Preparation and Micro Mechanical Properties of Nano-Al2O3 Particles Strengthened Ni-based Composite Coatings

AbstractNi-based composite solution containing nano-Al2O3 particles was prepared by high-energy mechanical and chemical processes. The microstructure and nano-particle content of nano-Al2O3/Ni composite coatings were determined by SEM, EDS and TEM. The micro mechanical properties were tested by nano-indentation technique, and the strengthening mechanism was analyzed. The results show that 85 percent of particles in the solution are dispersed in size of nano meter, nano-particles co-deposited in the coating increases by a factor of 53 percent and the structure of the composite coating is more compact and uniform than that of Ni coating. Nano-Al2O3/Ni coatings exhibit excellent micro mechanical properties, the nanohardness and Young's modulus are 7.04GPa and 225GPa respectively, which are attributed to finer crystals strengthening, dispersion strengthening and high- density dislocations strengthening

Development of Weed Detection Method in Soybean Fields Utilizing Improved DeepLabv3+ Platform

Accurately identifying weeds in crop fields is key to achieving selective herbicide spraying. Weed identification is made difficult by the dense distribution of weeds and crops, which makes boundary segmentation at the overlap inaccurate, and thus pixels cannot be correctly classified. To solve this problem, this study proposes a soybean field weed recognition model based on an improved DeepLabv3+ model, which uses a Swin transformer as the feature extraction backbone to enhance the model’s utilization of global information relationships, fuses feature maps of different sizes in the decoding section to enhance the utilization of features of different dimensions, and adds a convolution block attention module (CBAM) after each feature fusion to enhance the model’s utilization of focused information in the feature maps, resulting in a new weed recognition model, Swin-DeepLab. Using this model to identify a dataset containing a large number of densely distributed weedy soybean seedlings, the average intersection ratio reached 91.53%, the accuracy improved by 2.94% compared with that before the improvement with only a 48 ms increase in recognition time, and the accuracy was superior to those of other classical semantic segmentation models. The results showed that the Swin-DeepLab network proposed in this paper can successfully solve the problems of incorrect boundary contour recognition when weeds are densely distributed with crops and incorrect classification when recognition targets overlap, providing a direction for the further application of transformers in weed recognition

Two-step catalytic co-pyrolysis of walnut shell and LDPE for aromatic-rich oil

A novel two-step catalytic co-pyrolysis (TSCCP) process is proposed through coupling advantages of conventional two-step catalytic pyrolysis (TSCP) and one-step catalytic co-pyrolysis (OSCCP) for producing aromaticrich oil using walnut shell (WNS) and LDPE as feedstock. Co-pyrolysis of three WNS components (hemicellulose, cellulose and lignin) with LDPE are performed to validate the necessity and rationality of TSCCP. And effects of first step pyrolysis temperature (T1) and residence time (Rt1) on product distributions of TSCCP are investigated. When T1 and Rt1 are 550 degrees C and 7.5 s respectively, the oil yield is increased by 59.1% and 15.7% respectively compared with that of conventional TSCP and OSCCP. The selectivity toward aromatics is as high as 82.5%, and the selectivity of oxygenates is reduced to less than 1%. The excellent results of TSCCP are attributed to preventing secondary reactions led by higher temperature for hemicellulose and cellulose components, the enhanced conversion due to activation effect from lignin component, and the synergetic effect between WNS-derived oxygenates and LDPE-derived hydrocarbons

Network Public Opinion Monitoring System for Agriculture Products Based on Big Data

The influence of online public opinion on agricultural product safety on the society is increasing. In order to correctly guide the direction of online public opinion on agricultural products, help the agricultural sector turn from passive to active public opinion, timely prevent the spread of negative public opinion, and reduce the negative impact on public opinion hot events, it is especially important to improve the ability of monitoring agricultural products’ network public opinion. This research is based on big data technology to develop an agricultural products’ network public opinion monitoring system that can collect, process, and analyze data in real time, discover and track hot topics, and calculate and visualize the polarity of public sentiment. The use of big data technology to increase the processing speed aims to strengthen the public’s supervision of the public opinion on the network security of agricultural products and provide an effective basis of the decision-making of relevant departments

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceSemantic not consistency, and knowledge base is difficult to reuse and sharing are the key problems affecting the system development and application. This paper studies how to express the soil fertility level information using of the ontology and generate OWL (Ontology Web Language) document, and how to make use of SWRL (Semantic Web Rule Language) to express inference rules. On this basis, this paper integrates SWRL rules editor and JESS (java expert shell system) rules engine, establishes the reasoning framework based on JESS reasoning engine, and realizes the productivity grade evaluation based on ontology and SWRL

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

Abstract：Semantic not consistency, and knowledge base is difficult to reuse and sharing are the key problems affecting the system development and application. This paper studies how to express the soil fertility level information using of the ontology and generate OWL (Ontology Web Language) document, and how to make use of SWRL (Semantic Web Rule Language) to express inference rules. On this basis, this paper integrates SWRL rules editor and JESS (java expert shell system) rules engine, establishes the reasoning framework based on JESS reasoning engine, and realizes the productivity grade evaluation based on ontology and SWRL

Analysis and Research on Rice Disease Identification Method Based on Deep Learning

Rice is one of the most important food crops in China and around the world. However, with the continuous transformation of human activities, the quality of climate, soil, and water sources has also changed, and disease affecting rice has become increasingly serious. Traditional artificial pest identification methods have been unable to adapt to the occurrence of a large number of diseases, and artificial naked eye identification also increases the uncertainty of the identification results, and cannot “suit the remedy to the case”, which will not cure the disease, or even achieve half the result with half the effort. In the incidence range of rice diseases, rice blast, rice false smut, and bacterial blight have the highest incidence rate, the greatest harm, and are the most representative. Therefore, this paper mainly focuses on the above three categories. In this paper, the identification of rice diseases is further studied. First, sample pictures of rice blast, rice false smut, and bacterial leaf blight diseases are collected. Due to the differences in the distance and light of the sample photos, their size and angle is biased. Therefore, some means are needed to unify the specifications of these images, so as to improve the efficiency of network model recognition. Neural network recognition needs to absorb many sample images to classify and learn features. The main research objects of this paper are rice blast, rice false smut, and bacterial wilt. Therefore, this paper also expands the data set for this kind of disease, and unifies the specifications through size cutting, angle change, and vertical symmetrical mirror image processing. Then, we built a new network model based on deep learning to realize the parameter initialization design. The accuracy of the rice disease identification model built at the beginning does not satisfy the practical requirements. In order to upgrade the model in depth, this experiment increases the entry point of analysis and research, and integrates four parameters: iteration times, batch size, learning rate, and optimization algorithm in order to strive for the optimization of the experimental results. In this study, the confusion matrix is selected as the evaluation standard, and experimental results with more objectivity and reference value are obtained through the horizontal comparison of visual graphics generator (VGG) and residual network (ResNet), two highly referential network models. The results show that the recognition accuracy of the optimized model is 98.64%, which achieves the goal of accurately identifying diseases

Research on the Construction and Implementation of Soil Fertility Knowledge Based on Ontology

International audienceSoil fertility is the comprehensive reflection of related factors and the related factors. Soil fertility evaluation knowledge is stored by relational database as usually, and it is difficult to show the correlation and constraints among attributes .In this paper, Nongan county farmland productivity data is as the research object, Using rough set approach to do attribute reduction, using ontology method to establish the soil fertility level knowledge base, using multi Agent technology to implement the prototype system, and complete the reuse and sharing of knowledge, lay the foundation for semantic level reasoning