MODELING THE DYNAMICS OF COMPLEX INTERACTION SYSTEMS: FROM MORPHOGENESIS TO CONTROL

2012 ◽  
Vol 22 (02) ◽  
pp. 1250025 ◽  
Author(s):  
N. CORSON ◽  
M. A. AZIZ-ALAOUI ◽  
R. GHNEMAT ◽  
S. BALEV ◽  
C. BERTELLE
Keyword(s):  
Neural Networks ◽  
Life Cycle ◽  
Complex Systems ◽  
Complex Networks ◽  
Complex Network ◽  
Complex Interaction ◽  
Modeling And Analysis ◽  
Urban Dynamics ◽  
System Life ◽  
Structural Aspects

The aim of this paper is to contribute to the modeling and analysis of complex systems, taking into account the nature of complexity at different stages of the system life-cycle: from its genesis to its evolution. Therefore, some structural aspects of the complexity dynamics are highlighted, leading (i) to implement the morphogenesis of emergent complex network structures, and (ii) to control some synchronization phenomena within complex networks. Specific applications are proposed to illustrate these two aspects, in urban dynamics and in neural networks.

Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios

2020 ◽  
Author(s):  
Brian Chell ◽  
Steven Hoffenson ◽  
Benjamin Kruse ◽  
Mark R. Blackburn
Keyword(s):  
Life Cycle ◽  
Systems Design ◽  
System Optimization ◽  
Optimization Under Uncertainty ◽  
Modeling And Analysis ◽  
Mission Success ◽  
Large Numbers ◽  
Key Stakeholders ◽  
System Life ◽  
Engineering Modeling

Abstract Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.

Application of Complex Network Theory in Computer Network Topology Optimization Research

2014 ◽  
Vol 989-994 ◽  
pp. 4237-4240
Author(s):  
Zhi Kun Wang
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Complex Network ◽  
Network Topology ◽  
Computer Network ◽  
Transportation Network ◽  
Power Grids ◽  
Research Network ◽  
Complex Network Theory ◽  
New Research

If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.

scholarly journals Role Model for Systems Engineering Application

2019 ◽  
Vol 1 (1) ◽  
pp. 1265-1274
Author(s):  
Iris Gräßler ◽  
Christian Oleff ◽  
Julian Hentze
Keyword(s):  
Life Cycle ◽  
Complex Systems ◽  
Systems Engineering ◽  
Role Model ◽  
Engineering Application ◽  
Industrial Applications ◽  
Entire System ◽  
Research Projects ◽  
Role Description ◽  
System Life

AbstractTwenty-three years ago, Sheard published a very well-known description of Systems Engineering roles. Each role represents and defines activities and tasks to be taken into account for performing Systems Engineering through the entire system life cycle. As today's more and more complex systems require different considerations and approaches, these activities and tasks have changed and thereby the description of the role model need to be updated. This work introduces the “Role Model for Systems Engineering Application”, which is adapted to today's circumstances, with the intention to give practitioners guidance in applying Systems Engineering. For this purpose, results from literature as well as from research projects and industrial applications experience were analyzed and combined to an updated role description.

scholarly journals Minimal Modifications of Deep Neural Networks using Verification

10.29007/699q ◽  
2020 ◽  
Author(s):  
Ben Goldberger ◽  
Guy Katz ◽  
Yossi Adi ◽  
Joseph Keshet
Keyword(s):  
Neural Networks ◽  
Life Cycle ◽  
Complex Systems ◽  
Real World ◽  
Deep Neural Networks ◽  
Proof Of Concept ◽  
Recent Advances ◽  
The Way

Deep neural networks (DNNs) are revolutionizing the way complex systems are de- signed, developed and maintained. As part of the life cycle of DNN-based systems, there is often a need to modify a DNN in subtle ways that affect certain aspects of its behav- ior, while leaving other aspects of its behavior unchanged (e.g., if a bug is discovered and needs to be fixed, without altering other functionality). Unfortunately, retraining a DNN is often difficult and expensive, and may produce a new DNN that is quite different from the original. We leverage recent advances in DNN verification and propose a technique for modifying a DNN according to certain requirements, in a way that is provably minimal, does not require any retraining, and is thus less likely to affect other aspects of the DNN’s behavior. Using a proof-of-concept implementation, we demonstrate the usefulness and potential of our approach in addressing two real-world needs: (i) measuring the resilience of DNN watermarking schemes; and (ii) bug repair in already-trained DNNs.

ANALISA DAN PERANCANGAN SISTEM INFORMASI AKUNTASI UNTUK PENGELOLAAN KEUANGAN UKM

2018 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Diana Laily Fithri
Keyword(s):  
Information System ◽  
Life Cycle ◽  
Modelling Language ◽  
System Life

AbstrakSebuah Usaha Kecil Menengah (UKM) yang ada dalam masyarakat merupakan   sebuah usaha yang dirintis oleh masyarakat  dengan beberapa produk maupun usaha yang berbeda-beda. Setiap usaha tersebut memiliki manajemen keuangan yang masih dilakukan secara manual, yaitu pembukuan dan perekapan data yang masih menggunakan buku dengan cara mengumpulkan dan mengelompokkan beberapa nota. Dengan adanya permasalahan tersebut maka dibuatlah analisa dan perancangan sistem informasi akuntansi yang dapat digunakan oleh para pemilik UKM dalam mengelola keuangan serta dapat mengontrol manajemen dalam UKM tersebut.  Karena proses yang manual tersebut, banyak pemilik UKM yang kebingungan dalam mengelola keuangannya, dan tidak dapat mengetahui modal awal ketika mendirikan usaha sampai dengan usaha UKM yang semakin pesat. Oleh karena itu, dibuatlah sebuah analisa dan perancangan sistem informasi  akuntansi yang dapat digunakan oleh banyak pemilik UKM dalam mengelola keuangannya. Tujuan dari penelitian ini adalah untuk mempermudah para pemilik UKM dalam mengelola keuangannya serta dapat mengatur pengelolaan manajemen yang teratur dalam UKM tersebut. Metode pengembangan yang digunakan adalah dengan metode ISLC (Information System Life Cycle) yang nantinya dalam tahapan tersebut juga dilakukan implementasi ke beberapa UKM. Perancangan yang digunakan dengan menggunakan UML (United Modelling Language) dan Database MySQL.Kata kunci— akuntansi, keuangan, , manajemen, produk

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

Information on evolutionary age in redundancy of complex network

2019 ◽  
Vol 33 (27) ◽  
pp. 1950331
Author(s):  
Shiguo Deng ◽  
Henggang Ren ◽  
Tongfeng Weng ◽  
Changgui Gu ◽  
Huijie Yang
Keyword(s):  
Principal Component Analysis ◽  
Complex Networks ◽  
Metabolic Network ◽  
Complex Network ◽  
Cellular Networks ◽  
Topological Structure ◽  
Quantitative Measure ◽  
Principal Component ◽  
Evolutionary Information ◽  
Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

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