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نتیجه جستجو - Decomposition

تعداد مقالات یافته شده: 175
ردیف عنوان نوع
61 An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction
یک مدل ماشین پیشرفته افراطی برای پیش بینی جریان رودخانه: پیشرفته ترین برنامه ها ، کاربردهای عملی در منطقه مهندسی منابع آب و جهت گیری تحقیقات آینده-2019
Despite the massive diversity in the modeling requirements for practical hydrological applications, there remains a need to develop more reliable and intelligent expert systems used for real-time prediction purposes. The challenge in meeting the standards of an expert system is primarily due to the influence and behavior of hydrological processes that is driven by natural fluctuations over the physical scale, and the resulting variance in the underlying model input datasets. River flow forecasting is an imperative task for water resources operation and management, water demand assessments, irrigation and agriculture, early flood warning and hydropower generations. This paper aims to investigate the viability of the enhanced version of extreme learning machine (EELM) model in river flow forecasting applied in a tropical environment. Herein, we apply the complete orthogonal decomposition (COD) learning tool to tune the output-hidden layer of the ELM model’s internal neuronal system, instead of the conventional multi-resolution tool (e.g., singular value decomposition). ToA-ELM, AdaBoost.RT-extreme learning machine; AI, artificial intelligence; ANFIS, adaptive neuro-fuzzy inference system; ANN, artificial neural network; ARIMA, autoregressive integrated moving average; AtmP, atmospheric pressure; B-ANN, bootstrap-artificial neural network; BCSO, binary-coded swarm optimization; B-ELM, bootstrap-extreme learning machine; C-ELM, complex-extreme learning machine; Cl−1, chloride; COD, complete orthogonal decomposition (COD); CRO-ELM, coral reefs optimization-extreme learning machine; DE-ELM, deferential evolution-extreme learning machine; DID, department of Irrigation and Drainage; DO, dissolved oxygen concentration; EC-SVR, evolutionary computation-based support vector machine; EDI, effective drought index; ELM, extreme learning machine; EELM, enhanced extreme learning machine; EEMD, ensemble empirical mode decomposition; EL-ANFIS, extreme learning adaptive neuro-fuzzy inference system; EMD, empirical mode decomposition; Ens, Nash-Sutcliffe coefficient; Ensemble-ELM, ensemble-extreme learning machine; EPR, evolutionary polynomial regression; ESNs, echo state networks; ETo, evapotranspiration; Fe, iron; Fr, Froude number; FS, factor of safety; GA-ELM, genetic algorithm-extreme learning machine; GCM, general circulation model; G-ELM, geomorphology extreme learning machine; GP, genetic programming; GRNN, generalized regression neural network; HCO3 -1, bicarbonate; HDSR, diffuse solar radiation; HRT, hydraulic retention time; I-ELM, integrated extreme learning machine; KELM, Kernelextreme learning machine; LST, land surface temperature; LASSO, least absolute shrinkage and selection operator; LSTM, long short-term memory network; LSSVM, least square support vector machine; MAE, mean absolute error; MARS, multivariate adaptive regression spline; MBFIPS, Multi-objective binary-coded fully informed particle swarm optimization; MC-OS-ELM, meta cognitive-online sequential-extreme learning machine; MLPNN, multi-linear perceptron neural network; MLR, multiple linear regression; MME, multi-model ensemble; NEMR, northeast monsoon rainfall; NO2 -1, nitrite; NO3 -1, nitrate; NO2, nitrogen dioxide; NT, total nitrogen; O3, ozone; OP-ELM, optimally pruned-extreme learning machine; OSELM, online sequential extreme learning machine; PCA, principal component analysis; pH, power of hydrogen; PM10, air pollution “suspended particulate matters”; PO4 -3, phosphorus; R-ELM, radial basis-extreme learning machine; r, determination coefficient; RE, relative error; RF, rainfall; RH, relative humidity; RHmax, maximum relative humidity; RHmean, mean relative humidity; RHmin, minimum relative humidity; RMSE, root mean square error; RVM, relevance vector machine; SaE-ELM, self-adaptive evolutionary-extreme learning machine; SC, specific conductance; S-ELM, sigmoid-extreme learning machine; SHr, sunshine hour; SR, solar radiation; SO4 -2, sulfate; SiO2, Silicon; SO2,
مقاله انگلیسی
62 Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems
ماشینهای یادگیری افراطی دو فاز با ترکیب کامل حالت تجربی گروه با الگوریتم نویز تطبیقی برای مشکلات پیش بینی رواناب چند مقیاسی-2019
Expert systems adopted in real-time multi-scale runoff prediction are useful decision-making tools for hydrologists but the stochastic nature of any hydrological variable can pose significant challenges in attaining a reliable predictive model. This paper advocates a data-driven approach used to design two-phase hybrid model (i.e., CVEE-ELM). The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) coupled with the variational mode decomposition (VMD) algorithms for better frequency resolution of the input datasets and the extreme learning machine (ELM) algorithm as the objective predictive model. In the first stage of the presented model design, notable frequencies in the predictor-target data series are uncovered, utilizing the CEEMDAN algorithm where the model’s inputs are decomposed into their respective Intrinsic Mode Functions (IMFs) and the Residual (Res) series. The second stage entails a VMD approach, used to decompose the yet-unresolved high frequencies (i.e., IMF1) into their variational modes, further discerning and establishing the data attributes to be incorporated into the ELM model to simulate the respective IMFs, Res and VM data series, aggregated as an integrative tool for multiscale runoff prediction. In the model evaluative phase, the hybrid CVEE-ELM is cross-validated with a single-phase hybrid ELM and an autoregressive integrated moving average (ARIMA) model to benchmark its accuracy for predicting 1-, 3- and 6-month ahead runoff in Yingluoxia watershed, Northwestern China. Two-phase hybrid model exhibits superior accuracy at all lead times, to accord with high degree of correlations between the observed and the forecasted runoff, a relatively large Nash-Sutcliffe and the Legate-McCabe Index. Taylor diagrams depict the two-phase hybrid CVEE-ELM model generated forecasts located close to a reference (i.e., a perfect) model, with a lower root-mean square centered difference, and a correspondingly large correlation for all forecast horizons, although the model’s accuracy for shorter lead times (1-month), as expected, are better than the 3- and 6-month horizon. The study shows that the two-phase hybrid CVEE-ELM model where an integration of two frequency resolution algorithms are made, is a preferred datadriven tool that can be explored for real-life decision-system design, particularly for hydrological forecasting problems that have significantly stochastic data features, and thus, will require reliable forecasts to be generated at multi-step horizons
Keywords: Expert system | Runoff | Integrated model | Complete ensemble empirical mode decomposition | adaptive noise (CEEMDAN) | Variational mode decomposition (VMD) | Extreme learning machine (ELM)
مقاله انگلیسی
63 Estimation of the postmortem interval based on the human decomposition process
برآورد فاصله بعد از مرگ بر اساس فرایند تجزیه انسانی-2019
Postmortem interval (PMI) estimations which are used as evidence in Dutch court are sometimes solely based on the experience of the forensic physician without a scientific background. The aim of this study was to investigate the degree of agreement between forensic physicians and their PMI estimations. Fifteen cases were selected from 1534 external postmortem investigations. Photographs of the human remains were presented to 89 forensic physicians in the Netherlands with the instruction to estimate the PMI based on their experience, knowing the remains were found indoors and in which season. Data analysis was conducted by using an interclass correlation (ICC) and Spearmans rank correlation coefficient. This study shows a poor correlation (ICC=0.254) between the PMI estimations of the 89 forensic physicians. It is therefore not advised that PMI estimations based on experience be used as evidence in court.
Keywords: Postmortem interval | Time since death | Decomposition | Human taphonomy | Dutch court
مقاله انگلیسی
64 F-SVD based algorithm for variability and stability measurement ofbio-signals, feature extraction and fusion for pattern recognition
الگوریتم مبتنی بر F-SVD برای تغییرپذیری و اندازه گیری پایداری سیگنالهای زیستی ، استخراج ویژگی و ترکیب برای تشخیص الگو-2019
tA support system with efficient learning framework helps eliciting complete knowledge of underly-ing phenomena of interest. It makes the analysis less-onerous, time-consuming and error-prone andthus promotes large scale applications. Such modeling requires profound understanding of availableinformation and its appropriate utilization. Albeit success of electromyogram (EMG) support systems,challenges still exits specifically in early phase of design mainly due to inherent variations and complexdata distribution patterns of signals. In this article, a frame singular value decomposition (F-SVD) basedmethod-generalizing Canonical correlation analysis for automatic classification of EMG signals to diag-nose amyotrophic lateral sclerosis (ALS), myopathy and normal subjects, is proposed. At first, signals aredecomposed to formulate a set of vectors and performed subspace transformation to demonstrate thevariability and stability of signals base on correlations between pairs of vectors. Besides, discrete Wavelettransformation is applied on generated vectors and correlation analysis is performed. Afterwards, tak-ing highly correlated statistical measures a set of compact feature distributions are estimated and fusedvia two recently proposed parallel and serial feature fusion models. Finally two global descriptors foreffective classifications of various EMG patterns are proposed. The efficacy of derived feature space isvalidated by intuitive, graphical and statistical analysis. The model performances are investigated overtwo datasets. It achieves accuracy of 98.10% and 97.60% over two and three-class groups of first datasetreceptively. Accuracy over second dataset is 100% with a specificity of 100% and sensitivities of 100%.This is first time that F-SVD is employed for automatic classification of EMG. Experiments results on var-ious datasets evince adequacy of our method. Further comparison of performance with state-of-the-artmethods depicts that our method comparable or superior in terms of various performance metrics
Keywords: Singular value decomposition (SVD) | Canonical correlation analysis (CCA) | Motor unit action potential (MUAP) | Collective correlation coefficient matrix (CCCM) | Electromyography (EMG )
مقاله انگلیسی
65 A new method for voidage correlation of gas-liquid mixture based on differential pressure fluctuation
یک روش جدید برای همبستگی تخلیه مخلوط گاز و مایع بر اساس نوسان فشار دیفرانسیل-2019
Differential pressure of gas-liquid mixture contains abundant information about fluid flow and its nature. In this paper, a differential pressure across a Venturi tube is measured for a gas-liquid mixture. Trend component and fluctuation components of the differential pressure are extracted using Extreme-Point Symmetric Mode Decomposition. The analysis shows that the mean of the trend component is related to liquid flowrate, and the amplitude of the fluctuation components is correlated to voidage and flow pattern. Hence, a fluctuation coefficient based approach to access voidage is proposed. This is based on the trend and fluctuation components. Since there exists an influence of gas/liquid flowrate, fluid density and voidage on fluctuation coefficient, their qualitative relationships are analyzed to find the appropriate variables to modify the fluctuation coefficient. Experimental data are used to determine the appropriate specific modification parameters. The modified fluctuation coefficient is found to be flow pattern dependent, and hence fuzzy pattern recognition is adopted to identify flow patterns combining statistics from differential pressure. Finally, a flow pattern-based correlation is proposed to estimate the voidage. Verifications through confrontation with experimental results show that the proposed correlation is effective in estimating voidage of mixtures
Keywords: Gas-liquid mixture | Differential pressure | Fluctuation | Voidage | Extreme-Point Symmetric Mode | Decomposition | Fuzzy pattern identification
مقاله انگلیسی
66 Principal components methodology : A novel approach to forecasting production from liquid-rich shale (LRS) reservoirs
متدولوژی اجزای اصلی: یک روش جدید برای پیش بینی تولید از مخازن سنگ نفت غنی از مایع (LRS)-2019
With increasing global demand for energy, the importance of unconventional shale oil and gas research cannot be over-emphasized. The oil and gas industry requires rapid and reliable means of forecasting production. Existing traditional decline curve analysis (DCA) methods have been limited in their ability to satisfactorily forecast production from unconventional liquid-rich shale (LRS) reservoirs. This is due to several causes ranging from the complicated production mechanisms to the ultra-low permeability in shales. The use of hybrid (combination) DCA models can improve results. However, complexities associated with these techniques can still make their application quite tedious without proper diagnostic plots, correct use of model parameters and some knowledge of the production mechanisms involved. This work, therefore, presents a new statistical data-driven approach of forecasting production from LRS reservoirs called the Principal Components Methodology (PCM). PCM is a technique that bypasses a lot of the difficulties associated with existing methods of forecasting and forecasts production with reasonable certainty. PCM is a data-driven method of forecasting based on the statistical technique of principal components analysis (PCA). In our study, we simulated production of fluids with different compositions for 30 years with the aid of a commercial compositional simulator. We then applied the Principal Components Methodology (PCM) to the production data from several representative wells by using Singular Value Decomposition (SVD) to calculate the principal components. These principal components were then used to forecast oil production from wells with production histories ranging from 0.5 to 3 years, and the results were compared to simulated data. Application of the PCM to field data is also included in this work. This study provides fresh initiatives into how production forecasting from unconventional LRS reservoirs can be done in a different way.
Keywords: Principal components | Liquid-rich shale | Unconventional resources | Production forecasting | Pattern recognition
مقاله انگلیسی
67 New graph distance for deformable 3D objects recognition based on triangle-stars decomposition
فاصله جدید نمودار برای تشخیص اشیاء قابل تغییر شکل 3 بعدی بر اساس تجزیه مثلث ستاره ها-2019
We address the problem of comparing deformable 3D objects represented by graphs such as triangu- lar tessellations. We propose a new graph matching technique to measure the distance between these graphs. The proposed approach is based on a new decomposition of triangular tessellations into triangle- stars. The algorithm ensures a minimum number of disjoint triangle-stars, provides improved dissimi- larity by covering larger neighbors and allows the creation of descriptors that are invariant or at least oblivious under the most common deformations. The present approach is based on an approximation of the Graph Edit Distance, which is fault-tolerant to noise and distortion, thus making our technique par- ticularly suitable for the comparison of deformable objects. Classification is performed with supervised machine learning techniques. Our approach defines a metric space using graph embedding and graph kernel techniques. It is proved that the proposed distance is a pseudo-metric. Its time complexity is de- termined and the method is evaluated against benchmark databases. Our experimental results confirm the performances and the accuracy of our system.
Keywords: Graph matching | Graph edit distance | Graph decomposition | Graph embedding | Graph metric | Graph classification | Pattern recognition| 3D object recognition | Deformable object recognition | Metric learning
مقاله انگلیسی
68 Pattern recognition in voltammetric signals by ASD trilinear decomposition
تشخیص الگو در سیگنال های ولتامتری با تجزیه سه جانبه ASD-2019
This work proposes a modern approach in separation of voltammetric current components which applies a pattern recognition strategy. Application of the trilinear decomposition algorithm ASD was first time demonstrated for detection and modelling of faradaic and capacitive components, recorded as total signal in DPV experiments. Using calibration set of the model substance i.e. K4Fe(CN)6, sampled with the frequency of 1 kHz, the signal components were extracted, which shape was consistent with the theoretic relations. Fitting in the area of peak expressed by r was 0.9957 for faradaic current and 0.9916 for capacitive component. Also the successive stages of the redox process was identified and interpreted. Essential influence for the quantitative analysis was observed. DP voltammograms utilizing all faradaic current samples were characterized by zero baseline and improved calibration line parameters. A wide application list of this modern strategy was also proposed.
Keywords: Multi-way pattern recognition | ASD trilinear decomposition | DPV | Faradaic and capacitive current components
مقاله انگلیسی
69 Measuring contagion risk in international banking
اندازه گیری ریسک گریزی و در بانکی بین المللی-2019
tWe propose a distress measure for national banking systems that incorporates not only banks’ CDSspreads, but also how they interact with the rest of the global financial system via multiple linkagetypes. The measure is based on a tensor decomposition method that extracts an adjacency matrix from amulti-layer network, measured using banks’ foreign exposures obtained from the BIS international bank-ing statistics. Based on this adjacency matrix, we develop a new network centrality measure that can beinterpreted in terms of a banking system’s credit risk or funding risk.
Keywords:International banking | Contagion risk | Multi-layer networks | Tensor decompositions
مقاله انگلیسی
70 Asymmetric color cryptosystem using chaotic Ushiki map and equal modulus decomposition in fractional Fourier transform domains
رمزنگاری رنگ نامتقارن با استفاده از نقشه آشوبی آشوبی و تجزیه مدول برابر در حوزه های تبدیل فوریه کسری-2019
A single channel optical asymmetric cryptosystem for color image in fractional Fourier transform (FrFT) domain is presented. Instead of commonly used RGB channels encryption system, the color image is encoded into grayscale format and encrypted in a single optical channel system. For the design of asymmetric approach, the effective trapdoor one-way function is calculated by employing equal modulus decomposition (EMD). To enhance the security of the proposed cryptosystem, the Ushiki chaotic system is performed to generate the random phase mask in FrFT and a random sequence for scrambling the private key. The sensitive initial values and chaotic data can be regarded as the additional keys and public key. Various numerical experiments are given to verify the validity and capability of the proposed color cryptosystem.
Keywords: Color image encryption | Asymmetric cryptography | Optical transform
مقاله انگلیسی
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