• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.74.2-75
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• 2021

We demonstrate that a deep learning classifier that only uses to gravitational wave (GW) detectors auxiliary channel data can distinguish various types of non-Gaussian noise transients (glitches) with significant accuracy, i.e., ≳ 80%. The classifier is implemented using the multi-scale neural networks (MSNN) with PyTorch. The glitches appearing in the GW strain data have been one of the main obstacles that degrade the sensitivity of the gravitational detectors, consequently hindering the detection and parameterization of the GW signals. Numerous efforts have been devoted to tracking down their origins and to mitigating them. However, there remain many glitches of which origins are not unveiled. We apply the MSNN classifier to the auxiliary channel data corresponding to publicly available GravitySpy glitch samples of LIGO O1 run without using GW strain data. Investigation of the auxiliary channel data of the segments that coincide to the glitches in the GW strain channel is particularly useful for finding the noise sources, because they record physical and environmental conditions and the status of each part of the detector. By only using the auxiliary channel data, this classifier can provide us with the independent view on the data quality and potentially gives us hints to the origins of the glitches, when using the explainable AI technique such as Layer-wise Relevance Propagation or GradCAM.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Journal of IKEEE
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• v.15 no.2
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• pp.113-121
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• 2011

This paper presents the design of a fast auxiliary channel bus for DisplayPort 1.2 interface. The fast auxiliary channel supports Manchester transactions at 1Mbps and fast auxiliary transactions at 780Mbps. The Manchester transaction is used for managing the main link and auxiliary channel and the fast auxiliary transaction is for data transfer via the auxiliary channel. Simplified serial bus architecture is proposed to be implemented in fast auxiliary channel. The fast auxiliary channel transmitter and receiver are implemented with 7,648 LUTs and 6,020 slice register synthesized in Xilinx Vertex4 FPGA and can be operated at 72MHz to support 720Mbps.

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

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.4
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• pp.511-514
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• 1999

Data pertaining to 1111 first lactation performance record of Karan Fries (Holstein-Friesian $\times$ Zebu) cows spread over a period of 21 years and sired by 72 bulls were used to examine the efficiency of sire indices for lactation milk production using auxiliary traits. First lactation length, first service period, first calving interval, first dry period and age at first calving were considered as auxiliary traits. The efficiency of this method was compared with simple daughter average index (D), contemporary comparison method (CC), least-square method (LSQ), simplified regressed least-squares method (SRLS) and best linear unbiased prediction (BLUP) for lactation milk production. The relative efficiency of sire evaluation methods using one auxiliary trait was lower (24.2-32.8%) in comparison to CC method, the most efficient method observed in this study. Use of two auxiliary traits at a time did not further improve the efficiency. The auxiliary sire indices discriminate better among bulls as the range of breeding values were higher in these methods in comparison to conventional sire evaluation methods. The rank correlation between breeding values estimated using auxiliary traits were high (0.77-0.78) with CC method. The rank correlation among auxiliary sire indices ranged from 0.98 to 0.99, indicating similar ranking of sire for breeding values of milk production in all the auxiliary sire indices.

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

• Korean Journal of Mathematics
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• v.22 no.3
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• pp.529-552
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• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1449-1454
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• 2004

For the research, we developed the hardware and software of the measurement system for on-line test and evaluation. The software controls the hardware of the measurement data and acts as a interface between users and the system hardware. In this paper, we studied performances of the auxiliary block for high-speed railway vehicle. In order to performance these works, the conversion system was developed. Using this system, we obtained the important results, such as voltage values for the battery charger and auxiliary block. Also, we diagnosed the faults and performed the evaluation for auxiliary block using the measurement system and data.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.185-191
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• 2006

Auxiliary power supply developed by domestic technology has very important function that not only effect on main power converter & inverter system, traction motor and train control system which are related to performance of train, but also influence on power supply for HVAC(Heat, Ventilation, Air-conditioning) and lighting device which are related to comfort of passengers. This paper shows characteristic test results of auxiliary power supply such as working condition and performance, which is associated with velocity of train, operating mode and surrounding equipment, through test running. Also it shows the results deduced from comparison analysis between designed data and manufactory test data as measuring in put voltage of auxiliary power supply. And, it propose a modification of design parameter for stabilizing operation and improving reliability.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.537-551
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• 2021

We conducted a study on a regression spline estimator with a few pre-specified auxiliary variables. For the implementation of the proposed estimators, we adapted a coordinate descent algorithm. This was implemented by considering a structure of the sum of the residuals squared objective function determined by the B-spline and the auxiliary coefficients. We also considered an efficient stepwise knot selection algorithm based on the Bayesian information criterion. This was to adaptively select smoothly functioning estimator data. Numerical studies using both simulated and real data sets were conducted to illustrate the proposed method's performance. An R software package psav is available.