Urban Walkability Design Using Virtual Population Simulation

We present a system to generate a procedural environment that produces a desired crowd behaviour. Instead of altering the behavioural parameters of the crowd itself, we automatically alter the environment to yield such desired crowd behaviour. This novel inverse approach is useful both to crowd simulation in virtual environments and to urban crowd planning applications. Our approach tightly integrates and extends a space discretization crowd simulator with inverse procedural modelling. We extend crowd simulation by goal exploration (i.e. agents are initially unaware of the goal locations), variable‐appealing sign usage and several acceleration schemes. We use Markov chain Monte Carlo to quickly explore the solution space and yield interactive design. We have applied our method to a variety of virtual and real‐world locations, yielding one order of magnitude faster crowd simulation performance over related methods and several fold improvement of crowd indicators.     

A Novel Artificial Intelligence Based Timing Synchronization Scheme for Smart Grid Applications

The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.

     

Towards a quantitative approach for comparing crowds

In this paper, we propose a new model to quantitatively compare global flow characteristics of two crowds. The proposed approach explores a 4‐D histogram that contains information on the local velocity (speed and orientation) of each spatial position, and the comparison is made using histogram distances. The 4‐D histogram also allows the comparison of specific characteristics, such as distribution of orientations only, speed only, relative spatial occupancy only, and combinations of such features. Experimental results indicate that the proposed quantitative metric correlates with visual inspection. Copyright © 2012 John Wiley & Sons, Ltd.     

Understanding people motion in video sequences using Voronoi diagrams

This work describes a model for understanding people motion in video sequences using Voronoi diagrams, focusing on group detection and classification. We use the position of each individual as a site for the Voronoi diagram at each frame, and determine the temporal evolution of some sociological and psychological parameters, such as distance to neighbors and personal spaces. These parameters are used to compute individual characteristics (such as perceived personal space and comfort levels), that are analyzed to detect the formation of groups and their classification as voluntary or involuntary. Experimental results based on videos obtained from real life as well as from a crowd simulator were analyzed and discussed.     

Topology preserving deformable image matching using constrainedhierarchical parametric models

In this paper, we address the issue of topology preservation in deformable image matching. A novel constrained hierarchical parametric approach is presented, that ensures that the mapping is globally one-to one and thus preserves topology in the deformed image. The transformation between the source and target images is parameterized at different scales, using a decomposition of the deformation vector field over a sequence of nested (multiresolution) subspaces. The Jacobian of the mapping is controlled over the continuous domain of the transformation, ensuring actual topology preservation on the whole image support. The resulting fast nonlinear constrained optimization algorithm enables to track large nonlinear deformations while preserving the topology. Experimental results are presented both on simulated data and on real medical images.     

Overall and Local Bread Expansion,Mechanical Properties,and Molecular Structure During Bread Baking: Effect of Emulsifying Starches

In order to determine the relationship between molecular structure of wheat bread dough, its mechanical properties, total and local bread expansion during baking and final bread quality, different methods (rheological, nuclear magnetic resonance, magnetic resonance imaging and general bread characterisation) were employed. The study was extended on wheat dough with starch modified by octenyl succinic anhydride (OSA) in order to generalise the results. The interest of investigating multi-scale changes occurring in dough at different phases of baking process by considering overall results was demonstrated. It was found that OSA starch improved the baking performance during the first phase of baking. This feature was due to a higher absorption of water by OSA starch granules occurring at temperatures below that of starch gelatinization, as confirmed by NMR, and consecutive higher resistance to deformation for OSA dough in this temperature range (20–60 °C). This was explained by a delayed collapse of cell walls in the bottom of the OSA dough. In the second phase of baking (60–80 °C), the mechanism of shrinkage reduced the volume gained by OSA dough during the first phase of baking due to higher rigidity of OSA dough and its higher resistance to deformation. MRI monitoring of the inflation during baking made it possible to distinguish the qualities and defaults coming from the addition of OSA starch as well as to suggest the possible mechanisms at the origin of such dough behaviour.     

A paradigm for controlling virtual humans in urban environment simulations

This paper presents a new architecture for simulating virtual humans in complex urban environments. The approach is based on the integration of six modules. Four key modules are used in order to manage environmental data, simulate human crowds, control interactions between virtual humans and objects, and generate tasks based on a rule-based behavioral model. The communication between these modules is made through a client/server system. Finally, all low-level virtual human actions are delegated to a single motion and behavioral control module. Our architecture combines various human and object simulation aspects, based on the coherent extraction and classification of information froma virtual city database. This architecture is discussed in this paper, together with a detailed case study example.     

Automated system to acquire fluorescence, polarization and anisotropy maps within liquid flows

Maps of polarization and anisotropy can be helpful for flow analysis systems (FIA, CFA, etc.) with reactions dependent on the intermolecular alignment as well as for dispersion control. Maps can be acquired manually, but when a scan over a sample area is required, the acquisition becomes tiresome and has low precision. The paper describes an automatic flexible system for high-precision sample positioning with closed loop self control, remote data acquisition and storage controlled by a BASIC program. The system was developed to acquire maps up to 850 mm(2) of the sample (liquid flows, solids, interfaces, etc.), with up to 100 mum(2) precision. To evaluate the equipment, performance is presented as the scan of a thin liquid film of monoethylene glycol (MEG) flowing on borosilicate. Tests were performed with and without surfactantes at submicellar concentrations: two concentrations of sodium dodecyl sulphate (SDS) and one of polyethylene oxide (PEO). For pure MEG, the intermolecular alignment initially increased, then decreased. When SDS was added, both polarization and anisotropy only increased progressively with the flow. This might be explained by the surfactant decrease of interfacial interaction. When PEO was added, both polarization and anisotropy decreased pronouncedly over the entire map, which might be due to macromolecular aggregates within the bulk generating misaligned molecular domains. The system presented as sample positioning repeatability of 0.1% and a high polarization reproducibility (error margin < 6 in 1000).