Due to the complicated and variable operating environment of wind turbines,a method based on k-means clustering algorithm is proposed to divide the operating condition for wind turbines and supplies the forceful found...
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ISBN:
(纸本)9781509046584
Due to the complicated and variable operating environment of wind turbines,a method based on k-means clustering algorithm is proposed to divide the operating condition for wind turbines and supplies the forceful foundation for making a maintenance *** this method,the fault characteristic analysis and the characteristic frequency calculation for the gear box and the generator are *** then,the vibration characteristics of the two subsystems are extracted by using the wavelet packet ***,the k-means clustering algorithm is used to classify the measured data from a wind *** to the central-limit theorem,the criterion is established to evaluate whether the state characteristic parameters are normal or ***'s beneficial to the early warning under the different working conditions of wind *** results show that this division is accurate and it is beneficial to evaluate the operating states of wind turbines.
This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a ro...
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ISBN:
(数字)9781510608221
ISBN:
(纸本)9781510608221
This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clusteringalgorithm is used to classify the pixels in each frame and the clusters' centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.
With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry’s attention. In order ...
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ISBN:
(纸本)9781509050369;9781509050352
With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry’s attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-meansclustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.
This paper presents a new method to preprocess the datasets, and an efficient way for assigning data points to clusters. The improved method avoids k-meansalgorithm sensitive to the initial centroids and instability,...
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This paper presents a new method to preprocess the datasets, and an efficient way for assigning data points to clusters. The improved method avoids k-meansalgorithm sensitive to the initial centroids and instability, and it avoids the large number of iterations, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means.
With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry's attention. In ord...
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ISBN:
(纸本)9781509050352
With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry's attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-meansclustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.
Nowadays, intrusion detection is a technology to effectively avoid a number of risks of network intrusion. The k-meansalgorithm is widely used in intrusion detection. But, the algorithm has some shortcomings, such as...
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ISBN:
(纸本)9781538636749
Nowadays, intrusion detection is a technology to effectively avoid a number of risks of network intrusion. The k-meansalgorithm is widely used in intrusion detection. But, the algorithm has some shortcomings, such as random selection of k value, sensitive selection of initial cluster centers, and low accuracy in clustering high-dimensional data. In order to make up for these shortcomings of the k-meansalgorithm, this paper proposes an AE-kmeans architecture which combines an autoencoder with the improved k-meansalgorithm. The AE-kmeans architecture realizes the dimension reduction and feature extraction of these original data by introducing an autoencoder, and uses the improved k-meansalgorithm to cluster these processed data. These improvements of the improved k-meansalgorithm mainly include two aspects: the first aspect, a new method is introduced to select initial cluster centers;the second aspect, the algorithm calculates the weight of each attribute with the coefficient of variation, and then uses these weights in Euclidean distance formula, resulting in a weighted Euclidean distance formula. Finally, the AE-kmeans architecture uses kDD CUP99 data set for intrusion detection simulation experiments. The Experimental results show that the AE-kmeans architecture not only enhances the ability to deal with high-dimensional data, but also improves the detection rate and reduces the error-detection rate compared with k-meansalgorithm.
This paper offers a hybrid short-term load forecasting (STLF) model using a Bayesian neural network (BNN) with a pre-processing stage consisting of a k-means clustering algorithm and time series analysis. The data clu...
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ISBN:
(纸本)9781509032709
This paper offers a hybrid short-term load forecasting (STLF) model using a Bayesian neural network (BNN) with a pre-processing stage consisting of a k-means clustering algorithm and time series analysis. The data clusters are time series analyzed to provide the most accurate data sets for each hour of the day. The final forecast is provided from the BNN output. California load data is used to determine the accuracy and processing speed of the proposed method. Additionally a comparison between BNN and other intelligent algorithms is provided using the same pre-processing stage to further gauge performance benchmarks.
According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we ...
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According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-meansclustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F-measure, and our results show that the algorithms can accurately classify the subjects' thoughts into corresponding action commands.
Organizations implement management systems to achieve better performance in different functions. One important area of management systems is "energy management systems". ISO 50001: 2011 proposed a systematic...
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Organizations implement management systems to achieve better performance in different functions. One important area of management systems is "energy management systems". ISO 50001: 2011 proposed a systematic framework for operation of an effective energy management system (EnMS). Resources should be used efficiently to obtain suitable performance. Thus, it is useful to evaluate resource utilization by considering the importance and efficiency of resources in processes. In this study, major functions of an EnMS are identified in accordance with ISO 50001. Then, for each function, processes and resources are specified and classified. Later, data are gathered, and appropriate measures are calculated. Finally, data analysis techniques are applied to propose strategic decisions for improvement. In addition, an oil refinery is considered as a case study.
In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accura...
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In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease transmission under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivity and specificity. The results show that support vector machine algorithm outperformed the other two simple algorithms and the neural network and genetic algorithms offered better prediction at the end, while the proposed combined approach is developed using different parameters and outperformed the simple methods.
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