Dr. Vergara-Rojas, Samuel

Electric forklifts are increasingly adopted in industrial environments due to their energy efficiency, reduced emissions, and lower operating noise compared to combustion alternatives. This paper presents a novel methodology for estimating the transported load weight in electric forklifts based on the output power signal of the DC–DC converter driving the electro-hydraulic lifting system. The proposed method leverages non-intrusive measurements of voltage and current to compute the lifting power, lifting speed, and energy, also allowing the computation of the lifting efficiency. The analysis confirmed that lifting energy is not linearly correlated with transported weight but lifting efficiency can be reasonably approximated as a function of lifting power and lifting speed, subsequently allowing the estimation of the transported mass. Experimental validation using 53 lifting events demonstrated that the methodology can estimate load weight with a reasonable mean absolute percentage error of 10.6% and 6.4% when using linear or multivariable regression analysis, respectively. These results demonstrate that the approach is sufficiently accurate for practical applications such as triggering load warnings when the estimated mass exceeds predefined safety thresholds.

Fiber-optic Mach–Zehnder interferometers are widely used in research areas such as telecommunications, spectroscopy, and quantum information. These optical structures are known to be affected by phase fluctuations that are usually modeled as multiparametric noise. This multidimensional noise must be stabilized or compensated for to enable fiber-optic Mach–Zehnder architectures for practical applications. In this work, we study the effectiveness of a modified Perturb-and-Observe (P&O) algorithm to control multidimensional phase noise in fiber-based multi-arm Mach–Zehnder interferometers. We demonstrate the feasibility of stabilizing multidimensional phase noise by numerical simulations using a simple feedback control scheme and analyze the algorithm’s performance for systems up to dimension 8×8. We achieved minimal steady-state errors that guarantee high optical visibility in complex optical systems with 𝑁×𝑁 matrices (with 𝑁=[2,3,4,5,6,7,8]).

Background Muscular synergies could represent the patterns of muscular activation used by the central nervous system (CNS) to simplify the production of movement. Studies in walking-running transitions described up to nine synergy modules, and an earlier activation of flexor and extension ankle muscular groups compared to running or walking. Our project aims to study the behaviour of muscle synergies in different stance and swing variations of walking-running (WRT) and running-walking (RWT) transitions. Methods Twenty-four trained men participated in this study. A variable speed protocol on a treadmill was developed to record the activity of 14 muscle during walking, running and relative transitions. The protocol was based on five ramps of 50 seconds each around ± 10 and 20% of the WRT speed. WRT and RWT were identified according to an abrupt change of the duty factor. Analysing surface electromyography using non-negative matrix factorization (NMF) we obtained synergy modules and temporal activation profiles. Alpha threshold for statistical tests set at 0.05. Results We described four different transition strides, two for increasing speed transitions, and two for decreasing speed transitions. Four to six synergy modules were found in each condition. According to the maximum cosine similarity results, the two identified WRT conditions shared five modules, while the two RWT conditions shared four modules. WRT and RWT overall shared 4.33 ± 0.58 modules. The activation profiles and centres of activation revealed differences among conditions. Discussion Transition occurred at step level, and transition strides were composed by walk-like and run-like steps. Compared with previous studies in running and walking, both transitions needed earlier activation of a comparable number of synergy modules. Synergies were affected by acceleration: during RWT the need to dissipate energy, to decrease the speed, was achieved by increasing the number of co-activating muscles. This was reflected in fewer synergy modules and different activation profiles compared to WRT. We believe that our results could be enforced in different applied fields, like clinical gait analysis, physiotherapy and rehabilitation, where plans including co-activation of specific muscular groups could be useful. Gait transitions are common in different sports, and therefore also application in training and sport science would be possible.

A theory of sedimentation-consolidation evolved in the last decades of the 20th century and is accepted today by researchers worldwide. This theory provides a reliable method of thickener design, simulation and control. However, a process model, simple or sophisticated, empirical or phenomenological, is useful only if it is possible to determine, in an objective way, its experimental parameters. Although it is important for a mineral processing plant to perform periodical laboratory test to determine thickening parameters, and adjust the operation in this way, laboratory tests not always represent the behavior of the material in a thickener. Research workers at the University of Concepción developed new instrumentation, algorithms and software to determine the material properties of the thickener feed, such as settling velocity of the suspension and the compressibility of the sediment produced. Work was made in a major Chilean copper mineral processing plant to test the new instrumentation. In this paper, the auditing of one molybdenum thickener and a tailings thickener are presented using the two new online instrument.

Electrical impedance tomography (EIT) is a technology that estimates the electrical properties of a body or a cross section. Its main advantages are its non-invasiveness, low cost and operation free of radiation. The estimation of the conductivity field leads to low resolution images compared with other technologies, and high computational cost. However, in many applications the target information lies in a low intrinsic dimensionality of the conductivity field. The estimation of this low-dimensional information is addressed in this work. It proposes optimization-based and data-driven approaches for estimating this low-dimensional information. The accuracy of the results obtained with these approaches depends on modelling and experimental conditions. Optimization approaches are sensitive to model discretization, type of cost function and searching algorithms. Data-driven methods are sensitive to the assumed model structure and the data set used for parameter estimation. The system configuration and experimental conditions, such as number of electrodes and signal-to-noise ratio (SNR), also have an impact on the results. In order to illustrate the effects of all these factors, the position estimation of a circular anomaly is addressed. Optimization methods based on weighted error cost functions and derivate-free optimization algorithms provided the best results. Data-driven approaches based on linear models provided, in this case, good estimates, but the use of nonlinear models enhanced the estimation accuracy. The results obtained by optimization-based algorithms were less sensitive to experimental conditions, such as number of electrodes and SNR, than data-driven approaches. Position estimation mean squared errors for simulation and experimental conditions were more than twice for the optimization-based approaches compared with the data-driven ones. The experimental position estimation mean squared error of the data-driven models using a 16-electrode setup was less than 0.05% of the tomograph radius value. These results demonstrate that the proposed approaches can estimate an object's position accurately based on EIT measurements if enough process information is available for training or modelling. Since they do not require complex calculations it is possible to use them in real-time applications without requiring high-performance computers.