Characterization of Complex Systems by Aperiodic Driving Forces

1989 ◽  
Author(s):  
Daniel Bensen ◽  
Michael Welge ◽  
Alfred Huebler ◽  
Norman Packard
Keyword(s):  
Complex Systems ◽  
Driving Forces

Characterization of Complex Systems by Aperiodic Driving Forces

1989 ◽  
pp. 209-212
Author(s):  
Daniel Bensen ◽  
Michael Welge ◽  
Alfred Hübler ◽  
Norman Packard
Keyword(s):  
Complex Systems ◽  
Driving Forces

THE CHARACTERIZATION OF COMPLEX SYSTEMS OF CHEMICAL REACTIONS

1989 ◽  
Vol 83 (1) ◽  
pp. 221-240 ◽  
Author(s):  
JOHN HAPPEL ◽  
PETER H. SELLERS
Keyword(s):  
Complex Systems ◽  
Chemical Reactions

scholarly journals Emergence and Self-Organisation of Complex Systems: the role of energy flows and information a philosophical approach

2018 ◽  
Vol 2 (1) ◽  
pp. 31 ◽  
Author(s):  
Norbert Fenzl
Keyword(s):  
Complex Systems ◽  
Driving Forces ◽  
Open Systems ◽  
Structural Information ◽  
Self Organization ◽  
Specific Information ◽  
Philosophical Approach ◽  
Energy Flows ◽  
Energy And Material Flows

How order emerges from noise? How higher complexity arises from lower complexity? For what reason a certain number of open systems start interacting in a coherent way, producing new structures, building up cohesion and new structural boundaries? To answer these questions we need to precise the concepts we use to describe open and complex systems and the basic driving forces of self-organization.   We assume that self-organization processes are related to the flow and throughput of Energy and Matter and the production of system-specific Information. These two processes are intimately linked together: Energy and Material flows are the fundamental carriers of signs, which are processed by the internal structure of the system to produce system-specific structural Information (Is). So far, the present theoretical reflections are focused on the emergence of open systems and on the role of Energy Flows and Information in a self-organizing process. Based on the assumption that Energy, Mass and Information are intrinsically linked together and are fundamental aspects of the Universe, we discuss how they might be related to each other and how they are able to produce the emergence of new structures and systems. 

Ionic Polymeric Conductor Nano Composites (IPCNCs) as Distributed Nanosensors and Nanoactuators

2008 ◽  
Vol 54 ◽  
pp. 70-81 ◽  
Author(s):  
Mohsen Shahinpoor
Keyword(s):  
Driving Forces ◽  
Electronic Materials ◽  
Electrical Characterization ◽  
Irreversible Thermodynamics ◽  
Basic Material ◽  
Nano Composites ◽  
Chemical Plating ◽  
Fundamental Properties ◽  
Equivalent Circuit Modeling

This article covers advances made in connection with Ionic Polymeric-Conductor Nano Composites (IPCNCs) as distributed biomimetic nanosensors, nanoactuators, nanorobots and artificial muscles. A review of the fundamental properties and characteristics of IPCNCs will first be presented. This summary will include descriptions of the basic materials' molecular structure and subsequent procedure to manufacture the basic material for chemical plating and electroactivation. Further described are chemical molecular plating technologies to make IPCNCs, nanotechnologies of manufacturing and trapping of nanoparticles, SEM, TEM, SPM and AFM characterization of IPMNCs, biomimetic sensing and actuation characterization techniques, electrical characterization and equivalent circuit modeling of IPCNCs as electronic materials. A phenomenological model of the underlying sensing and actuation mechanisms is also presented based on linear irreversible thermodynamics with two driving forces, an electric field and a solvent pressure gradient and two fluxes, electric current density and the ionic+solvent flux. The presentation concludes with a number of videos and some live demos.

Preparation and characterization of a series of porous anion-exchanger chelating fibers and their adsorption behavior with respect to removal of cadmium(ii)

RSC Advances ◽  
2016 ◽  
Vol 6 (116) ◽  
pp. 115222-115237 ◽  
Author(s):  
Mingqiang Liu ◽  
Zhongan Tao ◽  
Huicai Wang ◽  
Fei Zhao ◽  
Qiang Sun
Keyword(s):  
Complex Systems ◽  
Anion Exchanger ◽  
High Salt ◽  
Adsorption Behavior ◽  
Salt Complex ◽  
Salt Systems

The aim of the present study was to investigate a series of porous anion-exchanger chelating fibers (PP-g-AA-Am), prepared using polypropylene (PP) for the removal of Cd(ii) in non-salt systems and in high-salt complex systems.

scholarly journals Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2624 ◽  
Author(s):  
Sara Hernández Sánchez ◽  
Rubén Fernández Pozo ◽  
Luis Hernández Gómez
Keyword(s):  
Global Positioning System ◽  
Driving Forces ◽  
Movement Direction ◽  
Motion Sensors ◽  
Forward Movement ◽  
Significant Acceleration ◽  
Global Positioning ◽  
Vehicle Movement

Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.

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