Plot-based Method Research Articles

Deep Learning-Assisted Multivariate Analysis for Nanoscale Characterization of Heterogeneous Beam-Sensitive Materials.

Nanoscale materials characterization often uses highly energetic probes which can rapidly damage beam-sensitive materials, such as hybrid organic-inorganic compounds. Reducing the probe dose minimizes the damage, but often at the cost of lower signal-to-noise ratio (SNR) in the acquired data. This work reports the optimization and validation of principal component analysis (PCA) and nonnegative matrix factorization for the postprocessing of low-dose nanoscale characterization data. PCA is found to be the best approach for data denoising. However, the popular scree plot-based method for separation of principal and noise components results in inaccurate or excessively noisy models of the heterogeneous original data, even after Poissonian noise weighting. Manual separation of principal and noise components produces a denoised model which more accurately reproduces physical features present in the raw data while improving SNR by an order of magnitude. However, manual selection is time-consuming and potentially subjective. To suppress these disadvantages, a deep learning-based component classification method is proposed. The neural network model can examine PCA components and automatically classify them with an accuracy of >99% and a rate of ∼2 component/s. Together, multivariate analysis and deep learning enable a deeper analysis of nanoscale materials' characterization, allowing as much information as possible to be extracted.

Using carpet plots to analyze blood transit times in the brain during hypercapnic challenge magnetic resonance imaging.

Blood arrival time and blood transit time are useful metrics in characterizing hemodynamic behaviors in the brain. Functional magnetic resonance imaging in combination with a hypercapnic challenge has been proposed as a non-invasive imaging tool to determine blood arrival time and replace dynamic susceptibility contrast (DSC) magnetic resonance imaging, a current gold-standard imaging tool with the downsides of invasiveness and limited repeatability. Using a hypercapnic challenge, blood arrival times can be computed by cross-correlating the administered CO2 signal with the fMRI signal, which increases during elevated CO2 due to vasodilation. However, whole-brain transit times derived from this method can be significantly longer than the known cerebral transit time for healthy subjects (nearing 20s vs. the expected 5-6s). To address this unrealistic measurement, we here propose a novel carpet plot-based method to compute improved blood transit times derived from hypercapnic blood oxygen level dependent fMRI, demonstrating that the method reduces estimated blood transit times to an average of 5.32s. We also investigate the use of hypercapnic fMRI with cross-correlation to compute the venous blood arrival times in healthy subjects and compare the computed delay maps with DSC-MRI time to peak maps using the structural similarity index measure (SSIM). The strongest delay differences between the two methods, indicated by low structural similarity index measure, were found in areas of deep white matter and the periventricular region. SSIM measures throughout the remainder of the brain reflected a similar arrival sequence derived from the two methods despite the exaggerated spread of voxel delays computed using CO2 fMRI.

Harmonic and Interharmonic Estimation using Poincaré Filtering Assisted ESPRIT Method

Accurate estimation of harmonics/interharmonics components is a crucial task in the electric power system. The parametric methods available for this purpose like Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) give accurate result but suffer from very high computational burden. While on the other hand non-parametric methods like Fast Fourier Transform (FFT) are fast but have low spectral resolution. This paper proposes a Poincare plot-based periodicity filter to detect the presence of interharmonics and then selecting an algorithm based on the requirement of spectral resolution. The proposed Poincare plot-based method has been thoroughly tested for its accuracy by varying different parameters associated with interharmonics components. The method has been tested on synthetic signals as well as experimental validation has also been performed by analyzing the current signal from aeration blower installed at GIFT city, Gujarat.

Determining the number of canonical correlation pairs for high-dimensional vectors

For two random vectors whose dimensions are both proportional to the sample size, we in this paper propose two ridge ratio criteria to determine the number of canonical correlation pairs. The criteria are, respectively, based on eigenvalue difference-based and centered eigenvalue-based ridge ratios. Unlike existing methods, the criteria make the ratio at the index we want to identify stick out to show a visualized “valley-cliff” pattern and thus can adequately avoid the local optimal solutions that often occur in the eigenvalues multiplicity cases. The numerical studies also suggest its advantage over existing scree plot-based method that is not a visualization method and more seriously underestimates the number of pairs than the proposed ones and the AIC and $$C_p$$ criteria that often extremely over-estimate the number, and the BIC criterion that has very serious underestimation problem. A real data set is analyzed for illustration.

Novel Density Poincaré Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions.

Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is difficult as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a novel density Poincaré plot-based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. First, we propose the generation of this new density Poincaré plot which is derived from the difference of the heart rate (DHR) and provides the overlapping phase-space trajectory information of the DHR. Next, from this density Poincaré plot, several image processing domain-based approaches including statistical central moments, template correlation, Zernike moment, discrete wavelet transform and Hough transform features are used to extract suitable features. Subsequently, the infinite latent feature selection algorithm is implemented to rank the features. Finally, classification of AF vs. PAC/PVC is performed using K-Nearest Neighbor, Support vector machine (SVM) and Random Forest (RF) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 10 AF and 10 PAC/PVC subjects. Results- During the segment-wise 10-fold cross-validation, SVM achieved the best performance with 98.99% sensitivity, 95.18% specificity and 97.45% accuracy with the extracted features. In subject-wise scenario, RF achieved the highest accuracy of 91.93%. Moreover, we further validated the proposed method using two other databases: wearable armband ECG data and the Physionet AFPDB. 100% PAC detection accuracy was obtained for both databases without any further training. Our proposed density Poincaré plot-based method showed superior performance when compared with four existing algorithms; thus showing the efficacy of the extracted image domain-based features. From intensive care unit's ECG to wearable armband ECGs, the proposed method is shown to discriminate PAC/PVCs from AF with high accuracy.

Biomass Estimation of Eaglewood (<i>Aquilaria filaria</i> (Oken) Merr.) in the Karst Ecosystem of West Papua

Eaglewood is Indonesia’s important trade commodity in the form of resins from several infected species of Thymelaeaceae. The basis to determine its international trade quota through CITES is derived from the estimated eaglewood-producing species grown in their habitat. This paper aims to estimate the biomass of eaglewood, Aquilaria filaria, in the karst ecosystem of West Papua. We conducted a plot-based method and calculated the biomass of A. filaria using a diameter-based allometric equation and simulated using a bootstrap procedure. The results showed that 15,500 tons of naturally infected eaglewood are estimated in the karst ecosystem of West Papua.

An improved Poincaré plot-based method to detect atrial fibrillation from short single-lead ECG

PurposeWith the popularity of wearable mobile devices, increasing numbers of applications use single-lead electrocardiograms (ECGs), which provides information about heart rate and rhythm to detect cardiac diseases. Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, it is very important to detect AF early. In this study, we propose a Poincaré plot-based method for AF detection that has the advantages of requiring few calculations and short detection segments. MethodOur method first extracts the first-order differential RR interval (ΔRRI) sequences from a segmented ECG data, and then the polar coordinate transformation is performed on the Poincaré plot of the ΔRRI to obtain the phase-based distribution. Two features, the distribution width Dw and the average distribution height Dh are extracted from the phase-based distribution to classify the AF and non-AF episodes. Five PhysioNet databases were used for the assessment, which contain 3,843.3 hours of data from 229 subjects. Long-Term AF Database was selected as the training set, and the remaining four databases were used as the testing sets. ResultsIn the testing sets, the results yield good sensitivity/specificity (97.91%/99.14%) with 60 second ECG signal segments, which are better than those of the existing the Poincaré plot-based methods, and the method also performs well with 20 second ECG signal segments (sensitivity/specificity of 97.28%/98.35%). ConclusionThe detection performance of this method is good under different condition, and has the advantage of easy implementation, which makes it promising for application in the detection and monitoring of AF.

A novel common and specific features extraction-based process monitoring approach with application to a hot rolling mill process

In this paper, a new common and specific features extraction-based process monitoring method is proposed for multimode processes with common features. Based on the common basis vectors, the common features that reflect the common information among multimode data can be obtained. The specific features corresponding to the individual properties of each mode are likewise obtained using the specific basis vectors. Moreover, the two basis vectors can be updated using a migration method when the new mode data are available in the database. A Kullback–Leibler distance-based metric is developed to measure the changes occurred in both two features. A derivative contribution plot-based method is finally proposed to isolate the root-cause variables leading to abnormal changes. The whole proposed methods are applied to an actual hot rolling mill (HRM) process, where common settings for different steel products and specific characteristics for each steel product exist. It is shown that the proposed method can successfully extract common features in an HRM process, and can present better monitoring performance compared with existing methods.

An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images

Advanced monitoring and mapping of forest areas using the latest technological advances in satellite imagery is an alternative solution for sustainable forest management compared to conventional ground measurements. Remote sensing products have been a key source of information and cost-effective options for monitoring changes in harvested areas. Despite recent advances in satellite technology with a broad variety of spectral and temporal resolutions, monitoring the areal extent of harvested forest areas in managed forests is still a challenge, primarily due to the highly dynamic spatiotemporal patterns of logging activities. Our goal was to introduce a plot-based method for monitoring harvested forest areas from very high-resolution (VHR), low-cost satellite images. Our method encompassed two data categories, which included vegetation indices (VIs) and texture analysis (TA). Each group of data was used to model the amount of harvested volume both independently and in combination. Our results indicated that the composition of all spectral bands can improve the accuracy of all models of average volume by 23.52 RMSE reduction and total volume by 33.57 RMSE reduction. This method demonstrated that monitoring and extrapolation of the calculated relation and results from smaller forested areas could be applied as an automatic remote-based supervised monitoring method over larger forest areas.

Artificial neural network and phasor data‐based islanding detection in smart grid

Islanding is an unusual condition in a power system where the generating station continues to supply the local load after one or multiple transmission line outage. This study develops a new islanding detection technique using the artificial neural network (ANN) classifier, which is provided with synchronised phasor measurements from a nine-bus Western Electricity Coordinating Council power system. An excessive number of data frames are generated in the phasor data concentrator. Before sending these data to the classifier, multiplier-based method (MBM) and Andrews plot-based method (APBM) are applied for dimension reduction and feature extraction. Comparisons are prepared with other dimension reduction algorithms. The accuracy of the classifier has been increased by increasing the number of hidden layers, the best accuracy is observed at a certain level for APBM. Non-detection zone (NDZ) for APBM is also evaluated. It is observed that the classification accuracy, and the detection time change when the neural network is retrained. All the results are compared and analysed statistically. This method can perform faster compared to other existing algorithms with an excellent accuracy and smaller NDZ.

A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape

Agricultural land now exceeds forests as the dominant global biome. Because of their global dominance, and potential expansion or loss, methods to estimate biomass and carbon in agricultural areas are necessary for monitoring global terrestrial carbon stocks and predicting carbon dynamics. Agricultural areas in the tropics have substantial tree cover and associated above ground biomass (AGB) and carbon. Active remote sensing data, such as airborne LiDAR (light detection and ranging), can provide accurate estimates of biomass stocks, but common plot-based methods may not be suitable for agricultural areas with dispersed and heterogeneous tree cover. The objectives of this research are to quantify AGB of a tropical agricultural landscape using a tree-based method that directly incorporates the size of individual trees, and to understand how landscape estimates of AGB from a tree-based method compare to estimates from a plot-based method. We use high-resolution (1.12 m) airborne LiDAR data collected on a 9280-ha region of the Azuero Peninsula of Panama. We model individual tree AGB with canopy dimensions from the LiDAR data. We apply the model to individual tree crown polygons and aggregate AGB estimates to compare with previously developed plot-based estimates. We find that agricultural trees are a distinct and dominant part of our study site. The tree-based approach estimates greater AGB in pixels with low forest cover than the plot-based approach, resulting a 2-fold difference in landscape AGB estimates between the methods for non-forested areas. Additionally, one third of the total landscape AGB exists in areas having <10% cover, based on a global tree cover product. Our study supports the continued use and development of allometric models to predict individual tree biomass from LiDAR-derived canopy dimensions and demonstrates the potential for spatial information from high-resolution data, such as relative isolation of canopies, to improve allometric models.

Relaxing LMI Conservatism Using Nyquist Plots and Its Application to Robust Mechatronics Synthesis

In this paper, we propose a Nyquist plot-based method for finite-frequency reshaping of the mechanical plant considering a low-order controller, which significantly reduces the large gap between actual robust stability and desired probability tolerance for closed-loop stability $(1-\epsilon )$ due to linear matrix inequality conservatism. The solution region for the open-loop Nyquist plot is derived based on the simultaneous use of two Nyquist planes. The chance-constrained robust stability criterion is represented on one Nyquist plane using a square region centered at $(-1+j0)$ , which has to be avoided over all frequencies. The other Nyquist plane contains commonly used graphical constraints from performance specifications. Our simulation results using the proposed graphical method achieves a robust stability of 97.1% based on a desired probability tolerance of $(1-\epsilon )=0.70$ for closed-loop robust stability. As such, a high-bandwidth control system with the desired disturbance attenuation capabilities while ensuring stable zeros is achieved as compared with the existing methods in the literature.

An assessment of uncertainty in volume estimates for stands reconstructed from tree stump information

Assessment of pre-harvest stand conditions after unplanned tree removals often requires reconstruction of the stand based on stump information. Prediction of diameter at breast height (d.b.h.) from stump measurements is a common practice because d.b.h. is usually a necessary precursor for estimating diameter distributions and predicting tree volume. Although not a widespread exercise, tree volumes are sometimes predicted directly from stump dimensions. Regardless of the approach taken, statistical models are invariably used in some manner and the model predictions are erroneously assumed to be without error. In this study, several methods for tree volume prediction arising from stump information were evaluated for the contribution of model-related uncertainty to the error in population estimates of total volume. When the entire population was enumerated, the model-related uncertainty was 1–2 per cent of the estimate depending on the volume estimation method. Sampling approaches based on individual stumps and 0.042 ha plots were evaluated, where the total uncertainty due to both model and sampling error was considerably larger when using the plot-based method. Generally, the smallest amount of error was present when predicting d.b.h. and then estimating tree volume from d.b.h. The uncertainty was largest for estimation of tree volume directly from stump dimensions when sampling proportions were ~0.35 or smaller; otherwise, the largest uncertainty resulted from prediction of d.b.h. and merchantable height which were both used as predictor variables in the volume model.

On Using Nyquist Plots for Relaxing Convex Conservative Approximations in Design of Robust Mechatronic Systems

Abstract: In this paper, a Nyquist plot-based method which reduces the conservatism associated with Linear Matrix Inequality (LMI) approximation of ambiguous chance constraints is proposed for the design of robust mechatronic systems. The mechanical plant and controller are redesigned over finite frequencies by shaping the open loop Nyquist plot to avoid an ambiguous chance-constrained robust stability disc. Commonly used graphical constraints such as the sensitivity disc are also included in order to satisfy the performance specifications. Our simulation results using the proposed graphical method achieves a robust stability of 91.8% based on a desired probability tolerance for closed-loop stability of (1 — e) = 0.70 as compared with LMI approximation, which requires the standard deviation of the parametric uncertainties to be reduced by four times in order to satisfy an identical (1 — e), and achieves a robust stability of 100%.

Estimations of temperature deviations in chromatographic columns using isenthalpic plots. I. Theory for isocratic systems

We propose to use constant enthalpy or isenthalpic diagrams as a tool to estimate the extent of the temperature variations caused by the mobile phase pressure drop along a chromatographic column, e.g. of its cooling in supercritical fluid and its heating in ultra-performance liquid chromatography. Temperature strongly affects chromatographic phenomena. Any of its variations inside the column, whether intended or not, can lead to significant changes in separation performance. Although instruments use column ovens in order to keep constant the column temperature, operating conditions leading to a high pressure drop may cause significant variations of the column temperature, both in the axial and the radial directions, from the set value. Different ways of measuring these temperature variations are available but they are too inconvenient to be employed in many practical situations. In contrast, the thermodynamic plot-based method that we describe here can easily be used with only a ruler and a pencil. They should be helpful in developing methods or in analyzing results in analytical laboratories. Although the most effective application area for this approach should be SFC (supercritical fluid chromatography), it can be applied to any chromatographic conditions in which temperature variations take place along the column due to the pressure drop, e.g. in ultra-high pressure liquid chromatography (UHPLC). The method proposed here is applicable to isocractic conditions only.

Automated extraction of tree and plot-based parameters in citrus orchards from aerial images

Highlights A plot-based method to extract fruit trees in high spatial resolution images is proposed. This methodology is applicable to large agricultural land parcel identification systems. Each tr...

A Bayesian Plotting Method for Fractional Factorial Data Analysis

This paper proposes a plot-based method for fractional factorial data analysis. The proposed plot is called a “response-probability model analysis plot” (RPMAP) because it displays the predicted responses associated with alternative models and decisions versus the model posterior probabilities. Benefits of the proposed method include unique information about whether the current state of model uncertainty and achievement of objectives warrants additional experimentation. Also, in some cases, the RPMAP leads to settings with arguably superior robustness to model uncertainty compared with normal probability plots or Posterior probability plots. The methods are illustrated using the well-studied injection molding data and another real-world case study. In both cases, new insights are gained with potential value to practitioners.

Novel fuelbed characteristics associated with mechanical mastication treatments in northern California and south-western Oregon, USA

Mechanically masticated fuelbeds are distinct from natural or logging slash fuelbeds, with different particle size distributions, bulk density, and particle shapes, leading to challenges in predicting fire behavior and effects. Our study quantified some physical properties of fuel particles (e.g. squared quadratic mean diameter, proportion of non-cylindrical particles) and surface fuel loading with planar intercept and plot-based methods in 10 mechanically masticated sites in northern California and south-western Oregon. Total woody fuel load differed among masticated sites, ranging from 15.3 to 63.4 Mg ha–1, with the majority of the load concentrated in the 10-h (53.7%) and 1-h (29.2%) time-lag classes. Masticated fuels were densely packed, with total depths ranging from 4.6 to 8.0 cm and fuelbed bulk densities ranging from 45.9 to 115.3 kg m–3. To accurately quantify loading in masticated fuelbeds, we recommend using a hybrid methodology, where 1-h and 10-h fuel loadings are estimated using a plot-based method and 100-h and 1000-h fuel loadings are estimated using the standard planar intercept method. Most masticated fuelbeds differed in loading by fuel class and fuelbed depth, when compared with existing natural and slash-based fuelbeds, suggesting new fire behavior fuel models specific to masticated fuelbeds may be warranted.

“Distance-variable” estimators for sampling and change measurement

The estimation procedure described is a simple technique that is applicable to virtually any plot-based sampling method and virtually any measured variable. It can be retrofitted to any existing fixed or variable plot over time by simply knowing the distance from the sampled object to the sample point. These estimators are illustrated for sampling over time as the plot size changes. An example is variable-plot sampling in forestry. Traditional estimates from sample plots can be geometrically viewed as a series of “disc shapes” where the same estimate is used for an object no matter how near the sample point is to that selected object. “Distance-variable” (DV) or “shaped” estimators have the same average value over the plot area, with some very important advantages. We believe that the DV estimate will be shown to reduce the variance of growth measurement compared with simple difference estimators. Traditional “disc” estimators are a special case of the more general DV estimators. There are no difficulties with the use of current edge-effect correction techniques, and the calculation of statistics is virtually identical to traditional methods.

Developing a wetland IBI with statewide application after multiple testing iterations

An index of biotic integrity (IBI) is a frequently used approach for assessing the ecological integrity of streams with fish and macroinvertebrates the faunal assemblages most commonly used as indicator taxa. The IBI approach has been much less commonly applied to wetlands, despite the legal, policy and scientific need to assess wetland condition and develop ecological performance goals for wetland creation, restoration and enhancement. While some IBIs are sophisticated systems with statewide application that have undergone one or more testing iterations, many published IBIs are derived from single data sets of a single class of aquatic resource with limited geographic application. The State of Ohio initiated development of a wetland IBI using vascular plants in 1996. Sampling methods were investigated and ultimately a plot-based method was adopted. Potential attributes and different human disturbance gradients were evaluated in several studies. Ultimately, IBIs for emergent, forest and shrub dominated wetlands were developed. Data from the Vegetation IBI-emergent (VIBI-E) is presented to illustrate this process. Subsequent testing and refinement is a critical step in the development of a robust IBI with more than local application. Throughout its initial development (R2 = 0.863, p < 0.001), first major testing iteration (R2 = 82.2%, p < 0.001), second test iteration (R2 = 75.0%, p < 0.001) and third test iteration (R2 = 82.1%, p < 0.001), the VIBI-E has remained significantly correlated with the disturbance gradient. Eight of the original 10 metrics proposed continued to have significant and interpretable relationships with the disturbance gradient, with 4 metrics remaining completely unchanged, and 4 undergoing relatively minor modifications, and 2 being replaced. The VIBI-E and its component metrics were also evaluated against a new disturbance gradient (Landscape Development Index or LDI), derived from land use percentages within a 1 km radius of the wetlands, that was not used during VIBI-E development. The VIBI-E score and 9 of 10 metrics were significantly correlated with the LDI disturbance gradient providing separate confirmation of the VIBI. The Vegetation IBI-E consistently and reliably assessed wetland condition across the whole range of wetland types throughout Ohio's ecological regions.