A Study on Causes of Errors of Enterprise R&D Statistics & Accounting Data

Abstract: The level of the R&D statistics quality is directly related to the accuracy of the S&T policy research and making, so to explore the causes of the R&D statistical error is great significance for improving the R&D statistics quality. On the basis of the results of previous studies and seek expert advice, the paper summarizes the possible causes of the R&D statistical error. Through field research to get first-hand data of Shaanxi province, the paper identifies the key causes of the R&D statistical error based on principal component analysis. We found that: the key causes of the R&D statistical error are followed by inconsistent of R&D statistical and accounting port, lack of training to the R&D fill staff, not allowed to grasp the connotation and extension of the R&D indicators and more difficult to obtain data. Therefore, the R&D statistical work should: on the one hand to strengthen among the various departments exchange and communication to clarify the port of R&D statistical and accounting; the other hand should be carried out theoretical studies to solve technical problems in the accounting and define the connotation and extension of the R&D indicators; Moreover, it should be do R&D statistics training to improve professional quality of R&D fill staff.Key words: Principal component analysis; R&D statistics; Statistical erro

A Novel Algorithm of Stochastic Chance-Constrained Linear Programming and Its Application

The computation problem is discussed for the stochastic chance-constrained linear programming, and a novel direct algorithm, that is, simplex algorithm based on stochastic simulation, is proposed. The considered programming problem in this paper is linear programming with chance constraints and random coefficients, and therefore the stochastic simulation is an important implement of the proposed algorithm. By theoretical analysis, the theory basis of the proposed algorithm is obtained and, by numerical examples, the feasibility and validness of this algorithm are illustrated. The detailed algorithm procedure is given, which is easily converted into the executable codes of software tools. Then, we compare it with some algorithms to verify its superiority. Finally, a practical example is presented to show its practicability

Genuine full characterization of partially coherence beam

For partially coherent light fields with random fluctuations, the intensity distributions and statistics have been proven to be more propagation robust compared with coherent light. However, its full potential in practical applications has not been realized due to the lack of four-dimensional optical field measurement. Here, a general modal decomposition method of partially coherent light field is proposed and demonstrated. The decomposed random modes can be used to, but not limited to, reconstruct average intensity, cross spectral density and orthogonal decomposition properties of the partially coherent light fields. Due to its versatility and flexibility, this method provides a powerful tool to further reveal light field invariant or retrieve embedded information after propagation through complex media. The Gaussian-shell-model beam and partially coherent Gaussian array are used as examples to demonstrate the reconstruction and even prediction of second-order statistical characteristics. This method is expected to pave the way for applications of partially coherent light in optical imaging, optical encryption and anti-turblence optical communication

Efficient Aerial Data Collection with UAV in Large-Scale Wireless Sensor Networks

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data