Identification of the Inverse Dynamics Model: A Multiple Relevance Vector Machines Approach

2008 ◽  
pp. 387-394 ◽  
Author(s):  
Chuan Li ◽  
Xianming Zhang ◽  
Shilong Wang ◽  
Yutao Dong ◽  
Jing Chen
Keyword(s):  
Inverse Dynamics ◽  
Dynamics Model ◽  
Relevance Vector Machines ◽  
Vector Machines

Estimation of Muscle Forces About the Ankle During Gait in Healthy and Neurologically Impaired Subjects

2011 ◽  
pp. 320-347
Author(s):  
Daniel N. Bassett ◽  
Joseph D. Gardinier ◽  
Kurt T. Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Muscle Activation ◽  
Inverse Dynamics ◽  
Biomechanical Model ◽  
Forward Dynamics ◽  
Muscle Forces ◽  
Dynamics Model ◽  
Joint Moments ◽  
Neurologically Impaired ◽  
Model Predictions ◽  
Contraction Dynamics

This chapter describes a biomechanical model of the forces about the ankle joint applicable to both unimpaired and neurologically impaired subjects. EMGs and joint kinematics are used as inputs and muscle forces are the outputs. A hybrid modeling approach that uses both forward and inverse dynamics is employed and physiological parameters for the model are tuned for each subject using optimization procedures. The forward dynamics part of the model takes muscle activation and uses Hill-type models of muscle contraction dynamics to estimate muscle forces and the corresponding joint moments. Inverse dynamics is used to calibrate the forward dynamics model predictions of joint moments. In this chapter we will describe how to implement an EMG-driven hybrid forward and inverse dynamics model of the ankle that can be used in healthy and neurologically impaired people.

scholarly journals Developing a forecasting model of concrete compressive strength using relevance vector machines

2014 ◽  
Vol 3 (2) ◽  
pp. 224 ◽  
Author(s):  
Mohammad Awwad
Keyword(s):  
Compressive Strength ◽  
Early Stage ◽  
Mixing Time ◽  
Relevance Vector Machine ◽  
Strength Increase ◽  
Concrete Compressive Strength ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Strength Material ◽  
Cement Mixture

We analyze results of two experiments that tested effect of adding Silica on the compressive strength of concrete at early stage and after long period. The two experiments evaluated different silica/cement ratios for different mixing periods. Adding Silica to concrete mix produce high early strength material which is highly desirable in airports and highways. More than 90 samples of different silica/cement ratios are tested for compressive strength at 3 and 28 days. Test results showed high early up to 60 MPa. Strength increase is proportional with the increase of silica/cement ratio and mixing time with maximum at ratio of 15/100 and 30 minutes mixing time. A relevance Vector Machine (RVM) model is developed to predict concrete compressive strength using concrete mixture inputs information. RVM model predictions matched experimental data closely. The developed model can be used to predict compressive strength in future periods based on initial information related to cement mixture. Keywords: Relevance Vector Machine, Silicate Percent, Prediction Model, Milling Time, Compressive Strength, Concrete.

scholarly journals DRACP: a novel method for identification of anticancer peptides

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Amino Acids ◽  
Chemical Characteristics ◽  
Surgical Operation ◽  
Anticancer Peptides ◽  
Relevance Vector Machines ◽  
Vector Machines ◽  
Amino Acids Composition ◽  
Novel Method ◽  
Hydrophilic Residues ◽  
Experimental Technology

Abstract Background Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs. Results Firstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets. Conclusion We developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.

Quaternion-Based Inverse Dynamics Model for Expressing Aerobatic Aircraft Trajectories

2009 ◽  
Vol 32 (4) ◽  
pp. 1388-1391 ◽  
Author(s):  
Rick G. Drury ◽  
James F. Whidborne
Keyword(s):  
Inverse Dynamics ◽  
Dynamics Model ◽  
Aircraft Trajectories

Relevance vector machines for multivariate calibration purposes

2008 ◽  
Vol 22 (11-12) ◽  
pp. 686-694 ◽  
Author(s):  
Noslen Hernández ◽  
Isneri Talavera ◽  
Angel Dago ◽  
Rolando J. Biscay ◽  
Marcia M. Castro Ferreira ◽  
...  
Keyword(s):  
Multivariate Calibration ◽  
Relevance Vector Machines ◽  
Vector Machines
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