Agent-based Crowd Simulation Modelling for a Gaming Environment

Crowd simulation study has become a favorite subject in the computer graphics community in the past three decades. It usually is a sub-function within many applications such as video games, films, and public security. This thesis proposes an independent crowd simulation model that is capable of running an Agent-based method through a gaming environment. It can simulate realistic human crowds with user-controllable features to provide a gaming-like experience. Our approach features an enhanced rendering system based on Distinguishable Agents Generating Method (DAGM). This method can generate distinguishable and scalable 3D human models in real-time. We also introduce our Multi-layer Collision System (MCS), which features a collision-message collection system and an evaluation processing system. We also introduce Building & City-planning Generating System (BCGS) for the purpose of setting up obstacles for the crowd during an evacuation simulation. Moreover, in this thesis, we also extend the study to other aspects such as crisis training and human animations to provide a complete agent-based crowd simulation model

The development of local solar irradiance for outdoor computer graphics rendering

Atmospheric effects are approximated by solving the light transfer equation, LTE, of a given viewing path. The resulting accumulated spectral energy (its visible band) arriving at the observer’s eyes, defines the colour of the object currently on the line of sight. Due to the convenience of using a single rendering equation to solve the LTE for daylight sky and distant objects (aerial perspective), recent methods had opt for a similar kind of approach. Alas, the burden that the real-time calculation brings to the foil had forced these methods to make simplifications that were not in line with the actual world observation. Consequently, the results of these methods are laden with visual-errors. The two most common simplifications made were: i) assuming the atmosphere as a full-scattering medium only and ii) assuming a single density atmosphere profile. This research explored the possibility of replacing the real-time calculation involved in solving the LTE with an analytical-based approach. Hence, the two simplifications made by the previous real-time methods can be avoided. The model was implemented on top of a flight simulator prototype system since the requirements of such system match the objectives of this study. Results were verified against the actual images of the daylight skies. Comparison was also made with the previous methods’ results to showcase the proposed model strengths and advantages over its peers

A Characterization Of Low Cost Simulator Image Generation Systems

Report identifies and briefly discusses the characteristics that should be considered in the evaluation, comparison, and selection of low cost computer image generation systems to be used for simulator applications

Multi-Objective Optimization for Cooling and Interior Natural Lighting in Buildings for Sustainable Renovation

In order to achieve the ‘nearly zero-energy’ target and a comfortable indoor environment, an important aspect is related to the correct design of the transparent elements of the building envelope. For improving indoor daylight penetration, architectural solutions such as light shelves are nowadays commercially available. These are defined as horizontal or inclined surfaces, fixed or mobile, placed on the inner and/or the outer side of windows, with surface features such to reflect the sunlight to the interior. Given the fact that these elements can influence different domains (i.e., energy need, daylighting, thermal comfort, etc.), the aim of this paper is to apply a multi-objective optimization method within the design of this kind of technology. The case study is a student house in the University of Athens Campus, subject to a deep energy renovation towards nZEB, under the frame of H2020 European project Pro-GET-onE (G.A No 723747). Starting from the numerical model of the building, developed in EnergyPlus, the multi-objective optimization based on a genetic algorithm is implemented. The variables used are various light shelves configurations by differing materials and geometry, as well as different window types and interior context scenarios. Finally, illuminance studies of the pre- and post-retrofit building are also provided through Revit illuminance rendering

Optimizing a Model-Agnostic Measure of Graph Counterdeceptiveness via Reattachment

Recognition of an adversary's objective is a core problem in physical security and cyber defense. Prior work on target recognition focuses on developing optimal inference strategies given the adversary's operating environment. However, the success of such strategies significantly depends on features of the environment. We consider the problem of optimal counterdeceptive environment design: construction of an environment which promotes early recognition of an adversary's objective, given operational constraints. Interpreting counterdeception as a question of graph design with a bound on total edge length, we propose a measure of graph counterdeceptiveness and a novel heuristic algorithm for maximizing counterdeceptiveness based on iterative reattachment of trees. We benchmark the performance of this algorithm on synthetic networks as well as a graph inspired by a real-world high-security environment, verifying that the proposed algorithm is computationally feasible and yields meaningful network designs.Comment: 15 pages, 11 figure

An Adaptive Intelligent Integrated Lighting Control Approach for High-Performance Office Buildings

abstract: An acute and crucial societal problem is the energy consumed in existing commercial buildings. There are 1.5 million commercial buildings in the U.S. with only about 3% being built each year. Hence, existing buildings need to be properly operated and maintained for several decades. Application of integrated centralized control systems in buildings could lead to more than 50% energy savings. This research work demonstrates an innovative adaptive integrated lighting control approach which could achieve significant energy savings and increase indoor comfort in high performance office buildings. In the first phase of the study, a predictive algorithm was developed and validated through experiments in an actual test room. The objective was to regulate daylight on a specified work plane by controlling the blind slat angles. Furthermore, a sensor-based integrated adaptive lighting controller was designed in Simulink which included an innovative sensor optimization approach based on genetic algorithm to minimize the number of sensors and efficiently place them in the office. The controller was designed based on simple integral controllers. The objective of developed control algorithm was to improve the illuminance situation in the office through controlling the daylight and electrical lighting. To evaluate the performance of the system, the controller was applied on experimental office model in Lee et al.’s research study in 1998. The result of the developed control approach indicate a significantly improvement in lighting situation and 1-23% and 50-78% monthly electrical energy savings in the office model, compared to two static strategies when the blinds were left open and closed during the whole year respectively.Dissertation/ThesisDoctoral Dissertation Architecture 201

Detection Sensor Placement Algorithm for Protection Against Attacks Using Drones

V dnešní době je použití dronů oblíbené nejen pro rekreační, ale i komerční účely. Drony mohou asistovat lidem při mnoha činnostech, zahrnující například dálkový sběr informací, letecké snímání a filmování, ale i dodávku zdravotního materiálu. Nicméně, jejich dostupnost a potenciál využití představuje i různá nebezpečí, proti kterým je potřeba se umět ochránit. Tato práce se zabývá návrhem algoritmu pro optimální rozmísťování kamer a akustických senzorů v oblasti monitorovaného sektoru, za účelem detekce potenciálně nebezpečných dronů, pracujícího v reálném 3D prostředí včetně okluze kamery. Algoritmus umožňuje vysokou míru uživatelské parametrizace, zahrnující prioritní oblasti, maximální cenu senzorové sítě, ceny senzorů nebo vícenásobné pokrytí. Součástí aplikace je také grafické rozhraní, které zobrazuje monitorovaný sektor, prioritní oblasti a senzorovou síť. První část práce popisuje komerční využití dronů v dnešní době a nebezpečí, které představují, existující systémy pro detekci a neutralizaci dronů a incidenty zaznamenané v minulých letech. Dále popisuje možné řešení a přístupy k problému optimálního rozmísťování senzorů. Druhá část práce se zabývá popisem aplikace, představením jednotlivých komponent a konfiguračních parametrů a detailním popisem navrhovaného algoritmu. Závěr práce se soustředí na vyhodnocení navrhovaného algoritmu pomocí experimentů simulujících scénáře, ve kterých by ochrana proti dronům byla nutná.Nowadays, the use of drones is common for both recreational and commercial purposes. Drones can assist people in many activities including remote sensing, aerial photography and filming as well as medical supply delivery. However, their availability and potential for use also pose various threats that need to be protected against. The goal of this thesis is to propose an algorithm to solve the problem of optimal sensor placement around the monitored sector, for the purpose of detection of possibly dangerous drones. The algorithm assumes a realistic 3D environment and deals with camera occlusion as well. It also offers a high level of user parameterization that involves the priority areas, maximal cost of the sensor network, sensor prices, or multiple coverage. A part of the application is also a graphical user interface (GUI) displaying the monitored sector, priority areas, and the computed sensor network. The first part of the thesis describes today's commercial use of drones, the threats posed by the drones, existing systems for drone detection and neutralization, and the recorded drone incidents. It further discusses possible approaches and solutions to the problem of optimal sensor placement. The second part of the thesis devotes to the description of the application, the introduction of individual components and configuration parameters, and a detailed description of the proposed algorithm. The end of the thesis focuses on evaluating the proposed algorithm using experiments that simulate scenarios in which the protection against drones would be necessary

Conservative occlusion culling for urban visualization using a slice-wise data structure

Cataloged from PDF version of article.In this paper, we propose a framework for urban visualization using a conservative from-region visibility algorithm based on occluder shrinking. The visible geometry in a typical urban walkthrough mainly consists of partially visible buildings. Occlusion-culling algorithms, in which the granularity is buildings, process these partially visible buildings as if they are completely visible. To address the problem of partial visibility, we propose a data structure, called slice-wise data structure, that represents buildings in terms of slices parallel to the coordinate axes. We observe that the visible parts of the objects usually have simple shapes. This observation establishes the base for occlusion-culling where the occlusion granularity is individual slices. The proposed slice-wise data structure has minimal storage requirements. We also propose to shrink general 3D occluders in a scene to find volumetric occlusion. Empirical results show that significant increase in frame rates and decrease in the number of processed polygons can be achieved using the proposed slice-wise occlusion-culling as compared to an occlusion-culling method where the granularity is individual buildings. © 2007 Elsevier Inc. All rights reserved

Revisiting Poisson-disk Subsampling for Massive Point Cloud Decimation

Scanning devices often produce point clouds exhibiting highly uneven distributions of point samples across the surfaces being captured. Different point cloud subsampling techniques have been proposed to generate more evenly distributed samples. Poisson-disk sampling approaches assign each sample a cost value so that subsampling reduces to sorting the samples by cost and then removing the desired ratio of samples with the highest cost. Unfortunately, these approaches compute the sample cost using pairwise distances of the points within a constant search radius, which is very costly for massive point clouds with uneven densities. In this paper, we revisit Poisson-disk sampling for point clouds. Instead of optimizing for equal densities, we propose to maximize the distance to the closest point, which is equivalent to estimating the local point density as a value inversely proportional to this distance. This algorithm can be efficiently implemented using k nearest-neighbors searches. Besides a kd-tree, our algorithm also uses a voxelization to speed up the searches required to compute per-sample costs. We propose a new strategy to minimize cost updates that is amenable for out-of-core operation. We demonstrate the benefits of our approach in terms of performance, scalability, and output quality. We also discuss extensions based on adding orientation-based and color-based terms to the cost function