scholarly journals CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams

2021 ◽  
Vol 15 (3) ◽  
pp. 1-30
Author(s):  
Kijung Shin ◽  
Euiwoong Lee ◽  
Jinoh Oh ◽  
Mohammad Hammoud ◽  
Christos Faloutsos
Keyword(s):  
Estimation Error ◽  
Accurate Estimation ◽  
Input Stream ◽  
Streaming Algorithm ◽  
Triangle Counting ◽  
Graph Streams ◽  
Unbiased Estimates ◽  
And Storage ◽  
Link Recommendation ◽  
Multiple Machines

Given a graph stream, how can we estimate the number of triangles in it using multiple machines with limited storage? Specifically, how should edges be processed and sampled across the machines for rapid and accurate estimation? The count of triangles (i.e., cliques of size three) has proven useful in numerous applications, including anomaly detection, community detection, and link recommendation. For triangle counting in large and dynamic graphs, recent work has focused largely on streaming algorithms and distributed algorithms but little on their combinations for “the best of both worlds.” In this work, we propose CoCoS , a fast and accurate distributed streaming algorithm for estimating the counts of global triangles (i.e., all triangles) and local triangles incident to each node. Making one pass over the input stream, CoCoS carefully processes and stores the edges across multiple machines so that the redundant use of computational and storage resources is minimized. Compared to baselines, CoCoS is: (a) accurate: giving up to smaller estimation error; (b) fast : up to faster, scaling linearly with the size of the input stream; and (c) theoretically sound : yielding unbiased estimates.

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.

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.

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 The use of dosed and herbage n-alkanes as markers for the determination of herbage intake

1986 ◽  
Vol 107 (1) ◽  
pp. 161-170 ◽  
Author(s):  
R. W. Mayes ◽  
C. S. Lamb ◽  
Patricia M. Colgrove
Keyword(s):  
Perennial Ryegrass ◽  
Accurate Estimation ◽  
Once Daily ◽  
Herbage Intake ◽  
Cyclical Pattern ◽  
Unbiased Estimates ◽  
Grazing Sheep ◽  
Supplementary Feed ◽  
Palmitic Acids

SUMMARYThe recovery in the faeces of the n-alkanes of herbage (odd-chain, C27–C35) and of dosed artificial alkanes (even-chain, C28 and C32) was studied in twelve 4-month-old castrated male lambs. The lambs received three levels of cut, fresh perennial ryegrass or a mixed diet of perennial ryegrass (0·70) and a barley-based concentrate (0·30) (500–900 g D.M./day). C28 and C32 n-alkanes (130 mg each), absorbed onto shredded paper, were given once daily for 17 days to test whether the recoveries of herbage and dosed alkanes were similar to enable their use as markers for determining the herbage intake of grazing sheep. Stearic and palmitic acids (130 mg each) were given with the dosed alkanes to half of the animals with the objective of facilitating emulsification of the dosed alkanes within the digestive tract.With the exception of C27 n-alkane, the faecal recoveries of all alkanes were unaffected by diet, feeding level or emulsifying agent. Faecal recovery of odd- chain herbage n-alkanes increased with increasing C-chain length. The recovery of the dosed C28 n-alkane was slightly greater than the recoveries of both C27, and C29 n-alkanes of herbage. The recoveries of the dosed C32 n-alkane and the herbage C33-alkane were the same.The mean herbage intake estimated using C33 and C32 n-alkanes was identical to the actual herbage intake. Other alkane pairs gave slight underestimates of herbage intake ranging from 3·5% for the C28–C29 pair to 7·6% for the C27–C28 pair. No cyclical pattern of n-alkane excretion throughout the day was observed. Examination of daily variations in faecal alkane concentrations indicated that the start of alkane dosing should precede the sampling of faeces by at least 6 days.These results suggest that accurate estimation of herbage intake in grazing sheep is possible from the simultaneous use of dosed C32 and herbage C33 n-alkanes as markers.The method may be particularly useful in enabling unbiased estimates of herbage intake to be made in animals receiving supplementary feed.

scholarly journals Triangle Counting in Dynamic Graph Streams

Algorithmica ◽  
2015 ◽  
Vol 76 (1) ◽  
pp. 259-278 ◽  
Author(s):  
Laurent Bulteau ◽  
Vincent Froese ◽  
Konstantin Kutzkov ◽  
Rasmus Pagh
Keyword(s):  
Dynamic Graph ◽  
Triangle Counting ◽  
Graph Streams

A Discrete-Time Nonlinear Estimator for an Orthosis-Aided Gait

2014 ◽  
Author(s):  
Ashwin Dani ◽  
Nitin Sharma
Keyword(s):  
Discrete Time ◽  
Lower Limb ◽  
Estimation Error ◽  
System Modeling ◽  
Estimation Algorithm ◽  
Accurate Estimation ◽  
Nonlinear Functions ◽  
State Dependent ◽  
Fast Convergence Rate ◽  
Measurement Units

To achieve automatic operation of a powered orthosis-aided gait or functional electrical stimulation-based walking restoration, accurate estimation of the leg angles is of utmost importance. Various phases of walking last for a short duration of time; thus, an accurate estimator is required with a fast convergence rate. To overcome this challenge, this paper presents a discrete-time nonlinear estimation algorithm to estimate lower-limb angles during an orthosis-aided walking. To this end, we use measurements from 6 degree-of-freedom (DOF) inertial measurement units (IMUs) to estimate the lower limb angles. The estimator is based on a state-dependent coefficient (SDC) linearization or extended linearization of the nonlinear functions. A combination of multiple discrete SDCs is used to compute an optimal gain of the nonlinear estimator based on uncertainty minimization criteria. The nonlinear estimator is robust to uncertainties in system modeling and sensor noise/bias from the IMUs. Monte Carlo simulation studies reveal that the estimator outperforms widely used discrete-time extended Kalman (EKF) filter with respect to average root-mean squared estimation error (RMSE) criteria.

scholarly journals Two-hop distance estimation in wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Estimation Method ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Distance Information ◽  
Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

scholarly journals Development of second and third-trimester population-specific machine learning pregnancy dating model (Garbhini-GA2) derived from the GARBH-Ini cohort in north India

2021 ◽  
Author(s):  
Nikhita Damaraju ◽  
Ashley Xavier ◽  
Ramya Vijayram ◽  
Bapu Koundinya Desiraju ◽  
Sumit Misra ◽  
...  
Keyword(s):  
Antenatal Care ◽  
Mean Squared Error ◽  
Estimation Error ◽  
Population Data ◽  
First Trimester ◽  
Specific Model ◽  
North India ◽  
Accurate Estimation ◽  
Third Trimester ◽  
Middle Income

Background: The prevalence of preterm birth (PTB) is high in lower and middle-income countries (LMIC) such as India. In LMIC, since a large proportion seeks antenatal care for the first time beyond 14-weeks of pregnancy, accurate estimation of gestational age (GA) using measures derived from ultrasonography scans in the second and third trimesters is of paramount importance. Different models have been developed globally to estimate GA, and currently, LMIC uses Hadlock's formula derived from data based on a North American cohort. This study aimed to develop a population-specific model using data from GARBH-Ini, a multidimensional and ongoing pregnancy cohort established in a district hospital in North India for studying PTB. Methods: Data obtained by longitudinal ultrasonography across all trimesters of pregnancy was used to develop and validate GA models for second and third trimesters. The first trimester GA estimated by ultrasonography was considered the Gold Standard. The second and third trimester GA model named, Garbhini-GA2 is a multivariate random forest model using five ultrasonographic parameters routinely measured during this period. Garbhini-GA2 model was compared to Hadlock and INTERGROWTH-21st models in the TEST set by estimating root mean-squared error, bias and PTB rate. Findings: Garbhini-GA2 reduced the GA estimation error by 23-45% compared to the published models. Furthermore, the PTB rate estimated using Garbhini-GA2 was more accurate when compared to published formulae that overestimated the rate by 1.5-2.0 times. Interpretation: The Garbhini-GA2 model developed is the first of its kind developed solely using Indian population data. The higher accuracy of GA estimation by Garbhini-GA2 emphasises the need to apply population-specific GA formulae to improve antenatal care and better PTB rate estimates.

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