Can social media usage of scientific literature predict journal indices of AJG,SNIP and JCR? An altmetric study of economics

Scientometrics - Altmetrics are often praised as an alternative or complement to classic bibliometric metrics, especially in the social sciences discipline. However, empirical investigations of...     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

两阶段领域自适应学习

针对领域自适应问题中源域和目标域的联合分布差异最小化问题,提出两阶段领域自适应学习方法.在第一阶段考虑样本标签和数据结构的判别信息,通过学习一个共享投影变换,使投影后的共享空间中边缘分布的差异最小.第二阶段利用源域标记数据和目标域非标记数据学习一个带结构风险的自适应分类器,不仅能最小化源域和目标域条件分布差异,还能进一步保持源域和目标域边缘分布的流形一致性.在3个基准数据集上的实验表明,文中方法在平均分类准确率和Kappa系数两项评价指标上均表现较优.     

A two-pass approach for handling out-of-vocabulary words in a large vocabulary recognition task

This paper addresses the problem of recognizing a vocabulary of over 50,000 city names in a telephone access spoken dialogue system. We adopt a two-stage framework in which only major cities are represented in the first stage lexicon. We rely on an unknown word model encoded as a phone loop to detect OOV city names (referred to as ‘rare city’ names). We use SpeM, a tool that can extract words and word-initial cohorts from phone graphs from a large fallback lexicon, to provide an N-best list of promising city name hypotheses on the basis of the phone graph corresponding to the OOV. This N-best list is then inserted into the second stage lexicon for a subsequent recognition pass.Experiments were conducted on a set of spontaneous telephone-quality utterances; each containing one rare city name. It appeared that SpeM was able to include nearly 75% of the correct city names in an N-best hypothesis list of 3000 city names. With the names found by SpeM to extend the lexicon of the second stage recognizer, a word accuracy of 77.3% could be obtained. The best one-stage system yielded a word accuracy of 72.6%. The absolute number of correctly recognized rare city names almost doubled, from 62 for the best one-stage system to 102 for the best two-stage system. However, even the best two-stage system recognized only about one-third of the rare city names retrieved by SpeM. The paper discusses ways for improving the overall performance in the context of an application.     

基于深度学习的表面缺陷检测方法综述

近年来, 基于深度学习的表面缺陷检测技术广泛应用在各种工业场景中. 本文对近年来基于深度学习的表面缺陷检测方法进行了梳理, 根据数据标签的不同将其分为全监督学习模型方法、无监督学习模型方法和其他方法三大类, 并对各种典型方法进一步细分归类和对比分析, 总结了每种方法的优缺点和应用场景. 本文探讨了表面缺陷检测中三个关键问题, 介绍了工业表面缺陷常用数据集. 最后, 对表面缺陷检测的未来发展趋势进行了展望.     

Flatness Based Control of an Industrial Robot Joint Using Secondary Encoders

Due to their compliant structure, industrial robots without precision-enhancing measures are only to a limited extent suitable for machining applications. Apart from structural, thermal and bearing deformations, the main cause for compliant structure is backlash of transmission drives. This paper proposes a method to improve trajectory tracking accuracy by using secondary encoders and applying a feedback and a flatness based feed forward control strategy. For this purpose, a novel nonlinear, continuously differentiable dynamical model of a flexible robot joint is presented. The robot joint is modeled as a two-mass oscillator with pose-dependent inertia, nonlinear friction and nonlinear stiffness, including backlash. A flatness based feed forward control is designed to improve the guiding behaviour and a feedback controller, based on secondary encoders, is implemented for disturbance compensation. Using Automatic Differentiation, the nonlinear feed forward controller can be computed in a few microseconds online. Finally, the proposed algorithms are evaluated in simulations and experimentally on a real KUKA Quantec KR300 Ultra SE.     

Adaptive covariate acquisition for minimizing total cost of classification

Machine Learning - In some applications, acquiring covariates comes at a cost which is not negligible. For example in the medical domain, in order to classify whether a patient has diabetes or not,...     

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Neural Computing and Applications - In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly...     

Bibliometric analysis on tendency and topics of artificial intelligence over last decade

Microsystem Technologies - Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and...     

Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach

Neural Computing and Applications - Selecting a subset of candidate features is one of the important steps in the data mining process. The ultimate goal of feature selection is to select an optimal...