Estimation of structural modal parameters by fourier transform with an optimal window

An adaptive Fourier Transform （FT） with an optimal window has been proposed for the time-frequency analysis of nonstationary time series. The method allows for a good estimation of both frequency and amplitude of the spectrum and can be easily applied to the general case of time-varying signals. The evaluation of the proposed approach has been performed on measured time-varying signals from a suspension bridge model and a steel frame model whose data have the typical non-stationary characteristics. The numerical results show that the proposed approach can overcome some of the difficulties encountered in the classic Fourier transform technique and can achieve higher computation accuracy.

Alexa Fluor 488-conjugated cholera toxin subunit B optimally labels neurons 3-7 days after injection into the rat gastrocnemius muscle

Neural tract tracing is used to study neural pathways and evaluate neuronal regeneration following nerve injuries.However,it is not always clear which tracer should be used to yield optimal results.In this study,we examined the use of Alexa Fluor 488-conjugated cholera toxin subunit B(AF488-CTB).This was injected into the gastrocnemius muscle of rats,and it was found that motor,sensory,and sympathetic neurons were labeled in the spinal ventral horn,dorsal root ganglia,and sympathetic chain,respectively.Similar results were obtained when we injected AF594-CTB into the tibialis anterior muscle.The morphology and number of neurons were evaluated at different time points following the AF488-CTB injection.It was found that labeled motor and sensory neurons could be observed 12 hours post-injection.The intensity was found to increase over time,and the morphology appeared clear and complete 3-7 days post-injection,with clearly distinguishable motor neuron axons and dendrites.However,14 days after the injection,the quality of the images decreased and the neurons appeared blurred and incomplete.Nissl and immunohistochemical staining showed that the AF488-CTB-labeled neurons retained normal neurochemical and morphological features,and the surrounding microglia were also found to be unaltered.Overall,these results imply that the cholera toxin subunit B,whether unconjugated or conjugated with Alexa Fluor,is effective for retrograde tracing in muscular tissues and that it would also be suitable for evaluating the regeneration or degeneration of injured nerves.

Aging Diversity Analysis and State of Health Estimation of LiFePO_(4)Batteries

In this paper,battery aging diversity among independent cells was studied in terms of available capacity degradation.During the aging process of LiFePO_(4)batteries,the phenomenon of aging diversity can be observed.When batteries with same specification were charged and discharged repeatedly under the same working conditions,the available capacity of different cell decreased at different rates along the cycle number.In this study,accelerated aging tests were carried out on multiple new LiFePO_(4)battery samples of different brands.Experimental results show that under the same working conditions,the actual available capacity of all cells decreased as the number of aging cycle increased,but an obvious aging diversity was observed even among different cells of same brand with same specification.This aging diversity was described and analysed in detail,and the common aging features of different cells beneath this aging diversity was explored.Considering this aging diversity,a probability density concept was adopted to estimate battery’s state of health(SOH).With this method,a relationship between battery SOH and its aging feature parameter was established,and a dynamic sliding window optimization technique was designed to ensure the optimal quality of aging feature extraction.Finally,the accuracy of this SOH estimation method was verified by random test.

Application of the Common Offset Seismic Reflection Method to Urban Active Fault Surveys

The method and principle of common offset seismic surveys as well as the field data gathering and processing technique were introduced briefly. Through two urban active fault survey examples in Fuzhou and Shenyang, the efficiency and limitation of using the common offset seismic reflection technique to carry out urban active fault surveys were probed. The results show that this technique has the properties of high resolving power, better reconstruction of subsurface structures, and real-time analyzing and interpretation of investigation results on site. This method can be used to quickly locate objects under investigation accurately in the areas with thinner Quaternary overburdens and strong bedrock interface fluctuations.

一种基于“最优捕集窗口”设计的全球化空气取水系统

Atmospheric water harvesting(AWH)is a promising solution to the water shortage problem.Current sorption-based AWH(SAWH)systems seldom obtain both wide climatic adaptability and high energy efficiency due to the lack of thermodynamic optimization.To achieve the ideal harvesting circulation in SAWH systems,the“optimal harvesting window”(OHW)design based on thermodynamic analysis was first proposed and validated by our prototype.The“OHW”theory indicates the water production rate and energy efficiency could be improved by properly reducing the adsorption temperature.As the humidity increases,the optimal adsorption temperature should be closer to the dew point of the environment.Experimental results revealed that,loaded with 3 kg widely adopted silica gel,the daily water production could reach 5.76-17.64 L/d with ultrahigh energy efficiency of 0.46-1.5 L/kWh.This prototype could also achieve optimal performance in wide climatic conditions in terms of 13-35℃and 18%-72%RH.Lastly,the performance of photovoltaic(PV)-driven SAWH was evaluated.Results showed that a 1 m^(2)PV panel could generate 0.66-2 L water per day in Shanghai throughout the year,the highest in opening literature.Notably,this work introduces a promising concept that can help achieve large-scale,ultra-fast,energyefficient AWH worldwide.

The infuence of window size on remote sensing-based prediction of forest structural variables

Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics that afect the quality of the prediction model depends on window size.However,suitable window sizes are usually determined by trial and error.There are a limited number of published studies evaluating appropriate window sizes for diferent remote sensing data.This research investigated the efect of window size on predicting forest structural variables using airborne LiDAR data,digital aerial image and WorldView-3 satellite image.Results:In the WorldView-3 and digital aerial image,signifcant diferences were observed in the prediction accuracies of the structural variables according to diferent window sizes.For the estimation based on WorldView-3 in black pine stands,the optimal window sizes for stem number(N),volume(V),basal area(BA)and mean height(H)were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.In oak stands,the R^(2) values of each moving window size were almost identical for N and BA.The optimal window size was 400 m^(2) for V and 600 m^(2) for H.For the estimation based on aerial image in black pine stands,the 800 m^(2) window size was optimal for N and H,the 600 m^(2) window size was optimal for V and the 1000 m^(2) window size was optimal for BA.In the oak stands,the optimal window sizes for N,V,BA and H were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.The optimal window sizes may need to be scaled up or down to match the stand canopy components.In the LiDAR data,the R^(2) values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion:This study illustrated that the window size has an efect on the prediction accuracy in estimating forest structural variables based on remote sensing data.Moreover,the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

Human motion prediction using optimized sliding window polynomial fitting and recursive least squares

Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.