Reliable Centralized Control of PMSM

In this paper, we introduce the development methodology of a reliable centralized control applied to a synchronous permanent magnet machine. The proposed system is nonlinear, we linearize around a point of application. The resulting model will then be used to reproduce the dynamic behavior of the machine for a reliable control. The controller is based on the standard h infinite to increase performance, reduce measurement noise, and to tolerate the outage of certain sensors. To illustrate the results, we made a comparison between a standard state feedback control and reliable h infinite robust control. The simulation results shows, that the system in case of technical placements poles loses classic performance in the presence of an outage, that the reliable centralized robust control remain satisfactory performance even in the presence of outage.

The Research of Maneuverability Modeling and Environmental Monitoring Based on a Robotic Dolphin

In this study, the C-turning, pitching, and flapping propulsion of a robotic dolphin during locomotion were explored. Considering the swimming action required of a three-dimensional (3D) robotic dolphin in the ocean, we propose a maneuverability model that can be applied to the flapping motion to provide precise and stable movements and function as the driving role in locomotion. Additionally, an added tail joint allows for the turning movement with efficient parameters obtained by a fluid-structure coupling method. To obtain a mathematical model, several disturbance signals were considered, including systematic uncertainties of the parameters, the perpetually changing environment, the interference from obstacles with effective fuzzy rules, and a sliding mode of control. Furthermore, a combined strategy of environment recognition was used for the positional control of the robotic dolphin, incorporating sonar, path planning with an artificial potential field, and trajectory tracking. The simulation results show satisfactory performance of the 3D robotic dolphin with respect to flexible movement and trajectory tracking under the observed interference factors.

MODEL PREDICTIVE CONTROL OF AN AUV USING DE-COUPLED APPROACH

This paper considers the decoupled dynamics and control of an Autonomous Underwater Vehicle (AUV). The decoupled model consists of speed, steering and depth subsystems. Generally AUV model is unstable and nonlinear. The central theme of this paper is the development of model predictive control (MPC) for underwater robotic vehicle for ocean survey applications. The proposed MPC for decoupled structure can have simple implementation. Simulation results have been presented which confirm satisfactory performance. Decoupled approach is well suitable for applying control.

The use of the combined cycle for the generation of electric energy by the gases obtained from the carbonization of wood using

The effluents from carbonization or pyrolysis and wood charcoal have aggregated thermal energy and, in conventional charcoal kilns, part of the wood is burned to ignite the burning in the kilns and the effluents generated are dispensed in the atmosphere and in the soil, which causes energy losses and environmental pollution. In this study we seek a clean and sustainable alternative to produce energy, in addition to the search for a system with satisfactory performance in the generation of electric energy. The objective of this study was to evaluate how much electricity can be produced from wood carbonization effluents by ONDATEC technology, using the Brayton and Rankine cycle, also known as Combined Cycle. This method presents a high power generation efficiency, around 50%, compared to other generation systems. A field experiment was carried out from October 21st to 24th, 2010 to determine the calorific value of wood carbonization effluents, using a microwave oven, (condensable and non-condensable gas), in the city of Uberaba -Mg, Brazil. The data generated in this study reveals important information for companies looking for a way to produce clean and renewable electricity from reforestation wood, in addition to the effort to minimize environmental pollution, ensure sustainability in production systems and the growing search for new sources of energy. A complete description of the experiment, including details of the project, is presented in this work.

Estimation of Domain Means from Business Surveys in the Presence of Stratum Jumpers and Nonresponse

Misclassified frame records (also called stratum jumpers) and low response rates are characteristic for business surveys. In the context of estimation of the domain parameters, jumpers may contribute to extreme variation in sample weights and skewed sampling distributions of the estimators, especially for domains with a small number of observations. There is limited literature about the extent to which these problems may affect the performance of the ratio estimators with nonresponse-adjusted weights. To address this gap, we designed a simulation study to explore the properties of the Horvitz-Thompson type ratio estimators, with and without smoothing of the weights, under different scenarios. The ratio estimator with propensity-adjusted weights showed satisfactory performance in all scenarios with a high response rate. For scenarios with a low response rate, the performance of this estimator improved with an increase in the proportion of jumpers in the domain. The smoothed estimators that we studied performed well in scenarios with non-informative weights, but can become markedly biased when the weights are informative, irrespective of response rate. We also studied the performance of the ’doubled half’ bootstrap method for variance estimation. We illustrated an application of the methods in a real business survey.

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

iEnhancer-CLA: Self-attention-based interpretable model for enhancers and their strength prediction

Enhancer is a class of non-coding DNA cis-acting elements that plays a crucial role in the development of eukaryotes for their transcription. Computational methods for predicting enhancers have been developed and achieve satisfactory performance. However, existing computational methods suffer from experience-based feature engineering and lack of interpretability, which not only limit the representation ability of the models to some extent, but also make it difficult to provide interpretable analysis of the model prediction findings.In this paper, we propose a novel deep-learning-based model, iEnhancer-CLA, for identifying enhancers and their strengths. Specifically, iEnhancer-CLA automatically learns sequence 1D features through multiscale convolutional neural networks (CNN), and employs a self-attention mechanism to represent global features formed by multiple elements (multibody effects). In particular, the model can provide an interpretable analysis of the enhancer motifs and key base signals by decoupling CNN modules and generating self-attention weights. To avoid the bias of setting hyperparameters manually, we construct Bayesian optimization methods to obtain model global optimization hyperparameters. The results demonstrate that our method outperforms existing predictors in terms of accuracy for identifying enhancers and their strengths. Importantly, our analyses found that the distribution of bases in enhancers is uneven and the base G contents are more enriched, while the distribution of bases in non-enhancers is relatively even. This result contributes to the improvement of prediction performance and thus facilitates revealing an in-depth understanding of the potential functional mechanisms of enhancers.

An Improved DCP-Based Image Defogging Algorithm Combined with Adaptive Fusion Strategy

Dark channel prior (DCP) has been widely used in single image defogging because of its simple implementation and satisfactory performance. This paper addresses the shortcomings of the DCP-based defogging algorithm and proposes an optimized method by using an adaptive fusion mechanism. This proposed method makes full use of the smoothing and “squeezing” characteristics of the Logistic Function to obtain more reasonable dark channels avoiding further refining the transmission map. In addition, a maximum filtering on dark channels is taken to improve the accuracy of dark channels around the object boundaries and the overall brightness of the defogged clear images. Meanwhile, the location information and brightness information of fog image are weighed to obtain more accurate atmosphere light. Quantitative and qualitative comparisons show that the proposed method outperforms state-of-the-art image defogging algorithms.

A Fluorescence Kinetic-Based Aptasensor Employing Stilbene Isomerization for Detection of Thrombin

It is important to detect thrombin due to its physiological and pathological roles, where rapid and simple analytical approaches are needed. In this study, an aptasensor based on fluorescence attenuation kinetics for the detection of thrombin is presented, which incorporates the features of stilbene and aptamer. We designed and synthesized an aptasensor by one-step coupling of stilbene compound and aptamer, which employed the adaptive binding of the aptamer with thrombin to cause a change in stilbene fluorescence attenuation kinetics. The sensor realized detection of thrombin by monitoring the variation in apparent fluorescence attenuation rate constant (kapp), which could be further used for probing of enzyme–aptamer binding. In comprehensive studies, the developed aptasensor presented satisfactory performance on repeatability, specificity, and regeneration capacity, which realized rapid sensing (10 s) with a limit of detection (LOD) of 0.205 μM. The strategy was successful across seven variants of thrombin aptasensors, with tunable kapp depending on the SITS (4-Acetamido-4′-isothiocyanato-2,2′-stilbenedisulfonic acid disodium salt hydrate) grafting site. Analyte detection mode was demonstrated in diluted serum, requiring no separation or washing steps. The new sensing mode for thrombin detection paves a way for high-throughput kinetic-based sensors for exploiting aptamers targeted at clinically relevant proteins.

Robust GMTI Scheme for Highly Squinted Hypersonic Vehicle-Borne Multichannel SAR in Dive Mode

While there are recent researches on hypersonic vehicle-borne multichannel synthetic aperture radar in ground moving target indication (HSV-MC-SAR/GMTI), this article, which specifically explores a robust GMTI scheme for the highly squinted HSV-MC-SAR in dive mode, is novel. First, an improved equivalent range model (IERM) for stationary targets and GMTs is explored, which enjoys a concise expression and therefore offers the potential to simplify the GMTI process. Then, based on the proposed model, a robust GMTI scheme is derived in detail, paying particular attention to Doppler ambiguity arising from the high-speed and high-resolution wide-swath. Furthermore, it retrieves the accurate two-dimensional speeds of GMTs and realizes the satisfactory performance of clutter rejection and GMT imaging, generating the matched beamforming and enhancing the GMT energy. Finally, it applies the inverse projection to revise the geometry shift induced by the vertical speed. Simulation examples are used to verify the proposed GMTI scheme.