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Aiming at the problems that SSA is prone to fall into local optimal and relatively low accuracy during search iteration. This paper proposed an enhanced sparrow search algorithm with multiple strategies (ESSA). At first, ESSA applied Lévy flight and cloud based adaptive weights to refine the original discoverers’ position update equation, which expanded the search range and enriched population diversity of the algorithm. Secondly, ESSA adopted a fuzzy control based adaptive lens imaging strategy to get the reversed position of the current optimal position to enhance the algorithm’s ability to jump out of local optimal. This paper selected 12 test functions from CEC2017 testbed as benchmark to evaluate the performance of ESSA with standard SSA, and other 4 improved sparrow algorithms: ISSA, MSSSA, HSSA, and SHSSA. Experiment result shows that ESSA can achieve the supreme results among evaluated algorithms. This paper also applied ESSA to the 3D UAV path planning problem. The simulation result illustrates that ESSA can also find the supreme 3D paths for UAV. [ABSTRACT FROM AUTHOR]

Objective The applicability of current China's natural gas analysis and testing standards under hydrogen-blended conditions needs to be confirmed ed to promote the standardization of hydrogen-blended natural gas. Methods By analyzing China's natural gas analysis and testing standards, 10 standards related to hydrogen-blended natural gas have been selected and the applicability of natural gas composition analysis standards and physical property calculation standards under hydrogen-blended conditions has been studied. Results The results show that the maximum hydrogen concentration specified by the natural gas composition analysis standard represented by GB/T 13610-2020 Analysis of natural gas composition-Gas chromatography and the physical property calculation standard represented by GB/T 17747.2-2011 Natural gas-Calculation of compression factor- Part 2: Calculation using molar-composition analysis is 10%, and the relevant standards need to be revised under the condition of high hydrogen concentration. Besides, if the hydrogen is not separated downstream, the interchangeability of natural gas and its standardization need to be studied urgently. Conclusions The proposed formulation and revision framework has reference significance for the future standardization work of hydrogen-blended natural gas. [ABSTRACT FROM AUTHOR]

With the increasing demand for straw resource utilization under the rice-wheat rotation system in the middle and lower reaches of the Yangtze River, less tillage or no-tillage (conservation tillage) on rice stubble fields has become a new trend. A large amount of residual surface straw can easily cause the entanglement and blockage of the soil-contacting parts for the no-till planter, which leads to a sharp increase in power consumption and a decrease in passability of the machine. To predict and evaluate the performance of the soil-contacting components for no-tillage planters, it’s necessary to establish the interaction model of the implement-soil coupling system. Traditional models regard the straw scattered on the soil surface as a rigid body and fail to simulate the rupture and deformation characteristics of the straw under rotary tillage operating conditions, which reduces the prediction accuracy of the model. In order to overcome the shortcomings of the previous models, an improved flexible model of the straw was proposed and the corresponding discrete element model of the tool-straw-soil coupling system was constructed considering the effect of scattered straw on the soil surface was constructed in this work. The soil and straw model parameters were calibrated by repose angle and compression experiments, respectively. Then, the correctness of the improved straw model was verified by soil bin test. At the same time, in order to solve the winding and obstructing problems caused by the operation of the standard rotary blade, a novel structure scheme of trapezoidal straight blade is designed. On this basis, straw displacement, shaft torque, stubble crushing and burying performance of both standard and trapezoidal straight blades under rotary tillage conditions are analyzed. Simulation results show that the existence of straw flexibility reduces the displacement of the straw, and plays a buffering role in the interaction between the straw and the cutter. The standard blade has a larger soil disturbance ability and causes the straw to move a longer distance, while the trapezoidal straight blade has a stronger stubble smashing ability after breaking the soil as a result of sharp blade edge. Stubble crushing and burying performance of the trapezoidal straight blade is better than that of the standard blade and the groove section shapes of both standard and trapezoidal straight blades under rotary tillage conditions are trapezoidal and triangular, respectively. The groove depth of the standard blade is 57 mm and that of the trapezoidal straight blade is 52 mm. The bending structure of standard blade causes a large amount of soil to be scattered in the seed ditch, especially when the depth of furrow is large, which will lead to the phenomenon of over-burying and affect the development of seeds. After the trapezoidal straight blade operation, the shape of the groove is more regular, and the residual soil is less. Triangular impact peak exits in the torque of the cutter shaft. The torque of the cutter shaft under the operation of the trapezoidal straight blade is greater than that of standard blade, while the soil breaking performance of the standard blade is better than that of the trapezoidal straight blade. In addition, taking the bending angle, flank angle and blade height as design variables, the influence of the variance and the interaction of design variables on the regression model was analyzed using the response surface method and the simplified regression equation of the objective function (rotary tillage torque and soil breakage rate) was determined. Based on the NSGA-II algorithm, the structure of the trapezoidal straight blade is optimized, and the optimal structural parameters are determined as follows: bending angle (150°), flank angle (60°), and blade height (88 mm). Through the comparative analysis of field experiments, it can be seen that the average torque and peak torque of cutter shaft were reduced by 11.70% and 6.28%, respectively, and the soil breaking rate is increased from 82.53% to 89.22%, which verifies the effectiveness of the optimization design of the tool structure. [ABSTRACT FROM AUTHOR]

In order to solve the problems of unstable and low efficiency in the work space, a particle swarm optimization (PSO) algorithm with dynamic changes of inertia weight and learning factors was used to optimize the trajectory of 3-5-3 piecewise polynomial interpolation. Through the kinematic analysis of the forest work vehicle, four passing points in the working space were obtained, and the 3-5-3 piecewise polynomial interpolation was used for trajectory planning. Finally, the improved particle swarm optimization algorithm was used to optimize the trajectory running time under the kinematic constraints, and the optimization performance of the standard particle swarm optimization algorithm was compared. The results showed that the 3-5-3 piecewise polynomial interpolation curve using the improved particle swarm optimization algorithm had faster convergence speed, smaller fitness after convergence, and about 13% shorter running time, which effectively improved the working efficiency of the forest work vehicle. [ABSTRACT FROM AUTHOR]

Entity relation extraction is a fundamental task to detect a list of triplets, including two entities and the semantic relations between them. An overlapping relationship has caused the low performance of standard entity relation extraction in fishery in recent years. In this study, a novel entity relation extraction was proposed in fishery using the dual attention mechanism. First, a sentence classification and labeling strategy were selected to solve the difficulty in labeling overlapping relations in fishery standard texts. Second, an extraction model of standard entity relationship was established using a combination of dual attention, and Bidirectional Encoder Representations from Transformers-Bi-directional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). Five components were included from the bottom to top: BERT layer, BiLSTM layer, word attention mechanism, sentence attention, and CRF output layer. In the pre-trained BERT model, the dual-layer two-way conversion and decoding were used to automatically learn sentence features for the vector representation. In the BiLSTM model, the context feature of the target entity was learned from the BERT output. The word and sentence level attention was utilized to optimize the weight of target words and sentences in the paragraph, including the noise removal, and the accuracy of relationship extraction. The CRF decoder was used to represent the output of the attention layer in the form of sequence tags. Finally, a comparative experiment was designed to verify the effectiveness of the model. The results show that better overall performance was achieved in the entity relationship extraction using the dual attention on the fishery standard interaction (DLOU-FSI) dataset, where the accuracy, recall, and F1 value were 92.67%, 92.31%, and 92.49%, respectively. The overlapping relations were effectively solved in the extraction of fishery standard relations. In addition, the recognition accuracy rates in the seven relation categories were all higher than others. The accuracy rate, recall rate, and F1 value were greatly improved by more than 90% in the quotation relationship, regulation, release, proposal, drafting, and centralization. Nevertheless, the recall rate of the improved model was dropped slightly in the comparison test. The reason was that there was a sparse distribution of sentence samples in the related category of comparative relations. In the DLOU-FSI corpus, each standard text of fishery contained only 0-3 comparison relation triples, less than 1% of the total number of all relational triples. As such, the comprehensive relationship features were necessary to learn for the higher recognition in the relationship extraction task. In addition, the quality of corpus was determined on the extraction of fishery standard text relations. The higher quality and the more accurate recognition of the model were achieved, as the deep learning model was trained to learn more data. There was also a critical value, after which the model cannot be greatly improved. In the case of a model adaptation, the test data can be effectively expanded to increase the number and diversity of samples for a better overall effect of relationship extraction. This finding can lay a significant foundation for the construction of fishery standard knowledge graphs. [ABSTRACT FROM AUTHOR]