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

Stock Market Simulation

In this Interactive Qualifying Project (IQP), the group conducted a 14-week stock market simulation using three different trading strategies: technical, swing, and position trading. The team researched the fundamentals of the stock market and the basics of trading using tools and resources gathered from the Internet. Each member managed a portfolio using one trading strategy with an initial $500,000 to invest. Trading decisions were supported by market analysis techniques and results were exchanged in weekly conventions. The project gave the team members a valuable beginning stock trading experience and helped them to gain a better knowledge and understanding of the stock market. This IQP has built a strong foundation for potential investment in the future

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

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