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Hemispherical Resonator Gyroscope (HRG) is a classical high precision Coriolis Vibration Gyroscope (CVG), which performs attitude estimation of carrier by detecting the precession of standing wave of resonator, thus, the drift of standing wave of resonator has a great influence on the output accuracy of gyroscope, where the quality factor non-uniformity of resonator is one of main error sources. Ring electrode is a classical excitation structure of HRG because the standing wave can precess freely under its excitation, which makes the gyroscope have more accurate scale factor, larger measurement range and better dynamic characteristics. In this paper, the equations of motion of an ideal resonator excited by a ring electrode are derived by the elastic thin shell theory and Lagrange mechanical principle, then the corresponding equivalent mechanical model is established. According to the “average method”, it can be seen that the ideal resonator excited by the ring electrode works in integral mode, and any position in the circumferential direction of resonator can be a working point, which means that the quality factor non-uniformity has a great effect on the drift of standing wave. Therefore, the equations of motion of resonator with quality factor non-uniformity under the ring electrode excitation are deduced by the equivalent mechanical model, and the drift model of standing wave is established by the “average method”, it can be found that both the amplitude of quality factor non-uniformity and angle between the “inherent damping axis” and antinode axis of standing wave can affect the drift rate of standing wave. Moreover, the drift model indicates that if the input angular rate does not reach the threshold, the precession angular rate of standing wave will appear “self-locking” phenomenon, that is, the gyroscope will lose the integral effect.

Observation of dispersion in field situations has left three issues that may be better understood by applying advective transport phenomena. (1) In some experiments, the longitudinal dispersivity becomes constant with increasing pathlength and in other cases it remains growing. (2) Dispersivities reported from multiple comprehensive observations at a single site differ at similar pathlength in some cases more than a factor two. (3) The observed difference between the plume fronts and plume tails is not represented in the reported parameters. The analytic equations for advective transport phenomena at macro-scale of (De Lange 2019) describe the thickness of the affected flow-tube and the spread of the plume front and tail. The scale factor defines the size of the averaging domain and so of the initial phase. The new macro-scale correlation coefficient relates the growth of the longitudinal dispersivity beyond the initial phase to the aquifer heterogeneity. Using stochastic parameters for the aquifer heterogeneity, the parameters are quantified at fourteen field experiments in the USA, Canada and Europe enabling the comparison of calculated and reported final dispersivities. Using the quantified parameters, 146 reported and calculated dispersivities along the traveled paths show a good match. A dispersivity derived from the local plume growth may differ a factor of two from the aquifer-representative value. The growths of plume fronts and tails between two plume stages are assessed in fourteen cases and compared to calculated values. Distinctive parameters for the plume front and tail support better understanding of field situations. A user-ready spreadsheet is provided.

As a high-precision angular rate sensor, the Frequency-Modulated (FM) gyroscope based on MEMS craft has attracted more and more attention in recent years, owing to its advantages in low power consumption, low temperature sensitivity, very high bandwidth, and excellent scale factor stability. This review summaries the history and latest development of the FM gyroscope research. The theory of the frequency modulation effect of the Coriolis coupling is provided based on the method of rotating wave approximation in a systematic and explicit way. Typical FM gyroscope operation modes, such as QFM, IFM, LFM, and their working principles are introduced minutely. The error sources of FM gyroscopes, and the characters comparison between FM and AM gyroscopes are discussed in detail as well.

Relative gravity values were repeatedly measured at 19 gravity points in and around Sakurajima Volcano in October 2020 and March 2021, to monitor spatiotemporal mass variations associated with volcanic activity. The gravity values obtained from 1998 to 2021 showed a clear increase of up to +4.3 μGal/yr in the central part of the volcano. The gravity increase was even observed by the LaCoste’s G31 and G375 gravimeters; these gravimeters have been used at Sakurajima Volcano since 1975, and their temporal variations in reading values are negligibly small these days. These facts indicate that the gravity increase observed in the central Sakurajima is the actual one, because the artificial gravity change due to temporal variation of instrumental scale factor is expected to be small for the G31 and G375 gravimeters. The gravity increase can be explained by the point mass increase of 1.9×10¹⁰ kg/yr under the Kita-dake summit at the depth of 3 km below sea level. This result implies that volcanic mass continuously increases under Sakurajima Volcano, even during the quiet period of eruptive activities at the Minami-dake crater.

The main problem in cartography is that it is not possible to map/project/transform a spherical or ellipsoidal surface into a plane without distortions. The distortions of areas, angles, and/or distances are immanent to all maps. It is known that scale changes from point to point, and at certain points, the scale usually depends on the direction. The local linear scale factor c is one of the most important indicators of distortion distribution in the theory of map projections. It is not possible to find out the values of the local linear scale factor c in directions of coordinate axes x and y immediately from the definition of c. To solve this problem, in this paper, we derive new formulae for the calculation of c for a rotational ellipsoid. In addition, we derive the formula for computing c in any direction defined by dx and dy. We also considered the position and magnitude of the extreme values of c and derived new formulae for a rotational ellipsoid.

We characterised the displacement of a transmission X-ray tube target parallel to the beam axis due to thermal effects using interferometry. Displacements of up to 10 μm with a sensitivity of about 0.4 μm/W deposited power were measured relative to the X-ray tube's mechanical mounting position. They significantly influence the scale factor of dimensional computed tomography measurements by several 10−4 at high geometric magnifications. Finite element simulations revealed that the displacement is due to thermal deformation of the entire target assembly and helped to derive a linearised model that predicted the displacements within a few tenths of a micrometre. Radiographic measurements of a calibrated standard confirmed that the mechanical displacement of the target corresponded with the X-ray focal spot position. The findings help to increase the accuracy of dimensional X-ray computed tomography measurements and to ameliorate the design of transmission X-ray tubes.

Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics industries. The exceptional success of convolution neural networks (CNNs), has absolutely revolutionized the field of SISR. However, for most of the CNN-based SISR methods, excessive memory consumption in terms of parameters and flops, hinders their application in low-computing power devices. Moreover, different state-of-the-art SR methods collect different features, by treating all the pixels contributing equally to the performance of the network. In this paper, we take into consideration both the performance and the reconstruction efficiency, and propose a Light-weight multi-scale attention residual network (MSAR-Net) for SISR. The proposed MSAR-Net consists of stack of multi-scale attention residual (MSAR) blocks for feature refinement, and an up and down-sampling projection (UDP) block for edge refinement of the extracted multi-scale features. These blocks are capable of effectively exploiting the multi-scale edge information, without increasing the number of parameters. Specially, we design our network in progressive fashion, for substituting the large scale factors ( × 4) combinations, with small scale factor ( × 2) combinations, and thus gradually exploit the hierarchical information. In parallel, for modulation of multi-scale features in global and local manners, channel and spatial attention in MSAR block is being used. Visual results and quantitative metrics of PSNR and SSIM exhibit the accuracy of the proposed approach on synthetic benchmark super-resolution datasets. The experimental analysis shows that the proposed approach outperforms the other existing methods for SISR in terms of memory footprint, inference time and visual quality.

The residual intensity modulation (RIM) of the multifunction integrated optic circuit (MIOC) can be classified into the linear term and the nonlinear term, which can impact the performance of the fiber-optic gyroscope (FOG). According to the generation mechanism of the RIM and the demodulation principle of the FOG, a model is established to analyze the influence of RIM on the FOG performance. Simulation shows that both the linear and the nonlinear RIM can lead to a large and sudden change in the FOG output at reset points of the feedback step wave, as well as fluctuations within the reset period. The magnitude of the sudden change at the reset point is proportional to the difference between the linear RIMs of two branches in the MIOC, and the fluctuation within the period of the feedback step wave is more dependent on the nonlinear RIM. The relationship between the FOG output and the linear and nonlinear RIMs are verified by experiments. The RIM-induced output can result in the increase of the bias error and the scale factor error of the FOG, especially at small input angular velocity. Furthermore, the application of FOG for the real-time high-speed position tracking to stabilize the attitude control platform can be limited.

Recently, deep learning algorithms have demonstrated remarkable performance in object detection tasks and have applied to many fields such as intelligent networked vehicles and drones. Different from other fields like video surveillance, the fields of intelligent networked vehicles and drones only have resource-limited platform and have high requirements in model storage and real-time inference. To satisfy these requirements, model compression approaches are developed to reduce model parameters and computation. As channel pruning is a coarse-grained hardware-friendly weight pruning method, it is widely used in model compression. Most studies have only focused on utilizing the scale factor of Batch Normalization (BN) layers to determine whether to prune a certain channel. However, the shift factor turns to very important when its value is large. The channel with large shift factor has a great influence on subsequent layers. In our paper, we take shift factor into consideration to protect these channels. We first introduce network building blocks to analyze YOLOv3-SPP3 model and demonstrate the importance of shift factor on the channel saliency in the pruning process. Then we present a new pruning method called high-ratio channel pruning (HRCP) to improve the performance of the pruned model based on definition of the channel saliency with scale factor and shift factor of BN layer. Our experimental results show that HRCP performs better than SlimYOLOv3 when model volumes are reduced by 50%, 88%, and 92% accordingly. In high-ratio pruning, our HRCP can be pruned more 9.7% volumes than SlimYOLOv3 under the same performance condition.

This paper presents an in-run mode-matching method for MEMS gyroscopes based on the power symmetry between the sideband signals in the sense mode. The method takes advantage of the power symmetry of both the noise and the Coriolis response within the upper sideband (USB) and the lower sideband (LSB) of the driving frequency. The power difference is taken as a mismatch detection and is fed to the mode-matching controller, which regulates the tuning electrodes. During the matching control, the history of the estimated mode order is recorded to adaptively tune the controller gain, speeding up the tracking process and reducing the steady ripples. The experiments exhibited that the frequency loop could converge to the steady-state within a reasonable time and with 0.28Hz peak-peak fluctuation. By the mode-matching, the scale factor of the gyroscope under test was boosted by ten times; the angle random walk was enhanced from 8.13deg/h/sqrt(Hz) to 1.99deg/h/sqrt(Hz); the bias instability was improved from 2.09deg/h to 1.12deg/h. The method proposed in this paper can be implemented with a general platform that MEMS gyroscopes usually utilize.

SUMMARY The scale factor of each relative gravimeter must be calibrated both before and after the fieldwork of a terrestrial gravity survey, to reduce uncertainties and ensure high precision. Conventionally, such calibration is a time-consuming process performed following well-established baselines. We propose a new Bayesian method to estimate the scale factor in a hybrid gravity network that includes several absolute gravity observation stations. In this approach, the scale factor is estimated as a hyperparameter using the Akaike Bayesian information criterion and using known absolute gravity stations in the network or/and calibrated instruments as constraints. Testing the sensitivity of the gravity values and the residuals of the gravity difference between two successive stations to the change of the scale factor demonstrates the robustness of this method. We also test the sensitivity of the estimated scale factor in the presence of Gaussian noise and the non-linear instrumental drift rate. Moreover, if the maximum absolute gravity interval is greater than 60 per cent of the range of gravity values in the network, or if the known scale factors of calibrated gravimeters are well calibrated, this approach can provide reasonable estimates of the daily drift rate and the unknown scale factors, where the latter has an error of

The continuously developing Global Navigation Satellite Systems (GNSS), in particular with the latest operational BDS and Galileo, provide good opportunities to enhance the performance of precise positioning. This study evaluates the performance of high-rate precise point positioning (PPP) applied for seismogeodesy with very recent BDS and Galileo. The static experiments with 20-Hz GNSS data show that Galileo and BDS-3/BDS-2 achieve comparable performances, with average 3D RMS values of 0.6–1.3 cm and 0.8–1.2 cm, respectively. In the dynamic experiments, we use a single-axis earthquake simulator of Quanser Shake Table II to simulate seismic waveforms of real earthquakes and collect 20-Hz GNSS data to evaluate the PPP performance. Taking the displacements recorded by the embedded encoder of the shake table as the truth, the accuracies of PPP displacements during the seismic events are 4–8 mm for GPS, 6–9 mm for BDS-2, and 3–6 mm for Galileo and BDS-3/BDS-2. By combining BDS-3 with BDS-2, the positioning accuracy of BDS is improved by 50% with respect to BDS-2 only. Galileo and BDS-3/BDS-2 perform slightly better than GPS, which is likely because the two latest operational systems use the more precise passive hydrogen maser clocks and have lower signal-in-space range error. The multi-GNSS solutions with GPS + Galileo + BDS-3/BDS-2 further improve the accuracy reaching 2–5 mm. We also identify a scale factor in the high-rate PPP displacements, which is probably induced by the dynamic stress error and related to the oscillating frequency. The scale factor reaches the maximum of 1.71 at an oscillating frequency around 2.5 Hz and then descends as the frequency increases. The improved performance of high-rate PPP augmented by the recent BDS-3 and Galileo is helpful to improve the practical level of high-rate GNSS in seismogeodesy and expand its applications to many other high-rate and high-accuracy applications.

This manuscript discusses the utility of maximal period linear binary pseudorandom sequences [also referred to as m-sequences or maximum length sequences (MLSs)] and linear frequency-modulated (LFM) sweeps for the purpose of measuring travel-time in ocean-acoustic experiments involving moving sources. Signal design and waveform response to unknown Doppler (waveform dilation or scale factor) are reviewed. For this two-parameter estimation problem, the well-known wide-band ambiguity function indicates, and moving-source observations corroborate, a significant performance benefit from using MLS over LFM waveforms of similar time duration and bandwidth. The comparison is illustrated with a typical experimental setup of a source suspended aft of the R/V Sally Ride to a depth of∼10 m and towed at∼1 m/s speed. Accounting for constant source motion, the root mean square travel-time variability over a 30 min observation interval is 53 μs (MLS) and 141 μs (LFM). For these high signal-to-noise ratio channel impulse response data, LFM arrival-time fluctuations mostly appear random while MLS results exhibit structure believed to be consistent with source (i.e., towed transducer) dynamics. We conclude with a discussion on signal coherence with integration times up to 11 MLS waveform periods corresponding to ∼27 s.

The reduction of temperature effect is vitally important for high precision sensors. This paper introduces the theoretical analysis and experimental verification of an in-situ compensation method on the temperature coefficient of the scale factor for a single-axis electromagnetic force-balance feedback MEMS accelerometer. A micro thermistor, fabricated together with the MEMS accelerometer, is integrated to in-situ sense the temperature variation of the accelerometer. The thermistor is connected to the voltage-to-current resistance in series, which compensates the variation of the scale factor of the accelerometer that is influenced by temperature variation. The experimental results reveal that the temperature coefficient of the scale factor of the compensated accelerometer is within 6 ppm/°C for the temperature range from 25°C to 50°C, which is reduced by 68 times compared with the uncompensated accelerometer. Benefiting from the compatible fabrication process and the in-situ compensation, the proposed method is promising to be low-cost and high-efficiency.

A z-axis decoupled multi-frame vibration MEMS gyroscope (DMFVMG) is presented in this paper. Firstly, the decoupled principle is employed and the sense frame, Coriolis frame and drive frame are arranged through inner fame, intermediate frame and outer frame, and the movement characteristic analysis indicates that the structure is fully decoupled. The structure is simulated through mode analysis, harmonic response and drop shock response. The simulation results show that the resonant frequencies of the Sense Mode and Drive Mode are 10094Hz and 10162Hz, and the maximum deformation and stress with the shock analysis is 7.14 $\mu {\mathrm{ m}}$ and 235.19MPa (among three different directions). Then, the structure is fabricated through MEMS processing, and P-type single-crystalline silicon wafer and 7740 glass wafer are used. After that, the structure is packaged in a ceramic vacuum tube shell and the resonant frequencies and quality values of the sense and drive mode are 10354.21Hz and 5364, 10438.23Hz and 5219, the error between fabrication and simulation is 2.58% and 2.72%. And the monitoring system of DMFVMG prototype is established by analog circuit with drive closed-loop and sense open-loop. Then, the DMFVMG prototype is tested through turntable, and the range, scale factor and intercept are ±300°/s, −19.06mV/°/s and 95.43mV respectively. Finally, five repeated bias tests are arranged and the angular random walking values and bias stability are 0.2408°/ $\surd \text{h}$ , 0.2358°/ $\surd \text{h}$ , 0.2413°/ $\surd \text{h}$ , 0.2397°/ $\surd \text{h}$ and 0.2480°/ $\surd \text{h}$ , 2.858°/h, 3.159°/h, 3.428°/h, 1.891°/h and 3.467°/h respectively.

In the rotational inertial navigation system (RINS), the horizontal gyro drift can be modulated when the IMU rotates around the z axis. At the same time, the scale factor error of the z gyro can be offset by forward and reverse rotation. However, in the forward and backward rotation, the gyro scale factor error will still cause the heading angle output error. Moreover, the change of the scale factor error of the fiber optic gyros(FOG) is sensitive to temperature changes, which makes it difficult to calibrate gyro scale factor error accurately. Therefore, in this paper, a method to calibrate the scale factor errors of two RINSs dynamically is proposed. During the movement of vehicle, the heading angle difference between the RINS1 and RINS2 is calculated real time, the scale factor errors of RINS1 and RINS2 could be estimated at the same time with the least squares filtering algorithm. The simulation experiments show that when RINS1 and RINS2 adopt a reasonable rotation strategy, the gyro scale factor errors were less than 0.5ppm. In the vehicle experiment, the heading angle error caused by the gyro scale factor error is kept below 5”. And the method significantly improves the problem that scale factor error affected by temperature changes during navigation process, and at the same time solves the problem of the calibration coefficient error in the dynamic situation.

Recently, deep learning achieves competitive accuracy and robustness and dramatically improves the performance of target scale estimation through pre-trained special network branches. Yet, a fast and robust scale estimation method is still a challenging problem for visual object tracking. Early correlation filter tracking algorithm uses a multiscale search method to estimate the scale with the constant number of scale factors and invariant aspect ratio, which is redundant for the video frames with little or no scale change. Also, an independent network branch for target scale state is proposed, but the training network needs an abundance of datasets, and the effect is not very stable for the unseen target object. Aiming at the problems of existing scale estimation solutions, several variable scale learning methods are proposed to explore the scale change of the target. Firstly, we proposed a variable scale factor learning method, which makes us rid of the commonly used multiscale search with the flaws of fixed scale factors. Secondly, we used a multiscale aspect ratio solution to make up for invariant aspect ratio. Thirdly, the first and second scale methods were combined to propose a variable scale aspect ratio estimation method. Finally, the proposed scale estimation methods were embedded into the state-of-the-art ECO (Efficient Convolution Operators) and ATOM (Accurate Tracking by Overlap Maximization) trackers to replace the original scale methods for verifying the effectiveness of our proposed method. Extensive experiments on OTB100, UAV123, TC128 and LaSOT datasets demonstrate that the tracking performance can be improved effectively by using the proposed scale methods.

Background: Object Relations Scale (ORS: Iume, Hirai, Aoki & Baba, 2006) was developed for accessing an individual’s object relation pattern. It consists of five domains: Insufficiency of Intimacy, Superficiality in Interpersonal Relations, Egoistic Manipulation, Excessive Need for Identification, and Abandonment Anxiety. However, its factor structure using confirmatory factor analysis (CFA) is still undetermined. Purpose: This study aims at evaluating the psychometric properties of ORS, in particular, confirming its factor structure using CFA, and examining the relationship of object relation maturity, to mental health as well as to psychological distress. Methods: The subjects of this study were 547 medical college students in Japan. CFA were conducted in order to determine the best fit model. The relationships of maturity level of one’s object relation pattern to his/her mental health as well as psychological distresses were examined by t-tests. Results: A four-factor model, a modified version of the original five-factor model showed the best fit. Among the four factors, three were those included in the original model. They were Insufficiency of Intimacy, Superficiality in Interpersonal Relations, and Abandonment Anxiety. The last factor consisted of items originally included in the remaining two factors, Egotistic Manipulation and Excessive Need for Identification. Each ORS subscale score positively correlated with that of mental health problems as well as those of psychological distresses of one or more domains at significant levels. Conclusion: The four-factor model, which does not necessarily negate the original five-factor model proposed by Iume et al., showed the best fit. Immaturity and instability in object relation relationship contributed to a variety of distresses as well as poor mental health.

Recent research on single image super-resolution (SISR) has achieved great success due to the development of deep convolutional neural networks. However, most existing SISR methods merely focus on super-resolution of a single fixed integer scale factor. This simplified assumption does not meet the complex conditions for real-world images which often suffer from various blur kernels or various levels of noise. More importantly, previous methods lack the ability to cope with arbitrary degradation parameters (scale factors, blur kernels, and noise levels) with a single model. A few methods can handle multiple degradation factors, e.g., noninteger scale factors, blurring, and noise, simultaneously within a single SISR model. In this work, we propose a simple yet powerful method termed meta-USR which is the first unified super-resolution network for arbitrary degradation parameters with meta-learning. In Meta-USR, a meta-restoration module (MRM) is proposed to enhance the traditional upscale module with the capability to adaptively predict the weights of the convolution filters for various combinations of degradation parameters. Thus, the MRM can not only upscale the feature maps with arbitrary scale factors but also restore the SR image with different blur kernels and noise levels. Moreover, the lightweight MRM can be placed at the end of the network, which makes it very efficient for iteratively/repeatedly searching the various degradation factors. We evaluate the proposed method through extensive experiments on several widely used benchmark data sets on SISR. The qualitative and quantitative experimental results show the superiority of our Meta-USR.

[ES] Uno de los elementos más influyentes en los vehículos ferroviarios es el amortiguador antilazo. Este elemento tiene el objetivo de aumentar la estabilidad del vehículo incrementando su velocidad crítica sin disminuir el confort. En este trabajo fin de máster se realiza un estudio sobre la influencia de los amortiguadores antilazo en el comportamiento dinámico de un vehículo ferroviario. En primer lugar, el proyecto consta de una introducción teórica sobre la dinámica ferroviaria de baja frecuencia, y también de la descripción de las normas EN 14363 y EN 12299, relacionadas con la aceptación dinámica de vehículos ferroviarios y su confort, respectivamente. En el trabajo, el principal software que se va a utilizar es el Adams Vi-Rail. Este programa se utiliza para realizar diferentes análisis dinámicos de confort (EN 12299) y de aceptación (EN 14363), con la finalidad de estudiar la influencia de los amortiguadores antilazo en el comportamiento dinámico del vehículo. El estudio se va a llevar a cabo modificando las propiedades del factor de escala de la curva fuerza-velocidad, y la rigidez en serie, que equivale a los silentblocks de unión con bogie y caja. La idea es ver qué combinación de valores proporciona un comportamiento más equilibrado entre prestaciones dinámicas y confort. También se estudia el comportamiento del amortiguador antilazo de la marca KONI modelo ENDURANCE line, con el objetivo de poder comprobar un modelo comercial con el modelo usado en Vi-Rail. Por último, a partir de los resultados obtenidos se propone una mejora del modelo de amortiguador antilazo. Los resultados obtenidos son satisfactorios para poder continuar con la línea de investigación en futuros trabajos., [EN] One of the most influential elements in rail vehicles is the yae damper. The purpose of this element is to increase the stability of the vehicle by increasing its critical speed without reducing comfort. In this master's thesis, a study is carried out on the influence of yaw dampers on the dynamic behavior of a railway vehicle. First of all, the project consists of a theoretical introduction on low frequency railway dynamics, and also the description of the standards EN 14363 and EN 12299, relating to the dynamic acceptance of rail vehicles and their comfort, respectively. In the work, the main software to be used is Adams Vi-Rail. This program is used to perform different dynamic analyses of comfort (EN 12299) and acceptance (EN 14363), in order to study the influence of yaw dampers. The study is going to be carried out by modifying the properties of the scale factor of the forcé-velocity curve, and the series stiffness, wich is equivalent to the silentblocks of bogie-box binfding. The idea is t osee what combination of values provides mor balanced behavior between dynamic and comfotr features. The behavior of the KONI yaw damper, model ENDURANCE line, is also studied, with the aim of being able to check a commercial model with the model used in Vi-Rail. Finally, based on the results obtained, an improvement of the yaw damper model is proposed. The results obtained are satisfactory to be able to continue with the research line in future works.