36,115 research outputs found
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
Multidisciplinary Design Optimization For Multi Purpose Design Of Truck
Multidisciplinary Design Optimization (MOO) problem are often are encountered in many industrial areas
and aspect. MOO refers to the optimization of systems of subsystem (disciplines). The optimization of
the full system is often reduced to hierarchy of optimizations at subsystem and system levels.
Multidisciplinary Design Optimization is a body of methods and techniques for performing the above
optimization so as to balance the design considerations at the system and detail levels.
This paper will be presents an application of MOO for multi purpose design of truck, case study in
Toyota Dyna. That body of truck can be used for some purpose, example for fluid tanker, mixer truck,
bulk and box truck and other purpose. The successful design requires harmonization of a number of
objectives and con5traint. However, dimensionality of such optimization and the complexity and
expense of the underlying analysis suggest a decomposition approach to enable concurrent execution
of smaller and more manageable task. We identify that the fundamental approach to modeling the
design depend on lumped mass, vehicle fixed coordin2te system, motion variable, earth fixed
coordinate system, euler angles, and force. The master problem for multi purpose design of body truck
is to maximize the contain capacity, with design variable are length, wide and high of the body. The
sub problem are minimize the centrifugal force, roll threshold and suspension.
The result of this paper will be giving the optimum condition of the body dimension and helpful the
design to reduce time for design. Finally, quality of product will be increase, reducing cost and
thereby being competitive
Multiscale, thermomechanical topology optimization of self-supporting cellular structures for porous injection molds
Purpose
This paper aims to establish a multiscale topology optimization method for the optimal design of non-periodic, self-supporting cellular structures subjected to thermo-mechanical loads. The result is a hierarchically complex design that is thermally efficient, mechanically stable and suitable for additive manufacturing (AM).
Design/methodology/approach
The proposed method seeks to maximize thermo-mechanical performance at the macroscale in a conceptual design while obtaining maximum shear modulus for each unit cell at the mesoscale. Then, the macroscale performance is re-estimated, and the mesoscale design is updated until the macroscale performance is satisfied.
Findings
A two-dimensional Messerschmitt Bolkow Bolhm (MBB) beam withstanding thermo-mechanical load is presented to illustrate the proposed design method. Furthermore, the method is implemented to optimize a three-dimensional injection mold, which is successfully prototyped using 420 stainless steel infiltrated with bronze.
Originality/value
By developing a computationally efficient and manufacturing friendly inverse homogenization approach, the novel multiscale design could generate porous molds which can save up to 30 per cent material compared to their solid counterpart without decreasing thermo-mechanical performance.
Practical implications
This study is a useful tool for the designer in molding industries to reduce the cost of the injection mold and take full advantage of AM
Novel CMOS RFIC Layout Generation with Concurrent Device Placement and Fixed-Length Microstrip Routing
With advancing process technologies and booming IoT markets, millimeter-wave
CMOS RFICs have been widely developed in re- cent years. Since the performance
of CMOS RFICs is very sensi- tive to the precision of the layout, precise
placement of devices and precisely matched microstrip lengths to given values
have been a labor-intensive and time-consuming task, and thus become a major
bottleneck for time to market. This paper introduces a progressive
integer-linear-programming-based method to gener- ate high-quality RFIC layouts
satisfying very stringent routing requirements of microstrip lines, including
spacing/non-crossing rules, precise length, and bend number minimization,
within a given layout area. The resulting RFIC layouts excel in both per-
formance and area with much fewer bends compared with the simulation-tuning
based manual layout, while the layout gener- ation time is significantly
reduced from weeks to half an hour.Comment: ACM/IEEE Design Automation Conference (DAC), 201
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