A qualitative characteristic scheme and a fast distance prediction method of multi-probe azimuthal gamma-ray logging in geosteering

2021 ◽  
Vol 199 ◽  
pp. 108244
Author(s):  
Zhen Qin ◽  
Bin Tang ◽  
Dong Wu ◽  
Shaocheng Luo ◽  
Xiugang Ma ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Prediction Method ◽  
Qualitative Characteristic ◽  
Characteristic Scheme ◽  
Distance Prediction

scholarly journals Adaptive Safe Distance Prediction Using MPC for Bridge Cranes Considering Anti-Swing in Dynamic Environment

2020 ◽  
Author(s):  
Huili Chen ◽  
Guoliang Liu ◽  
Guohui Tian ◽  
Jianhua Zhang ◽  
Ze Ji
Keyword(s):  
Dynamic Environment ◽  
Prediction Method ◽  
Bridge Crane ◽  
Safe Distance ◽  
Moving Obstacles ◽  
Dangerous Situation ◽  
The Mean ◽  
Prediction Control ◽  
Distance Prediction ◽  
Mean Time

<div>In dynamic environment, the suddenly appeared </div><div>human or other moving obstacles can affect the safety of the </div><div>bridge crane. For such dangerous situation, the bridge crane </div><div>must predict potential collisions between the payload and the </div><div>obstacle, keep safe distance while the swing of the payload must </div><div>be considered in the mean time. Therefore, the safe distance is </div><div>not a constant value, which must be adaptive to the relative </div><div>speed of the bridge crane. However, as far as we know, the </div><div>mathematical model between the safe distance and the relative </div><div>speed of the bridge crane has never been fully discussed. In </div><div>this paper, we propose a safe distance prediction method using </div><div>model prediction control (MPC), which can make sure that the </div><div>crane can stop before the obstacle, and avoid possible collisions, </div><div>while the relative speed and anti-swing are both considered. The </div><div>experimental results prove the effectiveness of our idea.</div>

scholarly journals Adaptive Safe Distance Prediction Using MPC for Bridge Cranes Considering Anti-Swing in Dynamic Environment

2020 ◽  
Author(s):  
Huili Chen ◽  
Guoliang Liu ◽  
Guohui Tian ◽  
Jianhua Zhang ◽  
Ze Ji
Keyword(s):  
Dynamic Environment ◽  
Prediction Method ◽  
Bridge Crane ◽  
Safe Distance ◽  
Moving Obstacles ◽  
Dangerous Situation ◽  
The Mean ◽  
Prediction Control ◽  
Distance Prediction ◽  
Mean Time

<div>In dynamic environment, the suddenly appeared </div><div>human or other moving obstacles can affect the safety of the </div><div>bridge crane. For such dangerous situation, the bridge crane </div><div>must predict potential collisions between the payload and the </div><div>obstacle, keep safe distance while the swing of the payload must </div><div>be considered in the mean time. Therefore, the safe distance is </div><div>not a constant value, which must be adaptive to the relative </div><div>speed of the bridge crane. However, as far as we know, the </div><div>mathematical model between the safe distance and the relative </div><div>speed of the bridge crane has never been fully discussed. In </div><div>this paper, we propose a safe distance prediction method using </div><div>model prediction control (MPC), which can make sure that the </div><div>crane can stop before the obstacle, and avoid possible collisions, </div><div>while the relative speed and anti-swing are both considered. The </div><div>experimental results prove the effectiveness of our idea.</div>

scholarly journals Study of Real-Valued Distance Prediction For Protein Structure Prediction with Deep Learning

2020 ◽  
Author(s):  
Jin Li ◽  
Jinbo Xu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
3D Structure ◽  
Prediction Method ◽  
Structure Modeling ◽  
Contact Prediction ◽  
Real Value ◽  
3D Structure Modeling ◽  
Distance Prediction

AbstractInter-residue distance prediction by deep ResNet (convolutional residual neural network) has greatly advanced protein structure prediction. Currently the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then employ a novel method to fold a protein by the predicted mean and deviation. Our findings include: 1) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); 2) our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; 3) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling; and 4) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models.

scholarly journals REALDIST: Real-valued protein distance prediction

2020 ◽  
Author(s):  
Badri Adhikari
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
Three Dimensional ◽  
Prediction Method ◽  
3D Models ◽  
Classification Problem ◽  
Intermediate Step ◽  
Protein Distance ◽  
Multi Class Classification ◽  
Distance Prediction

AbstractProtein structure prediction continues to stand as an unsolved problem in bioinformatics and biomedicine. Deep learning algorithms and the availability of metagenomic sequences have led to the development of new approaches to predict inter-residue distances—the key intermediate step. Different from the recently successful methods which frame the problem as a multi-class classification problem, this article introduces a real-valued distance prediction method REALDIST. Using a representative set of 43 thousand protein chains, a variant of deep ResNet is trained to predict real-valued distance maps. The contacts derived from the real-valued distance maps predicted by this method, on the most difficult CASP13 free-modeling protein datasets, demonstrate a long-range top-L precision of 52%, which is 17% higher than the top CASP13 predictor Raptor-X and slightly higher than the more recent trRosetta method. Similar improvements are observed on the CAMEO ‘hard’ and ‘very hard’ datasets. Three-dimensional (3D) structure prediction guided by real-valued distances reveals that for short proteins the mean accuracy of the 3D models is slightly higher than the top human predictor AlphaFold and server predictor Quark in the CASP13 competition.

scholarly journals Improving deep learning-based protein distance prediction in CASP14

2021 ◽  
Author(s):  
Zhiye Guo ◽  
Tianqi Wu ◽  
Jian Liu ◽  
Jie Hou ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Prediction Method ◽  
Learning Method ◽  
Sequence Alignments ◽  
Evolutionary Features ◽  
Protein Distance ◽  
Distance Prediction

AbstractAccurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the F1 measure. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps.

scholarly journals Empirical multi-wavelength prediction method for Very High Energy Gamma-ray emitting BL Lacs

2016 ◽  
Vol 12 (S324) ◽  
pp. 239-240
Author(s):  
V. Fallah Ramazani ◽  
E. Lindfors ◽  
K. Nilsson
Keyword(s):  
Gamma Ray ◽  
Prediction Method ◽  
High Energy ◽  
Linear Regression Method ◽  
Cherenkov Telescopes ◽  
Multi Wavelength ◽  
Very High Energy ◽  
Energy Gamma ◽  
Bl Lacs ◽  
Very High

AbstractWe present the most up-to-date and complete multi-wavelength correlation analysis on luminosity properties of TeV BL Lacs. Correlation function (power law or linear) parameters are calculated based on linear regression method. Using the lower energy luminosities of a sample of 182 non-TeV BL Lacs and the generated functions, minimum level of VHE gamma-ray emission was calculated for each non-TeV BL Lacs. This multi wavelength prediction method gives us a list of best candidates to be observed with current generation of Imaging Air Cherenkov Telescopes.

Fast Prediction Method of Radon Concentration in Environment Air

2014 ◽  
Vol 539 ◽  
pp. 819-822
Author(s):  
He Xi Wu ◽  
Qiang Lin Wei ◽  
Bo Yang ◽  
Qing Cheng Liu
Keyword(s):  
Relative Error ◽  
Radon Concentration ◽  
Gamma Ray ◽  
Transport Model ◽  
Prediction Method ◽  
Field Tests ◽  
Mean Value ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error ◽  
Fast Prediction

Base on the theory that 222Rn can transport in any medium, fast prediction model of radon concentration in environment air can be acquired. And it has been proved accurate by an experiment in laboratory. Many field tests also showed that the average absolute relative error is 8.78% between mean value of measurement and that of fast prediction. It can be predict fleetly the radon concentration by 226Ra which is acquired from the airborne gamma-ray spectra. The relative error between measurement and model is-11.7%. Therefore, the transport model can be effectively applied to predict radon concentration in environment air.

On the possible gamma-ray source in Cygnus

1967 ◽  
Vol 31 ◽  
pp. 469-471
Author(s):  
J. G. Duthie ◽  
M. P. Savedoff ◽  
R. Cobb
Keyword(s):  
Gamma Rays ◽  
Gamma Ray ◽  
Right Ascension

A source of gamma rays has been found at right ascension 20h15m, declination +35°, with an uncertainty of 6° in each coordinate. Its flux is (1·5 ± 0·8) x 10-4photons cm-2sec-1at 100 MeV. Possible identifications are reviewed, but no conclusion is reached. The mechanism producing the radiation is also uncertain.

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