An approach to solving the forward kinematics of the 5-RPUR (3T2R) parallel manipulator

Jaime Gallardo-Alvarado,Mario A. Garcia-Murillo,Luis D. Aguilera-Camacho,Luis A. Alcaraz-Caracheo,X. Yamile Sandoval-Castro 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.3

This work is devoted to simplifying the formulation and solution of the closure equations associated with the forward kinematic problem (FKP) of the 5-RPUR parallel manipulator, a limited-DOF robot able to perform 3T2R motion. The analysis yields a set of eighteen nonlinear equations that are solved numerically through a combination of the homotopy continuation method and the usual Newton-Raphson technique. Unlike existing methods, the proposed approach is easy to follow and can be easily translated into computer codes. Numerical examples are provided with the aim to illustrate the potential and correctness of the proposed method.

Parallel Multithreaded Processing for Data Set Summarization on Multicore CPUs

Ordonez, Carlos,Navas, Mario,Garcia-Alvarado, Carlos Korean Institute of Information Scientists and Eng 2011 Journal of Computing Science and Engineering Vol.5 No.2

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.

Parallel Multithreaded Processing for Data Set Summarization on Multicore CPUs

Carlos Ordonez,Mario Navas,Carlos Garcia-Alvarado 한국정보과학회 2011 Journal of Computing Science and Engineering Vol.5 No.2

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.

Exploring data processing strategies in NGS target enrichment to disentangle radiations in the tribe Cardueae (Compositae)

Herrando-Moraira, Sonia,Calleja, Juan Antonio,Carnicero, Pau,Fujikawa, Kazumi,Galbany-Casals, Mercè,Garcia-Jacas, Nú,ria,Im, Hyoung-Tak,Kim, Seung-Chul,Liu, Jian-Quan,Ló,pez-Alvarado Elsevier 2018 Molecular phylogenetics and evolution Vol.128 No.-

<P><B>Abstract</B></P> <P>Target enrichment is a cost-effective sequencing technique that holds promise for elucidating evolutionary relationships in fast-evolving lineages. However, potential biases and impact of bioinformatic sequence treatments in phylogenetic inference have not been thoroughly explored yet. Here, we investigate this issue with an ultimate goal to shed light into a highly diversified group of Compositae (Asteraceae) constituted by four main genera: <I>Arctium</I>, <I>Cousinia</I>, <I>Saussurea</I>, and <I>Jurinea</I>. Specifically, we compared sequence data extraction methods implemented in two easy-to-use workflows, PHYLUCE and HybPiper, and assessed the impact of two filtering practices intended to reduce phylogenetic noise. In addition, we compared two phylogenetic inference methods: (1) the concatenation approach, in which all loci were concatenated in a supermatrix; and (2) the coalescence approach, in which gene trees were produced independently and then used to construct a species tree under coalescence assumptions. Here we confirm the usefulness of the set of 1061 COS targets (a nuclear conserved orthology loci set developed for the Compositae) across a variety of taxonomic levels. Intergeneric relationships were completely resolved: there are two sister groups, <I>Arctium</I>-<I>Cousinia</I> and <I>Saussurea</I>-<I>Jurinea</I>, which are in agreement with a morphological hypothesis. Intrageneric relationships among species of <I>Arctium</I>, <I>Cousinia</I>, and <I>Saussurea</I> are also well defined. Conversely, conflicting species relationships remain for <I>Jurinea</I>. Methodological choices significantly affected phylogenies in terms of topology, branch length, and support. Across all analyses, the phylogeny obtained using HybPiper and the strictest scheme of removing fast-evolving sites was estimated as the optimal. Regarding methodological choices, we conclude that: (1) trees obtained under the coalescence approach are topologically more congruent between them than those inferred using the concatenation approach; (2) refining treatments only improved support values under the concatenation approach; and (3) branch support values are maximized when fast-evolving sites are removed in the concatenation approach, and when a higher number of loci is analyzed in the coalescence approach.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Target enrichment resolved relationships among the four genera of the Cardueae. </LI> <LI> Bioinformatic choices can largely affect the phylogenetic reconstructions. </LI> <LI> Filtering strategies improve support values only under concatenation analyses. </LI> <LI> The coalescence approach yields higher topological robustness than concatenation. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>