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The thermal inertia of buildings brings considerable flexibility to the building heating and cooling loads, which is believed to be a promising demand response resource in energy systems. However, it is challenging to utilize the thermal inertia of buildings in the operation of energy systems because of the complicated thermal dynamics and high computational cost. This paper proposes a data-driven aggregate thermal dynamic model (ATDM) for the multi-zone building and building cluster, respectively, which offers an equivalent and low-complexity building model for the operation and control of energy systems. The ATDM consists of the aggregation equation and the state equation. The former projects the detailed real states of buildings into the characteristic state (i.e., aggregate state) using an affine function, and the latter describes the thermal dynamics of buildings using the aggregate state. The ATDM is formulated for two practical load control strategies for the building cluster, including direct load control and indirect load control. Then, the constrained nonlinear regression model is proposed to estimate the model parameters and occupant behavior, for which an efficient algorithm based on the block coordinate descent method is developed by exploiting the decomposable structure of the regression model. Simulation results based on real-world data show that the root mean square error and mean absolute percentage error for the multi-zone building (or building cluster) are below 0.72 °C and 1.44% (or 0.32°C and 1.39%), respectively, verifying the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents an instrument for finding the location of an object in 3-D space. The location is determined using estimates of the object’s distance from static anchor points with known coordinates. This process involves three main challenges: 1) a way of measuring the Euclidean distance between two points; 2) estimating system parameters such as the position of the anchor points, the speed of sound, and data acquisition delay; and 3) computing the position of the object from its distance to the anchor points. This paper addresses each of these challenges. A wideband acoustic signal is used for distance estimation. Nonlinear regression techniques are used to estimate the positions of the anchors and jointly optimize other system parameters. After calibration, a similar method is also used, this time using the optimized parameter set, to estimate the position of the target object. The design and performance of a prototype system, based on the proposed method, are discussed. The prototype, using five anchors and 13 reference locations, achieved a mean positioning accuracy of 0.86 mm over a test volume spanning 0.5 m $\times0.5$ m $\times0.25$ m. [ABSTRACT FROM AUTHOR]

The article introduces several papers published within the issue, including one on inversing rig functions through nonlinear regression, another on capturing the motion of viscous thin film flows on a curved surface and a paper on using Smoothed Particle Hydrodynamics in computer animation.

The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumption. However, its performance may degrade dramatically when the noises are heavy tailed. To cope with this problem, this paper proposes two new nonlinear filters, namely the linear regression maximum correntropy EKF (LRMCEKF) and nonlinear regression maximum correntropy EKF (NRMCEKF), by applying the maximum correntropy criterion (MCC) rather than the MMSE criterion to EKF. In both filters, a regression model is formulated, and a fixed-point iterative algorithm is utilized to obtain the posterior estimates. The effectiveness and robustness of the proposed algorithms in target tracking are confirmed by an illustrative example. [ABSTRACT FROM AUTHOR]

In this paper, finite element simulations are used to analyze the plastic deformation of the solder ball and its stress and strain distributions. The bonding forces that depend on the forming temperature are investigated. The contact areas between the solder ball and the copper trace are also discussed. The largest contact area is regarded as the optimization condition during the thermal compression bonding processes. The effects of different pillar-trace shift ratios on the contact areas are also discussed to provide a guideline for avoiding product failure. Finally, an empirical equation adopting a four-parameter logistic nonlinear regression model for the pillar-trace gap in terms of the forming temperature and bonding forces is established. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach for performing blind source separation (BSS) in nonlinear mixtures is proposed, and their separability is studied. It is shown that this problem can be solved under a few assumptions, which are satisfied in most practical applications. The main idea can be considered as transforming a time-invariant nonlinear BSS problem to local linear ones varying along the time, using the derivatives of both sources and observations. Taking into account the proposed idea, numerous algorithms can be developed performing the separation. In this regard, an algorithm, supported by simulation results, is also proposed in this paper. It can be seen that the algorithm well separates the mixed sources, however, as the conventional linear BSS methods, the nonlinear BSS suffers from ambiguities, which are discussed in this paper. [ABSTRACT FROM PUBLISHER]

The launching process of multistage synchronous induction coilgun (MSSICG) is a complex process involving multifield coupling, so it is time-consuming to iteratively optimize its structural parameters by stochastic optimization algorithm. A nonlinear regression modeling method of MSSICG based on mainstream machine learning methods is proposed in this paper. Rooted in the current filament model (CFM) verified by prototype experiment, the sample set for training and testing could be obtained by a hybrid experimental design method. The least-squares support vector machine (LSSVM), the kernel extreme learning machine (KELM), and the eco state network (ESN) were employed to learn the training samples. In order to improve the accuracy of the prediction model, the chicken swarm optimization (CSO) was introduced to pretrain the hyperparameters of the LSSVM and the KELM as well as the parameters of the dynamic reservoir of the ESN. The results show that the predictive modeling of MSSICG based on CSO-KELM has better accuracy and generalization performance. Based on the obtained regression model, the CSO algorithm was used to optimize the structural parameters of a five-stage coilgun. It turns out that the optimization based on nonparametric model has higher computational efficiency than the optimization method which requires large-scale iterative calculation. This provides a novel idea for the engineering design of the MSSICG. [ABSTRACT FROM AUTHOR]

This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these parameter-measurement pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. PERK admits a simple implementation as per-voxel nonlinear lifting of MRI measurements followed by linear minimum mean-squared error regression. We demonstrate PERK for $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimation, a well-studied application where it is simple to compare PERK estimates against dictionary-based grid search estimates and iterative optimization estimates. Numerical simulations as well as single-slice phantom and in vivo experiments demonstrate that PERK and other tested methods produce comparable $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimates in white and gray matter, but PERK is consistently at least $140\times $ faster. This acceleration factor may increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel. [ABSTRACT FROM AUTHOR]

In this paper, we present a method of ship size extraction for Sentinel-1 synthetic aperture radar (SAR) images, which is composed of the image processing stage and the regression stage. In order to achieve extraction with high accuracy, considering the data characteristics of Sentinel-1 images, we propose to use the dual-polarization fusion and the nonlinear regression with the gradient boosting. The experiments and analyses on a relatively large data set show that: 1) compared with the existing and related studies, the proposed method achieves an improved performance. The extraction errors are pushed under one pixel, and they are 4.66% (8.80 m) and 7.01% (2.17 m) for length and width, respectively; 2) the dual-polarization information fusion does improve the size extraction accuracy; and 3) the nonlinear regression does exploit the relationship between the influential factors and the size parameters and provide a better performance than the linear regression. The experimental results verify that the proposed design is suitable for ship size extraction in Sentinel-1 SAR images. [ABSTRACT FROM AUTHOR]

In this paper, we study the sparse linear regression problem under the Local Differential Privacy (LDP) model. We first show that polynomial dependency on the dimensionality $p$ of the space is unavoidable for the estimation error in both non-interactive and sequential interactive local models, if the privacy of the whole dataset needs to be preserved. Similar limitations also exist for other types of error measurements and in the relaxed local models. This indicates that differential privacy in high dimensional space is unlikely achievable for the problem. With the understanding of this limitation, we then present two algorithmic results. The first one is a sequential interactive LDP algorithm for the low dimensional sparse case, called Locally Differentially Private Iterative Hard Thresholding (LDP-IHT), which achieves a near optimal upper bound. This algorithm is actually rather general and can be used to solve quite a few other problems, such as (Local) DP-ERM with sparsity constraints and sparse regression with non-linear measurements. The second one is for the restricted (high dimensional) case where only the privacy of the responses (labels) needs to be preserved. For this case, we show that the optimal rate of the error estimation can be made logarithmically dependent on $p$ (i.e., $\log p$) in the local model, where an upper bound is obtained by a label-privacy version of LDP-IHT. Experiments on real world and synthetic datasets confirm our theoretical analysis. [ABSTRACT FROM AUTHOR]

Precise monitoring of the chlorophyll type “a” (chl-a) concentration is critical in determining the level of production of oxygen and, consequently, the health conditions of inland aquatic ecosystems. This paper addresses two important issues in building precise and robust regression models for chl-a concentration from remote sensing data: the presence of multimodality in the sensor data distribution and the scarcity of information available to properly label the data. In order to effectively deal with the aforementioned issues, we propose an iterative learning framework (iterative transductive environmental modeling system) based on the principles of transductive learning that combines data-driven regression-based modeling with an iterative nonlinear classification process. The classification procedure, contingent on the maximum margin principle, generates data sets associated with each statistical modality. The classified data are labeled through a process of consecutive selection of the best candidate samples. Different selection mechanisms are discussed. The proposed method was applied in the empirical assessment of chl-a concentration from MEdium Resolution Imaging Spectrometer and Moderate Resolution Imaging Spectroradiometer satellite data and validated by in situ measurements in Lake Winnipeg in Manitoba, Canada. [ABSTRACT FROM PUBLISHER]

Information about the strength of gas sources in buildings has a number of applications in the area of building automation and control, including temperature and ventilation control, fire detection, and security systems. In this paper, we consider the problem of estimating the strength of a gas source in an enclosure when some of the parameters of the gas transport process are unknown. Traditionally, these problems are either solved by the maximum-likelihood method, which is accurate but computationally intensive, or by recursive least squares (also Kalman) filtering, which is simpler but less accurate. In this paper, we suggest a different statistical estimation procedure based on the concept of method of moments. We outline techniques that make this procedure computationally efficient and amenable for recursive implementation. We provide a comparative analysis of our proposed method based on experimental results, as well as Monte Carlo simulations. When used with the building control systems, these algorithms can estimate the gaseous strength in a room both quickly and accurately and can potentially provide improved indoor air quality in an efficient manner. [ABSTRACT FROM PUBLISHER]

The authors have recently proposed a model, based on Monte Carlo methods, to simulate light interaction with sand. In this paper, principal component analysis and regression techniques are applied to yield a compact analytical representation of the spectral reflectance signatures produced by the model. This analytical formulation compares well with the original model and is appropriate for applications demanding interactive rates. Examples are provided comparing the original model to the proposed formulation for three hypothetical sand samples. The effect of water content on reflectance is demonstrated for these samples. Additionally, examples are provided comparing the original and proposed models for three sand samples from the U.S. Army Topographic Engineering Center spectral database. [ABSTRACT FROM AUTHOR]

In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-of- the-art solutions. [ABSTRACT FROM AUTHOR]

Ear-electroencephalography (EEG) is a promising tool for practical brain–computer interface (BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical scalp-EEG system. However, an ear-EEG has a natural constraint of electrode location (e.g., limited in or around the ear) for acquiring informative brain signals sufficiently. Achieving reliable performance of ear-EEG in specific BCI paradigms that do not utilize brain signals on the temporal lobe around the ear is difficult. For example, steady-state visual evoked potentials (SSVEPs), which are mainly generated in the occipital area, have a significantly attenuated and distorted amplitude in ear-EEG. Therefore, preserving the high level of decoding accuracy is challenging and essential for SSVEP BCI based on ear-EEG. In this paper, we first investigate linear and nonlinear regression methods to increase the decoding accuracy of ear-EEG regarding SSVEP paradigm by utilizing the estimated target EEG signals on the occipital area. Then, we investigate an ensemble method to consider the prediction variability of the regression methods. Finally, we propose an error correction regression (ECR) framework to reduce the prediction errors by adding an additional nonlinear regression process (i.e., kernel ridge regression). We evaluate the ECR framework in terms of single session, session-to-session transfer, and subject-transfer decoding. We also validate the online decoding ability of the proposed framework with a short-time window size. The average accuracies are observed to be 91.11±9.14%, 90.52±8.67%, 86.96±12.13%, and 78.79±12.59%. This paper demonstrates that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed ECR framework. [ABSTRACT FROM AUTHOR]

This paper proposes the use of a nonlinear inversion technique for the extraction of the directional ocean wave spectrum from bistatic high-frequency surface wave radar (HFSWR) Doppler data. The extraction method is combined with nonlinear regression expressions, solely based on the Doppler data, to retrieve wind speed and direction. Once the initialization parameters have been defined, a blind iterative algorithm based on Tikhonov regularization in Hilbert Scales is used to extract the nondirectional spectrum. The extracted spectrum is then used to determine the directional factor, which is assumed to be described by a cosine-power model. The proposed method yields fairly good results with synthetic noise-contaminated HFSWR data with a priori regularization parameters. [ABSTRACT FROM AUTHOR]

In this paper we develop an adaptive transform-domain technique based on rational function systems. It is of general importance in several areas of signal theory, including filter design, transfer function approximation, system identification, control theory etc. The construction of the proposed method is discussed in the framework of a general mathematical model called variable projection. First we generalize this method by adding dimension type free parameters. Then we deal with the optimization problem of the free parameters. To this order, based on the well-known particle swarm optimization (PSO) algorithm, we develop the multi-dimensional hyperbolic PSO algorithm. It is designed especially for the rational transforms in question. As a result, the system along with its dimension is dynamically optimized during the process. The main motivation was to increase the adaptivity while keeping the computational complexity manageable. We note that the proposed method is of general nature. As a case study the problem of electrocardiogram (ECG) signal compression is discussed. By means of comparison tests performed on the PhysioNet MIT-BIH Arrhythmia database we demonstrate that our method outperforms other transformation techniques. [ABSTRACT FROM AUTHOR]

In most solutions to state estimation problems, e.g., target tracking, it is generally assumed that the state transition and measurement models are known priori. However, there are situations where the model parameters or the model structure itself are not known a priori or are known only partially. In these scenarios, standard estimation algorithms like the Kalman filter and the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. In this paper, the nonlinear state estimation problem with possibly non-Gaussian process noise in the presence of a certain class of measurement model uncertainty is considered. It is shown that the problem can be considered as a special case of maximum-likelihood estimation with incomplete data. Thus, in this paper, we propose an EM-type algorithm that casts the problem in a joint state estimation and model parameter identification framework. The expectation (E) step is implemented by a particle filter that is initialized by a Monte Carlo Markov chain algorithm. Within this step, the posterior distribution of the states given the measurements, as well as the state vector itself, are estimated. Consequently, in the maximization (M) step, we approximate the nonlinear observation equation as a mixture of Gaussians (MoG) model. During the M-step, the MoG model is fit to the observed data by estimating a set of MoG parameters. The proposed procedure, called EM-PF (expectation-maximization particle filter) algorithm, is used to solve a highly nonlinear bearing only tracking problem, where the model structure is assumed unknown a priori. It is shown that the algorithm is capable of modeling the observations and accurately tracking the state vector. In addition, the algorithm is also applied to the sensor registration problem in a multi-sensor fusion scenario. It is again shown that the algorithm is successful in accommodating an unknown nonlinear model for a target tracking scenario. [ABSTRACT FROM AUTHOR]

Monitoring the condition of a dc-link capacitor is important for the reliability-critical voltage source inverter (VSI) systems. With the ever increasing application of a metalized poly propylene film (MPPF) capacitor, a new challenge for dc-link capacitor condition monitoring (CM) is presented due to the very small equivalent series resistance and capacitance end-of-life criterion (typically 2%–5%) of an MPPF capacitor. To improve the accuracy of capacitance estimation, a novel quasi-online CM method is proposed in this paper, which introduces an LC resonance intentionally when system is not in operation. Experimental work is carried out to validate the method and the results show that 1% capacitance estimation accuracy can be achieved in a 3-kW induction machine variable frequency drive system. Because of the signal measurement at large amplitude (e.g., nominal current) and the time-based multi-parameters identification algorithm, the method is accurate and inherently insensitive to the system parameters variation, measurement noise, and effect of temperature drift. In practice, the method can be implemented using the system available sensors with no need of hardware modification. Moreover, since the resonance current is limited and decays shortly, the condition of a dc-link capacitor can be monitored safely and frequently on-site in a VSI system. [ABSTRACT FROM AUTHOR]

We propose a novel adaptive kernel-based regression method for complex-valued signals: the generalized complex-valued kernel least-mean-square (gCKLMS). We borrow from the new results on widely linear reproducing kernel Hilbert space (WL-RKHS) for nonlinear regression and complex-valued signals, recently proposed by the authors. This paper shows that in the adaptive version of the kernel regression for complex-valued signals we need to include another kernel term, the so-called pseudo-kernel. This new solution is endowed with better representation capabilities in complex-valued fields since it can efficiently decouple the learning of the real and the imaginary part. Also, we review previous realizations of the complex KLMS algorithm and its augmented version to prove that they can be rewritten as particular cases of the gCKLMS. Furthermore, important conclusions on the design of the kernels are drawn that help to greatly improve the convergence of the algorithms. In the experiments, we revisit the nonlinear channel equalization problem to highlight the better convergence of the gCKLMS compared to previous solutions. Also, the flexibility of the proposed generalized approach is tested in a second experiment with non-independent real and imaginary parts. The results illustrate the significant performance improvements of the gCKLMS approach when the complex-valued signals have different properties for the real and imaginary parts. [ABSTRACT FROM AUTHOR]

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, the current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can, in turn, suffer from the loss of structural details in synthesized images. Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss, and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T1- and T2- weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to the previous state-of-the-art methods. Our synthesis approach can help improve the quality and versatility of the multi-contrast MRI exams without the need for prolonged or repeated examinations. [ABSTRACT FROM AUTHOR]

Cables are subject to local impedance faults, or soft faults, e.g., following mechanical alterations. While their occurrence can be detected, no simple procedure exists for assessing whether a fault is critical and requires intervention. Previous work has demonstrated that the amplitude of echoes generated by time-domain reflectometry (TDR) does not measure how severe such faults are, hindering attempts at introducing early warning schemes that could prevent these faults from eventually evolving into hard faults, i.e., open or short circuits. This paper introduces a model-based identification procedure that is capable of accurately inferring how severe an impedance modification along a cable is, together with its length. It has the advantage of operating at lower frequencies than other more complex identification methods, while being intrinsically stable and well defined since faults are identified and located in separate steps, without requiring nonlinear regression techniques. The proposed method is also shown to remove the typical ambiguities found in the interpretation of TDR signals by reinstating a single reflection peak in reflectograms. General feasibility conditions for the identification of impedance faults are discussed, proving that only sufficiently long faults can univocally be identified. The accuracy of the proposed method is tested against experimental results obtained for faults of increasing severity in coaxial cables, for which TDR is shown to yield accurate estimates only when testing over a bandwidth almost $10\times $ wider. [ABSTRACT FROM AUTHOR]

In this paper, a reconstruction approach for simulating the output performance of a satellite's solar array (SA) is proposed. This approach introduces an Earth-reflected irradiance and a degradation model that affect the performance in-orbit to reconstruct a typical single diode model. The main feature of this reconstruction is that the computational procedure is convenient and can be programmed to the microcomputer system for real-time performance monitoring. Based on limited information from the manufacturer's datasheet, the single diode model is parameterized. In addition, an effective method of determining initial values is presented to avoid nonconvergence in the parameterization. Moreover, Earth-reflected irradiance on a body-mounted SA is simulated analytically with the parallel and perpendicular view factors. The degradation of the SA performance is modeled by a nonlinear regression method. Finally, the reconstructed model is applied to simulate the SA performance of microsatellite Tianxun-1 (TX-1) developed by Nanjing University of Aeronautics and Astronautics. The measured results over four years in space verify the validity of the proposed reconstruction approach. [ABSTRACT FROM AUTHOR]

Compared to HVAC, the application experience of composite/polymer insulators in HVDC transmission lines is limited. However, the tendency to utilize them in HVDC transmission lines, where pollution flashover is a major problem, is growing due to their water-repellent feature. This trend demands understanding insulator flashover performance and recognizing key parameters in the design and dimensioning of insulators. This paper presents the measurement and analytical results for dc pollution flashover of silicon rubber insulators at extra heavy pollution conditions. Two approaches are employed to model flashover voltage gradient and quantify the contribution of salt deposit density, specific leakage distance, average diameter, shed spacing to shed depth ratio, and form factor to insulator pollution performance. Developing a nonlinear regression model, the effect of the mentioned parameters on pollution flashover is studied. Then, the artificial neural network (ANN) and randomization method are used to develop a model to interpret parameters contribution to pollution flashover, and the results are validated with those of the regression method. Finally, by applying an ANN-based randomization approach, a multi-input model is created to quantify the significance of different parameters in flashover voltage gradient. The model ranks specific leakage distance and average diameter, respectively, as the most and least important geometrical characteristics in pollution flashover performance of SiR insulators. The method and results can be a source of information to optimize the design and dimensioning of HVDC SiR insulators especially in severe pollution conditions. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Subthalamic nucleus (STN) local field potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. In this paper, we introduce a behavior detection method capable of asynchronously detecting the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from the STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity for detecting finger movement. Our experimental results, using the recordings from 11 patients with PD, demonstrate that this approach is applicable for behavior detection in the majority of subjects (average area under curve of 70±12%). [ABSTRACT FROM PUBLISHER]

This paper presents a real-time energy management algorithm for hybrid ac/dc microgrids involving sustainable energy and hybrid energy storage. This hybrid storage system consists of super capacitors (SC) for ultra-fast load matching beside lithium-ion batteries for relatively long term load buffering. The energy management algorithm aims mainly at managing the energy within the system such that the effect of pulsed (short duration) loads on the power system stability is minimized. Moreover, an average annual saving of around 7% is achieved by shifting loads to off-peak hours. The expected energy needed during a future peak, the time of its occurrence and the current state of charge of both elements of the hybrid storage system are all examples of the inputs to the algorithm. A nonlinear regression technique is used to obtain mathematical models for the uncertain quantities including load and sustainable energy curves. The results show a significant improvement for the system in terms of voltage and power stability by applying the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The relationship between a control movement and the effect most expected by a population is known as a direction-of-motion stereotype. Such stereotypes are important in order to study the impact of different design variables on user performance. In this paper, a neural network-based method is proposed to quantify the causal connection among design variables and to compute their relative influences on the two performance measures of user response time and response accuracy. Based on the experimental data presented here, the best operating condition with which to optimize each of the measures is suggested. Some useful observations about the relationship between design variables and measures are also presented and discussed. In ergonomics, there have been a lack of useful quantitative methods for investigating the mappings between different displays and controls under a variety of operating conditions. The major contribution of this study is to provide some insight into the usefulness of quantitative methods in evaluating display-control compatibility. [ABSTRACT FROM PUBLISHER]

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, having style, living location, and weather conditions. Males and females may' also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for the statistical characterization of a measurand is presented. A description of how different bootstrap techniques can be applied in practice to estimate successfully a measurand probability density function (pdf) is given. When the direct observation of a quantity of interest is practically impossible such as in nondestructive testing, it is necessary to estimate such quantity, which is also called measurand. The statistical characterization of any estimator is important, because all the uncertainty features can be accessible to qualify such estimator. On the other hand, most of the time, the large-scale repetition of an experiment is not economically feasible, so that the Monte Carlo methods cannot be used directly for uncertainty characterization. Bootstrap methods have proved to be a potentially useful alternative. Moreover, a biased bootstrap recent technique, with which robust parameter estimates are obtained, is used. This technique is extended to be used in measurand estimation. An extended nested bootstrap improvement for the measurand pdf estimation is also presented. These techniques are applied to a realistic multidimensional measurand-estimation problem of groove dimensioning using remote field eddy current inspection. Measurand uncertainty characterization using the bootstrap techniques generally gives an accurate pdf estimation. [ABSTRACT FROM AUTHOR]

Static noise margin is one of the key metrics to estimate the likelihood of failure of a 6T-static random-access memory (SRAM) cell. This paper proposes a technique to accurately estimate the stability of a conventional SRAM cell without modifying the cell structure. The main idea is to measure the specific cell's currents with variant supply levels via the bit lines. The measured currents are used to estimate the read stability and the write ability through a nonlinear regression. The R^\bf 2 (coefficient of determination) of the stability estimation is as high as 0.95 when applied to an arbitrary data set. As typical stability definitions require an access to the internal node of a 6T-SRAM cell, alternative measurable stability metrics for read and write are surveyed and modified to improve the correlation with the conventional stability definition. With this alternative stability and the cell currents, the conversion rules from currents to the stability can be found from the measurement data. Simulation results show that the estimation error sigma is as small as 2.44% and 3% for the read stability and write-ability estimation, respectively. Validity of the idea is verified by Monte Carlo simulations by using SRAM models in a 45-nm CMOS technology. [ABSTRACT FROM PUBLISHER]

The estimation of the parameters of defects from eddy current nondestructive testing data is an important tool to evaluate the structural integrity of critical metallic parts. In recent years, several works have reported the use of artificial neural networks (ANNs) to deal with the complex relation between the testing data and the defect properties. To extract relevant features used by the ANN, principal component analysis, wavelet decomposition, and the discrete Fourier transform have been proposed. In this paper, a method to estimate dimensional parameters from eddy current testing data is reported. Feature extraction is based on the modeling of the testing data by a template of additive Gaussian functions and nonlinear regressions to estimate their parameters. An ANN was trained using features extracted from a synthetic data set obtained with finite-element modeling of the eddy current probe. The proposed method was applied to both simulated and measured data, providing good estimates. [ABSTRACT FROM AUTHOR]

This paper evaluates nonlinear retrieval methods to derive atmospheric properties from hyperspectral infrared sounding spectra, with emphasis on the retrieval of temperature, humidity, and ozone atmospheric profiles. We concentrate on the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-A satellite data for the future Meteosat Third Generation Infrared Sounder (MTG-IRS). The methods proposed in this work are compared in terms of both accuracy and speed with the current MTG-IRS L2 processing concept, which processes MetOp-IASI and proxy MTG-IRS data. The official chain consists of a principal component extraction, typically referred to as empirical orthogonal functions (EOF) and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering: 1) other linear feature extraction methods instead of EOF, such as partial least squares; and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks and kernel ridge regression as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models yield better results than linear retrieval for both MetOp-IASI and MTG-IRS synthetic and real data. Averaged gains throughout the column of +1.8 K and +2.2 K are obtained for temperature profile estimation from MetOp-IASI and IRS data, respectively. Similar gains are obtained for the estimation of dew point temperatures. In both variables, these improvements are more noticeable in lower atmospheric layers. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. The nonlinear statistical approach is successfully compared to optimal estimation (OE) in terms of accuracy, bias and computational cost. These results confirm the potential of statistical nonlinear inversion techniques for the retrieval of atmospheric profiles. [ABSTRACT FROM AUTHOR]

This paper evaluates state-of-the-art parametric and nonparametric approaches for the estimation of leaf chlorophyll content (Chl), leaf area index, and fractional vegetation cover from space. The parametric approach involves comparison of established and generic narrowband vegetation indices (VIs) and the Normalized Area Over reflectance Curve method, which calculates the continuum spectral region sensitive to Chl. However, as not all available bands take part in these spectral algorithms, it remains unclear whether optimal estimations are achieved. Alternatively, the nonparametric approach is based on Gaussian process (GP) techniques and allows inclusion of all bands. GP builds a nonlinear regression as a linear combination of spectra mapped to a high-dimensional space. Moreover, GP provides an indication of the most contributing bands for each parameter, a weight for the most relevant spectra contained in the training data set, and a confidence estimate of the retrieval. GP has previously demonstrated to be competitive in accuracy with support vector regression and neural networks. Results from hyperspectral Compact High Resolution Imaging Spectrometer data over the Spanish Barrax test site show that GP outperformed the VIs in assessing the vegetation properties when using at least four out of the 62 bands. GP identified most contributing bands in the red and red edge and, to a lower extent, in the blue and NIR parts of the spectrum. Since the proposed GP method is able to build robust relationships between the parameter of interest and only a few bands, it is a promising approach for multispectral data as well. [ABSTRACT FROM AUTHOR]

In this paper, we present a new analytical method for estimating the parameters of Delta-Lognormal functions and characterizing handwriting strokes. According to the Kinematic Theory of rapid human movements, these parameters contain information on both the motor commands and the timing properties of a neuromuscular system. The new algorithm, called XZERO, exploits relationships between the zero crossings of the first and second time derivatives of a lognormal function and its four basic parameters. The methodology is described and then evaluated under various testing conditions. The new tool allows a greater variety of stroke patterns to be processed automatically. Furthermore, for the first time, the extraction accuracy is quantified empirically, taking advantage of the exponential relationships that link the dispersion of the extraction errors with its signal-to-noise ratio. A new extraction system which combines this algorithm with two other previously published methods is also described and evaluated. This system provides researchers involved in various domains of pattern analysis and artificial intelligence with new tools for the basic study of single strokes as primitives for understanding rapid human movements. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a comparison of different techniques for making statistical inference about a measurement system model. This comparison involves results when two main assumptions are made: 1) the unknowable behavior of the probability density function (pdf) ℘z(e) of errors since the real measurement systems are always exposed to continuous perturbations of an unknown nature and 2) the assumption that, after some experimentation, one can obtain sufficient information that can be incorporated into the modeling as prior information. The first assumption leads us to propose the use of two approaches, which permit building hybrid algorithms; such approaches are the nonparametric bootstrap and the kernel methods. The second assumption makes possible the exploration of a Bayesian framework solution and Monte Carlo Markov chain auxiliary that is used to access the a posteriori pdf of the measurement. For both assumptions over ℘(e) and the model, different classical criteria can be used; one also uses an extension of a recent criterion of entropy minimization. The entropy criterion is constructed on the basis of a symmetrized kernel estimate ℘^n,h (e) of ℘(e). Finally, a comparison between results obtained with the different schemes proposed by De Ia Rosa is presented. [ABSTRACT FROM AUTHOR]

We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input. [ABSTRACT FROM PUBLISHER]

This article takes a public policy perspective and captures the implications of sectoral logistical capabilities for export competitiveness to obtain directives for developmental interventions in the logistics sector. We specifically develop a logistical policy intervention framework that links trade logistics performance and exports at the country-level with the objective to identify strategies for a nation's logistics policy formulation. Theoretically, the framework relies on the “macro-institutional view of the logistics systems environment,” which is an emerging global value chain perspective to model global trade scenarios. This perspective accounts for the influence of diverse operational capabilities at a sectoral scale, multiple institutions, and socio-economic environments in governing the performance of a nation's logistics systems in global trade. Methodologically, a rigorous nonlinear regression modeling approach highlights nonlinear discontinuities associated with the impact of logistical policy interventions fostering sectoral logistical capabilities (for example, developing logistics infrastructure or improving the state of logistics service quality in the nation) to boost the export viability of a country in the global economy. The results also highlight critical environmental bottlenecks that govern the efficacy of such interventions. This article pioneers to mainstream the public policy underpinning of logistics systems, and via this contextualization, it contributes to the agenda of mastering policy challenges in engineering and technology management. [ABSTRACT FROM AUTHOR]

We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization. Sparsity is of great importance in many scientific areas such as image and signal processing, medical imaging, compressed sensing, and machine learning, as it ensures robustness against noisy data and yields models that are easier to interpret due to the small number of relevant terms. It is common practice to enforce sparsity by adding the $\ell _1$ ℓ 1 -norm as a penalty term. In order to gain a better understanding and to allow for an informed model selection, we directly solve the corresponding multiobjective optimization problem (MOP) that arises when minimizing the main objective and the $\ell _1$ ℓ 1 -norm simultaneously. As this MOP is in general non-convex for nonlinear objectives, the penalty method will fail to provide all optimal compromises. To avoid this issue, we present a continuation method specifically tailored to MOPs with two objective functions one of which is the $\ell _1$ ℓ 1 -norm. Our method can be seen as a generalization of homotopy methods for linear regression problems to the nonlinear case. Several numerical examples – including neural network training – demonstrate our theoretical findings and the additional insight gained by this multiobjective approach. [ABSTRACT FROM AUTHOR]

The Earth orbiting spacecrafts use lithium-ion batteries as an energy storage device. Each battery is made up of several series- and parallel-connected cells. The constraints concerning the cell parameters, obtained by testing, drive the positioning of the cells in the battery. Positioning by cell sorting to meet the required battery performance can be approached through the genetic algorithm (GA). A GA improved cell sorting algorithm (GICSA) is proposed in this work. It sorts the cell parameters randomly with GA to match them within a given set of tight constraints. It sorts different cell types and numbers of cells for different battery configurations. It gives the practically best possible number of matched cells for the battery. [ABSTRACT FROM AUTHOR]

We introduce a novel general regression model that is based on a linear combination of a new set of non-local basis functions that forms an effective feature space. We propose a training algorithm that learns all the model parameters simultaneously and offer an initialization scheme for parameters of the basis functions. We show through several experiments that the proposed method offers better coverage for high-dimensional space compared to local Gaussian basis functions and provides competitive performance in comparison to other state-of-the-art regression methods. [ABSTRACT FROM AUTHOR]