Sensor Space Research Articles

Simulations for Planning of Liquid Hydrogen Spill Test

In order to better understand the complex pooling and vaporization of a liquid hydrogen spill, Sandia National Laboratories is conducting a highly instrumented, controlled experiment inside their Shock Tube Facility. Simulations were run before the experiment to help with the planning of experimental conditions, including sensor placement and cross wind velocity. This paper describes the modeling used in this planning process and its main conclusions. Sierra Suite’s Fuego, an in-house computational fluid dynamics code, was used to simulate a RANS model of a liquid hydrogen spill with five crosswind velocities: 0.45, 0.89, 1.34, 1.79, and 2.24 m/s. Two pool sizes were considered: a diameter of 0.85 m and a diameter of 1.7. A grid resolution study was completed on the smaller pool size with a 1.34 m/s crosswind. A comparison of the length and height of the plume of flammable hydrogen vaporizing from the pool shows that the plume becomes longer and remains closer to the ground with increasing wind speed. The plume reaches the top of the facility only in the 0.45 m/s case. From these results, we concluded that it will be best for the spacing and location of the concentration sensors to be reconfigured for each wind speed during the experiment.

Three-Dimensional Performance Evaluation of Hemispherical Coriolis Vibratory Gyroscopes

In this paper, the oscillation patterns and characteristics of gyroscopic reaction to rotation-induced Coriolis force and phase relations are reviewed by examining the main principles of operation of Coriolis vibratory gyroscopes based on the dynamic relations and proposed improvements in performance using parameter changes. Coriolis vibratory gyroscopes (CVGs) are among the most modern applicable gyroscopes in position detection that have replaced traditional gyroscopes due to some great features of the design of vibrating proof mass and elastic suspension. Given the key characteristics of capacitive versus piezoelectric excitation technologies for determining the vibration type in sensors, their operating principles and equations have completely changed. Therefore, two-dimensional finite element analysis is required to evaluate their optimal performance. Since the sensor space is constantly vibrating, a general equation is presented in this paper to explain the impact of parameters on the frequency of different operating modes. The main purposes of building vibrating gyroscopes are replacing the constant spinning of the rotor with a vibrating structure and utilizing the Coriolis effect, based on which the secondary motion of the sensitive object is generated according to the external angular velocity.

Theta power and functional connectivity as neurophysiological markers of executive functions in individuals with cognitive complaints in daily life

Impairments in executive functions (EFs) are common across psychological disorders. Research into the neural oscillations underlying EFs has the potential to help understand these impairments and contribute to the development of interventions. The aim of this study is to assess theta power and functional theta connectivity in the sensor space of the regions of the superordinate network for the core EFs: conflict monitoring, response inhibition, set-shifting, and working memory updating. We recruited adults with self-reported everyday EFs complaints and formed two groups: one with attention deficit hyperactivity disorder (ADHD) (n=27) and another without any diagnosis (n=22), and compared them to controls (n=21) on the Stroop, Stop-signal, Switching, and N-back task using EEG. Power and functional connectivity analyses were conducted for four regions of interest: frontal-midline, frontolateral left and right, and parietal region. For all four EFs, the groups showed a dynamical increase in theta power over time in the four regions of interest, as well as in functional theta connectivity between these regions. Group differences were found especially for conflict monitoring, with differences in theta power in the frontal-midline and frontolateral right region. These neural markers are also associated with behavioural performance and complaints in daily life. For set-shifting, group differences were less pronounced and for response inhibition and working memory updating no group differences were observed.

One-Step Crank-Nicolson Direct-Splitting Algorithm with Enhanced Absorption to Evaluate Low-Pressure Discharge for Satellite Sensors in Outer Space.

Low-pressure discharge causes air ionization resulting in performance degeneration or failure for the satellite sensors in outer space. Here, a one-step Crank-Nicolson Direct-Splitting (CNDS) algorithm is proposed to evaluate the electrical behavior of satellite sensors under the low-pressure discharge circumstance. To be more specific, the CNDS algorithm is proposed in the Lorentz medium, which can accurately analyze the ionized air and generated plasma. Higher order perfectly matched layer (PML) is modified in the Lorentz medium to efficiently terminate the unbounded lattice. It can be concluded that the proposed algorithm shows entire considerable performance in the low-pressure discharge evaluation. The proposed PML formulation behaviors enhanced absorbing performance compared with the existing algorithm. Through the experiments, it can be observed that the low-pressure discharge phenomenon causes performance variation, which shows a significant influence on the satellite sensors. Meanwhile, results show considerable agreement between the simulation and experiment results which indicates the effectiveness of the algorithm.

Assessment of Indoor Household Air Quality Using SentinAir's Cost-effective Sensor

According to the World Health Organization, particulate matter (2.5 m) is responsible for more than 4 million deaths worldwide. In real-time, low-cost sensors have assisted in the measurement of PM indoors. SentiAir, a low-cost instrument used in this study, monitors particulate matter (1, 2.5, and 10), as well as nitrogen dioxide, sulphur dioxide, carbon dioxide, ozone, temperature, and relative humidity. The goal of this study was to place the sensor in a typical household indoor space and evaluate all variables for 30 days as an initial investigation assessment. The sensor's proper procedure was strictly observed. PM1 (17.80 µg/m3), PM2.5 (25.21 µg/m3), PM10 (27.61 µg/m3), CO2 (419.7 ppm), O3 (24.75 ppb), NO2 (66.52 ppb), SO2 (48.04 ppb), temperature (34.1 °C), and humidity were the results (mean) (64%). Once those findings were compared to those of the WHO, it was discovered that PM2.5 and PM10 were well within the 24-hour guideline values of 25 and 50 µg/m3, respectively. However, PM2.5 may pose a risk. Temperature and humidity had a significant impact on the PM and gases. Cooking, especially frying and baking, produced a great increment in PM indoors.

Effective seismic force retrieval from surface measurement for SH-wave reconstruction

We present a new method to obtain dynamic body force at virtual interfaces to reconstruct shear wave motions induced by a source outside a truncated computational domain. Specifically, a partial differential equation (PDE)-constrained optimization method is used to minimize the misfit between measured motions at a limited number of sensors on the ground surface and their counterparts reconstructed from optimized forces. Numerical results show that the optimized forces accurately reconstruct the targeted ground motions in the surface and the interior of the domain. The proposed optimization framework yields a particular force vector among other valid solutions allowed by the domain reduction method (DRM). Per this optimized or inverted force vector, the reconstructed wave field is identical to its reference counterpart in the domain of interest but may differ in the exterior domain from the reference one. However, we remark that the inverted solution is valid and introduce a simple post-process that can modify the solution to achieve an alternative force vector corresponding to the reference wave field. We also study the desired sensor spacing to accurately reconstruct the wave responses for a given dominant frequency of interest. We remark that the presented method is omnidirectionally applicable in terms of the incident angle of an incoming wave and is effective for any given material heterogeneity and geometry of layering of a reduced domain. The presented inversion method requires information on the wave speeds and dimensions of only a reduced domain. Namely, it does not need any information on the geophysical profile of an enlarged domain or a seismic source profile outside a reduced domain. Thus, the computational cost of the method is compact even though it leads to the high-fidelity reconstruction of wave response in the reduced domain, allowing for studying and predicting ground and structural responses using real seismic measurements.

An optimized deployment strategy of smart smoke sensors in a large space

An optimized deployment strategy of smart smoke sensors in a large space

Processing Multicomponent Blade Tip Timing Experimental Data by Independent Component Analysis

Abstract Blade tip timing (BTT) is a simple, robust, and nonintrusive method for measuring the rotor blade vibrations in turbomachinery. Using this method, the analysis of vibrations characterized by multiple spectral components typical, for example, of flutter is challenging. This chapter proposes a probabilistic method able to separate and identify multiple harmonic components sampled according to a BTT-like schema. The data are divided into batches of fixed length called snapshots, which are interpreted as realization of random vectors. The statistical properties of the subspace spanned by such random vectors is used to identify the number of components present in the signal (i.e., number of active modes), to separate the components and estimate their frequency and amplitude. These results are obtained by applying sequentially the principal component analysis (PCA), the independent component analysis (ICA), and the harmonic matching (HM). The proposed technique is applied to experimental data obtained from a test rig with fixed disk and traveling load. The time-resolved measurements are resampled according to the sampling pattern induced by specified sensor spacing.

Space-Based Sensor Tasking Using Deep Reinforcement Learning

To maintain a robust catalog of resident space objects (RSOs), space situational awareness (SSA) mission operators depend on ground- and space-based sensors to repeatedly detect, characterize, and track objects in orbit. Although some space sensors are capable of monitoring large swaths of the sky with wide fields of view (FOVs), others—such as maneuverable optical telescopes, narrow-band imaging radars, or satellite laser-ranging systems—are restricted to relatively narrow FOVs and must slew at a finite rate from object to object during observation. Since there are many objects that a narrow FOV sensor could choose to observe within its field of regard (FOR), it must schedule its pointing direction and duration using some algorithm. This combinatorial optimization problem is known as the sensor-tasking problem. In this paper, we developed a deep reinforcement learning agent to task a space-based narrow-FOV sensor in low Earth orbit (LEO) using the proximal policy optimization algorithm. The sensor’s performance—both as a singular sensor acting alone, but also as a complement to a network of taskable, narrow-FOV ground-based sensors—is compared to the greedy scheduler across several figures of merit, including the cumulative number of RSOs observed and the mean trace of the covariance matrix of all of the observable objects in the scenario. The results of several simulations are presented and discussed. Additionally, the results from an LEO SSA sensor in different orbits are evaluated and discussed, as well as various combinations of space-based sensors.

A Soft, Fast and Versatile Electrohydraulic Gripper with Capacitive Object Size Detection

AbstractSoft robotic grippers achieve increased versatility and reduced complexity through intelligence embodied in their flexible and conformal structures. The most widely used soft grippers are pneumatically driven; they are simple and effective but require bulky air compressors that limit their application space and external sensors or computationally expensive vision systems for pick verification. In this study, a multi‐material architecture for self‐sensing electrohydraulic bending actuators is presented that enables a new class of highly versatile and reconfigurable soft grippers that are electrically driven and feature capacitive pick verification and object size detection. These electrohydraulic grippers are fast (step input results in finger closure in 50 ms), draw low power (6.5 mW per finger to hold grasp), and can pick a wide variety of objects with simple binary electrical control. Integrated high‐voltage driving electronics are presented that greatly increase the application space of the grippers and make them readily compatible with commercially available robotic arms.

Combined sensor selection and node location optimization for reducing the localization uncertainties in wireless sensor networks

Local Positioning Systems (LPS) address the limitations of Global Navigation Satellite Systems in harsh environments. LPS must attain the optimal location of their sensors in space for optimal performance, which is known as the Node Location Problem. In addition, not all the sensors under coverage present the best performance for determining the location. For this purpose, the optimal selection of the best combination of nodes requires addressing the Sensor Selection Problem. In this paper, for the first time in the literature, we present a methodology for the combined optimization of both problems which may attain improved results than separated optimizations. Results show that the consideration of SSP based sensor selection strategies during the NLP suppose a reduction up to 10% in the localization uncertainties with respect to traditional baseline sensor selection strategies of the NLP, thus proving the effectiveness of the devised technique.

Design and analysis of Hermite function‐based artificial neural network controller for performance enhancement of photovoltaic‐integrated grid system

AbstractThis article proposes a Hermite function‐based artificial neural network (HeANN) controller for shunt compensation in photovoltaic (PV) interfaced grid system. Various power quality (PQ) issues such as load change, poor power factor, reactive power demand and current harmonics are experienced by distribution system. The proposed HeANN algorithm is utilized to improve the PQ issues of the distribution system. Shunt active power filter (SAPF) is operated in current control mode and injects the compensating current at the point of common coupling (PCC). The inverter is connected with the green energy resource, that is, PV at its direct current terminals and contributes its power to fulfil the load active power demand. The parameters of HeANN are trained in real time. A prototype hardware of SAPF is developed using sensor circuits and space 1104. The simulation results are validated by experimental results. The comparative analysis of the results establishes the superiority of the proposed control technique to other control techniques such as synchronous reference frame, second order generalized integrator and back propagation neural network in terms of convergence, computational burden and total harmonic distortion of grid current.

Stabilization by switching of parabolic PDEs with spatially scheduled actuators and sensors

This paper addresses a switched sampled-data control design for stabilization of Kuramoto-Sivashinsky equation under the Dirichlet/periodic boundary conditions with spatially scheduled actuators. It is supposed that discrete-time point-like or averaged measurements are available. The system is known to be stabilizable by static output-feedback employing several distributed in space actuators and sensors, but is not stabilizable by only one of the actuator–sensor pairs. Does there exist a switching stabilizing static output-feedback such that at all times, only one actuator–sensor pair is active? We give a positive answer and find the appropriate switching sampled-data control law. The proposed switching controller can be implemented either by N actuators and sensors placed in each subdomain (here switching control may reduce the energy that the system spends) or by using one actuator–sensor pair that can move to the active subdomain. For implementation of the control law by moving actuators and sensors, we take into account a moving time by treating it as an additional switching between the open-loop system and the closed-loop switched system. The guidance of active (or mobile) actuators and sensors is provided by using output-dependent switching. Constructive conditions are derived to ensure that the resulting closed-loop system is regionally stable by means of the Lyapunov approach. Numerical example illustrates the efficiency of the method.

Localization of beta power decrease as measure for lateralization in pre-surgical language mapping with magnetoencephalography, compared with functional magnetic resonance imaging and validated by Wada test.

Objective: Atypical patterns of language lateralization due to early reorganizational processes constitute a challenge in the pre-surgical evaluation of patients with pharmaco-resistant epilepsy. There is no consensus on an optimal analysis method used for the identification of language dominance in MEG. This study examines the concordance between MEG source localization of beta power desynchronization and fMRI with regard to lateralization and localization of expressive and receptive language areas using a visual verb generation task. Methods: Twenty-five patients with pharmaco-resistant epilepsy, including six patients with atypical language lateralization, and ten right-handed controls obtained MEG and fMRI language assessment. Fourteen patients additionally underwent the Wada test. We analyzed MEG beta power desynchronization in sensor (controls) and source space (patients and controls). Beta power decrease between 13 and 35 Hz was localized applying Dynamic Imaging of Coherent Sources Beamformer technique. Statistical inferences were grounded on cluster-based permutation testing for single subjects. Results: Event-related desynchronization of beta power in MEG was seen within the language-dominant frontal and temporal lobe and within the premotor cortex. Our analysis pipeline consistently yielded left language dominance with high laterality indices in controls. Language lateralization in MEG and Wada test agreed in all 14 patients for inferior frontal, temporal and parietal language areas (Cohen's Kappa = 1, p < 0.001). fMRI agreed with Wada test in 12 out of 14 cases (85.7%) for Broca's area (Cohen's Kappa = 0.71, p = 0.024), while the agreement for temporal and temporo-parietal language areas were non-significant. Concordance between MEG and fMRI laterality indices was highest within the inferior frontal gyrus, with an agreement in 19/24 cases (79.2%), and non-significant for Wernicke's area. Spatial agreement between fMRI and MEG varied considerably between subjects and brain regions with the lowest Euclidean distances within the inferior frontal region of interest. Conclusion: Localizing the desynchronization of MEG beta power using a verb generation task is a promising tool for the identification of language dominance in the pre-surgical evaluation of epilepsy patients. The overall agreement between MEG and fMRI was lower than expected and might be attributed to differences within the baseline condition. A larger sample size and an adjustment of the experimental designs are needed to draw further conclusions.

Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces.

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.

The Limited Aperture Sparse Array for DOA Estimation of Coherent Signals: A Mutual-Coupling-Optimized Array and Coherent DOA Estimation Algorithm

The paper investigates DOA estimation of coherent Signals with the limited aperture sparse array. Mutual coupling between the sensors of the array cannot be ignored in practical radar with a limited aperture of array sensors, which will result in a degradation in the performance of Direction of Arrival (DOA) estimation. This paper proposes a Mutual-coupling-optimized array (MCOA) with a limited aperture in this scenario to reduce the mutual coupling effect. Firstly, we prove the sparse uniform linear array (SULA) has the smallest mutual coupling leakage when the array aperture and the number of sensors is determined. Secondly, we modify the spacing of the array sensors in SULA to make sure that the spacing between all array sensors and the reference sensor are coprime aiming to estimate DOA without spatial aliasing. Thirdly, we give an expression for the array element spacing arrangement with reduced mutual coupling leakage. Finally, the coherent signals are well resolved by the Sparse Bayesian Learning (SBL) algorithm. Numerous simulations are conducted to validate the advantages of the proposed array compared to several sparse arrays for estimating coherent signals in the presence of mutual coupling.

Examining localization error in MEG for the complex auditory brainstem response (cABR) across early development

The complex auditory brainstem response (cABR) has gained wide research interest over the last decade as it is argued to reflect early sensory encoding of complex sounds, such as speech, along the auditory pathway. Recent adoption of cABR recording in MEG, instead of 3 EEG electrodes, allows further examination of the underlying sources. In adults, while the onset component of cABR to transient events (e.g., consonants) can be reliably localized to the auditory brainstem, the frequency-following component to longer periodic events (e.g., vowels, lexical tones) may contain contribution from auditory cortex. This study thus expands to evaluate the localization accuracy of both onset response and FFR through simulation, both of which are crucial for speech. Simulated source activities are placed in the auditory brainstem versus superior temporal gyrus. Using the forward model, the activities are projected to MEG sensor space with realistic room noise and head movement. The simulated sensor space signal then undergoes inverse modeling to calculate source activation. Differences between the simulated source activity versus calculated source activity are calculated to reflect localization accuracy. This process is repeated using infant head models at 3, 6, 9, and 12 months and adult head model to examine changes across ages.

Optimal sensor spacing in IoT network based on quantum computing technology

Quantum computing has gained an advantage in recent years due to its compatibility and usage in various fields. The IoT environment is huge, and almost the whole world relies on it to acquire efficient data transmissions. Combining quantum computing with the IoT improves the system’s performance to a drastic level. The proposed technique focuses on introducing quantum computing into the IoT to optimise the sensor space without degrading the sensing ability of the sensors. To optimise the sensor space, the Quantum Computing based Rider Optimisation (QCRO) is introduced with optimal global search behaviour inspired by the Rider Optimisation Algorithm (ROA). After optimising sensor space, the Glued Tree based on Continuous quantum Walks (GTCW) is used to minimise the energy loss in the network. The continuous quantum walks are introduced into the Glued Trees (GT) to find the optimal path that can provide a minimised error rate. The experimental results demonstrated the effectiveness of the proposed approach against the other existing techniques in terms of data accuracy, data temporal efficiency and data cost.

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

It is a challenging task to track objects moving along an unknown trajectory. Conventional model-based controllers require detailed knowledge of a robot’s kinematics and the target’s trajectory. Tracking precision heavily relies on kinematics to infer the trajectory. Control implementation in parallel robots is especially difficult due to their complex kinematics. Vision-based controllers are robust to uncertainties of a robot’s kinematic model since they can correct end-point trajectories as error estimates become available. Robustness is guaranteed by taking the vision sensor’s model into account when designing the control law. All camera space manipulation (CSM) models in the literature are position-based, where the mapping between the end effector position in the Cartesian space and sensor space is established. Such models are not appropriate for tracking moving targets because the relationship between the target and the end effector is a fixed point. The present work builds upon the literature by presenting a novel CSM velocity-based control that establishes a relationship between a movable trajectory and the end effector position. Its efficacy is shown on a Delta-type parallel robot. Three types of experiments were performed: (a) static tracking (average error of 1.09 mm); (b) constant speed linear trajectory tracking—speeds of 7, 9.5, and 12 cm/s—(tracking errors of 8.89, 11.76, and 18.65 mm, respectively); (c) freehand trajectory tracking (max tracking errors of 11.79 mm during motion and max static positioning errors of 1.44 mm once the object stopped). The resulting control cycle time was 48 ms. The results obtained show a reduction in the tracking errors for this robot with respect to previously published control strategies.

3D sensor network location spatial positioning technology based on machine learning

Abstract The purpose of this paper is to combine machine learning to locate the 3D sensor network space. Real life is mostly a three-dimensional environment. Whether it is a factory in manufacturing or a vegetation base in agriculture, it needs to be monitored and positioned. In this paper, the localization algorithm is discussed to a certain extent. This paper firstly introduces the relevant background and organizes related work. It also wrote related algorithms, such as ranging-based positioning algorithms in the free space of wireless sensors. It shows the positioning link by introducing the wireless sensor network structure system and node structure. And this paper summarizes the Bounding-box Method positioning principle, TDOA algorithm principle, and TDOA positioning principle. It then describes the gradient boosting tree classification algorithm based on machine learning, and focuses on the admiral boosting tree classification algorithm related to the experiment. This paper also describes the ranging technology combining RSSI algorithm and DV-Hop algorithm in three-dimensional space, and mentions two algorithms of RSSI and DV-Hop. In the fourth part, the machine learning coordinate prediction accuracy improvement experiment and the three-dimensional space positioning algorithm optimization experiment and result analysis are carried out. It is proved by experiments that the model evaluation effect of the gradient boosting tree classification algorithm in machine learning is the best. It can be applied to the calculation of relative position coordinates of label nodes. It then carried out the three-dimensional positioning effect test experiment of IDV-Hop algorithm. This shows that when the network density in the experimental environment reaches more than 12, the localization coverage of IDV-Hop algorithm and DV-Hop algorithm are both higher than 91%. Finally, the hybrid algorithm of RSSI and DV-Hop algorithm is used to compare the positioning accuracy, positioning coverage and bad node rate with these two algorithms. It draws the stability of the hybrid algorithm and its effects, and finally discusses and summarizes the experiments.