Numerical and experimental studies on the effectiveness of time-varying electromagnetic fields in reducing electron density

When an aircraft or a hypersonic vehicle re-enters the atmosphere,the plasma sheath generated can severely attenuate electromagnetic wave signals,causing the problem of communication blackout.A new method based on time-varying E×B fields is proposed to improve on the existing static E×B fields and mitigate the radio blackout problem.The use of the existing method is limited by the invalid electron density reduction resulting from current density j=0 A m^(-2)in plasma beyond the Debye radius.The most remarkable feature is the introduction of a time-varying electric field to increase the current density in the plasma to overcome the Debye shielding effect on static electric field.Meanwhile,a magnetic field with the same frequency and phase as the electric field is applied to ensure that the electromagnetic force is always acting on the plasma in one direction.In order to investigate the effect of time-varying E×B fields on the plasma electron density distribution,two directions of voltage application are considered in numerical simulation.The simulation results indicate that different voltage application methods generate electromagnetic forces in different directions in the plasma,resulting in repulsion and vortex effects in the plasma.A comparison of the vortex effect and repulsion effect reveals that the vortex effect is better at reducing the electron density.The local plasma electron density can be reduced by more than 80%through the vortex effect,and the dimensions of the area of reduced electron density reach approximately 6 cm×4 cm,meeting the requirements of electromagnetic wave propagation.Besides,the vortex effect of reducing the electron density in RAM-C(radio attenuation measurements for the study of communication blackout)reentry at an altitude of 40 km is analyzed.On the basis of the simulation results,an experiment based on a rectangular-window discharge device is proposed to demonstrate the effectiveness of the vortex effect.Experimental results show that time-varying E×B fields can reduce the electron density in plasma of 3 cm thickness by 80%at B=0.07 T and U_(0)=1000 V.The investigations confirm the effectiveness of the proposed method in terms of reducing the required strength of the magnetic field and overcoming the Debye shielding effect.Additionally,the method is expected to provide a new way to apply a magnetic window in engineering applications.

An Improvement of Data Cleaning Method for Grain Big Data Processing Using Task Merging

Data quality has exerted important influence over the application of grain big data, so data cleaning is a necessary and important work. In MapReduce frame, parallel technique is often used to execute data cleaning in high scalability mode, but due to the lack of effective design, there are amounts of computing redundancy in the process of data cleaning, which results in lower performance. In this research, we found that some tasks often are carried out multiple times on same input files, or require same operation results in the process of data cleaning. For this problem, we proposed a new optimization technique that is based on task merge. By merging simple or redundancy computations on same input files, the number of the loop computation in MapReduce can be reduced greatly. The experiment shows, by this means, the overall system runtime is significantly reduced, which proves that the process of data cleaning is optimized. In this paper, we optimized several modules of data cleaning such as entity identification, inconsistent data restoration, and missing value filling. Experimental results show that the proposed method in this paper can increase efficiency for grain big data cleaning.

Determination of potassium sorbate and sorbic acid in agricultural products using THz time-domain spectroscopy

t The aim of this study was to investigate the feasibility of detecting potassium sorbate(PS)and sorbic acid(SA)in agricultural products using THz time-domain spectroscopy(THz-TDS).The absorption spectra of PS and SA were measured from 0.2 to 1.6 THz at room temperature.The main characteristic absorption peaks of PS and SA in polyethylene and powdered agricultural products with different weight ratios were detected and analyzed.Interval partial least squares(iPLS)combined with a particle swarm optimization and support vector classification(PSO-SVC)algorithm was proposed in this paper.iPLS was used for frequency optimization,and the PSO-SVC algorithm was used for spectrum analysis of the preservative content based on the optimal spectrum ranges.Optimized PSO-SVC models were obtained when the THz spectrum from the PS/SA mixture was divided into 11 or 12 subintervals.The optimal penalty parameter c and kernel parameter g were found to be 1.284 and 0.863 for PS(0.551-1.487 THz),1.374 and 0.906 for SA(0.454-1.216 THz),respectively.The preliminary results indicate that THz-TDS can be an effective nondestructive analytical tool used for the quantitative detection of additives in agricultural products.

Optical techniques in non-destructive detection of wheat quality:A review

Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.

Computer vision-based method for monitoring grain quantity change in warehouses

Regularly checking the quantity of stored grain in warehouses is essential for the grain safety of a country.However,current manual inspection ways fail to get real-time measurement results and require spending a lot of manpower and resources.In this paper,we proposed a computer vision-based method to automatically monitor the change in grain quantity of a granary.The proposed method was motivated from the observation that warehouse managers can use a camera to remotely monitor the grain security of a granary,which determines whether grain quantity is reduced by checking the distance between the grain surface and the grain loading line at the outlet of a granary.To this end,images were first captured by a camera,and a two-level spatial constraints-based SVM classifier was learned to detect the grain surface and the grain loading line of the images.During the test phase,the detected result of a test image obtained by SVM was further refined by Grab Cut with higher order potentials to get the more accurate segmentation result.Finally,the area between the grain surface and the grain loading line was calculated,and then compared with the previous measured one to determine whether the grain surface had dropped.The experiment results validate the effectiveness of the two-level spatial constraints SVM and the strategy for monitoring the change in grain quantity.