Optimal Sensor Position Research Articles

Silent Speech Recognition with Strain Sensors and Deep Learning Analysis of Directional Facial Muscle Movement.

Silent communication based on biosignals from facial muscle requires accurate detection of its directional movement and thus optimally positioning minimum numbers of sensors for higher accuracy of speech recognition with a minimal person-to-person variation. So far, previous approaches based on electromyogram or pressure sensors are ineffective in detecting the directional movement of facial muscles. Therefore, in this study, high-performance strain sensors are used for separately detecting x- and y-axis strain. Directional strain distribution data of facial muscle is obtained by applying three-dimensional digital image correlation. Deep learning analysis is utilized for identifying optimal positions of directional strain sensors. The recognition system with four directional strain sensors conformably attached to the face shows silent vowel recognition with 85.24% accuracy and even 76.95% for completely nonobserved subjects. These results show that detection of the directional strain distribution at the optimal facial points will be the key enabling technology for highly accurate silent speech recognition.

Compressive sampling - based extrapolation of free surface displacement data from pressure measurements

This article deals with the problem of extrapolating the time history of the free surface displacement at a given point from wave pressure head records. This problem is relevant to marine engineering applications, where, for instance, the monitoring of marine structures must be arranged without resorting to expensive measurement techniques, or sensor failures must be circumvented through adequate signal processing techniques. Specifically, a Compressive Sensing (CS) based technique is developed by resorting to the concept of sparsity, which is implemented through formulating and solving a L1 – norm minimization problem.Relevant numerical examples show that the proposed CS approach allows extrapolating reliable free surface displacement time histories. Further, the data extrapolated via CS are in better agreement with target free surface data than standard Fourier - based approaches, while possessing the desirable feature of removing the subjective choice of a cut-off frequency requested by the standard techniques. CS limits are highlighted in terms of required optimal pressure sensor position below the mean water level, and of reliability in extrapolating the profiles of steep waves.

Pulsed Demand Modeling for the Optimal Placement of Water Quality Sensors in Water Distribution Networks

AbstractThis paper deals with the issue of the consistency of water distribution network (WDN) modeling concerning the nature of demand for the optimal water quality sensor placement. In this regard, a biobjective optimization for the sensor placement is carried out by minimizing the contamination extension or the contaminated population in the trade‐off with the number of sensors installed, by considering two alternative demand modeling conditions, that is, smooth and pulsed demand, respectively. The real layout of the two Italian WDNs is considered for the application of the methodology. The results show that for a given placement of sensors, the contamination extension always increases when the pulsed demand is considered. Furthermore, optimal sensor positions are affected by the pulsed demand especially in the case of highly looped WDN layouts and when the extent of contamination is used as objective function.

Design and Fabrication of Soft 3D Printed Sensors and Performance Analysis of the Soft Sensors in a C-leg as Sensing Element

In soft robotics, a recent challenge is to decrease the number of rigid components used tocreate entirely soft robots. A common rigid component used in soft robots is the rigid encoder, which should be replaced with a soft counterpart if possible. In this work, we de-sign and manufacture a soft sensor, which is embedded into a C-shaped leg of a soft, legged, miniature robot. Our main goal is to show that we can embed a soft sensor into and receive contact feedback from a soft C-shaped leg of our soft miniature quadruped. We test various sensor parameters using custom test setups to analyze the soft sensor performance. Our soft sensor design is iterated by experimentally investigating several sensor shape options. For the C-leg of the soft miniature quadruped, optimal sensor geometry and position for the sensor implementation are found from a discrete design space as the outcome of this work. We received feedback from the soft sensor and compared commercial encoder data to the soft sensor embedded C-leg data. We managed to detect the rotation speed of the C-leg with the accuracy of 87.5% on a treadmill and with the accuracy of %86.7 under free rotation of the C-leg. However, if connection loss occurs in the miniature slipring mechanism, the error percentage in estimating the rotational speed increases significantly.

Theoretical and experimental research on a new defrosting control strategy based on differential pressure sensor

During system operation, frosting of the (heat pump and refrigerating) evaporator is inevitable. As such, the determination of the defrosting start time is integral for maintaining efficient and stable operation of the system. In the present study, a dimensionless parameter, pressure loss factor (PLF), was proposed based on the differential pressure sensor to determine the defrosting start time. For PLF, only the pressure difference before and after the evaporator needs to be measured, allowing for the growth trend of the frost layer on the evaporator to be quantitatively described. Firstly, through preliminary experiments, the optimal layout position of the differential pressure sensor was determined. Subsequently, the optimal defrosting time and PLF start threshold were determined based on the heat transfer rate of the evaporator. Under different experimental conditions, frosting experiments were conducted to validate the reliability of the proposed method. The results reveal that the optimal measuring point of the differential pressure sensor of the evaporator (with double fans) was on the central axis. The average error between the pressure difference at the measuring point and the average pressure difference of the evaporator was 2.76% under different experimental conditions. By using the PLF defrosting control method, the defrosting start time was accurately determined with the maximum error of ±5.5%.

Robustness of Optimal Sensor Methods in Dynamic Testing–Comparison and Implementation on a Footbridge

One of the objectives of structural health monitoring (SHM) is to maximize the information while keeping the number of sensors, and consequently the cost of the sensor system, to a minimum. Besides, the sensor configurations must be robust in the sense that the feasibility of small errors inherent to the process must not lead to large variations in the final results. This paper presents novelties regarding the robustness evaluation to model and measurement errors of four of the most influential optimal sensor placement (OSP) methods: the modal kinetic energy (MKE) method; the effective independence (EFI) method; the information entropy index (IEI) method; and the MinMAC method. The four OSP methods were implemented on the Streicker Bridge, a footbridge located on the Princeton University Campus, to identify five mode shapes of the bridge. The mode shapes, obtained in a FE model’s modal analysis, were used as input data for the OSP analyses. The study indicates that the MKE method seems to be the most suitable method to estimate the optimal sensor positions: it provides a relatively large amount of information with the lowest computational time, and it outperforms the other three methods in terms of robustness in the usual range of number of sensors.

Analysing local failure scenarios to assess the robustness of steel truss-type bridges

Many of the steel bridge collapses occur in truss-type bridges. This is, in fact, the focus of this study involving an assessment of the robustness of this type of structures based on an actual bridge that the authors had extensively monitored and controlled. Robustness was assessed by means of computer simulations of various Damage Scenarios (DSs) to analyse the structural bridge capacity to efficiently activate Alternative Load Paths (ALPs). The computational models have been previously validated with the results of load tests on the bridge and a laboratory test on a full-scale bridge span. The DSs have considered a series of non-simultaneous failures in different elements. The results indicate that the structure is capable of not triggering a disproportionate collapse after each of the DSs with the help of the efficient activation of ALPs that required the contribution of other elements with extra-strength capacity as well as from the superstructure and the joints working under bending moments. The results were used as the basis for practical recommendations for: i) the design of new steel bridges and the retrofit of existing ones and ii) monitoring the structure for the optimal position of sensors to predict local failures that could spread to the rest of the bridge.

Bioinspired dynamic soaring simulation system with distributed pressure sensors.

Inspired by the albatross, this paper presents the construction of a dynamic soaring simulation system with distributed pressure sensors. The advantage of our system lies in harvesting energy from the wind shear layer and estimating the wind information using a pressure-based sensor system. Specifically, the dynamic soaring simulation system contains an offline training stage and an online estimation and control stage. In the offline training stage, computational fluid dynamics simulations are conducted and used as the data source. A surrogate model is established to correlate the local flow conditions and the surface pressure at optimal sensor positions. In the online estimation and control stage, through sensing the pressure information, the real-time wind velocity and wind gradient are estimated by the surrogate model trained in the offline stage. Moreover, wind information is adopted in the simulation of dynamic soaring control. In this study, the simulation system was applied to linear and circular path-following tasks. It was found that the dynamic soaring simulation system with distributed pressure sensors provides an acceptable estimation of wind velocity and wind gradient with a certain time delay caused by numerical differentiation.

Optimal sensor placement methodology of hydraulic control system for fault diagnosis

During the state monitoring and fault diagnosis of hydraulic control system, different kinds of sensors are used to collect fault signals. The arrangement of a limited number of sensors in the most reasonable positions of the hydraulic system, that is, to solve the problem on the optimal placement of sensors, is the key to improving efficiency of fault diagnosis. Aiming at fault diagnosis of hydraulic control system, this paper proposes a sensor placement methodology of hydraulic control system to determine the optimal number and position of sensors based on a discrete particle swarm algorithm. First, the model of fault propagation and sensor response time is evaluated by a simulation model. Second, a discrete optimization model for sensor placement is established. Finally, a discrete particle swarm optimization algorithm is used to calculate the optimal solution for the optimal placement of sensors. In the iterative process, a Monte Carlo simulation-based comparison algorithm is used for the evaluation and comparison of particle. The simulation case of typical multi-circuit hydraulic control systems proves that the proposed method has fast convergence speed and optimization results. A real case of a subsea blowout preventer control system shows that the proposed method reduces the number of sensors and data redundancy effectively. Compared with the traditional method, the robustness of the proposed system under the optimal solution is improved.

Attenuation Characteristics and Time Delay of PD Electromagnetic Wave Propagation in GIS Systems

Modeling of electromagnetic wave propagation inside a gas-insulated switchgear structure can be helpful for understanding and improving the utilization of partial discharge detection by providing information to help determine more optimal positioning of ultra-high frequency sensors. This paper studies the effect of insulating spacers, L- and T-type structures on electromagnetic wave propagation by simulating the time dependent wave propagation behavior for these arrangements within a gas-insulated switchgear busbar geometry. The attenuation characteristics and time delays produced by spacers, L- and T-type structures for partial discharge electromagnetic wave propagation behavior are presented. Emphasis is placed on comparison of attenuation characteristics and time delay between different observation points along the busbar both before and after the 90° bends formed by L- and T-type structures. Moreover, a comparative analysis of the radial electric field at the gas-insulated switchgear tank and its rate of change with time is also conducted. The paper demonstrates that if the location of the ultra-high frequency sensor is chosen appropriately based on knowledge of the physical structures, the attenuation effects introduced by L- and T-type structures could be reduced.

A pilot project for the long-term structural health monitoring of historic city gates

This paper reports on a pilot project for long-term structural health monitoring of historical city gates. This structural typology is what today remains of the defensive structures that characterised the medieval centres of many European cities. Even though in the last years the scientific community has paid great attention to the structural analysis and on the structural health monitoring of masonry towers, which are apparently similar to the typology of city gates, only a few studies explicitly approach such heritage buildings. In most cases, the surviving gates are immersed in the daily vehicular-traffic flows and hence continuously subjected to traffic-induced vibration. Consequently, a key point both for their conservation is the assessment of the optimal sensors position to be used for reliable structural health monitoring procedures. These issues are here discussed with reference to a specific case: the San Niccolò gate in Florence (Italy). First, an expeditious experimental campaign performed with a reduced number of accelerometers is reported. This test is aimed to identify a numerical model that is subsequently employed to design the optimal sensor position of long-term structural monitoring. This optimal sensor grid must be assessed to ensure a low-cost and sustainable dynamic monitoring system but, at the same time, to maximize the information contents.

Optimal Control of Air Conditioning Systems by Means of CO2 Sensors in Electric Vehicles.

Considering the consistent reduction in battery range due to the operation of the Heating Ventilation and Air Conditioning (HVAC) system, this study deals with the CO2 measurement inside the cabin of an electric crane and aims to reduce the energy consumption through the control of the air recirculation. A control strategy was defined and tested through an experimental set-up where the presence of a driver was simulated as a source of CO2. The cabin was placed inside a climatic wind tunnel and the benefits of this control strategy on the HVAC system energy consumption were assessed, both in the heating and the cooling modes. In addition, we discussed the optimal position of the CO2 sensor inside the cabin by comparing the results obtained from some sensors placed around the cabin occupant with the ones logged by three sensors in the breathing zone. Finally, an investigation of the uncertainty of the indirect measurement of the leakage flow and its dependence on the number of CO2 sensors installed in the cabin was made through the Monte Carlo method.

Towards decoding selective attention through cochlear implant electrodes as sensors in subjects with contralateral acoustic hearing

Objectives. Focusing attention on one speaker in a situation with multiple background speakers or noise is referred to as auditory selective attention. Decoding selective attention is an interesting line of research with respect to future brain-guided hearing aids or cochlear implants (CIs) that are designed to adaptively adjust sound processing through cortical feedback loops. This study investigates the feasibility of using the electrodes and backward telemetry of a CI to record electroencephalography (EEG). Approach. The study population included six normal-hearing (NH) listeners and five CI users with contralateral acoustic hearing. Cortical auditory evoked potentials (CAEP) and selective attention were recorded using a state-of-the-art high-density scalp EEG and, in the case of CI users, also using two CI electrodes as sensors in combination with the backward telemetry system of these devices, denoted as implant-based EEG (iEEG). Main results. In the selective attention paradigm with multi-channel scalp EEG the mean decoding accuracy across subjects was 94.8% and 94.6% for NH listeners and CI users, respectively. With single-channel scalp EEG the accuracy dropped but was above chance level in 8–9 out of 11 subjects, depending on the electrode montage. With the single-channel iEEG, the selective attention decoding accuracy could only be analyzed in two out of five CI users due to a loss of data in the other three subjects. In these two CI users, the selective attention decoding accuracy was above chance level. Significance. This study shows that single-channel EEG is suitable for auditory selective attention decoding, even though it reduces the decoding quality compared to a multi-channel approach. CI-based iEEG can be used for the purpose of recording CAEPs and decoding selective attention. However, the study also points out the need for further technical development for the CI backward telemetry regarding long-term recordings and the optimal sensor positions.

Employing LIVE Digital Twin in Prognostic and Health Management: Identifying Location of the Sensors

Digital Twins (DT) and their applications in prognostics and diagnostics have been rapidly growing and becoming highly valuable for various industries. They possess the ability to predict the Remaining Useful Life (RUL) of a device and avoid costly maintenance or failure. Digital Twins can be defined as a real-time bidirectional data exchange of a physical entity and its cyber manifestation. This technology relies on sophisticated development and effective sensor placement to excel in smart maintenance related fields. However, too many sensors can jeopardize the ability of the Digital Twin to perform effectively. A surplus of sensors corresponds with large data sets which increase the computational expense. LIVE Digital Twin is a comprehensive model-based solution to develop Digital Twins for asset management enabled through sensor communication. The LIVE structure is composed of four principle phases, Learn, Identify, Verify, and Extend, with the purpose of designing and optimizing the digital twin using multi-physics simulations. The goal of this research is to present the LIVE architecture to prescribe effective sensor locations for Digital Twins. Furthermore, without loss of generality, the Learn and Identify phases are applied in a case study to develop a maintenance solution for a Light Rail Transit system. To achieve this, various dynamic simulation data was collected from a high-fidelity model to calibrate the performance of a low fidelity model. The advantageous computational speed of low fidelity models makes it possible to quickly analyze various faulty conditions for the system. As the system degrades, the natural frequencies of the vibration deviate thus leading to potential resonance and system failure. In comparing the faulty system’s response to the healthy case, an optimal sensor position is located.

A Novel Genetic Algorithm with db4 Lifting for Optimal Sensor Node Placements

Target coverage algorithms have considerable attention for monitoring the target point by dividing sensor nodes into cover groups, with each sensor cover group containing the target points. When the number of sensors is restricted, optimal sensor node placement becomes a key task. By placing sensors in the ideal position, the quality of maximum target coverage and node connectivity can be increased. In this paper, a novel genetic algorithm based on the 2-D discrete Daubechies 4 (db4) lifting wavelet transform is proposed for determining the optimal sensor position. Initially, the genetic algorithm identifies the population-based sensor location and 2-D discrete db4 lifting adjusts the sensor location into an optimal position where each sensor can cover a maximum number of targets that are connected to another sensor. To demonstrate that the suggested model outperforms the existing method, A series of experiments are carried out using various situations to achieve maximum target point coverage, node interconnectivity, and network lifetime with a limited number of sensor nodes.

Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design

Human activity recognition (HAR) systems combined with machine learning normally serve users based on a fixed sensor position interface. Variations in the installing position will alter the performance of the recognition and will require a new training dataset. Therefore, we need to understand the role of sensor position in HAR system design to optimize its effect. In this paper, we designed an optimization scheme with virtual sensor data for the HAR system. The system is able to generate the optimal sensor position from all possible locations under a given sensor number. Utilizing virtual sensor data, the training dataset can be accessed at low cost. The system can help the decision-making process of sensor position selection with great accuracy using feedback, as well as output the classifier at a lower cost than a conventional training model.

Optimal Experimental Design for Inverse Identification of Conductive and Radiative Properties of Participating Medium

The conductive and radiative properties of participating medium can be estimated by solving an inverse problem that combines transient temperature measurements and a forward model to predict the coupled conductive and radiative heat transfer. The procedure, as well as the estimates of parameters, are not only affected by the measurement noise that intrinsically exists in the experiment, but are also influenced by the known model parameters that are used as necessary inputs to solve the forward problem. In the present study, a stochastic Cramér–Rao bound (sCRB)-based error analysis method was employed for estimation of the errors of the retrieved conductive and radiative properties in an inverse identification process. The method took into account both the uncertainties of the experimental noise and the uncertain model parameter errors. Moreover, we applied the method to design the optimal location of the temperature probe, and to predict the relative error contribution of different error sources for combined conductive and radiative inverse problems. The results show that the proposed methodology is able to determine, a priori, the errors of the retrieved parameters, and that the accuracy of the retrieved parameters can be improved by setting the temperature probe at an optimal sensor position.

Evaluation of a multi-sensor Leap Motion setup for biomechanical motion capture of the hand

The Leap Motion controller (LMC) offers a low-cost means of markerless hand tracking, however, its utility is limited by a small field of view and reliance on appropriate sensor positioning. A recent update from Leap Motion has enabled the use of a multiple LMC device on a single computer, allowing the tracking of hands from multiple orientations, potentially overcoming the aforementioned limitations. This study describes a method of implementing a multi-LMC setup and evaluates its effect on the validity and reliability of the derived kinematics. This study implemented a Kabsch algorithm and Kalman filter to re-orientate and fuse the trajectories captured by three LMC at different orientations. Reliability was assessed by comparing between-day differences in maximum joint angles (ΔMJA) and a calculated coefficient of multiple correlations (CMC). Validity was assessed by comparing the LMC to the gold standard, a Vicon markered motion capture (MMC) system, and calculating the ΔMJA and applying the linear fit method. The proposed method was evaluated by comparing the reliability and validity of the single-LMC setups to the multi-LMC setup. A multi-LMC setup proved successful in improving the reliability and validity of kinematic data, most notably where reliability and validity were poor and variation was high between the single-LMC setups. Findings suggest that through implementing the proposed method, limitations associated with single-LMC setups, notably its reliance on optimal sensor positioning, can be overcome.

Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis.

Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system’s performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around to for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.

Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation.

Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal‐to‐noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér‐Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM‐based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.