Cold Atom Cloud with High Optical Depth Measured with Large Duty Cycle

We present a cold atom system with a dark-line two-dimensional magneto-optical trap, to increase the atomic density by suppressing the atomic radiation pressure. Optical depth （OD） and duty cycle are used to evaluate the system performance. We demonstrate a 100% increase in OD with the dark line, and obtain an ultrahigh OD of 264 with 10% for the duty cycle. Also, with an efficient dark line region, the OD could maintain above i00 with duty cycle as high as 30%. The cold atomic ensemble with an ultrahigh OD with a 10%-30% duty cycle is particularly advantageous in quantum i^formation processing and communication.

A Cost-benefit Measuring Model of Green Products in Manufacturing Industry

The paper builds up a cost-benefit measuring model of green products in manufacturing industry throughout its full life cycle, which can quantify green products＇ cost and benefit completely and correctly under the circumstance of satisfying enterprise, customer, environment and society. It also puts forth an operable method to estimate social benefit by opportunity cost and establishes a profit maximization-programming model. The model can be applied to justify whether some kinds of green products should be developed and produced.

3D shape measurement of larger complex objects based on fringe cycle correction

The grating fringe on the reference plane is broadened in the intersecting axis system because of oblique-angle projection. In order to solve this problem, we study the theoretical model of the temporal phase unwrapping method based on the fringe cycle correction. We also study the 3D shape measurement theoretical model of the larger complex objects after considering the coordinate deviation and lens distortion. Experimental results demonstrate that the fringe cycle on the reference plane can be corrected to a constant value, the lens distortion can be corrected, and 3D shape of larger complex objects can be accurately measured.

Output Prediction of Helical Microfiber Temperature Sensors in Cycling Measurement by Deep Learning

The inconsistent response curve of delicate micro/nanofiber(MNF)sensors during cycling measurement is one of the main factors which greatly limit their practical application.In this paper,we proposed a temperature sensor based on the copper rod-supported helical microfiber(HMF).The HMF sensors exhibited different light intensity-temperature response relationships in single-cycle measurements.Two neural networks,the deep belief network(DBN)and the backpropagation neural network(BPNN),were employed respectively to predict the temperature of the HMF sensor in different sensing processes.The input variables of the network were the sensor geometric parameters(the microfiber diameter,wrapped length,coiled turns,and helical angle)and the output optical intensity under different working processes.The root mean square error(RMSE)and Pearson correlation coefficient(R)were used to evaluate the predictive ability of the networks.The DBN with two restricted Boltzmann machines(RBMs)provided the best temperature prediction results(RMSE and R of the heating process are 0.9705℃and 0.9969,while the values of RMSE and R of the cooling process are 0.7866℃and 0.9977,respectively).The prediction results obtained by the optimal BPNN(five hidden layers,10 neurons in each layer,RMSE=1.1266℃,R=0.9957)were slightly inferior to those obtained by the DBN.The neural network could accurately and reliably predict the response of the HMF sensor in cycling operation,which provided the possibility for the flexible application of the complex MNF sensor in a wide sensing range.