scholarly journals Penggabungan Metode U-Control Chart dan Metode Automatic Clustering Differential Evolution untuk Penentuan Jumlah Klaster pada Metode K-Means

2019 ◽  
Author(s):  
Ahmad Ilham
Keyword(s):  
Differential Evolution ◽  
Control Chart ◽  
High Performance ◽  
Evaluation Method ◽  
A Priori ◽  
Differential Evolution Algorithm ◽  
Synthetic Data ◽  
Real Data ◽  
Activation Threshold ◽  
Automatic Clustering

Determining the number of clusters k-Means is the most populer problem among data mining researchers because of the difficulty to determining information from the data a priori so that the results cluster un optimal and to be quickly trapped into local minimums. Automatic clustering method with evolutionary computation (EC) approach can solve the k-Means problem. The automatic clustering differential evolution (ACDE) method is one of the most popular methods of the EC approach because it can handle high-dimensional data and improve k-Means drafting performance with low cluster validity values. However, the process of determining k activation threshold on ACDE is still dependent on user considerations, so that the process of determining the number of k-Means clusters is not yet efficient. In this study, the ACDE problem will be improved using the u-control chart (UCC) method, which is proven to be efficiently used to solve k-Means problems automatically. The proposed method is evaluated using the state-of-the-art datasets such as synthetic data and real data (iris, glass, wine, vowel, ruspini) from UCI repository machine learning and using davies bouldin index (DBI) and cosine similarity measure (CS) as an evaluation method. The results of this study indicate that the UCC method has successfully improved the k-Means method with the lowest objective values of DBI and CS of 0.470 and 0.577 respectively. The lowest objective value of DBI and CS is the best method. The proposed method has high performance when compared with other current methods such as genetic clustering for unknown k (GCUK), dynamic clustering pso (DCPSO) and automatic clustering approach based on differential evolution algorithm combining with k-Means for crisp clustering (ACDE) for almost all DBI and CS evaluations. It can be concluded that the UCC method is able to correct the weakness of the ACDE method on determining the number of k-Means clusters by automatically determining k activation threshold

Migration moveout analysis and depth focusing

Geophysics ◽  
1993 ◽  
Vol 58 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Claude F. Lafond ◽  
Alan R. Levander
Keyword(s):  
Heterogeneous Media ◽  
A Priori ◽  
Complex Structure ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Real Data ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Data Set ◽  
Velocity Models

Prestack depth migration still suffers from the problems associated with building appropriate velocity models. The two main after‐migration, before‐stack velocity analysis techniques currently used, depth focusing and residual moveout correction, have found good use in many applications but have also shown their limitations in the case of very complex structures. To address this issue, we have extended the residual moveout analysis technique to the general case of heterogeneous velocity fields and steep dips, while keeping the algorithm robust enough to be of practical use on real data. Our method is not based on analytic expressions for the moveouts and requires no a priori knowledge of the model, but instead uses geometrical ray tracing in heterogeneous media, layer‐stripping migration, and local wavefront analysis to compute residual velocity corrections. These corrections are back projected into the velocity model along raypaths in a way that is similar to tomographic reconstruction. While this approach is more general than existing migration velocity analysis implementations, it is also much more computer intensive and is best used locally around a particularly complex structure. We demonstrate the technique using synthetic data from a model with strong velocity gradients and then apply it to a marine data set to improve the positioning of a major fault.

The Complete Data Fusion as a ready to use tool for the exploitation of atmospheric Sentinel ozone profiles

2020 ◽  
Author(s):  
Nicola Zoppetti ◽  
Simone Ceccherini ◽  
Flavio Barbara ◽  
Samuele Del Bianco ◽  
Marco Gai ◽  
...  
Keyword(s):  
Data Fusion ◽  
A Priori ◽  
Synthetic Data ◽  
Simulated Data ◽  
Real Data ◽  
Low Earth Orbit ◽  
Complete Data ◽  
Atmospheric Composition ◽  
Horizon 2020 ◽  
Level 3

<p>Remote sounding of atmospheric composition makes use of satellite measurements with very heterogeneous characteristics. In particular, the determination of vertical profiles of gases in the atmosphere can be performed using measurements acquired in different spectral bands and with different observation geometries. The most rigorous way to combine heterogeneous measurements of the same quantity in a single Level 2 (L2) product is simultaneous retrieval. The main drawback of simultaneous retrieval is its complexity, due to the necessity to embed the forward models of different instruments into the same retrieval application. To overcome this shortcoming, we developed a data fusion method, referred to as Complete Data Fusion (CDF), to provide an efficient and adaptable alternative to simultaneous retrieval. In general, the CDF input is any number of profiles retrieved with the optimal estimation technique, characterized by their a priori information, covariance matrix (CM), and averaging kernel (AK) matrix. The output of the CDF is a single product also characterized by an a priori, a CM and an AK matrix, which collect all the available information content. To account for the geo-temporal differences and different vertical grids of the fusing profiles, a coincidence and an interpolation error have to be included in the error budget.<br>In the first part of the work, the CDF method is applied to ozone profiles simulated in the thermal infrared and ultraviolet bands, according to the specifications of the Sentinel 4 (geostationary) and Sentinel 5 (low Earth orbit) missions of the Copernicus program. The simulated data have been produced in the context of the Advanced Ultraviolet Radiation and Ozone Retrieval for Applications (AURORA) project funded by the European Commission in the framework of the Horizon 2020 program. The use of synthetic data and the assumption of negligible systematic error in the simulated measurements allow studying the behavior of the CDF in ideal conditions. The use of synthetic data allows evaluating the performance of the algorithm also in terms of differences between the products of interest and the reference truth, represented by the atmospheric scenario used in the procedure to simulate the L2 products. This analysis aims at demonstrating the potential benefits of the CDF for the synergy of products measured by different platforms in a close future realistic scenario, when the Sentinel 4, 5/5p ozone profiles will be available.<br>In the second part of this work, the CDF is applied to a set of real measurements of ozone acquired by GOME-2 onboard the MetOp-B platform. The quality of the CDF products, obtained for the first time from operational products, is compared with that of the original GOME-2 products. This aims to demonstrate the concrete applicability of the CDF to real data and its possible use to generate Level-3 (or higher) gridded products.<br>The results discussed in this presentation offer a first consolidated picture of the actual and potential value of an innovative technique for post-retrieval processing and generation of Level-3 (or higher) products from the atmospheric Sentinel data.</p>

Extraction of Specific Signals with Temporal Structure

2001 ◽  
Vol 13 (9) ◽  
pp. 1995-2003 ◽  
Author(s):  
Allan Kardec Barros ◽  
Andrzej Cichocki
Keyword(s):  
High Performance ◽  
Learning Algorithm ◽  
Temporal Structure ◽  
A Priori ◽  
Real Data ◽  
Primary Sources ◽  
Source Signal ◽  
Priori Information ◽  
Blind Extraction ◽  
Linear Mixtures

In this work we develop a very simple batch learning algorithm for semi-blind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis, we do not carry out the extraction in a completely blind manner; neither do we assume that sources are statistically independent. In fact, we show that the a priori information about the autocorrelation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm.

scholarly journals An IMM Filter Defined in the Linear-Circular Domain, Application to Maneuver Detection with Heading Only

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Karim El mokhtari ◽  
Serge Reboul ◽  
Georges Stienne ◽  
Jean Bernard Choquel ◽  
Benaissa Amami ◽  
...  
Keyword(s):  
A Priori ◽  
Synthetic Data ◽  
Real Data ◽  
Circular Domain ◽  
Circular Data ◽  
Von Mises Distribution ◽  
A Posteriori ◽  
Circular Distributions ◽  
Vehicle Maneuver ◽  
Von Mises

In this article, we propose a multimodel filter for circular data. The so-called Circular Interacting Multimodel filter is derived in a Bayesian framework with the circular normal von Mises distribution. The aim of the proposed filter is to obtain the same performance in the circular domain as the classical IMM filter in the linear domain. In our approach, the mixing and fusion stages of the Circular Interacting Multimodel filter are, respectively, defined from the a priori and from the a posteriori circular distributions of the state angle knowing the measurements and according to a set of models. We propose in this article a set of circular models that will be used in order to detect the vehicle maneuvers from heading measurements. The Circular Interacting Multimodel filter performances are assessed on synthetic data and we show on real data a vehicle maneuver detection application.

scholarly journals Choosing blindly but wisely: differentially private solicitation of DNA datasets for disease marker discovery

2014 ◽  
Vol 22 (1) ◽  
pp. 100-108 ◽  
Author(s):  
Yongan Zhao ◽  
Xiaofeng Wang ◽  
Xiaoqian Jiang ◽  
Lucila Ohno-Machado ◽  
Haixu Tang
Keyword(s):  
Evaluation Method ◽  
Scientific Discovery ◽  
A Priori ◽  
Real Data ◽  
Data Generation ◽  
Pilot Data ◽  
Disease Marker ◽  
Data Owner ◽  
Human Genomic ◽  
The Right

Abstract Objective To propose a new approach to privacy preserving data selection, which helps the data users access human genomic datasets efficiently without undermining patients’ privacy. Methods Our idea is to let each data owner publish a set of differentially-private pilot data, on which a data user can test-run arbitrary association-test algorithms, including those not known to the data owner a priori. We developed a suite of new techniques, including a pilot-data generation approach that leverages the linkage disequilibrium in the human genome to preserve both the utility of the data and the privacy of the patients, and a utility evaluation method that helps the user assess the value of the real data from its pilot version with high confidence. Results We evaluated our approach on real human genomic data using four popular association tests. Our study shows that the proposed approach can help data users make the right choices in most cases. Conclusions Even though the pilot data cannot be directly used for scientific discovery, it provides a useful indication of which datasets are more likely to be useful to data users, who can therefore approach the appropriate data owners to gain access to the data.

scholarly journals AIRBORNE GRAVITY GRADIOMETRY – DATA PROCESSING AND INTERPRETATION

2013 ◽  
Vol 31 (3) ◽  
pp. 427 ◽  
Author(s):  
Dionisio Uendro Carlos ◽  
Marco Antonio Braga ◽  
Henry F. Galbiatti ◽  
Wanderson Roberto Pereira
Keyword(s):  
Iron Ore ◽  
A Priori ◽  
Synthetic Data ◽  
Real Data ◽  
Geological Mapping ◽  
Airborne Gravity ◽  
Gravity Gradient ◽  
Gravity Gradiometry ◽  
Quadrilátero Ferrífero ◽  
Airborne Gravity Gradiometry

ABSTRACT. This paper discusses some processing techniques (all codes were implemented with open source software) developed for airborne gravity gradient systems, aiming at outlining geological features by applying mathematical formulations based on the potential field properties and its breakdown into gradiometric tensors. These techniques were applied to both synthetic and real data. These techniques applied to synthetic data allow working in a controlled environment, under- standing the different processing results and establishing a comparative parameter. These methodologies were applied to a survey area of the Quadrilátero Ferrífero to map iron ore targets, resulting in a set of very helpful and important information for geological mapping activities and a priori information for inversion geophysical models.Keywords: processing, airborne gravity gradiometry, iron ore exploration, FTG system, FALCON system. RESUMO. Neste trabalho apresentamos algumas técnicas de processamento (todos os códigos foram implementados em softwares livres) desenvolvidas para aplicação aos dados de aerogradiometria gravimétrica. Os processamentos foram aplicados tanto a dados sintéticos como a dados reais. A aplicação a dados sintéticos permite atuar em um ambiente controlado e entender o padrão resultante de cada processamento. Esses mesmos processamentos foram aplicados em uma área do Quadrilátero Ferrífero para o mapeamento de minério de ferro. Todos os resultados desses processamentos são muito úteis e importantes para o mapeamento geológicoe como informação a priori para modelos de inversão geofísica.Palavras-chave: processamento, dados de aerogradiometria gravimétrica, exploração de minério de ferro, sistema FTG, sistema FALCON.

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