scholarly journals Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands

2021 ◽  
Vol 15 ◽  
Author(s):  
Bingchen Liu ◽  
Li Jiang ◽  
Shaowei Fan ◽  
Jinghui Dai
Keyword(s):  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Principal Component ◽  
Past Research ◽  
Shape Descriptor ◽  
Gaussian Mixture ◽  
Human Hand ◽  
Mixture Regression ◽  
Low Dimensional ◽  
Anthropomorphic Hands

The proposal of postural synergy theory has provided a new approach to solve the problem of controlling anthropomorphic hands with multiple degrees of freedom. However, generating the grasp configuration for new tasks in this context remains challenging. This study proposes a method to learn grasp configuration according to the shape of the object by using postural synergy theory. By referring to past research, an experimental paradigm is first designed that enables the grasping of 50 typical objects in grasping and operational tasks. The angles of the finger joints of 10 subjects were then recorded when performing these tasks. Following this, four hand primitives were extracted by using principal component analysis, and a low-dimensional synergy subspace was established. The problem of planning the trajectories of the joints was thus transformed into that of determining the synergy input for trajectory planning in low-dimensional space. The average synergy inputs for the trajectories of each task were obtained through the Gaussian mixture regression, and several Gaussian processes were trained to infer the inputs trajectories of a given shape descriptor for similar tasks. Finally, the feasibility of the proposed method was verified by simulations involving the generation of grasp configurations for a prosthetic hand control. The error in the reconstructed posture was compared with those obtained by using postural synergies in past work. The results show that the proposed method can realize movements similar to those of the human hand during grasping actions, and its range of use can be extended from simple grasping tasks to complex operational tasks.

scholarly journals Optimizing the structure and movement of a robotic bat with biological kinematic synergies

2018 ◽  
Vol 37 (10) ◽  
pp. 1233-1252 ◽  
Author(s):  
Jonathan Hoff ◽  
Alireza Ramezani ◽  
Soon-Jo Chung ◽  
Seth Hutchinson
Keyword(s):  
Principal Component Analysis ◽  
Topological Structure ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Principal Component ◽  
Biologically Inspired ◽  
Wing Kinematics ◽  
Wing Motion ◽  
Kinematic Synergies ◽  
Low Dimensional

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

scholarly journals Joint identification of sex and sex-linked scaffolds in non-model organisms using low depth sequencing data

2021 ◽  
Author(s):  
Casia Nursyifa ◽  
Anna Bruniche-Olsen ◽  
Genis Garcia-Erill ◽  
Rasmus Heller ◽  
Anders Albrechtsen
Keyword(s):  
Dimensional Space ◽  
Sequence Similarity ◽  
Bird Species ◽  
Model Organism ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Model Organisms ◽  
Sequencing Data ◽  
Mammal Species ◽  
Low Dimensional

Being able to assign sex to individuals and identify autosomal and sex-linked scaffolds are essential in most population genomic analyses. Non-model organisms often have genome assemblies at scaffold level and lack characterization of sex-linked scaffolds. Previous methods to identify sex and sex-linked scaffolds have relied on e.g. sequence similarity between the non-model organism and a closely related species or prior knowledge about the sex of the samples to identify sex-linked scaffolds. In the latter case, the difference in depth of coverage between the autosomes and the sex chromosomes are used. Here we present "Sex Assignment Through Coverage" (SATC), a method to identify sample sex and sex-linked scaffolds from NGS data. The method only requires a scaffold level reference assembly and sampling of both sexes with whole genome sequencing (WGS) data. We use the sequencing depth distribution across scaffolds to jointly identify: i) male and female individuals and ii) sex-linked scaffolds. This is achieved through projecting the scaffold depths into a low-dimensional space using principal component analysis (PCA) and subsequent Gaussian mixture clustering. We demonstrate the applicability of our method using data from five mammal species and a bird species complex. The method is open source and freely available at https://github.com/popgenDK/SATC

scholarly journals The local Universe in the era of large surveys – I. Spectral classification of S0 galaxies

2020 ◽  
Vol 495 (4) ◽  
pp. 4135-4157 ◽  
Author(s):  
J L Tous ◽  
J M Solanes ◽  
J D Perea
Keyword(s):  
Dimensional Space ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Morphological Type ◽  
Physical Parameters ◽  
Star Forming ◽  
Interesting Possibility ◽  
Low Dimensional ◽  
Free Machine

ABSTRACT This is the first paper in a series devoted to review the main properties of galaxies designated S0 in the Hubble classification system. Our aim is to gather abundant and, above all, robust information on the most relevant physical parameters of this poorly understood morphological type and their possible dependence on the environment, which could later be used to assess their possible formation channel(s). The adopted approach combines the characterization of the fundamental features of the optical spectra of $68\, 043$ S0 with heliocentric z ≲ 0.1 with the exploration of a comprehensive set of their global attributes. A principal component analysis is used to reduce the huge number of dimensions of the spectral data to a low-dimensional space facilitating a bias-free machine-learning-based classification of the galaxies. This procedure has revealed that objects bearing the S0 designation consist, despite their similar morphology, of two separate subpopulations with statistically inconsistent physical properties. Compared to the absorption-dominated S0, those with significant nebular emission are, on average, somewhat less massive, more luminous with less concentrated light profiles, have a younger, bluer, and metal-poorer stellar component, and avoid high-galaxy-density regions. Noteworthy is the fact that the majority of members of this latter class, which accounts for at least a quarter of the local S0 population, show star formation rates and spectral characteristics entirely similar to those seen in late spirals. Our findings suggest that star-forming S0 might be less rare than hitherto believed and raise the interesting possibility of identifying them with plausible progenitors of their quiescent counterparts.

Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images

1996 ◽  
Vol 8 (6) ◽  
pp. 1321-1340 ◽  
Author(s):  
Joseph J. Atick ◽  
Paul A. Griffin ◽  
A. Norman Redlich
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Practical Interest ◽  
Principal Component ◽  
Shape From Shading ◽  
Shape Space ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Low Dimensional ◽  
Dimensional Object

The human visual system is proficient in perceiving three-dimensional shape from the shading patterns in a two-dimensional image. How it does this is not well understood and continues to be a question of fundamental and practical interest. In this paper we present a new quantitative approach to shape-from-shading that may provide some answers. We suggest that the brain, through evolution or prior experience, has discovered that objects can be classified into lower-dimensional object-classes as to their shape. Extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low-dimensional space. We carry out this proposal for an important class of three-dimensional (3D) objects: human heads. From an ensemble of several hundred laser-scanned 3D heads, we use principal component analysis to derive a low-dimensional parameterization of head shape space. An algorithm for solving shape-from-shading using this representation is presented. It works well even on real images where it is able to recover the 3D surface for a given person, maintaining facial detail and identity, from a single 2D image of his face. This algorithm has applications in face recognition and animation.

scholarly journals Low Dimensional Representations along Intrinsic Reaction Coordinates and Molecular Dynamics Trajectories Using Interatomic Distance Matrices

2019 ◽  
Author(s):  
Stephanie Hare ◽  
Lars Bratholm ◽  
David Glowacki ◽  
Barry Carpenter
Keyword(s):  
Molecular Dynamics ◽  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Physical Meaning ◽  
Interatomic Distance ◽  
Dimensional Space ◽  
Principal Component ◽  
Reaction Pathways ◽  
Reaction Coordinates ◽  
Low Dimensional

Low dimensional representations along reaction pathways were produced using newly created Python software that utilises Principal Component Analysis (PCA) to do dimensionality reduction. Plots of these pathways in reduced dimensional space, as well as the physical meaning of the reduced dimensional axes, are discussed.

scholarly journals A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5097 ◽  
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Changes ◽  
Dimensional Space ◽  
Principal Component ◽  
Majority Voting ◽  
Data Set ◽  
Centroid Distance ◽  
Low Dimensional ◽  
Reduced Data

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method.

scholarly journals Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks

2019 ◽  
Author(s):  
Di Jin ◽  
Bingyi Li ◽  
Pengfei Jiao ◽  
Dongxiao He ◽  
Weixiong Zhang
Keyword(s):  
Information Integration ◽  
Dimensional Space ◽  
Gaussian Mixture ◽  
Superior Performance ◽  
Network Structures ◽  
New Approach ◽  
Network Topologies ◽  
Node Attributes ◽  
Novel Method ◽  
Low Dimensional

Network embedding (NE) maps a network into a low-dimensional space while preserving intrinsic features of the network. Variational Auto-Encoder (VAE) has been actively studied for NE. These VAE-based methods typically utilize both network topologies and node semantics and treat these two types of data in the same way. However, the information of network topology and information of node semantics are orthogonal and are often from different sources; the former quantifies coupling relationships among nodes, whereas the latter represents node specific properties. Ignoring this difference affects NE. To address this issue, we develop a network-specific VAE for NE, named as NetVAE. In the encoding phase of our new approach, compression of network structures and compression of node attributes share the same encoder in order to perform co-training to achieve transfer learning and information integration. In the decoding phase, a dual decoder is introduced to reconstruct network topologies and node attributes separately. Specifically, as a part of the dual decoder, we develop a novel method based on a Gaussian mixture model and the block model to reconstruct network structures. Extensive experiments on large real-world networks demonstrate a superior performance of the new approach over the state-of-the-art methods.

Local preserving projections-based feature selection and Gaussian mixture model for machine health assessment

2011 ◽  
Vol 225 (7) ◽  
pp. 1703-1717 ◽  
Author(s):  
J Yu ◽  
M Liu ◽  
H Wu
Keyword(s):  
Feature Selection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Health Assessment ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Health State ◽  
Structure Information ◽  
Machine Health ◽  
Low Dimensional

The sensitivity of various features that are characteristics of machine health may vary significantly under different working conditions. Thus, it is critical to devise a systematic feature selection (FS) approach that provides a useful and automatic guidance on choosing the most effective features for machine health assessment. This article proposes a locality preserving projections (LPP)-based FS approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP can find a good linear embedding that preserves local structure information. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The LPP-based FS approach is based on unsupervised learning technique, which does not need too much prior knowledge to improve its utility in real-world applications. The effectiveness of the proposed approach was evaluated experimentally on bearing test-beds. A novel machine health assessment indication, Gaussian mixture model-based Mahalanobis distance is proposed to provide a comprehensible indication for quantifying machine health state. The proposed approach has shown to provide the better performance with reduced feature inputs than using all original candidate features. The experimental results indicate its potential applications as an effective tool for machine health assessment.

scholarly journals Hand kinematics of piano playing

2011 ◽  
Vol 106 (6) ◽  
pp. 2849-2864 ◽  
Author(s):  
Shinichi Furuya ◽  
Martha Flanders ◽  
John F. Soechting
Keyword(s):  
Degrees Of Freedom ◽  
Expectation Maximization Algorithm ◽  
Principal Component ◽  
Human Hand ◽  
Joint Motion ◽  
Joint Movement ◽  
Spatial Constraints ◽  
Multiple Degrees Of Freedom ◽  
Temporal And Spatial ◽  
And Cluster Analysis

Dexterous use of the hand represents a sophisticated sensorimotor function. In behaviors such as playing the piano, it can involve strong temporal and spatial constraints. The purpose of this study was to determine fundamental patterns of covariation of motion across joints and digits of the human hand. Joint motion was recorded while 5 expert pianists played 30 excerpts from musical pieces, which featured ∼50 different tone sequences and fingering. Principal component analysis and cluster analysis using an expectation-maximization algorithm revealed that joint velocities could be categorized into several patterns, which help to simplify the description of the movements of the multiple degrees of freedom of the hand. For the thumb keystroke, two distinct patterns of joint movement covariation emerged and they depended on the spatiotemporal patterns of the task. For example, the thumb-under maneuver was clearly separated into two clusters based on the direction of hand translation along the keyboard. While the pattern of the thumb joint velocities differed between these clusters, the motions at the metacarpo-phalangeal and proximal-phalangeal joints of the four fingers were more consistent. For a keystroke executed with one of the fingers, there were three distinct patterns of joint rotations, across which motion at the striking finger was fairly consistent, but motion of the other fingers was more variable. Furthermore, the amount of movement spillover of the striking finger to the adjacent fingers was small irrespective of the finger used for the keystroke. These findings describe an unparalleled amount of independent motion of the fingers.

scholarly journals Community Detection Based on DeepWalk Model in Large-Scale Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yunfang Chen ◽  
Li Wang ◽  
Dehao Qi ◽  
Tinghuai Ma ◽  
Wei Zhang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Large Scale ◽  
Dimensional Space ◽  
Complex Structure ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Large Scale Networks ◽  
Low Dimensional

The large-scale and complex structure of real networks brings enormous challenges to traditional community detection methods. In order to detect community structure in large-scale networks more accurately and efficiently, we propose a community detection algorithm based on the network embedding representation method. Firstly, in order to solve the scarce problem of network data, this paper uses the DeepWalk model to embed a high-dimensional network into low-dimensional space with topology information. Then, low-dimensional data are processed, with each node treated as a sample and each dimension of the node as a feature. Finally, samples are fed into a Gaussian mixture model (GMM), and in order to automatically learn the number of communities, variational inference is introduced into GMM. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large-scale networks. By further analyzing the excavated community structure, the organizational characteristics within the community are better revealed.

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