Chanyoung Park

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This paper investigates the effects of the number of coupon and element tests on uncertainty in element failure stress. In aircraft structural design, failure stress is first obtained from coupon tests, which is then used in predicting failure stress of structural element under combined loads. The mean and standard deviation of failure stress are expressed(More)
Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based(More)
To evaluate the effects of botulinum toxin type A (BTX-A) on mandible skeletal development by inducing muscle hypofunction. Four-week-old Sprague-Dawley rats (n=60) were divided into three groups: Group 1 animals served as controls and were injected with saline; Group 2 animals were injected unilaterally with BTX-A (the contralateral side was injected with(More)
For online product recommendation engines, learning high-quality product embedding that captures various aspects of the product is critical to improving the accuracy of user rating prediction. In recent research, in conjunction with user feedback, the appearance of a product as side information has been shown to be helpful for learning product embedding.(More)
Spin-Transfer Torque Magnetoresistive RAM (STT-MRAM) is being intensively explored as a promising on-chip last-level cache (LLC) replacement for SRAM, thanks to its low leakage power and high storage capacity. However, the write penalties imposed by STT-MRAM challenges its incarnation as a successful LLC by deteriorating its performance and energy(More)
The goal of RecSys Challenge 2015 [2] is: (1) to predict which user will end up with a purchase and if so, (2) to predict items that he/she will buy given click/purchase data provided by YOOCHOOSE. It is hard to achieve the goal of this Challenge because (1) the data does not contain user demographics information and it contains a lot of missing values and(More)
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