scholarly journals Robust Regrasping against Error of Grasping for Bin-picking and Kitting

2021 ◽  
Author(s):  
SHOGO ARAI ◽  
Kazuya Konada ◽  
Naoya Yoshinaga ◽  
Akinari Kobayashi ◽  
Kazuhiro Kosuge
Keyword(s):  
Visual Servoing ◽  
Singular Values ◽  
Target Object ◽  
General Purpose ◽  
Data Sets ◽  
Success Rates ◽  
Data Set ◽  
Image Jacobian ◽  
Bin Picking ◽  
Dual Arm Robot

<div>This study proposes a method of robust regrasping an object using a dual-arm robot with general-purpose hands, which is robust against the error of grasping. In this paper, one arm is assigned to hand over the object to the other arm that is named a receiver arm. The grasping error must be considered to increase the success rate of the regrasping since a hand-over arm first picks up the object with the general-purpose hand. In an online phase, the proposed method performs object positioning at an optimal pose at the time of regrasping using an image-based visual servoing (IBVS) approach to reduce the effect of the grasping error. In the planning phase, the proposed method computes the optimal pose for regrasping by maximizing the minimum singular values of the image Jacobian of IBVS to achieve a high positioning accuracy using a 3D model of the target object. To achieve the regrasping objects with various shapes robustly against image noises and changes in light environments, the image Jacobian of IBVS is computed by numerical differential using an actual data set. A large number of data sets corresponding to each candidate grasp are usually required for computing the image Jacobian. To reduce the number of data sets, we propose a conversion method of the image Jacobian requiring only one data set corresponding to one representative grasp. The experimental results show that the proposed method achieves regrasping of target objects with the general-purpose hands with high success rates and performs target object positioning with less than 0.7[mm] positioning error.</div>

scholarly journals Robust Regrasping against Error of Grasping for Bin-picking and Kitting

2021 ◽  
Author(s):  
SHOGO ARAI ◽  
Kazuya Konada ◽  
Naoya Yoshinaga ◽  
Akinari Kobayashi ◽  
Kazuhiro Kosuge
Keyword(s):  
Visual Servoing ◽  
Singular Values ◽  
Target Object ◽  
General Purpose ◽  
Data Sets ◽  
Success Rates ◽  
Data Set ◽  
Image Jacobian ◽  
Bin Picking ◽  
Dual Arm Robot

<div>This study proposes a method of robust regrasping an object using a dual-arm robot with general-purpose hands, which is robust against the error of grasping. In this paper, one arm is assigned to hand over the object to the other arm that is named a receiver arm. The grasping error must be considered to increase the success rate of the regrasping since a hand-over arm first picks up the object with the general-purpose hand. In an online phase, the proposed method performs object positioning at an optimal pose at the time of regrasping using an image-based visual servoing (IBVS) approach to reduce the effect of the grasping error. In the planning phase, the proposed method computes the optimal pose for regrasping by maximizing the minimum singular values of the image Jacobian of IBVS to achieve a high positioning accuracy using a 3D model of the target object. To achieve the regrasping objects with various shapes robustly against image noises and changes in light environments, the image Jacobian of IBVS is computed by numerical differential using an actual data set. A large number of data sets corresponding to each candidate grasp are usually required for computing the image Jacobian. To reduce the number of data sets, we propose a conversion method of the image Jacobian requiring only one data set corresponding to one representative grasp. The experimental results show that the proposed method achieves regrasping of target objects with the general-purpose hands with high success rates and performs target object positioning with less than 0.7[mm] positioning error.</div>

scholarly journals RegioSQM20: Improved Prediction of the Regioselectivity of Electrophilic Aromatic Substitutions

2020 ◽  
Author(s):  
Nicolai Ree ◽  
Andreas Gõller ◽  
Jan H. Jensen
Keyword(s):  
Open Source ◽  
Tight Binding ◽  
Electrophilic Aromatic Substitution ◽  
Data Sets ◽  
Aromatic Substitution ◽  
Success Rates ◽  
Proton Affinities ◽  
Data Set ◽  
Tautomeric Forms ◽  
Near Future

<div> <div> <div> <p>We present RegioSQM20, a new version of RegioSQM (<i>Chem. Sci</i>. 2018, 9, 660), which predicts the regioselectivities of electrophilic aromatic substitution (EAS) re- actions from the calculation of proton affinities. The following improvements have been made: The open source semiempirical tight binding program xtb is used instead of the closed source MOPAC program. Any low energy tautomeric forms of the input molecule are identified and regioselectivity predictions are made for each form. Finally, RegioSQM20 offers a qualitative prediction of the reactivity of each tautomer (low, medium, or high) based on the reaction center with the highest proton affinity. The inclusion of tautomers increases the success rate from 90.7% to 92.7%. RegioSQM20 is compared to two machine learning based models: one developed by Struble et al. (<i>React. Chem. Eng</i>. 2020, 5, 896) specifically for regioselectivity predictions of EAS reactions (WLN) and a more generally applicable reactivity predictor (IBM RXN) de- veloped by Schwaller et al. (<i>ACS Cent. Sci</i>. 2019, 5, 1572). RegioSQM20 and WLN offers roughly the same success rates for the entire data sets (without considering tau- tomers), while WLN is many orders of magnitude faster. The accuracy of the more general IBM RXN approach is somewhat lower: 76.3%-85.0%, depending on the data set. The code is freely available under the MIT open source license and will be made available as a webservice (regiosqm.org) in the near future. </p> </div> </div> </div>

scholarly journals Visual Servoing of Humanoid Dual-Arm Robot with Neural Learning Enhanced Skill Transferring Control

2018 ◽  
Vol 15 (02) ◽  
pp. 1750023 ◽  
Author(s):  
Chenguang Yang ◽  
Junshen Chen ◽  
Zhaojie Ju ◽  
Andy S. K. Annamalai
Keyword(s):  
Control System ◽  
Visual Servoing ◽  
Target Position ◽  
Target Object ◽  
Least Square ◽  
Skill Transfer ◽  
Deterministic Learning ◽  
Neural Learning ◽  
Dual Arm Robot ◽  
Dual Arm

This paper presents a novel combination of visual servoing (VS) control and neural network (NN) learning on humanoid dual-arm robot. A VS control system is built by using stereo vision to obtain the 3D point cloud of a target object. A least square-based method is proposed to reduce the stochastic error in workspace calibration. An NN controller is designed to compensate for the effect of uncertainties in payload and other parameters (both internal and external) during the tracking control. In contrast to the conventional NN controller, a deterministic learning technique is utilized in this work, to enable the learned neural knowledge to be reused before current dynamics changes. A skill transfer mechanism is also developed to apply the neural learned knowledge from one arm to the other, to increase the neural learning efficiency. Tracked trajectory of object is used to provide target position to the coordinated dual arms of a Baxter robot in the experimental study. Robotic implementations has demonstrated the efficiency of the developed VS control system and has verified the effectiveness of the proposed NN controller with knowledge-reuse and skill transfer features.

scholarly journals RegioSQM20: Improved Prediction of the Regioselectivity of Electrophilic Aromatic Substitutions

2020 ◽  
Author(s):  
Nicolai Ree ◽  
Andreas Gõller ◽  
Jan H. Jensen
Keyword(s):  
Open Source ◽  
Tight Binding ◽  
Electrophilic Aromatic Substitution ◽  
Data Sets ◽  
Aromatic Substitution ◽  
Success Rates ◽  
Proton Affinities ◽  
Data Set ◽  
Tautomeric Forms ◽  
Near Future

<div> <div> <div> <p>We present RegioSQM20, a new version of RegioSQM (<i>Chem. Sci</i>. 2018, 9, 660), which predicts the regioselectivities of electrophilic aromatic substitution (EAS) re- actions from the calculation of proton affinities. The following improvements have been made: The open source semiempirical tight binding program xtb is used instead of the closed source MOPAC program. Any low energy tautomeric forms of the input molecule are identified and regioselectivity predictions are made for each form. Finally, RegioSQM20 offers a qualitative prediction of the reactivity of each tautomer (low, medium, or high) based on the reaction center with the highest proton affinity. The inclusion of tautomers increases the success rate from 90.7% to 92.7%. RegioSQM20 is compared to two machine learning based models: one developed by Struble et al. (<i>React. Chem. Eng</i>. 2020, 5, 896) specifically for regioselectivity predictions of EAS reactions (WLN) and a more generally applicable reactivity predictor (IBM RXN) de- veloped by Schwaller et al. (<i>ACS Cent. Sci</i>. 2019, 5, 1572). RegioSQM20 and WLN offers roughly the same success rates for the entire data sets (without considering tau- tomers), while WLN is many orders of magnitude faster. The accuracy of the more general IBM RXN approach is somewhat lower: 76.3%-85.0%, depending on the data set. The code is freely available under the MIT open source license and will be made available as a webservice (regiosqm.org) in the near future. </p> </div> </div> </div>

Using Artificial Intelligence to Predict Decisions of the Turkish Constitutional Court

2021 ◽  
pp. 089443932110103
Author(s):  
Mehmet Fatih Sert ◽  
Engin Yıldırım ◽  
İrfan Haşlak
Keyword(s):  
Artificial Intelligence ◽  
Freedom Of Expression ◽  
Constitutional Court ◽  
Small Data ◽  
Data Sets ◽  
Success Rates ◽  
Data Set ◽  
Average Accuracy ◽  
Small Data Sets ◽  
The Subject

This article draws on an artificial intelligence (AI) technique to predict whether an individual application regarding the Turkish Constitutional Court's public morality and freedom of expression cases leding to a “violation” or a “nonviolation” decision. To this end, four different data sets have been composed, preclassification and fundamental word embeddings steps have been made on each data set. Multilayer perceptron, which is based on artificial neural networks, has been used for the prediction of the case decisions. We have predicted the court’s decisions on these cases with the high success rates (average accuracy of 90%) by using the subject or reasoning sections of texts of the cases as data. The subject section of the cases constituting only a very small part of the data has yielded the highest accuracy. The article has demonstrated that a basic AI technique can be successful in achieving accurate predictions even with relatively small data sets derived from well-structured court rulings.

scholarly journals Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

2019 ◽  
Vol 9 (5) ◽  
pp. 115 ◽  
Author(s):  
Ömer Türk ◽  
Mehmet Siraç Özerdem
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Data Sets ◽  
Great Success ◽  
Success Rates ◽  
Learning Networks ◽  
Eeg Signals ◽  
Data Set ◽  
Conventional Methods

The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.

scholarly journals RegioSQM20: improved prediction of the regioselectivity of electrophilic aromatic substitutions

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Nicolai Ree ◽  
Andreas H. Göller ◽  
Jan H. Jensen
Keyword(s):  
Open Source ◽  
Tight Binding ◽  
Electrophilic Aromatic Substitution ◽  
Data Sets ◽  
Aromatic Substitution ◽  
Success Rates ◽  
Proton Affinities ◽  
Data Set ◽  
Tautomeric Forms ◽  
Source Program

AbstractWe present RegioSQM20, a new version of RegioSQM (Chem Sci 9:660, 2018), which predicts the regioselectivities of electrophilic aromatic substitution (EAS) reactions from the calculation of proton affinities. The following improvements have been made: The open source semiempirical tight binding program is used instead of the closed source program. Any low energy tautomeric forms of the input molecule are identified and regioselectivity predictions are made for each form. Finally, RegioSQM20 offers a qualitative prediction of the reactivity of each tautomer (low, medium, or high) based on the reaction center with the highest proton affinity. The inclusion of tautomers increases the success rate from 90.7 to 92.7%. RegioSQM20 is compared to two machine learning based models: one developed by Struble et al. (React Chem Eng 5:896, 2020) specifically for regioselectivity predictions of EAS reactions (WLN) and a more generally applicable reactivity predictor (IBM RXN) developed by Schwaller et al. (ACS Cent Sci 5:1572, 2019). RegioSQM20 and WLN offers roughly the same success rates for the entire data sets (without considering tautomers), while WLN is many orders of magnitude faster. The accuracy of the more general IBM RXN approach is somewhat lower: 76.3–85.0%, depending on the data set. The code is freely available under the MIT open source license and will be made available as a webservice (regiosqm.org) in the near future.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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