检索结果-南通市图书馆

Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks

引用

Energy and AI 2024年第3期17卷 313-326页

作者： Justin Münch Jan Priesmann Marius Reich Marius Tillmanns Aaron Praktiknjo Mario Adam University of Applied Sciences Duesseldorf Centre of Innovative Energy SystemsMuensterstr.15640476 DuesseldorfGermany RWTH Aachen University E.ON Energy Research CenterChair for Energy System Economics(FCN-ESE)Mathieustrasse 1052074 AachenGermany

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

关键词： Security of electricity supply Probabilistic simulation Metamodeling artificial neural networks Regression

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Energy Disaggregation of Industrial Machinery Utilizing artificial neural networks for Non-intrusive Load Monitoring

引用

Energy and AI 2024年第3期17卷 342-356页

作者： Philipp Pelger Johannes Steinleitner Alexander Sauer Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart 70569Germany Institute for Energy Efficiency in Production(EEP) University of StuttgartStuttgart 70569Germany

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

关键词： Non-intrusive load monitoring Energy transparency Energy consumption evaluation Industrial manufacturing artificial neural networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics

引用

Digital Communications and networks 2022年第2期8卷 162-173页

作者： Wafa Batayneh Enas Abdulhay Mohammad Alothman Jordan University of Science and Technology Mechanical Engineering DepartmentIrbid22110 Jordan Jordan University of Science and Technology Biomedical Engineering DepartmentIrbid22110 Jordan

Surface Electromyography(sEMG)plays a key role in many applications such as control of Human-Machine Interfaces(HMI)and neuromusculoskeletal *** has strongly nonlinear relations to joint kinematics and reflects the subjects’intention in moving their *** relations have been traditionally examined by either integrated biomechanics and multi-body dynamics or gesture-based classification ***,these methods have drawbacks that limit their *** from them,joint kinematics can be continuously reconstructed from sEMG via estimation approaches,for instance,the artificial neural networks(ANNs).The Comparison of different ANNs used in different studies is difficult,and in many cases,*** current study focuses on fairly evaluating four types of ANN over the same dataset and conditions in proportional and simultaneous estimation of 15 hand joint angles from 10 sEMG *** presented ANNs are Feedforward,Cascade-Forward,Radial Basis Function(RBFNN),and Generalized Regression(GRNN).Each ANN is applied to its special parametric *** the methods efficiently solved the regression problem of the complex multi-input multi-output *** RBFNN has the best performance over the others with a 79.80%mean correlation coefficient over all joints,and its accuracy reaches as high as 92.67%in some ***,the highest accuracy over individual joints is 93.46%,which is achieved via the *** good accuracy suggests that the proposed approaches can be used as alternatives to the previously adopted ones and can be employed effectively to synchronously control multi-degrees of freedom HMI and for general multi-joint kinematics estimation purposes.

关键词： Surface electromyography Kinematics estimation artificial neural networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

引用

Geo-Spatial Information Science 2022年第3期25卷 353-364页

作者： Abdelkrim Bouasria Khalid Ibno Namr Abdelmejid Rahimi El Mostafa Ettachfini Badr Rerhou Faculty of Sciences Chouaib Doukkali UniversityEl JadidaMorocco Agricultural Development Department Doukkala Regional Agricultural Development OfficeEl JadidaMorocco Soil Science Department Hassan II Agronomic and Veterinary InstituteRabatMorocco

In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is *** task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi *** study area is located in the Doukkala irrigated perimeter in *** data can provide an alternative and fill this gap at a low *** to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable *** study aims to compare SOM prediction using Multiple Linear Regression(MLR)and artificial neural networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the *** image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,*** contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,*** pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.

关键词： Digital soil mapping soil organic matter remote sensing multiple linear regression artificial neural networks irrigated area Doukkala Morocco

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

引用

Chinese Physics B 2022年第2期31卷 233-241页

作者： Hao Luo Yi-Jun Wang Wei Ye Hai Zhong Yi-Yu Mao Ying Guo School of Automation Central South UniversityChangsha 410083China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Applied Science Jiangxi University of Science and TechnologyGanzhou 341000China

Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret ***,the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD *** this paper,we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks(ANN),which can be merged in post-processing with less additional *** ANN-based training scheme,enables key prediction without exposing any raw *** results show that the error between the predicted values and the true ones is in a reasonable *** CVQKD system can be improved in terms of the secret key rate and the parameter estimation,which involves less additional devices than the traditional CVQKD system.

关键词： quantum key distribution artificial neural networks secret key rate parameter estimation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Assessment of pressure drop in conical spouted beds of biomass by artificial neural networks and comparison with empirical correlations

引用

Particuology 2022年第11期70卷 1-9页

作者： Juan F.Saldarriag Yuby Cruz Idoi Estiati Mikel Tellabide Martin Olazar Department of Civil and Environmental Engineering Universidad de los AndesCarrera 1Este#19A-40BogotaColombia Department of Chemical Engineering University of the Basque CountryBarrio Sarriena s/nLeioaSpain

Pressure drop is an essential parameter in the operation of conical spouted beds(CSB)and depends on its geometric factors and materials *** materials,like biomass,are complex to treat and,unlike other gas–solid contact methods,CSB turn out to be a suitable technology for their *** neural networks were used in this study for the prediction of operating and peak pressure drops,and their performance has been compared with that of empirical correlations reported in the ***,a multi-layer perceptron network with backward propagation was used due to its ability to model non-linear multivariate *** fitting of the experimental data of both operating and peak pressure drop was significantly better than those reported in the literature,specifically in the case of the peak pressure drop,with R^(2) being ***,artificial neural networks have been proven suitable for the prediction of pressure drop in CSB.

关键词： Pressure drop artificial neural networks Biomass Spouted bed

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

The use of artificial neural networks to estimate seismic damage and derive vulnerability functions for traditional masonry

引用

Frontiers of Structural and Civil Engineering 2020年第3期14卷 609-622页

作者： Tiago Miguel FERREIRA Joao ESTEVAO Rui MAIO Romeu VICENTE ISISE Institute of Science and Innovation for Bio-Sustainability(IB-S)Department of Civil EngineeringUniversity of MinhoGuimaraes 4800-058Portugal Department of Civil Engineering University of AlgarveFaro 8005-139Portugal RISCO Department of Civil EngineeringUniversity of AveiroAveiro 3810-193Portugal

ABSTRACT This paper discusses the adoption of artificial Intelligence-based techniques to estimate seismic damage,not with the goal of replacing existing approaches,but as a mean to improve the precision of empirical *** such,damage data collected in the aftermath of the 1998 Azores earthquake(Portugal)is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on artificial neural networks(ANNs).The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability asssment methodology,which is subsequently used as input to both *** results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each ***,a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve *** general terms,the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach,which has revealed to be quite ***,the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

关键词： artificial neural networks seismic vulnerability masonry buildings damage estimation,vulnerability curves

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load

引用

Earthquake Engineering and Engineering Vibration 2018年第4期17卷 903-915页

作者： Meysam Ramezani Akbar Bathaei Amir K.Ghorbani-Tanha School of Civil Engineering College of Engineering University of Tehran

High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, artificial neural networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.

关键词： artificial neural networks tuned mass damper wind load auto-regressive model optimal frequency anddamping

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Proton exchange membrane fuel cells modeling based on artificial neural networks

引用

Journal of University of Science and Technology Beijing 2005年第1期12卷 72-77页

作者： YudongTian XinjianZhu GuangyiCao FuelCellResearchInstitute ShanghaiJiaotongUniversityShanghai200030China

To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

关键词： fuel cells proton exchange membrane artificial neural networks improved BP algorithm modeling

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using artificial neural networks

引用

Biomedical and Environmental Sciences 2007年第5期20卷 398-403页

作者： QAISAR MAHMOOD PING ZHENG DONG-LEI WU XU-SHENG WANG HAYAT YOUSAF EJAZ UL-ISLAM MUHAMMAD JAFFAR HASSAN GHULAM JILANI MUHAMMAD RASHID AZIM Department of Environmental Engineering College of Environment and Resource Science Zhejiang University Hangzhou 310029 Zhejiang China Institute of Statistical Genetics Zhejiang University Hangzhou 310029 Zhejiang China tUniversity of Arid Agriculture Rawalpindi Pakistan University of Arid Agriculture Rawalpindi Pakistan Federal Government Post-graduate College Sector H-8 Islamabad Pakistan

Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis （PCA） before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant （WTP） can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.

关键词： artificial neural networks Effluent sulfide prediction Effluent nitrite prediction Principal components analysis Wastewater treatment ASO reactor

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：