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This work enters in the scheme of work at The Multibody and Mechatronic Systems Laboratory of the University of Liège, that aims at optimizing trajectory control by taking into account robot flexibility. A Delta robot ``ABB IRB 340'' will one of the subjects of this study. This work will focus on designing a ``Rigid Body Dynamic Model'' of this robot as a step towards the goals of the Multibody and Mechatronics Systems Laboratory.
kinematics --- dynamics --- robotics --- model identification --- multibody modeling --- multibody mechanics --- Delta robot --- feedfoward --- control --- Samcef --- Mecano --- Ingénierie, informatique & technologie > Ingénierie mécanique
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Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales-from ecosystems to individual cells and from years to milliseconds. For these networks, the concept "the whole is greater than the sum of its parts" applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution-even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on "Biological Networks" showcases advances in the development and application of in silico network modeling and analysis of biological systems.
Pathway crosstalk --- Alzheimer’s disease --- Bioenergy crops --- Model identification --- Metabolic networks --- Host–pathogen interactions --- Single cell --- Parameter sensitivity --- Tuberculosis --- Multivariate statistical analysis --- Systems biology --- Biological networks --- Mathematical modeling --- Lignin biosynthesis --- Design of experiments
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Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties: Autonomy-Individuals that create the swarm robotic system are autonomous robots. They are independent and can interact with each other and the environment. Large number-They are in large number, enabling cooperation. Scalability and robustness-A new unit can be easily added to the system, so the system can be easily scaled. A greater number of units improves the performance of the system. The system is quite robust to the loss of some units, as some units still remain to perform, although the system will not perform to its maximum capabilities. Decentralized coordination-The robots communicate with each other and with their environment to make final decisions. Flexibility-The swarm robotic system has the ability to generate modularized solutions to different tasks.
n/a --- self-organization --- signal source localization --- multi-robot system --- sensor deployment --- parallel technique --- shape normalization --- genetic algorithm --- multiple robots --- optimization --- improved potential field --- optimal configuration --- autonomous docking --- asymmetrical interaction --- comparison --- behaviors --- patterns --- self-assembly robots --- congestion control --- surface-water environment --- target recognition --- coordinate motion --- UAV swarms --- formation reconfiguration --- swarm robotics --- swarm intelligence --- artificial bee colony algorithm --- obstacle avoidance --- fish swarm optimization --- search algorithm --- robotics --- time-difference-of-arrival (TDOA) --- formation --- mobile robots --- formation control --- meta-heuristic --- event-triggered communication --- search --- virtual structure --- 3D model identification --- surveillance --- event-driven coverage --- scale-invariant feature transform --- system stability --- Swarm intelligence algorithm --- bionic intelligent algorithm --- unmanned aerial vehicle --- underwater environment --- artificial flora (AF) algorithm --- swarm behavior --- weighted implicit shape representation --- Cramer–Rao low bound (CRLB) --- environmental perception --- particle swarm optimization --- modular robots --- cooperative target hunting --- virtual linkage --- multi-AUV --- consensus control --- panoramic view --- nonlinear disturbance observer --- sliding mode controller --- path optimization --- Swarm Chemistry --- multi-agents --- Cramer-Rao low bound (CRLB)
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Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.
polyacrylonitrile-based carbon fiber --- n/a --- coagulation bath --- binder dissolution --- sensitivity analysis --- simulation --- neural networks --- kernel development --- thermodynamics --- phytochemicals --- wave resonance --- natural extracts --- population balance model --- optimization --- vane --- parameter estimation --- grey-box model --- observability --- optimal clustering --- energy --- idling test --- data-mining --- extents --- computational fluid dynamics --- scrap dissolution --- Combined Heat and Power --- dynamic optimization --- scrap melting --- swelling --- engineering --- dry-jet wet spinning process --- fluid bed granulation --- point estimation method --- algebraic modeling language --- Design of Experiments --- costing stopping --- materials --- hydration --- SOS programming --- kinetics --- moisture content --- CHP legislation --- model predictive control --- graph theory --- robust optimization --- dynamic converter modelling --- partial least square regression --- uncertainty --- state decoupling --- utility management --- fluidized bed drying --- reactor coolant pump --- condensation --- wheat germ --- cooking --- maximum wave amplitude --- moving horizon estimation --- gray-box model --- chemistry --- barley --- machine learning --- heat and mass balance --- equality constraints --- porridge --- process model validation --- Pharmaceutical Processes --- mathematical model --- model identification --- Mammalian Cell Culture --- process modeling --- parameter correlation
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