• Journal of Digital Contents Society
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• v.15 no.6
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• pp.711-719
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• 2014

Along with the advent of ubiquitous computing environment, it is becoming a part of our common life style that the demands for enjoying the wireless internet using intelligent portable device such as smart phone and iPad, are increasing anytime or anyplace without any restriction of time and place. The recommending service becomes a very important technology which can find exact information to present users, then is easy for customers to reduce their searching effort to find out the items with high purchasability in e-commerce. Traditional mining association rule ignores the difference among the transactions. In order to do that, it is considered the importance of type of merchandise or service and then, we suggest a new recommending service using mining sequential pattern based on weight to reflect frequently changing trends of purchase pattern as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

• Steel and Composite Structures
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• v.34 no.1
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• pp.17-34
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• 2020

This paper investigates the flexural behavior of concrete beams reinforced longitudinally with either steel bars, molded glass-fiber reinforced polymer (GFRP) grating mesh or pultruded glass-fiber reinforced polymer (GFRP) grating mesh, under four-point bending. The variables included in this study were the type of concrete (normal weight concrete, perlite concrete and vermiculite concrete), type of the longitudinal reinforcement (steel bars, molded and pultruded GFRP grating mesh) and the longitudinal reinforcement ratio (between 0.007 and 0.035). The influences of these variables on the load-midspan deflection curves, bending stiffness, energy absorption and failure modes were investigated. A total of fifteen beams with a cross-sectional dimension of 160 mm × 210 mm and an overall length of 2400 mm were cast and divided into three groups. The first group was constructed with normal weight concrete and served as a reference concrete. The second and third groups were constructed with perlite concrete and vermiculite concrete, respectively. An innovative type of stirrup was used as shear reinforcement for all beams. The results showed that the ultimate load of the beams reinforced with pultruded GFRP grating mesh ranged between 19% and 38% higher than the ultimate load of the beams reinforced with steel bars. The bending stiffness of all beams was influenced by the longitudinal reinforcement ratio rather than the type of concrete. Failure occurred within the pure bending region which means that the innovative stirrups showed a significant resistance to shear failure. Good agreement between the experimental and the analytical ultimate load was obtained.

• Analytical Science and Technology
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• v.7 no.4
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• pp.471-476
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• 1994

The capability of PIXE (Proton Induced X-ray Emission) method for the precision measurement of coating thickness has been tested by measuring several gold coated copper plates. Two different experimental methods are applied and compared. The results are compared with those by the weight measurement and proton RBS (Rutherford Backscattering Spectrometry). The advantage of the method is that it can be also used for the nondestructive thickness measurement of this layers on large-scaled samples or archeological samples which cannot be placed in a vacuum chamber.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.443-446
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• 2007

A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12B
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• pp.767-778
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• 2007

In this paper, we propose a Intelligent Service Reasoning (ISR) model using data mining in smart home environments. Our model creates a service tree used for service reasoning on the basis of C4.5 algorithm, one of decision tree algorithms, and reasons service that will be offered to users through quantitative weight estimation algorithm that uses quantitative characteristic rule and quantitative discriminant rule. The effectiveness in the performance of the developed model is validated through a smart home-network simulation.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.545-552
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• 2004

Association rules mining, which is one of data mining technologies, searches data among which are frequent and related to each other in database. But, although the data are not of frequent and rare in database, they have the enough worth of business information if the data ares important and strongly related to each other, In this paper, we propose the algorithm discovering association rules that consist of data, which are rare but, important and strongly related to each other in database. The proposed algorithm was evaluated through simulation. We found that the proposed algorithm discovered efficiently association rules among data, which are not frequent but, important.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.499-508
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• 2015

High utility itemset(HUI) mining refers to the discovery of itemsets with high utilities which are not less than a user-specified minimum utility threshold, by considering both the quantities and weight factors of items in a transaction database. Recently the utility-list based HUI mining algorithms have been proposed to avoid numerous candidate itemsets and the algorithms need the costly join operations. In this paper, we propose a new HUI mining algorithm, using the utility-list with additional attributes of transaction utility and common utility of itemsets. The new algorithm decreases the number of join operations and efficiently prunes the search space. Experimental results on both synthetic and real datasets show that the proposed algorithm outperforms other recent algorithms in runtime, especially when datasets are dense or contain many long transactions.

• Journal of the Ergonomics Society of Korea
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• v.14 no.1
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• pp.91-96
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• 1995

The purpose of this study was two-fold : (1) to investigate the difference between the lifting capacities based on three different ergonomic approaches; a) the biomechanical, b) the physiological, and c) the phsychophysicalo approach, and the 1991 revised NIOSH Equation, and (2) to develop a comprehensive model for deter- mining maximum weight of lift.

• Safety and Health at Work
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• v.14 no.4
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• pp.457-466
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• 2023

Background: During hot environment work tasks with whole-body enclosed anti-bioaerosol suit, the combined effect of heavy sweating and exhaled hot humid air may cause the N95 medical respirator to saturate with water/sweat (i.e., water-blocking). Methods: 32 young male subjects with different body mass indexes (BMI) in whole-body protection (N95 medical respirator + one-piece protective suit + head covering + protective face screen + gloves + shoe covers) were asked to simulate waste collecting from each isolated room in a seven-story building at 27-28℃, and the weight, inhalation resistance (R_f), and aerosol penetration of the respirator before worn and after water-blocking were analyzed. Results: All subjects reported water-blocking asphyxia of the N95 respirators within 36-67 min of the task. When water-blocking occurred, the R_f and 10-200 nm total aerosol penetration (P_t) of the respirators reached up to 1270-1810 Pa and 17.3-23.3%, respectively, which were 10 and 8 times of that before wearing. The most penetration particle size of the respirators increased from 49-65 nm before worn to 115-154 nm under water-blocking condition, and the corresponding maximum size-dependent aerosol penetration increased from 2.5-3.5% to 20-27%. With the increase of BMI, the water-blocking occurrence time firstly increased then reduced, while the R_f, P_t, and absorbed water all increased significantly. Conclusions: This study reveals respirator water-blocking and its serious negative impacts on respiratory protection. When performing moderate-to-high-load tasks with whole-body protection in a hot environment, it is recommended that respirator be replaced with a new one at least every hour to avoid water-blocking asphyxia.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.50-57
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• 2006

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.