Deep Neural Network for Robust Speech Recognition With Auxiliary Features From Laser-Doppler Vibrometer Sensor

Recently, the signal captured from a laser Doppler vibrometer (LDV) sensor been used to improve the noise robustness automatic speech recognition (ASR) systems by enhancing the acoustic signal prior to feature extraction. This study proposes another approach in which auxiliary features extracted from the LDV signal are used alongside conventional acoustic features to further improve ASR performance based on the use of a deep neural network (DNN) as the acoustic model. While this approach is promising, the best training data sets for ASR do not include LDV data in parallel with the acoustic signal. Thus, to leverage such existing large-scale speech databases, a regres- sion DNN is designed to map acoustic features to LDV features. This regression DNN is well trained from a limited size parallel signal data set, then used to form pseudo-LDV features from a massive speech data set for parallel training of an ASR system. Our experiments show that both the features from the limited scale LDV data set as well as the massive scale pseudo-LDV features are able to train an ASR system that significantly outperforms one using acoustic features alone, in both quiet and noisy environments

Perceived Stress After Acute Myocardial Infarction: A Comparison Between Young and Middle-Aged Women Versus Men

Objective: The aim of the study was to examine how psychological stress changes over time in young and middle-aged patients after experiencing an acute myocardial infarction (AMI) and whether these changes differ between men and women. Methods: We analyzed data obtained from 2358 women and 1151 men aged 18 to 55 years hospitalized for AMI. Psychological stress was measured using the 14-item Perceived Stress Scale (PSS-14) at initial hospitalization and at 1 month and 12 months after AMI. We used linear mixed-effects models to examine changes in PSS-14 scores over time and sex differences in these changes, while adjusting for patient characteristics and accounting for correlation among repeated observations within patients. Results: Overall, patients' perceived stress decreased over time, especially during the first month after AMI. Women had higher levels of perceived stress than men throughout the 12-month period (difference in PSS-14 score = 3.63, 95% confidence interval = 3.08 to 4.18, p < .001), but they did not differ in how stress changed over time. Adjustment for patient characteristics did not alter the overall pattern of sex difference in changes of perceived stress over time other than attenuating the magnitude of sex difference in PSS-14 score (difference between women and men = 1.74, 95% confidence interval = 1.32 to 2.16, p < .001). The magnitude of sex differences in perceived stress was similar in patients with versus without post-AMI angina, even though patients with angina experienced less improvement in PSS-14 score than those without angina. Conclusions: In young and middle-aged patients with AMI, women reported higher levels of perceived stress than men throughout the first 12 months of recovery. However, women and men had a similar pattern in how perceived stress changed over time

Effect of Cooling Rate on the Formation of Nonmetallic Inclusions in X80 Pipeline Steel

Nonmetallic inclusions have a strong influence on the hydrogen-induced cracking (HIC) and sulfide stress cracking (SSC) in pipeline steels, which should be well controlled to improve the steel resistance to HIC and SSC. The effects of cooling rate on the formation of nonmetallic inclusions have been studied both experimentally and thermodynamically. It was found that the increasing cooling rate increased the number density and decreased the size of the inclusions, while the inverse results were obtained by decreasing the cooling rate. Furthermore, as the cooling rate decreased from 10 to 0.035 K/s, the inclusions were changed from Al2O3-CaO to Al2O3-CaO-MgO-CaS. At a high cooling rate, the reaction time is short and the inclusions cannot be completely transformed which should be mainly formed at high temperatures. While, at low cooling rate, the inclusions can be gradually transformed and tend to follow the equilibrium compositions

Comparing the performance of three data weighting methods when allowing for time-varying selectivity

How to properly weight composition data is an important ongoing research topic for fisheries stock assessments, and multiple methods for weighting composition data have been developed. Although several studies indicated that properly accounting for time-varying selectivity can reduce estimation biases in population biomass and management-related quantities, no study to date has compared the performance of widely used data-weighting methods when allowing for time-varying selectivity. Here, we conducted four simulation experiments on this topic, aiming to provide guidance on weighting age-composition data given time-varying selectivity. The first simulation experiment showed that over-weighting should be avoided in general and even when estimating time-varying selectivity. The second simulation experiment compared three data-weighting methods (McAllister–Ianelli, Francis, and Dirichlet-multinomial), within which the Dirichlet-multinomial method outperformed the other two methods when selectivity is time-varying. The third and fourth simulation experiments further showed that given time-varying selectivity, the Dirichlet-multinomial method still performed well when age-composition data were over-dispersed and when the level of selectivity variation needed to be simultaneously estimated. Our simulation results support using the Dirichlet-multinomial method when estimating time-varying fishery selectivity. Also, the simulations suggest that improving stock assessments by accounting for time-varying selectivity requires simultaneously addressing data weighting and time-varying selectivity.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Influencing Factors on Forest Biomass Carbon Storage in Eastern China – A Case Study of Jiangsu Province

Forest vegetation plays a crucial role in improving the ecological environment and maintaining the regional ecological balance. However, most studies pay little attention to the factors that can impact forest biomass carbon storage (FBCS). This research estimated the FBCS by combining relevant forest inventory data and models of continuous functions for biomass expansion factor. A modeling equation was then established and applied to examine the impact of socioeconomic factors on FBCS in Jiangsu, a coastal province in Eastern China, as a case study. The results showed that Jiangsu’s FBCS increased by 20.28 Tg from 2005 to 2010, showing a prominent carbon sink effect but with spatial imbalance among the changes in carbon storage. Jiangsu’s FBCS is significantly affected by land use factors (e.g., forest area and cultivated area), population factors (e.g., population density and urbanization), and economic development factors (e.g., GDP). Relatively speaking, the forest area and GDP had positive effects on FBCS, while cultivated area, population density, and urbanization had significant negative effects

A new semi-parametric method for autocorrelated age- and time-varying selectivity in age-structured assessment models

Selectivity is a key parameter in stock assessments that describes how fisheries interact with different ages and sizes of fish. Here, we introduce a new semi-parametric selectivity method, which we implement and test in Stock Synthesis. This selectivity method includes a parametric component and an autocorrelated non-parametric component, consisting of deviations from the parametric component. We explore the new selectivity method using two simulation experiments, which show that the two autocorrelation parameters for selectivity deviations of data-rich fisheries are estimable using either mixed-effect or simpler sample-based algorithms. When selectivity deviations of a data-rich fishery are highly autocorrelated, using the new method to estimate the two autocorrelation parameters leads to more precise estimations of spawning biomass and fully-selected fishing mortality. However, this new method fails to improve model performance in low data-quality cases where measurement error in the data overwhelms the pattern caused by the autocorrelated process. Finally, we use a case study involving North Sea herring to show that our new method substantially reduces autocorrelations in the Pearson residuals in fit to age-composition data.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD

Hotplug or ballooning : a comparative study on dynamic memory management techniques for virtual machines

In virtualization environments, static memory allocation for virtual machines (VMs) can lead to severe service level agreement (SLA) violations or inefficient use of memory. Dynamic memory allocation mechanisms such as ballooning and memory hotplug were proposed to handle the dynamics of memory demands. However, these mechanisms so far have not been quantitively or comparatively studied. In this paper, we first develop a runtime system called U-tube, which provides a framework to adopt memory hotplug or ballooning for dynamic memory allocation. We then implement fine-grained memory hotplug in Xen. We demonstrate the effectiveness of U-tube for dynamic memory management through two case studies: dynamic memory balancing and memory overcommitment. With these two case studies, we make a quantitative comparison between memory hotplug and ballooning. The experiments show that there is no absolute winner for different scenarios. Our findings can be very useful for practitioners to choose the suitable dynamic memory management techniques in different scenarios