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Item type:Publication, Soft Sensor Design for Restricted Variable Sampling Time(Elsevier, 2020-01-01)Difficult-to-obtain variables in industrial applications have led to the rise of soft sensors, which use prior system information and measurements to estimate these difficult-to-obtain variables. In real systems, the measurements that need to be estimated by a soft sensor are often infrequently measured or delayed. Sometimes, these delays and sampling time are variable in time. Though there are papers considering soft sensors in the presence of time delays and different sampling times, the variation of those parameters has not been considered when evaluating the adequacy of the soft sensors. Therefore, this paper will evaluate the impact of such variations for a data-driven soft sensor and propose modifications of the soft sensor that increase its robustness. The reliability of its estimate will be shown using the Bauer-Premaratne-Durán Theorem. Furthermore, the soft sensor will be simulated applying it to a continuous stirred tank reactor. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Soft sensor design for variable time delay and variable sampling time(Elsevier, 2020-08-01)Often industrial variables can be difficult to measure due to such factors as extreme conditions or complex compositions. In such cases, soft sensors have been developed that use available system information and measurements to estimate these difficult-to-obtain variables. In practice, the measurements that are to be estimated by a soft sensor are often infrequently measured or delayed. Occasionally, these sampling times or delays are time varying. At present, most research has considered these parameters to be time invariant, and thus, there is a need to consider the time-varying case. Therefore, this paper will evaluate the impact of time-varying delays and sampling times for the design of a data-driven soft sensor. Modifications will be proposed that will increase the robustness and performance of the soft sensor. The reliability of the estimate will be shown using the Bauer–Premaratne–Durán Theorem. Furthermore, the proposed soft sensor system will be tested using simulations of a continuous stirred tank reactor (CSTR) and an reverse osmosis plant. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate for the CSTR and reverse osmosis plant. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Effects of obstacles to innovation: are they complementary?(Centre international de psychosomatique, 2021-03-08)This paper investigates whether the effects of obstacles to firms’ propensity for and intensity of innovation were complementary in Peru, a middle-income developing country, during the period 2009-2011. The tests of complementarity are based on the estimation of two adjusted Crépon–Duguet–Mairesse (CDM) models that relate a firm’s decision to invest in science, technology and innovation activities (STI), the innovation process, and labor productivity. The estimations and tests yield four main results. First, there is evidence that the effects of obstacles to innovation are related and some are complementary. Second, firms’ size (particularly the largest ones) affects their decision to invest in STI. Third, under the assumption that obstacles are related, the intensity of investment in STI determines firms’ innovation outcomes. Lastly, robustness results suggest that human and physical capital and size are the most important factors that affect firms’ productivity.JEL Codes: O31, O3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Highly maneuverable target tracking under glint noise via uniform robust exact filtering differentiator with intrapulse median filter(Institute of Electrical and Electronics Engineers Inc., 2021-12-28)Highly maneuverable target tracking under glint noise and nonlinear uncertainties during course changes and terminal maneuvers has been solved suboptimally for many years via the interacting multiple model algorithm with the use of the Kalman filter (KF), unscented Kalman filter, and the extended Kalman filter (EKF). Also, nonlinear KFs such as the cubature Kalman filter have been proposed to improve nonlinear tracking without being able to filter out glint noise. To this end, the particle filter and some KF based on variational Bayesian approach have been proposed with very good results in filtering out glint noise. Nonetheless, it is difficult for state-of-the-art methods to achieve efficient filtering of glint noise and nonlinear tracking at the same time. On the other hand, robust exact differentiators, based on the super-twisting algorithm, have been used for many years in output-feedback control and state observation in order to obtain the derivatives of an input signal with theoretical finite-time exactness. However, their potential for target tracking applications has not been explored sufficiently. In this article, a uniform robust exact filtering differentiator with intrapulse median filtering is proposed to filter out glint noise at the sliding manifold, while offering nonlinear tracking robustness via high-degree super-twisting terms outside the sliding manifold. Numerical simulations comparing the proposed solution to other state-of-the-art methods were conducted, showing promising results. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-view data augmentation to improve wound segmentation on 3D surface model by deep learning(Institute of Electrical and Electronics Engineers Inc., 2021-01-01)Wound area segmentation really progressed with the emergence of deep learning, due to its robustness in uncontrolled lighting and no need to design hand-crafted features but two limits have still to be overcome: firstly, its performance relies on the size and quality of the training dataset in the medical field, where data annotation is costly and time-consuming; secondly the accuracy of the segmentation depends highly on the camera distance and angle and moreover perspective effects prevent measuring real surfaces in single views. To address concurrently these two issues, we propose to apply multi-view modeling: an image sequence is acquired around the wound site and enables wound 3D reconstruction. Then, a segmentation step is run to extract roughly the wound from the background in each view and to select the best view with an original strategy. This view provides the most accurate segmentation and the real wound bed area even on non planar wounds. Finally, this segmentation is backprojected in each view to generate a complete set of well annotated real images to reinforce the learning step of the neural network. In our experiments, we compare several strategies to select the best view in the image sequence. The proposed method, tested on a dataset of 270 images, outperforms standard deep learning approach based on a single view, as recorded with DICE index and IoU score which rise respectively from 36.53% to 86.3% and 29.48% to 77.09% for the wound class to achieve an overall DICE and IoU score of 93.04% and 86.61% including background class. These results attest to the robustness of our method and its improved accuracy in the wound segmentation task. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Urban road network resilience assessment on freight logistics by simulating disruptive events(Springer, 2022-01-01)The assessment of resilience in port road networks under disruptive events is a key issue related to urban logistics. This paper addresses an original simulation method to evaluate resilience using macro and micro simulation based on stochastic theory. The results provide insight into the resilience index of the network. This paper specifies the most influential network links around the area of influence produced by a logistics transport avenue in Lima-Peru. A function that includes redundancy and robustness of the system as a performance measure is proposed to measure resilience. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Warm Start Trajectory Reinforcement Learning for Multi-Mode Project Scheduling under Resource Constraints in UAV Manufacturing(RELX Group (Netherlands), 2025-01-01)1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Regime-switching, fiscal policy shocks and macroeconomic fluctuations in Peru(Elsevier BV, 2026-06-01)Following Chan and Eisenstat (2018a), we use a family of regime-switching models to analyze the evolution of fiscal shocks impacts on Peru’s economic growth from 1995Q1 to 2019Q4. Key findings include: (i) identification of two distinct economic regimes with different macroeconomic fundamentals tied to improvements in fiscal and monetary policy; (ii) enhanced model fit with the inclusion of regime switching volatility (RSV); (iii) a positive trend in the size of spending multipliers, though they remain below unity; (iv) during the 2008 Global Financial Crisis, capital expenditure shocks mitigated the decline in economic growth by 2 percentage points, highlighting their counter-cyclical potential. These findings are corroborated by robustness checks, which include changes in priors, variable reordering, adjustments in external and demand variables, and extending the sample to 2022Q4 to encompass the COVID-19 crisis.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Regularized Joint Estimator of the Nonlinearity Parameter and Attenuation Coefficient Using a Nonlinear Least-Squares Algorithm(SAGE Publications, 2025)The acoustic nonlinearity parameter (B/A) could enhance the diagnostic capabilities of conventional ultrasonography and quantitative ultrasound in tissues and diseases. Nonlinear acoustic propagation theory of plane waves has been used to develop a dual-energy model of the depletion of the fundamental related to the Gol’dberg number and subsequently to the B/A of media (a reference phantom is used as a baseline). The depletion method, however, needs a priori information of the attenuation coefficient (AC) of the assessed media. For this reason, recently, a work introduced a simultaneous estimator of the B/A and AC based on fitting depletion method measurements to a nonlinear model using the iterative algorithm Gauss-Newton Levenberg-Marquardt (GNLM). However, the GNLM method presented high sensitivity to the initial guess values of the algorithm which limits the robustness of the approach. In the present work, the Gauss-Newton method is combined with a total variation regularization approach (GNTV), which is achievable by expanding the nonlinear model of the GNLM method for joint estimation of the B/A and AC of all pixels of the parametric images instead of a block-wise approach. In addition, the GNTV used compounding data from several tone-burst transmissions at different center frequencies rather than only one narrowband tone-burst. The results suggest that incorporating regularization and increasing the number of frequencies improves the robustness of the GNTV compared to the GNLM method by accurately estimating B/A values in uniform and nonuniform experimental phantoms (mean relative error less than 18%). The best performance of B/A reconstruction was observed when the sample medium exhibited a constant Gol’dberg number.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Nonlinearity parameter estimation method from fundamental band signal depletion in pulse-echo using a dual-energy model(Acoustical Society of America, 2025)The estimation of the nonlinearity parameter (B/A) has the potential to be used in the clinical diagnosis of conditions such as liver steatosis. Recently, a pulse-echo method to estimate B/A based on the theory of the fundamental band amplitude depletion of weak waves, namely, the depletion method, was proposed. In the present work, the depletion method is presented with more technical detail. Then, the robustness of the depletion method is assessed by using simulations that diverge from the model requirements: (1) monochromatic plane wave propagation and (2) quadratic power-law frequency dependence attenuation. Regarding requirement (1), the results led to a critical finding that when using wideband pulses (37%–113% bandwidth), the bias of the B/A estimates is larger than the bias obtained using narrowband pulses (11%–28% bandwidth), even if requirement (2) holds. Regarding requirement (2), power-law frequency dependence closer to those of soft tissues, i.e., 1.1 or 1.2, using narrowband pulses presented bias of less than 10%. The use of narrowband pulses also was shown to be robust when the reference phantom and sample had attenuation mismatches of around 60%. Finally, the experimental feasibility of the depletion method was evaluated, showing results with good accuracy (bias <17%), which are consistent with the observations in the simulations.1