Gravity Moment Research Articles

Linking zonal winds and gravity – II. Explaining the equatorially antisymmetric gravity moments of Jupiter

ABSTRACT The recent gravity field measurements of Jupiter (Juno) and Saturn (Cassini) confirm the existence of deep zonal flows reaching to a depth of 5 per cent and 15 per cent of the respective radius. Relating the zonal wind-induced density perturbations to the gravity moments has become a major tool to characterize the interior dynamics of gas giants. Previous studies differ with respect to the assumptions made on how the wind velocity relates to density anomalies, on the functional form of its decay with depth, and on the continuity of antisymmetric winds across the equatorial plane. For the case of Jupiter, most of the suggested vertical structures exhibit a rather smooth radial decay of the zonal wind, which seems at odds with the observed secular variation of the magnetic field and the prevailing barotropy of the zonal winds. Moreover, the results relied on modifications of the surface zonal flows, an artificial equatorial regularization or ignored the equatorial discontinuity altogether. We favour an alternative structure, where the equatorially antisymmetric zonal wind in an equatorial latitude belt between ±21° remains so shallow that it does not contribute to the gravity signal. The winds at higher latitudes suffice to convincingly explain the measured gravity moments. Our results indicate that the winds are barotropic, i.e. constant along cylinders, in the outer $3000$ km and decay rapidly below. The preferred wind structure is 50 per cent deeper than previously thought, agrees with the measured odd gravity moments, is compliant with the requirement of an adiabatic atmosphere and unbiased by the treatment of the equatorial discontinuity. We discuss possible implications for the interpretation of the secular variation of Jupiter’s magnetic field.

뇌성마비 마비말장애 성인의 파찰음 실현 양상 분석*

This study aims to analyze the acoustic characteristics of Korean affricates produced by dysarthric speakers with cerebral palsy. Korean fricatives and affricates are the consonants that are prone to errors in dysarthric speech, but previous studies have focused only on fricatives. For this study, three affricates /tɕ, tɕ^h, t͈ɕ/ appearing at word initial and intervocalic positions produced by six mild-moderate male speakers of spastic dysarthria are selected from a QOLT database constructed in 2014. The parameters representing the acoustic characteristics of Korean affricates were extracted by using Praat: frication duration, closure duration, center of gravity, variance, skewness, kurtosis, and central moment. The results are as follows: 1) frication duration of the intervocalic affricates produced by dysarthric speakers was significantly longer than that of the non-disordered speakers; 2) the closure duration of dysarthric speakers was significantly longer; 3) in the case of the center of gravity, there was no significant difference between the two groups; 4) the skewness of the dysarthric speakers was significantly larger; and 5) the central moment of dysarthric speakers was significantly larger. This study investigated the characteristics of the affricates produced by dysarthric speakers and differences with non-disordered speakers.

Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades

Aiming at the problem of low resources utilization of rotating blades in the selection process of aeroengine fan rotor blades, this paper takes the first-order bending dispersion, first-order torque dispersion, and gravitational moment difference of rotor blades as the selection criteria and takes the minimum remaining blades as the optimization goal. An intelligent selection algorithm of blades based on the collocation degree of blades is proposed and achieves the efficient selection and full utilization of rotating blades. Aiming at the problem of multiple installations and multiple adjustments and low assembly success rate of fan rotor blades, this paper takes the gravity moment difference of the two blades at the diagonal position of 180° as the constraint and takes the minimum residual unbalance as the optimization objective, adopts the improved simulated annealing algorithm to optimize the assembly sequence of rotating blades, and greatly reduces the residual unbalance of blades, which is beneficial to reduce the number of assembly adjustments of blades. The optimizing selection and optimizing matching methods of rotating blades realize the full utilization and efficient assembly of blades and lays a foundation for the reliability and robustness of the assembly quality and service performance of blades.

Photothermal pneumatic wheel with high loadbearing capacity

Light-driven gravity-defying rotating devices are promising in many fields, but their applications are limited by low loadbearing capacity, induced stress and produced deformation. Here, we report fabrication of a photothermal pneumatic wheel (PPW) by combining several photothermal pneumatic fluidic devices, which was driven by photoinduced gravity moment. Light energy was firstly transferred into thermal energy of air, and the produced air pressure resulted in a gravity center shift to rotate the PPW. By carefully designing and manipulating the photothermal pneumatic actuating behaviors, a loadbearing of 16.6 g and self-sustained rotation were achieved. Various shapes and materials are suitable for the PPW, providing a platform for robotics, biomimetics and energy conversion.

Dynamics and reliability analysis of the deployment process of spring folding wing

A “X” type spring folding wing is taken as the research object in this paper, and schematic diagrams of unfolding and locking process are drawn according to its structure and working principle. The theoretical model of kinematics and dynamics which considering the gravity moment is established by simplifying the geometric model of deployment mechanism of the folding wing, so the curve of deployment angle and angular velocity of the outer wing during the deployment process can be obtained. The pulley diameter, preload force and stiffness of the spring-actuated are taken as random variables, and the deployment time and angular velocity are regarded as evaluation indicators. The Monte Carlo method is used to analyse the reliability of deployment process of the folding wing, and the reliability of the deployment process is obtained. The results show that the reliability of the deployment process of the spring folding wing is high.

A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data. The purpose of our research is to present an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks. To represent text documents, we introduce inverse gravity moment based term weighted word embedding model with trigrams. In this way, critical words/terms have higher values by keeping the word-ordering information. In our model, we present a three-layer stacked bidirectional long short-term memory architecture to identify sarcastic text documents. For the evaluation task, the presented framework has been evaluated on three-sarcasm identification corpus. In the empirical analysis, three neural language models (i.e., word2vec, fastText and GloVe), two unsupervised term weighting functions (i.e., term-frequency, and TF-IDF) and eight supervised term weighting functions (i.e., odds ratio, relevance frequency, balanced distributional concentration, inverse question frequency-question frequency-inverse category frequency, short text weighting, inverse gravity moment, regularized entropy and inverse false negative-true positive-inverse category frequency) have been evaluated. For sarcasm identification task, the presented model yields promising results with a classification accuracy of 95.30%.

Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment

Facial age estimation is of interest due to its potential to be applied in many real-life situations. However, recent age estimation efforts do not consider juveniles. Consequently, we introduce a juvenile age detection scheme called LaGMO, which focuses on the juvenile aging cues of facial shape and appearance. LaGMO is a combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM). Inspired by the formation of words from morphemes, we obtained facial appearance features comprising facial shape and wrinkle texture and represented them as terms that described the age of the face. By leveraging the implicit ordinal relationship between the frequencies of the terms in the face, TF-IGM was used to compute the weights of the terms. From these weights, we built a matrix that corresponds to the possibilities of the face belonging to the age. Next, we reduced the reference matrix according to the juvenile age range (0–17 years) and avoided the exhaustive search through the entire training set. LaGMO detects the age by the projection of an unlabeled face image onto the reference matrix; the value of the projection depicts the higher probability of the image belonging to the age. With Mean Absolute Error (MAE) of 89% on the Face and Gesture Recognition Research Network (FG-NET) dataset, our proposal demonstrated superior performance in juvenile age estimation.

An automatic software vulnerability classification framework using term frequency-inverse gravity moment and feature selection

Vulnerability classification is an important activity in software development and software quality maintenance. A typical vulnerability classification model usually involves a stage of term selection, in which the relevant terms are identified via feature selection. It also involves a stage of term-weighting, in which the document weights for the selected terms are computed, and a stage for classifier learning. Generally, the term frequency-inverse document frequency (TF-IDF) model is the most widely used term-weighting metric for vulnerability classification. However, several issues hinder the effectiveness of the TF-IDF model for document classification. To address this problem, we propose and evaluate a general framework for vulnerability severity classification using the term frequency-inverse gravity moment (TF-IGM). Specifically, we extensively compare the term frequency-inverse gravity moment, term frequency-inverse document frequency, and information gain feature selection using five machine learning algorithms on ten vulnerable software applications containing a total number of 27,248 security vulnerabilities. The experimental result shows that: (i) the TF-IGM model is a promising term weighting metric for vulnerability classification compared to the classical term-weighting metric, (ii) the effectiveness of feature selection on vulnerability classification varies significantly across the studied datasets and (iii) feature selection improves vulnerability classification.

Investigation of modal properties and layout of elastomer diaphragm tanks in telecommunication satellite

Elastomer diaphragm tanks have been adopted in many spacecraft applications, such as upper stages, space platforms, commercial and civil spacecraft as a propellant tank in which liquid propellant is covered with a diaphragm and compressed into a combustion chamber by gas pressure. Investigating the dynamic response and the influence of various parameters on the modal properties is a significant issue in the modeling of the space diaphragm tanks. In this work, the numerical study is presented to investigate the modal properties of a spherical fuel tank in both uncovered and covered cases with a flexible diaphragm as fluid–structure interacting situations. The filling volume ratio of fluid ranges is from 25 to 50%. Numeric dynamic simulations created by ANSYS software are verified by the experimental results of test tank and then, the validation of the numeric model is used to determine the natural frequencies and mode shapes of really elastomer diaphragm tank as the second case study for two conditions: fluid filling volume ratio of 80% that the tank is in full load condition and liquid filling volume ratio of 14% when the diaphragm completely inverted. After that, the best layout of diaphragm tanks in two-story satellite is detected by calculating deviations from center of gravity and cross moments of inertia objective functions in multi objective optimization software. Then the natural frequencies of satellite include diaphragm tanks computed and compared with natural frequencies and static loads of launch vehicle requirements. The achieved results of this study showed that the numeric simulations are able to renovate the experimental vibration characteristics of the diaphragm tank with a good precision. In addition numeric model can detect the best layout of diaphragm tank components.

Mechanical properties of soybean plants under various plant densities

Lodging can reduce grain yield and quality, especially in crops planted at high densities. Force analysis, which relates stem mechanical properties and morphological characteristics under high densities, was used to study lodging in soybean (Glycine max (L.) Merr.). We applied mechanical lodging and examined the relationships between stem bending moment of the breaking force and morphological characteristics in two soybean varieties of contrasting heights grown at four crop population densities (200000, 300000, 400000 and 500000 plants ha–1). The experiment was performed in a split-plot design during 2015–16. Measurements were made during the R1–R8 growth stages. Results showed that the full seed stage was the most sensitive period for lodging. Stem strength of the soybean plants increased with growth and development; however, during the R5–R6 stages, the weakened degree of stem strength was greater than the gravity moment. This was the primary reason for the soybeans tending to lodge during this period. In addition, increasing the planting density weakened the mechanical properties significantly. During breeding efforts to improve the soybean lodging resistance, more attention should be paid to strengthening soybean stems. This study also showed that the ratio of stem diameter to plant height could be a new quantitative index for evaluating the lodging resistance of soybeans.

Heavy Military Land Vehicle Mass Properties Estimation Using Hoisting and Pendulum Motion Method

Mass properties such as the centre of gravity location, moments of inertia, and total mass are of great importance for vehicle stability studies and deployment. Certain parameters are required when these vehicles need to be arranged inside an aircraft for the carrier to achieve proper mass balance and stability during a flight. These parameters are also important for the design and modelling process of vehicle rollover crash studies. In this study, the mass properties of a military armoured vehicle were estimated using hoisting and pendulum method. The gross total weight, longitudinal and vertical measurements were recorded by lifting the vehicle using a mobile crane and the data were used to estimate the centre of gravity. The frequency of vehicle oscillation was measured by applying swing motion with a small angle of the vehicle as it is suspended on air. The centre of gravity and mass moment of inertia were calculated using the vector mechanics approach. The outcomes and limitations of the approach as discussed in details.

Frontal crash seat belt restraint effectiveness and comfort accessories used by older occupants

Objective: Around a quarter of older occupants use some type of comfort or orthopedic aftermarket accessory on the vehicle seat while traveling in a vehicle. The aim of this study was to investigate the effect of comfort accessories on the performance of the seat belt restraint system in a frontal crash in terms of potential injury implications for older occupants.Methods: Eight frontal sled tests (43 km/h, 32 g) were carried out on a deceleration sled fitted with a three-point lap-sash seat belt and a front passenger seat from a common Australian passenger car for each test. A 5th percentile Hybrid III anthropometric test device (ATD) was positioned in the seat and measurements were recorded for head center of gravity acceleration, chest acceleration, neck forces and moments and sternal deflection. Tests were carried out in a baseline condition and with seven comfort accessories. Each comfort accessory was inserted between the ATD and vehicle seat as it is intended to be used, with the ATD otherwise positioned as close as possible to the baseline test position.Results: Initial distance between the seat belt anchor and ATD hip was associated with a statistically significant decrease in Head Injury Criterion and increase in sternal deflection. Submarining was related to the ATD torso recline angle and angle of the lap belt from the seat belt anchor.Conclusions: Accessories placed between the seat back and the lumbar region of an occupant have the potential to increase the risk of submarining due to a change in posture and should be avoided if such a change in posture when seated with an accessory is excessive. Sitting on seat cushions resulted in the greatest increase in seat belt anchor to hip distances and hence largest increase in sternal deflection. Given the fragility, frailty and particular importance of chest injuries among older vehicle occupants, further investigation is needed to determine whether these changes in ATD sternal deflection observed with seat cushion use results in injury threshold limits being exceeded and whether pretensioners and load limiters would ameliorate these effects without causing other negative changes in occupant response or kinematics.

Changes in design driving load cases: Operating an upwind turbine with a downwind rotor configuration

AbstractThis work considers the design driving load cases from a full design load basis analysis on an upwind turbine changed into a downwind configuration. The upwind turbine is a commercial class IIIA 2.1‐MW turbine, manufactured by Suzlon. The downwind turbine shows an increase in the normalized tower clearance by 6%, compared with the upwind concept. Removing the blade prebend increases the normalized minimum tower clearance by 17% in the downwind configuration compared with the upwind configuration. The extreme loads on the longitudinal tower bottom bending moment are seen to generally increase by 17% because of the overhanging gravity moment of the rotor‐nacelle assembly. The extreme blade root bending moments are reduced by 10% flapwise, because of the coning of the rotor in downwind direction. The fatigue loads suffer from the tower shadow, leading to an overall increase of the fatigue loads in the blades with up to 5% in flapwise direction in the downwind configuration. Because of blade deflection and coning direction, the downwind configuration shows a 0.75% lower annual energy production. Removing the prebend increases the annual energy production loss to 1.66%.

Improved inverse gravity moment term weighting for text classification

Text classification is one of the popular high dimensional classification problems where providing better feature vector representations explicitly improve classification performances. Thus, assigning appropriate weights to features or terms are crucial for obtaining effective feature vector representations. The methods used for weighting terms in text classification are called term weighting schemes. Although there exist some term weighting schemes for text classification, they are not fully effective and researchers still focus on proposing new term weighting schemes. In this study, two novel term weighting schemes namely SQRT_TF-IGMimp and TF-IGMimp derived from standard inverse gravity moment formula are proposed to improve weighting behaviors of existing TF-IGM scheme especially for some extreme cases. The performances of proposed schemes are compared with two standard IGM based schemes and five other state-of-the-art term weighting methods on both unbalanced (Reuters-21578) and balanced (20 Mini Newsgroups and 20 Newsgroups) datasets with KNN, SVM, and NN classifiers. Micro-F1 and macro-F1 are used as success measures. The experiments are conducted with various different feature sizes to examine the effects of the feature size on the success of weighting. The experimental results showed that the proposed SQRT_TF-IGMimp method generally outperformed all schemes including both standard TF-IGM and SQRT_TF-IGM schemes. However, the proposed TF-IGMimp scheme also showed mostly better performance than standard TF-IGM. To demonstrate validity of the proposed weighting scheme having maximum performance, t-test is also used and it can be stated that the performance gains obtained by the proposed SQRT_TF-IGMimp weighting scheme compared to standard SQRT_TF-IGM are statistically significant.

A linear approximation for the effect of cylindrical differential rotation on gravitational moments: Application to the non-unique interpretation of Saturn’s gravity

The higher order gravitational moments of a differentially rotating planet are greatly affected or even dominated by the near surface differential flow. Unlike the contribution from rigid body rotation, this part of the gravity field can be well estimated by linear theory when the differential rotation is on cylinders and the corresponding gravity field arises from the higher order moments directly introduced by that flow. In the context of an n = 1 polytrope, we derive approximate analytical formulas for the gravity moments. We find that ΔJ2n is typically at most a few times (−1)n+1aqd5/2, where a is the amplitude of the differential rotation (as a fraction of the background rigid body rotation), q=Ω2R3/GM is the usual dimensionless measure of rotation for the planet (mass M, radius R), and d << 1 is the characteristic depth of the flow as a fraction of the planetary radius. Applied to Saturn, with a set by the observed surface wind amplitude, we find first that the observed signs of the ΔJ2n are a trivial consequence of the definition of the corresponding Legendre polynomials, but the Cassini observations can not be explained by a simple exponentially decaying flow and instead require a substantial retrograde flow at depth and a larger d than the simple scaling suggests. This is consistent with the results reported by Iess et al. (2019). However, there is no fluid dynamical requirement that the flows observed in the atmosphere are a guide to the flows thousands of km deeper. We explore a wide range of flow depths and amplitudes which yield values for ΔJn that are acceptable within the error estimates and thus highlight the inherent non-uniqueness of inferences made from the higher order gravity moments.

Chicken swarm optimisation based clustering of biomedical documents and health records to improve telemedicine applications

The aim of this paper is to develop an efficient ontology enabled chicken swarm optimisation (CSO) based clustering algorithm with dynamic dimension reduction (DDR) to efficiently cluster biomedical documents and health records to facilitate telemedicine applications. A total of 350 documents and health records are collected from PubMed repository for telemedicine applications. First, the documents are pre-processed via semantic annotation and concept mapping while term frequency and inverse gravity moment (TF-IGM) factor is used to improve document representation and the modified n-gram resolves the substitution and deletion malpractices. DDR technique reduces feature space dimension and prunes non-useful text features to increase the clustering accuracy by tackling the high dimensionality problem. Finally, the clusters are formed by CSO clustering. Experimental simulations prove that the CSO-DDR clustering model is significantly efficient than the traditional algorithms and ensures reliable and adaptive telemedicine applications with better clustering of biomedical documents and health records.

Chicken swarm optimisation based clustering of biomedical documents and health records to improve telemedicine applications

The aim of this paper is to develop an efficient ontology enabled chicken swarm optimisation (CSO) based clustering algorithm with dynamic dimension reduction (DDR) to efficiently cluster biomedical documents and health records to facilitate telemedicine applications. A total of 350 documents and health records are collected from PubMed repository for telemedicine applications. First, the documents are pre-processed via semantic annotation and concept mapping while term frequency and inverse gravity moment (TF-IGM) factor is used to improve document representation and the modified n-gram resolves the substitution and deletion malpractices. DDR technique reduces feature space dimension and prunes non-useful text features to increase the clustering accuracy by tackling the high dimensionality problem. Finally, the clusters are formed by CSO clustering. Experimental simulations prove that the CSO-DDR clustering model is significantly efficient than the traditional algorithms and ensures reliable and adaptive telemedicine applications with better clustering of biomedical documents and health records.

Lower Limb Exoskeleton for Rehabilitation with Improved Postural Equilibrium

In this work we present a lower limb haptic exoskeleton suitable for patient rehabilitation, specifically in the presence of illness on postural equilibrium. Exoskeletons have been mostly conceived to increase strength, while in this work patient compliance with postural equilibrium enhancement is embedded. This is achieved with two hierarchical feedback loops. The internal one, closing the loop on the joint space of the exoskeleton offers compliance to the patient in the neighborhood of a reference posture. It exploits mechanical admittance control in a position loop, measuring the patient’s Electro Miographical (EMG) signals. The problem is solved using multi variable robust control theory with a two degrees of freedom setting. A second control loop is superimposed on the first one, operating on the Cartesian space so as to guarantee postural equilibrium. It controls the patient’s Center of Gravity (COG) and Zero Moment Point (ZMP) by moving the internal loop reference. Special attention has been devoted to the mechanical multi-chain model of the exoskeleton which exploits Kane’s method using the Autolev symbolic computational environment. The aspects covered are: the switching system between single and double stance, the system’s non-holonomic nature, dependent and independent joint angles, redundancy in the torque controls and balancing weight in double stance. Physical experiments to validate the compliance method based on admittance control have been performed on an elbow joint at first. Then, to further validate the haptic interaction with the patient in a realistic situation, experiments have been conducted on a first exoskeleton prototype, while the overall system has been simulated in a realistic case study.

Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applications

Clustering of biomedical documents has become a vital research concept due to its importance in the clinical and telemedicine applications. The clustering of the medical documents is being considered as a major issue because of its unstructured nature. This paper focuses on developing an efficient document clustering approach for the medical documents to be utilized in telemedicine applications. Most existing models utilize n-gram techniques for phrase identification and term, concept or semantic based models for clustering applications. However n-gram does not perform well when the original document has been modified while only hybrid models provide relatively improved clustering. The proposed document clustering approach is named as enriched semantic smoothing model which has been developed on the concept of Mesh ontology. As the semantic smoothing model is not effective in handling the density of general words, an improved model with term frequency and inverse gravity moment (TF-IGM) factor and improved background elimination is used. Unlike term frequency and inverse document frequency), TF-IGM precisely measure the class distinguishing power of a term by making use of the fine-grained term distribution across different classes of text in documents. The modified n-gram technique, which detects the cases of substitution and deletion in the documents and averts them, improves the phrases identification. The clustering efficiency of the k-means clustering and hierarchical clustering algorithms is improved by utilizing the proposed model. The experiments are made on Mesh ontology based PubMed documents with similarity measures and cluster validity indexes used for comparisons. The results show that the proposed approach of medical document clustering is highly accurate and thus improves the concepts of clinical practices and telemedicine.

The effect of differential rotation on Jupiter's low‐degree even gravity moments

AbstractThe close‐by orbits of the ongoing Juno mission allow measuring with unprecedented accuracy Jupiter's low‐degree even gravity moments J2, J4, J6, and J8. These can be used to better determine Jupiter's internal density profile and constrain its core mass. Yet the largest unknown on these gravity moments comes from the effect of differential rotation, which gives a degree of freedom unaccounted for by internal structure models. Here considering a wide range of possible internal flow structures and dynamical considerations, we provide upper bounds to the effect of dynamics (differential rotation) on the low‐degree gravity moments. In light of the recent Juno gravity measurements and their small uncertainties, this allows differentiating between the various models suggested for Jupiter's internal structure.