Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a su...
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This paper provides an oscillation trajectory optimization and control method for two-link underactuated manipulators(UMs) in a vertical *** proposed method solves the problem that the UMs cannot always enter the bala...
This paper provides an oscillation trajectory optimization and control method for two-link underactuated manipulators(UMs) in a vertical *** proposed method solves the problem that the UMs cannot always enter the balance region in the partitioning ***,we establish the system dynamic model,and analyze the system couple ***,we program an oscillation trajectory for the active link,and use the intelligent method to obtain the trajectory parameters,so ensuring the system can reach the area adjacent to the target position through tracking ***,we design the controller to realize the stable control at the target ***,the simulation results show the effectiveness and generality of the control strategy.
Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of o...
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Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.
Stable control and active disturbance rejection strategy is proposed for planar 2R underactuated robot via intelligent algorithm in this paper. At first, we build the dynamic model and describe the control characteris...
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Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel auto...
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Geological drilling process, owing to complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dim...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
Geological drilling process, owing to complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process.
Sample generation is an effective method to improve the performance of hyperspectral image classification by generating virtual samples for training sample expansion in the training process of classification. However,...
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The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. H...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications.
Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the world. In order to improve the early warning of disasters, a persistent monitoring method of multi-age...
Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the world. In order to improve the early warning of disasters, a persistent monitoring method of multi-agent systems is proposed in this work. To ensure that the agent's energy is never exhausted, the set invariance constraint is included in the optimization problem. The goal is to minimize the difference between the actual control input of the robot and the nominal control input corresponding to the task to be performed. Moreover, the control barrier function (CBF) is used to transform the forward invariance of a subset of the robot state space into a control input constraint. The coverage control method in an uncertain environment is verified by numerical simulation. This work provides new insights into effective monitoring and early warning of geo-hazards.
With the bursting of autonomous and assistant driving systems, traffic accident prediction has attracted increasing attention during the past few years. However, predicting traffic accidents is extremely challenging d...
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