KBNN Based on Coarse Mesh to Optimize the EBG Structures

The microwave devices are usually optimized by combining the precise model with global optimization algorithm. However, this method is time-consuming. In order to optimize the microwave devices rapidly, the knowledge-based neural network (KBNN) is used in this paper. Usually, the a priori knowledge of KBNN is obtained by the empirical formulas. Unfortunately, it is difficult to derive the corresponding formulas for the most electromagnetic problems, especially for complex electromagnetic problems; the formula derivation is almost impossible. We use precise mesh model of EM analysis as teaching signal and coarse mesh model as a priori knowledge to train the neural network (NN) by particle swarm optimization (PSO). The NN constructed by this method is simpler than traditional NN in structure which can replace precise model in optimization and reduce the computing time. The results of electromagnetic band-gap (EBG) structures optimally designed by this kind of KBNN achieve increase in the bandwidth and attenuation of the stopband and small passband ripple level which shows the advantages of the proposed KBNN method

Towards Successful Cloud Ordering Service

Background: The rise of cloud services has led to a drastic growth of e-commerce and a greater investment in development of new cloud services systems by related industries. For SaaS developers, it is important to understand customer needs and make use of available resources at as early as the system design and development stage. Objectives: This study integrates E-commerce Systems (ECS) Success model and Importance-Performance Analysis (IPA) into empirical research of the critical factors for cloud ordering system success. Methods/Approach: A survey research is conducted to collect data on customer perceptions of the importance and performance of each attribute of the particular cloud ordering service. The sample is further divided according to the degree of use of online shopping into high-usage users and low-usage users in order to explore their views regarding the system and generate adequate coping strategies. Results: Developers of online ordering systems can refer to the important factors obtained in this study when planning strategies of product/service improvement. Conclusions: The approach proposed in this study can also be applied to evaluation of other kinds of cloud services systems

Developing a Volume Model Using South NTS-372R Total Station without Tree Felling in a Populus canadensis Moench Plantation in Beijing, China

Volume table preparation using the traditional method and a collection model requires the harvest of approximately 200–300 trees of individual species. Although high precision could be achieved using that method, it causes huge damage to the forest. To minimize these losses, in this study, a South NTS-372R total station with a precise angle and distance measurement mode was used to measure 507 trees of Populus canadensis Moench without single tree felling. Moreover, the C# programming language was used in this study and the collected volume data were inserted in the total station. Using this method, a real-time precise measurement of volume could be achieved. After data collection, the optimal binary volume model of Populus canadensis Moench could be obtained through a comparative analysis. It turns out that the Yamamoto model is the optimal binary volume model (also known as two predictor variable model), with 0.9641 as the coefficient of determination (R2) and 0.19 m3 as the standard deviation of estimated value (SEE), which presents a good imitative effect. Moreover, it showed relative stability with the general relative error (TRE) of –0.12% and the mean system error (MSE) of –1.24%. The mean predicted error (MPE) of 1.18% and the mean predicted standard error (MPSE) of 9.25% showed high estimated precision of the average and individual tree volumes. The model has only three parameters, so it is suitable for volume table preparation. Finally, this study will present some new technical methods and means for volume modeling for further application in forestry

A γ\gamma-ray Quasi-Periodic modulation in the Blazar PKS 0301−-243?

We report a nominally high-confidence $\gamma$-ray quasi-periodic modulation in the blazar PKS 0301$-$243. For this target, we analyze its \emph{Fermi}-LAT Pass 8 data covering from 2008 August to 2017 May. Two techniques, i.e., the maximum likelihood optimization and the exposure-weighted aperture photometry, are used to build the $\gamma$-ray light curves. Then both the Lomb-Scargle Periodogram and the Weighted Wavelet Z-transform are applied to the light curves to search for period signals. A quasi-periodicity with a period of $2.1\pm0.3$ yr appears at the significance level of $\sim5\sigma$, although it should be noted that this putative quasi-period variability is seen in a data set barely four times longer. We speculate that this $\gamma$-ray quasi-periodic modulation might be evidence of a binary supermassive black hole.Comment: 9 pages, 8 figures; Accepted for publication in Ap