The Practical Application of Bio-Inspired PMA for the Detection of Partial Discharges in High Voltage Equipment.

In this paper, the practical application of a bio-inspired antenna for partial discharge (PD) detection in high voltage equipment was evaluated in order to validate the efficiency of using this technology for PD monitoring purposes. For this, PD measurements using the bio-inspired antenna were performed on operational 69 kV potential transformers (PT) in a real substation. After the field experiment, laboratory measurements using the IEC 60270 standard method and a bio-inspired antenna were performed, simultaneously, over the evaluated PT. The results obtained at the substation indicated suspicious frequencies of partial discharge activity in two out of three evaluated potential transformers, mainly for the frequencies of 461 MHz, 1366 MHz, 1550 MHz and 1960 MHz. During the laboratory tests, the presence of partial discharge activity over the suspicious potential transformers was confirmed with the detection of PD apparent charge levels above 20 pC. Finally, the frequency spectrum obtained from the PD signals detected by the bio-inspired antenna in the laboratory presented similar frequency values to those obtained during the practical application at the substation, making it a promising indicator for future defect classification studies using artificial intelligence.

Corrigendum: Ring attractor bio-inspired neural network for social robot navigation.

[This corrects the article DOI: 10.3389/fnbot.2023.1211570.].

Ring attractor bio-inspired neural network for social robot navigation.

Introduction: We introduce a bio-inspired navigation system for a robot to guide a social agent to a target location while avoiding static and dynamic obstacles. Robot navigation can be accomplished through a model of ring attractor neural networks. This connectivity pattern between neurons enables the generation of stable activity patterns that can represent continuous variables such as heading direction or position. The integration of sensory representation, decision-making, and motor control through ring attractor networks offers a biologically-inspired approach to navigation in complex environments. Methods: The navigation system is divided into perception, planning, and control stages. Our approach is compared to the widely-used Social Force Model and Rapidly Exploring Random Tree Star methods using the Social Individual Index and Relative Motion Index as metrics in simulated experiments. We created a virtual scenario of a pedestrian area with various obstacles and dynamic agents. Results: The results obtained in our experiments demonstrate the effectiveness of this architecture in guiding a social agent while avoiding obstacles, and the metrics used for evaluating the system indicate that our proposal outperforms the widely used Social Force Model. Discussion: Our approach points to improving safety and comfort specifically for human-robot interactions. By integrating the Social Individual Index and Relative Motion Index, this approach considers both social comfort and collision avoidance features, resulting in better human-robot interactions in a crowded environment.

Bio-Inspired Autonomous Navigation and Formation Controller for Differential Mobile Robots.

This article proposes a decentralized controller for differential mobile robots, providing autonomous navigation and obstacle avoidance by enforcing a formation toward trajectory tracking. The control system relies on dynamic modeling, which integrates evasion forces from obstacles, formation forces, and path-following forces. The resulting control loop can be seen as a dynamic extension of the kinematic model for the differential mobile robot, producing linear and angular velocities fed to the mobile robot's kinematic model and thus passed to the low-level wheel controller. Using the Lyapunov method, the closed-loop stability is proven for the non-collision case. Experimental and simulated results that support the stability analysis and the performance of the proposed controller are shown.

Design and Biomimicry: A Review of Interconnections and Creative Potentials.

The study and application of biological knowledge favor the creation of innovative projects in several areas, so it is necessary to better understand the use of these resources specifically in the field of design. Thus, a systematic review was undertaken to identify, describe, and analyze the contributions of biomimicry to design. For this purpose, the integrative systematic review model, called the Theory of Consolidated Meta-Analytical Approach, was used, carrying out a search on the Web of Science with the descriptors "design" and "biomimicry". For the period from 1991 to 2021, 196 publications were retrieved. The results were organized according to areas of knowledge, countries, journals, institutions, authors, and years. Citation, co-citation, and bibliographic coupling analyses were also performed. The investigation highlighted the following research emphases: the conception of products, buildings, and environments; the exploration of natural structures and systems to create materials and technologies; the use of biomimetic creative tools in product design; and projects focused on saving resources and implementing sustainability. It was noted that there was a tendency for authors to adopt a problem-based approach. It was concluded that the study of biomimicry can stimulate the development of multiple skills in design, improving creativity, and enhancing the potential integration of sustainability into production cycles.

A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification.

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.

Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot.

The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural networks, modulation neural networks, and nonlinear oscillation circuits, in order to make the automaton able to avoid obstacles and explore simulated and real environments. The performance achieved by using biologically inspired neural networks to solve the task at hand is similar to that of several works mentioned in the specialized literature. Furthermore, this work contributed to bridging the fields of computational neuroscience and robotics.

Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load.

This work aims to analyze two metaheuristics optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with six variations each, and compare them regarding their convergence, quality, and dispersion of solutions. The optimization target is the Gaussian Adaptive PID control (GAPID) to find the best parameters to achieve enhanced performance and robustness to load variations related to the traditional PID. The adaptive rule of GAPID is based on a Gaussian function that has as adjustment parameters its concavity and the lower and upper bound of the gains. It is a smooth function with smooth derivatives. As a result, it helps avoid problems related to abrupt increases transition, commonly found in other adaptive methods. Because there is no mathematical methodology to set these parameters, this work used bio-inspired optimization algorithms. The test plant is a DC motor with a beam with a variable load. Results obtained by load and gain sweep tests prove the GAPID presents fast responses with very low overshoot and good robustness to load changes, with minimal variations, which is impossible to achieve when using the linear PID.

Hydrodynamic cavitation through a bio-inspired fast-closing plunger mechanism: experiments and simulations.

Experimental and numerical results are reported for the internal and external flow fields evolving in a bio-inspired snapping plunger. The experimental evidence underlines the nature of the dynamic-coupling between the processes taking place inside and outside the device. Two main structures dictate the properties of the external flow field: a strong jet which is followed by a vortex ring. Internally, complex patterns of cavitating structures are simultaneously produced in the chamber and the venturi-like conduit. We find the cavitation cycle to be suitably described by the Rayleigh-Plesset model and, thus, proceed to characterize the coupling of both fields in terms of the fluctuations of the velocity. All main parameters, as well as the energy released to the fluid during the collapse, are found to be within the same order-of-magnitude of previously known experimental results for isolated bubbles of comparable size.

TiO2NTs bio-inspired coatings: revisiting electrochemical, morphological, structural, and mechanical properties.

By altering some synthesis variables, the morphology and structural properties of anodic TiO2nanotube arrays (TiO2NTs) can be tailored to a specific application. This study aims to investigate the effect of electrolyte-containing ions from human plasma and annealing temperature on structural, morphological, and mechanical parameters of TiO2NTs films, targeting its potential biomedical applications. Bio-inspired TiO2NTs were grown from Ticpand its Ti6Al4V alloy by potentiostatic anodization in the recently developed SBF-based electrolyte, maintained at 10 °C and 40 °C. The thermal investigation was performed by TGA/DSC and used to define the phase transition temperatures used for annealing (450 °C and 650 °C). Morphological and structural parameters were evaluated by FE-SEM, XRD, contact angle measurements, and nanoindentation. Results show that self-organized as-formed TiO2NTs were grown under all synthesis conditions with different wettability profiles for each substrate group. At 450 °C annealing temperature, the beginning of nanostructures collapse starts, becoming evident at 650 °C. The nanoindentation characterization reveals that both electrolyte and thermal annealing exhibited low effects on the hardness and Young's modulus. The tailoring of specific properties by different synthesis conditions could allow the individualization of treatments and better performancein vivo.

Remote RF excitation for small-bore MR imager at 15.2 T.

We report a method for remote excitation of the RF signal for preclinical-equivalent ultra high field Magnetic Resonance Imaging (MRI). A parallel-plate waveguide together with a bio-inspired surface coil were used to perform remote excitation experiments to acquire images with a small-bore MR imager at 15.2 T. The imager bore size limits the RF coil transmitter dimensions, so the Gielis super-formula was used to design an RF coil with small dimensions. Electromagnetic simulations of the principal mode were run to study the waveguide filled with air and loaded with a saline solution-filled tube. Radiation patterns were also computed in a semi-anechoic chamber for the same scenarios as above. A saline solution-filled spherical phantom and a formaldehyde-fixed mouse phantom were used to acquire images. Radiation patterns showed an omnidirectional distribution with no side lobes, and a very smooth behaviour with almost no loss of information in the saline solution-filled tube and without. The theoretical wave impedance was calculated and compared with simulated results showing an excellent correspondence. Spherical phantom image data and simulation results of B1 were contrasted and showed an important correlation. Ex vivo mouse images were of high quality and exhibited clear delineation of anatomical structures. These imaging results are in very good agreement with the simulations. Numerical, theoretical and experimental results validate this approach, using a bio-inspired surface coil with a simple waveguide for preclinical small-bore MRI at ultra high field.

3D Hermite Transform Optical Flow Estimation inLeft Ventricle CT Sequences.

Heart diseases are the most important causes of death in the world and over the years, thestudy of cardiac movement has been carried out mainly in two dimensions, however, it is important toconsider that the deformations due to the movement of the heart occur in a three-dimensional space.The 3D + t analysis allows to describe most of the motions of the heart, for example, the twistingmotion that takes place on every beat cycle that allows us identifying abnormalities of the heartwalls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiacmovement. In this work, we developed a new approach to determine the cardiac movement inthree dimensions using a differential optical flow approach in which we use the steered Hermitetransform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model ofthe human vision system (HVS). Our proposal was tested in complete cardiac computed tomography(CT) volumes ( 3D + t), as well as its respective left ventricular segmentation. The robustness tonoise was tested with good results. The evaluation of the results was carried out through errors inforwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained(interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, theinterpolation errors and normalized interpolation errors are very close and below the values reportedin ground truth flows. In the case of the 3D algorithm, the results were compared with another similarmethod in 3D and the interpolation errors remained below 0.1. These results of interpolation errorsfor complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a seriesof graphs are observed where the characteristic of contraction and dilation of the left ventricle isevident through the representation of the 3D optical flow.

Design and Application of a Metamaterial Superstrate on a Bio-Inspired Antenna for Partial Discharge Detection through Dielectric Windows.

The adaptation of dielectric windows as metamaterial superstrate over a bio-inspired Printed Monopole Antenna (PMA) was evaluated in order to improve the detection sensitivity of Ultra High Frequency (UHF) sensors designed for Partial Discharge (PD) measurement. For this purpose, rectangular and circular Split Ring Resonators (SRR) structures were designed and evaluated aiming to achieve a metamaterial superstrate that improves the characteristics of the bio-inspired PMA as the gain, bandwidth, and radiation pattern. Measurements of the PMA with metamaterial superstrate were carried out in an anechoic chamber and compared to the simulations performed. The results show that the metamaterial superstrate insertion did not impact the original operating bandwidth, covering most of the characteristic frequency range of PD activity. Moreover, this insertion resulted in a mean gain enhancement of 0.7 dBi regarding the reference PMA, resulting in an antenna with better sensitivity for PD detection (mean gain of 3.61 dBi). The PMA-metamaterial set PD detection sensitivity was evaluated through laboratory tests with a point-to-plane PD generator setup and in field with measurements from a 230 kV current transformer. The developed PMA-metamaterial set was able to detect, successfully, the activity of PD for both tests, being classified as an optimized sensor for PD detection through dielectric windows.

Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks.

One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods.

Design and Development of a Bio-Inspired UHF Sensor for Partial Discharge Detection in Power Transformers.

In this paper, the design and development of a bio-inspired UHF sensor for partial discharge detection in power transformers is presented. The UHF sensor was developed for external use in dielectric windows of power transformers. For this purpose, a microstrip antenna was designed with a radiating element shape based on the leaf of the Jatropha mollissima (Pohl) Baill plant. Then, an epoxy coating and an aluminium enclosure were developed to protect the antenna against corrosion and to provide mechanical support, external noise immunity, and a lifetime compatibility with power transformers. In order to verify the electrical parameters of the developed sensor, measurements of the gain and the reflection coefficient were performed in an anechoic chamber. Lastly, the antenna sensitivity for denominated partial discharge (PD) detection was compared with the IEC 60270 standard method. For this purpose, simultaneous tests were carried out in a partial discharge generator setup, composed of an oil cell with needle-plane electrodes. The experimental tests demonstrated the effectiveness of the sensor for detecting PD signals with apparent charge values higher than 35 pC.

Bio-inspired Printed Monopole Antenna Applied to Partial Discharge Detection.

A new, bio-inspired printed monopole antenna (PMA) model is applied to monitor partial discharge (PD) activity in high voltage insulating systems. An optimized sensor was obtained by designing a PMA in accordance with the characteristics of the electromagnetic signal produced by PD. An ultra-wideband (UWB) antenna was obtained by applying the truncated ground plane technique. The patch geometry was bio-inspired by that of the Inga Marginata leaf, resulting in a significant reduction in size. To verify the operating frequency and gain of the PMA, measurements were carried out in an anechoic chamber. The results show that the antenna operating bandwidth covers most of the frequency range of PD occurrence. Moreover, the antenna presented a good sensitivity (mean gain of 3.63 dBi). The antenna performance was evaluated through comparative results with the standard IEC 60270 method. For this purpose, simultaneous tests were carried out in a PD generator arrangement, composed by an oil cell with point-to-plane electrode configurations. The developed PMA can be classified as an optimized sensor for PD detection and suitable for substation application, since it is able to measure PD radiated signals with half the voltage levels obtained from the IEC method and is immune to corona discharges.

A novel bio-inspired optimization model based on Yellow Saddle Goatfish behavior.

Several species of fish live in groups to increase their foraging efficiency and reproduction rates. Such groups are considered self-organized since they can adopt different cooperative actions without the presence of an apparent leader. One of their most interesting collaborative behaviors found in fish is the hunting strategy presented by the Yellow Saddle Goatfish (Parupeneus cyclostomus). In this strategy, the complete group of fish is distributed in subpopulations to cover the whole hunting region. In each sub-population, all fish participate collectively in the hunt considering two different roles: chaser and blocker. In the hunt, a chaser fish actively tries to find the prey in a certain area whereas a blocker fish moves spatially to avoid the escape of the prey. In this paper, we develop the hunting model of Yellow Saddle Goatfish, which at some abstraction level can be characterized as a search strategy for optimization proposes. In the approach, different computational operators are designed in order to emulate this peculiar hunting behavior. With the use of this biological model, the new search strategy improves the optimization results in terms of accuracy and convergence in comparison to other popular optimization techniques. The performance of this method is tested by analyzing its results with other related evolutionary computation techniques. Several standard benchmark functions commonly used in the literature were considered to obtain optimization results. Furthermore, the proposed model is applied to solve certain engineering optimization problems. Analysis of the experimental results exhibits the efficiency, accuracy, and robustness of the proposed algorithm.

A global optimization algorithm inspired in the behavior of selfish herds.

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems.

Performance Characteristics of Bio-Inspired Metal Nanostructures as Surface-Enhanced Raman Scattered (SERS) Substrates.

The fabrication of high-performance plasmonic nanomaterials for bio-sensing and trace chemical detection is a field of intense theoretical and experimental research. The use of metal-silicon nanopillar arrays as analytical sensors has been reported with reasonable results in recent years. The use of bio-inspired nanocomposite structures that follow the Fibonacci numerical architecture offers the opportunity to develop nanostructures with theoretically higher and more reproducible plasmonic fields over extended areas. The work presented here describes the nanofabrication process for a series of 40 µm × 40 µm bio-inspired arrays classified as asymmetric fractals (sunflower seeds and romanesco broccoli), bilaterally symmetric (acacia leaves and honeycombs), and radially symmetric (such as orchids and lily flowers) using electron beam lithography. In addition, analytical capabilities were evaluated using surface-enhanced Raman scattering (SERS). The substrate characterization and SERS performance of the developed substrates as the strategies to assess the design performance are presented and discussed.

Analytical development of a binuclear oxo-manganese complex bio-inspired on oxidase enzyme for doping control analysis of acetazolamide.

A bio-inspired electrochemical sensor using a binuclear oxo-manganese complex was evaluated and applied in the detection of a substance associated with doping in sports: acetazolamide (ACTZ). Investigation was made of the influence of different experimental variables on the electrocatalytic oxidation of ACTZ by the bio-inspired sensor, such as pH and interfering species. The bio-inspired sensor showed the best response in the range from 5.00×10(-9) to 7.00×10(-8) mol L(-1) ACTZ, with a linear range from 5.00×10(-9) to 2.50×10(-8) mol L(-1) and a detection limit of 4.76×10(-9) mol L(-1). The sensor exhibited characteristics similar to the Michaelis-Menten model of an enzymatic electrode, due to the use of a multinucleated complex of manganese with µ-oxo units, which was able to mimic the properties of enzymes with manganese as a cofactor in their composition, such as Mn-containing oxidase. The determination of ACTZ with the bio-inspired sensor was evaluated using three different synthetic biological fluids (plasma, saliva, and urine), demonstrating its viability for use with real samples. The analysis of ACTZ in real urine samples using the bio-inspired sensor, simulating the method adopted by the World Anti-Doping Agency, which revealed viable, suggesting a new and promising platform to be used in these analysis.