• Journal of Korea Spatial Information System Society
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• v.7 no.2 s.14
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• pp.81-89
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• 2005

Declustering and parallel index structures are important research areas to improve a performance of databases. Previous researches proposed several distribution schemes for parallel R-trees, however there is no search schemes to be suitable for the index. In this paper, we propose schemes to improve the performance of range queries for distribute parallel indexes. The proposed schemes use the features that a parallel disk can read multiple nodes from various disks. The proposed schemes are verified using various implementations and performance evaluations. We propose new schemes which can read multiple nodes from multiple disks in contrast that to the previous schemes which can read a node from disk. The experimental evaluation shows that the proposed schemes give us the performance improvement by 40% from the previous researches.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.7
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• pp.679-686
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• 2002

Based on the alternating series expansion of error probability function due to phase noise in PSK systems, the performance evaluations for Tikhonov and Gaussian probability density functions were performed in this paper. The range of the signal-to-noise ratio of recovered carrier signal which provides the same dependency between the error performances by Tikhonov function and Gaussian function was analyzed via loss evaluation due to phase noise. The phase noise with 1/f$^2$ characteristic was generated based on the relationship of the phase noise spectral density and the modulation index for frequency modulation signal. Using the generated phase noise as the input signal for digital satellite communication receiver, the performance losses due to the phase noise were measured and evaluated with the analyzed performance characteristics.

• Journal of the Korean Society of Safety
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• v.26 no.6
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• pp.79-84
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• 2011

There have been so many studies related to job stress or musculoskeletal disorders(MSDs) in the shipbuilding industry. However, study of employee performance evaluation that reflect on the industrial features of the deckhouse manufacturing industry has not been established. Therefore, this study aimed to evaluate workers' job stress and work ability through statistical significance tests according to age, work types, years of service, work forms and sex. For this, the study conducted a job stress test using the Korean Occupational Stress Questionnaire Short Form, and employee performance evaluations using the Work Ability Index(WAI) of the Finnish Institute of Occupational Health (FIOH) on 536 workers in the deckhouse manufacturing industry. MINITAB statistics program was used for statistics analysis and significance was verified with the Kruskal-Wallis test, a corporate body of nonparametric statistical analysis. As a result, employee performance had significant differences, depending on age, work types, years of service and sex, and job stress also depended on the number of service year and work form. Additionally, it was found that employee performance decreased more as job stress increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.68-82
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• 2011

Wireless mesh networks (WMNs) provide high-speed backbone networks without any wired cable. Many researchers have tried to increase network throughput by using multi-channel and multi-radio interfaces. A multi-radio multi-channel WMN requires channel assignment algorithm to decide the number of channels needed for each link. Since the channel assignment affects routing and interference directly, it is a critical component for enhancing network performance. However, the optimal channel assignment is known as a NP complete problem. For high performance, most of previous works assign channels in a centralized manner but they are limited in being applied for dynamic network environments. In this paper, we propose a simple flow estimation algorithm and a hybrid channel assignment algorithm. Our flow estimation algorithm obtains aggregated flow rate information between routers by packet sampling, thereby achieving high scalability. Our hybrid channel assignment algorithm initially assigns channels in a centralized manner first, and runs in a distributed manner to adjust channel assignment when notable traffic changes are detected. This approach provides high scalability and high performance compared with existing algorithms, and they are confirmed through extensive performance evaluations.

• Tribology and Lubricants
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• v.39 no.2
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• pp.35-42
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• 2023

Bearing is a mechanical component that supports loads and transmits rotation. As the application of high-value-added products such as semiconductors, aviation, and robots have recently become diverse and more precise, an accurate bearing performance prediction and evaluation technology is required. Bearing performance evaluation can be divided into evaluations based on bearing theory and on numerical analysis. An evaluation based on numerical analysis is a technique that has been highlighted because the problems that remained unsolved owing to time problems can be solved through recent developments in computers. However, current studies have the disadvantage of not considering the essential changes over time and bearing rotation. In this study, bearing performance evaluation based on rigid body dynamic analysis considering rotation and load over time is performed. Rigid body dynamic analysis is performed for deep groove ball bearing to calculate the load applied by the ball. The reliability of the analysis is verified by comparing it with the results calculated using bearing theory. In addition, rigid body dynamic analysis is performed for automotive wheel bearings to calculate the contact angle and load applied by the ball for cases where axial load and radial load are applied, respectively. The effect of rotation and load over time is evaluated from these results.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.4
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• pp.49-72
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• 2020

In a rapidly changing environment, public libraries need performance measures that can comprehensively represent their activities in order to respond to various changes, demonstrate social value, and justify input resources. However, most of the performance evaluations have been focused on specific areas or specific analysis methods. Therefore, this study looked at the efforts of advanced countries of overseas libraries to develop performance evaluation tools for public libraries in order to develop performance management tools for domestic public libraries. The implications were derived by reviewing and analyzing the direction, evaluation system, and major performance evaluation areas of public library performance evaluation. The main result was that the perspective of public library performance evaluation was shifting from input-output-oriented to outcome or impact, and the performance evaluation area was linked to the development strategy of public libraries or the role of the library. And, the government and institutions were working together to establish an evaluation system for performance management of public libraries.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used