The classical, popular Hungarian algorithm for solving the “optimum assignment” problems (with its broad engineering/science applications) has been well-documented in the literature. Other (more efficient) variation...
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The classical, popular Hungarian algorithm for solving the “optimum assignment” problems (with its broad engineering/science applications) has been well-documented in the literature. Other (more efficient) variations of the Hungarian algorithm have also been extensively studied by the research communities. In this paper, the basic Hungarian algorithm is revisited, with the ultimate goal of developing a useful, user friendly, attractive Java computer animation for “effectively teaching” this basic/important optimum assignment algorithm. The final product from this work will help both the students and their instructor to not only mastering this technical subject, but also provide valuable tool for obtaining the solutions for homework assignments, class examinations, self-assessment tools, etc. A demo video of the Hungarian Algorithm's animation and result can be viewed online from any web browser using the website provided in reference [9].
In most of the traffic safety studies, both the identification of high-risk locations and the assessment of safety improvement solutions are done through the use of historical crash data. This study proposes an altern...
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ISBN:
(纸本)9781509018901
In most of the traffic safety studies, both the identification of high-risk locations and the assessment of safety improvement solutions are done through the use of historical crash data. This study proposes an alternative approach that makes use of traffic conflicts extracted from traffic video recordings for safety assessment. State-of-the-art computer vision techniques are used to extract vehicle trajectories automatically from 70 hours of traffic video data at two intersections. More specifically, a modified implementation of the Kanade-Lucas-Tomasi (KLT) feature tracker is used to extract the feature points and track those feature points frame by frame. The spectral embedding and the Dirichlet process Gaussian mixture model (DPGMM) are employed to cluster feature points that belong to the same object. The combination of each vehicle's individual trajectory with all the others' trajectories is then screened to identify all the possible vehicle pairs involved in conflict risk. Traffic conflict risks are identified after the time to collision (TTC) is computed for each vehicle pair. Hourly number of conflicts are found to follow a negative binomial distribution similar to number of crashes. A strong correlation is observed between the traffic conflicts and actual crashes, and thus the validity of using conflict data extracted from videos for safety assessment can be confirmed. The proposed approach has potential transferability and can be implemented by transportation agencies in other cities.
Results for ab initio no-core shell model calculations in a symmetry-adapted SU(3)-based coupling scheme demonstrate that collective modes in light nuclei emerge from first principles. The low-lying states of Li6, Be8...
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Results for ab initio no-core shell model calculations in a symmetry-adapted SU(3)-based coupling scheme demonstrate that collective modes in light nuclei emerge from first principles. The low-lying states of Li6, Be8, and He6 are shown to exhibit orderly patterns that favor spatial configurations with strong quadrupole deformation and complementary low intrinsic spin values, a picture that is consistent with the nuclear symplectic model. The results also suggest a pragmatic path forward to accommodate deformation-driven collective features in ab initio analyses when they dominate the nuclear landscape.
We present an error-controlled mesh refinement procedure for needle insertion simulation and apply it to the simulation of electrode implantation for deep brain stimulation, including brain shift. Our approach enables...
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We have developed a realistic nucleon-nucleon (NN) interaction, dubbed Daejeon16. We start from a SRG (similarity renormalization group) evolved chiral N3LO interaction. We then apply PETs (phase-equivalent transforma...
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Scalability of a low-cost, Intel Xeon-based, multi-Teraflop Linux cluster is tested for two high-end scientific applications: Classical atomistic simulation based on the molecular dynamics method and quantum mechanica...
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Scalability of a low-cost, Intel Xeon-based, multi-Teraflop Linux cluster is tested for two high-end scientific applications: Classical atomistic simulation based on the molecular dynamics method and quantum mechanical calculation based on the density functional theory. These scalable parallel applications use spacetime multiresolution algorithms and feature computational-space decomposition, wavelet-based adaptive load balancing, and spacefilling-curve-based data compression for scalable I/O. Comparative performance tests are performed on a 1024-processor Linux cluster and a conventional higher-end parallel supercomputer, 1184-processor IBM SP4. The results show that the performance of the Linux cluster is comparable to that of the SP4. We also study various effects, such as the sharing of memory and L2 cache among processors, on the performance.
作者：
ManWo NgDepartment of Modeling
Simulation and Visualization Engineering 1318 Engineering and Computational Sciences Building Old Dominion University Norfolk VA 23529 USA Department of Civil and Environmental Engineering
1318 Engineering and Computational Sciences Building Old Dominion University Norfolk VA 23529 USA
In ab-initio Configuration Interaction calculations, the nuclear wavefunction is expanded in Slater determinants of single-nucleon wavefunctions and the many-body Schrodinger equation becomes a large sparse matrix pro...
In ab-initio Configuration Interaction calculations, the nuclear wavefunction is expanded in Slater determinants of single-nucleon wavefunctions and the many-body Schrodinger equation becomes a large sparse matrix problem. The challenge is to reach numerical convergence to within quantified numerical uncertainties for physical observables using finite truncations of the infinite-dimensional basis space. We discuss strategies for constructing and solving the resulting large sparse matrix eigenvalue problems on current multicore computer architectures. Several of these strategies have been implemented in the code MFDn, a hybrid MPI/OpenMP Fortran code for ab-initio nuclear structure calculations that can scale to 100,000 cores and more. Finally, we will conclude with some recent results for 12C including emerging collective phenomena such as rotational band structures using SRG evolved chiral N3LO interactions.
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