Search

Abstract We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent, our method solves the two difficulties encountered in traditional video object segmentation: one is that the single frame calculation time is too long, and the other is that the current frame’s segmentation should use more information from past frames. The algorithm uses a global context (GC) module to achieve high-performance, real-time segmentation. The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time. Moreover, the prediction mask of the previous frame is helpful for the segmentation of the current frame, so we input it into a spatial constraint module (SCM), which constrains the areas of segments in the current frame. The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources. We added a refinement module to the decoder to improve boundary segmentation. Our model achieves state-of-the-art results on various datasets, scoring 80.1% on YouTube-VOS 2018 and a $${\cal J}{\rm{\& }}{\cal F}$$ J & F score of 78.0% on DAVIS 2017, while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

Abstract An electret-based mechanical antenna (EBMA), which can transmit extremely low frequency (ELF) electromagnetic signals, has the advantages of miniaturization and high transmitting efficiency, with great potential applications in air, underwater, and underground communications. To improve the charge density of the electret, which is a key factor in determining the radiation performance of an EBMA, this work proposes a fluorinated ethylene propylene/terpolymer of tetrafluoroethylene, hexafluoropropylene and vinylidene fluoride (FEP/THV) unipolar electret exhibiting negative polarity, reaching a total charge density up to −0.46 mC/m2 for each layer of electret. Long transmission distances can be achieved in sea water, soil, and air using a 3-layer-FEP/THV-based EBMA with a compact volume of 5 × 10−4 m3. As an application demonstration, binary ASCII-coded ELF information of “BUAA” is successfully transmitted with a power consumption

Botulinum neurotoxins (BoNTs) are the most toxic known proteins. Naturally occurring botulism in humans is caused by botulinum serotypes A, B, E, and F. Vaccination is an effective strategy to prevent botulism. In this study, a tetravalent botulinum vaccine (TBV) that can prevent serotypes A, B, E, and F was developed using the C-terminal receptor-binding domain of BoNT (Hc) as an antigen. To develop a suitable vaccine formulation, in vitro binding experiments of antigens and aluminum adjuvant in different buffers, and in vivo experiments of TBV at different antigen concentrations, were conducted. Our results showed that the optimal vaccine formulation buffer was a pH 6.0 phosphate buffer, and the suitable antigen concentration was 40 or 80 µg/ml of each antigen. A pilot-scale TBV was then prepared and evaluated for immunogenicity and stability. The results showed that TBV could elicit strong protective efficacy against each BoNT in mice, and remain effective after two years of storage at 4ºC, indicating that the preparation was stable and highly effective. Adsorption experiments also showed that the antigens could be well adsorbed by the aluminum adjuvant after 2 years of storage. Our results provide valuable experimental data supporting the development of a tetravalent botulinum vaccine, which is a promising candidate for the prevention of botulinum serotypes A, B, E, and F.

With the popularity of online-shopping, more and more delivery packages have led to stacking at sorting centers. Robotic detection can improve sorting efficiency. Standard datasets in computer vision are crucial for visual detection. A neuromorphic vision (NeuroVI) camera is a bio-inspired camera that can capture dynamic changes of pixels in the environment and filter out redundant background information with low latency. NeuroVI records pixel changes in the environment with the output of event-points, which are very suitable for the detection of delivery packages. However, there is currently no logistics dataset with the sensor, which limits its application prospects. This paper encodes the events stream of delivery packages, and converts the event-points into frame image datasets for recognition. Considering the falling risk during the packages' transportation on the sorting belt, another falling dataset is made for the first time. Finally, we combine different encoding images to enhance the feature-extraction on the YOLO network. The comparative results show that the new datasets and image-confusing network can improve the detection accuracy with the new NeuroVI.

The fan is one of the key components of the power transformer cooling system. The operating condition of fans determines transformers’ internal temperature rise and long-term reliability. However, at present, the fans’ condition monitoring only includes switch status (online) and regular maintenance (offline), online direct monitoring of the fans’ operating condition is lacking due to economic costs. In view of the above-mentioned problem, this paper proposes a transformer fan early fault detection method based on the oil exponent, which is monitored by the existing transformer top-oil temperature data, thereby detecting the abnormality of the fans. In this method, the oil exponent was chosen as the characteristic criterion. First, to obtain the range of oil exponent in different cooling modes, a set of physical models describing global oil flow and its interaction with air was established based on fluid dynamics and heat transfer principle. Then, regarding the constantly changing top-oil temperature, ambient temperature and load current, an oil exponent tracking algorithm using particle swarm optimization (PSO) was proposed within an improved IEC dynamic thermal model. The operation data from an oil-immersed transformer with a rated capacity of 120-MVA and rated voltage of 220-kV was selected to verify the above methods under two different scenarios.

Botulinum neurotoxins (BoNTs) are among the most toxic proteins. Vaccination is an effective strategy to prevent botulism. To generate a vaccine suitable for human use, a recombinant non-His-tagged isoform of the Hc domain of botulinum neurotoxin serotype E (rEHc) was expressed in Escherichia coli and purified by sequential chromatography. The immunogenicity of rEHc was evaluated in mice and dose- and time-dependent immune responses were observed in both antibody titers and protective potency. Then, the pilot-scale expression and purification of rEHc were performed, and its immunological activity was characterized. Our results showed rEHc has good immunogenicity and can elicit strong protective potency against botulinum neurotoxin serotype E (BoNT/E) in mice, indicating that rEHc is an effective botulism vaccine candidate. Further, we developed a novel antitoxin against BoNT/E by purifying F(abʹ)2 from pepsin-digested serum IgG of rEHc-inoculated horses. The protective effect of the F(abʹ)2 antitoxin was determined in vitro and in vivo. The results showed that our F(abʹ)2 antitoxin can prevent botulism in BoNT/E-challenged mice and effectively alleviate the progression of paralysis caused by BoNT/E to achieve therapeutic effects. Therefore, our results provide valuable experimental data for the production of a novel antitoxin, which is a promising candidate for the treatment of BoNT/E-induced botulism.

In modern manufacturing industry featured with automation and flexibility, the intelligent tool management for Computer Numeric Control (CNC) machine plays an essential role in manufacturing automation. The automatic tool recognition in terms of geometric shapes, materials and usage functions could facilitate the seamless integration with downstream process planning and scheduling processes. In this paper, a intelligent tool recognition system is proposed with a novel hybrid framework of multi-channel deep learning network with non-iterative and fast feedforward neural network to meet high efficiency and accuracy requirement in intelligent manufacturing. The combination of the fine-tuning Convolutional Neural Networks (CNNs) with the random parameter assignment mechanism of Extreme Learning Machines (ELMs) reach a balance in accurate feature extraction and fast recognition. In the proposed hybrid framework, features extracted from efficient CNNs are aggregated into robust ELM auto-encoders (ELM-AEs) to generate the compact but rich feature information, which are then feed to the subsequent single layer ELM network for tool recognition. The performance of proposed framework is verified on several standardized 3D shape retrieval and classification dataset, as well as on a self-constructed multi-view 3D data represented tool library database. Numerical experiments reveal a promising application perspective of proposed intelligent recognition system on manufacturing automation.

A typical way to predict the remaining useful life (RUL) of bearings is to predict certain health indicators (HIs) according to the historical HI series and forecast the end of life (EOL). The autoregressive neural network (ARNN) is an early idea to combine the artificial neural network (ANN) and the autoregressive (AR) model for forecasting, but the model is limited to linear terms. To overcome the limitation, this paper proposes an improved autoregressive integrated moving average with the recurrent process (ARIMA-R) method. The proposed method adds moving average (MA) components to the framework of ARNN, adding the long-range dependence and nonlinear factors. To deal with the recursive characteristics of the MA term, a process of MA component estimating is constructed based on the expectation-maximum method. In the concrete realization of the method, the rotation tree (RTF) is introduced in place of ANN to improve the prediction performance. The experiment on FEMTO datasets reveals that the proposed ARIMA-R method outperforms the ARNN method in terms of predictive performance evaluation indicators.

Micro-positioning stages, which are capable to meet the emerging industrial trend for performing various kinds of micro-manipulation and micro-assembly tasks are the crucial tools to miniaturize the products under ultra-high precision. The current main practice is to specifically tailor a monolithic positioning stage design for one kind of micro-manipulation application. However, the design and fabrication of such a specific flexure-based micro-positioning stage are expensive in both the time and financial investment. In order to increase the flexibility and functionality of micro-positioning stages, this paper proposes a novel reconfigurable planar micro-positioning stages platform based on different function modules. A functional model-based approach is developed to divide one micro-positioning stage into a set of functionally independent sub-models, which allows flexible composition resulting in an adjustable new stage. The robustness of the proposed platform is demonstrated using some typical micro-positioning stages benchmarking cases. The effectiveness of assembly using flexible function modules is verified to show the benefits of the proposed design platform for reconfigurable micro-positioning stages.

In this paper, a prescribed performance control scheme of the quadrotor unmanned aerial vehicle (UAV) under attitude and input saturation constraints is introduced. According to the underactuated feature, the quadrotor UAV system can be decomposed into an underactuated subsystem and a fully actuated subsystem. With the feedback linearization technique, a single nonlinear extended state observer (ESO) is proposed, and multiple observations are utilized to estimate both matched and unmatched disturbances, which not only can obtain a uniform convergence, but also reduces the complexity of the observer’s parameter adjustment. To improve system stability, an input saturation algorithm for each single rotor is introduced to modify the final control output. In addition, the limited attitude for the quadrotor UAV is also considered as a saturation constraint in the control scheme with a compensation auxiliary system. On this basis, dynamic surface control (DSC) with prescribed performance is adopted to guarantee the bounded convergence and steady-state error. All state errors of the closed-loop system are proven to be uniformly bounded using the Lyapunov theory, and the simulation results are given to demonstrate the stability, effectiveness, and superiority of the proposed control strategies at last.

This paper aims to investigate the mechanism by which dark septate endophytes (DSEs) enhance cadmium (Cd) tolerance in there host plants. Maize (Zea mays L.) was inoculated with a DSE, Exophiala pisciphila, under Cd stress at different concentrations (0, 5, 10, and 20 mg·kg−1). The results show that, under 20 mg/kg Cd stress, DSE significantly increased maize biomass and plant height, indicating that DSE colonization can be utilized to increase the Cd tolerance of host plants. More Cd was retained in DSE-inoculated roots, especially that fixed in the root cell wall (RCW). The capability of DSE to induce a higher Cd holding capacity in the RCW is caused by modulation of the total sugar and uronic acid of DSE-colonized RCW, mainly the pectin and hemicellulose fractions. The fourier-transform spectroscopy analysis results show that carboxyl, hydroxyl, and acidic groups are involved in Cd retention in the DSE-inoculated RCW. The promotion of the growth of maize and improvement in its tolerance to Cd due to DSEs are related to restriction of the translocation of Cd from roots to shoots; resistance of Cd uptake Cd inside cells; and the increase in RCW-integrated Cd through modulating RCW polysaccharide components.

In this paper, the problem of fluid–structure interaction of a circular membrane under liquid weight loading is formulated and is solved analytically. The circular membrane is initially flat and works as the bottom of a cylindrical cup or bucket. The initially flat circular membrane will undergo axisymmetric deformation and deflection after a certain amount of liquid is poured into the cylindrical cup. The amount of the liquid poured determines the deformation and deflection of the circular membrane, while in turn, the deformation and deflection of the circular membrane changes the shape and distribution of the liquid poured on the deformed and deflected circular membrane, resulting in the so-called fluid-structure interaction between liquid and membrane. For a given amount of liquid, the fluid-structure interaction will eventually reach a static equilibrium and the fluid-structure coupling interface is steady, resulting in a static problem of axisymmetric deformation and deflection of the circular membrane under the weight of given liquid. The established governing equations for the static problem contain both differential operation and integral operation and the power series method plays an irreplaceable role in solving the differential-integral equations. Finally, the closed-form solutions for stress and deflection are presented and are confirmed to be convergent by the numerical examples conducted.

Fusarium sp. strain ZH-H2 is capable to degrade high molecular weight polycyclic aromatic hydrocarbons (HMW-PAHs), smooth bromegrass (Bromus inermis Leyss.) can also degrade 4- to 6-ring PAHs. Pot experiments were conducted to investigate how bromegrass and different inoculum sizes of ZH-H2 clean up HMW-PAHs in agricultural soil derived from a coal mine area. The results showed that, compared with control, different sizes of inocula of ZH-H2 effectively degraded HMW-PAHs, with removal rates of 19.01, 34.25 and 29.26% for 4-, 5- and 6-ring PAHs in the treatment with 1.0 g kg−1 ZH-H2 incubation after 90 d. After 5 mon of cultivation, bromegrass reached degradation rate of these compounds by 12.66, 36.26 and 36.24%, respectively. By adding strain ZH-H2 to bromegrass, HMW-PAHs degradation was further improved up to 4.24 times greater than bromegrass (W), in addition to the degradation rate of Bbf decrease. For removal rates of both 5- and 6-ring PAHs, addition of 0.5 g kg−1 Fusarium ZH-H2 to pots with bromegrass performed better than addition of 0.1 g kg−1, while the highest concentration of 1.0 g kg−1 Fusarium ZH-H2 did not further improve degradation. Degradation of 4-ring PAHs showed no significant difference among different ZH-H2 incubations with bromegrass treatments. We found that the degradation rates of 4-, 5- and 6-ring PAHs in all treatments are significantly correlated in a positive, linear manner with activity of lignin peroxidase (LiP) (r=0.8065, 0.9350 and 0.9165, respectively), while degradation of 5- and 6-ring PAHs is correlated to polyphenoloxidase (PPO) activity (r=0.7577 and 07806). Our findings suggest that the combination of Fusarium sp. ZH-H2 and bromegrass offers a suitable alternative for phytoremediation of aged PAH-contaminated soil in coal mining areas, with a recommended inoculation size of 0.5 g Fusarium sp. ZH-H2 per kg soil.

Objective: To evaluate the effectiveness and safety of the Xingnao Kaiqiao needling method for treating acute ischemic stroke. Data Sources: We retrieved relevant randomized controlled trials involving Xingnao Kaiqiao acupuncture for treatment of acute ischemic stroke. The China National Knowledge Infrastructure, Weipu Information Resources System, Wanfang Medical Data System, Chinese Biomedical Literature Database, Cochrane Library, and PubMed were searched from June 2006 to March 2016. Data Selection: We analyzed randomized and semi-randomized clinical controlled trials that compared Xingnao Kaiqiao acupuncture with various control treatments, such as conventional drugs or other acupuncture therapies, for treatment of acute ischemic stroke. The quality of articles was evaluated according to the Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1), and the study was carried out using Cochrane system assessment methods. RevMan 5.2 was used for the meta-analysis of the included studies. Outcome Measures: The mortality rate, disability rate, activities of daily living (Barthel Index), and clinical efficacy were observed. Results: Twelve studies met the inclusion criteria for this review. The meta-analysis showed that between Xingnao Kaiqiao acupuncture and the control treatment, Xingnao Kaiqiao acupuncture reduced the disability rate [risk ratio (RR) = 0.51, 95% confidence interval (CI) = 0.27–0.98, z = 2.03, P < 0.05], elevated the activities of daily living (weighted mean difference = 12.23, 95% CI: 3.66–20.08, z = 2.80, P < 0.005), and had greater clinical efficacy (RR = 1.61, 95% CI: 1.23–2.09, z = 3.53, P < 0.0004). However, there was no significant difference in mortality rate (RR = 0.61, 95% CI: 0.15–2.45, z = 0.70, P > 0.05). Conclusion: The Xingnao Kaiqiao needling method is effective and safe for acute ischemic stroke. However, there was selective bias in this study, and the likelihood of measurement bias is high. Thus, more high-quality randomized controlled trials are needed to provide reliable evidence of the efficacy and safety of Xingnao Kaiqiao acupuncture in the treatment of acute ischemic stroke.

The existing studies indicate that the measurement formulas used in blister test techniques, which are used to measure the mechanical properties of thin-film/substrate systems, are usually given based on an approximation—that is, the applied direction of the uniformly distributed transverse load is always vertical, while the applied direction of the uniformly distributed gas pressure is always perpendicular to the surface of the thin film. This approximation will lead to a large measurement error. In this study, we obtained the analytical solution to the problem of axisymmetric deformation of blistering circular thin polymer films under the action of uniformly distributed gas pressure via the power series method. An example is given to illustrate the error caused by the approximation mentioned above, and the validity of the solution presented here is verified. The result shows that the chance of error caused by the approximation increases with the increase in the applied load, and it far exceeds the allowable error of measurement when the applied load is relatively large. In addition, the related experiments of the blistering circular thin polymer film under uniformly distributed gas pressure are carried out, and the experimental results are compared with the theoretical results. The comparison results show that the analytical solution given in this paper is correct. The solution presented here is of great significance to improve the measurement accuracy of the blister test technique.

In this paper, we analytically dealt with the usually so-called prestressed annular membrane problem, that is, the problem of axisymmetric deformation of the annular membrane with an initial in-plane tensile stress, in which the prestressed annular membrane is peripherally fixed, internally connected with a rigid circular plate, and loaded by a shaft at the center of this rigid circular plate. The prestress effect, that is, the influence of the initial stress in the undeformed membrane on the axisymmetric deformation of the membrane, was taken into account in this study by establishing the boundary condition with initial stress, while in the existing work by establishing the physical equation with initial stress. By creating an integral expression of elementary function, the governing equation of a second-order differential equation was reduced to a first-order differential equation with an undetermined integral constant. According to the three preconditions that the undetermined integral constant is less than, equal to, or greater than zero, the resulting first-order differential equation was further divided into three cases to solve, such that each case can be solved by creating a new integral expression of elementary function. Finally, a characteristic equation for determining the three preconditions was deduced in order to make the three preconditions correspond to the situation in practice. The solution presented here could be called the extended annular membrane solution since it can be regressed into the classic annular membrane solution when the initial stress is equal to zero.

In this study, a multi-parameter perturbation method is used for the solution of a functionally-graded, thin, circular piezoelectric plate. First, by assuming that elastic, piezoelectric, and dielectric coefficients of the functionally-graded materials vary in the form of the same exponential function, the basic equation expressed in terms of two stress functions and one electrical potential function are established in cylindrical coordinate system. Three piezoelectric coefficients are selected as perturbation parameters, and the established equations are solved by the multi-parameter perturbation method, thus obtaining up to first-order perturbation solutions. The validity of the perturbation solution obtained is verified by numerical simulations, based on layer-wise theory. The perturbation process indicates that adopting three piezoelectric coefficients as perturbation parameters follows the basic idea of perturbation theory—i.e., if the piezoelectricity may be regarded as a kind of introduced disturbance, the zero-order solution of the disturbance system corresponds exactly to the solution of functionally-graded plates without piezoelectricity. The result also indicates that the deformation magnitude of piezoelectric plates is smaller than that of plates without piezoelectricity, due to the well-known piezoelectric stiffening effect.

As an intelligent material, piezoelectric materials have been widely used in many intelligent fields, especially in the analysis and design of sensors and actuators; however, the vibration problems of the corresponding structures made of the piezoelectric materials are often difficult to solve analytically, because of their force−electric coupling characteristics. In this paper, the biparametric perturbation method was used to solve the free damping vibration problem of piezoelectric cantilever beams, and the perturbation solution of the problem solved here was given. A numerical example was given to discuss the influence of the piezoelectric properties on the vibration of piezoelectric cantilever beams. In addition, related vibration experiments of the piezoelectric cantilever beams were carried out, and the experimental results were in good agreement with the theoretical results. The results indicated that the biparametric perturbation solution obtained in this study is effective, and it may serve as a theoretical reference for the design of sensors and actuators made of piezoelectric materials.

The flexure-based fast tool servo, which is well adopted to manufacture non-circular section parts, has attracted many researchers’ attention for its cutting-edge characteristics in non-circular turning operation in terms of large output displacement, high bandwidth frequency, and fine surface processing. The geometric design and corresponding parameters configuration of the flexure hinge are deterministic to the performance of fast tool servo. In order to achieve a well-balanced stroke and natural frequency of the fast tool servo, this article proposes a harmony search–based multi-objective optimization method for setting of design parameters. Referring to the Lagrange method, the relationship between parameters of the flexure hinges and stiffness of the mechanism is derived and analyzed. Finite element analysis method is performed which demonstrates a fine agreement between the results from simulations and that from the proposed analytical solutions.

OBJECTIVE: To systematically evaluate the long-term effect and safety of Xingnao Kaiqiao needling method in ischemic stroke treatment. DATA RETRIEVAL: We retrieved relevant random and semi-random controlled trials that used the Xingnao Kaiqiao needling method to treat ischemic stroke compared with various control treatments such as conventional drugs or other acupuncture therapies. Searched databases included China National Knowledge Infrastructure, Weipu Information Resources System, Wanfang Medical Data System, Chinese Biomedical Literature Database, Cochrane Library, and PubMed, from May 2006 to July 2014. SELECTION CRITERIA: Two authors independently conducted literature screening, quality evaluation, and data extraction. The quality of articles was evaluated according to the Cochrane Reviewers′Handbook 5.1, and the study was carried out using Cochrane system assessment methods. RevMan 5.2 was used for meta-analysis of the included studies. MAIN OUTCOME MEASURES: Mortality rate, recurrence rate, and disability rate were observed. RESULTS: Nine randomized and semi-randomized controlled trials treating 931 cases of ischemic stroke were included in this review. Meta-analysis results showed that there were no significant differences in mortality reduction (risk ratio (RR) = 0.58, 95% confidence interval (CI): 0.17-1.93, Z = 0.89, P = 0.37) or recurrence rate (RR = 0.55, 95%CI: 0.18-1.70, Z = 1.04, P = 0.30) of ischemic stroke patients between the Xingnao Kaiqiao needling and control treatment groups. However, the Xingnao Kaiqiao needling method had a tendency towards higher efficacy in mortality reduction and recurrence rates. The Xingnao Kaiqiao needling method was significantly better than that of the control treatment in reducing disability rate (RR = 0.51, 95%CI: 0.27-0.98, Z = 2.03, P < 0.05). CONCLUSION: The Xingnao Kaiqiao needling method has a better effect than control treatment in reducing disability rate. The long-term effect of Xingnao Kaiqiao needling against ischemic stroke is better than that of control treatment. However, the limitations of this study limit the strength of the conclusions. Randomized controlled trials with a strict, reasonable design, and multi-center, large-scale samples and follow-up are necessary to draw conclusions about Xingnao Kaiqiao needling.

Pulse-width modulation rectifier plays an important role in the field of electric energy conversion. It has many advantages such as the grid side current sinusoidal, bidirectional energy flow, and unit power factor. In order to improve the power factor, this article proposes a new control method for the three-phase pulse-width modulation rectifier, that is, direct power control strategy based on virtual flux orientation. The original concept of AC motor stator flux is introduced into the proposed scheme for the three-phase pulse-width modulation rectifier so as to remove the grid voltage sampling circuit. The instantaneous power is estimated by the virtual flux vector orientation to realize the pulse-width modulation rectifier control in unbalanced condition. The two-order filter replaces the pure integrator in this process of implementation to solve the initial value problem of this pure integrator and achieve the better filtering property. With the estimation of virtual flux, the control strategy with space vector modulation realizes the fixed switching frequency to virtual flux orientation direct power control system, where the DC side output voltage is stable, the grid side current distortion rate is low, and anti-disturbance ability is strong. Experimental results show that this method has good performance in real three-phase grid condition. The control performance meets the requirements of power quality.

The wide applications of vector control improve the high-accuracy performance of alternating current (AC) adjustable speed system. In order to obverse the full-order flux and calculate the real-time speed, this article introduces the motor T equivalent circuit to build a full-order flux observer model, where the current and flux variables of stator and rotor are adopted. Model reference adaptive control is introduced to build the AC motor flux observer. The current output is used as feedback to build the feedback matrix. The calculation method of motor speed, which is part of the inputs of flux observation, is applied to realize the adaptive control. The concept of characteristic function is introduced to calculate the flux, of which the foundation is the variables of composite form of voltage and current models. The characteristic function is deduced as a relative-state variable function. The feedback matrix is improved and designed to ensure the motor flux observer is a smooth switch between current and voltage model in low and high speeds, respectively. Experimental results show that the feedback and characteristic model are feasible, and the vector control with speed sensorless based on the full-order flux observer has better performance and anti-disturbance.

The objective of this study was to determine the efficiency of different plant systems in capturing deep soil nitrate (NO3−) to reduce NO3− leaching in a field plot experiment using 15N labelling. The study was conducted on a calcareous alluvial soil on the North China Plains and the plant systems evaluated included alfalfa (Medicago sativa), American black poplar (Populus nigra) and cocksfoot (Dactylis). 15N-labelled N fertilizer was injected to 90 cm depth to determine the recovery of 15N by the plants. With conventional water and nutrient management, the total recovery of 15N-labeled NO3−-N was 23.4% by alfalfa after two consecutive growth years. The recovery was significantly higher than those by American black poplar (12.3%) and cocksfoot (11.4%). The highest proportion of soil residual 15N from the labeled fertilizer N (%Ndff) was detected around 90 cm soil depth at the time of the 1st year harvest and at 110-130 cm soil depth at time of the 2nd year harvest. Soil %Ndff in 0-80 cm depth was significantly higher in the alfalfa treatment than those in all the other treatments. The soil %Ndff below 100 cm depth was much lower in the alfalfa than those in all the other treatments. These results indicated that 15N leaching losses in the alfalfa treatment were significantly lower than by those in the black poplar and cocksfoot treatments, due to the higher root density located in nitrate labeling zone of soil profile. In conclusion, alfalfa may be used as a plant to capture deep soil NO3− left from previous crops to reduce NO3− leaching in high intensity crop cultivation systems of North China Plain.

The existing studies indicate polymers will present obviously different properties in tension and compression (bimodular effect) which is generally ignored because of the complexity of the analysis. In this study, a functionally graded piezoelectric cantilever beam with bimodular effect was investigated via analytical and numerical methods, respectively, in which a one-dimensional theoretical solution was derived by neglecting some unimportant factors and a two-dimensional numerical simulation was performed based on the model of tension-compression subarea. A full comparison was made to show the rationality of one-dimensional theoretical solution and two-dimensional numerical simulation. The result indicates that the layered model of tension-compression subarea also makes it possible to use numerical technique to simulate the problem of functionally graded piezoelectric cantilever beam with bimodular effect. Besides, the modulus of elasticity E* and the bending stiffness D* proposed in the one-dimensional problem may succinctly describe the piezoelectric effect on the classical mechanical problem without electromechanical coupling, which shows the advantages of one-dimensional solution in engineering applications, especially in the analysis and design of energy harvesting/sensing/actuating devices made of piezoelectric polymers whose bimodular effect is relatively obvious.

Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS) in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM), to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

In this study, we obtained an electroelastic solution for functionally graded piezoelectric circular plates under the action of combined mechanical loads which include the uniformly distributed loads on the upper surface of the plate and the radial force and bending moment at the periphery of the plate. All electroelastic materials parameters are assumed to vary according to the same gradient function along the thickness direction. The influence of different functionally graded parameters on the elastic displacement and elastic stress, as well as the electric displacement and electric potential, was discussed by a numerical example. The solution presented in this study is not only applicable to the case of combined loads, but also to the case of a single mechanical load. In addition, this solution reflects the influence of the function gradient on the pure piezoelectric plate, which is helpful to the refined analysis and optimization design of similar structures.

In this study, based on the pressure blister test technique, a theoretical study on the synchronous characterization of surface and interfacial mechanical properties of thin-film/substrate systems with residual stress was presented, where the problem of axisymmetric deformation of a blistering film with initial stress was analytically solved and its closed-form solution was presented. The expressions to determine Poisson’s ratios, Young’s modulus, and residual stress of surface thin films were derived; the work done by the applied external load and the elastic energy stored in the blistering thin film were analyzed in detail and their expressions were derived; and the interfacial adhesion energy released per unit delamination area of thin-film/substrate (i.e., energy release rate) was finally presented. The synchronous characterization technique presented here has theoretically made a big step forward, due to the consideration for the residual stress in surface thin films.

Intelligent fault diagnosis of complex machinery is crucial for industries to reduce the maintenance cost and to improve fault prediction performance. Acoustic signal is an ideal source for diagnosis because of its inherent characteristics in terms of being non-directional and insensitive to structural resonances. However, there are also two main drawbacks of acoustic signal, one of which is the low signal to noise ratio (SNR) caused by its high sensitivity and the other one is the low computational efficiency caused by the huge data size. These would decrease the performance of the fault diagnosis system. Therefore, it is significant to develop a proper feature extraction method to improve computational efficiency and performance in both periodic and irregular fault diagnosis. To enhance SNR of the acquired acoustic signal, the correlation coefficient (CC) method is employed to eliminate the redundant intrinsic mode functions (IMF), which comes from the decomposition procedure of pre-processing known as ensemble empirical mode decomposition (EEMD), because the higher the correlated coefficient of an IMF is, the more significant fault signatures it would contain, and the redundant IMF would compromise both the SNR and the computational cost performance. Singular value decomposition (SVD) and sample Entropy (SampEn) are subsequently used to extract the fault feature, by exploiting their sensitivities to irregular and periodic fault signals, respectively. In addition, the proposed feature extraction method using sparse Bayesian based pairwise coupled extreme learning machine (PC-SBELM) outperforms the existing pairwise-coupling probabilistic neural network (PC-PNN) and pairwise-coupling relevance vector machine (PC-RVM) by 1.8% and 2%, respectively, to achieve an accuracy of 93.9%. The experiments conducted on the periodic and irregular faults in the gears and bearings have demonstrated that the proposed hybrid fault diagnosis system is effective.

Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

Acoustic signals are an ideal source of diagnosis data thanks to their intrinsic non-directional coverage, sensitivity to incipient defects, and insensitivity to structural resonance characteristics. However this makes prevailing signal de-nosing and feature extraction methods suffer from high computational cost, low signal to noise ratio (S/N), and difficulty to extract the compound acoustic emissions for various failure types. To address these challenges, we propose a hybrid signal processing technique to depict the embedded signal using generally effective features. The ensemble empirical mode decomposition (EEMD) is adopted as the fundamental pre-processor, which is integrated with the sample entropy (SampEn), singular value decomposition (SVD), and statistic feature processing (SFP) methods. The SampEn and SVD are identified as the condition indicators for periodical and irregular signals, respectively. Moreover, such a hybrid module is self-adaptive and robust to different signals, which ensures the generality of its performance. The hybrid signal processor is further integrated with a probabilistic classifier, pairwise-coupled relevance vector machine (PCRVM), to construct a new fault diagnosis system. Experimental verifications for industrial equipment show that the proposed diagnostic system is superior to prior methods in computational efficiency and the capability of simultaneously processing non-stationary and nonlinear condition monitoring signals.

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox.