The samples of the newly-produced uncatalyzed aged wine,naturally-aged wine and electro-catalyzed wine are analyzed by nuclear magnetic resonance. The result shows theΔδof the naturally-aged wine is bigger than the newly-produced uncatalyzed aged wine and theΔδof the electro-catalyzed wine is bigger than the newly-produced uncatalyzed aged wine,which results from the association of hydrogen bond and the chemical shift of the water in naturally-aged wine and electro-catalyzed wine towards low field.
The research on energy consumption quota of metrology laboratory is an important part of building energy conservation from the perspective of total amount control. Through the research on the investigation method of basic energy consumption data, the type of quota index and the determination method of quota level. The analysis shows that the metrology laboratory adopts the method of unequal proportion classified sampling survey to obtain the basic data of energy consumption, adopts the energy consumption per unit area, considers the correction of energy consumption time and the proportion of auxiliary room area as the quota index, and uses the quota level method to determine the limit value, reference value and advanced value of energy consumption quota, which is fair, scientific and feasible.
To address the difficulty of obtaining the optimal driving strategy under the condition of a complex environment and changeable tasks of vehicle autonomous driving, this paper proposes an end-to-end autonomous driving strategy learning method based on deep reinforcement learning. The ideas of target attraction and obstacle rejection of the artificial potential field method are introduced into the distributed proximal policy optimization algorithm, and the APF-DPPO learning model is established. To solve the range repulsion problem of the artificial potential field method, which affects the optimal driving strategy, this paper proposes a directional penalty function method that combines collision penalty and yaw penalty to convert the range penalty of obstacles into a single directional penalty, and establishes the vehicle motion collision model. Finally, the APF-DPPO learning model is selected to train the driving strategy for the virtual vehicle, and the transfer learning method is selected to verify the comparison experiment. The simulation results show that the completion rate of the virtual vehicle in the obstacle environment that generates penalty feedback is as high as 96.3%, which is 3.8% higher than the completion rate in the environment that does not generate penalty feedback. Under different reward functions, the method in this paper obtains the highest cumulative reward value within 500 s, which improves 69 points compared with the reward function method based on the artificial potential field method, and has higher adaptability and robustness in different environments. The experimental results show that this method can effectively improve the efficiency of autonomous driving strategy learning and control the virtual vehicle for autonomous driving behavior decisions, and provide reliable theoretical and technical support for real vehicles in autonomous driving decision-making.
The aspherical measuring technology that based on computer-generated hologram (CGH) was introduced. The advantage of this method is that the phase shifts can be controlled digitally, no any mechanical moving and rotating element. By changing CGH coding which displayed on the Liquid Crystal Display (LCD), the wavefront and phase shifts in measuring system were induced. Based on the characteristics of aspherical measurement and LCD structure, the CGH encode technology used in LCD was discussed. Then a new encode method which applied to aspherical measurement was put forward. In this method, the LCD modulates functions of amplitude and phase was coexistent, and the character of LCD diffraction frequency spectrum was considered, and phase hologram was applied. This aspherical measuring technology is more flexible than usual method. In this paper, the hologram encode method based on LCD were illuminated in detail. In order to verify the correction of encode technology, the aspherical surface with standard wavefront was generated by coaxial hologram reconstruct system when hologram encode image was displayed on SONY LCX023 LCD, it interfere with the standard spherical wavefront, then the interferogram was sampled to computer by Charge Coupled Device (CCD) and A/D transfer, the wavefront of hologram reconstruct was obtained by image process finally. All calculation is completed by Matlab. An aspherical measuring system based on LCD was built experimentally. Both the theoretical analysis and experimental results demonstrate the feasibility of this approach.
In crop disease image segmentation, traditional convolutional neural networks have the problem of low image accuracy. For this reason, this paper proposes a segmentation method that combines conditional random fields with segnet networks. First, input the training set in the data set into the segnet network for training, and obtain the initial segmentation result and the network with updated parameters; secondly, the image segmented by the segnet network is sent to the conditional random field in the form of pixels and classification vectors corresponding to the pixels At the same time, construct an energy function to represent the relative relationship between pixels, and then keep training; in order to get the conditional random field in the training set, you need to adjust and optimize the image segmented by the segnet network to get it; then in the verification set, the segnet network and the weight parameters of the conditional random field model are further adjusted and optimized to obtain the final conditional random field and segnet network; finally, the final conditional random field and segnet network are applied to the crop disease image segmentation. The test results show that compared with the Otsu threshold and Grabcut segmentation methods, the segmentation accuracy P, recall rate R, and comprehensive evaluation index F of this method are all the maximum, and the average processing time of a single image is 0.86 s; compared with pspnet and deeplab methods, Under the premise that the segmentation accuracy P, the recall rate R, and the comprehensive evaluation index F are basically unchanged, the average processing time of a single image is significantly reduced. The results show that the best comprehensive effect can be achieved by segmenting crop disease images in complex environments using the method in this paper.
With the increase of world food demand, the intensity of cultivated land use also increased. To improve soil nutrient concentrations and crop yield, several straw returning techniques have been developed. Studies have shown that straw returning is beneficial to soil, but few studies have focused on the relationship between microbes and fertility in seasonal freeze-thaw periods. A two-year cropland experiment was set up that comprised three different straw return strategies, namely covering tillage with straw return for two years (CS), rotary tillage and straw return for two years (RS), rotary covering tillage with straw return (first year covering and the second year rotary tillage) (CRS), and conventional tillage with no straw return (CK). Illumina Miseq high throughput sequencing of 16S rRNA was applied to assess bacteria community structure. The relationship between bacteria community structure and changes in soil fertility induced by different straw incorporating during seasonal trends was studied. Our results showed that soil bacterial communities varied significantly during the soil seasonal freeze-thaw period in the northwest of Jilin province, China, and were influenced, to some extent, by the different straw returning procedures. Multidimensional analysis revealed that total phosphorus (TP), available nitrogen (AN), and total nitrogen (TN) were the major drivers of bacterial community structure. The co-occurrence network was divided into several modules. Notably, the major bacterial modules varied significantly in different sampling periods and different treatments. These results suggested that specific bacterial groups could contribute to soil fertility in relation to environmental fluctuations. Some bacterial groups (e.g., Pyrinomonadales, Rhizobiales, Sphingomonadales, and Xanthomonadales, in order level) were directly linked with specific environmental factors, indicating the key roles of these groups in soil fertility. In summary, the soil bacterial communities varied significantly during the freeze-thaw period and might play important roles in the degradation of straw. Thus, the straw return could enhance soil fertility.
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