检索结果-内蒙古大学图书馆

A combined prediction model based on secondary decomposition and intelligence optimization for carbon emission

APPLIED MATHEMATICAL MODELLING 2023年第1期121卷 484-505页

作者： Yang, Hong Wang, Maozhu Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. & COPY;2023 Elsevier Inc. All rights reserved.

关键词： Carbon emission Secondary decomposition intelligent optimization algorithm Weighted combination Error correction

来源：评论

学校读者我要写书评

暂无评论

Fractional-Order Model Predictive Control With Adaptive Parameters for Power Converter

引用

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2023年第3期11卷 2650-2660页

作者： Ke, Zipeng Wang, Jun Hu, Bo Peng, Zishun Zhang, Chao Yin, Xin Dai, Yuxing Hunan Univ Coll Elect & Informat Engn Changsha 410012 Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China Guizhou Univ Coll Elect Engn Guiyang 550029 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

In this article, we propose a fractional-order model predictive control (FOMPC) with adaptive parameters to improve the control accuracy of power converters, which introduces fractional-order calculus into the predictive control modeling process. Then, the adaptive parameter design method based on the intelligent optimization algorithm is proposed to achieve the optimal parameter combination of FOMPC under different operation conditions. Finally, a power factor correction (PFC) converter is established as a case study to validate the proposed control method. Compared to the conventional integer-order MPC, the proposed FOMPC with adaptive parameters can achieve a 49% total harmonic distortion (THD) reduction while transient response due to load changes was reduced by 57% without increasing the switching frequency and sacrificing efficiency.

关键词： Fractional-order calculus intelligent optimization algorithm model predictive control (MPC) power factor correction (PFC)

来源：评论

学校读者我要写书评

暂无评论

Spark-based intelligent parameter inversion method for prestack seismic data

引用

NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 4577-4593页

作者： Yan, Xuesong Zhu, Zhixin Hu, Chengyu Gong, Wenyin Wu, Qinghua China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China WuHan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China

Seismic exploration is an oil exploration method by utilizing seismic information. Useful reservoir parameter information can be gained through inversion of seismic information to effectively carry out exploration work. Prestack data are characterized by large data size and rich information. Rich reservoir parameter information can be obtained through inversion of prestack data. Due to mass prestack seismic data, existing single computer environment cannot satisfy computation requirement of huge data size. Thus, an efficient and fast method is urgently needed to solve the inversion problem of prestack seismic big data. Since local optimum may be easily caught when genetic algorithm is used to optimize elastic parameters, the inversion effect is not obvious. In particular, the optimization effect for the density parameters is not good. An intelligent optimization algorithm is proposed in this paper for elastic parameter inversion of prestack seismic data. The algorithm improves genetic manipulation. The improved algorithm has been used for model trial for log data, and good inversion effect has been achieved. The inverted elastic parameters well fit with the log curve of the theoretical model. The improved algorithm effectively improves the inversion accuracy of density parameters. In this paper, the algorithm has been implemented on Spark model, and the results show that the parallel model can effectively reduce operation time of the algorithm.

关键词： intelligent optimization algorithm Prestack seismic data Elastic parameter inversion Apache Spark

来源：评论

学校读者我要写书评

暂无评论

Stepwise Multi-Objective Parameter optimization Design of LLC Resonant DC-DC Converter

引用

ENERGIES 2024年第8期17卷 1919-1919页

作者： Yin, Miaomiao Luo, Quanming Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China

The LLC resonant converter, which is extensively utilized across various industrial fields, significantly depends on its parameters for performance optimization. This paper establishes a time-domain analytical model for the LLC resonant converter under Pulse Frequency Modulation (PFM) and proposes a multi-objective parameter optimization design method with stepwise constraints. The proposed method limits the resonant capacitor voltage while ensuring that the converter meets the voltage gain requirement and realizes Zero-Voltage Switching (ZVS). The converter's performance is then optimized with the objective of minimizing the switching frequency range, the resonant inductor current, and the RMS value of the switching current on the secondary side. Compared with the existing methods, the proposed method has the advantages of comprehensive consideration and wide application scenarios. Finally, a 1200 W experimental prototype was fabricated, with experimental results verifying the feasibility of the proposed optimization design method and demonstrating that the prototype's maximum efficiency reaches 96.54%.

关键词： DC/DC intelligent optimization algorithm LLC resonant converter parameter design

来源：评论

学校读者我要写书评

暂无评论

Adaptive Audio Steganography Based on Improved Syndrome-Trellis Codes

引用

IEEE ACCESS 2021年 9卷 11705-11715页

作者： Ying, Kaiyu Wang, Rangding Lin, Yuzhen Yan, Diqun Ningbo Univ Coll Informat Sci & Engn Ningbo 315211 Peoples R China

Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography. Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which controls the embedding position of the message in the cover signal. From another point of view, we implement adaptive steganography by improving the STC coding process (named Adaptive-STC). The parity-check matrix is the key to the encoding and extraction process of STC. In this work, we prove that the average embedding change probability of corresponding elements can be changed by adjusting its submatrix. Following that, we specially design an adaptive parity-check matrix to replace the designed distortion cost to restrict the embedding position. The generation of the adaptive parity-check matrix can be formulated as a multi-constrained integer programming problem in which the width of the submatrix is allocated at a fixed height. To solve this particular problem, we propose a targeted intelligent optimization algorithm (named GOAS) that can adaptively generate the parity-check matrix according to different audio cover. The experimental results show that the proposed method outperforms the state-of-the-art adaptive steganography with reduced embedding changes and improved audio quality while ensuring the ability against steganalysis.

关键词： Distortion Encoding Sparse matrices Generative adversarial networks Decoding Payloads Media Steganography syndrome-trellis codes parity-check matrix intelligent optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Mechanical properties evaluation of waste gangue-based cemented backfill materials based on an improved response surface model

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第2期31卷 3076-3089页

作者： Shi, Peitao Zhang, Jixiong Yan, Hao Zhou, Nan Zhu, Gaolei Zhao, Yang Chen, Peng China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Peoples R China Erdos Yutai Huasheng Resource Recycling Technol Co Ordos 017010 Peoples R China

Traditional mining methods damage the cultivated land and produce gangue waste that often contaminates the environment. Yet, these problems can be mitigated by transforming the waste into gangue-based cemented backfill material (GCBM), whose mechanical properties are crucial for surface protection. Therefore, in this study, an intelligent model based on laboratory tests was developed to evaluate the GCBM's mechanical properties. The strength tests and polynomial response surface model (PRSM) were used to analyze the non-linear correlation between the influencing factors and the uniaxial compressive strength (UCS). Meanwhile, the importance of multidimensional factors was analyzed by the mean impact value, revealing that concentration and gangue proportion are the most sensitive factors. In addition, an intelligent response surface model (IRSM) based on support vector regression model was constructed by enhancing an optimization algorithm with chaotic mapping and adaptive methods. The performance of the traditional PRSM and the novel IRSM was compared, and the IRSM was validated. The IRSM can predict UCS more efficiently and effectively than the traditional PRSM under high-dimensional factors, with R2 of 0.96 and MBE of 0.05. This indicated that the IRSM has the potential to promote coal mine waste reduction and environmental protection.

关键词： Mine waste disposal Gangue-based cemented backfill material Green mining Backfill mining intelligent optimization algorithm Support vector regression

来源：评论

学校读者我要写书评

暂无评论

optimization of Multiple Debris Removal Missions Using an Evolving Elitist Club algorithm

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2020年第1期56卷 773-784页

作者： Li, Haiyang Baoyin, Hexi Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

Space debris has become a great threat to space activities. We study and optimize space missions to remove multiple space debris. These missions are efficient and economical in stabilizing the orbital environment. In this paper, we propose an intelligent global optimization algorithm named the evolving elitist club algorithm (EECA), and we study its application in multiple debris removal missions. The crucial design element of EECA is the elitist club, which is a population created using ant colony optimization (ACO) and then evolved using genetic algorithm operators. We describe two types of multiple debris removal missions: 1) single spacecraft removal mission;and 2) multiple spacecraft removal mission. We optimize them using EECA. The proposed algorithm is compared with the conventional tree search method and other ACO algorithms. We show that EECA is more efficient than other algorithms. Furthermore, a time distribution (TD) EECA is proposed to handle time-dependent problems, and the time-continuous update mechanism of time-varying pheromone in TD EECA presents a novel idea for combinatorial-continuous problems.

关键词： Active debris removal global optimization intelligent optimization algorithm trajectory optimization

来源：评论

学校读者我要写书评

暂无评论

An optimal method for prediction and adjustment on byproduct gas holder in steel industry

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4588-4599页

作者： Zhang, Xiaoping Zhao, Jun Wang, Wei Cong, Liqun Feng, Weimin Dalian Univ Technol Res Ctr Informat & Control Dalian Peoples R China Shanghai Baosigh Software Co Ltd Shanghai Peoples R China

To maintain the balance of byproduct gas holder is an important task in optimal scheduling of byproduct energy in steel industry. However, this is often influenced by many factors and is difficult to obtain a precise mechanism model for analysis. In this paper, an optimal method for prediction and adjustment on byproduct gas holder is proposed. Considering the different operation styles of gasholders, both single and multiple gasholders level prediction models are established by machine learning methodology. And, a hybrid parameter optimization algorithm is developed to optimize the model for high prediction accuracy. Then, based on the predicted gasholder level, the optimal adjustment amount is calculated by a novel reasoning method to sustain the gasholder within safety zone. This method has been verified in the Energy Center of Baosteel, China. The results demonstrate that the proposed approach can precisely predict and adjust gasholders and provide a remarkable guidance for reasonable scheduling of byproduct gases. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Optimal scheduling of byproduct gases Gasholder level prediction Machine learning intelligent optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved GWO for large-scale function optimization and MLP optimization in cancer identification

引用

NEURAL COMPUTING & APPLICATIONS 2020年第5期32卷 1305-1325页

作者： Zhang, Xinming Wang, Xia Chen, Haiyan Wang, Doudou Fu, Zihao Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Hubei Canc Hosp Dept Gynaecol Tumour Wuhan 430079 Peoples R China

Grey wolf optimizer (GWO) is a novel nature-inspired algorithm, and it has the characteristics of strong local search ability but weak global search ability when dealing with some large-scale problems. So a GWO based on random opposition learning, strengthening hierarchy of grey wolves and modified evolutionary population dynamics (EPD), named as RSMGWO, is proposed. Firstly, a search way based on strengthening hierarchy of grey wolves is added;each grey wolf uses two kinds of updating modes, including a global-best search way based on random dimensions and the original search way of GWO, to improve the optimization performance. Secondly, a modified EPD is embedded to improve the optimization performance further. Finally, a random opposition learning strategy is merged to avoid falling into local optima. Experimental results on 19 different (especially large scale) dimensional benchmark functions and multi-layer perceptron (MLP) optimization for cancer identification show that compared with GWO and quite a few state-of-the-art algorithms, RSMGWO is able to provide more competitive results, in terms of both accuracy and convergence.

关键词： intelligent optimization algorithm Grey wolf optimizer (GWO) Opposition learning Large scale Cancer identification

来源：评论

学校读者我要写书评

暂无评论

optimization design of supply chain network based on BP neural network performance evaluation and feedback mechanism

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第23期36卷

作者： Wu, Yao Liu, Weiwei Shenyang Univ Technol Mech Engn Shenyang Peoples R China

This paper proposes a supply chain network design method suitable for multi-product and multi-inventory models, and uses the improved BP neural network to evaluate and provide feedback on the collaborative performance of the supply chain, adjusting the supply chain network design scheme on time. In the context of the Internet of Things (IoT) in manufacturing, it has been found that supply chain operations are difficult to meet personalized customer needs with high precision and quality. Therefore, we adopted a dynamic library strategy, supply chain network optimization model, hybrid algorithm, and the improved BP neural network to solve the above problems. First, this paper designs a corresponding inventory strategy selection mechanism for the various ordering methods of retailers in the manufacturing IoT environment. Based on this, we have constructed a dual objective model for a sustainable supply chain network to minimize total cost and maximize customer satisfaction. Second, we have developed a hybrid improved Grey Wolf and Whale algorithm (OLDGWOA) that can accurately solve the above model. The hybrid algorithm divides the population into two parts through opposition-based learning, and then we use the improved grey wolf algorithm and whale algorithm to solve the two populations, and seek the optimal solution in the results, resulting in a hybrid algorithm. Finally, we constructed a supply chain performance evaluation model and feedback mechanism based on the improved BP neural network to adjust inventory strategies and network design at any time. We also validated the developed model and algorithm through numerical examples, and the results showed that: (1) the hybrid algorithm has certain advantages in search and solution speed, (2) the advantages of supply chain network design based on supply chain performance evaluation and feedback mechanisms, and (3) the trade-off between ordering methods and inventory strategies, as well as the trade-off between location

关键词： BP neural network intelligent optimization algorithm inventory strategy selection manufacturing the Internet of Things supply chain network design

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：