丙烯酸酯橡胶柔性电极材料的制备及其性能研究

通过导电炭黑与多壁碳纳米管（MWCNT）并用制备丙烯酸酯橡胶（ACM）柔性电极材料,研究导电炭黑和MWCNT在ACM基体中的分散性及其并用比对柔性电极材料硫化特性、物理性能和导电性能的影响,并分析柔性电极材料与介电弹性体（DE）基体的粘合性能。结果表明：导电炭黑和MWCNT在ACM基体中具有良好的分散性;添加导电炭黑/MWCNT并用体系的ACM柔性电极材料的拉断伸长率远大于200%,满足DE发电机对于大形变的要求,且与仅添加导电炭黑的柔性电极材料相比,其硬度和弹性模量减小,柔韧性提高;随着MWCNT用量的增大,柔性电极材料的导电通路逐渐完善,导电性能提高;导电炭黑/MWCNT并用比为10/10的ACM柔性电极材料与ACM基DE基体共硫化后,两者的粘合强度达到5 N·mm^-1,且粘合稳定性较好。     

4种羧基型丙烯酸酯橡胶的性能对比

对比研究牌号2211和4212（国产）以及相应牌号PA526和PA522HF（进口）4种羧基型丙烯酸酯橡胶（ACM）的性能。结果表明：ACM PA526和4212的相对分子质量较大,ACM PA522HF的相对分子质量最小;ACM PA526和2211胶料的硫化速度较快;ACM PA526,2211和PA522HF硫化胶的耐热空气老化性能较好,190℃×72 h热空气老化后ACM2211硫化胶的拉伸强度最大;ACM4212和PA522HF硫化胶的抗压缩永久变形性能较好,ACMPA526和2211硫化胶的耐油性能较好,但ACM 2211硫化胶的耐低温性能较差。     

Consistent Video Filtering for Camera Arrays

Visual formats have advanced beyond single‐view images and videos: 3D movies are commonplace, researchers have developed multi‐view navigation systems, and VR is helping to push light field cameras to mass market. However, editing tools for these media are still nascent, and even simple filtering operations like color correction or stylization are problematic: naively applying image filters per frame or per view rarely produces satisfying results due to time and space inconsistencies. Our method preserves and stabilizes filter effects while being agnostic to the inner working of the filter. It captures filter effects in the gradient domain, then uses input frame gradients as a reference to impose temporal and spatial consistency. Our least‐squares formulation adds minimal overhead compared to naive data processing. Further, when filter cost is high, we introduce a filter transfer strategy that reduces the number of per‐frame filtering computations by an order of magnitude, with only a small reduction in visual quality. We demonstrate our algorithm on several camera array formats including stereo videos, light fields, and wide baselines.     

A GPU‐Adapted Structure for Unstructured Grids

A key advantage of working with structured grids (e.g., images) is the ability to directly tap into the powerful machinery of linear algebra. This is not much so for unstructured grids where intermediate bookkeeping data structures stand in the way. On modern high performance computing hardware, the conventional wisdom behind these intermediate structures is further challenged by costly memory access, and more importantly by prohibitive memory resources on environments such as graphics hardware. In this paper, we bypass this problem by introducing a sparse matrix representation for unstructured grids which not only reduces the memory storage requirements but also cuts down on the bulk of data movement from global storage to the compute units. In order to take full advantage of the proposed representation, we augment ordinary matrix multiplication by means of action maps, local maps which encode the desired interaction between grid vertices. In this way, geometric computations and topological modifications translate into concise linear algebra operations. In our algorithmic formulation, we capitalize on the nature of sparse matrix‐vector multiplication which allows avoiding explicit transpose computation and storage. Furthermore, we develop an efficient vectorization to the demanding assembly process of standard graph and finite element matrices.     

Finding a Clear Path: Structuring Strategies for Visualization Sequences

Little is known about how people structure sets of visualizations to support sequential viewing. We contribute findings from several studies examining visualization sequencing and reception. In our first study, people made decisions between various possible structures as they ordered a set of related visualizations (consisting of either bar charts or thematic maps) into what they considered the clearest sequence for showing the data. We find that most people structure visualization sequences hierarchically: they create high level groupings based on shared data properties like time period, measure, level of aggregation, and spatial region, then order the views within these groupings. We also observe a tendency for certain types of similarities between views, like a common spatial region or aggregation level, to be seen as more appropriate categories for organizing views in a sequence than others, like a common time period or measure. In a second study, we find that viewers’ perceptions of the quality and intention of different sequences are largely consistent with the perceptions of the users who created them. The understanding of sequence preferences and perceptions that emerges from our studies has implications for the development of visualization authoring tools and sequence recommendations for guided analysis.     

Modeling,Evaluation and Optimization of Interlocking Shell Pieces

While the 3D printing technology has become increasingly popular in recent years, it suffers from two critical limitations: expensive printing material and long printing time. An effective solution is to hollow the 3D model into a shell and print the shell by parts. Unfortunately, making shell pieces tightly assembled and easy to disassemble seem to be two contradictory conditions, and there exists no easy way to satisfy them at the same time yet. In this paper, we present a computational system to design an interlocking structure of a partitioned shell model, which uses only male and female connectors to lock shell pieces in the assembled configuration. Given a mesh segmentation input, our system automatically finds an optimal installation plan specifying both the installation order and the installation directions of the pieces, and then builds the models of the shell pieces using optimized shell thickness and connector sizes. To find the optimal installation plan, we develop simulation‐based and data‐driven metrics, and we incorporate them into an optimal plan search algorithm with fast pruning and local optimization strategies. The whole system is automatic, except for the shape design of the key piece. The interlocking structure does not introduce new gaps on the outer surface, which would become noticeable inevitably due to limited printer precision. Our experiment shows that the assembled object is strong against separation, yet still easy to disassemble.