This work describes a method for calibration of the Velodyne HDL-64E scanning LIDAR system. The principal contribution was expressed by a pattern calibration signature, the mathematical model and the numerical algorithm for computing the calibration parameters of the LIDAR. In this calibration pattern the main objective is to minimize systematic errors due to geometric calibration factor. It describes an algorithm for solution of the intrinsic and extrinsic parameters. Finally, its uncertainty was calculated from the standard deviation of calibration result errors.

Knowing exactly how much solar radiation reaches a particular area is helpful when planning solar energy installations. In recent years the use of renewable energies, especially those related to photovoltaic systems, has had an impressive up-tendency. Therefore, mechanisms that allow us to predict solar radiation are essential. This work aims to present results for predicting solar radiation using optimization with the Random Forest (RF) algorithm. Moreover, it compares the obtained results with other machine learning models. The conducted analysis is performed in Queretaro, Mexico, which has both direct solar radiation and suitable weather conditions more than three quarters of the year. The results show an effective improvement when optimizing the hyperparameters of the RF and Adaboost models, with an improvement of 95.98% accuracy compared to conventional methods such as linear regression, with 54.19%, or recurrent networks, with 53.96%, without increasing the computational time and performance requirements to obtain the prediction. The analysis was successfully repeated in two different scenarios for periods in 2020 and 2021 in Juriquilla. The developed method provides robust performance with similar results, confirming the validity and effectiveness of our approach.

Formulas do not estimate renal function with acceptable precision and accuracy. We compared 51 creatinine-based and/or cystatin c-based formulas with a gold standard (iohexol plasma clearance) in 193 renal transplant recipients using concordance correlation coefficient, total deviation index, coverage probability and the error in chronic kidney disease (CKD) stage classification. No formula showed a concordance correlation coefficient greater than 0.90 (average for creatinine-based formulas: ∼0.70 and for cystatin c-based formulas: ∼0.85). A wide total deviation index was observed: approximately 70% (creatinine-based) and approximately 50% (cystatin c-based), indicating that 90% of the estimations showed bounds of error of ±70% or ±50%, respectively, compared with the gold standard. No formula included 90% of the estimations within a coverage probability of ±10%. Half the CKD stages classified by creatinine-based formulas were incorrect, mainly due to overestimation of renal function. One of 3 CKD stages diagnosed by cystatin c-based formulas was incorrect, with both overestimation and underestimation. Overall, the formulas showed very low precision and accuracy and a high degree of error in reflecting real renal function. In conclusion, formulas do not properly reflect renal function in kidney transplantation, which makes their use in clinical practice unreliable. Moreover, their use in clinical trials should be avoided.

A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM) Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs) due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component’s harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems.