In this study, exhaust gas emissions are predicted using longshort-termmemory (LSTM) algorithm and minimum engine data, such as intake air temperature, emission gas temperature, and injection timing. Unlike existing...
详细信息
In this study, exhaust gas emissions are predicted using longshort-termmemory (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.
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 requ...
详细信息
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 longshort-termmemory (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.
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 a...
详细信息
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 longshort-termmemory (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.
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 distr...
详细信息
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
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...
详细信息
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 longshort-termmemory (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 algorithmterminates the printing if any anomaly is detected.
In this paper, a neural algorithm based on longshort-termmemory (LSTM) architecture able to model the energy demand as a time sequence application and predict trends based on key identified factors that influence en...
详细信息
ISBN:
(纸本)9781728107509
In this paper, a neural algorithm based on longshort-termmemory (LSTM) architecture able to model the energy demand as a time sequence application and predict trends based on key identified factors that influence energy consumption is proposed.
Development and successful implementation of Artificial Intelligence concepts with a focus on Neural Networks in different technical environments raise the question of their applicability in the transition from classi...
详细信息
ISBN:
(纸本)9781479975143
Development and successful implementation of Artificial Intelligence concepts with a focus on Neural Networks in different technical environments raise the question of their applicability in the transition from classic power grids to smart grids. In this paper power grid status is evaluated based on deviation between measured system parameters and design values while also considering historical values.
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 m...
详细信息
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 shor...
详细信息
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 ...
详细信息
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 longshorttermmemory (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.
暂无评论