Search

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate monitoring of soil moisture content (SMC) in agricultural fields can greatly contribute to the utilization of water resources and sustainable development. Low SMC can cause the soil to harden during crop growth, which in turn affects the absorption of the crops' water and nutrients. In this study, the field-scale wheat canopy spectra were gathered to quantitatively estimate and map the SMC using an unmanned aerial vehicle (UAV) hyperspectral sensor. The study area was selected as Fukang City, Xinjiang Uygur Autonomous Region, China (87°51'15′′E, 44°21'14′′N) at the transition zone between a desert and an oasis. The soil samples were also collected concurrently with the hyperspectral data. 70 sampling units of 0.5 m×0.5 m were evenly selected in the target field, where the GPS locations were recorded. The wheat canopy spectra were smoothed using the savitzky-golay (SG). The spectral data were then transformed using the continuous wavelet transform (CWT) with seven different wavelet functions (bior4.4, coif4, db4, fk14, haar, rbio3.9, and sym 4). The wavelet coefficients were extracted by the genetic algorithm (GA), and finally combined with partial least squares (PLSR), Support Vector Machine (SVM), artificial neural network (ANN), radom forest (RF), and extreme gradient boosting (XGBoost) to predict the SMC. A neural Network was used to predict the SMC, and then map the SMC at a spatial scale. The results demonstrate that the accuracy of SMC estimation greatly increased using the GA-based feature band selection. The determination coefficient (R²) of the feature band was 0.35-0.82 with the wavelet coefficients, while the R² of the complete wavelet band ranged from 0.20 to 0.44. Furthermore, the R² of SMC reached 0.82, 0.72, 0.79, 0.76, and 0.46, respectively, using PLSR, SVM, ANN, RF, and XGBoost models. The best estimation accuracy was achieved in the feature band with the db4 wavelet coefficients, compared with the rest wavelet functions. The predicted and measured values were remarkably similar. The GA significantly reduced the number of variables to maximize the feature factors in the remote sensing bands. The majority of the feature bands were retained to remove the redundant bands that frequently presented in the hyperspectral data. The more stable ANN model was achieved in the small number of sample points on the big sample, even though the accuracy was lower than that of PLSR. Consequently, the perfect estimation accuracy of SMC with excellent mapping was obtained in the continuous wavelet transform and GA feature extraction of the hyperspectral data acquired by the UAV. The improved inversion can be expected to achieve accurate SMC prediction using crop growth period spectra at the field scale. The finding can provide a strong reference for high-precision SMC monitoring using remote sensing. [ABSTRACT FROM AUTHOR]

Assessing the sustainable development of agriculture is a hot topic in global agricultural research and an important task of rural revitalization and agricultural upgrading in the new era. Reform and opening up promote the rapid development of the agricultural economy, but the conflict between agricultural development and agricultural resources and ecological environment is serious. Since the 18th National Congress of the CPC, the sustainable development of agriculture has been given more prominence. It is of great significance to study the evaluation of agricultural sustainable development. Firstly, this paper studies the relevant literature on agricultural sustainable development evaluation at home and abroad. Based on wavelet transform, support vector machine(SVM), genetic algorithm (GA)and ant colony optimization(ACO) algorithm, a combination evaluation method of wavelet and intelligent optimization support vector machine is proposed. Secondly, by referring to the existing evaluation index system and combining with the actual situation of China's agricultural development, the alternative index set of agricultural sustainable development evaluation is established. Using SPSS software, the significance and correlation of 150 primary indexes are analyzed, and an evaluation index system including 62 indexes is established. Thirdly, using one-dimensional discrete stationary wavelet analysis, data denoising processing, genetic algorithm, ant colony algorithm, optimize the parameters of support vector machine, get better penalty parameters, kernel function, insensitivity coefficient, and then train the support vector machine. Fourth, taking 31 provinces (municipalities) in China as an example, this paper evaluates the agricultural sustainable development by applying the wavelet-GASVM model and the wavelet-ACOSVM model, and using the GA-SVM model and the ACO-SVM model to evaluate the agricultural sustainable development of 31 provinces (municipalities) in China. Fifthly, the reliability of the wavelet intelligent optimization SVM method is verified by changing the number of test sets and conducting multiple tests. The evaluation and simulation results show that: 1) The mean square error of agricultural sustainable development evaluation of Wavelte-GASVM is 9.641×10-5, and the correlation coefficient is 0.980; however, under the same training set and test set, the mean square error and correlation coefficient obtained by GASVM are 0.006 and 0.979, respectively. 2)The mean square error of Wavelet-ACOSVM agricultural sustainable development evaluation was 9.318×10-5, and the correlation coefficient was 0.972; the mean square error MSE and correlation coefficient of ACO-SVM are 0.016 and 0.953, respectively, under the same training set and test set. 3) Change the number of test sets and perform several tests and compare the results of GASVM model and ACO-SVM model before and after wavelet denoising respectively. It can be seen that under the GASVM model, the mean square error of the running results after wavelet denoising is smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Under the ACOSVM model, the mean square error of the running result after wavelet denoising is also smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Conclusion: The prediction accuracy and convergence speed of agricultural sustainable development evaluation by wavelet-GASVM and wavelet-ACOSVM are better than GA-SVM and ACO-SVM, respectively, and the effect of agricultural sustainable development evaluation by wavelet-GASVM is more ideal. This study provides a method reference for agricultural sustainable development assessment. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To rationally allocate air traffic rights（ATR）and avoid vicious competition of airlines for scarce passenger air traffic right resources，the air traffic right allocation problem（ATRAP）is proposed originally， and a mixed integer programming （MIP） model for this problem was established. The model aims at reasonably allocating scarce ATR resources to domestic airlines and international airports. With the known origin-destination（OD）distribution of passengers of two countries，the objective of this model is to minimize the travel mileage of passengers，and comprehensively considers the level of international hub airport，the competitiveness level of airlines and market share. Constraints include air traffic right resources，airline operating capacity，flight load，etc. Then a genetic algorithm is designed to solve the problem，and the optimization solver named CPLEX as well as the genetic algorithm is used to carry out calculation example experiments to verify the correctness of the model and the effectiveness of the algorithm. Finally，the practical application of the allocation of ATR between China and France is carried out. [ABSTRACT FROM AUTHOR]

This study aims to realize the joint deployment of multiple agricultural machinery stations, particularly for the real-time job orders. A mathematical model with a fuzzy time window was also established to minimize the total scheduling time and the number of dispatching agricultural machinery. Some factors were comprehensively considered, such as farmers' satisfaction, the cooperation of multiple agricultural machinery stations, the number of orders, the area of farmland, and the location coordinates. An improved genetic method (GA) with excellent parent genes was designed to fulfill the task of multi machine station responding to the demand of multi farmland. At the same time, the agricultural machinery was allocated in the shortest time to implement the operation requirements of each farmland, according to the shortest path. A case study was carried out to verify the model and the visual interface, including three stations of agricultural machinery and 35 operation orders of farmland in a certain area around Wuhan, Hubei Province of China. The results showed that an excellent searching and stable convergence were achieved in the scheduling system of agricultural machinery. Specifically, the reduction rate of the total scheduling distance was 9.89%, and the reduction rate of the number of agricultural machinery was 15.38%, when the fuzzy membership degree was 0.8. An optimal number of real-time orders accepted by a single farm station was not more than 20, according to the actual situation of the agricultural machinery quantity in each station. Furthermore, the improved GA presented a better performance than the hybrid genetic in general, indicating the less calculation time of the deployment, the more reasonable allocation of tasks, and the reduced scheduling distance. The multi-site and multi-machine cooperative instant repose scheduling was also considered the joint deployment agricultural machinery and fuzzy time window in the modeling. There was a higher accuracy of the scheduling operation on agricultural machinery, and the fully considered satisfaction of farmers, even though the complexity of model increased, compared with the scheduling operation at a single agricultural machinery station. In the scheduling algorithm, the crossover and mutation operators were improved to reduce the risk of the operation data falling into the local optimal solution with the less running time. Consequently, the scheme can be widely expected to completely deal with agricultural machinery scheduling under complex backgrounds, fully meeting the cooperative operation of multiple agricultural machinery stations for the real-time operation needs of farmers. This finding can provide a strong support to the cost-saving and high efficiency of operation on agricultural machinery in modern agriculture. [ABSTRACT FROM AUTHOR]

A rotation irrigation is normally implemented in water-shortage regions, such as Xinjiang area of western China with widespread drip irrigation. Nevertheless, the conventional rotation irrigation group is generally divided into the fixed districts in the manual calculation. A better solution is thus highly demanding in this inefficient calculation. The rotation irrigation group is also representing the prominent regional and hydraulic characteristics in recent years. Therefore, it is necessary to clarify how to divide the rotation irrigation group in practice. Furthermore, the operation of the irrigation system needs to gradually complete the whole plot irrigation under the turn-on and turn-off valve sequence of several branch pipes in a rotation irrigation group. Correspondingly, there is a relatively high labor intensity in the operational mode, but with low investment cost, simple maintenance, and wide popularization, compared with the automatic operation of drip irrigation. In this study, a hybrid mathematical model was proposed to explore a better solution using the flow balance and structural constraints in the technical standards and division principles. Neighborhood characteristics were determined from the spatial distribution of branch pipes in the rotation irrigation group. The neighborhood search strategy and the repair of infeasible solution were given in the radius threshold range, further to find the critical path of the maximum extent. Four algorithms were selected to solve the model separately, including the genetic algorithm (GA), Greedy-GA, Tyson polygon-GA (Voronoi-GA), and Grid GA. The GA adopted the branch pipe model of actual number coding. The chromosome coding indicated that the branch pipe was opened in that group. The initial population was constructed using the Random, Greedy, Voronoi polygon, and Grid. The spatial distribution of branch pipe was applied in the neighborhood search strategy, further to serve as the structural feature in the rotation irrigation group. A uniform crossover strategy was adopted for the crossover and mutation of the offspring population. A competitive and elite retention strategy was adopted for the selection mechanism. Furthermore, the neighborhood structure represented the use of characteristic information. In the case of the rotation irrigation group, the spatial distribution of branch pipes presented critical structural characteristics. The specific search procedure was: first to establish the adjacency matrix of branch pipe, then to generate the reachable matrix using the Warshall, finally to calculate the critical path set for each valve, thereby converting the code. In a matrix form, the fundamental and non-key matrix was generated using a reachable matrix. Then the temporary path set was calculated and sorted to compare the valves in the non-critical matrix row for the standard deviation index. Feasible solutions were selected to update the matrix using four indicators from standard deviation, path length, connectivity, and running time. Furthermore, an optimal combination was achieved, where the mean flow rate in the rotation irrigation group was 260.06 m³/h, the standard deviation was 10.9 m³/h, the path length was 7 342.6 m, the mean path length was 8 105.2m, the mean path length within the group was 386.4 m, and the mean connectivity was 22. The neighborhood search strategy of the critical path was adopted to balance the distribution of branch pipes under the condition of objective function, suitable for daily management and maintenance. The Grid-GA and repair presented an excellent performance on the rotation operating group in a grip irrigation, particularly without the dimensional disaster in the multi-dimensional combination. Consequently, the hybrid optimal model and algorithm here can meet the hydraulic calculation and engineering requirements in modern agriculture. [ABSTRACT FROM AUTHOR]

The goal of this study was to obtain an optimal nutrient solution formula of cucumber for substrate cultivation based on the comprehensive evaluation of cucumber yield, quality and fertilizer use efficiency. The concentrations of nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg) were designed by a five-level (1/2) quadratic orthogonal rotation combination design method to obtain a total 23 treatments. The experiment was carried out in Yangling, Shaanxi of China (34°28′N, 108°07′E) from March 22 to July 10, 2018. The cucumber ‘Bonai 526’ was planted in substrate bags. The substrate had the organic matter content of 185.42 g/kg, the pH value of 7.28, and the electrical conductivity of 2.05 mS/cm. During the experiments, the cucumber yield was determined. The quality indexes including soluble protein, vitamin C and total soluble sugar content, organic acid and nitrate content were measured. The comprehensive evaluation score of cucumber yield and quality was calculated by a linear weighted model and then its relationship with concentrations of four elements was fitted by a quadratic regression model. The effect of two and three factors on the comprehensive score of cucumber yield and quality were analyzed. Subsequently, a multi-objective optimization model and a genetic algorithm were used to obtain the optimal nutrient solution formula. The results showed that the optimal treatments were different based on different yield and quality indexes, indicating that the comprehensive evaluation is necessary. Comprehensive evaluation scores of the 23 treatments were obtained and used for ranking. Different treatments of solution formula had significant effects on the comprehensive evaluation scores of cucumber yield and quality. A model was established to describe the relationship between of comprehensive evaluation scores of cucumber yield and quality and the coding levels of N, K, Ca and Mg (R2 =0.99, P<0.001). From the model, the coupling effects of four elements on comprehensive evaluation scores were explored and the results showed that the coupling effects of N and K, and N and Ca on the comprehensive evaluation scores of cucumber yield and quality was significant (P<0.05)．The interaction between N and Ca coupling had a positive effect, whilst the interaction between N and K coupling had a negative effect. Similarly, the element use efficiency models of the four elements were also established with the R2 values between 0.710 and 0.944. The multi-objective optimization model and genetic algorithm evaluation method suggested that the optimal combination of N, K, Ca and Mg concentrations in nutrient solution was 14.83, 6.89, 3.55 and 4.17 mmol/L, respectively. Under the optimal conditions, the comprehensive scores of optimal cucumber yield and quality were 38.52×10-3 while the comprehensive scores of element use efficiency of N, K and Ca were 55.09%, 63.77% and 30.55%, respectively. The optimal nutrient solution formula was better than the commonly used Japanese Yamazaki cucumber special formula. The cucumber yield per plant, soluble protein, vitamin C and total soluble sugar content of the former were 21.07%, 40.91%, 53.33% and 11.48% higher than those obtained by the latter, while the organic acid and nitrate content of the former were significantly lower than those of the latter by 4.67% and 16.78%, respectively. The findings provide an insightful guidance for high yield and quality of substrate-cultivated cucumber production, and further for the scientific management of nutrient solution in cucumber farming. [ABSTRACT FROM AUTHOR]

This study aims to propose a collaborative control strategy with the task assignment and whole area coverage for heterogeneous groups of agricultural robots under the complex environment of a farmland. The new system was also utilized to reasonably allocate the workload and work area of each robot in the collaborative operation of agricultural robot groups, and thereby to improve the overall work efficiency in precision agriculture. Various performance parameters were considered, including the energy consumption, failure rate, historical workload, and service quality of agricultural robots. Taking the overall efficiency of a robot team as the optimization goal, the agricultural robots were selected to effectively perform the specific task, where the workload of each participating robot was determined in the collaboration control framework. The complex working environment of agricultural robots was set, according to the actual characteristics in the digital ecological circular farm of the Shandong University of Technology and Zibo Hefeng Seed Company, China. A field experiment was carried out on the whole area coverage strategy of robot groups. The farmland that separated by the intertwining road was taken as a primary partitioning, according to the actual farm working environment. The primary partitions were rasterized for environmental modeling operations, and thereby the common working range of robot groups was set to the unit length of cells in the grid. Specifically, the irregular obstacle was treated by the expansion operation in binary morphology of image processing, when the edge line of obstacle cannot align to the edge line of raster in the simulation. In the modeling of rasterized environment and the treatment of obstacle expansion, the raster partitioning at the second level was established inside the first level partitioning. The merging operation between the raster partitions was also conducted to reduce the number of partitionings in the raster diagram. The operations expansion and reduction were carried out for the enlarged obstacles after the partition merging. The Genetic Algorithm(GA) and Hhybrid Particle Swarm Optimization (HPSO) were combined to improve the traditional GA crossover operation in computer-assisted support systems. The genetic diversity and chromosome population structure were utilized to enhance the convergence speed, where the chromosome was crossed with the chromosome of real-time optimal fitness value in iterative process. The GA concept of chromosome population diversity was established to consider the differences of fitness value for the chromosome in GA, and the species diversity in the different phases, thereby to set the adaptive crossover probability and mutation probability. The improved GA was then used to solve the problem of traversal sequence in a depth first search algorithm, when traversing over the partitions of the first and the second level. The path search rules were set in the second level partition, where the starting point and end point of a traversal were determined in the depth-first search algorithm. The work area of robot was allotted according to the workload of robot using the depth first search algorithm, when traversing in the raster chart. The traversal rules of robot in the working area were set by A* algorithm and eight-neighborhood search algorithm, further to realize the complete coverage of robot groups on the whole region. The simulation results showed that the path length of traversing each partition, iteration times, and the converge time to the optimal solution in the improved GA were 2.8%, 69.5%, and 64.2% less than those of the traditional GA, while, 9.3%, 19.0%, and 9.9% less than those of the simulated annealing algorithm. When the total area of obstacles remained unchanged, the total area repetition rate of robot population covering the whole area increased, as the number of obstacles increased. The total area repetition rate of obstacle with the highest dispersion in the map was 23.4% in the simulation experiment. Four types of special farmland were set, where there were different shapes but the same total amount of workload. 7 obstacles with the same positions, shapes, sizes and quantities were set in each special shaped farmland. The total area repetition rates of robot were 16.7%, 13.1%, 11.9%, and 6.7%, when traversing the four farmlands. The experimental results showed that the task assignment and regional coverage scheme can achieve the robot group at work full coverage of work area within the prescribed time. The work strategy of robot groups can provide a theoretical support for collaborative operation of agricultural robot groups on the complex environment in modern agriculture. [ABSTRACT FROM AUTHOR]

To achieve efficient dispatching of agricultural machinery in China during the farming season and ensure the successful completion of farming operations, the precise scheduling of Beidou agricultural machinery was designed based on the combination of genetic algorithm and WiFi clustering algorithm. In the designed scheme，the soil moisture data was firstly collected according to the internet of things technology to predict in advance whether the field needed agricultural machinery. By Beidou satellite navigation， the job field and the agricultural machinery were located to obtain the position information, and the GIS technology was used to calculate the trajectory. Through the genetic algorithm， the iterative deduction of agricultural machinery and farmland in a region was simulated to screen out the optimal agricultural machinery scheduling. According to the schedule ， the farmer's operation needs were accurately and quickly solved. Under the no-signal environment, the agricultural machinery was positioned by the WiFi clustering algorithm to master the situation of agricultural machinery.The results show that the scheme is simple, easy to use, efficient, convenient and adaptable. It can help the local agricultural machinery stations to accurately dispatch agricultural machinery and improve the informationization and modernization level of Chinese agriculture to accelerate the process of agricultural modernization. [ABSTRACT FROM AUTHOR]

Green crop is generally used as the foreground in image segmentation of agricultural visual navigation system because of its obvious chromaticity difference from the basic image background. However, for the segmentation of maize stubble row, there are many backgrounds in the field harvested by combine harvester, such as naked land surface, maize residues, which has little color difference with the maize stubble row. To achieve accurate and quick segmentation of maize stubble row, an image segmentation approach of maize stubble row based on genetic algorithm and threshold filtering noise was proposed in this paper. Firstly, the RGB color space was selected to accomplish the segmentation, which is a basic color space and widely used in machine vision automatic guidance systems. Secondly, to reduce the difficulty of image segmentation, the region of interest (ROI) was selected by calculating the maximum of column gray value accumulation. Thirdly, the gray-scale image of the maize stubble row was obtained through the optimized gray-scale operator. Besides, the genetic algorithm was often used in global optimization, which was used to optimize the gray-scale operator. Specially, the maize stubble row image’s pixel was divided into 3 classes: land surface, residues in rows and stubble tip incision, and the 3 classes were selected as the sample to optimize the gray-scale operator by genetic algorithm. Then, the segmentation of single threshold method was used to segment the gray-scale image. Furthermore, the segmentation of single threshold method is widely used in image segmentation because of its high efficiency and convenience. Lastly, the morphological corrosion treatment (MCT) and threshold noise filtering algorithm (TNFA) were applied to guarantee the integrity of the maize stubble row region and the noise points on both sides of the maize stubble row removal. In order to verify the effect of the method proposed in this paper, 200 test images were captured from Zhuozhou Experimental Station of Conservation Tillage Research Center of Ministry of Agriculture in Baoding City, China in each October during 2014－2016. The acquisition was often on sunny day, aiming at collecting images under different natural lighting conditions, different positions in maize stubble row field. The results showed that the average relative object area error (ARAE) by our method was only 24.68%, while the AREA of the iteration method and the OTSU method were 90.67% and 86.42%, respectively. The average processing time of a test image based on this paper presented algorithm was 0.16s, which was much more time-consuming than the OTSU method (0.07s), while the processing time of our method was less than the iteration method (0.25s). Therefore, the method presented in this paper achieved better performance than other methods in maize stubble row segmentation, and was effective to segment the maize stubble row in the complicated backgrounds. The presented method can provide precise basis for detection of guidance line in maize straw covering field. [ABSTRACT FROM AUTHOR]