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The load resisting capacity of structural members will decrease when they are subjected to long-term sustained load. Such phenomenon is widely known as the duration of load effect, which is mainly caused by the damage accumulation in the material. The deterioration mechanism of the material is a typical stochastic process which is influenced by a large variety of parameters involving complex physical and chemical process. Although classical models have been proposed to evaluate the duration of load effect, it is nearly impossible to quantify the influence of various parameters and to achieve an accurate estimation. To optimize the combination of complexity and goodness-of-fit, a neural network model is proposed in this paper to evaluate the duration of load effect in wood structural members. Taking individual uncertainties into consideration, the proposed model treats the damage in wood as a Markov process and can estimate the residual strength distribution of the investigated wood structural members under long-term sustained load. The coefficient of determination reaches above 95% under sustained loading scenario, and it shows good adaptability across different wood properties. Moreover, the model can be adapted to continuously varied loading scenarios with a 98% coefficient of determination. This research aims to provide a useful and straightforward tool for accurately predicting the duration of load effect in wood structural members, and the proposed algorithm can be easily modified to deal with similar engineering problems for other construction materials.

In view of the unreasonable timing and quantitative soot blowing operation of coal-fired boiler heating surface, this paper takes the data of cleaning factor as the monitoring index, puts forward the minimum cost rate as the optimization objective, and establishes the soot blowing optimization strategy with ash accelerated deposition model based on gamma process. Firstly, gamma distribution is used to describe the ash characteristics of coal-fired boiler heating surface, and an accelerated ash deposition model is established with a continuous non negative random variable which obeys exponential distribution; then, the soot blowing optimization model of boiler heating surface is established with the minimum average cost rate function. Finally, taking the off-line data of the heating surface cleaning factor of a 300MW Coal-fired Unit as an example, the PSO multi-objective optimization algorithm is used to optimize, and the minimum average cost rate is obtained and verified.

Aiming at ash and slag of boiler heated surface will reduce the heat transfer efficiency and security. This paper adopts clearness factor as the indicator to monitor healthy condition of the boiler heated surface, and put forward a model combining empirical mode decomposition (EMD) and long short-term memory (LSTM) model to predict boiler ash accumulation in the future. EMD can decompose the time series into a series of frequency-stable intrinsic mode functions. In addition, the special gate structure inside LSTM makes it possible to mine the long-term dependencies in the time series. The combination of the two increases the prediction accuracy of the time series. It is verified by simulation software that the model has a satisfactory accuracy in predicting healthy condition of the boiler heated surface, and the feasibility and effectiveness of the model are verified.

The research on the dual relationship between electrical LC oscillation and ball mechanical oscillation is of great significance to explore the similar dual phenomena in nature, and can provide reference cases for the exploration of unknown physical relations. In this paper, the change process of current, voltage, electric energy and other physical quantities during LC and RLC oscillation circuit operation is analyzed; in the aspect of ball mechanical oscillation, two motion models of spring simple harmonic vibration and sliding down the bowl wall are proposed, and the physical quantities such as velocity, potential energy and kinetic energy in the motion process are analyzed; finally, according to the physical quantities of LC oscillation and mechanical oscillation, the process and results are solved The duality analysis shows that there is a certain dual relationship between the two groups of physical quantities.

The line loss rate of transformer district has been paid close attention by power supply companies. With the accumulation of line loss sample data, artificial neural network algorithm has been widely used in line loss calculation. In view of the influence of the type and quantity of input variables on the calculation accuracy of artificial neural network model, this paper proposes an artificial neural network line loss calculation method based on improved k-means clustering and grey relational analysis. Aiming at the problem that the line loss characteristics of different transformer district are significantly different, the improved k-means algorithm which optimizes the initial clustering center is used to classify the transformer districts. Further, in order to reduce the network training burden caused by too many input variables, the correlation between electrical characteristic indicators and line loss rate is determined by grey relational analysis, and the number of input variables in each type of transformer district is determined by K-fold cross validation method. The simulation results show that the method can improve the accuracy of line loss calculation, and provide a theoretical basis for scientific and reasonable loss reduction.

Cable-less micro-robots have exhibit great capabilities for remote applications in small and constrained environments, especially in biotechnology and healthcare. This paper presents a swimmer propelled by its undulatory deformation using magnetoelastic composite material. The robot can move freely on water surface driven through a function of magnetic field, such as forward, backward and steering with different velocity. The magnetic field is generated by 3D Helmholtz coils, and the locomotion of the micro swimming robot is controlled by different applied magnetic field. To understand the characteristics of the swimmer, the force balance equation is established, and the morphologies of swimmer with different magnetic field are analyzed and described through finite element analysis method. Furthermore, we discuss and analyze relationship between the parameters of magnetic field and the speed of the swimmer. The experimental results verified the speed model of the swimming robot, which is influenced by the frequency and strength of magnetic field.

The actuation and control of mini-scale swimming robots have exhibit promising potential in limited and constrained environments, such as biomedical, micro-assembly and microfluidic applications. However, closed-loop control is still a challenging for the precision and repeatability in those applications. In this paper, a new type of soft-micro swimming robot based on magnetoelastic composite material is designed, and the swimmer can move freely on the water surface controlled by a rotating magnetic field with visual servoing. The visual feedback control strategy is proposed based on Mean Shift Algorithm and Fast Template Matching. The algorithm includes three parts: prediction, template matching, target positioning and template updating. To understand the characteristics of the swimmer, the relationship between swimming speed and magnetic field strength and frequency is verified by the algorithm in experiments. Furthermore, the proposed path following method with visual servoing applied in this control system. The experimental results show that the algorithm runs fast, has high accuracy and good real-time performance, and can provide great potential in mini-scale robot applications.

Resistivity of boron-doped polycrystalline-silicon (polysilicon) film was investigated experimentally with wide range of doping concentration (5×1018~1.4×1020cm-3) and various annealing conditions (950~1050°C, 10-30mins), which results in sheet resistance of 89.5~149000Ω/°C and resistivity of 2.6×10-3~4.3Ω·cm. Mathematical fitting curve shows perfect consistency of resistivity as function of average dopant concentration lower than 8.5×1019cm-3. While at higher concentration where sheet resistance is lower than 170Ω/°C, it is verified that dependence of resistivity of annealing temperature can be represented by the average nano-scale grain size. The average diameter of heavily doped (1.4×1020cm-3) polysilicon is 85nm, 99nm and 111nm at 950°C, 1000°C, 1050°C, respectively. Combining curve-fit and annealing temperature, resistivity of polysilicon thin film can be precisely controlled with an error less than 10%. The resistivity changes with nano-scale grain size are discussed.

Solid-state nanopores have emerged as useful single-molecule sensors for DNA and proteins. Dry-oxygen oxidation was proposed to directly shrink pyramidal silicon (Si) pore arrays. With this method, inverted-pyramid Si nanopore arrays with feature sizes of over 60 nm, prepared using a combination of dry and wet etching, were shrunk to be less than 20 nm with nanometer precision. The shrinkage was found to be dominated by the deposition of the SiO2 layer on the nanopore surface and its surface-tension-driven mass flow. The inner structure of the nanopore after the shrinkage kept its typical inverted-pyramid shape, which theoretically permits high-resolution DNA sequencing. Furthermore, this method can process many nanopore samples at one time and reduce the inbuilt stress in the Si nanopores during the annealing.

This paper presents the recent results of utilization of micro-Raman spectroscopy to measure and characterize residual stress in polysilicon doubly-clamped MEMS resonators with small lateral size. Due to imprecise prediction of the magnitude of intrinsic residual stress, detrimental effect of the residual stress severely shifts the resonant frequency of MEMS resonator from the analytical pre-designed value. The stress is not only determined by the fabrication process but also related to the structural dimensions of resonators. In this work, microRaman spectroscopy was used to measure the residual stress of resonators with widths down to 2μm. The results show that the optimized resonator with length shorter than 50μm and width between 3.2μm and 4.1μm exhibits minimum residual stress.

A microelectromechanical-system (MEMS) variable capacitor with symmetric toggle structure is proposed to achieve an excellent linearity of the C-V response and a large capacitance tuning ratio. Based on lever principle, flexible top plate of the capacitor moves upwards when applying the voltage on the control electrodes increases, which results in a highly linear decrease of the capacitance with increasing control voltage. The proposed MEMS variable capacitor was modeled and simulated using ANSYS software and fabricated using surface micromachining process. The results show a high linearity factor (LF) of 96.3% in C-V response and a large tuning ratio of 160% in a low actuation voltage range from 0 V to 30 V. The LF even reaches 98.9% from 10 V to 30 V.

Proteins are crucial in the biological processes, and their structure determines whether they can function well or not. Since the theory presented by Anfinsen that proteins' space structure is entirely determined by the primary structure came out, it is possible for us to predict the structure of proteins through their primary structure without any experiment. In order to reach this target, the prediction problem can be formulated as an optimization problem that is set to find the lowest free energy conformation. In this paper, a hybrid Artificial Bee Colony (ABC) with Particle Swarm Optimization (PSO) Algorithm is used to solve this problem. Considering that the two algorithms have complementary characteristics, we combine them together and find out a better optimization results through this new approach. Experimental results have demonstrated the feasibility and effectiveness of our proposed approaches.

Resonant tunneling diodes (RTDs) based on double-barrier structure have been proved to have negative differential resistance effect, and the current-voltage characteristics of RTDs are a function of stress. In order to study of the piezoresistive effect in RTDs for application in MEMS gyroscope, a novel structure with folding-type orthogonal beams is designed. This kind of gyroscope has been fabricated by GaAs surface micromachining processes and bulk micromachining technology. For testing the basic properties of gyroscope, the drive-feedback circuits and double-RTD Wheatstone bridge measurement system are established. The experimental results verify that the electromagnetism driven approach and the piezoresistive effect of RTD used in Coriolis effect detection are feasible.

A novel structure MEMS gyroscope using the mutil-layers nano resonant tunneling film with meso-piezoresistive effect is introduced in this paper. According to the theory of drive and detection, the folding-type orthogonal beam structure is designed, and the simulation of ANSYS proves that the structure does not only meet the drive approach, but also meets the detection conditions. This kind of gyroscope has been fabricated by GaAs IC surface processes and control hole etching technology. The Wheatstone bridge circuit with negative differential resistance of RTD is established to detect the change of stress at the base of detection beam. According to the experiment results, the I–V characteristics of RTDs are changed obviously in the role of stress, and prove that the detection beam based on RTD is feasible.

This paper describes a new approach to estimating the speed of an induction motor from the measured terminal voltages and currents without the use of a speed/position sensor. The new observer uses a purely algebraic speed estimator to stabilize a dynamic speed estimator and it is shown that it has the potential to provide low speed (including zero speed) control of an induction motor under full rated load.

Previous work by the authors has described a differential-algebraic approach to speed estimation in induction motors. The method shows that the speed /spl omega/ can be found by solving for the roots of a polynomial in /spl omega/ whose coefficients are functions of the stator voltages, stator currents, and their derivatives. Preliminary experimental results are presented in which the speed is estimated using the differential-algebraic method.