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Aiming at the problem of insufficient details of retinal blood vessel segmentation in current research methods, this paper proposes a multiscale feature fusion residual network based on dual attention. Specifically, a feature fusion residual module with adaptive calibration weight features is designed, which avoids gradient dispersion and network degradation while effectively extracting image details. The SA module and ECA module are used many times in the backbone feature extraction network to adaptively select the focus position to generate more discriminative feature representations; at the same time, the information of different levels of the network is fused, and long-range and short-range features are used. This method aggregates low-level and high-level feature information, which effectively improves the segmentation performance. The experimental results show that the method in this paper achieves the classification accuracy of 0.9795 and 0.9785 on the STARE and DRIVE datasets, respectively, and the classification performance is better than the current mainstream methods. [ABSTRACT FROM AUTHOR]

Hybrid excitation synchronous motor (HESM) offers the advantages of a convenient flux regulation and a wide range of available speeds. As such, it may be conveniently employed in the fields of electric vehicle and aerospace. In this paper, based on a suitable control strategy for vector and speed partition, we put forward a speed control method for HESM which shows optimal efficiency. On the one hand, our method aims at minimizing the copper loss by an optimal configuration of the q-axis current, the d-axis current, and the excitation current under the constraint voltage at the armature end. On the other hand, we conduct a comprehensive analysis of the effects of the flux-weakening speed coefficient on the operating performance of the motors and suggest a self-adaptive control method to regulate that coefficient and further improve the overall performance of the motors, including their speed-range regulation, their efficiency, and their dynamical properties. The proposed flux-weakening control method has been assessed by simulations and HESM-driven experiments. Results have confirmed the feasibility and optimality of our method. [ABSTRACT FROM AUTHOR]

Numbers and locations of sensors and actuators play an important role in cost and control performance for active vibration control system of piezoelectric smart structure. This may lead to a diverse control system if sensors and actuators were not configured properly. An optimal location method of piezoelectric actuators and sensors is proposed in this paper based on particle swarm algorithm (PSA). Due to the complexity of the frame structure, it can be taken as a combination of many piezoelectric intelligent beams and L-type structures. Firstly, an optimal criterion of sensors and actuators is proposed with an optimal objective function. Secondly, each order natural frequency and modal strain are calculated and substituted into the optimal objective function. Preliminary optimal allocation is done using the particle swarm algorithm, based on the similar optimization method and the combination of the vibration stress and strain distribution at the lower modal frequency. Finally, the optimal location is given. An experimental platform was established and the experimental results indirectly verified the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS) algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP) TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance. [ABSTRACT FROM AUTHOR]

A hybrid adaptive active vibration control method is proposed for tall building vibration suppression. Tuned mass dampers are employed as the actuators, single degree of freedom tuned mass damper system and multi degree of freedom tuned mass damper system is modeled respectively. On the basis of feedforward filter-x least square (FxLMS) algorithm and feedback FxLMS algorithm, a hybrid FxLMS algorithm is proposed. Finally the proposed method was implemented on a digital signal processor TMS320F28335, real time control system was constructed and real time suppression experiments were done to test the proposed algorithm, feedforward and feedback FxLMS algorithm on an experimental building platform with two floors. The experimental verification tests show that the proposed method is effective with good vibration control performance. [ABSTRACT FROM AUTHOR]

In recent years, the deep learning-based hyperspectral image (HSI) classification method has achieved great success, and the convolutional neural network (CNN) method has achieved good classification performance in the HSI classification task. However, the convolutional operation only works with local neighborhoods, and is effective in extracting local features. It is difficult to capture interactive features over long distances, which affects the accuracy of classification to some extent. At the same time, the data from HSI have the characteristics of three-dimensionality, redundancy, and noise. To solve these problems, we propose a 3D self-attention multiscale feature fusion network (3DSA-MFN) that integrates 3D multi-head self-attention. 3DSA-MFN first uses different sized convolution kernels to extract multiscale features, samples the different granularities of the feature map, and effectively fuses the spatial and spectral features of the feature map. Then, we propose an improved 3D multi-head self-attention mechanism that provides local feature details for the self-attention branch, and fully exploits the context of the input matrix. To verify the performance of the proposed method, we compare it with six current methods on three public datasets. The experimental results show that the proposed 3DSA-MFN achieves competitive classification and highlights the HSI classification task. [ABSTRACT FROM AUTHOR]

The buildup of fluegas contaminants in amine-based postcombustionCO2capture processes is an important concern due to itspotential impact on solvent degradation and reclaiming. In this research,in order to explore the impact of flue gas contaminants on solventthermal degradation, sodium nitrite, fly ash, sodium sulfate, andsodium thiosulfate were each added into carbon-loaded 5.0 M monoethanolamine(MEA) solution and the solutions were exposed to high-temperaturedegradation conditions. MEA degrades significantly more in the presenceof nitrite (5000 ppm) than MEA alone at the same amine mole concentration.The MEA degradation activation energy of MEA–nitrite solutionis calculated and found to be approximately one-seventh of that ofMEA solution without nitrite. Addition of nitrite not only enhanced generation of several MEA thermal degradation products but also induced formation of diethanolamine (DEA) and N-(2-hydroxyethyl)piperazin-3-one(HEPO), which have been known to form during MEA oxidative degradation.Interestingly, fly ash was observed to inhibit nitrite-induced MEAdegradation and greatly increase the MEA degradation activation energyof MEA–nitrite solution. This led to a reduction in accumulationof several degradation products. Fly ash, sodium sulfate, and sodiumthiosulfate by themselves were not shown to impact MEA thermal degradationrate. [ABSTRACT FROM AUTHOR]

ABSTRACT An improved multi-input multi-output filtered-X least mean square-based vibration control algorithm is proposed to solve the reference signal extraction problem for active vibration control system. The reference signal is constructed by the controller parameters and the vibration residual signal extracted directly from the vibrating structure, which is related to the external disturbance signal. Meanwhile, an FIR filter is adopted for online identification by adding white noise signal to the controller output as identification input signal; the identified model is substituted into the control algorithm, and the online secondary path identification is realized. Thus, the practical application problem of filtered-X least mean square algorithm is solved. The simulations and experiments show that the proposed algorithm is reliable and effective with good control performance. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system's fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%. [ABSTRACT FROM AUTHOR]

At present, the pose change of the face test sample is the main reason that affects the accuracy of face recognition, and the design of cross-pose recognition algorithm is a technical problem to be solved. In this paper, a cross-pose face recognition algorithm which integrates the regression iterative method and the interactive subspace method was proposed, and through the regression iteration, the target function converges rapidly and important characteristics of the sample were extracted. Then, the posture of cross-pose face image was estimated, and finally, the interactive subspace method was applied to judge the similarity of the human face. The experimental results of FERET face database (± 45°and ± 90° posture) and MIT-CBCL face database (N-fold cross-validation) showed that the proposed RIM-ISM algorithm had a higher recognition rate and robustness, and it could effectively solve the difficulty of cross-pose face recognition. [ABSTRACT FROM AUTHOR]

Noncoordinating solvents are intensively investigated and used in quantum dot (QD) synthesis since they generally are not involved in the chemical reaction. Here, high‐quality heavy‐metal‐free indium phosphide (InP) QDs are prepared by adopting a coordinating solvent synthesis method in which the trioctylamine (TOA) used is a one of a kind L‐type ligand that can coordinate with the In precursor and compete with the P precursor simultaneously, which decreases the nucleation sites and increases their growth rates, resulting in relatively larger‐sized InP QDs compared to those obtained by the conventional noncoordinating solvent of 1‐octadecene (ODE). By further coating the shell of ZnSe/ZnS, the core/shell structured InP QDs demonstrate a yellow emission with the highest photoluminescence quantum yields (PLQY) of up to 82%. The zinc chloride treatment via in situ ligand exchange further passivates the sulfide vacancies, which not only avoids the luminescence quenching of QDs but also facilitates the hole injection from the hole transport layer (HTL) to QDs. As a result, the resulting yellow InP QDs‐based light emitting diode (QLED) achieves a maximum external quantum efficiency (EQE) of 7.76% and an operational lifetime of 290 h at an initial luminance of 100 cd m−2. [ABSTRACT FROM AUTHOR]

With the merits of permanent magnet (PM) machine combining the possibility of controllable flux by field windings, hybrid excitation synchronous machine (HESM) is a novel candidate for wide speed range regulation application. The HESM with claw pole topology incorporates the advantages of the hybrid excited synchronous machine and claw pole machine. In this paper, the topology, operating principle and basic mathematical model of HESM are analyzed and established, respectively. Then, the control model of the HESM is deduced on the basis of the space vector control method, and the operating performance of the HESM across its entire operating range is also investigated. A new adaptive control strategy for the HESM drive system is proposed and designed on the basis of analyzing the influences of the flux-weakening coefficient, respectively, which can operate in both flux-enhancing and flux-weakening conditions. The correctness and effectiveness of the proposed adaptive control strategy and drive system design are verified by simulation and experimental results, which demonstrate that the adaptive control strategy maximizes the range of speed regulation while exhibits the high efficiency. [ABSTRACT FROM AUTHOR]

Active noise suppression for applications where the controlled system response varies with time is a difficult problem, especially for time varying nonlinear systems with large model error. On the basis of adaptive switching supervisory control theory, an adaptive supervisory switching control algorithm is proposed with a new controller switching strategy for active noise suppression of duct-like application. Real time experimental verification tests show that the proposed algorithm is effective with good noise suppression performance. [ABSTRACT FROM AUTHOR]

This correspondence focuses on the analysis and implementation of multi-input multi-output (MIMO) filtered-u least mean square (FULMS) algorithm for active vibration suppression of a cantilever smart beam with surface bonded lead zirconate titanate patches. By analysing a single-input single-output FULMS algorithm, the MIMO FULMS controller structure is given. Then an active vibration control experimental platform is established, with optimal placement of the actuators and sensors based on the maximal modal force rule. Simulation contrast analysis of FULMS algorithm and the most famous filtered-x least mean square (FXLMS) algorithm is performed while the reference signal is extracted from the exciter as well as directly from the controlled structure. Simulation results show that if the feedback information reflects the reference signal collected by the reference transducers, the FXLMS controller could hardly suppress the vibration while the FULMS controller is still effective. Then the actual control experiment is performed, and the result confirms the simulation results. The designed MIMO FULMS vibration controller has a good control performance, suppressing the vibration significantly with rapid convergence. [ABSTRACT FROM AUTHOR]

Abstract: Tricyclodecan-9-yl-xanthogenate (D609) has in vivo and in vitro antioxidant properties. D609 mimics glutathione (GSH) and has a free thiol group, which upon oxidation forms a disulfide. The resulting dixanthate is a substrate for glutathione reductase, regenerating D609. Recent studies have also shown that D609 protects brain in vivo and neuronal cultures in vitro against the potential Alzheimer’s disease (AD) causative factor, Aβ(1–42)-induced oxidative stress and cytotoxicity. Mitochondria are important organelles with both pro- and antiapoptotic factor proteins. The present study was undertaken to test the hypothesis that intraperitoneal injection of D609 would provide neuroprotection against free radical-induced, mitochondria-mediated apoptosis in vitro. Brain mitochondria were isolated from gerbils 1 h post injection intraperitoneally (ip) with D609 and subsequently treated in vitro with the oxidants Fe2+/H2O2 (hydroxyl free radicals), 2,2-azobis-(2-amidinopropane) dihydrochloride (AAPH, alkoxyl and peroxyl free radicals), and AD-relevant amyloid β-peptide 1–42 [Aβ(1–42)]. Brain mitochondria isolated from the gerbils previously injected ip with D609 and subjected to these oxidative stress inducers, in vitro, showed significant reduction in levels of protein carbonyls, protein-bound hydroxynonenal [a lipid peroxidation product], 3-nitrotyrosine, and cytochrome c release compared to oxidant-treated brain mitochondria isolated from saline-injected gerbils. D609 treatment significantly maintains the GSH/GSSG ratio in oxidant-treated mitochondria. Increased activity of glutathione S-transferase, glutathione peroxidase, and glutathione reductase in brain isolated from D609-injected gerbils is consistent with the notion that D609 acts like GSH. These antiapoptotic findings are discussed with reference to the potential use of this brain-accessible glutathione mimetic in the treatment of oxidative stress-related neurodegenerative disorders, including AD. [Copyright &y& Elsevier]

Permanent magnet vernier machine (PMVM) based on flux modulation principle has attracted increasing attention in low-speed direct-drive applications due to its high torque capability. A novel flux concentrating multi-tooth splitting poles permanent magnet vernier machine (FCMSP-PMVM) was proposed and designed, which has higher flux density relative to the conventional surface ones. The analytical cogging torque expression of the machine is derived by Maxwell energy method and Fourier series expansion to comprehensively provide insight into the variation of the cogging torque with main parameters. The number of flux modulation pole (FMP) per stator tooth, the circumferential width and radial depth of each FMP, pole arc coefficient, namely N 𝐹𝑀𝑃 , k d , k p and α p are defined to investigated the relationship between machine unique parameters and its cogging torque. Analysis and computation of the cogging torque characteristic of the proposed flux concentrating PMVM are studied by time-step finite element analysis (FEA). The FEA results demonstrate that N 𝐹𝑀𝑃 , k d and k p are the key parameters to the cogging torque optimization for the proposed machine. It is also confirmed that the pole arc coefficient has small influence to the cogging torque of the proposed machine compared with the traditional PM machines. Finally, a FCMSP-PMVM prototype is built to verify the above analysis and computation. The measured performance of the constructed prototype machine has confirmed the correctness of finite-element modeling results. Experience gained from this study has led to develop a set of design rules for the concentrated winding PMVM. [ABSTRACT FROM AUTHOR]

With the rapid development of drone technology, drones are frequently used in all aspects of urban safety, such as emergency disaster monitoring, public safety monitoring, traffic monitoring, violation monitoring and so on, which further speeds up the pace of smart city construction. For small and medium-sized cities, the cost of buying drone is too high, and renting drone is more cost-effective. However, the leasing transactions between government departments and drone leasing companies are faced with problems such as distrust, price disputes, and increased costs caused by third-party participation. To solve these problems, this paper proposes a multi-party secure and convenient transaction framework based on alliance blockchain, which helps to build trust between drone leasing companies and government departments without worrying about privacy leakage and transaction data tampering. In addition, an intelligent pricing strategy based on contract theory is designed, which can not only maximize the utility of government departments, but also encourage drone companies to actively participate. Simulation analysis shows that the proposed drone services transaction mechanism based on blockchain and intelligent pricing strategy is very effective. [ABSTRACT FROM AUTHOR]