• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2310-2332
• /
• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.4
• /
• pp.1309-1317
• /
• 2006

The paper considers some Bayes estimators of the finite population mean with auxiliary information under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. The proposed estimators are quite robust in general. Numerical methods of finding Bayes estimators under these heavy tailed priors are given, and are illustrated with an actual example.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.17 no.4
• /
• pp.298-305
• /
• 2012

This paper proposes a soft switching boost converter with an auxiliary resonant circuit. The auxiliary resonant circuit is added to a general boost converter and that is composed of one switch, one diode, one inductor and two capacitors. The resonant network helps the main switch to operate with a zero voltage switching(ZVS) and auxiliary switch also operates under the zero voltage and zero current conditions. The soft switching range is extended by the auxiliary switch and it is possible to control the proposed converter with a pulse width modulation(PWM). The ZVS and ZCS techniques make switching losses decreased and efficiency of the system improved. A theoretical analysis is verified through the simulation and experiment.

• The Korean Journal of Applied Statistics
• /
• v.20 no.2
• /
• pp.333-343
• /
• 2007

The auxiliary renewal function has an important role in analyzng queues in which the either of the inter-arrival time and the service time of customers is not exponential. As like the renewal function, the auxiliary renewal function is hard to compute although it can be defined theoretically. In this paper, we suggest two approximations for auxiliary renewal function and compare the two with the true value of auxiliary renewal function which can be computed in some special cases.

• Communications for Statistical Applications and Methods
• /
• v.10 no.3
• /
• pp.665-678
• /
• 2003

In this paper we suggest the new calibration estimator, which is called to the recalibration estimator, and its variance estimator using two-phase sampling technique according to the auxiliary information having strong correlation with the variable of interest under the unit nonresponse. In this unit nonresponse situation, an available information may exists at the level of whole population or the first-phase sample. The proposed recalibration estimator derives from the first and second phase weights respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.237-246
• /
• 2019

In order to improve the accuracy of the rating prediction of the recommendation model, not only user-item rating data are used but also consider auxiliary information of item such as comments, tags, or descriptions. The traditional approaches use a word-level model of the bag-of-words for the auxiliary information. This model, however, cannot utilize the auxiliary information effectively, which leads to shallow understanding of auxiliary information. Convolution neural network (CNN) can capture and extract feature vector from auxiliary information effectively. Thus, this paper proposes character-level deep-Convolution Neural Network based matrix factorization (Char-DCNN-MF) that integrates deep CNN into matrix factorization for a novel recommendation model. Char-DCNN-MF can deeper understand auxiliary information and further enhance recommendation performance. Experiments are performed on three different real data sets, and the results show that Char-DCNN-MF performs significantly better than other comparative models.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.56 no.2
• /
• pp.109-114
• /
• 2007

This paper summarize about the auxiliary electrode measured a ground resistance. The method to measure a ground resistance is the fall-of-potential method to using an auxiliary electrode. And an auxiliary electrode must be set up on the ground. Today it is so difficult to set up the auxiliary electrode on the ground because of many concrete building and many paved roads. So this paper is regarding a trust analysis of the ground resistance measurement by the substitute auxiliary electrode. It substituted a iron structure around the building, a neutral line multiplex ground to earth, a wire net for auxiliary electrode. This information is confirmed bv compared with the measurement value.

• Journal of the Korean Statistical Society
• /
• v.27 no.3
• /
• pp.331-348
• /
• 1998

In this paper, we have proposed some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean in the presence of auxiliary information. These estimators are compared with the classical ratio estimator and a subjective Bayes estimator utilizing the auxiliary information in terms of "posterior robustness" and "procedure robustness" Also, we have addressed the issue of choice of sampling design from a robust Bayesian viewpoint.

• Journal of radiological science and technology
• /
• v.46 no.6
• /
• pp.485-491
• /
• 2023

As an auxiliary tool for fixing the patient's posture when taking an X-ray, sponges with high radiolucencies are laminated in various thicknesses. This study aimed to evaluate the usefulness of an auxiliary tool for hand oblique projection X-ray by manufacturing it with a uniform thickness by 3D printing and comparing it with existing sponge tools. In the auxiliary tool, radiolucency was measured at the stairs where each finger was located, and pixel information values were compared in the digital imaging and communications in medicine(DICOM) image. Contrast to noise ratio(CNR) and signal to noise ratio(SNR) were compared by shooting the hand phantom and the auxiliary tool together. As the thickness of the sponge tool increased, radiolucency decreased by 15.52% and pixel information value increased by 20.61%. The transmittance of the 3D printing tool increased by 0.82%, and the pixel information value differed by 5.66%. CNR and SNR increased by 20.03% and 22.42% in 3D printing compared to existing sponge tools. while taking hand oblique projection, maintaining the thickness of the auxiliary tool uniformly through 3D printing maintains high radiolucency and minimal impact on medical images, and increases CNR and SNR, making it useful as an auxiliary tool for taking hand oblique projection.

• The Korean Journal of Applied Statistics
• /
• v.16 no.1
• /
• pp.33-44
• /
• 2003

Typically, it should be use auxiliary variable for calibrating the survey nonreponse in census or sampling survey. Where, if the dimension of auxiliary information is large, then it nay be spend a lot of computing time, and difficult to handle data set. Also because the variance estimator depends on the dimension of auxiliary variables, the variance estimator becomes underestimator. To deal with this problem, we propose the variable selection methods for calibration estimation procedure in unit nonreponse situation and we compare the efficiency by simulation study.