Determination of the Sign of g factors for Conduction Electrons Using Time-resolved Kerr Rotation

The knowledge of electron g factor is essential for spin manipulation in the field of spintronics and quantum computing. While there exist technical difficulties in determining the sign of g factor in semiconductors by the established magneto-optical spectroscopic methods. We develop a time resolved Kerr rotation technique to precisely measure the sign and the amplitude of electron g factor in semiconductors

Control System Design of Shunt Active Power Filter Based on Active Disturbance Rejection and Repetitive Control Techniques

To rely on joint active disturbance rejection control (ADRC) and repetitive control (RC), in this paper, a compound control law for active power filter (APF) current control system is proposed. According to the theory of ADRC, the uncertainties in the model and from the circumstance outside are considered as the unknown disturbance to the system. The extended state observer can evaluate the unknown disturbance. Next, RC is introduced into current loop to improve the steady characteristics. The ADRC is used to get a good dynamic performance, and RC is used to get a good static performance. A good simulation result is got through choosing and changing the parameters, and the feasibility, adaptability, and robustness of the control are testified by this result

A Knowledge Mapping Analysis of Digital Photogrammetry Research Using CiteSpace

In order to clearly understand the current status and application trends of digital photogrammetry domestic and overseas research, taking the core journals of Web of Science as the data source, using bibliometric methods and CiteSpace to carry out statistical analysis of the relevant literature of digital photogrammetry research. The results show that since 2011, the research literature on digital photogrammetry has shown a steady growth year by year.  Digital photogrammetry is most closely related to the three disciplines of geology, earth science integration, and physical geography; countries such as the United States, the United Kingdom, Italy, and China publish the most papers, and these countries have strong research capabilities. Lane SN and Chandler JH have been shared with a high number of citations, who are representative scholars in this field; Digital photogrammetry contains multiple research directions. This article studies the research hotspots and frontiers of digital photogrammetry through keyword co-occurrence analysis and mutation detection analysis

Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

Asphalt Mixture with Scrap Tire Rubber and Nylon Fiber from Waste Tires: Laboratory Performance and Preliminary M‐E Design Analysis

Scrap tire rubber and nylon fiber are waste materials that could potentially be recycled and used to improve the mechanical properties of asphalt pavement. The objective of this research was to investigate the properties of scrap tire rubber and nylon fiber (R‐F) modified warm mix asphalt mixture (WMA). The high‐temperature performance was estimated by the Hamburg wheel-tracking testing (HWTT) device. The low‐temperature cracking performance was evaluated by the disk‐shaped compact tension (DCT) test and the indirect tensile strength (IDT) test. The stress and strain relationship was assessed by the dynamic modulus test at various temperatures and frequencies. The extracted asphalt binder was evaluated by the dynamic shear rheometer (DSR). Pavement distresses were predicted by pavement mechanistic‐empirical (M‐E) analysis. The test results showed that: (1) The R‐F modified WMA had better high‐temperature rutting performance. The dynamic modulus of conventional hot mix asphalt mixture (HMA) was 21.8% ~ 103% lower than R‐ F modified WMA at high temperatures. The wheel passes and stripping point of R‐F modified WMA were 2.17 and 5.8 times higher than those of conventional HMA, respectively. Moreover, the R‐F modified warm mix asphalt had a higher rutting index than the original asphalt. (2) R‐F modified WMA had better cracking resistance at a low temperature. The failure energy of the R‐F modified WMA was 24.3% higher than the conventional HMA, and the fracture energy of the R‐F modified WMA was 7.7% higher than the conventional HMA. (3) The pavement distress prediction results showed the same trend compared with the laboratory testing performance in that the R‐F modified WMA helped to improve the IRI, AC cracking, and rutting performance compared with the conventional HMA. In summary, R‐F modified WMA can be applied in pavement construction

REAL-TIME MONITORING DEFORMATION OF BUILDING USING PHOTOGRAPHY DYNAMIC MONITORING SYSTEM

The spatial structure building is a type of building system; it is necessary to monitor deformation to determine its stability and robustness. Under the dynamic deformation of structures, it is challenging to determine appropriate zero image (the reference image) if we use the PST-IM- MP (photograph scale transformation-image matching-motion parallax) method to obtain the deformation of structures. This paper offers the Z-MP (zero-centered motion parallax) method to solve these problems and offers PDMS (Photography Dynamic Monitoring System) based on the digital photography system to monitor the dynamic deformation of the tennis stadium located in Jinan Olympic Sports Center. The results showed that the spatial structures of the tennis stadium were robust, and the deformations were elastic and within the permissible value. Compared with the PST-IM-MP method, the Z-MP method is more suitable for deformation monitoring structures under real-time deformation. This paper indicates PDMS has advantages of the simplicity of operations, automation, and the ability of non-contact dynamic deformation monitoring for multiple points in a short period. In the future, it will have broader application prospects

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201