Efficient and Effective Duplicate Detection in Hierarchical Data

2020 ◽  
Vol 17 (8) ◽  
pp. 3548-3552
Author(s):  
M. S. Roobini ◽  
P. B. S. Sumanth Kumar ◽  
P. B. Raviteja Reddy ◽  
Anitha Ponraj ◽  
J. Aruna
Keyword(s):  
High Accuracy ◽  
Cutting Edge ◽  
Hierarchical Data ◽  
Copy Detection ◽  
Bayesian Algorithm ◽  
Social Data ◽  
Xml Data ◽  
Duplicate Detection ◽  
Novel Technique

Although there is a long profession on distinguishing duplicates, just a handful in social data arrangements center around copy detection in ever more complex progressive systems, including XML data. Right now, present a novel technique for XML duplicate discovery, Renamed XMLDup. XMLDup utilizes a Bayesian algorithm defining the chance of duplicating two XML components, taking into account the data within the components, but also how data is structured. Likewise, to improve the effectiveness of Unit Review, Novel technique for pruning, equipped for noteworthy increases over the un-streamlined calculation rule, is introduced. We demonstrate through trials that our estimate is can accomplish high accuracy via trials we show our estimation is outflanking another cutting-edge duplicate discovery arrangement, both as far as proficiency and of adequacy.

Lithography-Based Ceramic Manufacturing: A Novel Technique for Additive Manufacturing of High-Performance Ceramics

2014 ◽  
Vol 88 ◽  
pp. 60-64 ◽  
Author(s):  
Martin Schwentenwein ◽  
Peter Schneider ◽  
Johannes Homa
Keyword(s):  
Additive Manufacturing ◽  
High Performance ◽  
Bending Strength ◽  
New Technology ◽  
High Accuracy ◽  
Ceramic Suspension ◽  
Novel Technique ◽  
Ceramic Manufacturing ◽  
Manufacturing Technologies ◽  
Very High

Albeit widely established in plastic and metal industry, additive manufacturing technologies are still a rare sight in the field of ceramic manufacturing. This is mainly due to the requirements for high performance ceramic parts, which no additive manufacturing process was able to meet to date.The Lithography-based Ceramic Manufacturing (LCM)-technology which enables the production of dense and precise ceramic parts by using a photocurable ceramic suspension that is hardened via a photolithographic process. This new technology not only provides very high accuracy, it also reaches high densities for the sintered parts. In the case of alumina a relative density of over 99.4 % and a 4-point-bending strength of almost 430 MPa were realized. Thus, the achievable properties are similar to conventional manufacturing methods, making the LCM-technology an interesting complement for the ceramic industry.

Design of the Precision Detection Device for the Key Dimension of Bending Mold

2011 ◽  
Vol 52-54 ◽  
pp. 84-90
Author(s):  
Ji Bin Tong ◽  
Hui Yuan ◽  
Yong Ming Wang ◽  
Xiao Liu Yu
Keyword(s):  
High Accuracy ◽  
Rapid Response ◽  
Cutting Edge ◽  
Good Stability ◽  
Edge Angle ◽  
In Series ◽  
Rotational Freedom

The key to guarantee bending mold interchangeability is the precise detection of the cutting edge angle, symmetry and the distance from its center to the fitting surface of CNC bending mold. According to the features of the Key Dimension of the Bending Mold (KDBM), it is detected using a 3-DOF mechanical arm connected in series. The mechanical arm has a horizontal DOF, a vertical DOF, and a rotational freedom of the gauge head which is placed at the end of the mechanical arm. The Precision Detection Device (PDD) for KDBM that we have designed has high accuracy, good stability and rapid response.

scholarly journals Efficient and Effective Duplicate Detection in Hierarchical Data

2013 ◽  
Vol 25 (5) ◽  
pp. 1028-1041 ◽  
Author(s):  
L. Leitão ◽  
P. Calado ◽  
M. Herschel
Keyword(s):  
Hierarchical Data ◽  
Duplicate Detection

scholarly journals Precise Volumetric Measurements of Any Shaped Objects with a Novel Acoustic Volumeter

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 760
Author(s):  
Sydoruk ◽  
Kochs ◽  
van Dusschoten ◽  
Huber ◽  
Jahnke
Keyword(s):  
High Accuracy ◽  
Measured Data ◽  
Supplementary Information ◽  
Single Frequency ◽  
Average Radius ◽  
True Density ◽  
Plant Seeds ◽  
Novel Technique ◽  
Cylindrical Pores ◽  
Measurement Principle

We introduce a novel technique to measure volumes of any shaped objects based on acoustic components. The focus is on small objects with rough surfaces, such as plant seeds. The method allows measurement of object volumes more than 1000 times smaller than the volume of the sensor chamber with both high precision and high accuracy. The method is fast, noninvasive, and easy to produce and use. The measurement principle is supported by theory, describing the behavior of the measured data for objects of known volumes in a range of 1 to 800 µL. In addition to single-frequency, we present frequency-dependent measurements that provide supplementary information about pores on the surface of a measured object, such as the total volume of pores and, in the case of cylindrical pores, their average radius-to-length ratio. We demonstrate the usefulness of the method for seed phenotyping by measuring the volume of irregularly shaped seeds and showing the ability to “look” under the husk and inside pores, which allows us to assess the true density of seeds.

scholarly journals AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

2021 ◽  
Author(s):  
Sara Capponi ◽  
Shangying Wang ◽  
Simone Bianco
Keyword(s):  
Binding Affinity ◽  
High Accuracy ◽  
Atomistic Simulations ◽  
Small Subset ◽  
Novel Technique ◽  
Neural Network Algorithm ◽  
Dynamics Simulations ◽  
Simulation Time ◽  
High Binding Affinity ◽  
Human Receptor

AbstractWe present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy mutations with low binding affinity from mutations with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the the SARS-CoV-2 spike protein and the human receptor ACE2.

scholarly journals AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

2021 ◽  
Vol 44 (10) ◽  
Author(s):  
Sara Capponi ◽  
Shangying Wang ◽  
Erik J. Navarro ◽  
Simone Bianco
Keyword(s):  
Binding Affinity ◽  
High Accuracy ◽  
Atomistic Simulations ◽  
Small Subset ◽  
Novel Technique ◽  
Neural Network Algorithm ◽  
Dynamics Simulations ◽  
Simulation Time ◽  
High Binding Affinity ◽  
Human Receptor

Abstract We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2. Graphic abstract

scholarly journals USING DEEP LEARNING AND HOUGH TRANSFORMATIONS TO INFER MINERALISED VEINS FROM LIDAR DATA OVER HISTORIC MINING AREAS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1561-1568
Author(s):  
J. Gallwey ◽  
C. Yeomans ◽  
M. Tonkins ◽  
J. Coggan ◽  
D. Vogt ◽  
...  
Keyword(s):  
High Accuracy ◽  
Mining Activity ◽  
Lidar Data ◽  
Learning Approach ◽  
Geological Structures ◽  
Mining Areas ◽  
Novel Technique ◽  
Geological Maps ◽  
Historic Mining ◽  
Fine Tune

Abstract. This paper presents a novel technique to improve geological understanding in regions of historic mining activity. This is achieved through inferring the orientations of geological structures from the imprints left on the landscape by past mining activities. Open source high resolution LiDAR datasets are used to fine-tune a deep convolutional neural network designed initially for Lunar LiDAR crater identification. By using a transfer learning approach between these two very similar domains, high accuracy predictions of pit locations can be generated in the form of a raster mask of pit location probabilities. Taking the raster of the predicted pit location centres as an input, a Hough transformation is used to fit lines through the centres of the detected pits. The results demonstrate that these lines follow the patterns of known mineralised veins in the area, alongside highlighting veins which are below the scale of the published geological maps.

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