检索结果-内蒙古大学图书馆

Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

引用

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2024年第8期83卷 322-322页

作者： Wang, Yankun Sun, Xinshuang Wen, Tao Wang, Luqi Yangtze Univ Sch Geosci Wuhan 430100 Hubei Peoples R China Hubei Yangtze Univ Technol Dev Co Ltd Jiacha Cty Branch Shannan 856499 Xizang Peoples R China Minist Educ Engn Res Ctr Rock Soil Drilling & Excavat & Protec Wuhan 430074 Hubei Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China

This paper applies eight state-of-the-art metaheuristic optimization algorithms to optimize the kernel extreme learning machine (KELM) and compares the mean accuracy and stability of these algorithms in step-like displacement prediction of reservoir landslides. The Baishuihe landslide, Shuping landslide, and Baijiabao landslide were selected as study cases. A new index that can balance the goodness and error of predictions, the combined prediction precision criterion (CPC), was proposed for the evaluation of these algorithms. The results show that the mean prediction accuracy between different algorithms is small, up to only approximately 15%, but the difference between the prediction stabilities varies greatly, up to approximately 70%. All metaheuristic-optimized KELM models have high mean prediction accuracy, among which the horse herd optimization algorithm is better than the other algorithms in terms of both mean prediction accuracy and stability. Our results highlight the importance of ensemble predictions in attenuating the contingency of single metaheuristic-optimized machine learning (ML) models and improving their stability and demonstrate the impartiality of the CPC index in the comparative evaluation of models. The methodological framework of this article can provide a reference for future research on ML-based landslide displacement prediction.

关键词： metaheuristic optimization algorithm KELM Reservoir landslides Comparative study Displacement prediction

来源：评论

学校读者我要写书评

暂无评论

Wearable Sensor Based on Covalent Organic Framework Humidity Films for Long-Term Monitoring of Tomato Physiology Under Abiotic Stress

引用

ACS NANO 2024年第48期18卷 33105-33118页

作者： Huang, Liang He, Xinyang Hu, Jimin Qin, Caixun Huang, Chenxin Tang, Yu Zhong, Fenglin Kong, Xiangzeng Wei, Xuan Fujian Agr & Forestry Univ Coll Mech & Elect Engn Fujian Key Lab Agr Informat Sensoring Technol Fuzhou 350002 Fujian Peoples R China Natl Univ Singapore Dept Mat Sci & Engn Singapore 117576 Singapore Donghua Univ Coll Text Shanghai Frontier Sci Res Ctr Adv Text Shanghai 201620 Peoples R China Fujian Agr & Forestry Univ Coll Life Sci Fuzhou 350002 Peoples R China Fujian Agr & Forestry Univ Coll Hort Fuzhou 350002 Peoples R China Guangdong Polytech Normal Univ Acad Interdisciplinary Studies Guangzhou 510665 Peoples R China Fujian Agr & Forestry Univ State Key Lab Ecol Pest Control Fujian & Taiwan Cr Fuzhou 350002 Fujian Peoples R China

Global agricultural productivity is affected by plant stresses every year;as a consequence, monitoring and preventing plant stresses is a significant measure to protect the agro-ecological environment. Similar to the adoption of wearable devices to appraise human physiological information and disease diagnosis, however, in situ nondestructive monitoring of complex and weak physiological information in plants is an enormous challenge for the development of wearable sensors. Herein, to accurately analyze the changes of tomato internal information under multiple abiotic stresses in real-time, we introduce the covalent organic framework (COF) film synthesized by self-assembly layer by layer through the oil/water interface as a sensitive material to develop a multifilm-integrated wearable sensor capable of monitoring leaf surface humidity and leaf temperature. The flexible substrate can stretch with leaf growth to ensure the accuracy of long-term monitoring. Benefiting from the performance characteristics, such as ultrahigh sensitivity (S) of 0.8399 nA/%RH and an extremely low-resolution (Delta RH) value of 0.0564%, which could amplify the conducted signal, and the long-term stability of COFMOP-TAPB, the transpiration information on tomatoes under 10 abiotic stresses can be monitored continuously and with high precision over a long period by applying the COF-based sensor on the lower surface of the leaf at the upper end of the stem morphology. Finally, we employ metaheuristic optimization algorithms to predict the time series of the internal physiological change trend of tomatoes in the future so that farmers can take corresponding preventive measures in time to ensure the healthy growth of tomatoes.

关键词： plant wearable sensor plant physiological information covalent organic framework nonbiological stress metaheuristic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improvement of medical data security using SABES optimization algorithm

引用

JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12929-12965页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet St Longowal Inst Engn & Technol Dept Comp Sci & Engn Longowal 148106 Punjab India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Punjab India

This paper proposes a bit-level cryptographic model for encrypting medical signals, patient information, clinical data, and images of various modalities. The model has utilized chaotic maps due to properties like non-periodicity, nonlinearity, ergodicity, deterministic, unpredictable, and sensitive to the initial conditions. Three chaotic maps are employed in a chained sequence at different levels of the model to elevate the randomness and provide uniform key distribution with larger key space. Also, the self-adaptive bald eagle search optimization algorithm has been employed to enhance the properties of chaotic maps by optimizing the initialization and control parameters. The cyclic redundancy check approach is involved to detect any change in the data during the transmission at the receiver side along with circular shift to make encrypted data resilient of differential attack. Therefore, the encryption model boosts the unpredictability, security, and protection against a wide range of threats. The encryption model performance efficiency is demonstrated using different types of security analysis to show resistance against statistical, entropy, differential, cropping, and brute-force attacks.

关键词： Chaotic system Medical data metaheuristic optimization algorithm Parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid prediction method for solar photovoltaic power generation using normal cloud parrot optimization algorithm integrated with extreme learning machine

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Liu, Huachen Cai, Changlong Li, Pangyue Tang, Chao Zhao, Mingwei Zheng, Xinyan Li, Yifeng Zhao, Yiran Liu, Chenxi Xian Technol Univ Sch Optoelect Engn Xian 710032 Peoples R China Xian Technol Univ Sch Armament Sci & Technol Xian 710032 Peoples R China

As the energy crisis environmental concerns rise, harnessing renewable energy sources like photovoltaics (PV) is critical for sustainable development. However, the seasonal variability and random intermittency of solar power pose significant forecasting challenges, threatening grid stability. Therefore, this paper proposes a novel renewable energy hybrid forecasting method, NCPO-ELM, to adequately capture spatial and temporal dependencies within meteorological data crucial for accurate predictions. To effectively optimize the performance of the Extreme Learning Machine (ELM), the Normal Cloud Parrot optimization (NCPO) algorithm is developed, inspired by Pyrrhura Molinae parrots' flock behavior and cloud model theory. NCPO integrates five unique search strategies and utilizes a random structure to explore and exploit. By introducing the normal cloud model to generate random samples with specific distributions, the algorithm enhances solution space coverage. NCPO is subsequently employed to optimize the Single-Layer Feedforward Network (SLFN) hidden layer hyperparameters, yielding optimal weights and biases for the output layer, thereby reducing benchmark ELM's sensitivity to noise and instability from random initialization. The actual forecasting results of PV stations across different regions demonstrate that the proposed NCPO-ELM shows superior prediction accuracy and performance compared to existing hybrid forecasting approaches, particularly for time series with diverse data characteristics and seasonal variations.

关键词： Photovoltaic power generation prediction metaheuristic optimization algorithm Normal cloud model Extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis

引用

GAIT & POSTURE 2024年 107卷 293-305页

作者： Xu, Datao Zhou, Huiyu Quan, Wenjing Jiang, Xinyan Liang, Minjun Li, Shudong Ugbolue, Ukadike Chris Baker, Julien S. Gusztav, Fekete Ma, Xin Chen, Li Gu, Yaodong Ningbo Univ Fac Sports Sci Ningbo Peoples R China Univ Pannonia Fac Engn Veszprem Hungary Eotvos Lorand Univ Savaria Inst Technol Szombathely Hungary Univ West Scotland Sch Hlth & Life Sci Paisley Renfrew Scotland Obuda Univ Fac Hlth & Safety Budapest Hungary Hong Kong Baptist Univ Dept Sport & Phys Educ Hong Kong Peoples R China Szecheny Istvan Univ Vehicle Ind Res Ctr Gyor Hungary Fudan Univ Huashan Hosp Dept Orthoped Shanghai Peoples R China

Background: Finding the best subset of gait features among biomechanical variables is considered very important because of its ability to identify relevant sports and clinical gait pattern differences to be explored under specific study conditions. This study proposes a new method of metaheuristic optimization-based selection of optimal gait features, and then investigates how much contribution the selected gait features can achieve in gait pattern recognition. Methods: Firstly, 800 group gait datasets performed feature extraction to initially eliminate redundant variables. Then, the metaheuristic optimization algorithm model was performed to select the optimal gait feature, and four classification algorithm models were used to recognize the selected gait feature. Meanwhile, the accuracy results were compared with two widely used feature selection methods and previous studies to verify the validity of the new method. Finally, the final selected features were used to reconstruct the data waveform to interpret the biomechanical meaning of the gait feature. Results: The new method finalized 10 optimal gait features (6 ankle-related and 4-related knee features) based on the extracted 36 gait features (85 % variable explanation) by feature extraction. The accuracy in gait pattern recognition among the optimal gait features selected by the new method (99.81 % +/- 0.53 %) was significantly higher than that of the feature-based sorting of effect size (94.69 % +/- 2.68 %), the sequential forward selection (95.59 % +/- 2.38 %), and the results of previous study. The interval between reconstructed waveform-high and reconstructed waveform-low curves based on the selected feature was larger during the whole stance phase. Significance: The selected gait feature based on the proposed new method (metaheuristic optimization-based selection) has a great contribution to gait pattern recognition. Sports and clinical gait pattern recognition can benefit from population-based metaheurist

关键词： Gait pattern recognition Sports and clinical gait Biomechanics feature selection metaheuristic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

引用

JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2023年第2期13卷 661-689页

作者： Sheykhinasab, Amirhossein Mohseni, Amir Ali Bahari, Arash Barahooie Naruei, Ehsan Davoodi, Shadfar Aghaz, Aliakbar Mehrad, Mohammad ACECR Inst Higher Educ Isfahan Branch Esfahan Iran Islamic Azad Univ Dept Oil & Chem Engn Sci & Res Branch Tehran Iran Lamerd Higher Educ Ctr Petr Engn Dept Lamerd Fars Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia Sahand Univ Technol Fac Oil & Gas Engn Sahand Oil & Gas Res Inst Tabriz Iran Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahrood Iran

Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination (R-2) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R-2 shed light on the MELM-COA as the best-performing model in the training and testing stage

关键词： Prediction of permeability Heterogeneous carbonate reservoir Hybrid prediction model metaheuristic optimization algorithm Deep learning

来源：评论

学校读者我要写书评

暂无评论

Parameters and methods used in flood susceptibility mapping: a review

引用

JOURNAL OF WATER AND CLIMATE CHANGE 2023年第6期14卷 1935-1960页

作者： Kaya, Cagla Melisa Derin, Leyla IKCU Dept Geog Izmir Turkiye Ankara Univ Emergency & Disaster Management Program Ankara Turkiye

A correct understanding of the parameters and methods used in flood susceptibility mapping (FSM) is critical for identifying the strengths and limitations of different mapping approaches, as well as for developing methodologies. In this study, we examined scientific publications in the literature using WoS. Although the number of methods used is quite high (about 160 with submethods), the number of parameters used in these methods varies, with a maximum of 21 and a minimum of 5 parameters preferred. It was found that the most commonly used parameter has a preference rate of 97%, but there is no common parameter in 100% of the studies. The methods used for determining flood susceptibility include multi-criteria decision-making (MCDM) methods, physically based hydrological models, statistical methods, and various soft computing methods. Although the use of traditional statistical methods and MCDM methods is already high among researchers, the methods used in flood susceptibility analysis have evolved over the years from traditional human judgments to statistical methods based on big data and machine learning. In the reviewed studies, it was observed that machine learning, fuzzy logic, metaheuristic optimization algorithms, and heuristic search algorithms, which are soft computing methods, have been widely used in FSM in recent years.

关键词： flood susceptibility mapping (FSM) metaheuristic optimization algorithm parameters statistical methods

来源：评论

学校读者我要写书评

暂无评论

Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network algorithm

引用

Computers, Materials & Continua 2023年第4期75卷 1073-1088页

作者： Musaed Alrashidi Department of Electrical Engineering College of EngineeringQassim UniversityBuraidahSaudi Arabia

Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging *** numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural Networkalgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation *** NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54.

关键词： Weibull probability density function wind energy numerical estimation method metaheuristic optimization algorithm neural network algorithm

来源：评论

学校读者我要写书评

暂无评论

metaheuristic-based machine learning modeling of the compressive strength of concrete containing waste glass

引用

STRUCTURAL CONCRETE 2023年第4期24卷 5417-5440页

作者： Seghier, Mohamed El Amine Ben Golafshani, Emadaldin Mohammadi Jafari-Asl, Jafar Arashpour, Mehrdad Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Computat Mech Ho Chi Minh City Vietnam Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Monash Univ Dept Civil Engn Melbourne Vic Australia Univ Sistan & Baluchestan Fac Engn Dept Civil Engn Zahedan Iran

Waste glass (WG) can be used as fine aggregate and powder in concrete mixtures, preventing pollution induced by this non-biodegradable material. The properties of WG-included concrete should be examined before its practical use. Compressive strength (CS) is one of the most crucial characteristics of concrete, and the measurement of which needs time-consuming and expensive experiments. The use of machine learning (ML) methods for modeling the CS of concrete can help achieve more reliable and precise models. In this study, a comprehensive database of WG-included concrete was collected from the literature. Next, four ML methods, including support vector regression (SVR), least-square support vector regression (LSSVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron neural network (MLP) were served in the CS modeling. A recently proposed metaheuristic method, called marine predators algorithm (MPA), was proposed to optimize the control parameters of the ML models to guarantee generalized accuracy. Results indicate that the hybrid LSSVR-MPA model outperforms the other developed ML models comparing the error metrics with an RMSE = 2.447 MPa and R-2 = 0.983. The sensitivity analysis reveals that replacing the cement with WG powder decreases the CS, whereas serving the WG as the replacement for natural fine aggregate improves the CS.

关键词： compressive strength concrete machine learning metaheuristic optimization algorithm waste glass waste materials

来源：评论

学校读者我要写书评

暂无评论

An Efficient Hybrid Scheduling Framework for Optimal Workload Execution in Federated Clouds to Maintain Performance SLAs

引用

JOURNAL OF GRID COMPUTING 2023年第3期21卷 1-22页

作者： Kshatriya, Divya Lepakshi, Vijayalakshmi A. REVA Univ Sch Comp Sci & Applicat Bengaluru 560064 Karnataka India

A Federated cloud is a composition of several clouds where a single federated cloud manager (FCM) is responsible for communication with the cloud service providers (CSPs) of associated clouds to accomplish the task of resource management. Finding optimal schedules for the incoming workloads from users is a highly complex task, as these workloads are expected to be completed under different deadlines. The existing frameworks for workload scheduling in federated cloud faced serious drawbacks such as inefficiency, unable to meet the deadlines assigned by users, etc. To overcome such drawbacks, this work introduces a new and effective strategy based on an optimization algorithm to achieve optimal scheduling of workloads. The proposed architecture involves a single FCM to identify the load in different clouds through communication with CSPs. A load level calculator (LLC) and a workload partitioning module (WPM) are maintained by the FCM to analyze the load in each cloud. Further, based on the load factor (LF) computed, the incoming workloads are partitioned into sub-queues of different sizes and are forwarded to the main clouds. The respective CSPs of the clouds maintain a cloud workload queue (CWQ) to locate the workloads and analyze the resource requirements. The CSP executes a scheduler based on hybrid flow-directed whale optimization (HFDWO) to find the optimal VMs in the data centre (DC) that can run the incoming workloads in the queue. The workloads are scheduled accordingly, and evaluations are conducted through simulations in the CloudSim tool. The performance of the approach is analyzed using the GWA T-12 Bitbrains dataset under different metrics. The overall improvement attained by the proposed approach compared to the existing frameworks is 25% in terms of SLA violation rate, 12% in terms of execution cost, 11% in terms of resource utilization, 33% in terms of makespan, 19% in terms of throughput and 28% in terms of response time.

关键词： Federated cloud Workloads Virtual machines Optimal workload scheduling metaheuristic optimization algorithm Load level calculation Workload partitioning

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：