Author Archives: Juan-antonio Fernández-madrigal

A particular application of quick detection of changes in a signal: detecting changes of voltage regimes in the electric distribution network

September 3, 2021 11:25 ,

D. Macii and D. Petri, Rapid Voltage Change Detection: Limits of the IEC Standard Approach and Possible Solutions, IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 2, pp. 382-392, Feb. 2020, DOI: 10.1109/TIM.2019.2903617.

Rapid voltage changes (RVCs) are power quality (PQ) events characterized by small and fast transitions between two steady-state root-mean-square (rms) voltage levels. RVCs occur quite often at the distribution level and are expected to be even more frequent in the future due to the increasing penetration of dynamic loads and renewable-based generators in the smart grid. Unlike other PQ events, RVCs are less critical, but also more difficult to detect than dips/sags and swells, due to their smaller voltage variations. Nevertheless, they can be harmful to generators’ control systems and electronic equipment in general. Moreover, they strongly affect flicker. The IEC Standard 61000-4-3:2015 clearly describes an algorithm for RVC detection. However, this approach is poorly characterized in the scientific literature. In fact, it suffers from some drawbacks. In this paper, some of them (e.g., rate-dependent detection limits and detection delays) are analyzed in depth. In addition, an alternative approach based on the estimation of the rate of change of rms voltage is proposed. Multiple simulation results show that the approach considered is more sensitive to noise, but also faster, especially when not so fast RVCs occur. Moreover, it allows measuring the rate of change of rms voltage, which is currently disregarded in the IEC Standard.

Posted in: Electronics, Probability and statistics , Tagged:

Estimating parameters of periodic signals that are sampled with just two levels (0/1) in magnitude

September 3, 2021 11:20 ,

P. Carbone, J. Schoukens and A. Moschitta, Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements, IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 2, pp. 339-353, Feb. 2020, DOI: 10.1109/TIM.2019.2902023.

Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. Although several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is, binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is, the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to the published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function but provides solutions that are very close to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted.

Posted in: Electronics , Tagged:

A model of the psychomotor behaviour of humans intended to be useful for integration with robots

September 3, 2021 11:14 ,

Stephen Fox, Adrian Kotelba, Ilari Marstio, Jari Montonen, Aligning human psychomotor characteristics with robots, exoskeletons and augmented reality, Robotics and Computer-Integrated Manufacturing, Volume 63, 2020, DOI: 10.1016/j.rcim.2019.101922.

In previous production literature, the uncertainty of human behaviour has been recognized as a source of productivity, quality, and safety problems. However, fundamental reasons for the uncertainty of human behavior have received little analysis in the production literature. Furthermore, potential for these fundamental reasons to be aligned with production technologies in order to improve production performance has not been addressed. By contrast, in this paper, fundamental reasons for the uncertainty of human behaviour are explained through a model of psychomotor characteristics that encompasses physiology, past experiences, personality, gender, culture, emotion, reasoning, and biocybernetics. Through reference to 10 action research cases, the formal model is applied to provide guidelines for planning production work that includes robots, exoskeletons, and augmented reality.

Posted in: Human-robot interaction, Psycho-physiological bases of engineering , Tagged:

State of the art in standards for Robotics

September 3, 2021 11:10 ,

Z.M. Bi, Zhonghua Miao, Bin Zhang, Chris W.J. Zhang, The state of the art of testing standards for integrated robotic systems, Robotics and Computer-Integrated Manufacturing
Volume 63, June 2020, DOI: 10.1016/j.rcim.2019.101893.

Technology standards facilitate the transparency in market and the supplies of products with good quality. For manufacturers, standards make it possible to reduce the costs by mass production, and enhance system adaptabilities through integrating system modules with the standardized interfaces. However, International standards on industrial robots such as ISO-9283 were developed in 1998, and they have not updated since then. Due to every-increasing applications of robots in complex systems, there is an emerging need to advance existing standards on robots for a broader scope of system components and system integration. This paper gives an introduction of the endeavors by National Institute of Standards and Technology (NIST); especially, it overviews the recent progresses on the standardized tests of robotic systems and components. The presented work aims to identify the limitations of existing industrial standards and clarify the trend of technology standardizations for industrial robotic systems.

Posted in: Industrial robots , Tagged:

Symbol grounding through neural networks

September 3, 2021 11:06 ,

Shridhar M, Mittal D, Hsu D., INGRESS: Interactive visual grounding of referring expressions, The International Journal of Robotics Research. January 2020, DOI: 10.1177/0278364919897133.

This article presents INGRESS, a robot system that follows human natural language instructions to pick and place everyday objects. The key question here is to ground referring expressions: understand expressions about objects and their relationships from image and natural language inputs. INGRESS allows unconstrained object categories and rich language expressions. Further, it asks questions to clarify ambiguous referring expressions interactively. To achieve these, we take the approach of grounding by generation and propose a two-stage neural-network model for grounding. The first stage uses a neural network to generate visual descriptions of objects, compares them with the input language expressions, and identifies a set of candidate objects. The second stage uses another neural network to examine all pairwise relations between the candidates and infers the most likely referred objects. The same neural networks are used for both grounding and question generation for disambiguation. Experiments show that INGRESS outperformed a state-of-the-art method on the RefCOCO dataset and in robot experiments with humans. The INGRESS source code is available at https://github.com/MohitShridhar/ingress.

Posted in: Human-robot interaction, Psycho-physiological bases of engineering , Tagged:

Adapting perception to environmental changes explicitly

September 3, 2021 11:02 ,

Sriram Siva, Hao Zhang, Robot perceptual adaptation to environment changes for long-term human teammate following, The International Journal of Robotics Research. January 2020, DOI: 10.1177/0278364919896625.

Perception is one of the several fundamental abilities required by robots, and it also poses significant challenges, especially in real-world field applications. Long-term autonomy introduces additional difficulties to robot perception, including short- and long-term changes of the robot operation environment (e.g., lighting changes). In this article, we propose an innovative human-inspired approach named robot perceptual adaptation (ROPA) that is able to calibrate perception according to the environment context, which enables perceptual adaptation in response to environmental variations. ROPA jointly performs feature learning, sensor fusion, and perception calibration under a unified regularized optimization framework. We also implement a new algorithm to solve the formulated optimization problem, which has a theoretical guarantee to converge to the optimal solution. In addition, we collect a large-scale dataset from physical robots in the field, called perceptual adaptation to environment changes (PEAC), with the aim to benchmark methods for robot adaptation to short-term and long-term, and fast and gradual lighting changes for human detection based upon different feature modalities extracted from color and depth sensors. Utilizing the PEAC dataset, we conduct extensive experiments in the application of human recognition and following in various scenarios to evaluate ROPA. Experimental results have validated that the ROPA approach obtains promising performance in terms of accuracy and efficiency, and effectively adapts robot perception to address short-term and long-term lighting changes in human detection and following applications.

Posted in: Robot sensors , Tagged:

Quantizing a continuous POMDP into a finite MDP to preserve optimality

October 23, 2020 17:37 ,

Naci Saldi; Serdar Yüksel; Tamás Linder, Asymptotic Optimality of Finite Model Approximations for Partially Observed Markov Decision Processes With Discounted Cost, IEEE Transactions on Automatic Control ( Volume: 65, Issue: 1, Jan. 2020), DOI: 10.1109/TAC.2019.2907172.

We consider finite model approximations of discrete-time partially observed Markov decision processes (POMDPs) under the discounted cost criterion. After converting the original partially observed stochastic control problem to a fully observed one on the belief space, the finite models are obtained through the uniform quantization of the state and action spaces of the belief space Markov decision process (MDP). Under mild assumptions on the components of the original model, it is established that the policies obtained from these finite models are nearly optimal for the belief space MDP, and so, for the original partially observed problem. The assumptions essentially require that the belief space MDP satisfies a mild weak continuity condition. We provide an example and introduce explicit approximation procedures for the quantization of the set of probability measures on the state space of POMDP (i.e., belief space).

Posted in: Probability and statistics , Tagged: ,

A universal approximator for the value function in continuous-state VI

October 23, 2020 17:31 ,

William B. Haskell; Rahul Jain; Hiteshi Sharma; Pengqian Yu, TA Universal Empirical Dynamic Programming Algorithm for Continuous State MDPs, IEEE Transactions on Automatic Control ( Volume: 65, Issue: 1, Jan. 2020), DOI: 10.1109/TAC.2019.2907414.

We propose universal randomized function approximation-based empirical value learning (EVL) algorithms for Markov decision processes. The “empirical” nature comes from each iteration being done empirically from samples available from simulations of the next state. This makes the Bellman operator a random operator. A parametric and a nonparametric method for function approximation using a parametric function space and a reproducing kernel Hilbert space respectively are then combined with EVL. Both function spaces have the universal function approximation property. Basis functions are picked randomly. Convergence analysis is performed using a random operator framework with techniques from the theory of stochastic dominance. Finite time sample complexity bounds are derived for both universal approximate dynamic programming algorithms. Numerical experiments support the versatility and computational tractability of this approach.

Posted in: Probability and statistics , Tagged: , ,

Do we prefer that our predictions fit observations -to validate our expectations- or that they surprise us -to acquire new knowledge-?

October 23, 2020 16:59 ,

Clare Press, Peter Kok, Daniel Yon, The Perceptual Prediction Paradox, Trends in Cognitive Sciences, Volume 24, Issue 1, January 2020, Pages 4-6, DOI: 10.1016/j.tics.2019.11.003.

From the noisy information bombarding our senses, our brains must construct percepts that are veridical – reflecting the true state of the world – and informative – conveying what we did not already know. Influential theories suggest that both challenges are met through mechanisms that use expectations about the likely state of the world to shape perception. However, current models explaining how expectations render perception either veridical or informative are mutually incompatible. While the former propose that perceptual experiences are dominated by events we expect, the latter propose that perception of expected events is suppressed. To solve this paradox we propose a two-process model in which probabilistic knowledge initially biases perception towards what is likely and subsequently upweights events that are particularly surprising.

Posted in: Psycho-physiological bases of engineering , Tagged:

Similarities between motor control and cognitive control

October 23, 2020 16:56 ,

Harrison Ritz, Romy Frömer, Amitai Shenhav, Bridging Motor and Cognitive Control: It’s About Time!, Trends in Cognitive Sciences, Volume 24, Issue 1, January 2020, Pages 4-6, DOI: 10.1016/j.tics.2019.11.005.

Is how we control our thoughts similar to how we control our movements? Egger et al. show that the neural dynamics underlying the control of internal states exhibit similar algorithmic properties as those that control movements. This experiment reveals a promising connection between how we control our brain and our body.

Posted in: Psycho-physiological bases of engineering