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ObjectivesConsidering the difference of primary frequency regulation ability of wind turbines under different wind conditions in wind farms, the evaluation method of frequency regulation capability of wind turbines was optimized on the basis of sag control and inertia control strategy.MethodsAn improved multi-unit frequency modulation reference power cooperative control strategy was proposed.ResultsThe introduction of multi-unit cooperative control method can effectively improve the distribution of frequency modulation reference power among units, thus effectively adjust the degree of each unit participating in the primary frequency regulation of the system. An improved cooperative control strategy was introduced on the basis of inertia control and sag control. According to the operation state of the unit under the actual wind condition, the reference power of the unit which can effectively participate in the primary frequency modulation was evaluated. The introduction of frequency modulation capability coefficient can realize the energy distribution of the reference value of frequency modulation power among units.ConclusionsThe cooperative control strategy can effectively protect the speed of wind turbine and improve the frequency response of power grid.

This study aims to improve the cooperative operation efficiency of multiple unmanned intelligent harvesters and grain trucks. A control system of multi-machine cooperative harvesting was proposed in the intelligent agricultural machinery. Two types of rice harvesters and one-grain truck were also taken as the research objects. An improved model of a continuoustime Markov chain was established for the finite state processes in the collaborative harvesting operation, according to the constraints of control decisions. The unloading conditions of harvesters varied greatly over time. A grain truck only served one harvester at a time, while the service time depended on the total amount of grain harvested. The minimum total time of collaborative harvesting was highly required to configure the harvesting sequence of each harvester. Particularly, there was the tradeoff between harvesting first or unloading first in a collaborative grain unloading task. The time that the grain truck stayed in a certain state was independent of that in the system. The process of transferring from one state to another was also irrelated with the previous and subsequent state. The unloading sequence and time of each harvester were predicted and dynamically updated to reduce the non-operation time using the improved model. The simulation results show that the control system of multi-machine cooperative harvesting effectively reduced the non-operation time for the high working efficiency. The completion time of grain truck, harvesters 1 and 2 was reduced by 10.71%, 10.25%, and 17.28%, respectively, compared with the full warehouse calling for grain unloading. The average completion time of agricultural machinery was reduced by 13.58% for the cooperative harvesting task. The waiting time was also greatly reduced in the grain unloading area. The field experiments show that the control system of multiple intelligent agricultural machineries was realized in the cooperative autonomous operation of two rice harvesters and one-grain truck. In scenario 1, there was a similar time for harvesters 1 and 2 to call the grain truck. The non-operation time for the two harvesters was 3.45 and 6.95 min, respectively. Among them, the non-operation time was gradually reduced for harvester 2 to wait in the field. The non-operation time of harvesters 1 and 2 decreased by 71.25% and 42%, respectively, whereas, the harvest efficiency increased by 6.65% and 5.22%, respectively, compared with the full-call grain unloading. In scenario 2, the intelligent agricultural machinery was able to independently enter the grain unloading area. The average non-operation and the completion time of the two harvesters were 54.67 and 59.66 min, respectively. The non-operation time of harvesters 1 and 2 decreased by 77.64% (1.9 min) and 37.09% (5.85 min), respectively, whereas, the harvest efficiency increased by 12.07% and 5.78%, respectively. The autonomous operation of harvesting/unloading transportation was realized to reduce the non-operation time of harvester for the high efficiency. Two scenarios can be expected to allocate the other ones in the vast majority of fields. The findings can provide support to the intelligent harvesting collaborative operation in unmanned farms. The improved model can also be further optimized for the scenarios when a circle of harvesting operation cannot be completed in a larger field, due to the limited granary capacity of the harvester [ABSTRACT FROM AUTHOR]

Task allocation can dominate the energy consumption in agricultural equipment during operations. Particularly, energy efficiency can promote sustainable development in the context of carbon emissions peaking and carbon neutrality in modern agriculture. Among them, tasks can be programmed into integral parts of operations in multiple agricultural machinery. In the realm of collaborative multitasking, multiple agricultural machines can be utilized to execute the operation tasks within individual fields. The well-defined partitioning of their duties can substantially reduce the overlapping coverage areas. As such, this allocation can then translate into a more effective reduction in energy consumption. Furthermore, partial idle time can be offered for agricultural machinery, when the number of tasks exceeds the available number of machines. The task allocation can also have the potential to enable the simultaneous deployment of multiple agricultural machines within the same field. In this study, a tradeoff was provided for the operational pathway and the number of rows. The operational area was then established for each agricultural machine. The potential conflicts were also effectively resolved in the course of the multimachine collaboration. Taking the deep tillage operation model as a case study, both effective and energy-efficient collaborative operations were achieved within the agricultural machinery group. To this end, a systematic analysis was performed on the energy consumption during collaborative operations. Subsequently, a multi-objective optimization was proposed to minimize the energy consumption and the completion time of the agricultural machinery group, wherein the task volume was set as the optimization variable. INSGA-II was employed to optimize this model, including the initialization stage, logistic mapping and incorporating, as well as reverse learning. An improved heuristic method of population initialization was employed to enhance the population diversity. In addition, the barrier function was designed to prevent the solution in local optima. Moreover, the uniform crossover operator and simulated single-point mutation were combined to ensure the integrity of integer encoding. The efficacy and practicality of the improved model were systematically evaluated in real-world case studies using deep loosening operations. The results demonstrate that the improved model achieved a notable 4.35% reduction in machinery group energy consumption, and a 4.51% reduction in completion time, compared with the conventional NSGA-II. Furthermore, there was a remarkable 5.51% reduction in the machinery group energy consumption and an impressive 42.65% reduction in the completion time, compared with conventional operations. In essence, the optimization fully met the immediate needs of task allocation across various work scenarios during collaborative multi-machine operations in practical agricultural production. For instance, the allocation of operating rows can be expected to efficiently define the working areas in the seeding and harvesting machines with different working widths, compared with the conventional parallel operation. This innovation can also lead to more optimal solutions in group task allocation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graphics is the property of Journal of Graphics 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.)

ObjectiveThe technology of multi-machine convoy driving has emerged as a focal point in the field of agricultural mechanization. By organizing multiple agricultural machinery units into convoys, unified control and collaborative operations can be achieved. This not only enhances operational efficiency and reduces costs, but also minimizes human labor input, thereby maximizing the operational potential of agricultural machinery. In order to solve the problem of communication delay in cooperative control of multi-vehicle formation and its compensation strategy, the trajectory control method of multi-vehicle formation was proposed based on model predictive control (MPC) delay compensator.MethodsThe multi-vehicle formation cooperative control strategy was designed, which introduced the four-vehicle formation cooperative scenario in three lanes, and then introduced the design of the multi-vehicle formation cooperative control architecture, which was respectively enough to establish the kinematics and dynamics model and equations of the agricultural machine model, and laied down a sturdy foundation for solving the formation following problem later. The testing and optimization of automatic driving algorithms based on real vehicles need to invest too much time and economic costs, and were subject to the difficulties of laws and regulations, scene reproduction and safety, etc. Simulation platform testing could effectively solve the above question. For the agricultural automatic driving multi-machine formation scenarios, the joint simulation platform Carsim and Simulink were used to simulate and validate the formation driving control of agricultural machines. Based on the single-machine dynamics model of the agricultural machine, a delay compensation controller based on MPC was designed. Feedback correction first detected the actual output of the object and then corrected the model-based predicted output with the actual output and performed a new optimization. Based on the above model, the nonlinear system of kinematics and dynamics was linearized and discretized in order to ensure the real-time solution. The objective function was designed so that the agricultural machine tracks on the desired trajectory as much as possible. And because the operation range of the actuator was limited, the control increment and control volume were designed with corresponding constraints. Finally, the control increment constraints were solved based on the front wheel angle constraints, front wheel angle increments, and control volume constraints of the agricultural machine.Results and DiscussionsCarsim and MATLAB/Simulink could be effectively compatible, enabling joint simulation of software with external solvers. When the delay step size d=5 was applied with delay compensation, the MPC response was faster and smoother; the speed error curve responded faster and gradually stabilized to zero error without oscillations. Vehicle 1 effectively changed lanes in a short time and maintains the same lane as the lead vehicle. In the case of a longer delay step size d =10, controllers without delay compensation showed more significant performance degradation. Even under higher delay conditions, MPC with delay compensation applied could still quickly respond with speed error and longitudinal acceleration gradually stabilizing to zero error, avoiding oscillations. The trajectory of Vehicle 1 indicated that the effectiveness of the delay compensation mechanism decreased under extreme delay conditions. The simulation results validated the effectiveness of the proposed formation control algorithm, ensuring that multiple vehicles could successfully change lanes to form queues while maintaining specific distances and speeds. Furthermore, the communication delay compensation control algorithm enables vehicles with added delay to effectively complete formation tasks, achieving stable longitudinal and lateral control. This confirmed the feasibility of the model predictive controller with delay compensation proposed.ConclusionsAt present, most of the multi-machine formation coordination is based on simulation platform for verification, which has the advantages of safety, economy, speed and other aspects, however, there's still a certain gap between the idealized model in the simulation platform and the real machine experiment. Therefore, multi-machine formation operation of agricultural equipment still needs to be tested on real machines under sound laws and regulations.

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 disturbance suppression algorithm based on Minimax theory is proposed for solving the disturbance suppression of multi-machine power systems with coupling performance. A pseudo-generalized Hamilton form is established based on modifying generalized Hamilton system for multi-machine excitation system. By introducing the Minimax theory into the Hamiltonian power system with disturbances， the disturbance suppression controller is determined based on the worst disturbance situation. The degree of disturbance presumption can be avoided and the conservatism of the traditional method can be reduced. Simulation results show that the method and the control strategy can make the system state converge to the initial equilibrium point rapidly during large disturbances， which effectively improves the transient stability performance of power systems. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology 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.)

[Objective] The technology of multi-machine convoy driving has emerged as a focal point in the field of agricultural mechanization. By organizing multiple agricultural machinery units into convoys, unified control and collaborative operations can be achieved. This not only enhances operational efficiency and reduces costs, but also minimizes human labor input, thereby maximizing the operational potential of agricultural machinery. In order to solve the problem of communication delay in cooperative control of multi-vehicle formation and its compensation strategy, the trajectory control method of multi-vehicle formation was proposed based on model predictive control (MPC) delay compensator. [Methods] The multi-vehicle formation cooperative control strategy was designed, which introduced the four-vehicle formation cooperative scenario in three lanes, and then introduced the design of the multi-vehicle formation cooperative control architecture, which was respectively enough to establish the kinematics and dynamics model and equations of the agricultural machine model, and laied down a sturdy foundation for solving the formation following problem later. The testing and optimization of automatic driving algorithms based on real vehicles need to invest too much time and economic costs, and were subject to the difficulties of laws and regulations, scene reproduction and safety, etc. Simulation platform testing could effectively solve the above question. For the agricultural automatic driving multi-machine formation scenarios, the joint simulation platform Carsim and Simulink were used to simulate and validate the formation driving control of agricultural machines. Based on the single-machine dynamics model of the agricultural ma‐ chine, a delay compensation controller based on MPC was designed. Feedback correction first detected the actual output of the object and then corrected the model-based predicted output with the actual output and performed a new optimization. Based on the above model, the nonlinear system of kinematics and dynamics was linearized and discretized in order to ensure the real-time solution. The objective function was designed so that the agricultural machine tracks on the desired trajectory as much as possible. And because the operation range of the actuator was limited, the control increment and control volume were designed with corresponding constraints. Finally, the control increment constraints were solved based on the front wheel angle constraints, front wheel angle increments, and control volume constraints of the agricultural machine. [Results and Discussions] Carsim and MATLAB/Simulink could be effectively compatible, enabling joint simulation of software with external solvers. When the delay step size d=5 was applied with delay compensation, the MPC response was faster and smoother; the speed error curve responded faster and gradually stabilized to zero error without oscillations. Vehicle 1 effectively changed lanes in a short time and maintains the same lane as the lead vehicle. In the case of a longer delay step size d =10, controllers without delay compensation showed more significant performance degradation. Even under higher delay conditions, MPC with delay compensation applied could still quickly respond with speed error and longitudinal acceleration gradually stabilizing to zero error, avoiding oscillations. The trajectory of Vehicle 1 indicated that the effectiveness of the delay compensation mechanism decreased under extreme delay conditions. The simulation results validated the effectiveness of the proposed formation control algorithm, ensuring that multiple vehicles could successfully change lanes to form queues while maintaining specific distances and speeds. Furthermore, the communication delay compensation control algorithm enables vehicles with added delay to effectively complete formation tasks, achieving stable longitudinal and lateral control. This confirmed the feasibility of the model predictive controller with delay compensation proposed. [Conclusions] At present, most of the multi-machine formation coordination is based on simulation platform for verification, which has the advantages of safety, economy, speed and other aspects, however, there's still a certain gap between the idealized model in the simulation platform and the real machine experiment. Therefore, multi-machine formation operation of agricultural equipment still needs to be tested on real machines under sound laws and regulations. [ABSTRACT FROM AUTHOR]

The observer-based fuzzy H∞ controller design problem was considered for steam valve opening of multi-machine power systems which is consisting of turbo-generators. Firstly， T-S fuzzy model was used to establish the model for multi-machine power systems. Because sometimes the measurable state variables of actual power systems may not be used as the premise variables of the fuzzy rules， the observer-based conditions were derived to guarantee the H∞ performance. However， the conditions are not based on the linear matrix inequalities (LMIs)， which can only be solved by a two-step method. Then the LMIs-based conditions by only one step were proposed， in which the conservativeness of the conditions was reduced. Finally， the T-S fuzzy model was established for the steam valve opening of two-machine infinite bus power systems with local linearization method， and effectiveness of the proposed controller design was verified by the simulation results. [ABSTRACT FROM AUTHOR]

The fuzzy H∞ controller design problem was studied for steam valve opening of multi-machine power systems which consisting of turbo-generators with mixed time-varying delays. The mixed time-varying delays include the interval retarded-type discrete delay， the neutral-type discrete delay and the time-varying distributed delays. Fuzzy models were established， and the controllers of the systems were designed. Linear matrix inequalities (LMIs)-based H∞ stability conditions were derived. Moreover， the results were expanded to different conditions of time-delay， including H∞ stability condition with the fast time-varying distributed delays (i.e. the derivatives of distributed delay is larger than 1). Therefore， the obtained results are new and less conservative. Finally， simulation results for the steam value opening of three-machine infinite bus systems were provided to verify the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

Due to the current research literatures of scheduling problem with learning effect only limited to single or double machines flow-shop scheduling and simple learning models, this paper proposed a multi-machine flow-shop scheduling model with job-dependent learning effect and makespan criterion. As for solving the proposed model, it analyzed the disadvantage of polynomial algorithms and heuristic algorithms, and presented a idea applied intelligent algorithm to the model. Then, it developed a novel hybrid cuckoo search algorithm by designing IMM code to transform continuous variables into job permutation, and using a chaos mapping initialize population and random replacement policy of heuristic algorithm to improve population quality and dispersion, and integrating iterative greedy algorithm and the Metropolis criterion to improve the local search. Simulation experiments show the effectiveness and superiority of the hybrid cuckoo algorithm. [ABSTRACT FROM AUTHOR]

Due to the limited rated power of individual power electronic transformer (PET),many PETs are always connected in parallel in practice. High-order harmonic interactions occur when PETs are in operation,resulting in a rise in the amplitude of the output high-order harmonics. Thereby,the power quality is influenced. The safety of PETs is also threatened by the high-order harmonics. In order to control the switching harmonics among the parallel PETs,a pulse width modulation (PWM) synchronization strategy based on the mixed information which is the combination of cloud signals and edge signals is proposed in this article. The basic idea has two parts. Firstly,the PWM synchronization is realized according to the cloud signals when the communication system works well. Secondly,the realization of PWM synchronization is switched to edge signals during the communication faults. It is proved that the high-precision and high-reliability can be guaranteed by the proposed strategy. The high-order harmonics can be coordinated-controlled well. Doing so,the interaction of high-order harmonics among PETs can be eliminated. The proposed strategy can be easily implemented due to limited requirement of additional hardware. With using the proposed strategy,the power quality and the reliability of PET can be improved.

For the low efficient of existing coal shipment method, a new type of dynamic quantitative loading system of wheel loader was designed according to actual situation of coal enterprises. The hardware, software designs and data processing method of the system were introduced. The system does't need stop wheel loader to meter in midway, and coal weight could be weighed and shown on the screen during the loading operation. Multi-machine cooperative loading could be implemented by using configuration software, and the loading efficiency of loading machine was improved.

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This article addresses the bathing problem in multi-machine collaborative operations, proposing a method based on improved self-organizing iterative clustering. This approach circumvents the issues of traditional manual parameter setting in the self-organizing iterative clustering algorithm that is often inconvenient and non-intuitive. The proposed method allows multiple machines to autonomously adjust the parameters involved in the clustering process, given a small number of intuitive hyperparameters. The ultimate goal is to iterate toward reasonable editing results. Initially, this article focuses on selecting feature vectors for the multi-machine collaborative confrontation situation. It applies standardization and principal component analysis to high-dimensional multi-machine situation information to confirm the new vector space. This space mainly encompasses position information in three dimensions and speed information. Subsequently, the paper introduces the concept of neighborhood density discrimination from density clustering. This improves the merging and splitting operations of the traditional self-organizing iterative clustering method. It optimizes and reduces the artificial parameters involved in these operations, enhancing the intelligent autonomy for batch clustering tasks. Before optimization, artificial parameters primarily include the number of expected clusters, minimum number of points within a class, number of iterations, upper limit of standard deviation that limits data distribution within a class, and an allowable shortest distance indicator between classes. Post optimization, the artificial parameters are limited to the expected cluster quantity, minimum number of points, and the number of iterations within a single classification. These optimized parameters are relatively intuitive, and the algorithm output does not strongly correlate with the input parameters. Ultimately, the paper selects algorithm evaluation indicators, including Dunn, Davies–Bouldin, silhouette coefficient, and Calinski–Harabasz. It uses these to evaluate the proposed algorithms ISODATA+ and K-MEANS+, along with the original ISODATA algorithm, against multiple artificially synthesized data sets (completely random data, Gaussian-generated data, and sin-type data) and real-world scenarios. The experimental results suggest that while KMEANS+ shows significant advantages owing to multiple manually set hyperparameters, it requires constant debugging when adjusting parameters, which increases the complexity of the task. Compared with the original self-organizing iterative algorithm ISODATA, statistical results show that the improved algorithm has equivalent capabilities to the original algorithm. This demonstrates that the ISODATA+ algorithm maintains good clustering capabilities even after removing some artificial parameters. The batching results from actual scenario tests further illustrate the effectiveness of the improved self-organizing iterative clustering algorithm in specific application scenarios, demonstrating its practicability for future real-world applications.

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Most machinery can be hands-free and remotely operated in modern agriculture. Almost all tractors are equipped with some sort of GPS technology in recent years, indicating a step on the way to fully autonomous farms in the future. A series of multiple agricultural machinery have also been introduced to realize highly efficient plant and harvest, while reducing the risk of natural disasters for large-scale production in China. Particularly, the vehicle can travel on pre-mapped roads, even to move around the obstacle. However, the parallel operation is still widely used in current multiple machinery, indicating the fixed agricultural machinery and static fixed route in advance. Furthermore, there are often most uncertain scenarios, such as a sudden failure, temporary increase, and inconsistent work efficiency of agricultural machinery in the actual farming and harvesting. These uncertainties have also posed great challenges to the operation of multiple agricultural machinery. Therefore, it is necessary to explore the multi-machine dynamic path planning, whenever the information is accessible about the barrier, particularly when the environment tends to be unpredictable and changeable. Moreover, the future unmanned farm is highly requiring the large-scale operation of multiple agricultural machinery. In this study, a multi-machine dynamic path planning was proposed for the wheeled autonomous tractors in various uncertain scenarios using an Improved Iterative Greedy (IIG) algorithm. The total completion time was also taken as the comprehensive optimization objective. More importantly, an attempt was made to deal with the inefficient or even invalid path planning after the occurrence of uncertain scenarios. The experimental results show that the scheme of path planning was timely and efficiently adjusted in uncertain scenarios. An optimal path was also found for the different numbers and performances of agricultural machinery during an iterative process. The total operation time of IIG optimized operation path in rectangular farmland decreased by 35%, compared with the traditional side-by-side operation. Specifically, there was a significant optimization effect, as the performance of agricultural machinery varied greatly. Additionally, the total turning time was reduced by 17% after IIG optimization, compared with the original. Consequently, the optimization algorithm presented a remarkable performance in uncertain scenarios, indicating excellent robustness and environmental adaptability. The finding can also provide a strong reference for the path planning of multiple autonomous machinery in unmanned farmland. [ABSTRACT FROM AUTHOR]

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A great challenge has posed on the trans-regional operation of agricultural machinery, including many tasks, strong timeliness and relatively fixed operational sequence. Therefore, a scientific and reasonable deployment scheme is still lacking to efficiently maintain operations for the production tracking in agricultural machinery in recent years. This study aims to optimize the cooperative scheduling for the multi-task and multi-machine assignment in a large-scale farm using improved multi-parent genetic algorithm (GA). Firstly, the basic locations of farmland and agricultural machinery were easily acquired and stored, as well as the task requirements distributed by farmers, with the aid of agricultural Internet of Things (IoTs) and navigation system. A multi-machine coupled with multi-tasks was analyzed, where the actual continuous operation was required to be completed in a specified time on a farm, for example, the tillage, sowing, and fertilization. Some parameters were also initialized, including the operation sequence, and the number of agricultural machines. A scheduling model was then built using a time window under the boundary conditions of multi-type machines, the distance of operation deployment, the time of preparation and operation. Besides, the model must also satisfy the following basic rules: 1) Each machine can only work on one field at the same time; 2) each field can be arranged with multiple agricultural machineries of the same task if the agricultural machineries are sufficient; 3) the operation sequence of different tasks in each farmland is fixed for the special operational procedures, while the tasks in each farmland must be executed. After that, taking the minimum operation time as the optimization goal, a feasible scheduling system was proposed using the improved multi-parent genetic algorithm (IMPGA) for the task planning. The field ID was used as the gene in the process of encoding, while the frequency of ID in the chromosome corresponded to the task of field, as the task process cannot be changed. Two parts were divided after generating primary population, remarked as the excellent and good group. The population propagates were then used in the strategy of multi-parent crossover, where a relatively superior individual was chosen from the excellent group to intersect with two good individuals chosen from the good one. The mutation probability was designed to be adjustable, when the fitness of optimal chromosome in the population cannot change after several iterations. Finally, the performance of Java-based IMPGA was verified using a series of real farmland datasets, randomly generated farmland tasks, and agricultural machinery in the Tacheng Prefecture of Xinjiang of western China. The MATLAB software was also used to generate job deployment. The experimental results showed both GA and IMPGA effectively performed the multi-task and multi-machinery assignments. Both GA and IMPGA achieved the optimal solution, when the number of fields was 5. The optimal and average solutions of IMPGA increased by 3.77% and 3.56%, respectively, when the number of fields was 10. The optimal and average solutions of IMPGA increased by 1.63% and 3.76%, respectively, when the number of farmlands was 15. The optimal solution and the average solution of IMPGA were improved by 4.46% and 3.47%, respectively, when the number of farmlands was 20. The total average quality of optimal and average solutions of IMPGA increased by 2.47% and 2.70%, respectively, indicating a better performance of IMPGA deployment. This finding can provide a reasonable scheduling scheme for the cross-regional operation of agricultural machinery in the production of the large-scale unmanned farms. [ABSTRACT FROM AUTHOR]

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Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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 Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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 Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

Conveying technology of agricultural materials has been one of the most important bottlenecks to restricting the intelligent application of farms. In general, the conveying equipment can be used to transport the materials from the feeder to the receiver. It is a high demand for the long battery life of the receiver for efficient and continuous conveying in the long distance. An automation conveying technology of agricultural materials can mainly cover all production links of farming, management, and harvesting in the complex system engineering. In this study, the current automation conveying technologies were systematically reviewed to divide into the following four categories, according to the material characteristics and conveying: automatic conveying technology of solid seedling fertilizer, liquid water medicine fuel automatic filling, harvest automatic collection, and harvest automatic unloading technology. A clear development trend was also proposed for the intelligent transportation of agricultural materials, with the development of technologies, such as 3S (Remote Sensing, Global Position System, GPS, and Geographic Information System, GIS), sensing and detection, as well as the automatic control and information processing. The final goal was to optimize the coordination of the fleet for the complete point-to-point zero inventory distribution, further realize the intelligent transportation of agricultural materials. Four stages of materials transportation usually included the meeting, docking, conveying, and separating. The main challenges were focused on the precise docking of the feeder/receiver, as well as the highly efficient, stable, and flexible conveying of materials. Furthermore, the research progress was summarized for three key technologies of intelligent material conveying, including autonomous positioning and navigation, real-time monitoring of material flow, and real-time communication of fleets. Among them, the autonomous positioning and navigation technology can be used to accurately control the position and path between the feeder/receiver, in order to ensure the precise meeting and docking of the feeder/receiver for better intelligent material transportation. The sensors can be widely used to real-time monitor the material balance in the granary of the feeder/receiver for the highly efficient, stable, and flexible transportation of materials in the real-time monitoring technologies of material flow. Technical support can also be provided to realize intelligent material transportation in the real-time communication technology of the machine group. The communication task between the feeder and the feeder can be completed by the transmission of information and data between the machine and the ground control station, all of which can then be collected, transmitted, and processed efficiently in real time. The information and data between the feeder/receiver can be used for a rapid response to the actual situation. Therefore, it is very necessary to combine the technical requirements and application scenarios of farm intelligence. The entire production can be linked to the agricultural material transportation system, including the farmland material demand decision-making technology using spatiotemporal data, the agricultural machinery material abundance prediction using the farmland digital model and variable operation prescriptions, and various material vehicles. A prediction was given for the trend of multi-machine collaborative material distribution in the intelligent farm operation, particularly from the perspective of dynamic optimization of machine distribution path. Therefore, the agricultural material conveying technology can be expected to be more intelligent, user-friendly, and precise in the future. Anyway, intelligent farms can greatly contribute to optimizing agricultural production and resource allocation in sustainable agriculture. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology 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 Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

A simulation model combining immune and genetic algorithm is proposed to solve multi-machine multi-task crane scheduling in job shop. Simulation model represents the crane characteristics of natural working environments where spatial abstraction is based on the locations between work stations and cranes in the same span. During scheduling process, multi-machine multi-task confliction is settled considering spatial constraints with available crane task priorities. Crane scheduling is evaluated by simulation model, while optimization is iterated with immune and genetic algorithm. Crane scheduling in a major span of a steelmaking factory is made to validate the simulation model, and compare between feasible schemes is given. The result shows efficiency and feasibility in industry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation 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.)

Aiming at the problems of real-time defect detection and defect classification requirements for polishing shaft surface defects in production practice, the surface defect detection system for polishing shafts, defect image acquisition, image features, traditional detection methods, etc. were studied, and the treatment methods of shaft surface defect detection were summarized. Two detection systems based on parallel computing were proposed, i.e. cloud-based Spark multi-machine parallel distributed computer cluster detection system and singlemachine multi-core CUDA parallel processing detection system. Through the analysis of Spark architecture multi-machine distributed parallel computer theory, and analysis of the principle of mathematical parallelization combined with CUDA single-machine multi-core parallel operation theory, the experiments were conducted. The results indicate that the single-machine multi-core parallel processing detection system based on CUDA is suitable for real- time detection, and the multi-machine distributed parallel detection system based on Spark is not suitable for real-time detection systems, but is suitable for big data processing systems. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power 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.)

Based on the actual process of dipping sonar in on-call submarine searching, combined with the classic extended spiral search method, a multi machine dipping sonar on-call submarine searching model was established. A submarine motion model was built based on the possible avoidance measures that submarines may take during the on-call submarine searching. We calculate the variation of detection probability of suspended sonar with response time and response distance of dipping sonar. Moreover, we also analyze the military force requirements of dipping sonar for submarine search under different response time and response distance of dipping sonar. So the obtained relevant conclusions can provide theoretical basis for tactical decision-making of anti-submarine helicopter response submarine search.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 Electric Drive is the property of Electric Drive 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 Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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 Yellow River is the property of Editorial Board of Yellow River 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 Mechanical & Electrical Engineering is the property of Mechanical & Electrical Engineering Magazine 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 Zhejiang Electric Power is the property of Zhejiang Electric Power 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.)