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Designing an anatomical structure for a surgical procedure is not a simple task. It is especially true of the craniofacial area, which consists of bone tissues with very complex geometry. CAD modelers need appropriate knowledge and skills in medicine and technical sciences to fully use the currently available tools in related processes with the reconstruction of the craniofacial areas. The presented preliminary studies are based on four patients treated at the Department of Maxillofacial Surgery. The segmentation process of the mandible model was performed in the ITK SNAP software. The process of generating surface body models was performed in the Auto Surfacing module in Geomagic software using two different methods: organic and mechanical. Then compare both methods for the accuracy of generating a CAD model of the mandible based on a triangle mesh structure in the Focus Inspection and the GOM Inspect software.

With the rapid development of distributed renewables, DC transmission, and active loads, increasingly more power electronics are and will be introduced into distribution grids. This has led to an increasing need for a new synchronized measurement system that covers different scenarios and applications to improve the stability and reliability of power-electronics-enabled power grids. In this paper, a full-view synchronized measurement system (SYMS) for the renewables, controls, loads, and waveforms of power-electronics-enabled power distribution grids is established, and is composed of different types of synchronized measurement devices (SMDs) and a data center. A general design method for the synchrophasor estimation of SMDs in different scenarios is proposed by deducing phasor measurement error models. This is further applied in combination with the design of hardware and a communication protocol to develop SMDs for the measurement of the renewables, controls, loads, and waveforms of power-electronics-enabled power distribution grids. Then, a framework of the data center of the SYMS, as well as several potential applications based on real-time measurements, are presented and discussed. The SYMS is a comprehensive, shared, and open power system data platform for use by future researchers.

A vehicle simulation model, CO2MPAS supports the introduction of the new WLTP-based certification system for CO2 emissions in Europe. This paper investigates the possibility to use the underlying simulation methodology to accurately calculate CO2 emissions over real-world trips, thus to extend the use of the methodology beyond vehicle certification. As a reference, the analysis used measurement data obtained from four vehicles over two different routes under real-world driving conditions. The CO2 emissions were measured using portable emissions measurement systems. The formal CO2MPAS methodology and two modified versions of it that require a reduced number of input data were assessed about their capacity to predict the measured CO2 emissions. The analysis focused on the accuracy and uncertainty of the three different methodology configurations. As an additional benchmark, the analysis considered the CO2 emissions estimates obtained from the EMEP/EEA Guidebook methodology used for emissions inventorying in Europe. Results show that the basic CO2MPAS configuration demonstrates good performance in predicting CO2 emissions over on-road tests, reaching a prediction accuracy over an entire test trip of −0,3% and a standard deviation of 3,1%. The modified versions showed slightly higher biases up to 3% and uncertainties (5–7%), but remaining within reasonable limits considering the reduced number of inputs used in each case. Given its ability to predict CO2 emissions accurately on a local base, CO2MPAS could be used for the prediction of instantaneous CO2 emissions in traffic micro-simulation exercises.

Three-dimensional (3D) profile scanning plays a crucial role in the inspection of assembled large aircraft. In this paper, to achieve noncontact automatic measurements of the high-reflective profiles of large-scale curved parts and components, an automated noncontact system and method with high accuracy and high efficiency are presented. First, a hybrid 3D coordinate measurement system based on proximity sensors and cameras is proposed to obtain noncontact measurements while avoiding the influence of high reflection on the measurement accuracy. A hybrid measurement model that combines the one-dimensional distances measured by the proximity sensors and the 3D information obtained by cameras is proposed to determine high-accuracy 3D coordinates of the measured points. Then, a profile-driven 3D automated scanning method and strategy are designed to rapidly scan and reconstruct the profile within the effective range without scratching the profile or exceeding the measurement range of the proposed system. Finally, experiments and accuracy analyses are performed in situ on an assembled tailplane panel (approximately 1760 mm × 460 mm). The automated scanning process is completed in a timeframe of 208 s with an average error of less than 0.121 mm for profile reconstruction. Therefore, the proposed method is promising considering both the high accuracy and high efficiency requirements of profile inspections for large aircraft.

Transient stability assessment (TSA) plays an important role to ensure safe operation of power system in internet of energy (IoE). Many time domain simulation (TDS) based and transient energy function (TEF) based methods have been proposed to assess the transient stability of power system. With the Wide Area Measurement System (WAMS) and the Phasor Measure Units (PMUs) applied to observe the real-time data, methods of TSA based on the machine learning and data-driven are continuously studied. These kinds of methods can only assess the transient stability of power system when subjected to large disturbances. However, these kinds of methods can’t infer the type of event which leads to the collapse of the power system. In this paper, the Gated Graph Neural Network (GGNN) is applied to assess the power system transient stability and infer the type of event leading the instability of power system. Firstly, Conditional Generative Adversarial Network (CGAN) is applied to generate unstable samples making the training data more balanced. With the balanced data graph-structured and used to train the GGNN based TSA model, the GGNN based TSA model achieves better performances. Finally, the real-time data is input into the trained TSA model and the transient stability of power system is assessed. When the power system is considered unstable, the proposed TSA model can also infer the type of event leading the instability of power system, classifying the unstable state to the corresponding event. Simulations performed on the New England 39-bus system verify the effectiveness of the proposed method.

In 2014 a number of Capacitor Voltage Transformers (CVTs) exploded in the Swedish transmission grid. To address this problem, data driven methods for continuously monitoring CVTs were developed. Thanks to these methods, CVT capacitor punctures are now regularly noticed and addressed before causing serious damage. This paper describes the algorithms used to make this possible. Detecting all CVT capacitor punctures as they occur increases the transmission system stability, avoids equipment damage, lowers risks to personnel when visiting the substations and makes it possible to correct measurement errors in the energy measurement systems. Automatic supervision scripts, using energy meter data, are running continuously and will alert the service personal immediately when problems occur. With the algorithms proposed in this article, the Swedish National Grid (Svk) has detected many old punctures and expects no future capacitor punctures to go undetected. Detecting all new punctures enables Svk to save substantial future cost since planned actions now are possible.

Diagnosis of mechanical faults in manufacturing systems is critical for ensuring safety and saving costs. With the development of data transmission and sensor technologies, measuring systems can acquire massive amounts of multi-sensor data. Although Deep-Learning (DL) provides an end-to-end way to address the drawbacks of traditional methods, it is necessary to do deep research on an intelligent fault diagnosis method based on Multi-Sensor Data. In this project, a novel intelligent diagnosis method based on Multi-Sensor Fusion (MSF) and Convolutional Neural Network (CNN) is explored. Firstly, a Multi-Signals-to-RGB-Image conversion method based on Principal Component Analysis (PCA) is applied to fuse multi-signal data into three-channel RGB images. Then, an improved CNN with residual networks is proposed, which can balance the relationship between computational cost and accuracy. Two datasets are used to verify the effectiveness of the proposed method. The results show the proposed method outperforms other DL-based methods in terms of accuracy.

Pantograph slide is a vulnerable component because of the contact and friction between the pantograph and catenary (PAC) while the train runs with a high speed. Condition monitoring of the slide is a necessary and meaningful work to improve the operation efficiency and reduce the occurrence of accident. It is a hard work to measure the slide abrasion due to the dynamic and complicated environment. In this paper, an accurate edge extraction method based on image analysis is proposed, which aims at providing an image metric property based on vision measurement to detect the wear of slide. First, a subpixel edge detection method is proposed. The gradient in horizontal direction is first taken into account, which filters out the non-horizontal edges. Then, the upper and lower edges of the slide are located using the geometric restriction. The subpixel edge feature are also extracted in the image process, which can be used to the vision measurement system for monitoring the wear condition of the slide. The experiment results shows that the proposed algorithm can precisely detect the subpixel edges of the slide. The measurement error of the slide is less than 2 mm, which demonstrates that the proposed algorithm has a high accuracy in the large field of view.

Femtosecond laser processing is a non-thermal process with less heat effect on a target material and is applied to dielectric and difficult-to-machine materials, such as titanium alloy and diamond. Femtosecond laser processing has been used for 3-dimensional shape generation in recent years, and increasing accuracy and productivity are required for such generation by femtosecond lasers. However, a 2.5-dimensional computer-aided manufacturing (CAM) system, which is mainly used in femtosecond laser processing, does not satisfy the requirement for complicated 3-dimensional shape generation. Therefore, there is a need for the development of a 3-dimensional CAM system for high-precision and productive 3-dimensional processing. Furthermore, for the 3-dimensional CAM system, quantitative identification of a 3-dimensional removal shape through ablation is necessary. This study proposes the development of a simulation method to accurately predict the 3-dimensional removal shape through femtosecond laser processing, including the piecewise approximation of the ablation rate and absorptance correction. At the same time, a similar 3-dimensional shape as that found through the experimental results was obtained during the simulation by considering the mechanical errors of the laser processing machine in the simulation model. Furthermore, an on-machine measurement system was developed to efficiently identify the ablation parameters.

Slip status of drill string is system generated binary value computed by comparison of sensor generated real time hook load value with a minimum threshold value of hook load stored in measurement system. This research article describes a novel method of slip status detection by machine vision technology which helps overcome the constraints of slip status detection with legacy measurement method. It also helps improve the real time drilling data quality and optimize and automate drilling operations. A method to detect drill string slip status with high-resolution digital camera installed on mast near rig floor is described along with backend vision processing and communication modules, which generate binary values of slip status. The binary values are transferred in real time to drilling measurement system of rig to compute other drilling parameters like bit depth, hole depth and stand counters. This method includes deploying active optical sensors at the rig floor, obtaining 1-D, 2-D, or 3D image data, and processing it to obtain the status of drill string. Reliable measurement of slip status by machine vision helps reduce non-productive time (NPT) by reliable real time surveillance of drilling operations.

Background: Stereo-vision-based three-dimensional coordinates measurement technology has been widely applied in the military or civil fields. There are two problems that need to be solved. The first problem is that each camera’s internal parameters and the two cameras' external parameters need to be calibrated. To increase the measurement range, usually, the turntable is used with the stereo vision system together. The second problem is the calibration of the turntable. Objective: The aim of the study is to construct and calibrate a stereo-vision-based coordinates measurement system via a two-axis turntable. Methods: Considering that the internal parameters of each camera do not change during the measurement process and the complicated optimization process of one-step self-calibration, a two-step stereo vision calibration method is proposed. In the first step, we calibrate the internal parameters of each camera through a specially designed planar target with circular points. In the second step, on the basis of the calibrated results of the internal parameters, the two cameras' external parameters are calibrated through a simple target which could be distributed in the measurement volume. For the calibration of the two-axis turntable, we calibrated the rotation axes of the turntable and the coordinates of points in the 3D space could be measured considering the non-orthogonality of the axes. Results: Some experiments are provided to examine the calibration methods we proposed. They are the plane target measurement experiments; the standard ball center coordinates measurement experiments and target pose measurement experiments. Experiment results demonstrate the superiority of the calibration method we proposed. Conclusion: We studied the calibration methods of the stereo-vision-based coordinates measurement system via a two-axis turntable. The experimental results show the measurement accuracy of our system is less than 0.1mm.

Simultaneously and independently measuring two-dimensional (2D) displacement, one-dimensional (1D) for out-of-plane measurement and 1D for in-plane measurement, is important for practical applications. Although extrinsic fiber Fabry-Perot interferometers (EFPIs) have been reported for highly precise out-of-plane displacement measurements for many years, simultaneous measurement of 2D displacement with submicron accuracy has not been realized by a monolithic EFPI as far as we know. In this paper, a monolithic dual-cavity EFPI to realize 2D displacement measurement of a target is reported. By quantitatively analyzing and experimentally calibrating the linear dependences of the in-plane displacement sensitivity and interception on the cavity length, the effect of out-of-plane displacement on the in-plane displacement measurement is removed, which is the basis of the 2D displacement detection. 2D random movement detection and the repeatability of the system are investigated experimentally. Within the range of 10m for in-plane displacement, and 352m-358m for cavity length, demodulation errors better than 96nm are achieved. The 2D displacement measurement system based on the dual-cavity EFPI with the advantages of noncontact, high accuracy and compact size is promising to be applied in the 2D acceleration measurement, 2D rotor vibration measurement, 2D precise positioning in semiconductor manufacture, thermal expansion coefficient measurement, and so on.

In this paper, a measurement-based three-dimensional spatial modulation (3-D SM) system is first proposed in the existing literature. The major contributions of this work include: 1) a 3-D SM measurement system with a 16 16 virtual uniform rectangle array (URA) at the transmitter and a virtual uniform line array (ULA) with 16 elements at the receiver, 2) a comprehensive measurement activity indoors taking both line-of-sight (LOS) and none-LOS (NLOS) scenarios into account, and 3) the first measurement-based average bit error probability (ABEP) evaluation for 3-D SM system. Numerical results reveal that the widely used independent and identically distributed (i.i.d.) Rayleigh channel model significantly overestimates the performance of the SM system. Moreover, we find that the ABEP performance of 3-D SM is worse than that of the 2-D SM under the same spacing among the antenna array, while 3-D SM outperforms 2-D SM under the same length of the antenna array.

Developing accurate and efficient measurement systems for blade profiles has always been a vital issue in blade manufacturing. Nowadays, optical-based systems for measuring blade profiles have attracted significant interest due to their high efficiency and flexibility. However, as key steps in optical measurement of blades, system calibration and establishment of measurement datum are normally accomplished based on calibration objects, which may introduce uncertainty and affect the accuracy of the measurement. In this article, we propose a novel method for blade-profile measurement based on blade self-features. A measurement system based on a four-axis platform and laser scanning sensor (LSS) is developed. A calibration method based on the blade datum plane feature is introduced to eliminate the pose error of the system without using additional calibration objects. A feature-matching algorithm based on the covariance matrix of the blade local leading-edge feature is further proposed to establish the measurement datum. The proposed method requires smaller system-motion space and fewer LSS viewpoints, which would improve the efficiency and flexibility of the measurement system. Furthermore, less error accumulation can be achieved using our method, thus, resulting in higher measurement accuracy. The experimental results reveal the feasibility and good practical application prospect of this method.

The accuracy in the measurement of magnetization curve (M-H) for soft materials is easily influenced by the external static field in the unshielded environment. To eliminate the influence from the environmental field, the measurement system must be placed in a zero-gauss chamber. However, such a device is not widely available and also costly. In this work, the rapid M-H measurement system with a giant magnetoresistance (GMR) sensor serving as the zero-field detector is proposed. The M-H curve is obtained by integrating the voltage in the pickup coil to determine the magnetization of the test sample under a sweeping magnetic field. The environment field component along the direction of the sweeping field is suppressed by using the output of the GMR sensor as the trigger signal to determine the offset field in the measured M-H curve. The experimental results show that the M-H curves for various kinds of soft magnetic samples, e.g. mu-metal wire, can be obtained under the interference of an applied static field. The proposed method helps improve the accuracy of the rapid M-H measurement system in the unshielded environment.

In this study, we developed a neutron time-of-flight (nTOF) measurement system for a 1.7-MV tandem proton accelerator with a target covered with 300-nm-thick lithium (Li) layer. With implementation of beam chopping module after its ion source, the accelerator is configured to operate in pulsed-beam mode with a pulse width

Sensor layout of biomagnetic measurement systems is measured using a multiple coil array that generates a magnetic signal. However, the results of the sensor array measurement included the error of the coil array because the coil array was not calibrated. In this paper, we proposed a calibration method to obtain the effective geometry and uncertainty of the coil array. The calibration was performed based on the X-ray CT measurements and numerical analysis of the position and shape of the coils. The differences between the designed and calibrated coil array geometries were 0.6 mm, 0.7°, and 0.3 mm with respect to the center position, orientation of the normal vector, and radius of the coils, respectively. The uncertainty of the calibration was 0.3 mm and 1.70 in terms of the dimension and orientation, respectively. We measured the sensor array layout of a magnetoencephalography (MEG) system using a calibrated coil array geometry. The source localization accuracy of the MEG system was improved compared with that of the sensor array layout obtained using the designed coil array geometry. We revealed the necessity of calibrating the coil array, regardless of the precision of the fabrication.

As one of the most significant applications of nanopositioning technology, the tremendous development of atomic force microscopes (AFMs) has been witnessed these years. Essentially, the scanning motions of AFMs are generally driven by piezoelectric tube scanners (PTSs), whose cross-coupling effect hinders their high speed and high-precision positioning. Therefore, it becomes an urgent yet challenging mission to establish a niche model for the nonlinear cross-coupling dynamics of the PTS. As an emergent pure data-driven learning approach, the Koopman operator sheds some light on the PTS modeling methodology, which is thus adopted in this article to approximate the nonlinear cross-coupling dynamics of PTSs in an infinite-dimensional space. Moreover, an online high-order extended dynamic mode decomposition algorithm is proposed for the finite-dimensional approximation of the Koopman operator online. The merit of the present model lies in updating the identified cross coupling upon the arrival of new data in an incremental way. Finally, experiments are conducted to approximate the X–Y axes cross coupling of PTSs of an NTMDT Prima AFM, which verifies the effectiveness and superiority of the proposed modeling algorithm. This article is expected to pave the way from the Koopman operator learning theory to real applications in dynamics modeling of abundant nanoscale measurement systems.

В статті розглядається застосування різних систем одиниць вимірювання у медичних дослідженнях. На конкретних прикладах демонструється зневажливе ставлення до вибору та використання одиниць вимірювання. На прикладі системи транспорту кисню пропонуються підходи для універсалізації одиниць вимірювання в медицині.

Accurate topology is the basis of fine management and safe operation of distribution network. With the development of distributed generation, more and more users participate in the distribution network, which makes the flow direction of distribution network more complex and brings some difficulties to the topology identification of distribution network. The existing distribution network topology identification methods lack of multi period measurement data, and the utilization rate of multi-user data is low, which leads to the low accuracy of distribution network topology identification. To solve this problem, a distribution network topology identification method based on multi-user fusion data is proposed. Firstly, the existing distribution network measurement system is analyzed, and the data and characteristics that can be collected in the actual project are obtained. Then, according to the characteristics of different data sampling frequency and accuracy, multi-user data fusion is carried out, and a PMU based multi-user data time scale alignment method and a pseudo measurement generation method based on linear extrapolation are proposed. Finally, an optimization model of distribution network topology identification based on multi-user and multi period fusion data is proposed. By minimizing the error between the measured value and the estimated value in several time periods, the model realizes the identification of distribution network topology. Simulation results show that the method is correct and effective.

Sea state affects the coasts of all maritime regions, and it impacts anthropic infrastructures and influences the activities carried out in the sea as well as in its proximity. Sea-state monitoring is therefore important for a number of reasons, including the need to validate weather forecast models and study the impact of sea waves on anthropic infrastructures such as ports. In this letter, we combined the measurements of sea state collected with Ocean Seismic Integrated System (OS-IS), a seismic-based system developed by AGI (Assist in Gravitation and Instrumentation) srl and INGV (National Institute for Geophysics and Volcanology), with those obtained using a Wave Radar system. The aim was to prove that this combination could enhance the efficacy and reliability of sea monitoring, thanks to the complementarity of the two types of measurements in terms of area covered and spatial resolution. More specifically, the sea-state parameters provided by OS-IS were compared with those provided by a Wave Radar system. OS-IS data were collected from three microseismic stations installed near the Gulf of La Spezia, on the Ligurian Coast, whereas the Wave Radar system was installed at the center of the Cinque Terre National Park at about 12 km from the OS-IS system. The comparison between the measurements of significant wave height (Hs) and peak period (Tp) showed a good agreement, with a correlation index close to 0.9. The residual difference highlights the complementarity of the two systems and the potential of an integrated system that includes both. In particular, OS-IS provides measurements over large, offshore areas, whereas the Wave Radar System provides high spatial-resolution measurements over areas near the coast.

International audience; We have measured the three-dimensional coordinates of retroreflecting glass spheres using a multilateration technique. To this end, the distances between each target and our measurement heads have been determined by an in-house prototype of electro-optical absolute distance meter. The used targets are spheres of glass refractive index n=2 and of diameter 14.2 mm. They offer a much larger aperture than corner cubes and a lower cost, but they present a low reflectivity. However, this paper shows that with a proper collimation of the laser beams it is possible to reach a range of operation of 20 m. The standard uncertainty on such distances is better than 11 µm, including the mechanical errors of the gimbal mechanisms of our aiming systems. This uncertainty has been validated thanks to a comparison with an interferometric bench. In addition, the measurement of the coordinates of 16 glass spheres using a self-calibration multilateration algorithm has also confirmed the performances of the developed system: the standard deviation on the position errors has been estimated to about 10 µm.

Flow accelerated corrosion (FAC) is a type of pipe corrosion in which the pipe thickness decreases depending on the fluid flow conditions. In nuclear power plants, FAC mainly occurs in the carbon steel pipes of a secondary system. However, because the temperature of a secondary system pipe is over 150 °C, in situ monitoring using a conventional ultrasonic non-destructive testing method is difficult. In our previous study, we developed a waveguide ultrasonic thickness measurement system. In this study, we applied a waveguide ultrasonic thickness measurement system to monitor the thinning of the pipe according to the change in pH. The Korea Atomic Energy Research Institute installed FAC-proof facilities, enabling the monitoring of internal fluid flow conditions, which were fixed for ~1000 h to analyze the effect of the pH. The measurement system operated without failure for ~3000 h and the pipe thickness was found to be reduced by ~10% at pH 9 compared to that at pH 7. The thickness of the pipe was measured using a microscope after the experiment, and the reliability of the system was confirmed with less than 1% error. This technology is expected to also be applicable to the thickness-reduction monitoring of other high-temperature materials.

Nonlinearity of a digital-to-time converter (DTC) is pivotal to spur performance in DTC-based all-digital phase-locked-loops (ADPLL). In this paper, we characterize and analyze the mismatch of cascaded-delay-unit DTCs. Through an improved built-in-self-test (BIST) time-to-digital converter (TDC) assisted with phase-to-frequency detector (PFD), a measurement system of sub-half-ps accuracy is constructed to conduct the characterization. Fabricated in 28-nm CMOS, the DTC transfer functions are measured, and mismatches are compared against Monte-Carlo simulation results. The integral nonlinearity (INL) results are compared against each other and converted to the in-band fractional spur level when the DTC would be deployed in the ADPLL. The BIST-TDC system thus characterizes the on-chip delays without expensive equipment or complex setup. The effectiveness of adding a PFD into the ΔΣ loop is validated. The entire BIST system consumes 0.6mW with a system self-calibration algorithm to tackle the analog blocks' nonlinearities.

Accurate volume measurement of flying droplets in the Mura-free manufacturing of inkjet printed organic light-emitting diodes (IJP OLEDs) remains a challenge due to the micrometer scale and high speeds of the droplets. Most existing vision-based methods pay little attention to image segmentation and estimate the droplet volume based on a symmetry assumption, which yields poor accuracy, especially for droplets with an unknown shape. In this paper, an accurate measurement method for flying droplet volume based on stereo vision is proposed. First, an image acquisition technique based on stereo vision and synchronous triggering is used to obtain multiview high-resolution droplet images. Then, an accurate binocular droplet image segmentation algorithm is proposed to segment the images with different degrees of blur to achieve similar accuracy. Finally, a polar-coordinate-Hermite-interpolation-based droplet reconstruction (PHDR) algorithm is proposed to reconstruct droplets and to calculate volume on the basis of the contours obtained by the binocular droplet image segmentation algorithm without any symmetry assumptions. The droplet measurement system (DMS) is set up, and a series of experiments are conducted to verify the performance of the proposed method. The results show the advantages of our algorithms, that the volume measurement accuracy is ±3% and that the relative standard uncertainty is better than 2.1%.

This chapter aims to present the general context of a measurement system and basic concepts of measurement and related terms. The presentation develops according to a step-by-step, top-down strategy, which progressively characterizes measurement as (i) an empirical process, (ii) designed on purpose, (iii) whose input is a property of an object, and (iv) that produces information in the form of values of that property. These are proposed as necessary but not sufficient conditions for a process to be identified as a measurement: as such the contents of this chapter should be uncontroversial to be read and accepted by most, if not all, researchers and practitioners.

A photonics-based system for simultaneous angle of arrival (AOA) and frequency measurement is proposed and demonstrated. In the proposed system, an acousto-optic modulator (AOM) based optical frequency shift loop (OFSL) is used to generate an optical frequency-stepped local oscillator (LO). After balanced in-phase and quadrature (I/Q) down-conversion with the frequency-stepped LO, the frequency to be measured is obtained. Thanks to the balanced I/Q down-conversion, the image frequencies and undesired frequency-mixing components can be suppressed, making the system have better adaptability to multiple-target scenarios. In addition, the AOA from different RF sources can also be estimated based on the Van CittertZernike theorem, even if the working bands of these targets overlap with each other. In the proof-of-concept experiment, the measurement of multiple targets with different frequencies is achieved by frequency detection with 0.179 MHz resolution. Moreover, incoherent microwave sources at the same frequency band can also be distinguished in the angular direction, achieving the capacity of simultaneous frequency and AOA measurement of multiple-target.

The ability to monitor invasive sea lamprey (Petromyzon marinus) populations in the Laurentian Great Lakes is critical to protecting the region’s $7 billion USD fishing industry and preserving its biodiversity. Monitoring these invaders requires considerable fieldwork and human power, making remote lamprey detection systems attractive for their continuous monitoring capabilities and potential for workload reduction. However, a lack of available methods for detecting sea lamprey hampers development of such systems. Here we present a sensor composed of two exposed planar interdigitated electrodes (IDE) along with a DC measurement system for the detection of lamprey attachment underwater. Measuring voltage instead of impedance, reduces cost and signal processing complexity, making the device more attractive for field deployment. The system is calibrated to a baseline output voltage and deviations from this baseline occur when objects touch the IDE. Validation was done through testing on live adult sea lampreys using video recordings to correlate lamprey attachments to the sensor response. Three response types were identified corresponding to different attachments: sustained, short and sliding-sustained. Sensor response to sustained and sliding-sustained attachments showed a characteristic exponential decay whereas the response due to short attachments was indistinguishable from measurement noise. Lamprey size was found to have a weak linear correlation with both response parameters, positive for the voltage drop and negative for the time constant of voltage drop. A representative circuit for the lamprey-sensor interaction is proposed and simulated using element values calculated from the response parameters. The response of the model shows agreement with experimental data.

Instantaneous microwave frequency estimation enables numerous essential applications in the commercial, defense, and civilian marketplace. The advancement of applications is hindered by the bottleneck in electronic-based frequency measurement systems including narrow bandwidth, high errors rate, and low dynamic range. Photonics-based frequency estimation approaches not only increase the operation frequency range and provide rapid measurement response, but also benefit from immunity to electromagnetic interference and enhancement in system adaptability. Despite the unique advantages offered by photonics-based frequency estimation approaches, it is challenging to obtain linear mapping between the unknown frequency and the measured optical characteristics due to the nonlinear response in electro-optical devices, which consequently results in degradation in measurement precision and a complex calibration relationship. Therefore, it is critical to mitigate the challenge to achieve dynamic, adaptive, and high-precision estimation of microwave frequency. To this end, this paper presents the design and demonstration of a high-precision photonic based instantaneous frequency estimation system driven by machine learning. A three-layer deep neural network is used to tackle device nonlinearity and system noise, resulting in absolute error of < 50 MHz and root mean square error of 1.1 MHz.

The wide-area damping controller (WADC) using the remote signals obtained from wide-area measurement system (WAMS) as input is an effective measure to suppress interarea oscillations. Once the input signal of WADC is threatened by false data injection attacks (FDIAs), its performance will be decreased dramatically. To address this problem, this article proposes a resilient adaptive wide-area damping control (RAWADC) framework combined with linear state estimator (LSE) and goal representation heuristic dynamic programming (GrHDP). The LSE is proposed and adopted as a preprocessor to detect and correct the attacked data. By detecting and correcting FDIA, the integrity of the WAMS measurement signal is guaranteed. The GrHDP algorithm is employed to design the WADC, which not only does not require a detailed mathematical model of the system but can also adapt to variations of the operating conditions. Simulation results of the 16-machine 68-bus system with voltage source converter based HVdc indicate that the RAWADC can suppress interarea oscillation effectively under a wide range of operating conditions, different FDIA types and measurement noise.

Accurate in vivo measurement of finger joint kinematics is important for evaluation of treatment methods, implant designs, and for the development and validation of computer models of the hand. The main objective of this project was to develop a standardized finger kinematic measurement system employing electromagnetic (EM) tracking to measure in vivo finger motion pathways. A landmark digitization protocol was developed and used in vivo, in a biomechanical study using EM trackers secured to the finger segments. In vivo results for finger flexion/extension showed no significant differences between EM and goniometer results, 5°±3°; p = 0.735.

In industrial applications, copper wire is often used in the cable core or the motor armature. These copper wires are made by passing copper rods through the round-hole mold in a wire drawing machine. To reduce friction, it is necessary to use a lubricant mixed with oil and water for lubrication. The oil would be consumed over time, so it is required to add oil in a while to maintain lubrication. Generally, concentration sensors are used to monitor the concentration of lubricant to determine the timing of replenishing the oil. The principle of the sensor is by the intensity change of optical reflection to detect the concentration. However, copper chips scraped off from the wire during the drawing process will gradually accumulate on the surface of the sensor, thus causing sensor misdetection due to the shading of the light source. In this paper, an improved concentration sensing system is proposed to solve this problem. In the proposed system, a clean water tank is placed beside the oil tank, and the sensor is located in the clean water tank most of the time and is moved to the oil tank for measurement only at a specific time. After the measurement is completed, it will return to the clean water tank. This can greatly reduce the time the sensor is immersed in the oil tank, minimizing the chance that the sensor is shielded by copper scraps. To enable the sensor to move in this manner, a novel linkage design is proposed to meet several requirements in the movement of the mechanism. The mechanism is featured by its simple structure and only one driving device is needed. Furthermore, motion analyses of the mechanism are provided. Finally, we explain the working principle of the measurement system where the proposed linkage and the concentration sensing system are combined. To sum up the results, the proposed method can effectively expand the frequency of on-site maintenance from monthly to annual, which greatly reduces manpower requirements and effectively improves stability.

This paper proposes a methodology for calibration of industrial robots that uses a concept of measurement sub-regions, allowing low-cost solutions and easy implementation to meet the robot accuracy requirements in industrial applications. The solutions to increasing the accuracy of robots today have high-cost implementation, making calibration throughout the workplace in industry a difficult and unlikely task. Thus, reducing the time spent and the measured workspace volume of the robot end-effector are the main benefits of the implementation of the sub-region concept, ensuring sufficient flexibility in the measurement step of robot calibration procedures. The main contribution of this article is the proposal and discussion of a methodology to calibrate robots using several small measurement sub-regions and gathering the measurement data in a way equivalent to the measurements made in large volume regions, making feasible the use of high-precision measurement systems but limited to small volumes, such as vision-based measurement systems. The robot calibration procedures were simulated according to the literature, such that results from simulation are free from errors due to experimental setups as to isolate the benefits of the measurement proposal methodology. In addition, a method to validate the analytical off-line kinematic model of industrial robots is proposed using the nominal model of the robot supplier incorporated into its controller.

A distributed pressure sensor is proposed and designed for dynamic pressure measurement by using frequency-agile fast Brillouin optical time-domain analysis (BOTDA). A double coated single-mode fiber (SMF) is used as the sensing fiber because of its 1500- $\mu \text{m}$ thickness outer coating that can significantly improve the dependence of the Brillouin frequency shift on pressure. Then, the frequency-agile technique is used to generate frequency-agile pump pulse in BOTDA system so that a fast Brillouin gain spectrum distribution measurement can be achieved. An injection locked scheme is used as both optical filter and optical amplifier to improve the measurement accuracy. As a result, the static pressure sensitivity of the double coated fiber is −3.46 MHz/MPa with the pressure ranging from 0 to 30 MPa. Subsequently, a large dynamic range pressure measurement is performed with a sampling rate of 33.3 kHz and maximum sensing distance of 25 m. Furthermore, it is proved that the uncertainty of dynamic pressure measurement system is 0.03 MPa.

Integration of microgrid system with the conventional grid system is an alternative solution to fulfill the power demand in a Wide Area Measurement System. However, the microgrid connected to a grid system has many challenges like voltage sag, voltage unbalancing and power loss due the Distributed Generating (DG) sources used in the microgrid. The main objective of this paper is to model a microgrid system with Distributed Generating (DG) sources and Static Synchronous Compensator (STATCOM) to enhance the voltage profile and power flow and to reduce the power loss of the system. In this paper, a real-valued Damodar Valley Corporation grid-connected microgrid system with STATCOM controller is formed with the help of Power System Analysis Toolbox software and the voltage profile and power loss of the system are observed.

This is article provides an overview of the IEEE Instrumentation and Measurement Society Technical Committee TC-22 on Intelligent Measurement Systems (https://ieee-ims.org/technical-committee/tc-22). The scope of the TC-22 is related to fostering development and use of artificial intelligence, computational intelligence, and soft computing in measurement systems and related applications [1]–[3].

Electric thrusters are considered to be the most promising propulsion system of choice for space gravitational wave detection. However, the requirement of high accuracy and drag-free control results in significant challenges in thrust measurements. To better understand the thrust noise characteristics, this work proposes a time-frequency-domain method for thrust noise analysis, i.e., a scalogram method attained through wavelet analysis. Unlike the conventional Fourier transform method, it can provide 3-D relationships for the time-frequency and thrust noise. A free-state thrust measurement experiment identified that the environmental temperature produces the dominant impact in the thrust noise. During a 70,000 s measurement, even without thruster operation, the thrust noise increased from 0.3 μN/Hz1/2 to 19 μN/Hz1/2 as the environmental temperature increased from −1 °C to 3 °C. The corresponding frequency of peak thrust noise for the entire period tends to be the same of 0.1 Hz. Additionally, a Hall thruster thrust measurement experiment was conducted to verify the method's time-varying analysis capability for thrust noise. The complete time-frequency thrust noise variation throughout the entire operating process of a Hall thruster was observed. The results at different stages of thruster operation indicate that thrust noise has the greatest influence 150–300 s after ignition. This is because the thrust stand will take hundreds of seconds to reach thermal equilibrium after ignition. Another finding during the Hall thruster operation is that the 0.1 Hz frequency corresponding to the peak thrust noise is highly consistent with the frequency of peak thrust noise in the free-state thrust experiment. This reveals the fact that the 0.1 Hz frequency should be the dominant frequency for thrust noise caused by temperature drift in our thrust measurement system.

In this study, the ECT system was used to study the temporal and spatial distribution characteristics of bed density in an ADMFB under the influence of superficial gas velocity and static bed height. The results show that, In the axial distribution, the closer to the top of the bed at low gas velocity( 13 cm/s), the lower the density. In the radial distribution, the closer to the central axis, the lower the density. With the decrease of static bed height, the radial uniformity becomes worse. The optimal gas velocity is 12.5 cm/s, under which the bed spatial distribution is the most uniform, and the ecart probable moyen of spherical particles is 0.06. The separation density is smaller than the bed density and is close to the central axis density.

The interaction between the superconductor and the permanent magnet guideway (PMG) is very important in the system of high temperature superconducting (HTS) maglev. At present, HTS maglev has entered the key stage of engineering application. In order to study its dynamic characteristics and stability under higher speed conditions, an ultra-high-speed maglev test rig is developed to undertake this research task. The test rig is mainly composed of an inverter fed AC motor, the transmission mechanism, a brake, a guideway rotor, the control system, the HTS maglev measurement system and the permanent magnet electrodynamics levitation (EDL) measurement system. In the test rig, the circular guideways are rotated vertically, and the Halbach-array PMG and aluminum guideway are fixed inside the stainless-steel rotor to solve the centrifugal effect of high-speed rotation. In order to ensure that the test rig can run stably at the speed of 600 km/h, the design is strictly in accordance with the standard of 700 km/h during the development process. This paper verified the reliability of the important component through modeling and simulation of the guideway rotor. The ultra-high-speed maglev test rig has abundant functions, which can be used to study the high-speed dynamic characteristics and stability of HTS maglev and permanent magnet EDL, which has great practical significance to promote the research and development of maglev technology.

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time. This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.

Formation of irregularities on the inner surface of pipes is a common phenomenon that leads to corrosion and affects the functionality in the processing industries. Ultrasonic is known as one of the non-destructive means to address the formation of irregularities inside pipes. In this study, an ultrasonic measurement system is developed to detect the presence of internal irregularities in a pipe. An ultrasonic sensor EFC16T/R-2 with a frequency of 40 kHz was mounted outside the test pipe with a circular ring sensor unit. Different conditions of the inner pipe surface had caused fluctuations of the ultrasonic signal. The results show a low output voltage in the range of 2.1333 to 3.1334 V when no irregularities were detected. A higher output voltage was observed in the range of 5.4677 to 8.8667 V when irregularities occurred. The reconstructed images of irregularities had matched the actual condition of the pipe. Some images showed a slight inaccuracy of the position of the irregularities caused by the instability of the ultrasonic signals. Overall, the developed ultrasonic tomography is suitable as a tool for monitoring irregularities in a pipe. Key words: Ultrasonic, non-destructive method, pipe irregularities, tomography, instrumentation

Fringe projection profilometry (FPP) is a technique used for obtaining the three-dimensional shape of an object in fields such as reverse engineering, cultural heritage, and computer vision. The FPP measurement accuracy significantly depends on the arrangement of the measurement system, measurement space conditions, equipment setting, and object characteristics. However, experimental verification of the measurement conditions is time-consuming and expensive. This study presents a simulator that can set a broader range of measurement conditions. This simulator was developed using Unity, which is an integrated development environment for video games. The proposed simulator can be configured with a graphical user interface for device placement and operation, and it can freely adjust the aperture, exposure time, and depth-of-field of the camera. The measurement simulation results for a CAD model obtained using the proposed simulator are presented. The results indicate that camera defocus or unfit exposure time causes phase measurement errors.

Traditional bridge inspection methods mostly rely on the experience and expertise of inspectors in visual detection of cracks. Such visual inspection methods are not only time consuming but also could be affected by subjective judgment of inspectors and lack a unified inspection standard. To overcome the shortcomings of visual inspection, this study investigates the feasibilities of using an unmanned aerial vehicle (UAV) combined with laser projectors to measure surface cracks. All related parameters of the laser projectors that are fixed on the UAV are calibrated first in the laboratory. Then, the UAV with the laser projectors is used to capture images of surface cracks. The images from the UAV can be transformed to orthoimages using the laser projectors’ parameters and the image processing technology. Next, surface cracks in the orthoimages are automatically identified by a crack identification algorithm. Finally, the characteristics of surface cracks are evaluated by the image-based measurement technologies. To verify the accuracy of the proposed image-based measurement system (UAV with laser projectors), seven artificial cracks with different widths are measured using the proposed measurement system and the point cloud method (UAV with the global navigation satellite system) under different measurement distances. The test results demonstrate that while the distance between the UAV and the artificial crack is within 150 cm, the proposed image-based measurement system is able to measure crack widths more accurately than the GNSS point cloud method. In addition, the proposed image-based measurement system can automatically identify surface cracks and measure crack widths from UAV-captured images without manual measurements.

Gantry-type machines are normally large-scaled manufacturing equipment, such as contact/noncontact measuring machines and cutting machines. Its actual positional shift of the functional point (FP) from the ideal position in three directional components within the working volume is called the volumetric error and is mainly caused by geometric errors of each axis. Currently, the most popular volumetric error model with homogeneous transformation matrix (HTM) method is based on linkage-chain kinematics, which is lack of measurement principles in practice. Besides, most commercial instruments can only provide one-by-one geometric error measurement, which is too time consuming. In this paper, a new volumetric error modeling method developed by the author’s group, called the vector transfer (VT) method based on the Abbe principle and Bryan principle, was applied to a moving gantry-type optical measuring machine (OMM). The geometric errors in each axis were simultaneously measured by the developed multi-degree-of-freedom measuring systems (MDFMs) to reduce the measurement time and uncertainty. The volumetric errors of tested OMM were accurately assessed by experimental verification to the accuracy of 93%, which proved the effectiveness and applicability of the VT method.