99 results about "Denoising autoencoder" patented technology

The invention relates to the technical field of power equipment monitoring, in particular to a transformer fault diagnosis method, system and device based on multi-modal deep learning and a storage medium. Obtaining the volume fraction of gas dissolved in oil of the transformer, the local discharge capacity, the sleeve dielectric loss factor, the vibration data and the infrared image data; continuous wavelet transform processing based on integrated gradient is carried out on the vibration data, and key fault frequency bands are dynamically screened to generate a time-frequency map; inputting the numeric data into a stack-type denoising auto-encoder network to extract depth features; respectively inputting the infrared image and the radio frequency map into a double-branch convolution encoder for early feature fusion, and extracting map depth features through a stack type convolution auto-encoder network; and based on the Dempster-Shafer evidence theory, carrying out conflict resolution and evidence synthesis on the fault probability distribution output by the two types of modes, and outputting a diagnosis result.

The invention discloses an APT (Advanced Persistent Threat) network attack detection method based on meta-path learning and subgraph sampling, which relates to the technical field of network security, and comprises the following steps of: constructing a network entity relation graph, collecting and standardizing various log data and network topology information, and introducing an external knowledge base to excavate a potential relation; the attribute graph model is used for endowing rich attributes for the nodes and the edges; a meta-path is dynamically generated through an adaptive algorithm, the meta-path is optimized in combination with frequent sub-graph mining and historical attack data, a model in which an attention mechanism and a noise reduction auto-encoder are combined with a graph convolutional network is introduced, and node features are further extracted; defining a node influence propagation index to perform sub-graph sampling, and generating a hierarchical sub-graph; and finally, fusing sub-graph topology, nodes of meta-path learning and attribute features, and adopting multi-classifier ensemble learning to optimize the performance of the attack detection model. According to the method, the accuracy, timeliness and adaptability of APT attack detection are effectively improved, and the method is suitable for a complex dynamic network environment.

The invention relates to the technical field of data fusion, and discloses an underwater-based multi-source data feature fusion method, which comprises the following steps: acquiring ocean underwater sound data acquired by a multi-source sensor, preprocessing the ocean underwater sound data, inputting the preprocessed ocean underwater sound data into a trained denoising auto-encoder, extracting initial feature representation, and performing feature fusion on the initial feature representation; optimizing the K-means initial clustering center by adopting an energy valley optimization algorithm to obtain optimal initial clustering center configuration; operating a K-means algorithm based on the optimal initial clustering center configuration, performing clustering analysis on the initial feature representation, and selecting a feature fusion strategy according to a clustering result to obtain a comprehensive feature representation; and outputting a target recognition result through a classification algorithm according to the comprehensive feature representation. And the accuracy, the robustness and the efficiency of underwater target recognition are remarkably improved.

The invention discloses a wireless network high-speed switching automatic test optimization method based on edge computing, and relates to the field of wireless network switching. An improved gravitational search algorithm is combined with a GIS deployment node, multi-dimensional data is collected through an intelligent heterogeneous sensor and reinforcement learning, preprocessing is carried out through a denoising auto-encoder based on a GAN, modeling is carried out through space-time diagram convolution and an LSTM mixed model, a test case is generated through reinforcement learning Monte Carlo tree search, and testing is carried out on edge nodes in a distributed mode. And an optimization strategy is formulated through multi-agent deep reinforcement learning, and strategy implementation and feedback are realized through SDN and a block chain. According to the method, multi-dimensional acquisition, intelligent modeling, automatic testing and deep reinforcement learning optimization are realized, the high-speed switching performance is improved, the time delay and failure rate are reduced, the testing efficiency is improved, resource allocation is optimized, the network security is enhanced, and stable and efficient operation of the wireless network is guaranteed in an omnibearing manner.

The invention provides a magnetotelluric data denoising method and device based on a depth fusion model, is applied to a magnetotelluric data denoising system, and relates to the technical field of magnetotelluric data processing. A deep fusion model with a'noise feature recognition-signal selective reconstruction 'framework is established, input signals are pre-classified through a front noise detection module, the problem of false attenuation of effective signals in traditional end-to-end denoising is effectively avoided, a denoising auto-encoder containing a residual structure is utilized, and the denoising accuracy is improved. The deep feature extraction capability of the residual structure and the signal reconstruction advantage of the denoising auto-encoder are combined, the fidelity and integrity of the reconstructed signal are remarkably improved, the denoising process does not depend on manual intervention, and the efficiency and accuracy of data processing are greatly improved.

The invention provides a drug-target correlation prediction method based on a hierarchical representation learning framework, and the method comprises the steps: screening high-information-density nodes based on a dynamic fluctuation threshold value, reducing low-noise nodes, and reconstructing topological connection according to a virtual edge weight formula; performing tensor splicing on the drug molecular features extracted by the dynamic neighborhood search framework and the protein semantic features generated by the denoising auto-encoder to form drug-protein pair joint feature representation; a multi-head attention mechanism is utilized to allocate dynamic weights for multi-view features based on drug-protein pairs, multi-view feature vectors are spliced, after key information is screened through the attention mechanism, the key information is input into a full-connection neural network, and a correlation prediction value is output based on the full-connection neural network. The association prediction method solves the problems that the prediction precision of the model on the complex biological interaction is poor, and the understanding ability of the model on the multilevel feature learning association in the complex biological network is seriously limited.

The invention discloses an online monitoring method for emerging marine pollutants, and relates to the technical field of pollutant monitoring, a plurality of sensors are adopted to work at the same time, multi-dimensional data are collected in real time, and comprehensive pollutant information is provided; a multi-layer convolutional neural network CNN is utilized to perform automatic feature extraction, automatic learning and extraction of high-dimensional complex features in data, a combined model of a support vector machine SVM and a decision tree is combined, preliminary classification is performed by the SVM, refined classification is performed by the decision tree, and the classification accuracy is improved; a deep learning-based denoising automatic encoder DAE is adopted to carry out denoising processing on sensor data, the DAE effectively removes background noise in nonlinear and complex environments through a learning noise mode, in addition, a self-adaptive filter is used, filtering parameters are dynamically adjusted according to environmental changes, the denoising effect is further optimized, and the signal quality is improved.

The invention discloses a single-cell data deep clustering method and device based on double auto-encoders and a medium, and relates to the technical field of single-cell RNA sequencing. The method comprises the following steps: acquiring original gene data of a single cell; an scDAEC model is constructed; the scDAEC model comprises a dynamic combination multi-head attention mechanism, a denoising auto-encoder and a graph auto-encoder; the de-noising auto-encoder is constructed based on zero-expansion negative binomial distribution loss adjusted by a zero-expansion proportion adaptive weight; the graph self-encoder is constructed based on a graph convolutional network; and inputting the original gene data into the scDAEC model for clustering processing to obtain a single cell data clustering result. The framework constructed by the method disclosed by the invention has very strong generalization ability and high fault tolerance.

The invention discloses an online monitoring method for emerging marine pollutants, and relates to the technical field of pollutant monitoring, a plurality of sensors are adopted to work at the same time, multi-dimensional data are collected in real time, and comprehensive pollutant information is provided; a multi-layer convolutional neural network CNN is utilized to perform automatic feature extraction, automatic learning and extraction of high-dimensional complex features in data, a combined model of a support vector machine SVM and a decision tree is combined, preliminary classification is performed by the SVM, refined classification is performed by the decision tree, and the classification accuracy is improved; a deep learning-based denoising automatic encoder DAE is adopted to carry out denoising processing on sensor data, the DAE effectively removes background noise in nonlinear and complex environments through a learning noise mode, in addition, a self-adaptive filter is used, filtering parameters are dynamically adjusted according to environmental changes, the denoising effect is further optimized, and the signal quality is improved.

The invention relates to an industrial process soft measurement method, in particular to a soft measurement method of a space-time fused hierarchical enhancement denoising auto-encoder network, which is familiar with an actual industrial process and determines a process variable and a quality variable; industrial data are collected and divided; performing data preprocessing; noise is added into the processed data, the processed data is input into an enhanced de-noising auto-encoder network (HEDAE) to learn effective information, and global enhanced features are extracted; and inputting global enhancement features extracted by the HEDAE to the GRU and the CNN at the same time, designing an interactive gating mechanism (TSIF), fusing time and space features, constructing a hybrid model to realize quality variable prediction, and verifying model prediction performance according to an evaluation index. On-line prediction is carried out through a soft measurement model, an industrial product quality variable prediction value is obtained, a production operation process is monitored and controlled in real time, and a production strategy is adjusted to ensure production efficiency, improve product quality and realize green production.

The invention discloses an intelligent operation and maintenance management method and system based on multi-modal AI, and belongs to the technical field of data artificial intelligence and industrial intelligent operation and maintenance crossing. The method comprises the steps that multi-modal data is collected and preprocessed, an improved VMD is used for conducting modal component decomposition on vibration signals, a first-order energy moment and a second-order energy moment are calculated to generate an initial vibration feature vector, and the initial vibration feature vector is calculated; a de-noising auto-encoder SDAE is used to obtain a final vibration feature vector, and the multi-modal data comprises vibration signals, energy consumption, images and text data; an improved VMD method is introduced to be combined with a Lagrange multiplier and a self-adaptive step length mechanism, a frequency domain iteration optimization solving process is constructed, meanwhile, nonlinear and deep feature enhancement is achieved through a denoising self-encoder, and therefore the extraction capacity and stability of a system for key modal components in complex vibration signals are effectively improved.

The invention relates to a single-cell multi-omics data clustering method based on cross-omics feature fusion, and belongs to the field of single-cell omics analysis of computational biology. According to the technical scheme, the method comprises the following steps: step 1, preprocessing unicellular original data of different omics; step 2, using the preprocessed data to train a plurality of denoising auto-encoders to serve as single view feature extractors of each omics; step 3, training a de-noising auto-encoder as a common view feature extractor by combining the preprocessed different omics data; step 4, utilizing an attention mechanism to enhance public view features; step 5, enhancing cross-omics public representation by using comparative learning; and 6, performing clustering analysis and cell type identification by directly using the fused low-dimensional feature representation obtained by the model. The method is used for solving the problem of clustering analysis of single-cell multi-omics data in the field of bioinformatics, and is particularly applied to multi-omics data fusion and cell type identification.

The present invention discloses a method for preprocessing underwater direction of arrival estimation, which changes the training target and structure of a BERT deep learning model, trains the model in a supervised manner based on four training targets, obtains an improved BERT model, and inputs array signal receiving data of a one-dimensional uniform linear array into the improved BERT model to obtain array signal denoising receiving data and an estimated value of the sound speed. The present invention improves the BERT deep learning model, uses the improved BERT as a denoising autoencoder to preprocess the array signal receiving data, improves the signal-to-noise ratio of the output signal, and estimates the sound speed at the same time. The denoised receiving data and the estimated sound speed obtained after the preprocessing are used as the input of the existing DOA estimation method, which can reduce the influence of noise and sound speed on DOA estimation, and improve the estimation performance of the existing DOA estimation method in an environment where the underwater sound speed is uncertain and the signal-to-noise ratio is low.

The application discloses an online monitoring method of a voltage transformer based on independent component analysis, which samples information of historical data, steady-state data and real-time data of signals output by the voltage transformer, constructs a data set from the historical data, and imports the data set into an initial SDAE network model based on a sparse denoising autoencoder to perform dynamic training, uses the steady-state data to fine-tune parameters based on the SDAE network model obtained through offline training, and thus obtains encoding data of the SDAE network model; uses an independent component analysis method to perform independent component decomposition by taking the encoding data as input; calculates sample statistics and overall statistical threshold, compares real-time statistics with the overall statistical threshold, and if the real-time statistics are lower than the overall statistical threshold, it is determined that the state of the voltage transformer is normal in this round of judgment; otherwise, it is considered that there is an abnormal voltage transformer in the voltage transformer group.

The present invention discloses a method, apparatus, device, and storage medium for detecting anomalies in drone flight data. These methods relate to the field of drone device anomaly detection and address the low accuracy of anomaly detection in drone flight data in the prior art. The method comprises: obtaining historical flight data of a drone, wherein the historical flight data includes multiple flight parameters; inputting the historical flight data into an anomaly detection model for detection, and obtaining detection results corresponding to the multiple flight parameters. The anomaly detection model is constructed by connecting the output of a pretrained denoising autoencoder and the input of a pretrained long-short-term memory neural network. The present invention combines the denoising autoencoder and the long-short-term memory neural network for anomaly detection, thereby improving the accuracy of anomaly detection.

A point cloud denoising method based on a denoising autoencoder. First, the point cloud data is processed, and the point cloud denoising problem is treated as a local problem. The neighborhood of each point is taken and randomly sampled. Secondly, the Transform layer appropriately destroys the input data to create obstacles for subsequent feature extraction. Then, the point cloud is aligned using the rotation matrix calculated by principal component analysis, rotating the point cloud to the same angle. Then, the Encoder layer extracts potential features from the damaged data through a multi-layer perceptron and uses maximum pooling to enhance translation invariance, rotation invariance, and scale invariance. Finally, the Decoder layer of the network decodes the potential features through full convolution and outputs the predicted displacement of the noise point to complete the denoising. The present invention removes noise as efficiently as possible while maintaining the geometric characteristics of the point cloud data.

This invention discloses a blast furnace ironmaking process monitoring method based on RBNCVA. First, a stacked denoising autoencoder (SDAE) is used to model historical blast furnace fault data, extracting robust wide nonlinear features. This feature helps to handle the complex nonlinearity of the blast furnace ironmaking process and is resistant to interference from noise and outliers. Then, a canonical variate analysis (CVA) method is used to analyze the relationship between past and future feature vectors to address the dynamic nature of the blast furnace. Finally, statistics are calculated using a probability density function obtained using kernel density estimation. This method reduces the false alarm rate in the blast furnace ironmaking monitoring process and significantly improves the fault detection rate and fault sensitivity.

The application discloses an intelligent online laboratory process data anomaly detection method, in particular to an intelligent online laboratory process anomaly detection method based on a particle swarm optimization algorithm and a deep sparse denoising autoencoder, which comprises the following steps: S101, collecting running process data based on an intelligent online laboratory equipment and building a sample database; S102, performing Min-Max normalization preprocessing operation on the data; S103, building an anomaly detection model based on a deep sparse denoising autoencoder (DSDAE), so that the model can learn the data characteristics of normal data after training; S104, optimizing the number of hidden layer neurons of the DSDAE model by using a particle swarm (PSO) algorithm, and establishing an anomaly detection model based on PSO-DSDAE; S105, determining a model reconstruction residual tolerance threshold by using a cumulative sum algorithm, and establishing an anomaly judgment mechanism; S106, training the anomaly detection model based on PSO-DSDAE by using dataset data, and realizing anomaly data detection by comparing whether the reconstruction residual between the model output and the input exceeds the threshold.

The present invention discloses a method, device and storage medium for predicting operating parameters of a generator set based on a multi-scale time series data fusion model. The multi-scale time series data fusion model includes a data denoising module and a data fusion module. The data denoising module includes a denoising autoencoder, and the data fusion module includes a one-dimensional deep convolutional neural network and a bidirectional long short-term memory network. The one-dimensional deep convolutional neural network includes a convolutional layer, a pooling layer and a fully connected layer, and the bidirectional long short-term memory network is embedded in the one-dimensional deep convolutional neural network. The method for predicting the operating parameters of the generator set includes: collecting real-time data of each operating parameter affecting the operating mode of the generator set and performing preprocessing; inputting the preprocessed real-time data of each operating parameter into a pre-trained multi-scale time series data fusion model to obtain prediction results of each operating parameter of the generator set. The present invention can reduce the noise of the original data, improve the data fusion accuracy and the prediction accuracy.

The application provides a natural gas pipeline anomaly detection method and system based on a hybrid deep neural network, wherein the method comprises normalizing feature data of a natural gas pipeline to obtain input feature values; constructing a stacked sparse denoising autoencoder deep neural network model as a first hybrid deep neural network according to the input feature values; constructing a cost function according to the input feature values, performing unsupervised feature learning of the first hybrid deep neural network by using the cost function, and obtaining a second hybrid deep neural network; adding a supervised classifier to the second hybrid deep neural network to obtain a third hybrid deep neural network; inputting the input feature values into the third hybrid deep neural network to obtain output feature values; calculating a maximum probability value of the output feature values by using the supervised classifier; and reducing a difference between the maximum probability value of the output feature values and a label. The application can improve anomaly detection accuracy and reduce the false positive rate of anomaly detection when an intrusion attack or an anomaly occurs.

The present invention discloses a method for evaluating the health state of an unmanned aerial vehicle (UAV) based on an enhanced spatio-temporal graph neural network, including: collecting the flight parameters of the UAV to be evaluated, and inputting the flight parameters and the adjacency matrix at each moment into the trained spatio-temporal graph neural network, and outputting the prediction label corresponding to each moment through the spatio-temporal graph neural network to obtain the category of the health state of the UAV; wherein, when the spatio-temporal graph neural network is trained, firstly, a flight parameter data set of the UAV in different health states is obtained and a new data set is generated by using a denoising autoencoder generative adversarial network; kinematic and dynamic modeling is performed on the flight parameters of the UAV to obtain a correlation relationship model among the flight parameters; the flight parameters are used as nodes, and the connection relationship between the nodes is determined based on the correlation relationship model, and an adjacency matrix is constructed as a spatio-temporal graph; the samples in the new data set and the adjacency matrix are used as spatio-temporal data to train the spatio-temporal graph neural network.

The application discloses a CNN denoising method based on distance-Doppler information, comprising the following steps: acquiring continuous multiple frames of radar distance-Doppler images, and constructing a convolutional neural network model; introducing an effective loss solving function to the convolutional neural network model for training; inputting a current frame image, a previous frame image of the current frame and a previous two frame image of the current frame into the trained convolutional neural network model to obtain a denoised image of the current frame image, wherein the convolutional neural network model comprises a self-adaptive moving encoder module combined with a convolution block attention module, effectively avoiding the problems of redundant parameters and serious information loss caused by forward convolution and maximum pooling of a convolutional autoencoder and other convolution-based denoising autoencoders. Meanwhile, the feature map is adaptively encoded, and those feature maps containing target information are selectively emphasized, and those feature maps containing interference information are as much as possible ignored.

The embodiment of the present application provides a kind of training method for trajectory observation data denoising model of motor target, the model includes preprocessing unit and denoising autoencoder, the method comprises: obtaining training set, each sample in training set includes input data and label, input data is the multiple frames of observation data obtained by sensor in predetermined time interval observing target movement track, label is the real movement track of target in corresponding time;Model is trained using training set, and the parameters of model are updated based on the loss calculated, wherein, preprocessing unit carries out denoising preprocessing to input data, and denoising autoencoder carries out coding and decoding to input data after denoising preprocessing, and the movement track after denoising is obtained based on the output of coding and decoding of denoising autoencoder Model, the model capable of effectively denoising observation data is obtained by the training method of the present application, to improve the accuracy of model to motor target movement track estimation.

The application provides a power system inertia analysis method, device, equipment and storage medium, wherein the method comprises: estimating the equivalent inertia of the power system using a physical model; correcting the equivalent inertia of the power system using a pre-trained data model to obtain updated equivalent inertia, wherein the data model adopts a denoising autoencoder, training labels are generated by including fine models of various types of inertia sources in the training process, and the update speed is adjusted by a momentum factor when updating the gradient; input the updated equivalent inertia into a simulation platform, add perturbations to each inertia node in the simulation platform to obtain the frequency of each inertia node, and calculate the distribution of each inertia node based on the updated equivalent inertia, the frequency of each inertia node and the center frequency of the inertia of the power system. The application proposes a hybrid intelligent driving scheme guided by a physical model and corrected by a data model, which can balance the calculation efficiency and prediction accuracy as a whole.

The present invention discloses a wind power frequency modulation step-by-step inertia control method based on deep learning. This method mainly includes the following three steps: 1) Obtain the optimal wind power frequency modulation step-by-step inertia control parameters using the Golden Eagle optimization algorithm in time-domain simulation and generate a data set; 2) Extract features from the data set based on a stacked denoising autoencoder; 3) Generate an optimal wind power frequency modulation step-by-step inertia control scheme by learning data features based on a deep neural network. The present invention can quickly and accurately provide the optimal wind power frequency modulation step-by-step inertia control scheme under the current system when power imbalance occurs in the power system; it can prompt the wind farm to actively provide power support for the power system, effectively participate in the power system frequency control, timely suppress the system frequency drop, is beneficial to compensating for the system inertia, effectively improves the quality and efficiency of wind power frequency modulation, provides an important scheme for power system planning and decision-making work, and has great significance for the safety and stability of power system operation.

The invention relates to a training method of an unsupervised language translation model and a language translation method and device. The implementation scheme is as follows: performing grammatical analysis and coding processing on unsupervised training corpora to obtain a grammatical feature vector; respectively inputting the training corpora into the corresponding monolingual word embedding layer and the shared word embedding layer to obtain monolingual word embedding and shared word embedding, and generating semantic vectors according to the monolingual word embedding and the shared word embedding; the shared word embedding layer is constructed based on a mixed corpus containing a source language and a target language; performing fusion processing on the grammar feature vector and the semantic vector to obtain a fusion vector of the training corpus; training an unsupervised pre-training model by using the fusion vector; and initializing an encoder of the language translation model by adopting a weight parameter of the trained unsupervised pre-training model, and performing unsupervised training on the language translation model by alternately executing a de-noising automatic encoder and a reverse translation task. According to the invention, the accuracy, fluency and robustness of translation can be improved.

The invention discloses a turbine fault diagnosis method and related device, and the method comprises the steps: collecting an original vibration signal of a turbine, carrying out the coding processing of the original vibration signal of the turbine through a denoising self-encoder, and achieving the signal dimension reduction compression through the hidden layer dimension constraint, and obtaining a coding signal; inputting the coded signals into a convolutional neural network, extracting local features through a convolutional layer, and combining the local features into a feature map with a higher depth dimension after the local features are screened by a pooling layer; and inputting the feature map with a higher depth dimension into a full connection layer, finely adjusting network parameters through a back propagation algorithm in a supervised learning mode, and outputting a classification result of turbine fault types and positions. According to the method, huge operation parameters of the turboset are trained through deep learning, and automatic output of fault feature type positions and intelligent diagnosis of operation health conditions are completed without depending on an existing expert diagnosis system.

The present invention discloses a radar signal modulation recognition method based on convolutional denoising and Resnet50. The implementation scheme of the present invention is as follows: perform time-frequency analysis on radar signals; generate a training set; construct a convolutional denoising autoencoder network; train the convolutional denoising autoencoder network; construct a deep residual neural network 19-Resnet50; use a transfer learning strategy to train the deep residual neural network 19-Resnet50; identify the in-pulse modulation type of radar signals. The present invention uses a convolutional denoising autoencoder network to realize the denoising of the time-frequency image of radar signals under low signal-to-noise ratio, and improves the quality of the time-frequency feature image of radar signals; the present invention uses a deep residual neural network 19-Resnet50 to extract deeper image classification features, and can identify the in-pulse modulation types of various radar signals in a low signal-to-noise ratio environment, having the advantages of strong anti-noise ability, a large number of recognized radar signal types, and high recognition accuracy.

The present invention relates to a method for identifying fraudulent calls with a width autoencoder that integrates an attention mechanism, and belongs to the field of big data technology. S1: Based on the user service data of a telecom operator, extract and preprocess user basic information, voice communication data, SMS communication data, and mobile phone APP access data, perform feature processing on each form, and perform encrypted caller association integration according to external association rules; S2: Based on the preprocessed and associated data, construct a denoising autoencoder to compress the input into a low-dimensional space representation, and reconstruct the output through the representation; S3: Construct a width learning feature generation model, input the encoded data and the original data into the model to generate feature nodes, encoding feature nodes, enhancement nodes, and encoding enhancement nodes, and reconstruct the node distribution based on the user dimension; S4: Use the attention mechanism model to extract channel and space features; S5: Divide the data set, train the model and tune the parameters, obtain the trained model, and output the final prediction result of the fraud data.

A generalized zero-sample fault diagnosis method for thermal power generation water supply pump groups based on fault similarity belongs to the field of generalized zero-sample fault diagnosis for thermal power generation water supply pump groups. The present invention extracts fault-related features from historical production data by constructing a feature extraction model of a fault-related stacked denoising autoencoder; based on shallow expert knowledge, a similarity matrix between seen faults and seen faults, as well as a similarity matrix between seen faults and unseen faults are constructed. The similarity matrix between seen faults is calculated using seen fault data, and combined with shallow expert experience, a similarity matrix mapping relationship between data and expert knowledge is formed; based on an offline model, seen faults and unseen faults of the water supply pump group are diagnosed in real time. The present invention can diagnose the fault type of the water supply pump group in real time, overcomes the dependence on deep-level knowledge such as the cause, location and degree of the fault, and the defects of domain offset, and ensures the normal operation of the water supply pump group in thermal power generation.