An Improved Affinity Propagation Clustering Algorithm Based on Entropy Weight Method and Principal Component Analysis

2016 ◽  
Vol 9 (6) ◽  
pp. 227-238 ◽  
Author(s):  
Wang Limin ◽  
Zhang Li ◽  
Han Xuming ◽  
Ji Qiang ◽  
Mu Guangyu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Affinity Propagation ◽  
Entropy Weight ◽  
Affinity Propagation Clustering ◽  
Entropy Weight Method ◽  
Weight Method

scholarly journals A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis

2016 ◽  
Vol 19 (03) ◽  
pp. 382-390 ◽  
Author(s):  
Martina Siena ◽  
Alberto Guadagnini ◽  
Ernesto Della Rossa ◽  
Andrea Lamberti ◽  
Franco Masserano ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Enhanced Oil Recovery ◽  
Parameter Space ◽  
Oil Recovery ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Bayesian Clustering ◽  
Global Features

Summary We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through principal-component analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesian-clustering algorithm. Considering the cluster that includes the EOR field under evaluation, an intercluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components (PCs) and (b) the fraction of variance associated with each PC is taken as weight of the Euclidean distance that we determine. As a test bed, we apply our approach on three fields operated by Eni. These include light-, medium-, and heavy-oil reservoirs, where gas, chemical, and thermal EOR projects were, respectively, proposed. Our results are (a) conducive to the compilation of a broad and extensively usable database of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.

scholarly journals Automatic spike sorting for high-density microelectrode arrays

2018 ◽  
Vol 120 (6) ◽  
pp. 3155-3171 ◽  
Author(s):  
Roland Diggelmann ◽  
Michele Fiscella ◽  
Andreas Hierlemann ◽  
Felix Franke
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Microelectrode Arrays ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Recording Electrodes

High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the “curse of dimensionality” and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

scholarly journals Integrated energy service demand evaluation based on AHP and entropy weight method

2020 ◽  
Vol 185 ◽  
pp. 01046
Author(s):  
Xianjun Qi ◽  
Mucong Zhou
Keyword(s):  
Clustering Algorithm ◽  
Rapid Development ◽  
Assessment Model ◽  
Entropy Weight ◽  
Analytic Hierarchy ◽  
Energy Services ◽  
Energy Service Demand ◽  
Entropy Weight Method ◽  
Energy Service ◽  
Weight Method

In the context of the energy revolution, integrated energy services have ushered in a period of opportunities for rapid development. To assess the demand of users of integrated energy services, we establish indexes of demand of integrated energy service users. We got the assessment model based on the analytic hierarchy process (AHP) and entropy weight method. The proposed assessment model is tested on 50 users, and K-means clustering algorithm is used to cluster users. The characteristics of the service requirements of each type are analysed.

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes

2008 ◽  
Vol 57 (10) ◽  
pp. 1659-1666 ◽  
Author(s):  
Kris Villez ◽  
Magda Ruiz ◽  
Gürkan Sin ◽  
Joan Colomer ◽  
Christian Rosén ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Process Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Process Data ◽  
Nitrogen And Phosphorus ◽  
Online Data ◽  
Data Set

A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective discrimination between normal and abnormal operational conditions. The second iteration of the procedure allowed a clear-cut discrimination of applied operational changes in the SBR history. Important to add is that this procedure helped identifying some changes in the process behaviour, which would not have been possible, had we only relied on visually inspecting this online data set of the SBR (which is traditionally the case in practice). Hence the PCA based clustering methodology is a promising tool to efficiently interpret and analyse the SBR process behaviour using large historical online data sets.

Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission

2015 ◽  
Vol 741 ◽  
pp. 183-187 ◽  
Author(s):  
Yong Liu ◽  
Biao Ma ◽  
Yu Yan ◽  
Chang Song Zheng
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Atomic Emission ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
State Evaluation ◽  
Power Shift ◽  
Fuzzy C Means Clustering ◽  
Friction Surfaces

Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle components and higher cumulative contributing rate. To detect the threshold point at where the wear-stage upgraded, the Fuzzy C-means clustering algorithm was applied to process the eigenvalues of principle components. Furthermore, it is demonstrated that the principle components relate to the worn-out state of friction pairs or transmission parts. In general, the introduction of KPCA has contributed to assess the wear-stage at where the machine situates and the accurate worn-out state of various transmission parts.

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