Nanometer-Level Comparison of Three Spindle Error Motion Separation Techniques

2005 ◽  
Vol 128 (1) ◽  
pp. 180-187 ◽  
Author(s):  
Eric Marsh ◽  
Jeremiah Couey ◽  
Ryan Vallance
Keyword(s):  
State Of The Art ◽  
The State ◽  
Numerical Error ◽  
Separation Techniques ◽  
Error Separation ◽  
Level Measurement ◽  
Rotationally Symmetric ◽  
Error Motion ◽  
Aerostatic Spindle ◽  
Better Than

This work demonstrates the state of the art capabilities of three error separation techniques for nanometer-level measurement of precision spindles and rotationally-symmetric artifacts. Donaldson reversal is compared to a multi-probe and a multi-step technique using a series of measurements carried out on a precision aerostatic spindle with a lapped spherical artifact. The results indicate that subnanometer features in both spindle error motion and artifact form are reliably resolved by all three techniques. Furthermore, the numerical error values agree to better than one nanometer. The paper discusses several issues that must be considered when planning spindle or artifact measurements at the nanometer level.

Ultrathin RuRh@(RuRh)O2 core@shell nanosheets as stable oxygen evolution electrocatalysts

2020 ◽  
Vol 8 (31) ◽  
pp. 15746-15751 ◽  
Author(s):  
Kai Wang ◽  
Bolong Huang ◽  
Weiyu Zhang ◽  
Fan Lv ◽  
Yi Xing ◽  
...  
Keyword(s):  
Oxygen Evolution ◽  
State Of The Art ◽  
The State ◽  
Oxide Shell ◽  
Core Shell ◽  
Stable Oxygen ◽  
Better Than

We report a novel architecture of ultrathin RuRh@(RuRh)O2 core/shell nanosheets with a core of ultrathin metallic RuRh nanosheets and a shell of (RuRh)O2 oxides as a superb electrocatalyst toward the oxgen evolution reaction (OER), better than most of the state-of-the-art Ru-based or Ir-based electrocatalysts. Moreover, the RuRh@(RuRh)O2 core/shell nanosheets exhibit good durability because the (RuRh)O2 oxide shell protects the normally labile RuRh NS core against dissolution during the OER process.

scholarly journals Category Trees - Classifiers that Branch on Category

2021 ◽  
Vol 12 (06) ◽  
pp. 65-76
Author(s):  
Kieran Greer
Keyword(s):  
State Of The Art ◽  
The State ◽  
Biological Analogy ◽  
Category Type ◽  
Benchmark Datasets ◽  
Batch Input ◽  
Distinguishing Features ◽  
Incorrect Data ◽  
Better Than

This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.

scholarly journals Classifier for Activities with Variations

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3529 ◽  
Author(s):  
Rabih Younes ◽  
Mark Jones ◽  
Thomas Martin
Keyword(s):  
Activity Recognition ◽  
State Of The Art ◽  
The State ◽  
Daily Activities ◽  
Controlled Environment ◽  
Novel Approach ◽  
Specific Activities ◽  
Application Specific ◽  
Complex Activities ◽  
Better Than

Most activity classifiers focus on recognizing application-specific activities that are mostly performed in a scripted manner, where there is very little room for variation within the activity. These classifiers are mainly good at recognizing short scripted activities that are performed in a specific way. In reality, especially when considering daily activities, humans perform complex activities in a variety of ways. In this work, we aim to make activity recognition more practical by proposing a novel approach to recognize complex heterogeneous activities that could be performed in a wide variety of ways. We collect data from 15 subjects performing eight complex activities and test our approach while analyzing it from different aspects. The results show the validity of our approach. They also show how it performs better than the state-of-the-art approaches that tried to recognize the same activities in a more controlled environment.

Radial Error Motion Measurement of Ultraprecision Axes of Rotation With Nanometer Level Precision

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Qiang Shu ◽  
Mingzhi Zhu ◽  
XingBao Liu ◽  
Heng Cheng
Keyword(s):  
Spherical Surface ◽  
Eccentric Rotation ◽  
Nonlinearity Error ◽  
Motion Measurement ◽  
Error Separation ◽  
Radial Error ◽  
Lateral Offset ◽  
Error Motion ◽  
Measurement Efficiency ◽  
Aerostatic Spindle

Error motion of an ultraprecision axis of rotation has great influences on form error of machined parts. This paper gives a complete error analysis for the measurement procedure including nonlinearity error of capacitive displacement probes, misalignment error of the probes, eccentric error of artifact balls, environmental error, and error caused by different error separation methods. Nonlinearity of the capacitive displacement probe targeting a spherical surface is investigated through experiments. It is found that the additional probe output caused by lateral offset of the probe relative to the artifact ball greatly affects the measurement accuracy. Furthermore, it is shown that error motions in radial and axial directions together with eccentric rotation of the artifact lead to lateral offset. A novel measurement setup which integrates an encoder and an adjustable artifact is designed to ensure measurement repeatability by a zero index signal from the encoder. Moreover, based on the measurement setup, once roundness of the artifact is calibrated, roundness of the artifact can be accurately compensated when radial error motion is measured, and this method improves measurement efficiency while approaches accuracy comparable to that of error separation methods implemented alone. Donaldson reversal and three-probe error separation methods were implemented, and the maximum difference of the results of the two methods is below 14 nm. Procedure of uncertainty estimation of radial error motion is given in detail by analytical analysis and Monte Carlo simulation. The combined uncertainty of radial error motion measurement of an aerostatic spindle with Donaldson reversal and three-probe methods is 14.8 nm and 13.9 nm (coverage k = 2), respectively.

scholarly journals ULoc

2021 ◽  
Vol 5 (3) ◽  
pp. 1-31
Author(s):  
Minghui Zhao ◽  
Tyler Chang ◽  
Aditya Arun ◽  
Roshan Ayyalasomayajula ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Power Consumption ◽  
State Of The Art ◽  
Tracking System ◽  
The State ◽  
Location Estimation ◽  
High Bandwidth ◽  
Time Of Travel ◽  
Localization And Tracking ◽  
State Of Art ◽  
Better Than

A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.

scholarly journals Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

2019 ◽  
Author(s):  
Li Rui ◽  
Zheng Shunyi ◽  
Duan Chenxi ◽  
Yang Yang ◽  
Wang Xiqi
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Empirical Studies ◽  
The State ◽  
Attention Mechanism ◽  
Spatial Features ◽  
Better Than

In recent years, more and more researchers have gradually paid attention to Hyperspectral Image (HSI) classification. It is significant to implement researches on how to use HSI's sufficient spectral and spatial information to its fullest potential. To capture spectral and spatial features, we propose a Double-Branch Dual-Attention mechanism network (DBDA) for HSI classification in this paper, Two branches aer designed to extract spectral and spatial features separately to reduce the interferences between these two kinds of features. What is more, because distinguishing characteristics exist in the two branches, two types of attention mechanisms are applied in two branches above separately, ensuring to exploit spectral and spatial features more discriminatively. Finally, the extracted features are fused for classification. A series of empirical studies have been conducted on four hyperspectral datasets, and the results show that the proposed method performs better than the state-of-the-art method.

scholarly journals Syllable-Based Indonesian Automatic Speech Recognition

2020 ◽  
Vol 12 (4) ◽  
pp. 720-728
Author(s):  
Danny Henry Galatang ◽  
◽  
Suyanto Suyanto ◽  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
State Of The Art ◽  
The State ◽  
Speech Corpus ◽  
Advanced Method ◽  
Acoustic Models ◽  
The Future ◽  
End To End ◽  
Better Than

The syllable-based automatic speech recognition (ASR) systems commonly perform better than the phoneme-based ones. This paper focuses on developing an Indonesian monosyllable-based ASR (MSASR) system using an ASR engine called SPRAAK and comparing it to a phoneme-based one. The Mozilla DeepSpeech-based end-to-end ASR (MDSE2EASR), one of the state-of-the-art models based on character (similar to the phoneme-based model), is also investigated to confirm the result. Besides, a novel Kaituoxu SpeechTransformer (KST) E2EASR is also examined. Testing on the Indonesian speech corpus of 5,439 words shows that the proposed MSASR produces much higher word accuracy (76.57%) than the monophone-based one (63.36%). Its performance is comparable to the character-based MDS-E2EASR, which produces 76.90%, and the character-based KST-E2EASR (78.00%). In the future, this monosyllable-based ASR is possible to be improved to the bisyllable-based one to give higher word accuracy. Nevertheless, extensive bisyllable acoustic models must be handled using an advanced method.

scholarly journals Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Yeli Feng ◽  
Daniel Jun Xian Ng ◽  
Arvind Easwaran
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
State Of The Art ◽  
Autonomous Driving ◽  
The State ◽  
Theory And Practice ◽  
Data Sets ◽  
Variational Autoencoder ◽  
Distribution Shifts ◽  
Better Than

Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS). One source of uncertainty arises from distribution shifts in the input data between training and test scenarios. Detecting such distribution shifts in real-time is an emerging approach to address the challenge. The high dimensional input space in CPS applications involving imaging adds extra difficulty to the task. Generative learning models are widely adopted for the task, namely out-of-distribution (OoD) detection. To improve the state-of-the-art, we studied existing proposals from both machine learning and CPS fields. In the latter, safety monitoring in real-time for autonomous driving agents has been a focus. Exploiting the spatiotemporal correlation of motion in videos, we can robustly detect hazardous motion around autonomous driving agents. Inspired by the latest advances in the Variational Autoencoder (VAE) theory and practice, we tapped into the prior knowledge in data to further boost OoD detection’s robustness. Comparison studies over nuScenes and Synthia data sets show our methods significantly improve detection capabilities of OoD factors unique to driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented. Finally, we customized one proposed method into a twin-encoder model that can be deployed to resource limited embedded devices for real-time OoD detection. Its execution time was reduced over four times in low-precision 8-bit integer inference, while detection capability is comparable to its corresponding floating-point model.

Simulating SST Teleconnections to Africa: What is the State of the Art?

2013 ◽  
Vol 26 (15) ◽  
pp. 5397-5418 ◽  
Author(s):  
David P. Rowell
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Coupled Model ◽  
The State ◽  
Sub Saharan Africa ◽  
Intercomparison Project ◽  
Rainfall Anomalies ◽  
Sub Saharan ◽  
Better Than

Abstract This study provides an overview of the state of the art of modeling SST teleconnections to Africa and begins to investigate the sources of error. Data are obtained from the Coupled Model Intercomparison Project (CMIP) archives, phases 3 and 5 (CMIP3 and CMIP5), using the “20C3M” and “historical” coupled model experiments. A systematic approach is adopted, with the scope narrowed to six large-scale regions of sub-Saharan Africa within which seasonal rainfall anomalies are reasonably coherent, along with six SST modes known to affect these regions. No significant nonstationarity of the strength of these 6 × 6 teleconnections is found in observations. The capability of models to represent each teleconnection is then assessed (whereby half the teleconnections have observed SST–rainfall correlations that differ significantly from zero). A few of these teleconnections are found to be relatively easy to model, while a few more pose substantial challenges to models and many others exhibit a wide variety of model skill. Furthermore, some models perform consistently better than others, with the best able to at least adequately simulate 80%–85% of the 36 teleconnections. No improvement is found between CMIP3 and CMIP5. Analysis of atmosphere-only simulations suggests that the coupled model teleconnection errors may arise primarily from errors in their SST climatology and variability, although errors in the atmospheric component of teleconnections also play a role. Last, no straightforward relationship is found between the quality of a model's teleconnection to Africa and its SST or rainfall biases or its resolution. Perhaps not surprisingly, the causes of these errors are complex, and will require considerable further investigation.

Practical Picture Processing

1974 ◽  
Vol 32 ◽  
pp. 338-339
Author(s):  
T. A. Welton
Keyword(s):  
Radiation Damage ◽  
Coherence Length ◽  
Spatial Information ◽  
State Of The Art ◽  
Coherent Radiation ◽  
The State ◽  
Energy Spread ◽  
Electron Micrograph ◽  
Picture Processing ◽  
Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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