检索结果-内蒙古大学图书馆

Application of Physical Model Test-Based long short-term memory algorithm as a Virtual Sensor for Nitrogen Oxide Prediction in Diesel Engines

引用

INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2023年第2期24卷 585-593页

作者： Shin, Dalho Jo, Seongin Kim, Hyung Jun Park, Suhan Konkuk Univ Dept Mech Engn Seoul 05029 South Korea Chonnam Natl Univ Dept Mech Engn Gwangju 61186 South Korea Natl Inst Environm Res Transportat Pollut Res Ctr 42 Hwangyeong Ro Incheon 22689 South Korea Konkuk Univ Sch Mech & Aerosp Engn Seoul 05029 South Korea

In this study, exhaust gas emissions are predicted using long short-term memory (LSTM) algorithm and minimum engine data, such as intake air temperature, emission gas temperature, and injection timing. Unlike existing modeling analysis methods, deep learning does not require various vehicle specifications and data, and the correlation between the measured data is derived by itself;therefore, it can serve as a virtual emission sensor. As it is difficult to analyze the correlation between the deep learning and test data from actual road cars because of the complex environment, an experimental single-cylinder diesel engine is used in this study. The intake air temperature is varied from 0 degrees C to 100 degrees C, and the injection timing is varied for nitrogen oxide measurement. Consequently, nitrogen oxide is successfully predicted with a high correlation R-2 of 0.994 using minimal engine data.

关键词： Deep learning long short-term memory algorithm Intake temperature Injection timing Indicated specific nitrogen oxide (ISNOx) Indicated mean effective pressure (IMEP)

来源：评论

学校读者我要写书评

暂无评论

Improving solar radiation source efficiency using adaptive dynamic squirrel search optimization algorithm and long short-term memory

引用

FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Khafaga, Doaa Sami Alhussan, Amel Ali Eid, Marwa M. El-kenawy, El-Sayed M. Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia Delta Univ Sci & Technol Fac Artificial Intelligence Mansoura Egypt Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura Egypt

Artificial intelligence and machine learning are used to optimize the design parameters of renewable energy sources, which are now regarded as vital components in current clean energy sources. As a result, system requirements can be reduced, and a well-designed system can improve performance. Artificial intelligence approaches in renewable energy sources and system design would significantly cut optimization time while maintaining high modeling accuracy and optimum performance. This study examines machine learning in depth, emphasizing how it can be used in developing renewable energy sources because of the vast range of technologies it can use. This paper approximates the hourly tilted solar irradiation using climate factors. The irradiance is estimated using a hybrid ensemble-learning approach. This approach combines a proposed adaptive dynamic squirrel search optimization algorithm (ADSSOA) with long short-term memory (LSTM) methods. To the best of our knowledge, this combination has not been used for solar radiation. The results are analyzed and contrasted with the outcomes of several recent swarm intelligence algorithms, such as the genetic algorithm, particle swarm optimization, and gray wolf optimizer. The binary ADSSOA approach performed as expected, with an average error of 0.1801 and a standard deviation of 0.0656. The ADSSOA-LSTM model had the lowest root mean square error (0.000388) compared to LSTM's (0.001221). In addition, the statistical analysis uses 10 iterations of each presented and evaluated method to provide accurate comparisons and reliable results.

关键词： forecasting solar radiation al-Biruni Earth radius metaheuristic algorithm artificial intelligence long short-term memory algorithm

来源：评论

学校读者我要写书评

暂无评论

LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor

引用

ENERGIES 2024年第10期17卷 2340页

作者： Lachekhab, Fadhila Benzaoui, Messouada Tadjer, Sid Ahmed Bensmaine, Abdelkrim Hamma, Hichem Univ MHamed Bougara Fac Hydrocarbon & Chem Appl Automat Lab Boumerdes 35000 Algeria Univ MHamed Bougara Inst Elect & Elect Engn Appl Automat Lab Boumerdes 35000 Algeria Univ MHamed Bougara Fac Hydrocarbon & Chem Electrificat Ind Enterprises Lab Boumerdes 35000 Algeria

Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data's underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques' applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor's variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures;LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the com

关键词： long short-term memory algorithm deep learning model autoencoder model anomaly detection electrical machine

来源：评论

学校读者我要写书评

暂无评论

An analysis of market power in Iran's electricity market with machine learning

引用

INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第4-5期45卷 489-502页

作者： Rostamnia, Naser M. Kharazmi Univ Dept Econ Tehran Iran Kharazmi Univ Dept Econ Karaj Iran

The Iranian electricity market was reformed over the last three decades primarily to promote competition and improve its production efficiency. This paper provides an analysis of competition in the Iranian electricity market. Although other works have provided similar assessments, none has provided a thorough probe over a long period. This paper analyses the Herfindahl Hirschman Index (HHI) of the market for the last decade which has not been done. Also, the paper forecasts the index in the market for the next year to project its direction. long short-term memory (LSTM) was implemented to forecast the indices in an efficient computational time. Grid search is used to select the optimal model for forecasting, and interactions analyses provide insights into the parameter options that lead to significantly improved accuracies. The results show that the market was unconcentrated from 2012 to 2021. Also, the forecasts show that the market will remain unconcentrated for the next year. Furthermore, the analysis shows that the entrance of new powerplants into the market could reduce the concentration in the market.

关键词： market power analysis Herfindahl Hirschman index long short-term memory algorithm hyperparameter optimisation grid search

来源：评论

学校读者我要写书评

暂无评论

Development and comparison of machine-learning algorithms for anomaly detection in 3D printing using vibration data

引用

PROGRESS IN ADDITIVE MANUFACTURING 2024年第2期9卷 529-541页

作者： Kumar, Rishi Sangwan, Kuldip Singh Herrmann, Christoph Ghosh, Rishi Sangwan, Mukund Birla Inst Technol & Sci Pilani Pilani Campus Pilani 333031 India Tech Univ Carolo Wilhelmina Braunschweig Inst Machine Tools & Prod Technol IWF Chair Sustainable Mfg & Life Cycle Engn Langer Kamp 19B D-38106 Braunschweig Germany Shiv Nadar Univ Greater Noida 201314 Uttar Pradesh India

3D printing is an emerging technology that converts digital models directly into physical objects. However, abnormal vibrations during the 3D printing process significantly affect the product quality, and also lead to possible failures of the printer components. This paper aims at developing machine-learning algorithms for anomaly detection or abnormal behavior of a 3D printer using vibration data. The proposed algorithms utilize vibration data from a sensor mounted on the printer. Data are then trained and validated developing four machine-learning algorithms to detect anomalies due to the structural or mechanical defects of the printer. Performances of the proposed four algorithms were evaluated and compared. It was found that the proposed long short-term memory (LSTM) algorithm has the best accuracy of 97.17% as compared to other algorithms. The novelty of the present work lies in detecting anomalies with high accuracy due to structural or mechanical faults in 3D printers using a low-cost sensor. The significance of the current work lies in its ability to achieve error-free 3D printing, resulting in less material waste, reduced human intervention and costs, and improved product quality by detecting potential anomalies during printing. The proposed algorithm terminates the printing if any anomaly is detected.

关键词： 3D printing Anomaly detection Machine learning long short-term memory algorithm Vibration sensor

来源：评论

学校读者我要写书评

暂无评论

The Bayesian CNN-LSTM Mixed Hybrid algorithm Model of the Photovoltaic short-term Output Forecasting 3

The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Ph...

引用

3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Dong, Enchong Guo, Yuxi North China Electric Power University School of Control and Computer Engineering Beijing China Shenzhen College of International Education Shenzhen China

ISBN: (纸本)9798350393224

Due to the volatility, randomness, and intermittency of photovoltaic power generation, it is difficult to accurately forecast its output. This paper proposes a Bayesian-optimized CNN-LSTM mixed neural model for a short-term photovoltaic output prediction model. First of all, the Xinjiang photovoltaic power station dataset is selected and preprocessed in this study. Secondly, the CNN method is applied for data feature extraction. And then the Bayesian optimization algorithm is used to analyze the LSTM super parameter optimization. Finally, the data, after undergoing feature extraction, are input into the Bayes theorem-LSTM model for forecast generation. The results show that the proposed algorithm has realized the accurate prediction of photovoltaic power, getting better than most other single benchmark models. These findings not only highlight the improved efficacy of the proposed algorithm but also affirm its potential applicability and value for practical implementation. © 2023 IEEE.

关键词： Bayesian algorithm Convolutional Neural Network algorithm long short-term memory algorithm Photovoltaic power output prediction

来源：评论

学校读者我要写书评

暂无评论

Energy Price Prediction on the Romanian Market using long short-term memory Networks 54

Energy Price Prediction on the Romanian Market using Long Sh...

引用

54th International Universities Power Engineering Conference (UPEC)

作者： Ioanes, Andrei Tirnovan, Radu Tech Univ Cluj Napoca Fac Elect Engn Cluj Napoca Romania

ISBN: (纸本)9781728133492

Transition to a market-based economy has reached, eventually, the production of electrical energy in Romania. Historically considered a never-ending resource, the producers did not have to interact with the consumer and more specific with the demands of the consumers. This paper proposes a neural algorithm based on long short-term memory (LSTM) architecture able to assess the price of energy as a time sequence application and predict trends based on the interaction between resources availability and demand.

关键词： Power Grid Demand Curve Machine Learning long short-term memory algorithm

来源：评论

学校读者我要写书评

暂无评论

Robust multi-step wind speed forecasting based on a graph-based data reconstruction deep learning method

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期238卷

作者： Wang, Ke Tang, Xiao-Yu Zhao, Shumian Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Shuren Univ Coll Informat Sci & Technol Hangzhou 310015 Peoples R China Carl von Ossietzky Univ Oldenburg Inst Phys D-26129 Oldenburg Germany Carl von Ossietzky Univ Oldenburg ForWind D-26129 Oldenburg Germany

Against global warming, wind energy has increasingly become a stable form of power supply. Accurate prediction of wind speed is crucial for turbine control and wind farm dispatch, contributing to stable and continued wind energy utilization. However, it is very difficult to accomplish satisfactory wind speed forecasting, especially multi-step forecasting, due to the stochastic, random and volatile characteristic of wind speed series with complex fluctuations. This study is elaborated to propose a novel method based on "data graph"reconstruction and long short term memory (LSTM) network, to achieve accurate and robust multi-step wind speed forecasting. To obtain implicit correlations between wind speed series, the time series data are reconstructed into matrices like "data graphs". To achieve better computing efficiency and forecasting accuracy, the convolutional neural network (CNN) is adopted to extract the features in the "data graphs". Then the extracted data graph features are imported into the bidirectional-LSTM (bi-LSTM) network module, which takes the input in forward and backward directions, respectively, to extract more temporal information. For adequate performance assessment, experiments are carried out on data sets of an actual wind farm located in a mountainous region in China. The results show that the proposed method outperforms classic meta-model based and hybrid-model based methods in accuracy, stability and computing efficiency. The results reveal that the data graph reconstruction combined with the CNN is able to extract the hidden features of the wind speed time series data. The proposed CNN-bi-LSTM method effectively improve the multi-step wind speed prediction accuracy.

关键词： Renewable energy Multi-step prediction Feature selection long short-term memory algorithm Graphical data design

来源：评论

学校读者我要写书评

暂无评论

Development of Machine Learning algorithm for Characterization and Estimation of Energy Consumption of Various Stages during 3D Printing 55

Development of Machine Learning Algorithm for Characterizati...

引用

55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Kumar, Rishi Ghosh, Rishi Malik, Rohan Sangwan, Kuldip Singh Herrmann, Christoph Birla Institute of Technology and Science Pilani Pilani Campus 333031 India Sustainable Manufacturing and Life Cycle Engineering Langer Kamp 19b Braunschweig38106 Germany

Energy usage in industries is one of the major contributors for climate change, biodiversity loss and resource scarcity. Technological advancements in digitalization led by Industry 4.0 facilitates affordable energy monitoring systems. This allows comprehensive understanding of the primary energy needs and improvement in the areas of inefficiency of a modern manufacturing system. Machine learning has the potential to reveal untapped insights, providing decision support for sustainable manufacturing by improving environmental performances, significant savings, and operational opportunities. The objectives of this research paper are to develop a machine learning algorithm for characterization, and to estimate the energy consumption of various stages in 3D printing. Machine learning model is developed using long short-term memory algorithm, and is trained, validated, and deployed for the classification of various stages during 3D printing process. Furthermore, energy consumption in each stage is estimated based on Simpson's rule. The characterization of stages is useful for understanding the energy consumption in each stage during the 3D printing process and providing decision support to practitioners in improving the areas of energy and time inefficiencies. © 2022 The Authors. Published by Elsevier B.V.

关键词： 3D Printing long short-term memory algorithm Machine Learning Stage Characterization

来源：评论

学校读者我要写书评

暂无评论

Development of Machine Learning algorithm for Characterization and Estimation of Energy Consumption of Various Stages during 3D Printing

引用

Procedia CIRP 2022年 107卷 65-70页

作者： Rishi Kumar Rishi Ghosh Rohan Malik Kuldip Singh Sangwan Christoph Herrmann Birla Institute of Technology and Science Pilani Pilani Campus India 333031 Technische Universität Braunschweig - Institute of Machine Tools and Production Technology (IWF) Chair of Sustainable Manufacturing and Life Cycle Engineering Langer Kamp 19b 38106 Braunschweig Germany

Energy usage in industries is one of the major contributors for climate change, biodiversity loss and resource scarcity. Technological advancements in digitalization led by Industry 4.0 facilitates affordable energy monitoring systems. This allows comprehensive understanding of the primary energy needs and improvement in the areas of inefficiency of a modern manufacturing system. Machine learning has the potential to reveal untapped insights, providing decision support for sustainable manufacturing by improving environmental performances, significant savings, and operational opportunities. The objectives of this research paper are to develop a machine learning algorithm for characterization, and to estimate the energy consumption of various stages in 3D printing. Machine learning model is developed using long short-term memory algorithm, and is trained, validated, and deployed for the classification of various stages during 3D printing process. Furthermore, energy consumption in each stage is estimated based on Simpson’s rule. The characterization of stages is useful for understanding the energy consumption in each stage during the 3D printing process and providing decision support to practitioners in improving the areas of energy and time inefficiencies.

关键词： 3D Printing Machine Learning long short-term memory algorithm Stage Characterization

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

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

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：