37200200
A topological hierarchy-based approach to toolpath planning for multi-material layered manufacturing
This paper proposes a topological hierarchy-based approach to toolpath planning for multi-material layered manufacturing (MMLM) of heterogeneous prototypes. The approach facilitates control of MMLM and increases the fabrication efficiency of complex objects by generating multi-toolpaths that avoid redundant tool movements and potential collisions. It uses a topological hierarchy-sorting algorithm to group complex multi-material slice contours into families connected by a parent-and-child relationship. Subsequently, a sequential toolpath planning algorithm generates multi-toolpaths for sequential deposition of materials without redundant tool movements. To reduce build time further, a concurrent toolpath planning algorithm generates collision-free multi-toolpaths to control the tools that deposit materials concurrently. It uses parametric polygons to construct tool envelopes for contour families of the same material property to simplify detection of tool collisions. The tightness of polygons can be controlled to suit the processing speed and the optimality of the resulting concurrent toolpaths. The proposed approach has been implemented as an integral part of a multi-material virtual prototyping (MMVP) system that can process complex slice contours for planning, stereoscopic simulation, and validation of multi-toolpaths. It may be adapted for subsequent control of MMLM processes.
19996434
A multi-material virtual prototyping system
Abstract This paper proposes a multi-material virtual prototyping system for digital fabrication of heterogeneous prototypes. It consists mainly of a topological hierarchy-sorting algorithm for processing slice contours, and a virtual simulation system for visualisation and optimisation of multi-material layered manufacturing (MMLM) processes. The topological hierarchy-sorting algorithm processes the hierarchy relationship of complex slice contours. It builds a parent-and-child list that defines the containment relationship of the slice contours, and subsequently arranges the contours in an appropriate sequence, which facilitates toolpath planning for MMLM by avoiding redundant tool movements. The virtual simulation system simulates MMLM processes and provides stereoscopic visualisation of the resulting multi-material prototypes for quality analysis and optimisation of the processes.
5224987
Differential learning algorithms for decorrelation and independent component analysis
Decorrelation and its higher-order generalization, independent component analysis (ICA), are fundamental and important tasks in unsupervised learning, that were studied mainly in the domain of Hebbian learning. In this paper we present a variation of the natural gradient ICA, differential ICA, where the learning relies on the concurrent change of output variables. We interpret the differential learning as the maximum likelihood estimation of parameters with latent variables represented by the random walk model. In such a framework, we derive the differential ICA algorithm and, in addition, we also present the differential decorrelation algorithm that is treated as a special instance of the differential ICA. Algorithm derivation and local stability analysis are given with some numerical experimental results.
23325537
A spanning tree of the 2m-dimensional hypercube with maximum number of degree-preserving vertices
Abstract For an n -dimensional hypercube Q n , the maximum number of degree-preserving vertices in a spanning tree is 2 n / n if n =2 m for an integer m . (If n ≠2 m , then the maximum number of degree-preserving vertices in a spanning tree is less than 2 n / n .) We also construct a spanning tree of Q 2 m with maximum number of degree-preserving vertices.
3185620
A simple algorithm for homeomorphic surface reconstruction
Abstract 1 Introduction The problem of computing a piecewise linear approximation to a surface from a set of sam- ple points on the surface has been a focus of research in solid modeling and graphics due to its many applications. The input to this sur- face reconstruction problem consists of the three dimensional coordinates of the sampled points. The crust algorithm of [1] reconstructs a surface with topological and geometric guarantees using the Voronoi diagram of the input point set. We present new observations that simplify both the algorithm and the proofs for the crust, and we give for the first time a proof that the crust is homeomorphic to the input surface. *Dept. of Computer Science, U. of Texas, Austin TX 78712. e-mail: amenta@cs.utexas.edu tDept, of Computer Science, U. of Texas, Austin, TX 78712. e-mail: sunghee@cs.utexas.edu SDept. of Computer and Information Science, Ohio State U., Columbus, OH 43210. e-mail: t amaldey¢ci s. ohio-state, edu §Dept. of Computer and Information Science, Ohio State U., Columbus, OH 43210. e-mail: leekha@cis, ohio-state, edu
16177959
Multi-Node Global Directory Construction in Peer-to-Peer Systems
In P2P systems, the speed of searching for desired lists of sharable files is one of the most important measurement issues in evaluation of overall system performance. Although the searching schemes of existing non-structured P2P systems have been improved to enhance speed considerably, they still never reach to the level of those of the centralized and structured systems. In this paper, we propose a multi-node global directory in P2P systems, where all the lists of sharable resources of the joining peers are aggregated and divided into the chosen nodes while keeping collating sequence as a whole to make one big linked list. Thus in indexed sequential searching, the system provides us with convenience of using the command like 'dir' of MS-DOS, and in general keyword searching, which is naturally impossible for the structured P2P systems, the searching speed is even faster than those of centralized P2P systems since a small list segment is scanned at each node synchronously in parallel
9780462
Learning-Based SMT Processor Resource Distribution via Hill-Climbing
The key to high performance in simultaneous multithreaded (SMT) processors lies in optimizing the distribution of shared resources to active threads. Existing resource distribution techniques optimize performance only indirectly. They infer potential performance bottlenecks by observing indicators, like instruction occupancy or cache miss counts, and take actions to try to alleviate them. While the corrective actions are designed to improve performance, their actual performance impact is not known since end performance is never monitored. Consequently, potential performance gains are lost whenever the corrective actions do not effectively address the actual bottlenecks occurring in the pipeline. We propose a different approach to SMT resource distribution that optimizes end performance directly. Our approach observes the impact that resource distribution decisions have on performance at runtime, and feeds this information back to the resource distribution mechanisms to improve future decisions. By evaluating many different resource distributions, our approach tries to learn the best distribution over time. Because we perform learning on-line, learning time is crucial. We develop a hill-climbing algorithm that efficiently learns the best distribution of resources by following the performance gradient within the resource distribution space. This paper conducts an in-depth investigation of learning-based SMT resource distribution. First, we compare existing resource distribution techniques to an ideal learning-based technique that performs learning off-line. This limit study shows learning-based techniques can provide up to 19.2% gain over ICOUNT, 18.0% gain over FLUSH, and 7.6% gain over DCRA across 21 multithreaded workloads. Then, we present an on-line learning algorithm based on hill-climbing. Our evaluation shows hill-climbing provides a 12.4% gain over ICOUNT, 11.3% gain over FLUSH, and 2.4% gain over DCRA across a larger set of 42 multiprogrammed workloads