Load Sensor Research Articles

Body side-specific changes in sensorimotor processing of movement feedback in a walking insect.

Feedback from load and movement sensors can modify timing and magnitude of the motor output in the stepping stick insect. One source of feedback is stretch reception by the femoral chordotonal organ (fCO), which encodes such parameters as the femorotibial (FTi) joint angle, the angular velocity, and its acceleration. Stimulation of the fCO causes a postural resistance reflex, during quiescence, and can elicit the opposite, so-called active reaction (AR), which assists ongoing flexion during active movements. In the present study, we investigated the role of fCO feedback for the difference in likelihood of generating ARs on the inside vs. the outside during curve stepping. We analyzed the effects of fCO stimulation on the motor output to the FTi and the neighboring coxa-trochanter and thorax-coxa joints of the middle leg. In inside and outside turns, the probability for ARs increases with increasing starting angle and decreasing stimulus velocity; furthermore, it is independent of the total angular excursion. However, the transition between stance and swing motor activity always occurs after a specific angular excursion, independent of the turning direction. Feedback from the fCO also has an excitatory influence on levator trochanteris motoneurons (MNs) during inside and outside turns, whereas the same feedback affects protractor coxae MNs only during outside steps. Our results suggest joint- and body side-dependent processing of fCO feedback. A shift in gain may be responsible for different AR probabilities between inside and outside turning, whereas the general control mechanism for ARs is unchanged.NEW & NOTEWORTHY We show that parameters of movement feedback from the tibia in an insect during curve walking are processed in a body side-specific manner, and how. From our results it is highly conceivable that the difference in motor response to the feedback supports the body side-specific leg kinematics during turning. Future studies will need to determine the source for the inputs that determine the local changes in sensory-motor processing.

Single-component force balance for the measurement of low-magnitude mean aerodynamic loads

A novel force balance is developed for the measurement of low-magnitude mean aerodynamic forces. The measurement of such small loads is often desired for low Reynolds number wind tunnel experiments, however commercially available options for force measurement do not have high enough sensitivity or accuracy. A force balance is designed to measure forces that range from mN to hundreds of milli-Newtons in magnitude. The sensitivity of the balance is derived theoretically, and the actual value is determined from calibration. The force balance demonstrates both a high degree of linearity, and low hysteresis over the desired load range. The measurement capability of the force balance is validated using measurements of the drag force on a circular cylinder, and the lift force on a square cylinder for Reynolds numbers between 1100 and 10 000. In each case, the force measured by the newly developed force balance shows good agreement with data from literature, and a level of accuracy currently not possible with commercially available sensors for such low-magnitude loads.

Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference

Understanding the underlying structure of building occupant dynamics is crucial to improving the effectiveness and energy efficiency of commercial buildings, as occupants fundamentally drive building design and operation. In current practice, we largely account for occupant behavior in the design and management of buildings through rudimentary schedules of presence or absence. However, the increasing availability of embedded sensors—such as plug load sensors—offers an opportunity not only to monitor occupants’ activity patterns but also to use these patterns to gain insight into the network structure of occupants. In this letter, we present a statistical methodology for inferring this network, which comprises social, spatial, and organizational ties among occupants. We apply our method to a 7-person office environment in Northern California, and we compare the inferred networks to ground truth social, spatial, and organizational networks obtained through validated survey questions. We demonstrate that this approach offers insights into the complex nature of occupant dynamics, which can ultimately serve as inputs into building design strategies that minimize energy consumption and improve occupant well-being.

Load imbalances existed as determined by a sensor after conventional gap balancing with a tensiometer in total knee arthroplasty.

To evaluate intercompartmental load intraoperatively with a sensor after conventional gap balancing with a tensiometer during total knee arthroplasty (TKA). Fifty sensor-assisted TKA procedures were performed prospectively between August and September 2018 with a cruciate-retaining prosthesis. After applying a modified measured resection technique, conventional balancing between resected surfaces was achieved. The equal and rectangular flexion-extension gaps were confirmed using a tensiometer at 90° and 5°-7° (due to posterior tibial slope) of knee flexion. Then, the load distribution was evaluated intraoperatively with a sensor placed on trial implants in the positions of knee flexion (90° flexion) and extension (10° flexion). The proportion of coronal load imbalance (medial load - lateral load ≥ ± 15lb) was 56% in extension and 32% in flexion (p = 0.023). The proportion of sagittal load imbalance (extension load - flexion load ≥ ± 15lb) was 36% in the medial compartment and 4% in the lateral compartment (p < 0.001). An additional procedure for load balancing was performed in 74% of knees. Coronal and sagittal load imbalances existed as determined by the sensor even after the achievement of appropriate conventional gap balance. The additional rebalancing procedure was performed for balanced loads in 74% of the knees after conventional balancing. The use of an intraoperative load sensor offers the advantage of direct evaluation of the load on TKA implants. IV.

Novel carbon-nanotube-enhanced composite load sensor to monitor the whole-life structural performance of buildings

This paper presents research on novel load sensors for use in building structures carried out by the principal investigator in his multidisciplinary doctorate research. The sensors consist of carbon-nanotube-enhanced cement matrix and polymer composites. They record a change in electrical resistance when subjected to a change in imposed load. The sensor types are subjected to load tests, Raman spectroscopy characterisation and scanning electron microscopy tests. The gauge factors of the preferred sensor types are calculated and compared with those obtained from the literature. A critique of the preferred novel sensor system is provided.

The Inspection on Obstructive Sleep Apnea Severity Detection using a Deep Learning Access

As of late, crucial endeavors are created to research thoroughgoing sleep observant to anticipate sleeprelated clutters. variable sleep organize grouping has attained unbelievable enthusiasm among specialists in well-being information science. A soft induction framework is received to assess the division of sleep organize. At that time, a starter sleep profundity is decided. Besides, a restricted state machine is made to tell apart the sleep stage changes. the excellence between our examination and alternative existing investigations is that, first, each the load sensors and also the pulse device area unit utilized; at that time, the soft induction and a restricted state machine area unit bestowed, that provide United States of America the next truth than the standard techniques to assess the sleep prepare. preventive sleep disorder (OSA) may be a typical sleep issue caused by abnormal reposeful. The seriousness of OSA will prompt various aspect effects, as an example, fulminant viscus death (SCD). Polysomnography (PSG) may be a very best quality level for OSA analysis. It records various sign from the patient's body for in any event one entire night and figures the ApneaHypopnea Index (AHI) that is that the amount of symptom or respiration occurrences each hour. This value is then accustomed prepare patients into OSA seriousness levels. The principle focal points of our projected technique incorporate easier data acquisition, prompt OSA seriousness recognition, and undefeated part extraction while not space learning from ability. Programmed sleep-organize arrangement models were worked with sturdy and explainable AI techniques (support vector machine and call tree).

Numerical Recreation of Field Cases on a Biofidelic Human FE Model Involving Deformable Less-Lethal Projectiles

Since the 1970s, there has been an increased interest in the use and development of less lethal kinetic energy (LLKE) projectiles. These weapons are mainly used by law enforcement officers worldwide in cases of riot and crowd control. Even if such projectiles are designed to incapacitate an individual with a low risk of serious injuries, the literature indicates a risk of thorax injuries like rib fractures and lung and heart contusions when a projectile strikes the body. Among published articles discussing the wounding potential of LLKE projectiles, two are particularly well documented describing two field cases involving deformable projectiles (eXact iMpact™ and Flash-Ball®). However, one question remains: how to properly exploit field cases in order to find both injury thresholds and guidelines for the design of LLKE projectiles? Hence, the authors focus their research on the use of numerical tools as the finite element (FE) method to recreate the real impact conditions on a biofidelic human torso FE model. It requires the accurate modeling of the deformable projectiles at dynamic strain rates. To address that issue, the study begins by several ballistic experiments on a rigid wall equipped with a load sensor and a high-speed camera. The impact force measured and high-speed images are directly used in an inverse procedure to optimize model parameters of the concerned projectile to correlate impact experiments and modeling. Finally, this is followed by the impact modeling on the human torso FE model and the identification of model-dependent numerical metrics suitable to predict lung and heart contusions. While efforts need to be pursued, this present research provides an interesting step towards designing effective and reliable LLKE projectiles.

Remote Patient Health and Medicine Monitoring by Data Logging using IOT

Considering the day by day rapid increase in the population of the world, providing appropriate healthcare to elderly or unwell people becomes a crucial issue and needs high attention from mainly medical, also industrial and academic fields of the society. Nowadays on the Internet of Things (IoT), there is extensive research on finding the solution for improving the quality of life of the people. In this Project we are presenting a prototype of a health monitoring system which is based on IoT and makes use of wearable sensors, thereby providing remote and continuous monitoring of a patient’s health. The system continuously monitors the patient’s heartbeat, temperature and medicines consumed, by using a web application. Various sensors will be used such as heartbeat sensor and temperature sensor for patients as well as load sensor to get data about how many medicines patient have and when to refill the medicine box. Based on the values acquired from temperature and heartbeat sensor predictions are given about the patient’s condition. All sensors will be communicating with an application through a remote web server where all data will be hosted using IoT protocols.

Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra.

Weigh-in-motion systems are installed in pavements or on bridges to identify and reduce the number of overloaded vehicles and minimise their adverse effect on road infrastructure. Moreover, the collected traffic data are used to obtain axle load characteristics, which are very useful in road infrastructure design. Practical application of data from weigh-in-motion has become more common recently, which calls for adequate attention to data quality. This issue is addressed in the presented paper. The aim of the article is to investigate the accuracy of 77 operative weigh-in-motion stations by analysing steering axle load spectra. The proposed methodology and analysis enabled the identification of scale and source of errors that occur in measurements delivered from weigh-in-motion systems. For this purpose, selected factors were investigated, including the type of axle load sensor, air temperature and vehicle speed. The results of the analysis indicated the obvious effect of the axle load sensor type on the measurement results. It was noted that systematic error increases during winter, causing underestimation of axle loads by 5% to 10% for quartz piezoelectric and bending beam load sensors, respectively. A deterioration of system accuracy is also visible when vehicle speed decreases to 30 km/h. For 25% to 35% of cases, depending on the type of sensor, random error increases for lower speeds, while it remains at a constant level at higher speeds. The analysis also delivered a standard steering axle load distribution, which can have practical meaning in the improvement of weigh-in-motion accuracy and traffic data quality.

Load Sensor Instability and Optimization of MEMS-based Tensile Testing Devices

MEMS-based tensile testing platforms are very powerful tools for the mechanical characterization of nanoscale materials, as they allow for testing of micro/nano-sized components in situ electron microscopes. In a typical configuration, they consist of an actuator, to deliver force/displacement, and a load sensor, which is connected to the sample like springs in series. Such configuration, while providing a high resolution force measurement, can cause the onset of instability phenomena, which can later compromise the test validity. In the present paper such phenomena are quantitatively discussed through the development of an analytical model, which allows to find a relationship between the rise of instability and the sensor stiffness, which is the key parameter to be optimized.

Comparison of regression methods for transverse load sensor based on optical fiber long-period grating

In this work, we report the comparison of regression methods in a long-period grating (LPG) for transverse strain measurement. We analyze the transverse strain sensing characteristics, such as load intensity and azimuthal angle, based on the birefringence effect induced in LPG sensor. Therefore, we employ the different orthogonal responses of the grating to develop regression methods, which allow the estimation of the strain behavior of the LPG sensor. The predictive performances of these interrogation models are compared in terms of square correlation coefficient (R2) and root mean square error (RMSE). Finally, the results indicate that the best method to predict load intensity is the Fourth-Degree Polynomial Fit, whereas the artificial neural network (ANN) model could be successfully employed to predict the azimuthal angle.

Effect of Yttrium on Hot Tearing Susceptibility of Mg–6Zn–1Cu–0.6Zr Alloys

The effect of Yttrium (Y) addition on hot tearing susceptibility of Mg–6Zn–1Cu–xY–0.6Zr (x = 0, 1, 2, 3) alloys was investigated using a constrained rod casting apparatus which monitored the hot tearing formation with a load sensor and a data acquisition system. The solidification characterization of the investigated alloys was studied through thermal analysis. The thermal analysis results revealed that as-cast Mg–6Zn–1Cu–xY–0.6Zr alloys are composed of primary α-Mg dendrites and several types of second phases, MgZnCu, MgZn2, Mg3YZn6 and/or Mg3Y2Zn3. The hot tearing susceptibility is affected by the addition of Y content that significantly decreases the hot tearing susceptibility of Mg–6Zn–1Cu–xY–0.6Zr alloys due to the grain refinement and high feeding capacity. In addition, the analysis of microstructures and fracture surfaces indicated that liquid film theory and feeding theory are mainly dominant on the hot tearing mechanisms.

Estimation and Control of Wind Turbine Tower Vibrations Based on Individual Blade-Pitch Strategies

In this brief, we present a method to estimate the tower fore-aft velocity based upon measurements from blade load sensors. In addition, a tower dampening control strategy is proposed based upon an individual blade pitch control architecture that employs this estimate. The observer design presented in this brief exploits the Coleman transformations that convert a time-varying turbine model into one that is linear and time-invariant, greatly simplifying the observability analysis and subsequent observer design. The proposed individual pitch-based tower controller is decoupled from the rotor speed regulation loop and hence does not interfere with the nominal turbine power regulation. Closed-loop results, obtained from high fidelity turbine simulations, show close agreement between the tower estimates and the actual tower velocity. Furthermore, the individual-pitch-based tower controller achieves a similar performance compared with the collective-pitch-based approach but with negligible impact upon the nominal turbine power output.

Temperature- and strain- insensitive transverse load sensing based on optical fiber reflective Lyot filter

For the first time to our knowledge, we propose and experimentally demonstrate a transverse load sensor using an optical fiber reflective Lyot filter. Such an optical fiber reflective Lyot filter consist of a fiber polarizer, a segment of polarization-maintaining fiber and a single mode fiber based fiber reflector. By demodulating the wavelength of the interference dip, the proposed device exhibits a desirable high transverse load sensitivity of 64.72 nm/(N/mm) without temperature and strain confusion, which provides a new option for the transverse load measurement and further expands the application of the fiber Lyot filter.

Detection of broken wires in elevator wire ropes with ultrasonic guided waves and tone-burst wavelet

Elevator wire ropes with polymer cores hold and hoist heavy fluctuating loads in a corrosive environment. Such working condition causes metal fatigue, which together with abrasion around pulleys leads to progressive loss of the metallic cross section. This can be seen in the forms of a roughened and pitted surface of the ropes, reduction in diameter, and broken wires. Therefore, their deterioration must be monitored so that any unexpected damage or corrosion can be detected before it causes a fatal accident. Ultrasonic-guided wave-based inspection, which has proved its capability in nondestructive testing of platelike structures such as tubes and pipes, can monitor the cross section of wire ropes in their entire length from a single point. However, guided waves have drawn less attention for defect detection purposes in wire ropes. This article reports the condition monitoring of a steel wire rope from a hoisting elevator with broken wires as a result of corrosive environment and fatigue. Finite element analysis was conducted as a baseline to study guided wave propagation in wire ropes and plot dispersion curves. Guided wave propagation in wire ropes was experimentally investigated on a newly built cable stretching machine equipped with a load sensor under different amount of tensile loading. To expose the indication of broken wires, the recorded signals were analyzed by tailor-made continuous wavelet transform called tone burst wavelet.

A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine.

Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons. An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time.

Novel physical network algorithm for indirect measurement of polished rod load of beam‐pumping unit

A novel neural network algorithm for indirect measurement of the polished rod load of the beam-pumping unit is proposed. The dynamometer card is a two-dimension graph of polished rod load versus horse head position, and it is widely used for working state monitoring of pumping unit. The drift problem of load sensor makes the direct measurement of polished rod load not economical and practical, so indirect measurement of polished rod load is crucial. Through the analysis of the mechanical characteristics of the pumping unit, a neural network with physical meaning (physical network) is designed. The experiment shows that the physical network can simulate the energy transfer process properly with only 18 training data items, which is about 1% of other data-driven models, and maintains good performance under different down-hole conditions.

Tire⁻Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems.

Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of weigh-in-motion (WIM) systems, this paper proposes a neural network-based method integrating identification and predication. A backpropagation neural network for signal classification (BPNN-i) was designed to identify ideal samples acquired by load sensors closest to the tire-pavement contact area. After that, ideal samples were used to predict the gross vehicle weight by using another backpropagation neural network (BPNN-e). The dataset for training and evaluation was collected from a multiple-sensor WIM (MS-WIM) system deployed in a public road. In our experiments, 96.89% of samples in the test set had an estimation error of less than 5%.

Cytomegalovirus Infection May Trigger Adult-Onset Still's Disease Onset or Relapses.

Previous studies have revealed that several micro-organisms, especially DNA viruses, have been associated with adult-onset Still's disease (AOSD). However, there are no studies on the relationship between the presence of viral infections in AOSD patients with disease occurrence and reactivation. In the present study, we aimed to investigate the presence of antibodies against virus, virus DNA load and nucleic acid sensors in AOSD patients. Anti-viral antibodies were measured by enzyme-linked immunosorbent assay (ELISA) in plasma samples from 100 AOSD patients and 70 healthy controls (HCs). The copy number of cytomegalovirus (CMV) DNA in 100 AOSD patients was detected by PCR. The expression levels of nucleic acid sensors interferon gamma-inducible protein 16 (IFI16) and absent in melanoma 2 (AIM2) in peripheral blood mononuclear cell (PBMC) and skin from AOSD patients and HCs were analyzed by PCR and immunohistochemistry. The levels of antibodies against CMV were significantly higher in AOSD patients compared to HCs. Moreover, the level of anti-CMV IgM antibody was significantly increased in patients with fever, sore throat, arthralgia and rash. CMV DNA was found in plasma of AOSD patients with disease new-onset and relapse. Furthermore, the copy number of CMV DNA significantly increased in patients with fever, sore throat, arthralgia and rash. And the significant associations of the CMV DNA level with the levels of leukocytes, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α) were observed. Moreover, we found an upregulation of cytoplasmic DNA-sensing receptor IFI16 and AIM2 in PBMC and skin from AOSD patients. In conclusion, our results showed that CMV infection may play a role in the initiation or amplification of inflammatory responses in AOSD.

Fiber optic load sensor using microbend-deformer

Monitoring volume in traffic load is necessary in order to manage the increase of traffic load volume. One of the technologies has been used in that case is Weigh-in-Motion (WIM). Conventional load sensors used in WIM technology are piezoelectric, capacitive mats, bending plate, strain gauge, and load cell. However, the sensors have problems such as electromagnetic interference, corrosion, complex installation and high installation costs. In order to solve those problems, an optical fiber load sensor based on microbend using micro-deformer is being proposed. Optical fiber deformer consists of micro grain sand and silicone rubber mixture with certain compositions. The proposed sensor showed promising result with good sensitivity and measure range. The proposed sensor showed the maximum sensitivity performance around 0.0239 mV.N−1, the maximum microbend sensitivity of 0.548 mV.mm−1 and the maximum measurement range of 30000 N that can be applied in WIM system.