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As an effective structure health monitoring technology, acoustic emission (AE) has been developed rapidly in various fields over recent years. AE source localization is the core element of AE technology. In this paper, a Node Block Location Method (NBLM) based on the collaborative model of node and block matrix was proposed to meet the high-precision requirement of source localization in the quasi-cylindrical structure with complex hole. NBLM can not only bypass the empty area, but also fit the actual propagation of elastic waves better in the complex boundaries. In order to evaluate the effectiveness of NBLM, lead-breaking tests were carried out on two cylindrical structure: one with square hole and the other with circular hole. The lead-breaking points were used as AE source in the test. The source coordinates were inverted by three separate location methods according to arrivals. Test results verify the superiority of NBLM and show that the location accuracy of the NBLM has been greatly improved compared to the existing methods.

In recent years, with the rapid development of deep learning and the Internet, Weibo and e-commerce platforms play an important role in people's daily lives. User data and Weibo comments on e-commerce platforms hide rich emotional information. At the same time, these massive amounts of data also attract more researchers to participate in the research of short text data such as online shopping and Weibo comments, researchers can analyze these data to help businesses increase sales and improve product quality. On the other hand, it also provides a reference for consumers to fully understand the attributes of commodities. How to dig deeper into the potential emotional information of short texts is the most important thing. The emergence of neural networks has pushed natural language processing sentiment analysis to a hot topic, this article uses a combination of convolutional neural network and attention mechanism to solve this problem. This paper proposes a two-way long and short memory network model based on multichannel data. The model first uses shallow learning features such as emotional part of speech extracted from short texts to map to high-dimensional space to form 3 high-dimensional feature vectors, combine the three feature vectors with word embedding and input them into BiLSTM form three channels, the attention mechanism is added to the three fully connected layers, and finally input to the SoftMax classification layer. The experimental results show that compared with the traditional text convolutional neural network, the method proposed in this paper has a significant improvement in the accuracy of short text sentiment analysis tasks.

High-intensity focused ultrasound (HIFU) has been used successfully as new modality for the tumor treatments. Quantitative estimation of acoustic intensity at higher power output is important to the ablation planning, but the current approach is usually limited to the low power. Recently, infrared thermometry has been used to estimate acoustic intensity in the focused ultrasound field with the assumption of single frequency or in the linear mode. At higher power or in the therapy mode, nonlinear propagation effects will generate harmonics of the fundamental frequency. In this paper, a theoretical framework of quantitative method was introduced to estimate harmonic intensity in focused ultrasound field using infrared camera. The percentage differences between the derived results and the simulation are

Human movement biomechanical analyses are considered as an essential tool in the clinical management and basic research of neurological and orthopedic conditions. Also, application of a biomechanical model for the estimation of the activity of muscles during rehabilitation training sessions could assist therapists to conduct personalized rehabilitation therapy and understand inter muscle dynamics of body part motions. In this paper, we propose a subject-specific neuromusculoskeletal (NMS) prediction model for the estimation of individual muscle forces during elbow extension and flexion. The NMS model was established by initial guesses and then the parameters of muscles were optimized and modified using the surface electromyography (sEMG) signals and motion data. Then specific muscles forces were predicted using the subject-specific NMS model during elbow flexion and extension. The results show geat accuracy and reliability of the optimized NMS model. This subject-specific NMS model by merging sEMG signals and motion data together, shows great potential to provide an improvement to quantitatively assess upper limb function and improve diagnosis and management of both neurological and orthopaedic conditions in rehabilitation.

In order to combine multi-views into one ultra-wide single-view for present surveillance desire, a video fusion scheme based on FPGA is proposed in this paper. The geometric transformation and image fusion algorithms used in our scheme are simulated by Matlab for verification and optimization. Then, a combination of two 640*480 video images captured by cameras is implemented in a Altera FPGA platform DE2-115. Moreover, the weighted fusion method is used to realize video fusion for better performance; and larger size of 1280*768@60Hz video images' combination are also realized in DE2-115. Experiments show that the proposed approach can realize the mosaic of two video images with a delay of 16 clock cycles, i.e. a total of 0.16us. The error induced by the fusion process is less than 1 pixel. The implementation in DE2-115 for two RGB 1280*768@60 pixel size image combination can be 1743 times faster than software implementation with a good splicing performance.

To extend the coverage of base stations, the same frequency repeater is widely used in modern communication network. However, there exists coupling echo interference between transmitting antenna and receiving antenna of the repeater station, which affects the system stability and the signal quality. The echo interference is in the same frequency with the useful signals of base stations, which cannot be eliminated by traditional frequency-domain filtering. To address this problem, this paper proposes an adaptive echo cancellation algorithm by inducing an additional reference sequences, which is targeted to apply in the hardware platform of Fujian Jingao FDD-TLE same frequency repeater. In this paper, the working principle of the algorithm is described firstly; then the algorithm is analyzed by MATLAB simulation, and finally verified in FPGA. The comparisons between before and after echo cancellation using the proposed algorithm in time domain and frequency domain shows that this algorithm can effectively suppress echo interference, and the output signal spectrum is significantly improved. Compared with the adaptive filtering algorithm based on the transform domain, this algorithm does not need Fourier transform, which has lower computational complexity and takes less resource. Moreover, this algorithm has a wide application scope and does not depend on the specific modulation scheme of the relaying signal.

Torsion bar need to be pressed into output shaft for electrical power steering assembly, but the tolerance value should be determined when designing the torsion bar and output shaft model. There is initial penetration between torsion bar and output shaft finite element after meshing the 3D model, then study and eliminate the penetration, and find the maximum equivalent stress and deformation of X axis is 0 when offset value is -0.077mm after simulation based on different offset value . The tolerance value is calculated based on interfere fit between torsion bar and output shaft which let the torsion bar can not slip when 50Nm external load applied. Verify the analysis result by testing at test bench, and the test result is closed to the analysis result.

Renewable-energy-based grids development needs new methods to maintain the balance between the load and generation using the efficient energy storages models. Most of the available energy storages models do not take into account such important features as the nonlinear dependence of efficiency on lifetime and changes in capacity over time horizon, the distribution of load between several independent storages. In order to solve these problems the Volterra integral dynamical models are employed. Such models allow to determine the alternating power function for given/forecasted load and generation datasets. In order to efficiently solve this problem, the load forecasting models were proposed using deep learning and support vector regression models. Forecasting models use various features including average daily temperature, load values with time shift and moving averages. Effectiveness of the proposed energy balancing method using the state-of-the-art forecasting models is demonstrated on the real datasets of Germany's electric grid., 6 pages; accepted to 38th China Control Conference 2019

Since 21st Century, wind energy has become the world's fastest growing energy due to its characteristics of safety, cleanness and abundance as the demand for energy has doubled. This paper firstly explicitly introduces the distribution of China's wind energy resources and the features of wind resources in major provinces and cities of China. Then, the development current status of China's wind power is analyzed. The results indicate that China's wind energy resources have large reserves with wide distribution and huge development potential and in a rapid development. Since 2009, the yearly newly installed capacity and the cumulative installed capacity of China's wind power have been taking the first place in the world ranking for consecutively 5 years. Although wind power industry maintains high growth, the growth of cumulative wind power capacity is at a slow pace in China. Meanwhile, annual installed wind power capacity is in a negative annual growth due to the impact of the economic crisis. The proportion of different models of annual wind turbines in China shows that the trend of future development is towards large capacity wind turbines.

In this paper, the melt blending method was used to prepare isotactic polypropylene (iPP) samples. The nucleating agents N, N'-dicyclohexyl-2,6-naphthalenedicarboxamide (TMB-5) was chosen to induce the β-crystal. The quenching, 130oC isothermal crystallization, natural cooling molding processes were used to control α- and / or β-crystals in the iPP samples. The crystalline modifications and crystallinity were analyzed by X-ray diffraction (XRD) and differential scanning calorimeter (DSC) analysis. The mechanical and thermal degradation properties of these samples were investigated by tensile test and thermogravimetric analysis (TGA). The breakdown tests and the volume resistivity tests were conducted to characterize the electrical property of the samples. The experimental results show that the three molding processes does affect the content and thickness of β-crystals in the iPP sample, and the natural cooling and quenching molding will produce more β-crystals. Compared to the microcrystallites of α-crystal, the growth of β-crystals effectively improved the thermal and electrical properties of iPP, such as the thermal stability and breakdown strength. On another hand, the thinner β-crystal can effectively improve the mechanical and provide better stability in volume resistivity as temperature increases. Based on the above results, the mechanism of crystalline modifications influence on the electrical property of iPP discussed.

When the disturbance of ship is compensated, it can't get the specific motion parameters in time. In order to solve this problem, the motion attitude needs to be predicted in advance, and the reliable data also needs to be provided for the wave compensation system. This paper introduces a method of motion attitude prediction based on BP neural network. The method solves the learning problem of hidden layer by selecting the BP neural network model, and the results show that using such method can effectively improve the prediction speed and precision of motion attitude.

This letter presents a novel three-mode cavity bandpass filter using the corner cuts as a perturbation element. These corner cuts are employed not only to achieve the desired coupling degree between degenerate modes in a single rectangular cavity, but also to create the expected cross-coupling effect for improvement of out-of-band rejection skirt. Based on this triple-mode cavity, a double-cavity filter with five poles is designed. In order to improve the out-of-band rejection and harmonic suppression, a coaxial cavity is further inserted into the two triple-mode cavities to realize a bandpass filter with six in-band transmission poles. Simulated frequency responses are well confirmed by fabricating a prototype cavity bandpass filter and testing its two-port S-parameters.

There are nonlinearities in an electro-hydraulic shaking table, causing harmonic distortion when it corresponds to a sinusoidal excitation, due to higher harmonics in the acceleration response. This paper aims to develop a harmonic identification method for a hydraulic shaking table by using Fourier series based Back Propagation (BP) network. The learning problem of hidden layer is solved by selecting appropriate BP network model, which has a single input, a single output, and two hidden layers for each harmonic. The hidden layer is excited by trigonometric function, and the weights are adjusted by the gradient descent method. The Lyapunov theorem is adopted to limit the network's the learning rate in order to guarantee the convergence of the algorithm. The network's weights are adjusted by the identification error between the sinusoidal response and the identified signal. Each harmonic's amplitude and phase are directly computed from the trained weights. Experimental results show that the proposed scheme can effectively identify each harmonic's amplitude and phase with high precision.

This paper presents a novel band-pass filter with four transmission zeros based on SIW structure. In this structure, we use TE 10d mode and TE 20d mode achieve the typical CT filter that introduces one transmission zero. While coupling microstrip line for source-load coupling will introduce the other three, which is controllable by coupling length and gap. In 8.4GHz band, the filter has return loss more than −20dB, and out-of-band rejection more than −17dB.

This paper presents a novel dual-mode dielectric filter, utilizing two degenerate modes, TM 120 and TM 210 . The structure consists of a cuboid dielectric resonator short-circuited on top and bottom surface by a rectangular metallic cavity. The frequencies of resonant modes are tunable by metallic screw while modes coupling coefficient controllable by metallic ring. On this basis, we design single-cavity, and double-cavity dual-mode dielectric filter for LTE band.

Vascular tree angiogram is an important tool for computer-aided diagnosis on the vascular disease. The vascular edge detection in the angiogram is an important step in the angiogram analysis and computer-aided diagnosis, which is the basis on the segmentation of angiogram. This paper studies an improved edge detection method based on several classical methods. The experiments demonstrate the improved method has good performance in angiogram edge detection. This method is easy to be used in the processing of angiogram.

This article describes a novel modeling method for a rigid-flexible coupled planetary gear transmission system, which consists of rigid and elastic ring gear subsystems. The rigid gear subsystem model was established using the lumped parameter method while the elastic ring gear subsystem was established using the finite element method. According to the compatible state of deformation between mesh force and ring gear deformation, the natural frequency and vibration modes were determined, and the distribution rules of the natural frequency for this coupled system were established. The vibration modes were classified into six different modes based on the vibration characteristics of the system. This article provides a theoretical basis for the dynamic design of a rigid-flexible coupled planetary gear transmission system.

Based on beam pumping unit, an new method of design beam hanger to dynamic loading and simulation in ADAMS software is presented, and beam pumping unit's structure and working principle are introduced in this paper first. Secondly, using the development function of macro command in ADAMS software, combining with the connect method of bushing and contact through theoretical analysis and calculation to develop beam hangers and managed to get the virtual prototype of beam pumping unit. Finally, using the function of spline to simulate the indicator diagram and loading into suspension point, making dynamic simulation in ADAMS software. The simulation results reflected the actual motion law of the horse head of beam pumping unit, proved the feasibility of the new modeling method and provided the theory base for further research.

The large-scale wind turbine blade contour design and the choice of wing section are the core technology in wind power generation; both affect the wind turbine performance and the energy efficiency. This paper presents the redesign principles and methods for large-scale wind turbine blade which are based on the analysis of blade CAD model from reverse engineering, looking for geometric features, exploring basic constraint factors on blade shape, and determining parameters of blade cross section calculation formula. Blade design has been completed using this self-developed blade aerofoil automatic generation system. The results showed that the redesign meets the production requirements.

The purpose of our present study is to understand the Superconductor-Insulator Transition. In this paper we report tunnel experiments on Bi2Sr2Ca1-xYxCu2O8+δ (Y-Bi2212) crystals with x = 0, 0.01, 0.05, 0.1, 0.11 and 0.12. The results are discussed about Copper pairs existing. Tunneling spectra were measured for Y-Bi2212 crystal-SiO-Ag planar junctions. For superconducting samples the V-shape gap structure and zero bias conductance peaks (ZBCP) were observed. While neither superconducting gap nor ZBCP was observed for insulating samples. These results suggest that Cooper pairs break up on the insulating side of SIT. In our spectra there were also several characteristic behaviors.

Human activities have profoundly influenced natural ecosystems, especially wetlands. There is an urgent need for comprehensively understanding the causes, processes and consequences of human-induced environmental impacts. This paper presented rapid landscape change and differentiate the impact of human activities on reductions in wetland natural vegetation in the Lixiahe wetlands based on remote sensing data. We quantified land-use change in the study area using classified Landsat TM (1988-10-25, 2006-05-20) and ETM+ (2000-09-16) images. Over the study period, wetland natural vegetation declined by 69%, which was largely attributable to aquaculture development. The loss speed of natural vegetation rose significantly with increasing proximity to rural settlements and secondary roads, with a curve of the inverted "U" shaped response to the presence of towns. These trends are reasonable in that wetland use activities in the study area are directly associated with the settlements and roads in the rural areas at local scales, but are also indirectly associated with the major transportation network from a regional perspective. Meanwhile, we should notice that this change was not of a continuous linear feature, and instead it showed a typical sub-segment function characteristic, which indicated that the impact of the driving factors on natural vegetation was non-linear.

New organizational unit of gridthe micro-grid is an integral part of smart distribution network, with the advantages of flexible and efficient use of new energy, IEC61850 communication makes the electrical equipment of different manufacturers interoperate in one system. In this paper digital substation is divided into substation layer, bay layer and process layer based on the IEC61850 communication, this paper analyses the three-layered structure of the digital substation communication systems and the communication of smart distribution grid with micro-gird, explores the information interactive when there are faults in the grid and the feeder terminal unit(FTU)for information modeling, discusses the application of IEC61850 communication in smart distribution grid containing micro-grid.

By means of linear matrix inequality (LMI), a robust stability analysis method is presented to discuss the robust stability for uncertain T-S fuzzy singular systems. Based on the Lyapunov stability theory, a sufficient condition which makes the uncertain T-S fuzzy singular systems robust stability is obtained. Moreover, an ellipse constraint is given to change the input/output constraints into a group of LMI bindings by using elliptic invariant domain and S-Procesure methods. Finally, a sufficient condition for uncertain T-S fuzzy singular systems robust stable and satisfying the input/output constraints has been derived.

In this note, the problem of robust stability for uncertain Takagi-Sugeno (T-S) fuzzy singular systems with constraints is considered. Firstly, based on the generalized Lyapunov stability theory and linear matrix inequality LMI technique, a sufficient condition for unconstrained T-S fuzzy singular systems robust stability is got. And then give an ellipse constraint where the initial state to change the time-domain constraints into a group of LMI binding by using elliptic invariant domain and S-Procesure methods, a sufficient condition for system robust stable and satisfying the time-domain constraints has been obtained.

We develop an algorithm RSFA to perform nonlinear blind source separation with temporal constraints. The algorithm is based on slow feature analysis using random Fourier features for shift invariant kernels, followed by a selection procedure to obtain the sought-after signals. This method not only obtains remarkable results in a short computing time, but also excellently handles situations where there are multiple types of mixtures. In kernel methods, since the problem is unsupervised, the need of multiple kernels is ubiquitous. Experiments on music excerpts illustrate the strong performance of our method.

Considering the limitation such as premature convergence and low local convergence speed of genetic algorithm, some improvements were made for classical genetic algorithm. Firstly, a help operator was used to help individuals of population according to the given probability. Secondly, the genetic individuals were separated into male individuals and female individuals, and consanguinity was fused into individuals. Two individuals with different sex could reproduce the next generation only if they were distant consanguinity individuals. Based on this improved genetic algorithm, an evolved neural network algorithm named IGA-BP algorithm was proposed. In this algorithm, genetic algorithm was used to optimize and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. The disadvantage of neural networks that their structure and parameters were decided stochastically or by one's experience was overcome in this way and the surge of algorithm was restrained. IGA-BP algorithm was used to recognize handwritten numerals, a recognition model of handwritten numerals based on BP neural network was found, and the handwritten numeral recognition scheme based on IGA-BP algorithm was proposed. The experimental results show that this algorithm is better than SGA-BP algorithm and traditional BP algorithm in both speed and precision of convergence, We can obtain a better recognition effect using this algorithm.

In order to overcome the limitation such as premature convergence and low global convergence speed etc. of genetic algorithm, some improvements were made for classical genetic algorithm. The genetic individuals were separated into male individuals and female individuals, and consanguinity was fused into individuals. Two individuals with different sex could reproduce the next generation only if they were distant consanguinity individuals. A help operator was used to help individuals according to the given probability. Based on the improved genetic algorithm, a thoroughly evolutionary neural network algorithm named IGA-BP algorithm was proposed. In IGA-BP algorithm, genetic algorithm was used firstly to evolve and design the structure and all training parameters of neural network roundly. Then, training samples were used to search the optimal solution again. The disadvantage of neural network that its structure and training parameters were decided stochastically or by one's experience was overcome. IGA-BP algorithm was used to diagnose power transformer faults. A fault diagnosis model of power transformer was founded based on neural network. The illustrational results show that this algorithm is better than traditional BP algorithm in both speed and precision of convergence. We can realize a fast and accurate diagnosis for power transformer faults by this algorithm.