Scale Estimation Research Articles

Development of shear-wave velocity profiles and kappa compatible with Ground Motion model for Taiwan

A new velocity model (GMM) compatible with a ground motion prediction model (GMM) for Taiwan is presented in this study. The model is developed using the data set extracted from the flatfile compiled for Taiwan Senior Seismic Hazard Analysis Committee (SSHAC) level 3 project (NCREE, 2015). The Phung et al. (2020) ergodic Sa GMM was used as a backbone model for estimating the site amplification in the frequency domain using inverse Random vibration theory (IRVT). The main motivation for developing a velocity model is that the estimation of the site scaling in terms of the time-averaged shear-wave velocity over the top 30 m (VS30), the method also provides the corresponding depth-dependent VS(z) profiles and the value for the selected VS30 value as a result of the inverse quarter-wave-length method (IQWL). Not all of the empirical amplification can be explained by the 1-D VS profiles andvalues. For soft sites (VS30 < 500 m/s), and intermediate periods (0.5-2.0 sec), there is additional amplification in the empirical data which is attributed to 3-D path and site effects. Using the proposed method, the resulting GMM provides a VS profile, k, and 3-D effect for each VS30 value, which provides a more informative handoff of ground-motion information for use in site-specific site response studies.

Adaptive Nadaraya-Watson kernel regression estimators utilizing some non-traditional and robust measures: a numerical application of British food data

In nonparametric regression research, estimation of regression function is a prime concern. Recently, researchers developed some modified Nadaraya-Watson (N-W) regression estimators utilizing robust mean, median and harmonic mean. In this paper, we propose to utilize the additive combination of non-traditional measures i.e. (Hodges-Lehmann, Mid-Range, Tri-Mean, Quartile-Deviation) with the robust minimum covariance determinant (MCD) scale estimator in (N-W) regression estimator. Utilizing these measures, we get some new versions of (N-W) regression estimator. We also attempted to derive the properties of the proposed versions, such as bias, variance, mean square error (MSE), and mean integrated square error (MISE). The proposed estimators are compared with some of the existing estimators available in literature through a simulation study, utilizing two artificial populations. We also incorporated real-life application by taking British food data set denoted as Engel95, and assess the predictive ability of nonparametric regression, based on proposed and existing N-W estimators.

A multi-time scale schedulable capacity evaluation method for stations considering user wishes

As a flexible resource, electric vehicles (EVs) can realize various auxiliary services such as peak shaving, reactive power optimization, fault recovery, and emergency power supply. It is essential to quickly and accurately evaluate the EV schedulable capacity to realize auxiliary services. In this context, this paper first simulates EV charging demand using trip chain and Monte Carlo (MC) methods based on the 2017 National Household Travel Survey (NHTS2017) results to provide a multi-time scale estimation of EV charging station schedulable capacity to provide a data foundation. Then, considering incentive price, price sensitivity, and user credit, a demand response model is established to describe the uncertainty of user response and constraints such as differentiated demand on both sides of the grid user, battery charging and discharging state, battery loss, and response uncertainty are integrated to establish a multi-time scale evaluation model of the schedulable capacity of EV charging stations. Finally, the effectiveness of the proposed evaluation model is verified by simulation, and the effects of site area, incentive price, and dispatching time scale on the maximum schedulable capacity of EV charging stations are analyzed.

A case report assessing the utility of a low-cost tracking GPS device for monitoring terrestrial mammal movements

BackgroundAccurate data on animal movements can highlight behavioural and ecological issues, such as territorial interactions, barriers to migration patterns, including compromised movement corridors, or avoidance of deteriorating habitats, and disease transmission, thus helping in conservation decision making. This study examines the utility of mOOvement global positioning system (GPS) tags, a low-cost cattle ear tag tracking device to monitor movements of terrestrial mammal populations in South Australia.ResultsStationary tags (n = 40) were used to test horizontal accuracy with a median location error of 33.26 m (IQR = 16.9–59.4), and maximum recorded error of 410 m. The locational accuracy was weakly influenced by the horizontal dilution of precision (HDOP), a measure of satellite availability and geometry, and overhead canopy cover. Numerous tags produced infrequent and inconsistent readings, median of 12 records per day (IQR = 6–12), correlating negatively with the tag’s distance from the centrally located LoRa antenna; however, some tags recorded fewer than one position per day.ConclusionsWe propose that the primary cause of mOOvement tag inadequacy is the use of only the GPS satellite constellation (USA, 1978), which does not provide adequate coverage in either satellite number or geometry in the sky at the − 35° latitude to calculate accurate positions regularly over 24 h, unlike the multiple constellations available in the global navigation satellite system (GNSS). We conclude that GPS tags are unsuitable for studies requiring high locational accuracy or identification of an individual’s social interactions, where the GPS constellation has a limited number of satellites available during prolonged periods. They can, however, be used to provide estimates of home range size or track large scale daily movements of animals in more equatorially located regions.

Robust confidence intervals for the process capability index Cpk with bootstrap improvement

The process capability index (PCI), C pk , one of the widely used tools for assessing the capability of a manufacturing process, expresses the deviation of the process mean from the midpoint of the specification limits. The C pk is known to perform well under the general assumption that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and sample standard deviation are used for the estimation of the PCI. However, the sample mean and sample standard deviation are quite sensitive to data contamination and this will result in underperformance of C pk . Therefore, in this article, we propose alternatives to the conventional method by replacing the sample mean and sample standard deviation with robust location and scale estimators. We also propose a method for constructing a robust PCI C pk confidence interval which lends itself to robust statistical hypothesis testing. The robust hypothesis testing methods based on this confidence interval are shown to be quite efficient when the data are normally distributed yet also outperform the conventional method when data contamination exists.

Postseismic gravity changes after the 2011 Tohoku earthquake observed by superconducting gravimeters at Mizusawa, Japan

Postseismic gravity changes after the 2011 Tohoku earthquake (Mw9.0) were investigated using the data from superconducting gravimeters (SGs) at Mizusawa, Japan. The data in the period from 2014 to 2021 were used in the analysis. The SG data were first corrected for instrumental drift using the results of absolute gravity measurements. Then, correction for the hydrological effect was applied based on physical modeling of soil moisture. Finally, the effect of vertical displacement of the station (free-air effect) was corrected using GNSS data. After these corrections, residual gravity indicated a long-term increase, with its rate gradually decreasing with time. This fact suggests that viscoelastic relaxation after the earthquake played an important role in producing the long-term gravity changes. Fitting a decaying exponential function of time to the residual series yielded 89.4 ± 4.4 µGal (1 µGal = 10–8 ms–2) as the total gravity change and 635 ± 17 days as the characteristic time scale. In addition to the ground-based observations, the data from satellite gravity missions GRACE/GRACE-FO were analyzed to retrieve gravity changes at Mizusawa. Similar analysis of the satellite-based data yielded 15.2 ± 1.6 µGal as the total gravity change and 3444 ± 599 days as the characteristic time scale. The difference in the estimates of the total gravity change, of a factor of about 6, from the ground-based and the satellite-based observations may be attributed to the limited spatial resolution in the latter method. The difference in the estimates of the time scale, of a factor of about 1/5, may originate from the difference in the depth where the two kinds of gravimetry are mainly sensitive. Referring to recent theoretical studies on postseismic deformations after the 2011 Tohoku earthquake, our results can be interpreted consistently by assuming the existence of a layer of viscoelastic materials with viscosity 2times {10}^{18} text{Pa s} underneath the Tohoku area of Japan.Graphical

Multi-Zone Infection Risk Assessment Model of Airborne Virus Transmission on a Cruise Ship Using CONTAM

Since the onset of the pandemic, the cruise industry has faced substantial challenges, experiencing an 81% year-on-year decline in 2020. Notably, China’s cruise industry has remained shuttered for nearly two years. The conventional epidemiological investigations relying on human memory have proven inadequate in regard to swiftly and reliably identifying high-risk populations, thus leading to excessive pandemic prevention or other inappropriate measures. Furthermore, current research endeavors have not adequately addressed the critical issues of isolation location selection and the estimation of isolation scale within multi-zone indoor environments. Therefore, how to control the epidemic with minimal impact on the public has become an urgent problem. To solve the problems mentioned above, a multi-zone infection risk assessment model of airborne virus transmission was proposed to rapidly qualify the risk of infection, identify the high-risk population, and provide guidance. The model can be divided into two parts, including a multi-zone airflow model and a risk assessment model based on the Wells–Riley model. A large in-service ro-ro passenger ship already in service was used for the application of the proposed method. The results show that the number of isolated rooms shall be at least 5% of the occupancy and the isolation location can be chosen through the multi-zone infection risk assessment model. This paper provides insights into risk assessment to mitigate the spread of epidemics on a large cruise ship, and the method can be easily applied to all kinds of multi-zone indoor environments.

Representative velocity scale of Rayleigh‐Bénard convection with shear‐thinning fluids

AbstractAlthough natural convection is frequently encountered in various chemical processes, Rayleigh number (Ra) cannot be defined fully in shear‐thinning fluid systems. In particular, the velocity scale, which is necessary to estimate the effective viscosity of the system, should be discussed carefully. Thus, in this study, the representative velocity scale of Rayleigh‐Bénard (RB) convection, which is a typical example of natural convection, with shear‐thinning fluids was investigated based on the velocity fields obtained using computational fluid dynamics. Numerical simulations revealed that the critical temperature difference at which RB convection starts to fully develop decreases with an increase in the shear‐thinning property. The shear‐thinning property also induced subcritical bifurcation. In addition, the velocity scale of convection increases with an increase in the shear‐thinning property. Thus, the shear‐thinning property is considered to accelerate convection. Compared with several types of velocity scales used by other researchers, significant deviations from the actual scale were observed. Therefore, a new type of velocity scale, including the buoyant to viscous force ratio, arbitrary parameter, and thermal diffusivity, was proposed. The proposed velocity scale allowed an approximate estimation of the actual velocity scale. Although further investigation of the validity is necessary with varying geometries and rheological parameters, this velocity scale will be useful for controlling RB convection with Newtonian/shear‐thinning fluids.

The reactor-based perspective on finite-rate chemistry in turbulent reacting flows: A review from traditional to low-emission combustion

In flames, turbulence can either limit or enhance combustion efficiency by means of strain and mixing. The interactions between turbulent motions and chemistry are crucial to the behaviour of combustion processes. In particular, it is essential to correctly capture non-equilibrium phenomena such as localised ignition and extinction to faithfully predict pollutant formation. Reactor-based combustion models — such as the Eddy Dissipation Concept (EDC) or Partially Stirred Reactor (PaSR) — may account for turbulence-chemistry interactions at an affordable computational cost by calculating combustion rates relying upon canonical reactors of small fluid size and timescale. The models may include multiscale mixing, detailed chemical kinetic schemes and high-fidelity multispecies diffusion treatments. Although originally derived for conventional, highly turbulent combustion, numerous recent efforts have sought to generalise beyond simple empirical correlations using more sophisticated relationships. More recent models incorporate the estimation of scales based on local variables such as turbulent Reynolds and Damköhler numbers, phenomenological descriptions of turbulence based on fractal theory or specific events such as extinction. These modifications significantly broaden the effective range of operating conditions and combustion regimes these models can be applied to, as in the particular case of Moderate or Intense Low-oxygen Dilution (MILD) combustion. MILD combustion is renown for its ability to deliver appealing features such as abated pollutant emissions, elevated thermal efficiency and fuel flexibility. This review describes the development and current state-of-the-art in finite-rate, reactor-based combustion approaches. Recently investigated model improvements and adaptations will be discussed, with specific focus on the MILD combustion regime. Finally, to bridge the gap between laboratory-scale canonical burners and industrial combustion systems, the current directions and the future outlook for development are discussed.

Robust Data-Driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty - Part I: Theory

Data-driven sparse estimation of distribution factors (DFs) facilitates online power flow sensitivity analysis for secure system operation. However, existing methods are vulnerable to time-varying non-Gaussian PMU measurement noise, bad data, and uncertain renewable energy sources (RESs). Moreover, they lack scalability to large-scale systems. This two-part paper proposes a robust and scalable sparse DF estimation framework considering PMU data quality and RES uncertainty. In this Part I, a novel Adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -Lasso estimator with theoretically guaranteed robustness is proposed. It mitigates the impacts of measurement and RES uncertainties to yield accurate dominant DF estimates while promoting sparsity. The key idea is to integrate the robust statistics theory with sparse representation techniques, in particular the Huber loss function, adaptively-weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization, concomitant scale estimate, and pseudo-residuals. Two important robustness properties of this estimator are theoretically proven, i.e., the bounded influence function and the asymptotic consistency of dominant DF estimates given limited samples. The breakdown points of this estimator to measurement and RES uncertainties are derived. Test results validate that the proposed estimator allows accurate estimation without relying on power flow models or massive operating data. It achieves significantly superior robustness over existing methods in multiple scenarios.

Scale-Aware Tracking Method with Appearance Feature Filtering and Inter-Frame Continuity.

Visual object tracking is a fundamental task in computer vision that requires estimating the position and scale of a target object in a video sequence. However, scale variation is a difficult challenge that affects the performance and robustness of many trackers, especially those based on the discriminative correlation filter (DCF). Existing scale estimation methods based on multi-scale features are computationally expensive and degrade the real-time performance of the DCF-based tracker, especially in scenarios with restricted computing power. In this paper, we propose a practical and efficient solution that can handle scale changes without using multi-scale features and can be combined with any DCF-based tracker as a plug-in module. We use color name (CN) features and a salient feature to reduce the target appearance model's dimensionality. We then estimate the target scale based on a Gaussian distribution model and introduce global and local scale consistency assumptions to restore the target's scale. We fuse the tracking results with the DCF-based tracker to obtain the new position and scale of the target. We evaluate our method on the benchmark dataset Temple Color 128 and compare it with some popular trackers. Our method achieves competitive accuracy and robustness while significantly reducing the computational cost.

The Home Environments of Infants of Mothers with Early, Remitted Clinical Depression and No Depression during the First Two Years Postpartum.

The current study examines stability, continuity, and group and gender differences in the home environments of infants of mothers with early, remitted clinical depression and no postpartum depression, overcoming methodological variations in the extant literature. Fifty-five mothers diagnosed with clinical depression (major or minor depression, dysthymia, or depressive disorder not otherwise specified) at 5 months and fully remitted by 15 and 24 months, and 132 mothers with no postpartum depression (Mage = 32.47; 69.7% European American) completed the Home Observation for Measurement of the Environment (HOME) Inventory Infant/Toddler version when their infants were 15 and 24 months old. No differences in stability estimates of the HOME scales were found between the groups. In terms of continuity, controlling for maternal education and infant birth order, HOME responsivity, involvement, and total score decreased, while HOME acceptance increased between 15 and 24 months in the full sample. There were no effects of group or gender. Results may point to the home environment as a key protective factor for infants of mothers with early, remitted clinical depression, or findings may suggest improved maternal parenting cognitions and practices following remission.

(Invited) Techno-Economic Analysis on Near-Term and Future Projections of Levelized Cost of Hydrogen for Low-Temperature Water Electrolysis Technologies

Hydrogen is seen as a potential energy intermediary between renewable energy sources and end-use applications including transportation, power, and industrial feedstocks. Core to this approach is low-cost hydrogen generation via water electrolysis. Significant research has been undertaken to improve the electrolysis stack technology, including enhancements in electrical efficiency, materials degradation, and total stack cost. However, relative to the total cost of installing an electrolysis plant, the stack contributes <50% of the initial capital investment. Therefore, from an electrolysis project perspective, the mechanical balance of plant and electrical systems required to operate the plant are equally important to understand.To explore the near-term and future cost projection for hydrogen production via electrolysis, Strategic Analysis, Inc. (SA) has developed a bottom-up project cost model for Alkaline, Proton Exchange Membrane (PEM), and Anion Exchange Membrane (AEM) electrolysis technologies. This project cost model incorporates: 1) Stack cost, derived from a Design for Manufacturing and Assembly (DFMA) process-based cost model developed by SA, 2) mechanical balance of plant, including process equipment, piping, valves, and instrumentation, derived from equipment quotes, scaling, database values, and Aspen estimates; 3) electrical balance of plant, including wiring, rectification, and electrical infrastructure upgrades, derived from time and material cost correlations; 4) Site Preparation, focused on green field installation; and 5) Construction Overhead, including engineering, procurement, and construction (EPC) costs and project contingency. The SA project cost model is fed into a Levelized Cost of Hydrogen (LCOH) model that accounts for electricity and water consumption, in addition to other operating costs, including labor, maintenance, and stack replacements.The SA project cost model and the LCOH model were used to conceptualize electrolysis system sizes from 100 MW to 1 GW for Alkaline, PEM, and AEM electrolysis systems. From a project perspective, electricity costs contribute 50-80% to the LCOH while capital and maintenance costs contribute the remaining 20-50% to the LCOH. Improvements in stack performance and cost offer incremental improvements in LCOH; however, larger reductions in hydrogen cost will only be possible through optimization of the stack cell voltage and operating current density in conjunction with reductions in net electricity price through integration with low-cost, probably renewable, electricity.Results from SA’s polarization performance optimization model show that lower cost Alkaline stacks, having somewhat lower performance (than PEM), tend to optimize at lower current densities leading to a larger stack active area, and benefit from low operational voltage to obtain higher conversion efficiencies. Higher cost PEM stacks with higher performance (than Alkaline stacks) tend to optimize at higher current densities to reduce the size/capital cost of the stacks and can afford a lower stack efficiency. Multivariable sensitivity analyses show the statistical variation in capital cost leading to the most likely range in LCOH for each technology. The results of this study provide guidance on where the largest incremental reductions in LCOH can be achieved on a project basis.

Estimating population size for California spotted owls and barred owls across the Sierra Nevada ecosystem with bioacoustics

Monitoring population size at ecosystem scales is difficult for most species of conservation concern. While assessing site occupancy at broad scales has proven feasible, rigorous tracking of changes in population size over time has not – even though it can provide a stronger basis for assessing population status and conservation-decision making. Therefore, we demonstrate how relatively low-intensity, ecosystem-scale passive acoustic monitoring (PAM) can be linked to local-density monitoring to estimate the population size of native California spotted owls (Strix occidentalis occidentalis) and invasive barred owls (S. varia) across the western Sierra Nevada, California. Based on a PAM sampling grid with 400 ha cells (the approximate home range size of these species), we estimated site occupancy to be between 0.42 (SE = 0.02) and 0.30 (SE = 0.02) for California spotted owls using relatively liberal and strict criteria, respectively, for considering a cell occupied. PAM-based site occupancy estimates within local-scale density monitoring study areas (range = 0.41–0.78 and 0.28–0.76 for liberal and strict criteria, respectively) were strongly and positively correlated with local density (range = 0.08–0.31 owl/km2) for this species. In contrast, ecosystem-wide site occupancy of barred owls was very low based on PAM (0.034, SE < 0.01), as were densities within local monitoring studies (range = 0–0.005 owls/km2). By scaling ecosystem-wide site occupancy estimates to densities estimated with local monitoring studies, we estimated that, depending on occupancy criteria, 2,218 (SE = 278) or 2,328 (SE = 489) California spotted owls occurred in the Sierra Nevada ecosystem in 2021. Thus, while California spotted owls are a rare subspecies, they were well-distributed across the Sierra Nevada. Because there were so few barred owl detections, we could not estimate ecosystem-scale abundance, which reflects the success of prior experimental removals in the region. In conclusion, our study provides a generalizable framework for estimating population size for territorial species with PAM at ecosystem scales when local-scale estimates of density are available. Thus, we demonstrate that this approach can provide novel and valuable insights into monitoring populations to aid species conservation.

Soft pinning: Experimental validation of static correlations in supercooled molecular glass-forming liquids.

Enormous enhancement in the viscosity of a liquid near its glass transition is a hallmark of glass transition. Within a class of theoretical frameworks, it is connected to growing many-body static correlations near the transition, often called "amorphous ordering." At the same time, some theories do not invoke the existence of such a static length scale in the problem. Thus, proving the existence and possible estimation of the static length scales of amorphous order in different glass-forming liquids is very important to validate or falsify the predictions of these theories and unravel the true physics of glass formation. Experiments on molecular glass-forming liquids become pivotal in this scenario as the viscosity grows several folds (), and simulations or colloidal glass experiments fail to access these required long-time scales. Here we design an experiment to extract the static length scales in molecular liquids using dilute amounts of another large molecule as a pinning site. Results from dielectric relaxation experiments on supercooled Glycerol with different pinning concentrations of Sorbitol and Glucose, as well as the simulations on a few model glass-forming liquids with pinning sites, indicate the versatility of the proposed method, opening possible new avenues to study the physics of glass transition in other molecular liquids.

Dynamical heterogeneity in active glasses is inherently different from its equilibrium behavior

Activity-driven glassy dynamics, while ubiquitous in collective cell migration, intracellular transport, dynamics in bacterial and ant colonies, etc., also extends the scope and extent of the as-yet mysterious physics of glass transition. Active glasses are hitherto assumed to be qualitatively similar to their equilibrium counterparts at an effective temperature, [Formula: see text]. Here, we combine large-scale simulations and an analytical mode-coupling theory (MCT) for such systems and show that, in fact, an active glass is inherently different from an equilibrium glass. Although the relaxation dynamics can be equilibrium-like at a [Formula: see text], effects of activity on the dynamic heterogeneity (DH), which is a hallmark of glassy dynamics, are quite nontrivial and complex. With no preexisting data, we employ four distinct methods for reliable estimates of the DH length scales. Our work shows that active glasses exhibit dramatic growth of DH and systems with similar relaxation times, and thus, [Formula: see text] can have widely varying DH. To theoretically study DH, we extend active MCT and find good qualitative agreement between the theory and simulation results. Our results pave avenues for understanding the role of DH in glassy dynamics and can have fundamental significance even in equilibrium.

Research on Calendar Aging for Lithium-Ion Batteries Used in Uninterruptible Power Supply System Based on Particle Filtering

The aging process of lithium-ion batteries is an extremely complex process, and the prediction of the calendar life of the lithium-ion battery is important to further guide battery maintenance, extend the battery life and reduce the risk of battery use. In the uninterruptible power supply (UPS) system, the battery is in a floating state for a long time, so the aging of the battery is approximated by calendar aging, and its decay rate is slow and difficult to estimate accurately. This paper proposes a particle filtering-based algorithm for battery state-of-health (SOH) and remaining useful life (RUL) predictions. First, the calendar aging modeling for the batteries used in the UPS system for the Shanghai rail transportation energy storage power station is presented. Then, the particle filtering algorithm is employed for the SOH estimation and RUL prediction for the single-cell battery calendar aging model. Finally, the single-cell SOH and RUL estimation algorithm is expanded to the pack and group scales estimation. The experimental results indicate that the proposed method can achieve accurate SOH estimation and RUL prediction results.

Examining the Psychometrics and Characteristics of the Assessing School Settings: Interactions of Students and Teachers

This article focuses on the psychometric properties and characteristics of the Assessing School Settings: Interactions of Students and Teachers (ASSIST), an observational assessment administered by trained external observers of teacher practices, classroom context, and student behaviors at the classroom level. Study 1 examines variability, reliability, and convergence between ASSIST scores with data from 3,298 classrooms nested within 185 elementary, middle, and high schools. We report school-level intraclass correlations (ICCs), standard deviations, means, and reliability estimates of ASSIST scales; investigate the correspondence among ASSIST-measured constructs with multilevel correlations; and explore school-level predictors of ASSIST global scale scores. Study 2 further examines reliability over time and convergent validity using repeated ASSIST and Classroom Assessment Scoring System–Secondary (CLASS-S) observations in classrooms of 335 teachers in 41 middle schools. The ASSIST global measures exhibited moderate to good reliability across three same-teacher observations (ICCs ranged from .69 to .82). Associations between all ASSIST and CLASS-S scales suggested close correspondence of the measures, especially at the teacher level and school level. Together, these findings highlight the utility of the ASSIST observational measure, both as a research and practice tool, across multiple school types and classroom contexts, and with potential to inform coaching to improve teachers’ classroom management.

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.

Improvement of research methodology mesostructures of atmospheric fronts on the basis of ground­based microwave radiometry

The paper considers the some results of experimental studies of the mesoscale structure of atmospheric fronts using a ground-based microwave water vapor radiometer. A methodology for studying the mesoscale structure of atmospheric fronts is described, which includes two stages: the stage of syn- optic (frontological) analysis and the stage of mesoscale analysis based on measurements of atmospheric moisture and cloud liquid water content using a microwave water vapor radiometer. The application of the methodology is demonstrated by the example of the analysis of the results of observation of the case of the passage of warm and cold fronts through the Leningrad region on September 9—14, 2020. Based on the analysis of the results of previous studies, the features of changes in the characteristics of moisture content during the passage of atmospheric fronts are generalized. The growth of the atmospheric water vapor and cloud liquid water content is delayed relative to the appearance of harbingers of a warm cloud front of the upper tier. The growth of moisture content characteristics has a wave-like character, which can be explained by the manifestation of the strip mesostructure of the front. Estimates of the characteristic scales of spatial heterogeneities in the structure of atmospheric fronts for the area under consideration differ from similar characteristics obtained by P. Hobbs for the northeastern coast of North America. In the course of mesoscale analysis, the type of atmospheric front crossing the observation point can be clarified. The results obtained determine the prospects for improving the methods of diagnostics and current forecasting of dangerous weather phenomena during the passage of atmospheric fronts using ground-based microwave radiometers