595 research outputs found
A macro-micro robot for precise force applications
This paper describes an 8 degree-of-freedom macro-micro robot capable of performing tasks which require accurate force control. Applications such as polishing, finishing, grinding, deburring, and cleaning are a few examples of tasks which need this capability. Currently these tasks are either performed manually or with dedicated machinery because of the lack of a flexible and cost effective tool, such as a programmable force-controlled robot. The basic design and control of the macro-micro robot is described in this paper. A modular high-performance multiprocessor control system was designed to provide sufficient compute power for executing advanced control methods. An 8 degree of freedom macro-micro mechanism was constructed to enable accurate tip forces. Control algorithms based on the impedance control method were derived, coded, and load balanced for maximum execution speed on the multiprocessor system
Sequence stratigraphy, fracture characterization, and rebound hardness analysis of the unconventional "Mississippian Limestone"/STACK play, north-central Oklahoma, USA
The "Mississippian Limestone"/STACK play in Oklahoma has been a prolific hydrocarbon play for decades. However, several critical aspects, all of which are valuable for reservoir characterization, such as core-based sequence stratigraphy and fracture distribution, and rebound hardness (RHN), are not well understood. To address these topics with an integrated approach, this study utilizes six cores from four counties in north-central Oklahoma and a time-equivalent outcrop in northwestern Arkansas, the latter of which is evaluated for a fracture analog.In all cores combined, seven mudstone, siltstone, and silty limestone facies are present that exhibit vertical cyclicity at various scales, defining a hierarchical sequence stratigraphic framework. (Sub)vertical, naturally mineralized fractures are common in all cores, with the highest average fracture intensity corresponding to the silty limestone-rich intervals (i.e., regressive phases of "third-order" sequences), which commonly show distinctively low gamma-ray values. These observations imply the potential value of sequence stratigraphy in characterizing and predicting fracture distribution in these unconventional reservoirs. In the outcrop, which is composed of carbonate mudstone and chert, similar types of fractures are present, with overall higher fracture intensity in chert. The distribution pattern of attribute data (height, kinematic aperture, spacing) is affected by lithology, fracture type, and fracture height, pointing to a cooperative role of lithology, fracture type, and fracture-bedding relationships in affecting fracture attributes. Because of different dominant lithologies, this outcrop does not work as a direct fracture analog for the play areas of this study. For RHN analysis, plug samples from the Vaca Muerta Formation provide supplemental data. 2D crossplots between the collected RHN data and the rock data (mineralogy, porosity, sonic velocity, elastic parameters) show correlative trends with clustering by facies groups, implying the effect of facies in the statistical pattern and the value of RHN for rock typing. Variable correlation coefficient suggests variable capabilities of RHN in predicting rock properties, which can be related to the multivariate control of RHN as suggested by leverage analysis. In addition, regression analysis indicates that RHN can potentially assist in the prediction of certain rock properties. These observations imply the potential value of RHN in reservoir characterization
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
Image super-resolution (SR) has witnessed extensive neural network designs
from CNN to transformer architectures. However, prevailing SR models suffer
from prohibitive memory footprint and intensive computations, which limits
further deployment on edge devices. This work investigates the potential of
network pruning for super-resolution to take advantage of off-the-shelf network
designs and reduce the underlying computational overhead. Two main challenges
remain in applying pruning methods for SR. First, the widely-used filter
pruning technique reflects limited granularity and restricted adaptability to
diverse network structures. Second, existing pruning methods generally operate
upon a pre-trained network for the sparse structure determination, hard to get
rid of dense model training in the traditional SR paradigm. To address these
challenges, we adopt unstructured pruning with sparse models directly trained
from scratch. Specifically, we propose a novel Iterative Soft
Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a
randomly initialized network at each iteration and tweaking unimportant weights
with a small amount proportional to the magnitude scale on-the-fly. We observe
that the proposed ISS-P can dynamically learn sparse structures adapting to the
optimization process and preserve the sparse model's trainability by yielding a
more regularized gradient throughput. Experiments on benchmark datasets
demonstrate the effectiveness of the proposed ISS-P over diverse network
architectures. Code is available at
https://github.com/Jiamian-Wang/Iterative-Soft-Shrinkage-SRComment: Accepted by ICCV 2023, code released at
https://github.com/Jiamian-Wang/Iterative-Soft-Shrinkage-S
Emerging Paradigms of Neural Network Pruning
Over-parameterization of neural networks benefits the optimization and
generalization yet brings cost in practice. Pruning is adopted as a
post-processing solution to this problem, which aims to remove unnecessary
parameters in a neural network with little performance compromised. It has been
broadly believed the resulted sparse neural network cannot be trained from
scratch to comparable accuracy. However, several recent works (e.g., [Frankle
and Carbin, 2019a]) challenge this belief by discovering random sparse networks
which can be trained to match the performance with their dense counterpart.
This new pruning paradigm later inspires more new methods of pruning at
initialization. In spite of the encouraging progress, how to coordinate these
new pruning fashions with the traditional pruning has not been explored yet.
This survey seeks to bridge the gap by proposing a general pruning framework so
that the emerging pruning paradigms can be accommodated well with the
traditional one. With it, we systematically reflect the major differences and
new insights brought by these new pruning fashions, with representative works
discussed at length. Finally, we summarize the open questions as worthy future
directions
A force-controllable macro-micro manipulator and its application to medical robots
This paper describes an 8-degrees-of-freedom macro-micro robot. This robot is capable of performing tasks that require accurate force control, such as polishing, finishing, grinding, deburring, and cleaning. The design of the macro-micro mechanism, the control algorithms, and the hardware/software implementation of the algorithms are described in this paper. Initial experimental results are reported. In addition, this paper includes a discussion of medical surgery and the role that force control may play. We introduce a new class of robotic systems collectively called Robotic Enhancement Technology (RET). RET systems introduce the combination of robotic manipulation with human control to perform manipulation tasks beyond the individual capability of either human or machine. The RET class of robotic systems offers new challenges in mechanism design, control-law development, and man/machine interface design. We believe force-controllable mechanisms such as the macro-micro structure we have developed are a necessary part of RET. Work in progress in the area of RET systems and their application to minimally invasive surgery is presented, along with future research directions
S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction
The technology of hyperspectral imaging (HSI) records the visual information
upon long-range-distributed spectral wavelengths. A representative
hyperspectral image acquisition procedure conducts a 3D-to-2D encoding by the
coded aperture snapshot spectral imager (CASSI) and requires a software decoder
for the 3D signal reconstruction. By observing this physical encoding
procedure, two major challenges stand in the way of a high-fidelity
reconstruction. (i) To obtain 2D measurements, CASSI dislocates multiple
channels by disperser-titling and squeezes them onto the same spatial region,
yielding an entangled data loss. (ii) The physical coded aperture leads to a
masked data loss by selectively blocking the pixel-wise light exposure. To
tackle these challenges, we propose a spatial-spectral (S^2-) Transformer
network with a mask-aware learning strategy. First, we simultaneously leverage
spatial and spectral attention modeling to disentangle the blended information
in the 2D measurement along both two dimensions. A series of Transformer
structures are systematically designed to fully investigate the spatial and
spectral informative properties of the hyperspectral data. Second, the masked
pixels will induce higher prediction difficulty and should be treated
differently from unmasked ones. Thereby, we adaptively prioritize the loss
penalty attributing to the mask structure by inferring the pixel-wise
reconstruction difficulty upon the mask-encoded prediction. We theoretically
discusses the distinct convergence tendencies between masked/unmasked regions
of the proposed learning strategy. Extensive experiments demonstrates that the
proposed method achieves superior reconstruction performance. Additionally, we
empirically elaborate the behaviour of spatial and spectral attentions under
the proposed architecture, and comprehensively examine the impact of the
mask-aware learning.Comment: 11 pages, 16 figures, 6 tables, Code:
https://github.com/Jiamian-Wang/S2-transformer-HS
Lightweight Image Super-Resolution with Information Multi-distillation Network
In recent years, single image super-resolution (SISR) methods using deep
convolution neural network (CNN) have achieved impressive results. Thanks to
the powerful representation capabilities of the deep networks, numerous
previous ways can learn the complex non-linear mapping between low-resolution
(LR) image patches and their high-resolution (HR) versions. However, excessive
convolutions will limit the application of super-resolution technology in low
computing power devices. Besides, super-resolution of any arbitrary scale
factor is a critical issue in practical applications, which has not been well
solved in the previous approaches. To address these issues, we propose a
lightweight information multi-distillation network (IMDN) by constructing the
cascaded information multi-distillation blocks (IMDB), which contains
distillation and selective fusion parts. Specifically, the distillation module
extracts hierarchical features step-by-step, and fusion module aggregates them
according to the importance of candidate features, which is evaluated by the
proposed contrast-aware channel attention mechanism. To process real images
with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve
block-wise image patches using the same well-trained model. Extensive
experiments suggest that the proposed method performs favorably against the
state-of-the-art SR algorithms in term of visual quality, memory footprint, and
inference time. Code is available at \url{https://github.com/Zheng222/IMDN}.Comment: To be appear in ACM Multimedia 2019, https://github.com/Zheng222/IMD
- …