Identification of Motion-Based Action Potentials in Neural Bundles

2014 ◽  
Vol 2 (2) ◽  
pp. 22-40 ◽  
Author(s):  
Volkhard Klinger ◽  
Arne Klauke
Keyword(s):  
Action Potentials ◽  
Learning Algorithm ◽  
Inverse Kinematic ◽  
Sensory Nerves ◽  
Key Factors ◽  
Agent Based ◽  
Nerve Signal ◽  
Multi Agent ◽  
And Control ◽  
Level Identification

Realizing a nerve signal based prostheses control or limb stimulation is a great challenge in medical technology. It requires a recording and an identification process of the motion-based action potentials of motor and sensory nerves within the corresponding neural bundle. Two additional key factors are used by multi agent-based learning algorithm: The anatomical disposition of the nerves within the neural bundle and the inverse kinematic. In this paper the authors introduce the Smart Modular Biosignal Acquisition, Identification and Control System and its application environment. They present the different process levels and their characteristic identification contribution and they give an overview of the multi-agent based identification framework. The authors show the verification environment and present results regarding the first-level identification procedure.

Benchmarking and Robust Multi-Agent-Based Production Planning and Control

2003 ◽  
Vol 36 (3) ◽  
pp. 249-254
Author(s):  
Daniel Frey ◽  
Jens Nimis ◽  
Heinz Wörn ◽  
Peter Lockemann
Keyword(s):  
Production Planning ◽  
Production Planning And Control ◽  
Agent Based ◽  
Planning And Control ◽  
Multi Agent ◽  
And Control

Agent Support for Design of Aircraft Parts

1998 ◽  
Author(s):  
Krishna N. Jha ◽  
Andrea Morris ◽  
Ed Mytych ◽  
Judith Spering
Keyword(s):  
Preliminary Design ◽  
Formal Representation ◽  
Web Based ◽  
Agent Based ◽  
Multi Agent ◽  
High Level ◽  
Legacy Applications ◽  
The Cost ◽  
And Control ◽  
Task Structures

Abstract Designing aircraft parts requires extensive coordination among multiple distributed design groups. Achieving such a coordination is time-consuming and expensive, but the cost of ignoring or minimizing it is much higher in terms of delayed and inferior quality products. We have built a multi-agent-based system to provide the desired coordination among the design groups, the legacy applications, and other resources during the preliminary design (PD) process. A variety of agents are used to model the various design and control functionalities. The agent-representation includes a formal representation of the task-structures. A web-based user-interface provides high-level interface to the users. The agents collaborate to achieve the design goals.

scholarly journals Framework of Experimental Design and Data Mining in Multi-agent Simulation

2022 ◽  
pp. 1-7
Author(s):  
Fa Zhang ◽  
Shi-Hui Wu ◽  
Zhi-Hua Song
Keyword(s):  
Complex System ◽  
Data Mining ◽  
Experimental Design ◽  
Complex Systems ◽  
Key Factors ◽  
Agent Based ◽  
Meta Model ◽  
Reduction Methods ◽  
Multi Agent ◽  
Using Data

Multi-agent based simulation (MABS) is an important approach for studying complex systems. The Agent-based model often contains many parameters, these parameters are usually not independent, with differences in their range, and may be subjected to constraints. How to use MABS investigating complex systems effectively is still a challenge. The common tasks of MABS include: summarizing the macroscopic patterns of the system, identifying key factors, establishing a meta-model, and optimization. We proposed a framework of experimental design and data mining for MABS. In the framework, method of experimental design is used to generate experiment points in the parameter space, then generate simulation data, and finally using data mining techniques to analyze data. With this framework, we could explore and analyze complex system iteratively. Using central composite discrepancy (CCD) as measure of uniformity, we designed an algorithm of experimental design in which parameters could meet any constraints. We discussed the relationship between tasks of complex system simulation and data mining, such as using cluster analysis to classify the macro patterns of the system, and using CART, PCA, ICA and other dimensionality reduction methods to identify key factors, using linear regression, stepwise regression, SVM, neural network, etc. to build the meta-model of the system. This framework integrates MABS with experimental design and data mining to provide a reference for complex system exploration and analysis.

scholarly journals A situation-based multi-agent architecture for handling misunderstandings in interactions

2015 ◽  
Vol 25 (3) ◽  
pp. 439-454 ◽  
Author(s):  
Thao Phuong Pham ◽  
Mourad Rabah ◽  
Pascal Estraillier
Keyword(s):  
Adaptive Systems ◽  
Agent Based ◽  
Consistency Management ◽  
Management Scenario ◽  
Multi Agent ◽  
Online Distance Learning ◽  
Online Distance ◽  
And Control ◽  
Elementary Building

AbstractDuring interactions, system actors may face up misunderstandings when their local states contain inconsistent data about the same fact. Misunderstandings in interactions are likely to reduce interactivity performances (deviation or deadlock) or even affect overall system behavior. In this paper, we characterize misunderstandings in interactions between system actors (that may be human users or system agents) in interactive adaptive systems. To deal with such misunderstandings and ensure state consistency, we present an agent-based architecture and a scenario structuring approach. The system includes several agents devoted to scenario unfolding, plot adaptation and consistency management. Scenario structuring is based on the notion of a situation that is an elementary building block dividing the interactions between systems’ actors into contextual scenes. This pattern supports not only scenario execution but consistency management as well. In order to organize and control interactions, the situation contextualizes interactions and activity of the system’s actors. It also includes prevention and tolerance agent-based mechanisms to deal with the misunderstandings and their causes. We validate our consistency management mechanisms using Uppaal simulation and provide some experimental results to show the effectiveness of our approach on an online distance learning case study

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