scholarly journals Quantitative Examinations of Internal Representations for Arm Trajectory Planning: Minimum Commanded Torque Change Model

1999 ◽  
Vol 81 (5) ◽  
pp. 2140-2155 ◽  
Author(s):  
Eri Nakano ◽  
Hiroshi Imamizu ◽  
Rieko Osu ◽  
Yoji Uno ◽  
Hiroaki Gomi ◽  
...  
Keyword(s):  
Trajectory Planning ◽  
Computational Models ◽  
Change Model ◽  
Trajectory Data ◽  
Internal Representations ◽  
Muscle Dynamics ◽  
Kinematic Space ◽  
Wide Range ◽  
Flexion Extension ◽  
Intrinsic Dynamic

Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. A number of invariant features of multijoint planar reaching movements have been observed in measured hand trajectories. These features include roughly straight hand paths and bell-shaped speed profiles where the trajectory curvatures between transverse and radial movements have been found to be different. For quantitative and statistical investigations, we obtained a large amount of trajectory data within a wide range of the workspace in the horizontal and sagittal planes (400 trajectories for each subject). A pair of movements within the horizontal and sagittal planes was set to be equivalent in the elbow and shoulder flexion/extension. The trajectory curvatures of the corresponding pair in these planes were almost the same. Moreover, these curvatures can be accurately reproduced with a linear regression from the summation of rotations in the elbow and shoulder joints. This means that trajectory curvatures systematically depend on the movement location and direction represented in the intrinsic body coordinates. We then examined the following four candidates as planning spaces and the four corresponding computational models for trajectory planning. The candidates were as follows: the minimum hand jerk model in an extrinsic-kinematic space, the minimum angle jerk model in an intrinsic-kinematic space, the minimum torque change model in an intrinsic-dynamic-mechanical space, and the minimum commanded torque change model in an intrinsic-dynamic-neural space. The minimum commanded torque change model, which is proposed here as a computable version of the minimum motor command change model, reproduced actual trajectories best for curvature, position, velocity, acceleration, and torque. The model’s prediction that the longer the duration of the movement the larger the trajectory curvature was also confirmed. Movements passing through via-points in the horizontal plane were also measured, and they converged to those predicted by the minimum commanded torque change model with training. Our results indicated that the brain may plan, and learn to plan, the optimal trajectory in the intrinsic coordinates considering arm and muscle dynamics and using representations for motor commands controlling muscle tensions.

Forest path condition monitoring based on crowd-based trajectory data analysis

2020 ◽  
pp. 1-18
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz
Keyword(s):  
Information Acquisition ◽  
Road Network ◽  
Road Networks ◽  
Trajectory Data ◽  
Country Road ◽  
Wide Range ◽  
Sport Activities ◽  
Path Condition ◽  
Spatio Temporal ◽  
The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

An efficient approach to converting three-dimensional image data into highly accurate computational models

2008 ◽  
Vol 366 (1878) ◽  
pp. 3155-3173 ◽  
Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Computational Models ◽  
Three Dimensional ◽  
Image Data ◽  
Model Material ◽  
Imaging Data ◽  
Material Inhomogeneity ◽  
Dimensional Image ◽  
Wide Range ◽  
Impact Modelling

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.

The attachment length in orificed hollow cathodes

2021 ◽  
Author(s):  
Christopher Wordingham ◽  
Pierre-Yves Taunay ◽  
Edgar Choueiri
Keyword(s):  
Electron Temperature ◽  
Plasma Density ◽  
Hollow Cathode ◽  
Computational Models ◽  
Closed Form Solution ◽  
Form Solution ◽  
Decay Length ◽  
Plasma Density Profile ◽  
Wide Range ◽  
Approximate Boundary Condition

Abstract A first-principles approach to obtain the attachment length within a hollow cathode with a constrictive orifice, and its scaling with internal cathode pressure, is developed. This parameter, defined herein as the plasma density decay length scale upstream of (away from) the cathode orifice, is critical because it controls the utilization of the hollow cathode insert and influences cathode life. A two-dimensional framework is developed from the ambipolar diffusion equation for the insert-region plasma. A closed-form solution for the plasma density is obtained using standard partial differential equation techniques by applying an approximate boundary condition at the cathode orifice plane. This approach also yields the attachment length and electron temperature without reliance on measured plasma property data or complex computational models. The predicted plasma density profile is validated against measurements from the NSTAR discharge cathode, and calculated electron temperatures and attachment lengths agree with published values. Nondimensionalization of the governing equations reveals that the solution depends almost exclusively on the neutral pressure-diameter product in the insert plasma region. Evaluation of analytical results over a wide range of input parameters yields scaling relations for the variation of the attachment length and electron temperature with the pressure-diameter product. For the range of orifice-to-insert diameter ratio studied, the influence of orifice size is shown to be small except through its effect on insert pressure, and the attachment length is shown to be proportional to the insert inner radius, suggesting high-pressure cathodes should be constructed with larger-diameter inserts.

AN ALGORITHM TO CALCULATE THE COMPONENTS OF ALL MUSCLE FORCES DURING EACH PHASES OF THE GAIT CYCLE, FOR THE PELVIC-FEMUR COMPLEX

2005 ◽  
Vol 05 (04) ◽  
pp. 539-548 ◽  
Author(s):  
SANTANU MAJUMDER ◽  
AMIT ROYCHOWDHURY ◽  
SUBRATA PAL
Keyword(s):  
Finite Element ◽  
Hip Joint ◽  
Computational Models ◽  
Gait Cycle ◽  
Muscle Force ◽  
Fe Analysis ◽  
Muscle Forces ◽  
Force Magnitude ◽  
Flexion Extension ◽  
Force Components

With the help of finite element (FE) computational models of femur, pelvis or hip joint to perform quasi-static stress analysis during the entire gait cycle, muscle force components (X, Y, Z) acting on the hip joint and pelvis are to be known. Most of the investigators have presented only the net muscle force magnitude during gait. However, for the FE software, either muscle force components (X, Y, Z) or three angles for the muscle line of action are required as input. No published algorithm (with flowchart) is readily available to calculate the required muscle force components for FE analysis. As the femur rotates about the hip center during gait, the lines of action for 27 muscle forces are also variable. To find out the variable lines of action and muscle force components (X, Y, Z) with directions, an algorithm was developed and presented here with detailed flowchart. We considered the varying angles of adduction/abduction, flexion/extension during gait. This computer program, obtainable from the first author, is able to calculate the muscle force components (X, Y, Z) as output, if the net magnitude of muscle force, hip joint orientations during gait and muscle origin and insertion coordinates are provided as input.

Unconscious mental states

2018 ◽  
Author(s):  
Georges Rey
Keyword(s):  
Computational Models ◽  
A Priori ◽  
Mental States ◽  
A Priori Knowledge ◽  
Pupillary Dilation ◽  
Mental Life ◽  
Mental Processes ◽  
The Unconscious ◽  
Wide Range ◽  
The Moment

Unconscious phenomena are those mental phenomena which their possessor cannot introspect, not only at the moment at which the phenomenon occurs, but even when prompted (‘Do you think/want/…?’). There are abundant allusions to many kinds of unconscious phenomena from classical times to Freud. Most notably, Plato in his Meno defended a doctrine of anamnesis according to which a priori knowledge of, for example, geometry is ‘recollected’ from a previous life. But the notion of a rich, unconscious mental life really takes hold in nineteenth-century writers, such as Herder, Hegel, Helmholtz and Schopenhauer. It is partly out of this latter tradition that Freud’s famous postulations of unconscious, ‘repressed’ desires and memories emerged. Partly in reaction to the excesses of introspection and partly because of the rise of computational models of mental processes, twentieth-century psychology has often been tempted by Lashley’s view that ‘no activity of mind is ever conscious’ (1956). A wide range of recent experiments do suggest that people can be unaware of a multitude of sensory cognitive factors (for example, pupillary dilation, cognitive dissonance, subliminal cues to problem-solving) that demonstrably affect their behaviour. And Weiskrantz has documented cases of ‘blindsight’ in which patients with damage to their visual cortex can be shown to be sensitive to visual material they sincerely claim they cannot see. The most controversial cases of unconscious phenomena are those which the agent could not possibly introspect, even in principle. Chomsky ascribes unconscious knowledge of quite abstract principles of grammar to adults and even newborn children that only a linguist could infer. Many philosophers have found these claims about the unconscious unconvincing, even incoherent. However, they need to show how the evidence cited above could be otherwise explained, and why appeals to the unconscious have seemed so perfectly intelligible throughout history.

scholarly journals Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

2020 ◽  
Vol 10 (2) ◽  
pp. 118 ◽  
Author(s):  
Muhammad Waqas Nadeem ◽  
Mohammed A. Al Ghamdi ◽  
Muzammil Hussain ◽  
Muhammad Adnan Khan ◽  
Khalid Masood Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Computational Models ◽  
Medical Image Analysis ◽  
Critical Discussion ◽  
Levels Of Abstraction ◽  
Discussion Section ◽  
Wide Range ◽  
Classification Prediction

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

scholarly journals Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics

2020 ◽  
Vol 7 (7) ◽  
pp. 191243
Author(s):  
Ayoub Lasri ◽  
Viktorija Juric ◽  
Maité Verreault ◽  
Franck Bielle ◽  
Ahmed Idbaih ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Cell Growth ◽  
Dna Methyltransferase ◽  
Computational Models ◽  
Phenotypic Selection ◽  
Stochastic Gene Expression ◽  
Phenotypic Resistance ◽  
Methylguanine Dna Methyltransferase ◽  
Wide Range

Glioblastoma (GBM) is the most aggressive malignant primary brain tumour with a median overall survival of 15 months. To treat GBM, patients currently undergo a surgical resection followed by exposure to radiotherapy and concurrent and adjuvant temozolomide (TMZ) chemotherapy. However, this protocol often leads to treatment failure, with drug resistance being the main reason behind this. To date, many studies highlight the role of O-6-methylguanine-DNA methyltransferase (MGMT) in conferring drug resistance. The mechanism through which MGMT confers resistance is not well studied—particularly in terms of computational models. With only a few reasonable biological assumptions, we were able to show that even a minimal model of MGMT expression could robustly explain TMZ-mediated drug resistance. In particular, we showed that for a wide range of parameter values constrained by novel cell growth and viability assays, a model accounting for only stochastic gene expression of MGMT coupled with cell growth, division, partitioning and death was able to exhibit phenotypic selection of GBM cells expressing MGMT in response to TMZ. Furthermore, we found this selection allowed the cells to pass their acquired phenotypic resistance onto daughter cells in a stable manner (as long as TMZ is provided). This suggests that stochastic gene expression alone is enough to explain the development of chemotherapeutic resistance.

scholarly journals Reports of the AAAI 2011 Conference Workshops

AI Magazine ◽  
2012 ◽  
Vol 33 (1) ◽  
pp. 57-70
Author(s):  
Noa Agmon ◽  
Vikas Agrawal ◽  
David W. Aha ◽  
Yiannis Aloimonos ◽  
Donagh Buckley ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
San Francisco ◽  
Computational Models ◽  
Action Planning ◽  
Human Robot Interaction ◽  
Human Computation ◽  
Risk Modeling ◽  
Learning Plan ◽  
Wide Range ◽  
Representation Techniques

The AAAI-11 workshop program was held Sunday and Monday, August 7–18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages; Analyzing Microtext; Applied Adversarial Reasoning and Risk Modeling; Artificial Intelligence and Smarter Living: The Conquest of Complexity; AI for Data Center Management and Cloud Computing; Automated Action Planning for Autonomous Mobile Robots; Computational Models of Natural Argument; Generalized Planning; Human Computation; Human-Robot Interaction in Elder Care; Interactive Decision Theory and Game Theory; Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action and Language; Lifelong Learning; Plan, Activity, and Intent Recognition; and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events.

scholarly journals Decoding the Real-Time Neurobiological Properties of Incremental Semantic Interpretation

2020 ◽  
Vol 31 (1) ◽  
pp. 233-247
Author(s):  
Hun S Choi ◽  
William D Marslen-Wilson ◽  
Bingjiang Lyu ◽  
Billi Randall ◽  
Lorraine K Tyler
Keyword(s):  
Sentence Comprehension ◽  
Computational Models ◽  
Spoken Language ◽  
Semantic Interpretation ◽  
Semantic Constraints ◽  
Incremental Processing ◽  
Prior Context ◽  
Wide Range ◽  
Preceding Context ◽  
Over Time

Abstract Communication through spoken language is a central human capacity, involving a wide range of complex computations that incrementally interpret each word into meaningful sentences. However, surprisingly little is known about the spatiotemporal properties of the complex neurobiological systems that support these dynamic predictive and integrative computations. Here, we focus on prediction, a core incremental processing operation guiding the interpretation of each upcoming word with respect to its preceding context. To investigate the neurobiological basis of how semantic constraints change and evolve as each word in a sentence accumulates over time, in a spoken sentence comprehension study, we analyzed the multivariate patterns of neural activity recorded by source-localized electro/magnetoencephalography (EMEG), using computational models capturing semantic constraints derived from the prior context on each upcoming word. Our results provide insights into predictive operations subserved by different regions within a bi-hemispheric system, which over time generate, refine, and evaluate constraints on each word as it is heard.

Hierarchical Multiscale Modeling of Tire–Soil Interaction for Off-Road Mobility Simulation

2019 ◽  
Vol 14 (6) ◽  
Author(s):  
Hiroki Yamashita ◽  
Guanchu Chen ◽  
Yeefeng Ruan ◽  
Paramsothy Jayakumar ◽  
Hiroyuki Sugiyama
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Computational Cost ◽  
Interaction Model ◽  
High Fidelity ◽  
Wheel Load ◽  
Mobility Performance ◽  
Wide Range ◽  
Computationally Intensive ◽  
Soil Bin

A high-fidelity computational terrain dynamics model plays a crucial role in accurate vehicle mobility performance prediction under various maneuvering scenarios on deformable terrain. Although many computational models have been proposed using either finite element (FE) or discrete element (DE) approaches, phenomenological constitutive assumptions in FE soil models make the modeling of complex granular terrain behavior very difficult and DE soil models are computationally intensive, especially when considering a wide range of terrain. To address the limitations of existing deformable terrain models, this paper presents a hierarchical FE–DE multiscale tire–soil interaction simulation capability that can be integrated in the monolithic multibody dynamics solver for high-fidelity off-road mobility simulation using high-performance computing (HPC) techniques. It is demonstrated that computational cost is substantially lowered by the multiscale soil model as compared to the corresponding pure DE model while maintaining the solution accuracy. The multiscale tire–soil interaction model is validated against the soil bin mobility test data under various wheel load and tire inflation pressure conditions, thereby demonstrating the potential of the proposed method for resolving challenging vehicle-terrain interaction problems.

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