An efficient adaptive space partitioning algorithm for electromagnetic scattering calculation of complex 3D models

The space partitioning algorithm based on the rounding and addressing operations has been proved to be an efficient space partitioning algorithm with the potential for real-time calculation.An improvement on this kind of space partitioning algorithms for solving complex 3D models is presented.Numerical examples show that the efficiency of the improved algorithm is better than that of the original method.When the size of most target elements is smaller than the size of spatial grids,the efficiency of the improved method can be more than four times of that of the original method.An adaptive method of space partitioning based on the improved algorithm is developed by taking the surface element density or the curvature as the threshold for deep partitioning and conducting the deep partitioning using the octree method.A computer program implementation for applying the method in some typical applications is discussed,and the performance in terms of the efficiency,reliability,and resource use is evaluated.Application testing shows that the results of the adaptive spacing partitioning are more convenient for the follow-up use than that of the basic uniform space partitioning.Furthermore,when it is used to calculate the electromagnetic scattering of complex targets by the ray tracing(RT)method,the adaptive space partitioning algorithm can reduce the calculation time of the RT process by more than 40%compared with the uniform space segmentation algorithm.

Complex Function Approximation Based on Multi-ANN Approach

This paper presents a multi-ANN approximation approach to approximate complex non-linear function. Comparing with single-ANN methods the proposed approach improves and increases the approximation and generalization ability, and adaptability greatly in learning processes of networks. The simulation results have been shown that the method can be applied to the modeling and identification of complex dynamic control systems.

A Fast Algorithm for Ray Tracing

A fast algorithm for ray tracing is presented, with which the specular reflection term of global illumination model is improved. A hybrid technique combining hierarchical bounding volumes and constant size box partitioning is presented and a fast box traversal algorithm is used. By this technique multiple ray intersections with objects that are in more than one box can be avoided. As a result, the speed of ray tracing is considerably increased.

Researches on Cartographic Database-Based Interactive Three-Dimensional Topographic Map

With the development of computer graphics, the three-dimensional (3D) visualization brings new technological revolution to the traditional cartography. Therefore, the topographic 3D-map emerges to adapt to this technological revolution, and the applications of topographic 3D-map are spread rapidly to other relevant fields due to its incomparable advantage. The researches on digital map and the construction of map database offer strong technical support and abundant data source for this new technology, so the research and development of topographic 3D-map will receive greater concern. The basic data of the topographic 3D-map are rooted mainly in digital map and its basic model is derived from digital elevation model (DEM) and 3D-models of other DEM-based geographic features. In view of the potential enormous data and the complexity of geographic features, the dynamic representation of geographic information becomes the focus of the research of topographic 3D-map and also the prerequisite condition of 3D query and analysis. In addition to the equipment of hardware that are restraining, to a certain extent, the 3D representation, the data organization structure of geographic information will be the core problem of research on 3D-map. Level of detail (LOD), space partitioning, dynamic object loading (DOL) and object culling are core technologies of the dynamic 3D representation. The object- selection, attribute-query and model-editing are important functions and interaction tools for users with 3D-maps provided by topographic 3D-map system, all of which are based on the data structure of the 3D-model. This paper discusses the basic theories, concepts and cardinal principles of topographic 3D-map, expounds the basic way to organize the scene hierarchy of topographic 3D-map based on the node mechanism and studies the dynamic representation technologies of topographic 3D-map based on LOD, space partitioning, DOL and object culling. Moreover, such interactive operation functions are explored, in this paper, as spatial query, scene editing and management of topographic 3D-map. Finally, this paper describes briefly the applications of topographic 3D-map in its related fields.

Fast,Exact and Robust Set Operations on Polyhedrons Using Localized Constructive Solid Geometry Trees

Regularized Boolean operations have been widely used in 3D modeling systems. However, evaluating Boolean operations may be quite numerically unstable and time consuming, especially for iterated set operations. A novel and unified technique is proposed in this paper for computing single and iterated set operations efficiently, robustly and exactly. An adaptive octree is combined with a nested constructive solid geometry （CSG） tree by this technique. The intersection handling is restricted to the cells in the octree where intersection actually occurs. Within those cells, a CSG tree template is instanced by the surfaces and the tree is converted to planebased binary space partitioning （BSP） for set evaluation; Moreover, the surface classification is restricted to the ceils in the octree where the surfaces only come from a model and are within the bounding-boxes of other polyhedrons. These two ways bring about the efficiency and scalability of the operations, in terms of runtime and memory. As all surfaces in such a cell have the same classification relation, they are classified as a whole. Robustness and exactness are achieved by integrating plane-based geometry representation with adaptive geometry predicate technique in intersection handling, and by applying divide-and-conquer arithmetic on surface classification. Experimental results demonstrate that the proposed approach can guarantee the robustness of Boolean computations and runs faster than other existing approaches.

Fine-tuning of pore-space-partitioned metal-organic frameworks for efficient C_(2)H_(2)/C_(2)H_(4) and C_(2)H_(2)/CO_(2) separation

Acetylene (C_(2)H_(2)) and ethylene (C_(2)H_(4)) both are important chemical raw materials and energy fuel gasses.But the effective removement of trace C_(2)H_(2)from C_(2)H_(4)and the purification of C_(2)H_(2)from carbon dioxide(CO_(2)) are particularly challenging in the petrochemical industry.As a class of porous physical adsorbent,metal-organic frameworks (MOFs) have exhibited great success in separation and purification of light hydrocarbon gas.Herein,we rationally designed four novel MOFs by the strategy of pore space partition(PSP) via introducing triangular tri(pyridin-4-yl)-amine (TPA) into the 1D hexagonal channels of acs-type parent skeleton.By modulating the functional groups of linear dicarboxylate linkers for the parent skeleton,a series of isoreticular PSP-MOFs (SNNU-278-281) were successfully obtained.The synergistic effects of suitable pore size and Lewis basic functional groups make these MOFs ideal C_(2)H_(2)adsorbents.The gas adsorption experimental results show that all MOFs have excellent C_(2)H_(2)uptakes.Specially,SNNU-278demonstrates a high C_(2)H_(2)uptake of 149.7 cm3/g at 273 K and 1 atm.Meanwhile,SNNU-278-281 MOFs also show extremely great C_(2)H_(2)separation from CO_(2)and C_(2)H_(4).The optimized SNNU-281 with highdensity hydroxy groups exhibits extraordinary C_(2)H_(2)/CO_(2)and C_(2)H_(2)/C_(2)H_(4)dynamic breakthrough interval times up to 31 min/g and 17 min/g under 298 K and 1 bar.

Optimal Maintenance Modeling for Systems with Multiple Non-Identical Units Using Extended DSSP Method

In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy;a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which “hard failure” and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems.

Incomplete Multi-View Clustering via Auto-Weighted Fusion in Partition Space

As a class of effective methods for incomplete multi-view clustering,graph-based algorithms have recently drawn wide attention.However,most of them could use further improvement regarding the following aspects.First,in some graph-based models,all views are forced to share a common similarity graph regardless of the severe consistency degeneration due to incomplete views.Next,similarity graph construction and cluster analysis are sometimes performed separately.Finally,the contribution difference of individual views is not always carefully considered.To address these issues simultaneously,this paper proposes an incomplete multi-view clustering algorithm based on auto-weighted fusion in partition space.In our algorithm,the information of cluster structure is introduced into the process of similarity learning to construct a desirable similarity graph,information fusion is performed in partition space to alleviate the negative impact brought about by consistency degradation,and all views are adaptively weighted to reflect their different contributions to clustering tasks.Finally,all the subtasks are collaboratively optimized in a united framework to reach an overall optimal result.Experimental results show that the proposed method compares favorably with the state-of-the-art methods.

A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization

Many isolation approaches, such as zoning search, have been proposed to preserve the diversity in the decision space of multimodal multi-objective optimization(MMO). However, these approaches allocate the same computing resources for subspaces with different difficulties and evolution states. In order to solve this issue, this paper proposes a dynamic resource allocation strategy(DRAS)with reinforcement learning for multimodal multi-objective optimization problems(MMOPs). In DRAS, relative contribution and improvement are utilized to define the aptitude of subspaces, which can capture the potentials of subspaces accurately. Moreover, the reinforcement learning method is used to dynamically allocate computing resources for each subspace. In addition, the proposed DRAS is applied to zoning searches. Experimental results demonstrate that DRAS can effectively assist zoning search in finding more and better distributed equivalent Pareto optimal solutions in the decision space.

Computation of Edge-Edge-Edge Events Based on Conicoid Theory for 3-D Object Recognition

The availability of a good viewpoint space partition is crucial in three dimensional （3-D） object recognition on the approach of aspect graph. There are two important events, depicted by the aspect graph approach, edge-：edge-edge （EEE） events and edge-vertex （EV） events. This paper presents an algorithm to compute EEE events by characteristic analysis based on conicoid theory, in contrast to current algorithms that focus too much on EV events and often overlook the importance of EEE events. Also, the paper provides a standard flowchart for the viewpoint space partitioning based on aspect graph theory that makes it suitable for perspective models. The partitioning result best demonstrates the algorithm＇s efficiency with more valuable viewpoints found with the help of EEE events, which can definitely help to achieve high recognition rate for 3-D object recognition.

Design and Implementation of an Extended Collectives Library for Unified Parallel C

Unified Parallel C （UPC） is a parallel extension of ANSI C based on the Partitioned Global Address Space （PGAS） programming model, which provides a shared memory view that simplifies code development while it can take advantage of the scalability of distributed memory architectures. Therefore, UPC allows programmers to write parallel applications on hybrid shared/distributed memory architectures, such as multi-core clusters, in a more productive way, accessing remote memory by means of different high-level language constructs, such as assignments to shared variables or collective primitives. However, the standard UPC collectives library includes a reduced set of eight basic primitives with quite limited functionality. This work presents the design and implementation of extended UPC collective functions that overcome the limitations of the standard collectives library, allowing, for example, the use of a specific source and destination thread or defining the amount of data transferred by each particular thread. This library fulfills the demands made by the UPC developers community and implements portable algorithms, independent of the specific UPC compiler/runtime being used. The use of a representative set of these extended collectives has been evaluated using two applications and four kernels as case studies. The results obtained confirm the suitability of the new library to provide easier programming without trading off performance, thus achieving high productivity in parallel programming to harness the performance of hybrid shared/distributed memory architectures in high performance computing.

分割氢键有机框架的孔道空间提升光催化CO_(2)还原效率

氢键有机框架材料(HOFs)在催化领域具有巨大的潜力,但其孔道空间在去除溶剂后易塌陷,限制了其在光催化能源转换和存储方面的应用.本研究采用孔道分割策略成功设计并合成了一种稳定孔道结构、高效光催化CO_(2)还原活性的HOF催化剂.H4TBAPy分子通过与HOF孔道中大量的CH…π相互作用锚定自身,并将HOF的大孔道分隔成多个小空间,显著增加了HOF框架的稳定性.同时,富电子属性的H4TBAPy分子提升了HOF体系中电子的传输速率,从而提高了催化活性.这项研究为设计稳定、高性能的HOFs光催化剂提供了新策略.