.1% better than Mk-means, and 5.8% better than EECPK-means protocol with respect to the parameter half energy consumption (HEC).

• Korean Journal of Occupational Health Nursing
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• v.24 no.3
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• pp.183-193
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• 2015

Purpose: The purpose of this study was to describe cleaning workers' working experiences in Korea. Methods: The data were collected in two focus-group interviews with 9 cleaning workers. The phenomenological analytic method suggested by Colaizzi was used to analyze the data. Results: Five theme clusters and thirteen themes emerged from the analysis. The first theme clusters, 'Dead-end choice' included Limits of elderly women workers, Financial difficulties, Lowered self-esteem. The second theme clusters, 'Facing with discriminatory working environments' included Fear and unfair working conditions. The third theme clusters, 'Potential health problems' included Physical overload, Repeated exposure to hazardous substances and Emotional labor. The fourth theme clusters, 'Excluded from protection of the law' included Gloomy reality and Sexual harassment. The fifth theme clusters, 'Desire to get out of social isolation' included Efforts to maintain the status, Desire to live confidently and Desire to change social recognition. Conclusion: The findings of the study provide understanding on cleaning workers' working experiences to explain by their vision and language and should ensure proper working conditions and environment to live a better life.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.209-219
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• 2008

Recently the number of Internet firms providing useful information like weather forecast data is growing. However most of such information is not prepared in accordance with customers' demand, resulting in relatively low customer satisfaction. To upgrade the service quality, it is recommended to devise a system for customers to get involved in the process of service production, which normally requires a huge investment on supporting computer systems like clusters. In this paper, as a way to cut down the budget for computer systems but to improve the performance, we extend the HPC cluster system to include other Internet servers working independently on the same LAN, to make use of their idle times. We also deal with some issues resulting from the extension, like the security problem and a possible deadlock caused by overload on some non-dedicated nodes. At the end, we apply the technique in the solution of some 2D grid problem.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.115-122
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• 2011

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.8-15
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• 2012

In this paper, we propose a new monitor for collecting job information from Hadoop clusters in real time. This monitor is made of two programs called Collector and Agent. Agent collects Hadoop cluster's node information and job information, and Collector analyzes the collected information and saves it in a database. Also, Collector was placed in a new node outside the Hadoop cluster so that it does not affect Hadoop's work and will not cause overload. When the proposed monitor was implemented and applied, the testbed cluster was able to detect the occurrence of dead nodes immediately. In addition, we were able to find Hadoop jobs which were inefficient and when we modified such jobs to further enhance the performance of Hadoop.

• Women's Health Nursing
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• v.21 no.4
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• pp.297-307
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• 2015

Purpose: This study was aimed to understand the meaning and essentials of the experience of burnout for hospital nurses with turnover intention. Methods: The design was a qualitative research of phenomenological study. Participants: Seven hospital nurses who had worked over three years and had experiences of turnover intention in a hospital with over 400 beds were included. Results: Nine meaningful themes related to burnout experiences and four theme clusters of 1) battery warning sounds almost out; 2) the player who hit the drum and double-headed drum; 3) the target flying arrow without a break; and 4) the pendulum swaying to turn over. Registered nurses (RNs) felt burnout with an overload of work and by the thought that it was illegal action for registered nurses to receive insufficient rewards for their work. RNs also experienced there were no problem solving strategies to verbal violence by patient and medical team. Conclusion: The findings show that burnout experiences for those who had turnover intention was developed from the insight that insufficient training to do work independently with over-load for nurses was not ethical. It suggests that it is necessary to rethink training systems for nursing and hospitals to relieve turnover intention.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.389-395
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• 2007

Collaborative filtering is a popular technique for information filtering to reduce information overload and widely used in application such as recommender system in the E-commerce domain. Collaborative filtering systems collect human ratings and provide Predictions based on the ratings of other people who share the same tastes. The quality of predictions depends on the number of items which are commonly rated by people. Therefore, it is difficult to apply pure collaborative filtering algorithm directly to dynamic collections where items are constantly added or removed. In this paper we suggest a method for managing dynamic collections. It creates taste space for items using a technique called Singular Vector Decomposition (SVD) and maintains clusters of core items on the space to estimate relevance of past and future items. To evaluate the proposed method, we divide database of user ratings into those of old and new items and analyze predicted ratings of the latter. And we experimentally show our method is efficiently applied to dynamic collections.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.11-16
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• 2019

People are subject to time sanctions in a busy modern society. Therefore, people find it difficult to eat simple junk food and even exercise, which is bad for their health. As a result, the incidence of chronic diseases is increasing. Also, the importance of making accurate and appropriate inferences to individual characteristics is growing due to unnecessary information overload phenomenon. In this paper, we propose a knowledge reasoning model using association rules and cluster analysis of multi-contexts. The proposed method provides a personalized healthcare to users by generating association rules based on the clusters based on multi-context information. This can reduce the incidence of each disease by inferring the risk for each disease. In addition, the model proposed by the performance assessment shows that the F-measure value is 0.027 higher than the comparison model, and is highly regarded than the comparison model.

• 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.