Efficient Online Object Tracking Scheme for Challenging Scenarios

Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.

Large Scale Reservoir Simulation Models for Regional Understanding of Inter-Field Communications - A Case Study Offshore Abu Dhabi

Reservoir simulation models aim to reproduce at well, sector and field level the pressure and production behavior observed in the historical data. The size and resolution of the models are essentially capped by the computational resources as the numerical computations are quite complex and hardware demanding. For this reason, the use of simulation models to understand inter-field communications at regional level have been always a challenge, rarely pursued, referring those analyses to simple material balance to evaluate influxes, lacking lateral vectors to identify where volumes are coming from, especially on cases of multiple field interactions. The work presented in this paper illustrates the value of merging existing field level simulations models into a large scale regional simulation grids, in order to understand pressure disturbances observed in multiple fields Offshore Abu Dhabi. The process of merging simulation models represents a big challenge considering the high variety of approaches used in the original models, different geology complexity, fluid characteristics, different depletion regimes and field development strategies. In this study, thousands of wells, 6 structures with different fluid and equilibrium regions were used to build the biggest reservoir simulation model in Abu Dhabi. The integration of the data pursues the replication of the existing static and dynamic models, addressing in parallel lateral and vertical upscaling issues when moving from very fine into coarser grids. Implications on the change of scale on the repeatability of the HCIIP volumes and the impact of pseudo relative permeability curves on the history match were carefully analyzed during the process. Evaluation of the impact of the simplifications over the overall quality of the model was of paramount importance, interrogating whether the simplifications affects the capability of the model for assessing the pressure communication and influxes among the fields. The regional simulation model allowed to understand the effects of the peripheral water injection of a giant field on the nearby satellite fields, also the effects of these interactions on the pressure and oil saturation changes through time. Fields and Structures separated way far (20 and 40 Km away) can eventually see pressure disturbances after very long periods of time (up to 300 psi in couple of decades in some cases). Although evidences for changes in pressure are very clear and supported by RFT/MDT time lapsed data, the work also proved that changes on saturations are not very evident or can be considered very marginal on fields separated by large distances. This work represents an alternative and more accurate approach for evaluating nearby field communications and to quantify the boundary conditions to restore models at original stage before nearby interferences, allowing proper initialization of the fine scaled simulation models on pre-production status.

Scale Symmetry and Friction

Dynamical similarities are non-standard symmetries found in a wide range of physical systems that identify solutions related by a change of scale. In this paper, we will show through a series of examples how this symmetry extends to the space of couplings, as measured through observations of a system. This can be exploited to focus on observations that can be used to distinguish between different theories and identify those which give rise to identical physical evolutions. These can be reduced into a description that makes no reference to scale. The resultant systems can be derived from Herglotz’s principle and generally exhibit friction. Here, we will demonstrate this through three example systems: the Kepler problem, the N-body system and Friedmann–Lemaître–Robertson–Walker cosmology.

Understanding the City-transport System of Urban Agglomeration through Improved Space Syntax Analysis

From the perspective of city-transport system, this article applies space syntax to analyze the physical integration of cities. Traditionally, space syntax is mainly applied to urban areas, buildings, and other scales. However, when space syntax is applied to the configuration analysis of urban agglomeration, the change of scale causes changes in spatial perception and human behavioral patterns. Thus, we present a new method of space syntax. This method defines the lane and track between entrance and exit, and city as node, which represents small-scale space. Infrastructure, such as stations, entrances, and exits, are defined as links. The urban agglomeration is thus transformed into a topological network, and then displayed as a bipartite graph of cities and routes. We take the urban agglomerations in the Yangtze River Middle Reaches (YRMR) as the case study area and analyze its spatial configuration from the perspectives of local and integral, interfaces at different scales, gaps, evolution of the dual foreground and background networks, and evolution of the transport networks. The results reveal the way cities integrate with each other and further reveal the multi-scale spatial structure of urban agglomeration.

Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database

NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

FROM PLUG MEASUREMENTS TO DYNAMIC SIMULATIONS: UPSCALING EFFECTS ON MODELED HYDROCARBON VOLUMES

Building reliable subsurface models requires detailed knowledge of both the rock and fluids involved. One critical petrophysical property determining the viability of a development is the hydrocarbon saturation. In 3D geological models, the saturation is populated via Saturation height models and free fluid levels. In populating a 3D model with meaningful properties, measurements at various scales are integrated. Core measurements acquired at resolution far superior to that used in the 3D models require a change of scale- upscaling step. The process of accurately predicting water saturation in the upscaled model is not trivial. Here we follow this process by employing a saturation height model (SHM) at different scales in relationship to various permeability realizations. Multiple choices available as inputs into the SHM in various ranges of sensitivity with respect to the free water level position as well as different rock quality are looked at. Various degrees of heterogeneity are studied by using synthetic data, the saturation prediction accuracy based on upscaled input rock properties (like arithmetic/geometric and harmonic upscaled permeability) is investigated. For homogeneous rocks a workflow is detailed with the purpose of detecting the upscaling limits highlighting the possible errors that might appear in the upscaling process. A counterintuitive result is that in the transition zone (the focus of this work) permeable rocks are more prone to errors than the less permeable ones. We also conclude that no alteration of the SHM is necessary in the upscaling process. Given the fact that rock quality enters the SHM and that permeability upscaling follows a route that ultimately attempts to honor well performance, a natural question is what the relevance of such a permeability model as input for the SHM is. Our results highlight the best choices for an upscaled SHM input (upscaled) permeability- not necessarily the upscaled permeability used in history matching. Smallest errors are shown to be resulting from using geometrical or 1/3 power law upscaled permeability.

On the scaling of wind turbine rotors

This paper formulates laws for scaling wind turbine rotors. Although the analysis is general, the article primarily focuses on the subscaling problem, i.e., on the design of a smaller-sized model that mimics a full-scale machine. The present study considers both the steady-state and transient response cases, including the effects of aerodynamic, elastic, inertial, and gravitational forces. The analysis reveals the changes to physical characteristics induced by a generic change of scale, indicates which characteristics can be matched faithfully by a subscaled model, and states the conditions that must be fulfilled for desired matchings to hold. Based on the scaling laws formulated here, the article continues by considering the problem of designing scaled rotors that match desired indicators of a full-scale reference. To better illustrate the challenges implicit in scaling and the necessary tradeoffs and approximations, two different approaches are contrasted. The first consists in a straightforward geometric zooming. An analysis of the consequences of zooming reveals that, although apparently simple, this method is often not applicable in practice, because of physical and manufacturing limitations. This motivates the formulation of scaling as a constrained optimal aerodynamic and structural matching problem of wide applicability. Practical illustrations are given considering the scaling of a large reference 10 MW wind turbine of about 180 m in diameter down to three different sizes of 54, 27, and 2.8 m. Results indicate that, with the proper choices, even models characterized by very significant scaling factors can accurately match several key performance indicators. Additionally, when an exact match is not possible, relevant trends can at least be captured.

Neural Systems Under Change of Scale

We derive a theoretical construct that allows for the characterisation of both scalable and scale free systems within the dynamic causal modelling (DCM) framework. We define a dynamical system to be “scalable” if the same equation of motion continues to apply as the system changes in size. As an example of such a system, we simulate planetary orbits varying in size and show that our proposed methodology can be used to recover Kepler’s third law from the timeseries. In contrast, a “scale free” system is one in which there is no characteristic length scale, meaning that images of such a system are statistically unchanged at different levels of magnification. As an example of such a system, we use calcium imaging collected in murine cortex and show that the dynamical critical exponent, as defined in renormalization group theory, can be estimated in an empirical biological setting. We find that a task-relevant region of the cortex is associated with higher dynamical critical exponents in task vs. spontaneous states and vice versa for a task-irrelevant region.

Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

Study on association solubilization and inhibition of scale in recirculating cooling water system under S-HGMF

The present study conducted an investigation on the effect of Superconducting-High Gradient Magnetic Field (S-HGMF) in the association solubilization of recirculating cooling water and the crystal form change of scale. The effects of magnetic flux density, flow rate and cycle-index on the solubility of scale-forming ions were investigated, and the effects of viscosity and surface tension on the molecular internal energy and order degree of circulating water were analyzed. The scale was ground and mixed with water and placed in a S-HGMF system to study the effect of S-HGMF on the crystal form change of CaCO3. The experimental results showed us that S-HGMF could increase the solubility of scale-forming ions. It could enhance the interaction between water molecules by increasing viscosity and reducing surface tension, so as to improve the stability of water quality, reduce ion precipitation, and achieve the effect of scale inhibition. At the same time, it could also change the crystal structure of CaCO3, promote the transformation of calcite to aragonite, and realize the purpose of scale inhibition. In a word, S-HGMF treatment can effectively solve the scaling problem of recirculating cooling water system, which provides a reference for scale inhibition of recirculating cooling water.