Discrete Data Samples Research Articles

A Controller Design for Approaching Disabled Satellites Based on Discrete Sample Points.

When approaching and removing a disabled satellite, the accuracy of the controller is imperative to the success of the mission because if the mission fails, more space debris can be produced due to satellite collision. To address this issue, a controller directly driven by discrete sample data points is proposed in this paper. First, the input vector for the controller is placed into a state space as a point. The state space also contains points constructed by the input vectors of pre-generated samples, which are created by the GPOPS planning algorithm along with control commands as sample output vectors. Then, an adjacent range is selected and the sample points within are collected. To accelerate the process, a series of data processing methods are implemented, including the dichotomy method, table look-up method, and random selection method. Finally, the control commands are computed using the iteratively reweighted least-squares algorithm with the assumption that similar inputs have similar outputs. According to the simulation results, the discrete point controller is more precise than the neural network controller.

Deep-Sea Seabed Sediment Classification Using Finely Processed Multibeam Backscatter Intensity Data in the Southwest Indian Ridge

In 2007, China discovered a hydrothermal anomaly in the Longqi hydrothermal area of the Southwest Indian Ridge. It was the first seabed hydrothermal area discovered in the ultraslow spreading ocean ridge in the world. Understanding the types of seabed sediments in this area is critical for studying the typical topography and geological characteristics of deep-sea seabed hydrothermal areas. The traditional classification of deep-seabed sediments adopts box sampling or gravity column sampling and identifies the types of seabed sediments through laboratory analysis. However, this classification method has some shortcomings, such as the presence of discrete sampling data points and the failure of full-coverage detection. The geological sampling in deep-sea areas is particularly inefficient. Hence, in this study, the EM122 multibeam sonar data collected in the Longqi hydrothermal area, Southwest Indian Ridge, in April 2019 are used to analyze multibeam backscatter intensity. Considering various errors in the complex deep-sea environment, obtaining backscatter intensity data can truly reflect seabed sediment types. Through unsupervised and supervised classification, the seabed sediment classification in the Longqi hydrothermal area was studied. The results showed that the accuracy of supervised classification is higher than that of unsupervised classification. Thus, unsupervised classification is primarily used for roughly classifying sediment types without on-site geological sampling. Combining the genetic algorithm (GA) and the support vector machine (SVM) neural network, deep-sea sediment types, such as deep-sea clay and calcareous ooze, can be identified rapidly and efficiently. Based on comparative analysis results, the classification accuracy of the GA-SVM neural network is higher than that of the SVM neural network, and it can be effectively applied to the high-precision classification and recognition of deep-sea sediments. In this paper, we demonstrate the fine-scale morphology and surface sediment structure characteristics of the deep-sea seafloor by finely processing high-precision deep-sea multibeam backscatter intensity data. This research can provide accurate seabed topography and sediment data for the exploration of deep-sea hydrothermal resources and the assessment of benthic habitats in deep-sea hydrothermal areas.

In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks.

American foulbrood is a dangerous bee disease that attacks the sealed brood. It quickly leads to the death of bee colonies. Efficient diagnosis of this disease is essential. As specific odours are produced when larvae rot, it was investigated whether an electronic nose can distinguish between colonies affected by American foulbrood and healthy ones. The experiment was conducted in an apiary with 18 bee families, 9 of which showed symptoms of the disease confirmed by laboratory diagnostics. Three units of the Beesensor V.2 device based on an array of six semiconductor TGS gas sensors, manufactured by Figaro, were tested. Each copy of the device was tested in all bee colonies: sick and healthy. The measurement session per bee colony lasted 40 min and yielded results from four 10 min measurements. One 10-min measurement consisted of a 5 min regeneration phase and a 5 min object-measurement phase. For the experiments, we used both classical classification methods such as k-nearest neighbour, Naive Bayes, Support Vector Machine, discretized logistic regression, random forests, and committee of classifiers, that is, methods based on extracted representative data fragments. We also used methods based on the entire 600 s series, in this study of sequential neural networks. We considered, in this study, six options for data preparation as part of the transformation of data series into representative results. Among others, we used single stabilised sensor readings as well as average values from stable areas. For verifying the quality of the classical classifiers, we used the 25-fold train-and-test method. The effectiveness of the tested methods reached a threshold of 75 per cent, with results stable between 65 and 70 per cent. As an element to confirm the possibility of class separation using an artificial nose, we used applied visualisations of classes. It is clear from the experiments conducted that the artificial nose tested has practical potential. Our experiments show that the approach to the problem under study by sequential network learning on a sequence of data is comparable to the best classical methods based on discrete data samples. The results of the experiment showed that the Beesensor V.2 along with properly selected classification techniques can become a tool to facilitate rapid diagnosis of American foulbrood under field conditions.

Records of the Laschamps geomagnetic polarity excursion from Black Sea sediments: magnetite versus greigite, discrete sample versus U-channel data

SUMMARY Magnetostratigraphic investigation of sediment cores from two different water depths in the SE Black Sea based on discrete samples, and parallel U-channels in one of the cores, yielded high-resolution records of geomagnetic field variations from the past about 68 ka. Age constrains are provided by three tephra layers of known age, accelerator mass spectrometry 14C dating, and by tuning element ratios obtained from X-ray fluorescence scanning to the oxygen isotope record from Greenland ice cores. Sedimentation rates vary from a minimum of ∼5 cm ka−1 in the Holocene to a maximum of ∼50 cm ka−1 in glacial marine isotope stage 4. Completely reversed inclinations and declinations as well as pronounced lows in relative palaeointensity around 41 ka provide evidence for the Laschamps geomagnetic polarity excursion. In one of the investigated cores also a fragmentary record of the Mono Lake excursion at 34.5 ka could be revealed. However, the palaeomagnetic records are more or less affected by greigite, a diagenetically formed magnetic iron sulphide. By definition of an exclusion criterion based on the ratio of saturation magnetization over volume susceptibility, greigite-bearing samples were removed from the palaeomagnetic data. Thus, only 25–55 per cent of the samples were left in the palaeomagnetic records obtained from sediments from the shallower coring site. The palaeomagnetic record from the deeper site, based on both discrete samples and U-channels, is much less affected by greigite. The comparison of palaeomagnetic data shows that the major features of the Laschamps polarity excursion were similarly recovered by both sampling techniques. However, several intervals had to be removed from the U-channel record due to the presence of greigite, carrying anomalous directions. By comparison to discrete sample data, also some directional artefacts in the U-channel record, caused by low-pass filtering of the broad magnetometer response functions, averaging across fast directional and large amplitude changes, can be observed. Therefore, high-resolution sampling with discrete samples should be the preferred technique when fast geomagnetic field variations, such as reversals and excursions, shall be studied from sedimentary records in the very detail.

Assessment of Nutrient Supply by a Tidal Jet in the Northern Strait of Georgia Based on a Biogeochemical Model

AbstractWe present a coupled three‐dimensional biological‐physical model for the Salish Sea and evaluate it by comparison to nitrate, silicate, and chlorophyll observations. It accurately reproduces nitrate concentrations with Willmott skill scores, root‐mean‐square error, and bias ranging from 0.84–0.95, 4.02–6.5 μM, and −2.33–1.84 μM, respectively, compared to three independent discrete sample data sets. A prominent feature of the model output is a tidal jet emanating from Discovery Passage producing a downstream plume of elevated surface nitrate. The signal is present from April to September, when surface nitrate is otherwise drawn down. It has a weak but statistically significant correlation to Discovery Passage tidal velocity (R=0.37, p<0.01). Within the turbulent jet and associated plume, the average rate of vertical nitrate supply due to mixing and advection across a depth of roughly 6 m is 0.46 μmol m−2 s−1 between 15 May and 20 August 2015, compared to 0.10 μmol m−2 s−1 for the northern Strait of Georgia as a whole. Close to Discovery Passage, where velocities and shear are strongest, the majority of the vertical nitrate flux is due to mixing. As velocities weaken downstream, vertical advection becomes more important relative to mixing, but vertical velocities also decrease. The tidal pulses out of Discovery Passage drive waves that contribute net upward nitrate flux as far south as Cape Lazo, 40 km away. The nitrate supply drives new production, consistent with existing observations. Similar dynamics have been described in many other tidally influenced coastal systems.

Salt Flushing, Salt Storage, and Controls on Selenium and Uranium: A 31‐Year Mass‐Balance Analysis of an Irrigated, Semiarid Valley

AbstractSalinity, selenium, and uranium pose water‐quality challenges for the Arkansas River in southeastern Colorado and other rivers that support irrigation in semiarid regions. This study used 31 years of continuous discharge and specific conductance (SC) monitoring data to assess interannual patterns in water quality using mass balance on a 120‐km reach of river. Discrete sampling data were used to link the SC records to salinity, selenium, and uranium. Several important patterns emerged. Consumptive use reduced discharge by a median value of 33% and drove corresponding increases in salinity and uranium concentrations. Increased water availability for irrigation from rainfall and upstream snowpack in 1995–1999 flushed additional salinity and uranium into the river in 1996–2000; average annual total dissolved solids (salinity) concentrations increased 25%, and loads increased 131%. Smaller flushing events have occurred, sometimes lagging an increase in water availability by about one year. The pattern indicates flushing of salts temporarily stored, evaporatively concentrated, or of geologic origin. Mobilization of selenium from the reach was minor compared to salinity and uranium, and net selenium removal from the river was suggested in some years. Several processes related to irrigation could be removing selenium. The results provide context for efforts to improve water quality in the Arkansas River and rivers in other semiarid regions.

Floating treatment wetland retrofit in a stormwater wet pond provides limited water quality improvements

Floating treatment wetlands (FTW) are an emerging management practice for improving water quality in stormwater wet ponds or other detention basins. Although there is substantial evidence of nutrient reductions by FTW in mesocosm-scale studies, findings from field-scale studies on the water quality benefits of FTW are less conclusive. A medium-sized stormwater wet pond (~0.8 ha) was partially divided using an impermeable liner and monitored for 280 d using a control and FTW treatment (~20–23% FTW coverage) to determine impact of FTW on concentrations of several water quality parameters at the field-scale. Discrete samples and high-frequency, multi-point monitoring were used to assess water quality differences. No significant differences between the two treatments were found for total nitrogen, total Kjeldahl nitrogen, or total phosphorus, based on discrete samples, and in situ monitoring showed dissolved oxygen was significantly lower in the FTW treatment by roughly 2.6 mg L−1. The FTW treatment had significantly higher nitrate concentrations in discrete samples, but this finding was not supported by the high-frequency data collected when considering the full monitoring period. The FTW had significantly lower total suspended solids, based on discrete and high-frequency sampling, and resulted in a 10–43% reduction in TSS (~25% mean reduction), relative to the control. Comparison of subsets of the high-frequency data to discrete sampling data demonstrated how limited discrete sampling can lead to partial information and potentially erroneous conclusions when assessing FTW treatment effect in stormwater, especially when differences in water quality chemistry are small.

A new spray deposition pattern measurement system based on spectral analysis of a fluorescent tracer

To complement shortages of discrete sampling data and improve the detection accuracy of droplet deposition in unmanned aerial vehicle (UAV) spraying, we developed a new spray deposition pattern measurement system (SDPMS) based on a fluorescent tracer and spectral analysis. Then, we evaluated the system performance in two field spraying experiments in comparison with water-sensitive paper results. The system comprises a fluorescence scanner and spectral analysis program. The fluorescence scanner includes an ultraviolet light, spectrometer, far end controller, stepping motor, and sample reel. First, 1.0% fluorescent tracer solution is sprayed, and the droplets are collected on a paper strip. Then, the paper strip is scanned with the fluorescence scanner, and a set of fluorescence intensity values is collected and processed by the spectral analysis program. Finally, the spray deposition pattern is calculated. The experimental results showed that the spray deposition pattern from the SDPMS had a 0.89 correlation coefficient with that of water-sensitive paper. A linear regression model between fluorescence intensity and deposit coverage was constructed, with a coefficient of determination of 0.91 (F = 61.8845, P < 0.001). In addition, a linear regression model between fluorescence intensity and volume rate was constructed, with a coefficient of determination of 0.89 (F = 51.6639, P < 0.001). The SDPMS and field experiments offer a good foundation for the development of an improved system compatible with UAV spraying.

Improved model of radial vibration in switched reluctance motor including magnetic saturation

This paper proposes an improved method for the prediction of radial vibration in switched reluctance motor (SRM) considering magnetic saturation. In this paper, the basic modeling principle is briefly introduced, it is based on the derivation that the peak acceleration is dependent on the product of phase current and current gradient idi/dt. However, the derivation may cause errors due to saturation effect. Thus in this paper, the discrete sample data are firstly acquired based on DC pulse measurement method, by which electromagnetic, torque and peak acceleration characteristics can all be acquired. Then the entire peak acceleration characteristics are obtained by improved Least Square Support Vector Machine (LSSVM). Based on the obtained static peak acceleration characteristics, the time-varied radial vibration model is established based on superposition of natural oscillations of dominant vibration modes. Finally, a simulation model is built up using MATLAB/Simulink. The good agreement between simulation and experiment shows that the proposed method for modeling is feasible and accurate, even under saturation. In addition, since LSSVM does not need any prior knowledge, it is much easier for modeling compared with other existing literatures.

An initial case study to deconvolve natural remanent magnetization of a continuous paleomagnetic sample using the software UDECON

Previous studies have compiled relative paleointensity data for the last 2–3 Ma from individual paleomagnetic records obtained from marine sediment cores. These records have mostly been obtained by pass-through measurements, which are known to smooth and alter magnetic signals. Among many efforts, a standalone open-use graphical software UDECON has been developed to deconvolve pass-through measurement data. As an initial case study to assess the applicability of the software to deconvolve natural remanent magnetization (NRM) of a continuous paleomagnetic sample, we chose 40 discrete samples from a piston core recovered in the northeast Pacific. We measured NRMs after alternating field demagnetization at 20 mT for both discrete samples and a simulated continuous sample, made by connecting the discrete samples. The discrete samples show centimeter-scale variations in NRM. Such variations are smoothed out and mostly disappear in the results of the simulated continuous sample. However, after using the software to deconvolve the data, the variations are almost completely restored. Good agreement between the discrete sample data and the deconvolved data indicates that the deconvolution by the software is very successful. We observe detailed features of a directional reversal in the data from the discrete samples and in the deconvolved data but not in the data from the simulated continuous sample. This emphasizes that the deconvolution analysis by the software is a powerful tool to extract detailed features from continuous paleomagnetic records obtained by pass-through measurements.

A Generalized Methodology for Data Analysis.

Based on a critical analysis of data analytics and its foundations, we propose a functional approach to estimate data ensemble properties, which is based entirely on the empirical observations of discrete data samples and the relative proximity of these points in the data space and hence named empirical data analysis (EDA). The ensemble functions include the nonparametric square centrality (a measure of closeness used in graph theory) and typicality (an empirically derived quantity which resembles probability). A distinctive feature of the proposed new functional approach to data analysis is that it does not assume randomness or determinism of the empirically observed data, nor independence. The typicality is derived from the discrete data directly in contrast to the traditional approach, where a continuous probability density function is assumed a priori. The typicality is expressed in a closed analytical form that can be calculated recursively and, thus, is computationally very efficient. The proposed nonparametric estimators of the ensemble properties of the data can also be interpreted as a discrete form of the information potential (known from the information theoretic learning theory as well as the Parzen windows). Therefore, EDA is very suitable for the current move to a data-rich environment, where the understanding of the underlying phenomena behind the available vast amounts of data is often not clear. We also present an extension of EDA for inference. The areas of applications of the new methodology of the EDA are wide because it concerns the very foundation of data analysis. Preliminary tests show its good performance in comparison to traditional techniques.

Empirical Data Analytics

In this paper, we propose an approach to data analysis, which is based entirely on the empirical observations of discrete data samples and the relative proximity of these points in the data space. At the core of the proposed new approach is the typicality—an empirically derived quantity that resembles probability. This nonparametric measure is a normalized form of the square centrality (centrality is a measure of closeness used in graph theory). It is also closely linked to the cumulative proximity and eccentricity (a measure of the tail of the distributions that is very useful for anomaly detection and analysis of extreme values). In this paper, we introduce and study two types of typicality, namely its local and global versions. The local typicality resembles the well-known probability density function (pdf), probability mass function, and fuzzy set membership but differs from all of them. The global typicality, on the other hand, resembles well-known histograms but also differs from them. A distinctive feature of the proposed new approach, empirical data analysis (EDA), is that it is not limited by restrictive impractical prior assumptions about the data generation model as the traditional probability theory and statistical learning approaches are. Moreover, it does not require an explicit and binary assumption of either randomness or determinism of the empirically observed data, their independence, or even their number (it can be as low as a couple of data samples). The typicality is considered as a fundamental quantity in the pattern analysis, which is derived directly from data and is stated in a discrete form in contrast to the traditional approach where a continuous pdf is assumed a priori and estimated from data afterward. The typicality introduced in this paper is free from the paradoxes of the pdf. Typicality is objectivist while the fuzzy sets and the belief-based branch of the probability theory are subjectivist. The local typicality is expressed in a closed analytical form and can be calculated recursively, thus, computationally very efficiently. The other nonparametric ensemble properties of the data introduced and studied in this paper, namely, the square centrality, cumulative proximity, and eccentricity, can also be updated recursively for various types of distance metrics. Finally, a new type of classifier called naive typicality-based EDA class is introduced, which is based on the newly introduced global typicality. This is only one of the wide range of possible applications of EDA including but not limited for anomaly detection, clustering, classification, control, prediction, control, rare events analysis, etc., which will be the subject of further research.

Research on Data Mining Technology Based on Weka Platform

Research uses Weka big data mining technology platform to analyze the data.The association rules miningmethods of discrete sample data by Weka technology,using SimpleKMeans clustering algorithm for clustering analysisof thesimulated sample data mining, common features of each type of data and the difference data between differentclusters from where, for a variety of data region division, analysis of different regions of the data distribution. For example, mining research project, a school in the college entrance examination scores data for the simulation sample, in Chinese, math and English college entrance examination scores as the object of analysis of large data mining, thepaper in science classes, the language, the total scores were compared with the distribution.The integrated use of statistical analysis and data mining technology, mining analysis on college entrance examination data deeply, get useful informationwith performance clustering, has strong theoreticalvalue, can help to the college entrance examination reform, give some guidance to high school education.

基于实测离散数据的含风电场电力系统概率潮流计算

随着风电接入容量的增加，利用常见的概率密度函数拟合其分布特征进行概率潮流(Probabilistic Load Flow, PLF)计算将产生较大误差，提出一种考虑离散分布输入变量的PLF计算方法，以期计算结果更符合实际情况。该方法仅需风电出力离散采样数据，结合离散变量函数统计矩点估计法和Gram-Charlier展开级数，便可估计出输出随机变量的统计特征，如期望、方差、累积分布等信息。采用IEEE 39节点系统验证了所提方法的有效性和实用性，结果表明：该方法不仅计算速度快，而且具有较高的精度，工程应用前景较好。 With the increasing penetration of wind sources, using common probability density function to fit the probability distribution to compute the probabilistic load flow (PLF) will lead to considerable error. We propose PLF calculation method considering discrete distribution of input variables to calculate more realistic results. This method only needs wind power discrete sampling data combined with point estimate method of discrete variable function statistical moments and Gram- Charlier expand series, to estimate the statistical characteristics of the output of the random variable, such as the information of expectation, variance, cumulative distribution and so on. The effectiveness and practicality of the proposed method is verified using IEEE 39-bus system, and the results show that: the calculation method is not only faster, but also has high accuracy, and it has better engineering application prospects.

A Critical Review of Discrete Soil Sample Data Reliability: Part 2—Implications

ABSTRACTPart 2 of this study investigates the implications of random, small-scale contaminant concentration variability in soil for reliance on discrete soil sample data to guide environmental investigations. Random variability around an individual point limits direct comparison of discrete sample data to risk-based screening levels. “False negatives” can lead to premature termination of an investigation or remedial action. Small-scale distributional heterogeneity of contaminants in soil is expressed as artificial, seemingly isolated “hot spots” and “cold spots” in isoconcentration maps. Surgical removal of hot spots can lead to erroneous conclusions regarding the magnitude of remaining contamination. The field precision of an individual discrete sample data set for estimation of means for a contaminant in a risk assessment is not directly testable. Omission of “outlier” data in order to force data to fit a geostatistical model distorts estimates of mean concentrations and introduces error into a risk assessment. The potential for such errors was pointed out in early USEPA guidance but largely ignored or misunderstood. Decision Unit and Multi Increment sample investigation methods, long known to the agricultural and mining industries, were specifically developed to overcome these inherent shortcomings of discrete sampling methods and provide more reliable and defensible data for environmental investigations.

Geophysical Tutorial: The function of interpolation

When interpreting seismic or well data in desktop software, it's easy to forget that the data are discretely sampled in space and time. Even when you look at an apparently continuous log curve, there are really only samples, typically every 0.1524 m (6 inches). When you display seismic data as wiggle traces, there are perhaps samples only every 2 or 4 ms in the time dimension. Some software lets you turn off the interpolation so you can see the discrete data samples, as in Figure 1a .

A Novel Approach to Evaluation of Pest Insect Abundance in the Presence of Noise

Evaluation of pest abundance is an important task of integrated pest management. It has recently been shown that evaluation of pest population size from discrete sampling data can be done by using the ideas of numerical integration. Numerical integration of the pest population density function is a computational technique that readily gives us an estimate of the pest population size, where the accuracy of the estimate depends on the number of traps installed in the agricultural field to collect the data. However, in a standard mathematical problem of numerical integration, it is assumed that the data are precise, so that the random error is zero when the data are collected. This assumption does not hold in ecological applications. An inherent random error is often present in field measurements, and therefore it may strongly affect the accuracy of evaluation. In our paper, we offer a novel approach to evaluate the pest insect population size under the assumption that the data about the pest population include a random error. The evaluation is not based on statistical methods but is done using a spatially discrete method of numerical integration where the data obtained by trapping as in pest insect monitoring are converted to values of the population density. It will be discussed in the paper how the accuracy of evaluation differs from the case where the same evaluation method is employed to handle precise data. We also consider how the accuracy of the pest insect abundance evaluation can be affected by noise when the data available from trapping are sparse. In particular, we show that, contrary to intuitive expectations, noise does not have any considerable impact on the accuracy of evaluation when the number of traps is small as is conventional in ecological applications.

Sensor data as a measure of native freshwater mussel impact on nitrate formation and food digestion in continuous-flow mesocosms

AbstractNative freshwater mussels can influence the aquatic N cycle, but the mechanisms and magnitude of this effect are not fully understood. We assessed the effects of Amblema plicata and Lampsilis cardium on N transformations over 72 d in 4 continuous-flow mesocosms, with 2 replicates of 2 treatments (mesocosms with and without mussels), equipped with electronic water-chemistry sensors. We compared sensor data to discrete sample data to assess the effect of additional sensor measurements on the ability to detect mussel-related effects on NO3– formation. Analysis of 624 sensor-based data points detected a nearly 6% increase in NO3– concentration in overlying water of mesocosms with mussels relative to mesocosms without mussels (p < 0.05), whereas analysis of 36 discrete samples showed no statistical difference in NO3– between treatments. Mussels also significantly increased NO2– concentrations in the overlying water, but no significant difference in total N was observed. We used the sensor data for phyt...

Comparison of methods for estimating fracture size and intensity applied to Aiguille Marbrée (Mont Blanc)

This article presents a multidisciplinary research devoted to correlate estimation of the rock mass characteristics with parameters and features obtained by the use of digital photography. The data obtained from remote slope analysis are then undertaken to a statistical analysis of discrete data samples comparing different survey areas in terms of shape and dimension of the sampling window. The method is developed by an application to the North rock face of the Aiguilles Marbrées (Mont Blanc). This slope was chosen as case of study since it represents an important high mountain location from the environmental and touristic point of view, where the severe working condition led to the application of innovative methodology. This site is monitored by a local authority (Fondazione Montagna Sicura) devoted to the territory safe control since a rock fall of considerable size occurred in September 2007. After a photogrammetric survey, an orthophotograph has been created; circular windows have been materialized on the orthophotograph to perform area sampling. Statistical analysis of the obtained data enables us to calculate parameters and estimators for rock mass classification.

Regularized Interpolation for Noisy Images

Interpolation is the means by which a continuously defined model is fit to discrete data samples. When the data samples are exempt of noise, it seems desirable to build the model by fitting them exactly. In medical imaging, where quality is of paramount importance, this ideal situation unfortunately does not occur. In this paper, we propose a scheme that improves on the quality by specifying a tradeoff between fidelity to the data and robustness to the noise. We resort to variational principles, which allow us to impose smoothness constraints on the model for tackling noisy data. Based on shift-, rotation-, and scale-invariant requirements on the model, we show that the L(p)-norm of an appropriate vector derivative is the most suitable choice of regularization for this purpose. In addition to Tikhonov-like quadratic regularization, this includes edge-preserving total-variation-like (TV) regularization. We give algorithms to recover the continuously defined model from noisy samples and also provide a data-driven scheme to determine the optimal amount of regularization. We validate our method with numerical examples where we demonstrate its superiority over an exact fit as well as the benefit of TV-like nonquadratic regularization over Tikhonov-like quadratic regularization.