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Self-Organization
Published in Mihai V. Putz, New Frontiers in Nanochemistry, 2020
Aranya B. Bhattacherjee, Suman Dudeja
Self-organization is a process where global order emerges out of the local interactions/correlations between the constituent parts of an initially disordered system. The process of self-organization can be spontaneous and can be tuned by some external parameter in some systems. It is often induced by random fluctuations that are amplified by positive feedback. The process of self-organization can be classical or quantum in nature, and it involves all the components of the system. The organization is robust against perturbations. Self-organization occurs in a variety of physical, chemical, biological, social, and cognitive systems such as the collection of atoms interacting with the electromagnetic field, chemical oscillations, bacterial colonies, animal swarming and neural networks.
Simulating Variable System Structures for Engineering Emergence
Published in Larry B. Rainey, Mo Jamshidi, Engineering Emergence, 2018
Umut Durak, Thorsten Pawletta, Tuncer Ören
Complexity is becoming the future landscape of technical systems. Emergence is defined as the coherent and novel macro-level patterns, properties, behavior, or structures that arise from the micro-level interactions among the elements of the complex systems. It is a self-organized order. Self-organization can be pronounced as a designed system characteristic that yields an adaptive process to acquire and maintain a structure without external control. This process executes as micro-level interactions among the system elements and leads to a macro-effect, namely emergence. Organic Computing (OC) aims at utilization of self-organization and emergence as observed in nature for the design of future technical systems (Schmeck 2005). It promotes providing technical systems with some degrees of freedom in low-level behavior for allowing them to self-organize in order to adopt while controlling them with high-level objectives and goals. The observer/controller architecture was proposed as a generic architectural concept for OC (Branke et al. 2006). It offers system governance through an observer that monitors the underlying system in order to report a quantified situation and a controller that evaluates the situation against the user objectives and takes the control actions. So it is not fully autonomous; the users affect the system by changing the objectives. The key characteristic of the architecture is allowing the underlying technical system to be dynamic in a way that it can change its structure and thereby change its behavior.
Sustainable Agriculture: Building Social-Ecological Resilience
Published in Saeid Eslamian, Faezeh Eslamian, Handbook of Drought and Water Scarcity, 2017
Mohammad Naser Reyhani, Saeid Eslamian, Alireza Davari
The ability to self-organize is particularly important in adaptive comanagement and is an essential element of adaptive capacity. Self-organization is an essential element to build resilience. An essential part of self-organization is creating a dynamic interplay between diversity and disturbance [19]. The resilience of a system is closely related to its capacity for self-organization because nature’s cycles involve renewal and reorganization [63]. An opportunity to self-organize can materialize after disturbance or crisis in the reorganization phase and may even result in alternative pathways or trajectories for social-ecological systems. Social memory seems to play an important role in the self-organization process and key individuals draw on social memories of other scales in the reorganization following change [20]. Creating opportunity for self-organization and cross-scale linkages may appear to be a self-evident goal, but there are many complications. Self-organization is often hampered by policies of centralization of decision-making, depriving the various levels of political organization from learning from their own mistakes [31].
Constructing a Demand-Driven College English Learning Environment in Higher Education Institutions
Published in Applied Artificial Intelligence, 2023
Self-organization is the ability of self-regulation, self-improvement and self-development of the system itself. In the EVLE, the formation of its self-organization ability should be realized through the design of the core monitoring and adjustment mechanism, which can be monitored manually or by artificial intelligence. E.g. When the system finds that the learner encounters learning difficulties through monitoring. The cause of the problem can be analyzed by a diagnostic system is the lack of learning resources. There is also a problem with teaching strategies. Or learning activities are not properly arranged: and then take action based on the analysis results. Or adjust the learning content, or change the teaching strategy. Or provide learners with individualized guidance and assistance, and ultimately solve problems and improve the virtual learning environment (Xiao-Dong and Hong-Hui 2020).
The concept of collaborative engineering: a systematic literature review
Published in Production & Manufacturing Research, 2022
Leonilde Varela, Goran Putnik, Fernando Romero
(Schuhmacher & Hummel, 2019) mentioned that conventional planning and control systems, which rely on predefined processes and central DM, are not capable to deal with the arising system’s complexity along the dimensions of changing goods, layouts and throughput requirements. The authors mention that the concepts of ‘self-organization’ in combination with ‘autonomous control’ provide promising solutions to solve these new requirements by using, among other things, the potential of autonomous, decentralized and target-optimized DM. The authors refer to intelligent logistical objects (e.g. smart products, bins and conveyor systems) which are able to communicate and interact with each other as well as with human workers. To investigate the potential of automation and human-robot collaboration for intralogistics, the authors propose a research project for the development what they call a collaborative tugger train based on LF principles.
Architecture and knowledge modelling for self-organized reconfiguration management of cyber-physical production systems
Published in International Journal of Computer Integrated Manufacturing, 2022
Timo Müller, Simon Kamm, Andreas Löcklin, Dustin White, Marius Mellinger, Nasser Jazdi, Michael Weyrich
The future of industrial automation will be shaped by the concept of cyber-physical systems (CPSs), which are physical systems with their own intelligence and cyber abilities, and will feature a high degree of intelligence (Wan et al. 2018; Grochowski et al. 2020; Vogel-Heuser et al. 2020). This is due to the promising potentials which CPSs offer for the production domain. Some of these are self-configuration or self-organization capabilities, which, e.g. lead to more cost-effective and efficient production. Furthermore, increasing demand to customize products (Zhang et al. 2016), shorter innovation and product life cycles (Köcher et al. 2020; Järvenpää, Siltala, and Lanz 2016) result in frequently changing production requirements. Therefore, objectives for production systems are becoming ever more unpredictable during the design phase of these systems. Consequently, adaptations of production systems by means of reconfigurations (i.e. adaptations during the operational phase) have to be carried out frequently. Production systems composed of multiple CPSs are also referred to as Cyber-Physical Production Systems (CPPSs). Hence, the reconfiguration of CPPSs has become an active field of research (Hengstebeck, Barthelmey, and Deuse 2018; Balzereit and Niggemann 2020; Engelsberger and Greiner 2018) that tackles the challenges of frequent changes during the operation of future production systems.