检索结果-内蒙古大学图书馆

SPA Combined with swarm intelligence optimization algorithms for Wavelength Variable Selection to Rapidly Discriminate the Adulteration of Apple Juice

引用

FOOD ANALYTICAL METHODS 2017年第6期10卷 1965-1971页

作者： Li, Ying Guo, Yajing Liu, Chang Wang, Wu Rao, Pingfan Fu, Caili Wang, Shaoyun Fuzhou Univ Coll Biol Sci & Engn Fuzhou 350108 Fujian Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350116 Fujian Peoples R China Zhejiang Gongshang Univ Sch Food Sci & Biotechnol Hangzhou 310018 Zhejiang Peoples R China

The application of wavelength variable selection before partial least squares (PLS) regression to rapidly discriminate the adulteration of apple juice by Fourier transform near-infrared (FT-NIR) was investigated in this study. Successive projections algorithm (SPA) combined with four swarm intelligence optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), group search optimizer (GSO), and firefly algorithm (FA), was applied to extract effective wavelength variables. The results demonstrated that the variable number of SPA-PSO-PLS models was validly improved with a wavelength variable of four. The accuracy of model was satisfactory with the coefficients of determination of prediction (R-p(2) = 0.9986) and good root mean square errors of prediction (RMSEP = 0.0628). The results suggested that SPA combined with swarm intelligence optimization algorithms for wavelength variable selection could rapidly and efficiently discriminate the adulteration of apple juice.

关键词： FT-NIR spectroscopy Successive projections algorithm swarm intelligence optimization algorithms Wavelength selection

来源：评论

学校读者我要写书评

暂无评论

Parameter Tuning in Modeling and Simulations by Using swarm intelligence optimization algorithms 9

Parameter Tuning in Modeling and Simulations by Using Swarm ...

引用

9th International Conference on Computational intelligence and Communication Networks (CICN)

作者： Tan, Rabia Korkmaz Bora, Sebnem Ege Univ Dept Comp Engn Izmir Turkey

ISBN: (纸本)9781509050017

Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results.. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.

关键词： swarm intelligence optimization algorithms Particle swarm optimization Firefly algorithms Modeling and Simulation Parameter tuning

来源：评论

学校读者我要写书评

暂无评论

Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features

引用

FOOD CHEMISTRY 2021年 338卷 127828-127828页

作者： Jiang, Hui Liu, Tong He, Peihuan Ding, Yuhan Chen, Quansheng Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Jiangsu Peoples R China

The fatty acid content of flour is an important indicator for determining the deterioration of flour. We propose a novel rapid measurement method for fatty acid content during flour storage based on a self-designed color-sensitive gas sensor array. First, a color-sensitive gas sensor array was prepared to capture the odor changes during flour storage. The sensor features were then optimized using genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO). Finally, back propagation neural network (BPNN) models were established to measure the fatty acid content during flour storage. Experimental results showed that the optimization effects of the three algorithms improved in the following order: GA < ACO < PSO, for the sensor features optimization. In the validation set, the determination coefficient of the best PSO-BPNN model was 0.9837. The overall results demonstrate that the models established on the optimized features can realize rapid measurements of fatty acid content during flour storage.

关键词： Flour Color-sensitive gas sensor array Sensor feature optimization swarm intelligence optimization algorithms Quantitative determination

来源：评论

学校读者我要写书评

暂无评论

Identification of the Water Inrush Source Based on the Deep Learning Model for Mines in Shaanxi, China

引用

MINE WATER AND THE ENVIRONMENT 2025年第1期44卷 133-148页

作者： Cui, Mingyi Hou, Enke Feng, Dong Che, Xiaoyang Xie, Xiaoshen Hou, Pengfei Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Shaanxi Peoples R China Shaanxi Prov Key Lab Geol Support Coal Green Explo Xian 710054 Shaanxi Peoples R China

Roof water inrush disasters in coal mines present safety risks, so swiftly and accurately identifying the source of inrush water is essential for disaster prevention and control. This research employed Python and the Tensorflow deep learning framework, based on a Deep Feedforward Neural Network (DFNN) and used swarm intelligence optimization algorithms (SIOA) to create six advanced models for identifying the source of roof water inrush in the Binchang mining area, while the characteristics of the input of the sudden water source were abstracted at multiple levels through the multiple hidden layers of neural networks to improve higher classification precision. To counteract overfitting during model development, the Dropout method was applied to enhance the model's hidden layers. Training of the models involved 80% of the collected sample data, while testing utilized the remaining 20%. The evaluation of training efficiency focused on cross-entropy loss and accuracy as primary measures. The study incorporated data from three major water-bearing strata across seven typical mines in the Binchang area, constructing models based on a dataset of 183 water samples. Routine ion concentration tests were conducted on these water samples, selecting eight chemical indicators K+ + Na+, Ca2+, Mg2+, Cl-, SO42-, HCO3-, TDS, and pH as input features, including essential parameters for accurate water source identification. In addition, the models were rigorously evaluated with multi-faceted indicators, including training time, confusion matrices, precision, recall, F1 scores, and graphs of validation loss and accuracy. Results revealed that the validation accuracy of all six SIOA-DFNN models surpassed 91%, much better than traditional neural networks. Specifically, the GWO-DFNN model achieved an outstanding 95.92% accuracy. This highlights the exceptional precision of the optimization algorithm in identifying the source of roof water inrush in the Binchang mining area. This research hi

关键词： Binchang mining area Groundwater Water hazards Hydrochemical analysis Neural networks swarm intelligence optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 13435-13453页

作者： Liu, Junnan Liu, Yifan Ding, Yongkang Henan Univ Sch Software Kaifeng 475001 Peoples R China

Heterogeneous multi-core processor has the ability to switch between different types of cores to perform tasks, which provides more space and possibility for realizing efficient operation of computer system and improving computer computing power. Current research focuses on heterogeneous multiprocessor systems with high performance or low power consumption to reduce system energy consumption. However, some studies have shown that excessive voltage reduction may lead to an increase in transient failure rates, reducing system reliability. This paper studies the energy optimal scheduling problem of HMSS with DVFS under the constraints of minimum time and reliability, and proposes an improved wild horse optimization algorithm (OIWHO), which improves the efficiency of heterogeneous task scheduling and shortens the task completion time. The algorithm uses the learning and chaos perturbation strategies based on opposition and crossover strategies to balance the search and utilization capabilities, and can further improve the performance of OIWHO. Compared with previous work, our proposed algorithm has more advantages than existing algorithms. Experimental results show that the average computing time of OIWHO algorithm is 12.58%, 11.42%, 7.53%, 4.20% and 3.21% faster than DRNN-BWO, PSO, GWO-GA, GACSH and OIWOAH, respectively. Especially when solving large-scale problems, our algorithm takes less time than other algorithms.

关键词： Distributed computing Heterogeneous multi-core processors High performance computing Task scheduling swarm intelligence optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Energy-Saving optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

引用

BUILDINGS 2024年第9期14卷 2842页

作者： Hu, Bin Guo, Yuhu Huang, Wenjun Jin, Jianxiang Zou, Mingxuan Zhu, Zhikun Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Huzhou Inst Ind Control Technol Huzhou 313000 Peoples R China Zhejiang YunTrol Intelligence Control Technol Co L Hangzhou 310053 Peoples R China

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

关键词： HVAC system energy consumption model ALOE swarm intelligence optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Feature selection based on snake optimizer for defect or damage classification in composite rolls via ultrasonic Rayleigh waves

引用

APPLIED ACOUSTICS 2024年 224卷

作者： Sun, Maoxun Zhang, Yue Li, Haolin Xiao, Biao Liu, Hongye Wang, Xiao Hu, Chaojie Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China Nantong Univ Sch Mech Engn Nantong 226019 Peoples R China Shanghai Inst Special Equipment Inspect & Tech Res Shanghai 200062 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai 200092 Peoples R China

Defects or damages on the surface or subsurface of composite rolls directly affect the quality of the rolled products, and their periodic inspection and accurate identification can provide a reference for the repair or replacement of composite rolls. Ultrasonic Rayleigh waves can non-destructively assess the surface or subsurface of composite rolls, during which similar original signals are perhaps generated for the defects or damages with different types or sizes, making it difficult to distinguish their types. Machine learning is proposed to solve these problems, and feature selection based on the ultrasonic Rayleigh waves mainly utilizes traditional methods such as ReliefF. However, these unsupervised methods have not been combined with specific classification algorithms. In this study, swarm intelligence optimization algorithms, including snake optimizer (SO), dung beetle optimizer (DBO), and grasshopper optimization algorithm (GOA), are investigated based on the support vector machine (SVM). Note that the utilized original signals are received on the left and right sides of defects or damages, and they are processed by segmentation, fast Fourier transform (FFT), and wavelet packet decomposition to build original feature sets. It is illustrated that Rayleigh waves passing through the defects or damages carry more valuable information about the types. In contrast, the ultrasonic waves reflected from the defects or damages can provide information about the types that the former does not contain. Furthermore, for the intelligent classification of defects or damages in composite rolls using ultrasonic Rayleigh waves, SO is more suitable for SVM and has certain advantages.

关键词： Composite rolls Defect or damage classification swarm intelligence optimization algorithms Snake optimizers

来源：评论

学校读者我要写书评

暂无评论

Improved swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

引用

SENSORS 2022年第1期22卷 118-118页

作者： Zi, Jiali Lv, Danju Liu, Jiang Huang, Xin Yao, Wang Gao, Mingyuan Xi, Rui Zhang, Yan Southwest Forestry Univ Coll Big Data & Intelligent Engn Kunming 650224 Yunnan Peoples R China Southwest Forestry Univ Sch Math & Phys Kunming 650224 Yunnan Peoples R China

In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does;it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle swarm optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.

关键词： speech separation cross-correlation blind source separation swarm intelligence optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Apple tree transpiration estimated using the Penman-Monteith model integrated with optimized jarvis model

引用

AGRICULTURAL WATER MANAGEMENT 2023年 276卷

作者： Xing, Liwen Zhao, Lu Cui, Ningbo Liu, Chunwei Guo, Li Du, Taisheng Wu, Zongjun Gong, Daozhi Jiang, Shouzheng Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China Nanjing Univ Informat Sci & Technol Sch Appl Meteorol Jiangsu Key Lab Agr Meteorol Nanjing 210044 Peoples R China China Agr Univ Ctr Agr Water Res China Beijing 100091 Peoples R China Chinese Acad Agr Sci Inst Environm & Sustainable Dev Agr State Engn Lab Efficient Water Use Crops & Disaste Beijing 100081 Peoples R China

Accurate estimates of plant transpiration (Tsf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (rc), the Penman-Monteith (PM) model is commonly used in Tsf modeling. However, the rc is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the rc of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environ-mental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (Rn), and air temperature (Ta), and of soil water content (theta a) on the accuracy of JA sub -models to calculate apple tree rc and Tsf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008-2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate rc. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of Tsf estimates with five empirical rc models, combined with the PM model. The results showed that sub-model JA3322, i.e., the third constraint for VPD, the third constraint for Rn, the second constraint for Ta, and the second constraint for theta a attained the best estimate of rc with a coefficient of determination (R2 = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m-1), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage

关键词： Canopy resistance Constraint function swarm intelligence optimization algorithms Growth period segmentation

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：