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Copyright of Telecommunication Engineering is the property of Telecommunication 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.)

To further improve the convergence and solution accuracy of competitive swarm optimizer, a variety of population entropy competitive particle swarm optimization algorithm (CSOPE) is proposed. Firstly, a nonlinear inertia weight adjustment strategy is proposed to balance the global exploration ability and local exploitation ability of particles. Secondly, a population state detection strategy based on entropy model is proposed, which calculates the population entropy by the standardized quartile difference and standardized median difference of the population. The population state is monitored by the difference in entropy values between adjacent generations of the population. When the population is in a convergence state, it uses gray wolf search to exploit winner particle locally to improve the convergence accuracy of the algorithm. The proposed CSOPE algorithm is compared with other 8 optimization algorithms on 21 test functions in CEC2008 and CEC2013, and the experimental results show that the solving accuracy and convergence of the CSOPE algorithm are significantly improved. The CSOPE algorithm is applied to the node localization problem in wireless sensor networks, and the results show that the CSOPE algorithm has high localization accuracy. [ABSTRACT FROM AUTHOR]

Objectives: In order to effectively filter out complex noise components in GNSS coordinate time series and extract effective signals, we construct a denoising method based on parameter-optimized variational modal decomposition (VMD). Methods: First, the combination of permutation entropy and mutual information is used as fitness function, and the optimal parameter combination of the mode decomposition number K and the quadratic penalty factor α of VMD is obtained by using grey wolf optimization algorithm (GWO). Then the GNSS coordinate time series is decomposed into K eigen mode function components by VMD. Finally, the sample entropy is used to determine the effective modal component, which is reconstructed as an effective signal, so as to realize the effective separation of signal and noise.The GWO-VMD method is compared and analyzed with the empirical mode decomposition (EMD), wavelet denoising (WD) and IVMD methods by using the simulated signal and the measured data from 20 reference stations of the crustal movement observation network of China for experiments. Results: The simulated signal experiments show that the three denoising evaluation indexes of root mean square error,correlation coefficient and signal-to-noise ratio of GWO-VMD denoising signal are better than EMD, WD and IVMD methods. The experiments on the measured data show that the GWO-VMD method can reduce the amplitude of noise significantly. In terms of the velocity uncertainty of the reference station, the overall GWO-VMD method reduces the velocity uncertainty better than the EMD, WD and IVMD methods. Conclusions: The GWOVMD method can more effectively remove the noise from GNSS coordinate time series and better preserve the original characteristics of the signal, which can provide reliable data for subsequent analysis and processing. [ABSTRACT FROM AUTHOR]

Aiming at the task assignment problem of heterogeneous UAV performing multi-type fixed tasks on the ground, a task allocation method based on Cooperative Multiple Task Allocation Problem (CMTAP) and Modified Chimp Optimization Algorithm (MChOA) is proposed. Firstly, the CMTAP model is established, and the combat scene is depicted by setting the elements in the quad, and a multi-objective evaluation function with the highest total return, the lowest threat cost, and the shortest total flight range is established. Secondly, the MChOA algorithm is used to complete the model solving: the chaotic reverse learning strategy is adopted to improve the diversity of the intial population distribution. In the iteration stage, the global and local search capabilities of the algorithm are better balanced by adopting the dynamic adjustment strategy of preemptive behavior. Comparative experiments show that compared with the original algorithm, the resulting task allocation scheme reduces the average total voyage in the two scale scenarios by 22.6% and 21.1%, respectively, while maintaining a higher task gain and a lower threat cost. [ABSTRACT FROM AUTHOR]

Underground gas storage is an important facility for regulating the supply and demand of natural gas, optimising the allocation of gas sources and ensuring the safety of gas supply. The traditional emergency response mechanism for gas storage often relies on emergency response agents’ experience and judgement, while there is a lack of a systematic and scientific decision support method to ensure the efficient transfer of information in the emergency response process and maximize the benefits of emergency response scheme. A gas storage reservoir emergency response optimization method was proposed, which constructs a gas storage reservoir emergency response model using the intelligent body cooperative response modeling language. Besides, a method of calculating the disposal benefit of emergency response scheme based on the depth-first search strategy was presented, which determines the optimal cooperative response scheme by using the benefit rate as a robustness indicator of the emergency response cooperative scheme. Finally, taking the emergency response process of Chongqing Xiangguosi Gas Storage Reservoir as an example, a case study was conducted. The results show that compared with the original emergency response scheme, the optimal cooperative response scheme reduces the end-of-response elapsed time by 38. 4% and improves the rate of return by 26. 61% . The coordinated response scheme combined with the optimisation algorithm is effective in reducing time consumption and increasing disposal yield [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 analysis of creep effects in finite element analysis depends on whether the exponential form of the creep coefficient can accurately reproduce the creep coefficient curve. This paper aims to develop a high-precision fitting method for arbitrary creep coefficient curves to achieve accurate finite element creep analysis. The core issue was examined, and an optimization algorithm was proposed. First, the simulated annealing algorithm was used to search for the global optimal solution, followed by the application of the Levenberg-Marquardt algorithm for precise calculations. Two examples were employed to verify the method. The results showed that: (1) The proposed creep coefficient curve fitting method could accurately fit the creep coefficient curve. (2) The case studies showed that the algorithm achieved a correlation coefficient ( R) above 0. 999 9 when fitting the creep coefficient curves according to highway and railway standards, indicating high fitting accuracy and strong general applicability. (3) The proposed algorithm featured a simple structure and clear hierarchy, allowing for easy implementation using common programming languages. This method provides valuable insights for the development of finite element program creep analysis modules or related scientific research on concrete creep behavior. [ABSTRACT FROM AUTHOR]

In response to the lack of fault diagnosis methods for bulb tubular hydroelectric units, this study utilizes a rotor fault simulation platform to simulate typical faults within these units as the basis. This research combines the Subtraction-Average-Based Optimizer (SABO) with Chaotic Mapping, to enhance Variational Mode Decomposition (VMD) for decomposing raw data. The components obtained are then analyzed using multiscale sample entropy, with the results fed into a BP neural network optimized by a multi-strategy improved Butterfly Optimization Algorithm (BOA) for fault diagnosis and classification identification. The results demonstrate a diagnostic accuracy of 98.75%, significantly superior to non-optimized models. This research provides a valuable addition to the existing fault diagnosis methods for bulb tubular hydroelectric units. [ABSTRACT FROM AUTHOR]

Hydrological model is an important tool for flood simulation, and most of the models contain a certain number of parameters. There? fore, parameter calibration is one of the keys to improve the precision of hydrological model runoff simulation and ensure the accuracy of mod? el results. As the optimization standard of parameter calibration process, there is currently no detailed classification system to provide a clear selection basis for the selection of objective function. In order to explore the effects of different types of objective functions on the calibration results of hydrological model parameters and the precision of runoff simulation, this paper comprehensively considered various types of objectives, such as Nash efficiency coefficient, Kling-Gupta coefficient, flood relative error, etc., and constructed an objective function classification system that includes high flow target, low flow target, flood volume target, flood process target and weighted target. According to the requirements of the current hydrological forecast information standard, some criteria were provided for the selection of objective function. Further, the Xinanjiang model was established, and the optimization algorithms of SCE-UA and NSGA-II were introduced. A variety of objective function selection schemes were set up for different algorithms, and the influence of different objective function schemes and optimization algorithms on the precision of runoff simulation was analyzed by taking Muma River in Hanjiang River basin as an example. The results show that using a single objective function can improve the simulation accuracy of hydrological elements embodied by the objective function; Selecting the appropriate objective function is the key to improve the precision of runoff simulation. Under the requirements of existing norms, selecting flood process target and high flow target is the best choice to ensure the accuracy of hydrological model. Compared with Nash efficiency coefficient, Kling Gupta coefficient has better performance in flood volume simulation accuracy and is a better choice for flood process target. The simulation results of weighted single target method and multi-target method are different in the high-flow part. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Cotton Textile Technology is the property of Cotton Textile 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.)

Copyright of Light Industry Machinery is the property of Light Industry Machinery 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 To use neural networks and optimization algorithms, establish an obesity level prediction model to assess obesity risk. Methods Perform correlation analysis on 2 111 recorded data collected from participants aged between 14 and 61 years old in Mexico, Peru, and Colombia, and establish a BP neural network obesity level prediction model. At the same time, optimize the number of hidden nodes and transfer function of the model through pruning to find the optimal network structure. In addition, the genetic algorithm and the simulated annealing algorithm were used to optimize the weights and thresholds of the model, ultimately establishing a high-precision and practical GASA-BP neural network obesity level prediction model. Results The R2 of the prediction model was 0.975 1, and the MAE was 0.352, indicating high prediction accuracy and strong practicality. In the process of predicting obesity levels in the model, weight index was the most important, with a correlation of 0.913 with obesity levels. The correlation between overweight members in the family was also relatively strong, with a correlation of 0.505. Conclusion The GASA-BP neural network prediction model performs better than other models in predicting obesity levels, and can make the most accurate prediction of obesity levels, providing guidance and reference for personalized obesity assessments and subsequent prevention and control measures. [ABSTRACT FROM AUTHOR]

To improve the ride comfort of commercial vehicle, the parameters of the suspension system of commercial vehicle were optimized. Based on the actual test data of the air spring, the stiffness characteristic curve was fitted, and the multi-body dynamic model of the front and rear suspension and the entire vehicle was established. The smoothness of the vehicle under random input and pulse input conditions was simulated. The parametric multi-platform co-simulation model of the suspension system was built, and the degree of influence of the design parameters of the suspension system on ride comfort was obtained through the global sensitivity analysis. The multi-objective response surface approximation model for vehicle ride comfort was established, and the multi-objective genetic optimization algorithm was used to optimize the parameters in the feasible region. The results show that under the random input conditions, the root mean square (RMS) values of the weighted acceleration at the driver and passenger positions are respectively reduced by 12.50% and 29.71% when the vehicle speed is 100 km/h. Under pulse input conditions, the maximum vertical accelerations at the driver and passenger positions are respectively reduced by 14.69% and 31.28% when the vehicle speeds are respective 20 km/h and 30 km/h, and the optimization effect is obvious. [ABSTRACT FROM AUTHOR]

For the problems of complex forest canopy images and poor segmentation accuracy, a novel canopy image segmentation method based on improved northern goshawk algorithm (INGO) by using multi-strategy fusion was proposed. Firstly, a random reverse learning strategy was introduced for the initialization of northern goshawk to increase population diversity and improve search efficiency. Adaptive weight factors were added in the exploration phase of the northern goshawk to improve the algorithm's search ability and accelerates its convergence speed. Non-linear convergence factors were introduced in the development stage of northern goshawk to balance global search and local development capabilities. Secondly, multiple-threshold Kapur entropy was employed as the fitness function, the improved algorithm was tested using eight benchmark functions, and the results demonstrated that it effectively enhanced both accuracy and search speed. Finally, the improved algorithm was applied to threshold segmentation research on forest canopy images, and comparative analysis was conducted on fitness value, peak signal-to-noise ratio of forest image segmentation (PSNR), structural similarity (SSIM), and feature similarity (FSIM) . Experimental results indicated that the improved algorithm can obtain more accurate forest canopy segmentation thresholds and higher segmentation accuracy [ABSTRACT FROM AUTHOR]

To address the issue of high communication cost under the federated learning framework in multi-tenant data centers, this paper proposed an optimization algorithm based on ternary evolutionary model parameters. Firstly, it constructed a federated learning architecture tailored to multi-tenant data centers for data privacy protection. Secondly, in response to the excessive communication overhead stemming from the implementation of the federated learning framework, which increasing interactions between tenants and the data center, it proposed an optimization algorithm that utilized ternary evolutionary model parameters. This algorithm aimed to reduce redundant communication in the exchange of model parameters between tenants and the data center by integrating the optimal local model with the evolutionary direction of ternary vectorized model parameters. Moreover, by analyzing privacy research based on federated learning, the algorithm effectively ensured the privacy of tenants participating in the training during the communication process. Finally, experimental results demonstrate that, while maintaining training accuracy, the proposed method can effectively reduce redundant communication costs by 30% compared to the federated averaging baseline algorithm. [ABSTRACT FROM AUTHOR]

The weighted set covering problem (WSCP) is a famous NP-hard problem. In order to solve the WSCP efficiently by using artificial bee colony algorithm(ABC), this paper proposed a novel binary ABC(nBABC). In nBABC, it proposed a global evolution operator which combined random learning and inheritance to improve the global exploration ability of the algorithm. Secondly, based on the dynamic adjustment strategy, it proposed an adaptive random inversion operator to maintain the balance between exploration and development. Based on the idea of approximate algorithm, it proposed the repair algorithm WSCP-GRA and optimization algorithm WSCP-GOA for dealing with the infeasible solution of WSCP, on the basis of which a new method for solving WSCP was given by using nBABC. In order to verify the efficiency of nBABC in solving WSCP, using it to solve 45 WSCP instances in OR-Library. The comparison with several algorithms shows that nBABC can obtain the optimal values of all instances, which is more competitive than the existing algorithms for solving WSCP. [ABSTRACT FROM AUTHOR]

The survival behavior of snakes in nature generates the snake optimization (SO), a meta-heuristic optimization algorithm. But the original snake optimization algorithm suffers from slow convergence and easy to fall into the local optimum, so this paper proposed an improved snake optimization algorithm that combined elite initialization and K-nearest neighbors improved snake optimizer (EKISO). Firstly, in order to improve the quality of the initial population, it proposed elite initialization during the population initialization stage, which generated high-quality initial population individuals based on elite individuals of the population. Secondly, optimizing the spiral foraging strategy with a shock factor expanded the search range during the global exploration stage and enhanced the algorithm's local escape capability. Finally, in the local exploitation stage, it proposed a position update method based on the K-nearest neighbor concept, enhancing the information interaction capability among population individuals, thus accelerating convergence speed and improving convergence accuracy. This method compared with seven other optimization algorithms using 14 classical test functions and 4 CEC2017 test functions, which proves that EKISO converges faster, has higher accuracy and is not easy to fall into local optimum. To further validate the practicality and feasibility of EKISO, this paper applied it to pressure vessel design problems. Experimental comparative analysis reveals that EKISO possesses certain advantages in dealing with real optimization problems. [ABSTRACT FROM AUTHOR]

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 Journal of Ordnance Equipment Engineering is the property of Chongqing University 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 Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 Chongqing University of Technology (Natural Science) is the property of Chongqing University 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 Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University 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 Natural Gas Industry is the property of Natural Gas Industry Journal Agency 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.)

It's proposed that a systematic average age of information optimization algorithm that integrates sensor energy supply, data transmission timeliness, and mobile user requirements. Firstly, an unmanned aerial vehicle assisted WSN is used to secure the energy collection and data transmission from the sensors. Secondly, AoI is introduced as a measure to jointly optimize multi-device scheduling, transmit power and UAV trajectories to establish a non-convex optimization problem with the objective of minimizing the average AoI of the sensors. Then, the non-convex problem is transformed into a convex one by means of constraint relaxation, variable substitution and successive convex approximation, and an iterative average AoI minimization algorithm is designed. Finally, the algorithm is verified through simulation, and the results show that the algorithm effectively improves the freshness of the sensor data while satisfying the mobile user experience. [ABSTRACT FROM AUTHOR]

To maximize the utilization of limited energy and achieve flexible and efficient grid connection for spacecraft power supply systems' a composite grid topology optimization model for power transmission and signal communication is proposed based on deep reinforcement learning (DRL). Various interpretable component models are employed based on knowledge distillation principles to analyze the optimization mechanism. Firstly' the transformation law of the control domain of the spacecraft bus voltage regulation in the on-orbit operation stage is analyzed' and the composite network topology model of power transmission and signal communication is established by combining the node propagation parameters. Secondly asynchronous advantage actor-critic (A3C) is utilized to adaptively optimize potential operational reliability risks in routing distribution and topology of the electrical signal transmission network. Finally various interpretable components are used to perform knowledge destination on the trained DRL model forming an interpretable quantitative analysis method. The proposed method theoretically predicts optimal grid-connected processes of space power supply under random shadow effects, providing theoretical support and reference for designing space power supply controllers under higher task requirements and complex environments. [ABSTRACT FROM AUTHOR]

The problem of lunar interferometric synthetic aperture radar (InSAR) satellite formation configuration design is studied. Firstly, the relative dynamic model of lunar InSAR satellite based on relative eccentricity/inclination (E/I) vector is established. Then, the long-term stable flight conditions of formation flying under the perturbation environment of lunar orbit are studied. Taking the relative altimetry accuracy and fuzzy altitude as optimization indexes, and considering the inter-satellite safety, the objective function of formation configuration design is given. In response to the existing research methods are unable to solve the multi-objective problem of configuration design, an improved multi-objective ant-lion optimization algorithm in applied to achieve the configuration design of lunar InSAR satellite formations. Simulation results show that the proposed algorithm in better in baseline utilization rate, which verities the adaptability and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Correctly and quickly measuring the attitude of aircraft is an important prerequisite for achieving precise docking missions for manned spacecraft. Traditional attitude measurement requires splicing three attitude parameters. This paper proposes an improved optimization algorithm that is applied to attitude measurement with electromagnetic wave structure vectors as reference, achieving synchronous, overall, and rapid measurement of attitude parameters. Firstly, a guidance vector in established based on the changing relationship between multiple electromagnetic vector sensors and received signals, the attitude rotation m the form of an exponential function in the guidance vector in expanded by Taylor approximation. Then an equal-step traversal algorithm combined with Newton method in used to quickly search the spectral peak of the space spectral function of multiple navigation signals, and the attitude measurement of the aircraft in completed. Simulation results show that the attitude measurement accuracy of this method is high and the error can be controlled at about 0. 01°, which improves the efficiency of attitude measurement. This method also has good anti-interference ability and has good application value. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication 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.)

A sound performance prediction method based on the artificial neural network (ANN) was proposed to meet the requirements of rapid prediction and optimization design of resonant sound-absorbing metamaterials. Firstly, a theoretical model was established for multilayer perforated resonant sound-absorbing metamaterials (MPRSMs) composed of microperforated panels and Helmholtz resonators, which was then verified through simulation and experiments; subsequently, a dataset was generated with the theoretical model, and in turn an ANN model was constructed by means of the back propagation (BP) neural network to build the mapping relationship between structural parameters and acoustic performances; afterwards, the trained ANN model was combined with the genetic algorithm to optimize the acoustic performance of the MPRSMs. The results show that, the trained ANN model can accurately predict the sound absorption performance of the MPRSMs, and the prediction efficiency improves by more than 50% compared to the theoretical model; the combination of the ANN model and the optimization algorithm can not only improve the optimization efficiency, but bring good low-frequency broadband sound absorption performance of the optimized structure. The ANN provides convenience for large-scale structural performance prediction calculations and has broad application prospects in structural design and optimization of metamaterials. [ABSTRACT FROM AUTHOR]

Mechanical metastructures have superior mechanical properties coming from the topology of sperially designed representative elements, and have attracted wide attention due to their mathematical basis, super normal features and broad applications. To optimize the design procedure, grasp the future trends and promote the interdisciplinary innovation, the fundamental design concepts and research advancements of mechanical metastructures were reviewed. First, the design methods for mechanical metastructures were classified based on the forward and inverse design concepts. Second, within the forward design category, the principles, applicable fields, and optimization directions of periodic superstructures, surface defect superstructures, and mathematical model-based superstructures were discussed. For the reverse design category, the progresses and existing problems in the application of optimization algorithms and learning algorithuns in the field of mechanical metastructures were analyzed, with future challenges and open issues concluded. [ABSTRACT FROM AUTHOR]

To improve the stability of underactuated robotic arms in the sorting process of fragile parts, a fuzzy PID control algorithm is proposed to optimize their grasping performance by improving the particle swarm algorithm. Firstly, the characteristics of the underactuated robotic arm grasping force control system were analyzed and a specific strategy was proposed that combines particle swarm algorithm optimization algorithm with fuzzy PID grasping force control system. Secondly, methods such as dynamic inertia weights were introduced into particle swarm algorithm to improve its iteration speed and avoid falling into local optima. On this basis, the improved particle swarm optimization algorithm was used to optimize the relevant parameters of the fuzzy PID controller, achieving online self-tuning of fuzzy rule weights and quantization factors, and solving the problem of PID parameters unable to be dynamically adjusted. Finally, the method was simulated and analyzed. The results show that the control algorithm designed in this paper can achieve stable grasping force within 0.8 s. with a steady-state error of less than 0.2%, and a disturbance tuning time of 0. 262 s. The transient response speed, control accuracy, and stability of the system are significantly improved. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to compare and analyze the applicability of modified Gash model and neural network model in simulating and predicting canopy interception of artificial forest to reveal the canopy interception and its response process of Robinia pseudoacacia in arid area, and to provide scientific basis for further understanding of forest eco-hydrological process and its regulation mechanism. [Method] Robinia pseudoacacia plantation in the east of the Yellow River of Ningxia was taken as the research object, the stemflow and throughfall were observed and the canopy interception was calculated. The modified Gash model and neural network model were used to simulate the canopy interception of Robinia pseudoacacia forest. [Result] (1) The throughfall, stemflow and canopy interception of Robinia pseudoacacia plantation in the study area were 154.19 mm, 5.61 mm and 16.5 mm, respectively, and the thresholds for throughfall and stemflow were 1.37 mm and 2.17 mm, respectively. (2) Both the Gash modified model and the optimized neural network model could better simulate the canopy interception of Robinia pseudoacacia. The absolute error, mean square error, root mean square error and mean absolute percentage error of the Gash modified model were 0.20%, 0.06%, 0.24% and 52.43%, respectively. The fitting accuracy of the simulation results reached 83%. Compared with the Gash modified model, the mean square error of the BP neural network algorithm model (SSA-BP) optimized by the sparrow search algorithm was reduced by 61.48%, the mean absolute error was reduced by 40.39%, the root mean square error was reduced by 37.93%, the mean absolute percentage error was reduced by 50.52%, and the coefficient of determination was increased by 1.2%. [Conclusion] In the simulation study of canopy interception, the BP neural network model with sparrow search algorithm has a good reliability, which can effectively reduce the simulation error and improve the prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

By analyzing the characteristics of collaborative decision-making behavior of heterogeneous clusters, this paper introduces quantum decision-making model to solve the autonomous decision-making problem of heterogeneous clusters. First, it is triggered from the OODA loop process to clarify the control mode of heterogeneous cluster collaboration. Then, aily ming at the specific problem of heterogeneous cluster cooperative cruise, the tasks of unmanned boat cluster and UAV cluster are split, and the application models of formation optimization and autonomous detection are constructed respectively. The formation optimization algorithm is designed to dynamically generate the best communication formation structure. The quantum decision cloud and feedback scoring mechanism are designed to select the most suitable decision content among many possible detection options. Finally, the effectiveness of formation optimization model and autonomous detection model in heterogeneous cluster cooperative cruise is demonstrated by simulation experiments. Experiments show that the quantum decision model has greater applicability and mobility in decision behavior selection and optimization than the conventional decision model. [ABSTRACT FROM AUTHOR]

In view of the low efficiency of transformer fault diagnosis, an improved grasshopper optimization algorithm is proposed by combining dissolved gas analysis in oil with artificial intelligence method to optimize the transformer fault diagnosis method of Bayesian network. The differential evolution algorithm and simulated annealing algorithm are used to improve the locust algorithm, which improve the optimization ability of the algorithm. The improved locust algorithm is applied to the Bayesian network structure learning to construct the transformer fault diagnosis model, and the method proposed in this study is used to diagnose the transformer fault. The experimental results show that the diagnosis accuracy of this method is 92.7%, which is higher than that of other algorithms [ABSTRACT FROM AUTHOR]

The GEO (Golden Eagle Optimizer) is a population based meta heuristic algorithm that simulates the cooperative hunting behavior of golden eagles. In view of the problem of poor solution accuracy and local optima traps in the GEO algorithm, this study proposes an improved MERGEO (Mapped Elitist Reverse GEO) algorithm. Based on the original algorithm, symmetric mapping search strategy, adaptive elite strategy and random backward learning mechanism, are used to balance the exploration and development stages of the algorithm, and obtain the ability to avoid local optimal and better optimization accuracy. The independent strategy effectiveness analysis, scalability analysis and optimization performance comparison with other algorithms are carried out on 10 benchmark test functions. The experimental results show that the improved MERGEO algorithm has strong competitiveness and good optimization ability. The improved algorithm is applied to the coverage optimization problem of wireless sensor networks and pressure vessel design problem, which verifies the practical application value of improved algorithm. [ABSTRACT FROM AUTHOR]

Maintaining population diversity and improving algorithm search efficiency are two major problems that need to be solved urgently in the multimodal multi-objective optimization. To address the above problems, this paper proposed a multimodal multi-objective brain storm optimization algorithm based on zoning search and reinforcement learning (MMBSO-ZSRL). In the MMBSO-ZSRL, the decision space was first decomposed into multiple subspaces to reduce the search difficulty and maintain the population diversity. Subsequently, the proposed algorithm used SARSA algorithm to balance the global exploration and local exploitation capabilities of the brain storm optimization algorithm. Additionally, the MMBSO-ZSRL utilized the special crowding distance to select individuals for guiding the population evolution. To verify the performance of the proposed algorithm, this paper selected six advanced multimodal multi-objective optimization algorithms and the IEEE CEC2019 multimodal multi-objective problem benchmark test suite for experiments. Experimental results demonstrate that the overall performance of the MMBSO-ZSRL is significantly better than that of compared algorithms. The proposed MMBSO-ZSRL can not only find the Pareto front with better diversity and approximation, but also find more Pareto optimal solutions in the decision space. [ABSTRACT FROM AUTHOR]

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 China Mechanical Engineering is the property of Editorial Board of China Mechanical 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 Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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 Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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 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 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 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.)

With the increasing penetration of distributed energy in power systems, the optimal power flow (OPF) analysis-based distribution network energy management system faces challenges such as high data dimensionality, long solution time, and large computational load. In response to these challenges, this paper aims to use the alternating direction method of multipliers (ADMM) to solve the OPF problem of active distribution systems in a fully distributed fashion. Considering the impact of distribution system partitioning on the performance of distributed algorithms, a data-driven network partitioning method based on measurement data is proposed to accelerate the convergence rate of the optimization algorithm. Moreover, different from traditional ADMM algorithms that rely more or less on certain global information, this paper integrates a consensus-based method to coordinate the balance problem at the interface between regions, thereby achieving a fully distributed solution to the OPF problem. In addition, this paper employs the LinDistFlow AC power flow approximation model to resolve the non-convexity challenges of the OPF problem. Finally, the effectiveness of the proposed method is verified through tests on IEEE distribution network cases of different sizes, and its performance in terms of error accuracy and computation time of the optimization algorithm is superior in comparison to other partitioning methods. [ABSTRACT FROM AUTHOR]

As more and more workflow applications are deployed in the cloud, how to optimize resource scheduling cost while satisfying workflow deadline constraints is a popular research area. In this paper, we propose a cost optimization with deadline constraint (CODC) algorithm. Firstly, workflow tasks are merged to reduce the data transfer overhead between different instances. Secondly, paying attention to the impact of parent and subtasks on the priority of the current task, the case of unmet task sub-deadlines is considered to select the instance that completes task execution earliest. Finally, the experimental results are compared with the existing algorithms on five workflows. CODC algorithm has a lower workflow execution cost compared to other three comparison algorithms. [ABSTRACT FROM AUTHOR]

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture 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 Ordnance Equipment Engineering is the property of Chongqing University 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.)