Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the...
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Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the apparent volatility and chaotic nature of the non-ferrous metal price sequence, forecasting it remains a difficult challenge. While prior research employed a variety of methodologies to forecast metal prices, they overlooked the critical role of chaos feature analysis and the necessity of error analysis, severely limiting prediction accuracy. This paper designs a novel non-ferrous metal price ensemble prediction system that incorporates data decomposition, phase space reconstruction, multi-objectiveoptimization, point prediction, and interval prediction. A combined kernel extreme learning machine based on the improved multi-objective lion swarm optimizationalgorithm is developed and theoretically explained to improve prediction accuracy and reliability. Additionally, the appropriate creation of the prediction interval based on the best-fit distribution of the point prediction error enabled the examination of various levels of uncertainty. In an empirical experiment using copper and aluminum prices from the London Metal Exchange, the proposed system demonstrated benefits in point and interval prediction, providing decision makers with useful prediction references.
This study focuses on ensuring the stable operation of the power grid by accurately forecasting the theoretical power generation capacity of wind turbine units, especially in scenarios integrating significant amounts ...
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This study focuses on ensuring the stable operation of the power grid by accurately forecasting the theoretical power generation capacity of wind turbine units, especially in scenarios integrating significant amounts of renewable energy into the grid. The forecasting process involves two key steps: initially forecasting wind speeds and then estimating theoretical power generation using wind turbine power conversion curves. This article proposes a wind speed forecasting system based on deep learning, integrating multiple hybrid models and employing deep learning algorithms to select the optimal wind speed hybrid forecasting model, optimized by the multi -objective mayfly optimizationalgorithm. Additionally, a wind energy conversion simulation system for wind turbines has been developed, precisely simulating the physical process of converting wind energy into electrical energy. This system, in conjunction with wind speed forecasting, estimates the theoretical power generation of wind farms. The results of this research hold significant practical implications for enhancing the operational efficiency of wind power, strengthening the grid's supply -demand balance, and increasing the economic and environmental value of wind power projects.
The traditional power grid planning lacks consideration of the uncertainty and correlation between wind and solar joint output in the same region, which poses challenges to the stable operation of the power system. Th...
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The traditional power grid planning lacks consideration of the uncertainty and correlation between wind and solar joint output in the same region, which poses challenges to the stable operation of the power system. Therefore, it is greatly important to consider the environmental and economic dispatch in light of the uncertainties and correlations associated with wind and solar energy. To tackle these issues, this paper introduces a dynamic environmental economic dispatch model that accounts for the uncertainties and correlations between wind and photovoltaic power based on their output characteristics. Initially, a probability model for photovoltaic-wind joint power is established using the Copula function. Subsequently, the Latin hypercube sampling method is employed alongside an improved K-means clustering technique to derive typical output scenarios. An adaptive multi-objective fireworks algorithm, featuring a differential selection strategy, is then utilized to enhance the environmental economic dispatch model. Finally, the IEEE 39 node system is used as an example to demonstrate the solution of the dynamic environmental and economic scheduling model. Simulation results reveal that the method for generating typical output scenarios presented in this paper effectively captures the uncertainties and correlations of photovoltaic-wind joint power. Furthermore, when compared to other optimizationalgorithms, the improved adaptive multi-objective fireworks algorithm proves to be more efficient in addressing the dynamic environmental economic dispatch challenges within the power system.
With the impact of urbanization, food shortage and environmental pollution, the sustainability and fairness of the current food supply system have attracted more and more attention. Taking reducing hunger as the main ...
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ISBN:
(纸本)9781665427098
With the impact of urbanization, food shortage and environmental pollution, the sustainability and fairness of the current food supply system have attracted more and more attention. Taking reducing hunger as the main constraint and ensuring the ecological environment and nutritional health as the secondary constraint, this paper constructs a food equity model to ensure the profitability of the food system, and uses the multi-objective optimization algorithm and the improved ideal solution to filter the optimal value in the optimal solution set.
This study presents a novel approach for optimizing the parameters of monolithic microwave integrated circuit (MMIC) functional units using machine-learning techniques and multi-objective optimization algorithms. We u...
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This study presents a novel approach for optimizing the parameters of monolithic microwave integrated circuit (MMIC) functional units using machine-learning techniques and multi-objective optimization algorithms. We utilize advanced machine-learning methods, including random forest, artificial neural networks (ANNs), and recurrent neural networks (RNNs), to construct highly accurate models that predict the performance of these units. These models are subsequently integrated with a multi-objective optimization algorithm, specifically the multi-objective particle swarm optimization (MOPSO), to generate inverse design solutions for both the geometric designs of the units and the fabrication parameters of the heterogeneous integration process. Our approach, which has been validated through chip fabrication and testing, has demonstrated its robustness as a tool for achieving optimal MMIC designs. It not only reduces the design time but also enhances the manufacturability of MMICs, thereby opening new avenues in microwave and RF circuit design.
For better performance, this paper proposes an optimizationobjective function strategy using the dual-mode matching theory of the Doherty power amplifiers (DPAs). The proposed dual-mode impedance error strategy can b...
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ISBN:
(纸本)9781665490375
For better performance, this paper proposes an optimizationobjective function strategy using the dual-mode matching theory of the Doherty power amplifiers (DPAs). The proposed dual-mode impedance error strategy can be used in the multi-objective optimization algorithm for the carrier and peaking output matching network designs in the DPA. For verification, a 1.5-2.6 GHz wideband DPA was designed to meet the impedance optimization requirements in the back-off power and saturation modes. The 6 dB back-off efficiency achieves 44.3%-56.4% over the whole frequency band, the saturation power of this DPA is 42.3-44.2 dBm with a gain of 8.4-10.2 dB in the interested band.
This paper proposes a novel image recognition method based on multi-objective evolutionary learning.A DenseNet with multi-objective evolutionary algorithm is applied to steel *** the densely connected operation,DenseN...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
This paper proposes a novel image recognition method based on multi-objective evolutionary learning.A DenseNet with multi-objective evolutionary algorithm is applied to steel *** the densely connected operation,DenseNet constructs a deep network learning framework,the data features can be extracted via the propagation with a smaller number of parameters,so as to acquire better classification *** to the difficulties in manually tuning the network structure and selecting hyperparameters,a multi-objective differential evolutionary algorithm with a knee solution is used to optimize the DenseNet *** order to balance the model's performance and complexity,lower loss and less computation are considered as optimized multi-objective functions of the network *** are conducted on the steel strip surface defect *** results illustrate that the proposed method is effective and can achieve various kinds of material defects classification *** the future work,we will try to develop an optimization model to recognize and segment steel strip surface defects effectively and then can be applie in smart industry.
In this paper,we study the path planning problem of the detection robot after the coal mine disaster,and arrange a robot to detect multiple target *** objective is to minimize the path length and the degree of danger ...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In this paper,we study the path planning problem of the detection robot after the coal mine disaster,and arrange a robot to detect multiple target *** objective is to minimize the path length and the degree of danger ***,the A-Star algorithm is used to obtain the path matrix of any two target *** an improved multi-objective optimization algorithm is proposed to find paths with Pareto optimal or near-optimal ***,the multi-objective optimization algorithm is compared with other multi-objectiveoptimization *** experimental results demonstrate that the algorithm can effectively obtain the detection path and minimize the length of the path and the degree of danger.
Developing a biomechanical model which connected with the actual anatomy of the human body is helpful to understand the human response to vibration. A finite element model of the seated human body with 175 cm in statu...
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Developing a biomechanical model which connected with the actual anatomy of the human body is helpful to understand the human response to vibration. A finite element model of the seated human body with 175 cm in stature and 68.6 kg in weight, which consists of seven segments, six joints and soft tissue, was established to reflect apparent mass based on the Hybrid III dummy model. By comparing the body segment mass percentages with previous data, the rationality of mass distribution in this model was verified. The biomechanical parameters play a crucial role in biodynamic modeling, while the joint and soft tissue parameters are difficult to choose due to the wide range of anthropometric parameters. In this study, the root-mean-square error between the calculated and the measured apparent mass was taken as objective function, and the effect of fifteen human parameters on the objective function was analyzed through sensitivity analysis. Then seven parameters with a considerable influence on the objective function were selected as design variables, and four approximate models were established for parameter optimization. Soft tissues and joint parameters of the model were determined by parameter identification, and the finite element model that can reflect vertical in-line and fore-and-aft cross-axis apparent mass of the human body without backrest was developed. The seated human model presented in this paper can also reflect the transmissibility from seat to the first thoracic spine and the main modes of the human body below 10 Hz, which is conducive to express the human response to vibration.
With the growing demand for a clean energy source, wind power is drawing increasing attention. However, its intermittence and fluctuation set strict restrictions on its development and applications. Although a vast am...
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With the growing demand for a clean energy source, wind power is drawing increasing attention. However, its intermittence and fluctuation set strict restrictions on its development and applications. Although a vast amount of research has been conducted on this subject, studies have failed to charac-terize the uncertainties of the growing intervals and have focus only on point prediction. Therefore, this paper proposes an interval prediction system that can effectively avoid the drawbacks of point forecasting. The system is composed of five units: a preprocessing unit, a feature selection unit, an optimization unit, a forecasting unit, and a result evaluation unit. The preprocessing unit, along with the feature selection unit, is applied to obtain the ideal input data. Then, the forecasting unit, whose key parameters are updated by the optimization unit, is used for interval prediction. The experimental results obtained from various evaluation metrics show that the accuracy of the developed system exceeds that of benchmark methods, and also confirm the possibility of applying the proposed method in the effective utilization of wind energy. (C) 2020 Elsevier Ltd. All rights reserved.
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