Search Results (3,453)

This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because they are both autonomous and passive at the same time and are therefore very attractive. Their calibration and systematic errors or bias are known problems, briefly discussed in the article due to their importance, and are relatively simple to solve. However, problems related to the accumulation of these errors over time and their autonomous and dynamic correction remain. This article proposes a solution to the problem of IMU tilt calibration, i.e., the pitch and roll and the accelerometer bias correction in dynamic conditions, and presents the process of calculating these parameters based on combined accelerometer and gyroscope records using a new approach based on measuring increments or differences in tilt measurement. Verification was performed by simulation under typical conditions and for many different inertial units, i.e., IMU devices, which brings the proposed method closer to the real application context. The article also addresses, to some extent, the issue of navigation, especially in the context of dead reckoning. Full article

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module’s structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module’s structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module’s structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module’s structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations. Full article

Pressure management is a fundamental and highly effective method for the management of real losses in water distribution networks and therefore reducing non-revenue water. In this work, a methodology is developed to assess leakages in the water distribution networks of small settlements. The settlement of Agios Germanos in the Municipality of Prespes is selected as a representative case study. The hydraulic modeling of the water distribution network in the study area is used to assess the hydraulic behavior of the existing infrastructure in its current state of operation and to find critical locations to install the necessary measuring equipment (pressure sensors, flow meters, water level sensors, and pressure reducing valves). This equipment is used to calibrate the hydraulic model, estimate leakages, and manage them effectively. Minimum night flow analysis is utilized to assess leakages in the studied network based on measurements of the hydraulic parameters from the equipment installed. The effects of pressure management on leakages are then examined by assessing the relationship between the pressure and leak flow rate in the selected settlement. Full article

The interest in using new technologies to obtain recordings of on-water kinetic variables for assessing the performance of elite sprint kayakers has increased over the last decades but systematic approaches are warranted to ensure the validity and reliability of these measures. This study has an innovative approach, and the aim was to develop a new versatile jig including reference force sensors for both the calibration and validation of mutual static and dynamic stroke forces as measured with instrumented paddles at the high force levels used in elite sprint kayaking. Methods: A jig was constructed using a modified gym weight stack and a frame consisting of aluminum profiles permitting a fastening of custom-made kayak paddle shaft and blade support devices with certified force transducers combined with a data acquisition system to record blade and hand forces during static (constant load) and dynamic conditions (by paddle stroke simulation). A linear motion path incorporating a ball-bearing equipped carriage with sensors for the measurement of vertical distance and horizontal displacement was attached to the frame for recordings of various position measures on the paddle. The jig design with all components is extensively described to permit replication. The procedures for assessing the accuracy of the jig force instrumentation are reported, and with one brand of instrumented paddle used as an example, methods are described for force calibration and validation during static and dynamic conditions. Results: The results illustrate that the measured force with the jig instrumentation was similar to the applied force, calculated from the applied accurate mass (within a −1.4 to 1.8% difference) and similar to the force as calculated from the applied mass with the weight stack (within a −0.57 to 1.16% difference). The jig was suitable for the calibration and validation of forces in a range relevant for elite sprint kayaking under both static and dynamic conditions. During static conditions with a force direction equal to the calibration conditions and a force range from 98 to 590 N, all values for the instrumented paddle were within a −3.4 to 3.0% difference from the jig sensor values and 28 of 36 values were within ±2%. During dynamic conditions with paddle stroke simulations at 60 and 100 strokes/min and a target peak force of 400 N, the common force variables as measured by the instrumented paddle were not significantly different from the same measures by the jig (values at 100 strokes/min: peak force; 406.9 ± 18.4 vs. 401.9 ± 17.2 N, mean force; 212.8 ± 15.4 vs. 212.0 ± 14.4 N, time to peak force; 0.17 ± 0.02 vs. 0.18 ± 0.02 s, force impulse; 90.8 ± 11.2 vs. 90.5 ± 10.8 Ns, impulse duration; 0.43 ± 0.03 vs. 0.43 ± 0.03 s). Conclusion: A novel jig with several new functions is presented that enables the calibration and validation of force measurements with instrumented paddles by providing standardized conditions for calibration and force validation during both static and dynamic conditions in a force range relevant to elite sprint kayaking. Full article

The National Oceanic and Atmospheric Administration (NOAA) 21 Visible Infrared Imaging Radiometer Suite (VIIRS) was successfully launched on 10 November 2022. To ensure the required instrument performance, a series of Post-Launch Tests (PLTs) were performed and analyzed. The primary calibration source for NOAA-21 VIIRS Reflective Solar Bands (RSBs) is the Solar Diffuser (SD), which retains the prelaunch radiometric calibration standard from prelaunch to on-orbit. Upon reaching orbit, the SD undergoes degradation as a result of ultraviolet solar illumination. The rate of SD degradation (called the H-factor) is monitored by a Solar Diffuser Stability Monitor (SDSM). The initial H-factor’s instability was significantly improved by deriving a new sun transmittance function from the yaw maneuver and one-year SDSM data. The F-factors (normally represent the inverse of instrument gain) thus calculated for the Visible/Near-Infrared (VISNIR) bands were proven to be stable throughout the first year of the on-orbit operations. On the other hand, the Shortwave Infrared (SWIR) bands unexpectedly showed fast degradation, which is possibly due to unknown substance accumulation along the optical path. To mitigate these SWIR band gain changes, the NOAA VIIRS Sensor Data Record (SDR) team used an automated calibration software package called RSBautoCal. In March 2024, the second middle-mission outgassing event to reverse SWIR band degradation was shown to be successful and its effects are closely monitored. Finally, the deep convective cloud trends and lunar collection results validated the operational F-factors. This paper summarizes the preliminary on-orbit radiometric calibration updates and performance for the NOAA-21 VIIRS SDR products in the RSB. Full article

The hydrological response (HR), generally defined as the relationship between rainfall and runoff, should be understood holistically within the processes of the conversion of rainfall to evapotranspiration, surface and subsurface runoff, groundwater flow, and streamflow. The objective of this study was to evaluate the HR of three predominant land use and land cover (LULC) types in the Colombian Andes at the micro-watershed scale. Experimental micro-watersheds were established to replicate LU (pasture, and a coffee agroforestry system) and LC (natural forest). The TETIS model was applied, calibrated, and verified, and the similarity between observed flows (using level sensors and volumetric gauges) and flows simulated by the model was evaluated, relating the HR to each type of LULC. The HR included an analysis of the Water Retention and Regulation Index—IRH and Base Flow Index—IFB. The best model fit and HR were found for the agroforestry system, with a moderate NSE (0.48), R² (0.7), RMSE (0.2), and BE (20.8%). On the other hand, a forest cover was found to guarantee the permanence of subsurface inputs and base flows to the river, as evidenced by high IRH, IFB, and water balance values. Natural forest land uses present high volumetric moisture content in the soil, corresponding to a high IFB. Full article

The development of systems for weighing vehicles in motion aims to introduce systems allowing automatic enforcement of regulations. HSWIM (high speed weight-in-motion) systems enable measurement of a mass of vehicles passing through a measurement station without disturbing the traffic flow. This article focuses on the calibration of a weighing station for moving vehicles, where strain gauge sensors are used to measure pressures. A solution was proposed to replace the calibration coefficients with calibration functions. The analysis was performed for two methods of determining wheel loads: based on the maximum of the signal from strain gauge sensors and on a method using the field under the signal and the vehicle’s speed. Calibration functions were determined jointly for all test vehicles and separately for each of them. The use of a calibration function for a specific vehicle type made it possible to determine wheel pressure and gross weight with a level of accuracy that allowed the weigh-in-motion station to be classified as a direct enforcement system. The achieved improvement in the accuracy of weighing in motion did not require any interference with the measurement station. The proposed change in the method of calibration and, ultimately, determination of wheel loads required only a change in the algorithm for determining wheel loads. Full article

In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring leaf wetness duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida’s Plant Science Research and Education Unit (PSREU) in Citra, FL, USA, and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production. Full article

On the human face, the lips are one of the most important anatomical elements, both morphologically and functionally. Morphologically, they have a significant impact on aesthetics, and abnormal lip morphology causes sociopsychological problems. Functionally, they play a crucial role in breathing, articulation, feeding, and swallowing. An apparatus that can accurately and easily measure the elastic modulus of perioral tissues in clinical tests was developed, and its measurement sensitivity was evaluated. The apparatus is basically a uniaxial compression apparatus consisting of a force sensor and a displacement sensor. The displacement sensor works by enhancing the restoring force due to the deformation of soft materials. Using the apparatus, the force and the displacement were measured for polyurethane elastomers with various levels of softness, which are a model material of human tissues. The stress measured by the developed apparatus increased in proportion to Young’s modulus, and was measured by the compression apparatus at the whole region of Young’s modulus, indicating that the relation can be used for calibration. Clinical tests using the developed apparatus revealed that Young’s moduli for upper lip, left cheek, and right cheek were evaluated to be 45, 4.0, and 9.9 kPa, respectively. In this paper, the advantages of this apparatus and the interpretation of the data obtained are discussed from the perspective of orthodontics. Full article

Essential for decision-making, measurement is a cornerstone of various fields including energy management. While direct methods exist for some quantities like length, most physico-chemical properties require indirect assessment based on observable effects. Historically, pressure was measured by the water column height, and temperature by mercury expansion. Recent advancements in artificial intelligence (AI) offer a transformative approach by combining vast datasets with traditional measurements. This holds immense potential for applications facing extreme conditions and involving complex fluids where measurement is extremely challenging (over 1500 K and 5 MPa). In this study, an AI model is evaluated to replace online rheometers (293–1173 K, 0.15–3.5 MPa). A machine learning model utilizes a neural network with up to 8000 neurons, eight hidden layers, and over 448 million parameters. Trained, tested, and validated on three experimental databases with over 600 test conditions, the New Generation Predicted Viscosity Sensor (NGPV sensor) achieves exceptional accuracy (less than 4.8 × 10⁻⁷ Pa·s). This virtualized sensor proves highly relevant for hypersonic airbreathing applications involving fuel degradation and energy conversion. It maintains excellent predictability (accuracy below 6 × 10⁻⁶ Pa·s) even at flow rates 10 times higher than calibration, surpassing traditional rheometers limited by calibration needs and a lower viscosity measurement threshold (10⁻⁴ Pa·s). Full article

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer’s gait and diagnose balance issues. This approach can be utilized to improve a user’s rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques. Full article

This paper presents the application of a compact and fully integrated LED quantum sensor based on the NV centers in diamond for current measurement in a busbar. The magnetic field measurements from the sensor are directly compared with measurements from a numerical simulation, eliminating the need for calibration. The sensor setup achieves an accuracy of 0.28% in the measurement range of 0–30 $\mathrm{A}$ DC. The integration of advanced quantum sensing technology with practical current measurement demonstrates the potential of this sensor for applications in electrical and distribution networks. Full article

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance, was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality. Full article

Although the NSGA-III algorithm is able to find the global optimal solution and has a good effect on the workshop scheduling optimization, the limitations in population diversity, convergence ability and local optimal solutions make it not applicable to certain situations. Thus, an improved NSGA-III workshop scheduling optimization algorithm is proposed in this work. It aims to address these limitations of the NSGA-III algorithm in processing workshop scheduling optimization. To solve the problem of individual elimination in the traditional NSGA-III algorithm, chaotic mapping is introduced in the improved NSGA-III algorithm to generate new offspring individuals and add the selected winning individuals to the offspring population as the parent population for the next iteration. The proposed algorithm was applied to a pressure sensor calibration workshop. A comparison with the traditional NSGA-III algorithm was conducted through a simulation analysis. The results show that the proposed algorithm can obtain a better convergence performance, improve the optimization ability and avoid falling into local optimal solutions. Full article

Marker-less hand–eye calibration permits the acquisition of an accurate transformation between an optical sensor and a robot in unstructured environments. Single monocular cameras, despite their low cost and modest computation requirements, present difficulties for this purpose due to their incomplete correspondence of projected coordinates. In this work, we introduce a hand–eye calibration procedure based on the rotation representations inferred by an augmented autoencoder neural network. Learning-based models that attempt to directly regress the spatial transform of objects such as the links of robotic manipulators perform poorly in the orientation domain, but this can be overcome through the analysis of the latent space vectors constructed in the autoencoding process. This technique is computationally inexpensive and can be run in real time in markedly varied lighting and occlusion conditions. To evaluate the procedure, we use a color-depth camera and perform a registration step between the predicted and the captured point clouds to measure translation and orientation errors and compare the results to a baseline based on traditional checkerboard markers. Full article