Design of an Intent Recognition System for Dynamic, Rapid Motions in Unstructured Environments

2021 ◽  
pp. 1-10
Author(s):  
Pooja R Moolchandani ◽  
Anirban Mazumdar ◽  
Aaron Young
Keyword(s):  
Estimation Error ◽  
Recognition System ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Degree Reduction ◽  
Wearable Robot ◽  
Intent Recognition ◽  
Physical Systems ◽  
Start Up ◽  
Increasing Demand

Abstract In this study, we developed an offline, hierarchical intent recognition system for inferring the timing and direction of motion intent of a human operator when operating in an unstructured environment. There has been an increasing demand for robot agents to assist in these dynamic, rapid motions that are constantly evolving and require quick, accurate estimation of a user's direction of travel.An experiment was conducted in a motion capture space with six subjects performing threat-evasion in 8 directions, and their mechanical and neuromuscular signals were recorded for use in our intent recognition system (XGBoost). Investigated against current, analytical methods, our system demonstrated superior performance with quicker direction of travel estimation occurring 140 ms earlier in the movement and a 11.6 degree reduction of error. The results showed that we could even predict movement start 100 ms prior to the actual, thus allowing any physical systems to start up. Our direction estimation had an optimal performance of 8.8 degrees, or 2.4% of the 360 degrees range of travel, using 3-axis kinetic data. The performance of other sensors and their combinations indicate that there are additional possibilities to obtain low estimation error. These findings are promising as they can be used to inform the design of a wearable robot aimed at assisting users in dynamic motions, while in environments with oncoming threats.

Automatic weaving method for three-dimensional composite preforms using vision system

2021 ◽  
pp. 004051752098238
Author(s):  
Siyuan Li ◽  
Zhongde Shan ◽  
Dong Du ◽  
Li Zhan ◽  
Zhikun Li ◽  
...  
Keyword(s):  
Relative Position ◽  
Vision System ◽  
Estimation Error ◽  
Three Dimensional ◽  
Fiber Reinforced Composites ◽  
Accurate Estimation ◽  
Digital Manufacturing ◽  
Relative Rotation ◽  
Reinforced Composites ◽  
Main Structure

Three-dimensional composite preform is the main structure of fiber-reinforced composites. During the weaving process of large-sized three-dimensional composite preform, relative rotation or translation between the fiber feeder and guided array occurs before feeding. Besides, the weaving needles can be at different heights after moving out from the guided array. These problems are mostly detected and adjusted manually. To make the weaving process more precise and efficient, we propose machine vision-based methods which could realize accurate estimation and adjustment of the relative position-pose between the fiber feeder and guided array, and make the needles pressing process automatic by recognizing the position of the weaving needles. The results show that the estimation error of relative position-pose is within 5%, and the rate of unrecognized weaving needles is 2%. Our proposed methods improve the automation level of weaving, and are conducive to the development of preform forming toward digital manufacturing.

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

scholarly journals Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

2015 ◽  
Vol 32 (6) ◽  
pp. 821-827 ◽  
Author(s):  
Enrique Audain ◽  
Yassel Ramos ◽  
Henning Hermjakob ◽  
Darren R. Flower ◽  
Yasset Perez-Riverol
Keyword(s):  
Machine Learning ◽  
Isoelectric Point ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Basis Set ◽  
Superior Performance ◽  
Supplementary Information ◽  
Training Dataset ◽  
Accurate Estimation ◽  
Prediction Methods

Abstract Motivation: In any macromolecular polyprotic system—for example protein, DNA or RNA—the isoelectric point—commonly referred to as the pI—can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge—and thus the electrophoretic mobility—of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: [email protected] Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

2017 ◽  
Vol 25 (4) ◽  
pp. 413-434 ◽  
Author(s):  
Justin Grimmer ◽  
Solomon Messing ◽  
Sean J. Westwood
Keyword(s):  
Treatment Effects ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Randomized Experiments ◽  
Heterogeneous Treatment Effects ◽  
Data Set ◽  
Heterogeneous Effects ◽  
The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

scholarly journals Improving MUSLE performance for sediment yield prediction at micro-watershed level using seasonal classified data

2018 ◽  
Vol 13 (3) ◽  
pp. 505-512
Author(s):  
Hamzeh Noor ◽  
Mohammad Rostami Khalaj
Keyword(s):  
Sediment Yield ◽  
Traditional Approach ◽  
Estimation Error ◽  
Superior Performance ◽  
North East ◽  
Erosion Processes ◽  
The North ◽  
East Of Iran ◽  
Different Seasons

Abstract Separating erosion data and assessing season-based models are of great importance considering the variation in soil erosion processes in different seasons, especially in semi-arid regions. However, evaluation of an erosion model using seasonal classification of data and at a micro-watershed level have rarely been considered. Therefore, the present study was conducted to evaluate the modified universal soil loss equation (MUSLE): 1) with the seasonal classification of data and 2) with the traditional approach (no classification of data), in the Sanganeh research micro-watershed. This watershed has an area of 1.2 ha and is located in the north east of Iran. The results showed that the original MUSLE overestimated the sediment yield in the study watershed. Also, after calibration of MUSLE, the seasonal classification of data (with a relative estimation error (RE) of 34%) showed its superior performance compared with the traditional calibration approach (with a RE of 62%). In this regard, the obtained REs of 33, 40, and 31% respectively for spring, autumn, and winter are within or close to the acceptable range.

scholarly journals Analysis of operating variables for Yerba mate leaves supercritical carbon dioxide extraction

2018 ◽  
Vol 24 (3) ◽  
pp. 231-238 ◽  
Author(s):  
Branislava Teofilovic ◽  
Nevena Grujic-Letic ◽  
Strahinja Kovacevic ◽  
Sanja Podunavac-Kuzmanovic ◽  
Slobodan Gadzuric
Keyword(s):  
Potassium Chloride ◽  
Current Source ◽  
Economic Feasibility ◽  
Superior Performance ◽  
Yerba Mate ◽  
Operating Variables ◽  
Carbon Dioxide Extraction ◽  
Increasing Demand ◽  
Positive Results

Given the increasing demand for potassium in Brazil, the mining and use of carnallite is becoming increasingly important, because the current source of potassium, sylvinite, is being depleted and there is a risk of shortages. Based on theoretical and practical data available in existing literature, this work describes the development, simulation, and economic feasibility of a process for dissolution and crystallization of potassium chloride from carnallite ore. Positive results were obtained following the application of the Hoffman diagram and determination of the corresponding equation. The proposed process provided over 85% potassium chloride crystallization, demonstrating its superior performance, compared to existing procedures.

Analytical Model for High–Level Area Estimation of FPGA Design

2016 ◽  
Vol 7 (2) ◽  
pp. 35-44
Author(s):  
Rachna Singh ◽  
Arvind Rajawat
Keyword(s):  
Early Stage ◽  
Estimation Error ◽  
Research Work ◽  
Performance Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Fpga Design ◽  
Estimation Model ◽  
Area Estimation ◽  
High Level

FPGAs have been used as a target platform because they have increasingly interesting in system design and due to the rapid technological progress ever larger devices are commercially affordable. These trends make FPGAs an alternative in application areas where extensive data processing plays an important role. Consequently, the desire emerges for early performance estimation in order to quantify the FPGA approach. A mathematical model has been presented that estimates the maximum number of LUTs consumed by the hardware synthesized for different FPGAs using LLVM.. The motivation behind this research work is to design an area modeling approach for FPGA based implementation at an early stage of design. The equation based area estimation model permits immediate and accurate estimation of resources. Two important criteria used to judge the quality of the results were estimation accuracy and runtime. Experimental results show that estimation error is in the range of 1.33% to 7.26% for Spartan 3E, 1.6% to 5.63% for Virtex-2pro and 2.3% to 6.02% for Virtex-5.

scholarly journals IoT-Based Digital Twin for Energy Cyber-Physical Systems: Design and Implementation

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4762 ◽  
Author(s):  
Ahmed Saad ◽  
Samy Faddel ◽  
Osama Mohammed
Keyword(s):  
Power Systems ◽  
Systems Design ◽  
Cyber Physical Systems ◽  
Superior Performance ◽  
Physical Systems ◽  
Digital Twin ◽  
Power And Control ◽  
Amazon Web Services ◽  
High Bandwidth ◽  
And Control

With the emergence of distributed energy resources (DERs), with their associated communication and control complexities, there is a need for an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system. The digital twin (DT) is a new concept that can unleash tremendous opportunities and can be used at the different control and security levels of power systems. This paper provides a methodology for the modelling of the implementation of energy cyber-physical systems (ECPSs) that can be used for multiple applications. Two DT types are introduced to cover the high-bandwidth and the low-bandwidth applications that need centric oversight decision making. The concept of the digital twin is validated and tested using Amazon Web Services (AWS) as a cloud host that can incorporate physical and data models as well as being able to receive live measurements from the different actual power and control entities. The experimental results demonstrate the feasibility of the real-time implementation of the DT for the ECPS based on internet of things (IoT) and cloud computing technologies. The normalized mean-square error for the low-bandwidth DT case was 3.7%. In the case of a high-bandwidth DT, the proposed method showed superior performance in reconstructing the voltage estimates, with 98.2% accuracy from only the controllers’ states.

Adaptive Rate Subscriber Loop Data Transmission using Ethernet Interfaces

1997 ◽  
Vol 07 (04) ◽  
pp. 273-281
Author(s):  
Norifumi Takaya ◽  
David E. Dodds ◽  
Carl D. McCrosky
Keyword(s):  
Data Transmission ◽  
Network Access ◽  
Access Technology ◽  
Start Up ◽  
Rate Transmission ◽  
Data Rates ◽  
Adaptive Rate ◽  
Web Access ◽  
Adaptive Rate Transmission ◽  
Increasing Demand

The increasing demand for Internet and World Wide Web access from the home has stimulated research into finding methods of providing access at rates greater than the 33.6 kb/s offered by current computer modems. Most copper telephone pairs have bandwidth capacities much greater than the 3.4 kHz voice-band. Using this excess bandwidth it is possible to substantially exceed current modem rates. This paper describes an inexpensive and readily deployable network access technology capable of providing bit rates ranging from hundreds of kb/s to potentially greater than 1 Mb/s on existing copper telephone lines. The usable bit rate, which varies depending on the length and gauge of the wire, is adaptively determined at system start up. The results of rate adaption testing are presented, as well as the results of throughput testing when TCP is used to provide flow control across the adaptive rate transmission line. It is also shown that current IBM compatible computers are only capable of supporting data rates of slightly more than 1 Mb/s through Ethernet adaptor cards; providing access rates beyong a few Mb/s is currently unnecessary.

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