Parameter Estimation and Predictive Speed Control of Chopper-Fed Brushed DC Motors

This paper presents an effective speed control method for brushed DC motors fed by a DC chopper using the concept of Finite Control Set-Model Predictive Control (FCS-MPC). As this control algorithm requires the parameters of the controlled object, the estimation of motor parameters is first performed by using two types of data. The first data includes the output speed response corresponding to the step input voltage to obtain the transfer function in the no-load regime. The second data consists of the motor speed and armature current when a load torque is applied to the motor shaft. The discrete-time equation of the motor armature circuit is used to obtain the future values of the armature circuit current and the motor speed. A cost function is defined based on the difference between the reference and predicted motor speed. The optimal switching states of the DC chopper are selected corresponding to the maximum value of the cost function. The performance of the proposed speed control algorithm is validated on an experimental system. The simulation and experimental results obtained show that the MPC controller can outperform the conventional proportional-integral (PI) controller

On the feasibility and use of teleseismic P wave coda autocorrelation for mapping shallow seismic discontinuities

Seismic body waves from distant earthquakes, which propagate near vertically beneath recording stations, provide tools for imaging shallow Earth structures with high vertical resolution. The most commonly used techniques such as P and S wave receiver functions utilize mode conversions from P to S waves or vice versa to retrieve information on the gradients of elastic properties in the crust and upper mantle. Here we demonstrate the feasibility and advantage of utilizing reflection signals through an improved method of teleseismic P wave coda autocorrelation. We recover clear reflections independently on vertical and radial components, which provide complementary constraints on the subsurface structures. Field data from two stations from different geological settings are analyzed, one of which is an ice station in Antarctica and the other is a bedrock station on the Kaapvaal craton in South Africa. The results from both analyses show the feasibility of the method to unveil P and S wave reflection signals from the ice-rock interface and the Moho discontinuity. Extensive synthetic experiments are set up to corroborate our results

Earth's Correlation Wavefield: Late Coda Correlation

Cross correlation of seismograms provides new information on the Earth both through the exploitation of ambient noise and specific components of earthquake records. Here we cross‐correlate recordings of large earthquakes on a planetary scale and identify a range of hitherto unobserved seismic phases in Earth's correlation wavefield. We show that both arrivals with the timing expected for the regular seismic wavefield and previously unexplained phases are produced by interference between seismic paths having the same ray parameter but with only a subset of propagation legs in common. This insight explains the origin and generation mechanism of the features of Earth's correlation wavefield and opens up new ways of addressing issues in global seismology. Strong similarity between observed and synthesized correlation wavefields indicates that the Earth's radial structure is remarkably well constrained in the intermediate period range

Sentiment classification on polarity reviews: an empirical study using rating-based features

We present a new feature type named rating-based feature and evaluate the contribution of this feature to the task of document-level sentiment analysis. We achieve state-of-the-art results on two publicly available standard polarity movie datasets: on the dataset consisting of 2000 reviews produced by Pang and Lee (2004) we obtain an accuracy of 91.6% while it is 89.87% evaluated on the dataset of 50000 reviews created by Maas et al. (2011). We also get a performance at 93.24% on our own dataset consisting of 233600 movie reviews, and we aim to share this dataset for further research in sentiment polarity analysis task

HYBRID END-TO-END APPROACH INTEGRATING ONLINE LEARNING WITH FACE-IDENTIFICATION SYSTEM

To date, facial recognition has been one of the most intriguing, interesting research topics over years. It requires some specific face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition as well; however, all of these algorithms derive from heavy deep learning architectures that cause limitations for development, scalability, flawed accuracy, and deployment into publicity with mere CPU servers. It also calls for large datasets containing hundreds of thousands of records for training purposes. In this paper, we propose a full pipeline for an effective face recognition application which only uses a small Vietnamese celebrity dataset and CPU for training that can solve the leakage of data and the need for GPU devices. It is based on a face vector-to-string tokens algorithm then saves face’s properties into Elasticsearch for future retrieval, so the problem of online learning in Facial Recognition is also tackled. Comparison with another popular algorithm on the dataset, our proposed pipeline not only outweighs the accuracy counterpart, but it also achieves a very speedy time inference for a real-time face recognition application

An automatic restoration scheme for switch-based networks

International audienceThis paper presents a fully automated distributed resilient routing scheme for switch-based or new generation router based networks. The failure treatment is done locally and other nodes in the network do not need to undertake special actions. In contrast to conventional IP routing schemes, each node routes the traffic on the basis of the entering arc and of the destination. The resulting constraint is that two flows to the same destination entering in a node by a common arc have to merge after this arc. It is shown that this is sufficient for dealing with all single link failure situations, assuming that the network is symmetric and two-link connected. Two heuristic approaches are proposed to handle the corresponding dimensioning problem for large network instances. The proposed method generalizes some methods of literature [6], [8] and provides more cost-efficient solutions

Relative Positional Encoding for Speech Recognition and Direct Translation

Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for modeling positions in this model was tailored for text modeling, and thus is less ideal for acoustic inputs. In this work, we adapt the relative position encoding scheme to the Speech Transformer, where the key addition is relative distance between input states in the self-attention network. As a result, the network can better adapt to the variable distributions present in speech data. Our experiments show that our resulting model achieves the best recognition result on the Switchboard benchmark in the non-augmentation condition, and the best published result in the MuST-C speech translation benchmark. We also show that this model is able to better utilize synthetic data than the Transformer, and adapts better to variable sentence segmentation quality for speech translation.Comment: Submitted to Interspeech 202

Effect of Different Types of Liquid Natural Rubbers on the Modification of DGEBA-Based Epoxy Resin

Diglycidyl ether of bisphenol A (DGEBA)-based epoxy and varying content of hydroxyl terminated liquid natural rubber (HTNR) or carboxyl-terminated liquid natural rubber (CTNR) were cured using an aromatic amine hardener. The ultimate aim of the study was to modify the brittle epoxy matrix by liquid rubber to improve the toughness characteristics. Tensile, flexural, and fracture toughness behaviors of neat as well as modified networks have been studied to observe the effect of different types of  liquid natural rubber modification. The morphological evolution of the toughened networks was examined by scanning electron microscope (SEM), and the observations were used effectively to explain the impact properties of the network having varying content of liquid natural rubbers. The results showed that the impact resistance of both HTNR-modified DGEBA and CTNR-modified DGEBA was superior to that of the pure epoxy resin, among which the impact resistance of CTNR-modified DGEBA was better than that of HTNR-modified DGEBA. For all the composites with HTNR or CTNR, the impact resistance increased with elastomer concentration up to 15.0 parts per hundred parts of resin (phr). Higher concentration of the elastomers resulted in larger particles and gave lower impact values. These results allow the conclusion that 15 phr is the maximum content of HTPB or CTNR that might be added in DGEBA composites for a positive effect upon the impact strength