Ghanem, Bernard
2013-01-01
This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. © 2012 Elsevier Inc. All rights reserved.
Visual Analysis of Swarm and Geomagnetic Model Data
Santillan Pedrosa, Daniel; Triebnig, Gerhard
2016-08-01
ESA Swarm data is available for anyone to use via the virtual research platform "VirES for Swarm" (http://vires.services). A highly interactive data manipulation and retrieval interface is provided for the magnetic products of the European Space Agency (ESA) Swarm constellation mission. It includes tools for studying various Earth magnetic models and for comparing them to the Swarm satellite measurements and given solar activity levels.
POLICE OFFICE MODEL IMPROVEMENT FOR SECURITY OF SWARM ROBOTIC SYSTEMS
I. A. Zikratov; A. V. Gurtov; T. V. Zikratova; Kozlova, E. V.
2014-01-01
This paper focuses on aspects of information security for group of mobile robotic systems with swarm intellect. The ways for hidden attacks realization by the opposing party on swarm algorithm are discussed. We have fulfilled numerical modeling of potentially destructive information influence on the ant shortest path algorithm. We have demonstrated the consequences of attacks on the ant algorithm with different concentration in a swarm of subversive robots. Approaches are suggested for inform...
Characterization and Modeling of Insect Swarms Using tools from Fluid Dynamics
2016-09-01
quantitatively measuring the flight trajectories of swarming insects and to use the resulting data to evaluate currently used models of collective...for quantitatively measuring the flight trajectories of swarming insects and to use the resulting data to evaluate currently used models of...exception was the introduction of a ground-based “swarm marker” to encourage swarm nucleation and to place the swarm in a convenient location. Once swarms
The Swarm Initial Field Model for the 2014 geomagnetic field
DEFF Research Database (Denmark)
Olsen, Nils; Hulot, Gauthier; Lesur, Vincent
2015-01-01
Data from the first year of ESA's Swarm constellation mission are used to derive the Swarm Initial Field Model (SIFM), a new model of the Earth's magnetic field and its time variation. In addition to the conventional magnetic field observations provided by each of the three Swarm satellites......, explicit advantage is taken of the constellation aspect by including East-West magnetic intensity gradient information from the lower satellite pair. Along-track differences in magnetic intensity provide further information concerning the North-South gradient. The SIFM static field shows excellent...
POLICE OFFICE MODEL IMPROVEMENT FOR SECURITY OF SWARM ROBOTIC SYSTEMS
Directory of Open Access Journals (Sweden)
I. A. Zikratov
2014-09-01
Full Text Available This paper focuses on aspects of information security for group of mobile robotic systems with swarm intellect. The ways for hidden attacks realization by the opposing party on swarm algorithm are discussed. We have fulfilled numerical modeling of potentially destructive information influence on the ant shortest path algorithm. We have demonstrated the consequences of attacks on the ant algorithm with different concentration in a swarm of subversive robots. Approaches are suggested for information security mechanisms in swarm robotic systems, based on the principles of centralized security management for mobile agents. We have developed the method of forming a self-organizing information security management system for robotic agents in swarm groups implementing POM (Police Office Model – a security model based on police offices, to provide information security in multi-agent systems. The method is based on the usage of police station network in the graph nodes, which have functions of identification and authentication of agents, identifying subversive robots by both their formal characteristics and their behavior in the swarm. We have suggested a list of software and hardware components for police stations, consisting of: communication channels between the robots in police office, nodes register, a database of robotic agents, a database of encryption and decryption module. We have suggested the variants of logic for the mechanism of information security in swarm systems with different temporary diagrams of data communication between police stations. We present comparative analysis of implementation of protected swarm systems depending on the functioning logic of police offices, integrated in swarm system. It is shown that the security model saves the ability to operate in noisy environments, when the duration of the interference is comparable to the time necessary for the agent to overcome the path between police stations.
Hierarchical Swarm Model: A New Approach to Optimization
Directory of Open Access Journals (Sweden)
Hanning Chen
2010-01-01
Full Text Available This paper presents a novel optimization model called hierarchical swarm optimization (HSO, which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O, based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.
Vladimir Bukovski : Zhenitba huzhe kurenija" / Vladimir Bukovski ; interv. Andrei Titov
Bukovski, Vladimir
2008-01-01
Intervjuu nõukogudeaegse dissidendi Vladimir Bukovskiga, kes vastab küsimustele, mis puudutavad tema poliitilist tegevust, suhtumist Venemaasse, kommunistidesse, Euroopa Liitu, NATO-sse ja eraelu. Vt. samas: Vladimir Bukovski: CV
Modelling Oil-Spill Detection with Swarm Drones
Directory of Open Access Journals (Sweden)
F. Aznar
2014-01-01
Full Text Available Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as ... A multi- objective optimization of hard turning using neural network modelling and swarm intelligence ... being used in this study), and these activated values in turn become the starting signals for the next adjacent ...
Directory of Open Access Journals (Sweden)
javed akhtar
2015-12-01
Full Text Available This research paper tends to highlight Vladimir Lenin’s views and attitude towards work and indolence. Therefore, he admires work, action and revolution, which are characteristics of the proletarians, and condemns lethargy, inertia, indolence, indecision and procrastination, which are peculiar to the surf-owning and land-owning feudal nobility. Vladimir Lenin condemns Ivan Goncharov’s most famous character Oblomov frequently in his speeches and writings, which represents the surf-owning and land-owning feudal nobility of the nineteenth-century Tsarist Russian social formation. In fact, Oblomov like other literary types have definite historical roots, which are closely related to the way of life of a particular class. In this manner, his class nature or Oblomovism typifies the sloth of the serf-owning and land-owning nobility. These traits of Oblomov have not become out-dated but the class they typify has become something of the past. Vladimir Lenin pays full attention to the lasting and broad-scale implications of Oblomov’s character, which crosses the limits of his social milieu and age, picking up the penetrating insight into the class nature of Oblomov’s character. Vladimir Lenin highlights the continuing relevance of Oblomov’s character in his own times, criticising Oblomov and Oblomovism and identifies his political rivals and enemies around him with Oblomov.
[Modeling of swarm formation as a consequence of autotaxis].
Senina, I N; Tiutiunov, Iu V
2002-01-01
The simple model of school and swarm formation is proposed within the frameworks of Eulerian space models (reaction-diffusion-advection system). Assuming that the schooling and the processes of birth-and-death act on different time scales, we have excluded the local kinetics of species (the reaction term) from the model. The spatial dynamics of animals is circumscribed by scalar field of density and vector field of velocity. The basis of animal aggregation in space is the ability of animals to move in certain direction, i.e. taxis. As an example of swarming strategy the behavior of midges is taken: we presume that individuals accelerate towards higher swarm density but change direction when the density exceeded some maximum. In other words, acceleration of movement is assumed to be proportional (with density-dependent coefficient of proportionality) to the gradient of species density. This statement poses the equation for species velocity. Thus, our model adds the differential equation for velocity of autotaxis to the standard advection-diffusion model. The linear analysis of 1D problem with zero-flux boundary conditions has showed that homogeneous nonzero equilibrium looses its stability when the movement rate of animals (coefficient of proportionality in velocity equation) overpasses some bifurcation value. The numerical experiments have confirmed analytical results, displaying stationary spatially heterogeneous solution (standing waves) for the detected supercritical value of the movement rate.
Analysis of the Emergence in Swarm Model Based on Largest Lyapunov Exponent
Directory of Open Access Journals (Sweden)
Yu Wu
2011-01-01
Full Text Available Emergent behaviors of collective intelligence systems, exemplified by swarm model, have attracted broad interests in recent years. However, current research mostly stops at observational interpretations and qualitative descriptions of emergent phenomena and is essentially short of quantitative analysis and evaluation. In this paper, we conduct a quantitative study on the emergence of swarm model by using chaos analysis of complex dynamic systems. This helps to achieve a more exact understanding of emergent phenomena. In particular, we evaluate the emergent behaviors of swarm model quantitatively by using the chaos and stability analysis of swarm model based on largest Lyapunov exponent. It is concluded that swarm model is at the edge of chaos when emergence occurs, and whether chaotic or stable at the beginning, swarm model will converge to stability with the elapse of time along with interactions among agents.
Sensei Vladimir Tarassov / Vladimir Tarassov ; interv. Viktoria Korpan, Taivo Paju
Tarassov, Vladimir
2005-01-01
Venemaal hinnatud ja Tallinnas elav juhtimiskoolitaja iseloomustab oma ametit, selgitab töötamist Venemaal, tutvustab kasutusel olevat metoodikat ning annab üldisi soovitusi algajale ärimehele äris edu saavutamiseks. Vt. samas: Eestimaa mees Vladimir Tarassov kuulub Venemaa kuulsamate koolitajate hulka; Sensei Vladimir Tarassov; CV
Mining Customer Change Model Based on Swarm Intelligence
Jin, Peng; Zhu, Yunlong
Understanding and adapting to changes of customer behavior is an important aspect of surviving in a continuously changing market environment for a modern company. The concept of customer change model mining is introduced and its process is analyzed in this paper. A customer change model mining method based on swarm intelligence is presented, and the strategies of pheromone updating and items searching are given. Finally, an examination on two customer datasets of a telecom company illuminates that this method can achieve customer change model efficiently.
P-adic valued models of swarm behaviour
Schumann, Andrew
2017-07-01
The swarm behaviour can be fully determined by attractants (food pieces) which change the directions of swarm propagation. If we assume that at each time step the swarm can find out not more than p - 1 attractants, then the swarm behaviour can be coded by p-adic integers. The main task of any swarm is to logistically optimize the road system connecting the reachable attractants. In the meanwhile, the transporting network of the swarm has loops (circles) and permanently changes, e.g. the swarm occupies some attractants and leaves the others. However, this complex dynamics can be effectively coded by p-adic integers. This allows us to represent the swarm behaviour as a calculation on p-adic valued strings.
Swarm magnetic and GOCE gravity gradient grids for lithospheric modelling
DEFF Research Database (Denmark)
Bouman, Johannes; Ebbing, Jörg; Kotsiaros, Stavros
contain more signal content than global models do. The patchwork of regional grids is presented as well as the subsequent error reduction through iterative downward and upward continuation using the Poisson integral equation. The promises and pitfalls are discussed of using grids at mean satellite...... mantle in the well-surveyed North-East Atlantic Margin. In particular, we present the computation of magnetic and gravity gradient grids at satellite altitude (roughly 450 km and 250 km above the Earth for Swarm and GOCE respectively). It is shown that regional solutions based on a tesseroid approach may...
Properties of a Formal Method to Model Emergence in Swarm-Based Systems
Rouff, Christopher; Vanderbilt, Amy; Truszkowski, Walt; Rash, James; Hinchey, Mike
2004-01-01
Future space missions will require cooperation between multiple satellites and/or rovers. Developers are proposing intelligent autonomous swarms for these missions, but swarm-based systems are difficult or impossible to test with current techniques. This viewgraph presentation examines the use of formal methods in testing swarm-based systems. The potential usefulness of formal methods in modeling the ANTS asteroid encounter mission is also examined.
DualTrust: A Trust Management Model for Swarm-Based Autonomic Computing Systems
Energy Technology Data Exchange (ETDEWEB)
Maiden, Wendy M. [Washington State Univ., Pullman, WA (United States)
2010-05-01
Trust management techniques must be adapted to the unique needs of the application architectures and problem domains to which they are applied. For autonomic computing systems that utilize mobile agents and ant colony algorithms for their sensor layer, certain characteristics of the mobile agent ant swarm -- their lightweight, ephemeral nature and indirect communication -- make this adaptation especially challenging. This thesis looks at the trust issues and opportunities in swarm-based autonomic computing systems and finds that by monitoring the trustworthiness of the autonomic managers rather than the swarming sensors, the trust management problem becomes much more scalable and still serves to protect the swarm. After analyzing the applicability of trust management research as it has been applied to architectures with similar characteristics, this thesis specifies the required characteristics for trust management mechanisms used to monitor the trustworthiness of entities in a swarm-based autonomic computing system and describes a trust model that meets these requirements.
A Model of the Earth's Magnetic Field From Two Year of Swarm Satellite Constellation Data
DEFF Research Database (Denmark)
Olsen, Nils; Finlay, Chris; Tøffner-Clausen, Lars
More than two year of data from ESA's Swarm constellation mission are used to derive a model of the Earth’s magnetic field and its time variation (secular variation). The model describes contributions from the core and lithosphere as well as large-scale contributions from the magnetosphere (and its...... satellites and alongtrack first differences we include the East-west magnetic gradient information provided by the lower Swarm satellite pair, thereby explicitly taking advantage of the constellation aspect of Swarm. We assess the spatial and temporal model resolution that can be obtained from two years...
DEFF Research Database (Denmark)
Olsen, Nils; Hulot, Gauthier; Lesur, Vincent
Earth-induced counterpart). We use data from geomagnetic quiet times (Kp less than 2o, time change of Dst-index less than 2 nT/hr) and dark regions (sun below horizon) and co-estimate the Euler angles describing the rotation between the vector magnetometer instrument frame and the North-East-Center (NEC......Almost one year of data from ESA's Swarm constellation mission are used to derive a model of the Earth’s magnetic field and its time variation (secular variation). The model describes contributions from the core and lithosphere as well as large-scale contributions from the magnetosphere (and its...... and temporal model resolution that can be obtained from one year of Swarm satellite data by comparison with other recent models that also include non-Swarm magnetic observations....
... A karavan idjot / Vladimir Simonov
Simonov, Vladimir
2006-01-01
Vladimir Putini terav kriitika lääneriikide sovetoloogide aadressil, kes oma poliitiliste seisukohtadega on jäänud eelmisesse sajandisse ning vaidlustavad Venemaa õiguse osaleda G8 töös. Kremlis toimunud pressikonverentsist välisajakirjanikele
Bristle-Bots: a model system for locomotion and swarming
Giomi, Luca; Hawley-Weld, Nico; Mahadevan, L.
2012-02-01
The term swarming describes the ability of a group of similarly sized organisms to move coherently in space and time. This behavior is ubiquitous among living systems: it occurs in sub-cellular systems, bacteria, insects, fish, birds, pedestrians and in general in nearly any group of individuals endowed with the ability to move and sense. Here we address the problem of the origin of collective behavior in systems of self-propelled agents whose only social capability is given by aligning contact interactions. Our model system consists of a collection of Bristle-Bots, simple automata made from a toothbrush and the vibrating device of a cellular phone. When Bristle-Bots are confined in a limited space, increasing their number drives a transition from a disordered and uncoordinated motion to an organized collective behavior. This can occur through the formation of a swirling cluster of robots or a collective dynamical arrest, according to the type of locomotion implemented in the single devices. It is possible to move between these two major regimes by adjusting a single construction parameter.
Gravity field models derived from Swarm GPS data
Teixeira da Encarnação, João; Arnold, Daniel; Bezděk, Aleš; Dahle, Christoph; Doornbos, Eelco; van den IJssel, Jose; Jäggi, Adrian; Mayer-Gürr, Torsten; Sebera, Josef; Visser, Pieter; Zehentner, Norbert
2017-04-01
The Swarm satellites, with primary mission to measure Earth's Magnetic Field, continue to provide high-quality hl-SST data. We use these data to derive the time-varying gravity field of the Earth up to Spherical Harmonic degree and order 12, on a monthly basis since December 2013. We combine the gravity field solutions computed with the data of all three satellites, as provided by a number of institutes, namely at the Astronomical Institute (ASU) of the Czech Academy of Sciences (Bezděk et al., 2016), the Astronomical Institute of the University of Bern (AIUB, Jäggi et al., 2016) and the Institute of Geodesy (IfG) of the Graz University of Technology (Zehentner et al., 2015) and demonstrate that this uninterrupted time series of gravity field models are in good agreement with the temporal variations observed by the GRACE satellites. Therefore, these data can be used to study large-scale mass changes globally, e.g. i) in the context of low-latency applications, such as the European Gravity Service for Improved Emergency Management project (http://egsiem.eu), ii) in those months where GRACE solutions are not available, and iii) as an important source of independent information for mitigating the GRACE/GRACE Follow-On gap.
Multiscale modelling and analysis of collective decision making in swarm robotics.
Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey
2014-01-01
We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.
Multiscale modelling and analysis of collective decision making in swarm robotics.
Directory of Open Access Journals (Sweden)
Matthias Vigelius
Full Text Available We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.
3D model generation using an airborne swarm
Energy Technology Data Exchange (ETDEWEB)
Clark, R. A.; Punzo, G.; Macdonald, M. [Department of Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow, G1 1XW (United Kingdom); Dobie, G.; MacLeod, C. N.; Summan, R.; Pierce, G. [Centre for Ultrasonic Engineering, University of Strathclyde, Glasgow, G1 1XW (United Kingdom); Bolton, G. [National Nuclear Laboratory Limited, Chadwick House, Warrington Road, Birchwood Park, Warrington, WA3 6AE (United Kingdom)
2015-03-31
Using an artificial kinematic field to provide co-ordination between multiple inspection UAVs, the authors herein demonstrate full 3D modelling capability based on a photogrammetric system. The operation of the system is demonstrated by generating a full 3D surface model of an intermediate level nuclear waste storage drum. Such drums require periodic inspection to ensure that drum distortion or corrosion is carefully monitored. Performing this inspection with multiple airborne platforms enables rapid inspection of structures that are inaccessible to on-surface remote vehicles and are in human-hazardous environments. A three-dimensional surface-meshed model of the target can then be constructed in post-processing through photogrammetry analysis of the visual inspection data. The inspection environment uses a tracking system to precisely monitor the position of each aerial vehicle within the enclosure. The vehicles used are commercially available Parrot AR. Drone quadcopters, controlled through a computer interface connected over an IEEE 802.11n (WiFi) network, implementing a distributed controller for each vehicle. This enables the autonomous and distributed elements of the control scheme to be retained, while alleviating the vehicles of the control algorithm’s computational load. The control scheme relies on a kinematic field defined with the target at its centre. This field defines the trajectory for all the drones in the volume relative to the central target, enabling the drones to circle the target at a set radius while avoiding drone collisions. This function enables complete coverage along the height of the object, which is assured by transitioning to another inspection band only after completing circumferential coverage. Using a swarm of vehicles, the time until complete coverage can be significantly reduced.
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high ...
Validation of Swarm accelerometer data by modelled nongravitational forces
Czech Academy of Sciences Publication Activity Database
Bezděk, Aleš; Sebera, J.; Klokočník, Jaroslav
2017-01-01
Roč. 59, č. 10 (2017), s. 2512-2521 ISSN 0273-1177 R&D Projects: GA MŠk(CZ) LG15003 Institutional support: RVO:67985815 Keywords : space-borne accelerometers * nongravitational accelerations * swarm mission Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 1.401, year: 2016
Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation
Energy Technology Data Exchange (ETDEWEB)
Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL
2009-12-01
To better understand insurgent activities and asymmetric warfare, a social adaptive model for modeling multiple insurgent groups attacking multiple military and civilian targets is proposed and investigated. This report presents a pilot study using the particle swarm modeling, a widely used non-linear optimal tool to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamically changing environment and to provide insight and understanding of insurgency warfare. Our results show that unified leadership, strategic planning, and effective communication between insurgent groups are not the necessary requirements for insurgents to efficiently attain their objective.
Pozzobon, Victor; Perre, Patrick
2017-10-16
This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.
Towards diagnostic tools for analysing Swarm data through model retrievals
DEFF Research Database (Denmark)
Kotsiaros, Stavros; Plank, Gernot; Haagmans, R.
polar orbits between 300 and 550 km altitude. Goal of the current study is to build tools and to analyze datasets, in order to allow a fast diagnosis of the Swarm system performance in orbit during the commission phase and operations of the spacecraft. The effects on the reconstruction of the magnetic......, position errors and data gaps. The magnitude of the different error sources is kept variable so that we not only compare the impact of different error sources, but investigate also the effects on the magnetic field reconstruction for different noise levels. Further extension of this approach will allow...... to test the influence of ionospheric residual signal or the impact of data selection on the lithospheric retrieval. Initially, the study considers one satellite and emphasises on the lithospheric field reconstruction, but in a second step it is extended to a realistic Swarm constellation of three...
Industry Cluster's Adaptive Co-competition Behavior Modeling Inspired by Swarm Intelligence
Xiang, Wei; Ye, Feifan
Adaptation helps the individual enterprise to adjust its behavior to uncertainties in environment and hence determines a healthy growth of both the individuals and the whole industry cluster as well. This paper is focused on the study on co-competition adaptation behavior of industry cluster, which is inspired by swarm intelligence mechanisms. By referencing to ant cooperative transportation and ant foraging behavior and their related swarm intelligence approaches, the cooperative adaptation and competitive adaptation behavior are studied and relevant models are proposed. Those adaptive co-competition behaviors model can be integrated to the multi-agent system of industry cluster to make the industry cluster model more realistic.
Dialogi ne pod zapis / Vladimir Fridljand
Fridljand, Vladimir
2005-01-01
Baltimaade ajakirjanikud kohtusid uudisteagentuuri RIA Novosti kutsel Moskvas Venemaa asevälisministri Vladimir Tshizhovi, politoloogi Sergei Karaganovi, opositsioonipartei Rodina liidri Dmitri Rogozini jt poliitikute, politoloogide ja ajakirjanikega
Kinetic order-disorder transitions in a pause-and-go swarming model with memory.
Rimer, Oren; Ariel, Gil
2017-04-21
A two dimensional model of self-propelled particles combining both a pause-and-go movement pattern and memory is studied in simulations. It is shown, that in contrast to previously studied agent based models in two-dimensions, order and disorder are metastable states that can co-exist at some parameter range. In particular, this implies that the formation and decay of global order in swarms may be kinetic rather than a phase transition. Our results explain metastability recently observed in swarming locust and fish. Copyright © 2017 Elsevier Ltd. All rights reserved.
Numerical Simulation of a Tumor Growth Dynamics Model Using Particle Swarm Optimization.
Wang, Zhijun; Wang, Qing
Tumor cell growth models involve high-dimensional parameter spaces that require computationally tractable methods to solve. To address a proposed tumor growth dynamics mathematical model, an instance of the particle swarm optimization method was implemented to speed up the search process in the multi-dimensional parameter space to find optimal parameter values that fit experimental data from mice cancel cells. The fitness function, which measures the difference between calculated results and experimental data, was minimized in the numerical simulation process. The results and search efficiency of the particle swarm optimization method were compared to those from other evolutional methods such as genetic algorithms.
Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms
Directory of Open Access Journals (Sweden)
Li Bing
2013-01-01
Full Text Available Recently, distributed coordination control of the unmanned aerial vehicle (UAV swarms has been a particularly active topic in intelligent system field. In this paper, through understanding the emergent mechanism of the complex system, further research on the flocking and the dynamic characteristic of UAV swarms will be given. Firstly, this paper analyzes the current researches and existent problems of UAV swarms. Afterwards, by the theory of stochastic process and supplemented variables, a differential-integral model is established, converting the system model into Volterra integral equation. The existence and uniqueness of the solution of the system are discussed. Then the flocking control law is given based on artificial potential with system consensus. At last, we analyze the stability of the proposed flocking control algorithm based on the Lyapunov approach and prove that the system in a limited time can converge to the consensus direction of the velocity. Simulation results are provided to verify the conclusion.
Long-range Acoustic Interactions in Insect Swarms - An Adaptive Gravity Model
Gorbonos, Dan; Ianconescu, Reuven; Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.; Gov, Nir S.
The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. We consider mating swarms of midges, which are thought to interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges' acoustic sensing, we show that our ``adaptive gravity'' model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. Additionally, the adaptive interactions open a new class of equations of motion, which may appear in other biological contexts.
A model of Earth’s magnetic field derived from 2 years of Swarm satellite constellation data
DEFF Research Database (Denmark)
Olsen, Nils; Finlay, Chris; Kotsiaros, Stavros
2016-01-01
dependence for n=7–15, demonstrates the possibility to determine high-quality field models from only 2 years of Swarm data, thanks to the unique constellation aspect of Swarm. To account for the magnetic signature caused by ionospheric electric currents at polar latitudes we co-estimate, together......More than 2 years of magnetic field data taken by the three-satellite constellation mission Swarm are used to derive a model of Earth’s magnetic field and its time variation. This model is called SIFMplus. In addition to the magnetic field observations provided by each of the three Swarm satellites......, explicit advantage is taken of the constellation aspect of Swarm by including East–West magnetic intensity and vector field gradient information from the lower satellite pair. Along-track differences of the magnetic intensity as well as of the vector components provide further information concerning...
DEFF Research Database (Denmark)
Finlay, Chris; Olsen, Nils; Tøffner-Clausen, Lars
Ten months of data from ESA's Swarm mission, together with recent ground observatory monthly means, are used to update the CHAOS series of geomagnetic field models with a focus on time-changes of the core field. As for previous CHAOS field models quiet-time, night-side, data selection criteria......th order spline representation with knot points spaced at 0.5 year intervals. The resulting field model is able to consistently fit data from six independent low Earth orbit satellites: Oersted, CHAMP, SAC-C and the three Swarm satellites. As an example, we present comparisons of the excellent model...
Tian, Haigang; Liang, Naifeng; Zhao, Puzhi; Wang, Xuan; Zhu, Chuangwei; Zhang, Sanchun; Wang, Wei
2017-06-01
With the purpose of predicting icing of transmission lines, a model updating approach is presented in this study. The changes of structural dynamic response of transmission lines that is caused by icing is studied firstly by using finite element method. Then, model updating method and particle swarm optimization is implemented to indentify the thickness of icing according to the alternation of natural frequencies. The results show that the proposed methodology is meaningful to monitor line icing.
Stability Analysis of Flock and Mill rings for 2nd Order Models in Swarming
Albi, G.; Balagué, D.; Carrillo, J. A.; von Brecht, J.
2013-01-01
We study the linear stability of flock and mill ring solutions of two individual based models for biological swarming. The individuals interact via a nonlocal interaction potential that is repulsive in the short range and attractive in the long range. We relate the instability of the flock rings with the instability of the ring solution of the first order model. We observe that repulsive-attractive interactions lead to new configurations for the flock rings such as clustering and fattening fo...
2010-05-05
employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map, allowing pheromone-like...matter of design, DSE-R-0808 employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map
Veel kord linna sotsiaaltoetustest / Vladimir Shokman
Šokman, Vladimir, 1948-
2002-01-01
Tartu abilinnapea Vladimir Shokman viitab Tartu Postimehe eksitusele ning selgitab, et Isamaaliit esitas 2002. a. algul eelnõu ettepanekuga alustada vältimatu abi korra väljatöötamist, mitte aga sotsiaaltoetuste korda ennast
Particle Swarm Optimization (PSO) for Magnetotelluric (MT) 1D Inversion Modeling
Grandis, Hendra; Maulana, Yahya
2017-04-01
Particle Swarm Optimization (PSO) is one of nature-inspired optimization algorithms that adopts swarm (insects, school of fish, flock of birds etc.) behaviour in search for food or common target in a collaborative manner. The particles (or agents) in the swarm learn from their neighbours as well as themselves regarding the promising area in the search space. The information is then used to update their position in order to reach the target. The search algorithm of a particle is dictated by the best position of that particle during the process (individual learning term) and the best particle in its surroundings (social learning term) at a particular iteration. In terms of optimization, the particles are models defined by their parameters, while the promising area in the model space is characterized by a low misfit associated with optimum models. Being a global search approach, PSO is suitable for nonlinear inverse problem resolution. The algorithm was applied to a simple minimization problem for illustration purpose. The application of PSO in geophysical inverse problem is demonstrated by inversion of synthetic magnetotelluric (MT) data associated with simple 1D models with satisfactory results in terms of model recovery as well as data misfit.
New tools for characterizing swarming systems: A comparison of minimal models
Huepe, Cristián; Aldana, Maximino
2008-05-01
We compare three simple models that reproduce qualitatively the emergent swarming behavior of bird flocks, fish schools, and other groups of self-propelled agents by using a new set of diagnosis tools related to the agents’ spatial distribution. Two of these correspond in fact to different implementations of the same model, which had been previously confused in the literature. All models appear to undergo a very similar order-to-disorder phase transition as the noise level is increased if we only compare the standard order parameter, which measures the degree of agent alignment. When considering our novel quantities, however, their properties are clearly distinguished, unveiling previously unreported qualitative characteristics that help determine which model best captures the main features of realistic swarms. Additionally, we analyze the agent clustering in space, finding that the distribution of cluster sizes is typically exponential at high noise, and approaches a power-law as the noise level is reduced. This trend is sometimes reversed at noise levels close to the phase transition, suggesting a non-trivial critical behavior that could be verified experimentally. Finally, we study a bi-stable regime that develops under certain conditions in large systems. By computing the probability distributions of our new quantities, we distinguish the properties of each of the coexisting metastable states. Our study suggests new experimental analyses that could be carried out to characterize real biological swarms.
A Secular Variation Model for Igrf-12 Based on Swarm Data and Inverse Geodynamo Modelling
Fournier, A.; Aubert, J.; Erwan, T.
2014-12-01
We are proposing a secular variation candidate model for the 12th generation of the international geomagnetic reference field, spanning the years 2015-2020. The novelty of our approach stands in the initialization of a 5-yr long integration of a numerical model of Earth's dynamo by means of inverse geodynamo modelling, as introduced by Aubert (GJI, 2014). This inverse technique combines the information coming from the observations (in the form of an instantaneous estimate of the Gauss coefficients for the magnetic field and its secular variation) with that coming from the multivariate statistics of a free run of a numerical model of the geodynamo. The Gauss coefficients and their error covariance properties are determined from Swarm data along the lines detailed by Thébault et al. (EPS, 2010). The numerical model of the geodynamo is the so-called Coupled Earth Dynamo model (Aubert et al., Nature, 2013), whose variability possesses a strong level of similarity with that of the geomagnetic field. We illustrate and assess the potential of this methodology by applying it to recent time intervals, with an initialization based on CHAMP data, and conclude by presenting our SV candidate, whose initialization is based on the 1st year of Swarm data This work is supported by the French "Agence Nationale de la Recherche" under the grant ANR-11-BS56-011 (http://avsgeomag.ipgp.fr) and by the CNES. References: Aubert, J., Geophys. J. Int. 197, 1321-1334, 2014, doi: 10.1093/gji/ggu064 Aubert, J., Finlay, C., Fournier, F. Nature 502, 219-223, 2013, doi: 10.1038/nature12574 Thébault E. , A. Chulliat, S. Maus, G. Hulot, B. Langais, A. Chambodut and M. Menvielle, Earth Planets Space, Vol. 62 (No. 10), pp. 753-763, 2010.
Hongying Jin; Linhao Li
2013-01-01
This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow prediction process, and data collecting module can collect and return the data through the destination devi...
A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model
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Di Zhou
2016-01-01
Full Text Available Due to its fast convergence and population-based nature, particle swarm optimization (PSO has been widely applied to address the multiobjective optimization problems (MOPs. However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO, a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to address MOPs. The employment of a novel communication mechanism between particles effectively slows down the descent speed of the swarm diversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithm is highly competitive on both convergence and diversity in solving the MOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing.
2017-05-25
motion,” provides a method that combines situational awareness, elusiveness, mass, speed, mobility, and surprise to physically and cognitively overwhelm...Napoleon’s Great Army at Ulm, provide operational shock and cognitive dissonance to opposing military systems and personnel. In Swarming and the...Scythians, Alexander used similar anti-swarm methods that bottlenose dolphins use to catch swarming fish. In order to catch fish utilizing swarms
A novel artificial immune clonal selection classification and rule mining with swarm learning model
Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.
2013-06-01
Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models
Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237
Social interactions in myxobacterial swarming.
Directory of Open Access Journals (Sweden)
Yilin Wu
2007-12-01
Full Text Available Swarming, a collective motion of many thousands of cells, produces colonies that rapidly spread over surfaces. In this paper, we introduce a cell-based model to study how interactions between neighboring cells facilitate swarming. We chose to study Myxococcus xanthus, a species of myxobacteria, because it swarms rapidly and has well-defined cell-cell interactions mediated by type IV pili and by slime trails. The aim of this paper is to test whether the cell contact interactions, which are inherent in pili-based S motility and slime-based A motility, are sufficient to explain the observed expansion of wild-type swarms. The simulations yield a constant rate of swarm expansion, which has been observed experimentally. Also, the model is able to quantify the contributions of S motility and A motility to swarming. Some pathogenic bacteria spread over infected tissue by swarming. The model described here may shed some light on their colonization process.
Gravity field models derived from Swarm GPS data
de Teixeira da Encarnacao, J.; Arnold, Daniel; Bezděk, Aleš; Dahle, Christoph; Doornbos, E.N.; van den IJssel, J.A.A.; Jäggi, Adrian; Mayer-Gürr, Torsten; Sebera, Josef; Visser, P.N.A.M.; Zehentner, Norbert
2016-01-01
It is of great interest to numerous geophysical studies that the time series of global gravity field models derived from Gravity Recovery and Climate Experiment (GRACE) data remains uninterrupted after the end of this mission. With this in mind, some institutes have been spending efforts to
A multi-class classification MCLP model with particle swarm ...
Indian Academy of Sciences (India)
But in this paper we propose a Multi-Class Classification MCLP model. We use PSO for fine-tuning the parameters of MCC-MCLP. KDD CUP 99 data set is used for performance evaluation of the proposed method. Our MCC-MCLP method classifies the data better and helps in fine-tuning the parameters with the help of PSO ...
Hybrid Swarm Algorithms for Parameter Identification of an Actuator Model in an Electrical Machine
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Ying Wu
2011-01-01
Full Text Available Efficient identification and control algorithms are needed, when active vibration suppression techniques are developed for industrial machines. In the paper a new actuator for reducing rotor vibrations in electrical machines is investigated. Model-based control is needed in designing the algorithm for voltage input, and therefore proper models for the actuator must be available. In addition to the traditional prediction error method a new knowledge-based Artificial Fish-Swarm optimization algorithm (AFA with crossover, CAFAC, is proposed to identify the parameters in the new model. Then, in order to obtain a fast convergence of the algorithm in the case of a 30 kW two-pole squirrel cage induction motor, we combine the CAFAC and Particle Swarm Optimization (PSO to identify parameters of the machine to construct a linear time-invariant(LTI state-space model. Besides that, the prediction error method (PEM is also employed to identify the induction motor to produce a black box model with correspondence to input-output measurements.
A Modular Simulation Framework for Assessing Swarm Search Models
2014-09-01
of Defense DoE design of experiments GUI Graphical User Interface HVU High Value Unit MAD Magnetic Anomaly Detector MOE Measure of Effectiveness MOP...Though the U.S. and allied military forces may compensate for this by employing other sensors, such as magnetic anomaly detectors (MAD) in the...detection. We maintain the range of searchers between twenty and forty agents to provide parity between the parameters of our search models. 55 We expect that
A coordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization
DEFF Research Database (Denmark)
Xu, Lizhong; Yang, Guangya; Xu, Zhao
2013-01-01
, detailed combined heat and power (CHP) model is developed. The part load performance of CHP is modeled by curve fitting method. Furthermore, electric heater is introduced into the model to improve the economy of Microgrid operation and enhance the flexibility of the Microgrid by electricity-heat conversion......This paper develops a coordinated electricity and heat dispatching model for Microgrid under day-ahead environment. In addition to operational constraints, network loss and physical limits are addressed in this model, which are always ignored in previous work. As an important component of Microgrid....... Particle swarm optimization (PSO) is employed to solve this model for the operation schedule to minimize the total operational cost of Microgrid by coordinating the CHP, electric heater, boiler and heat storage. The efficacy of the model and methodology is verified with different operation scenarios....
Modeling and simulation of dynamic ant colony's labor division for task allocation of UAV swarm
Wu, Husheng; Li, Hao; Xiao, Renbin; Liu, Jie
2018-02-01
The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of ;problem centered + evolutionary solution; and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony's labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs' real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.
Konkin, Vladimir
2006-01-01
Venemaa näitleja, kes sai tuntuks Pavel Kortshagini rolliga filmis "Kuidas karastus teras". Endast, teistest rollidest ja Vladimir Võssotskist, kellega näitleja tutvus teleseriaali "Kohtumispaika ei tohi muuta" võtetel
Vladimir Võssotski : planeta? ulitsa? tsvetok! / Deniss Ushakov
Ushakov, Deniss
2008-01-01
Vladimir Võssotski 70. sünniaastapäeva puhul meenutatakse viise, kuidas on püütud ja püütakse jäädvustada tema mälestust (raamatud, näitused, muuseumid, ausambad, tänavad jne). Sõna saavad ka poeg Nikita Võssotski ja teine abikaasa Ljudmila Abramova
Eurooplaste uus võimalus / Vladimir Putin
Putin, Vladimir, 1952-
2006-01-01
Ilmunud ka: Postimees : na russkom jazõke, Molodjozh Estonii 22. nov. lk. 6,7, Vesti 24. nov. lk. 5. Venemaa president Vladimir Putin annab hinnanguid Venemaa ja Euroopa Liidu suhete kohta algava Venemaa-Euroopa Liidu tippkohtumise eel. President kirjutab, et Venemaa kuulub Euroopasse ning näeb Euroopa Liidus liitlast ja koostööpartnerit
Vladimir Igorevich Arnold (1937–2010)
Indian Academy of Sciences (India)
IAS Admin
V I Arnold (referred to as Vladimir Igorevich or VI in Russian) was born into a strongly academic family, and recalls being given problems to solve at home, and later in school, from the age of five, and delighting in solving them. He entered the undergraduate class at the. Department of Mathematics and Mechanics of ...
Directory of Open Access Journals (Sweden)
Fei Wang
2017-07-01
Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.
A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization
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Lizhi Cui
2014-01-01
Full Text Available This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO, for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1 the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2 the GRCM-PSO method is able to handle the real HPLC-DAD data set.
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Wei Han
2013-01-01
Full Text Available An implicit reserve constraint unit commitment (IRCUC model is presented in this paper. Different from the traditional unit commitment (UC model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical method is applied to evaluate the reliability of UC solutions and to estimate the outage loss. The stochastic failures of generating units and uncertainties of load demands are considered while assessing the reliability. The artificial fish swarm algorithm (AFSA is employed to solve this proposed model. In addition to the regular operation, a mutation operator (MO is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated from 10 to 100 units system, and the testing results are compared with those obtained by genetic algorithm (GA, particle swarm optimization (PSO, and ant colony optimization (ACO in terms of total production cost and computational time. The simulation results show that the proposed method is capable of obtaining higher quality solutions.
Kim, Seongho; Li, Lang
2014-02-01
The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
DEFF Research Database (Denmark)
Olsen, Nils; Ravat, Dhananjay; Finlay, Chris
2017-01-01
We derive a new model, named LCS-1, of Earth’s lithospheric field based on four years (Sept 2006 – Sept 2010) of magnetic observations taken by the CHAMP satellite at altitudes lower than 350 km, as well as almost three years (April 2014 to December 2016) of measurements taken by the two lower...... Swarm satellites Alpha and Charlie. The model is determined entirely from magnetic “gradient” data (approximated by finite differences): the North-South gradient is approximated by first differences of 15 second along-track data (for CHAMP and each of the two Swarm satellites), while the East...
Application of Cat Swarm Optimization in testing Static Load Models for Voltage Stability
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G. Naveen Kumar
2016-03-01
Full Text Available Power System Load Modeling is a method which is used to model the power system and essential for voltage stability studies. Voltage stability defines the ability of a power network to maintain steady state voltages at all the buses under normal operating conditions, and when subjected to a disturbance. The research presented as part of this paper, deals with analysis of different static load models for voltage stability studies. The precision of the results are directly related to the load models used in this analysis. The method is analyzed using continuation power flow routine. Flexible AC Transmission System technology with a combination of Cat Swarm Optimization Meta Heuristic Search approach is applied to give a solution for the problem of instability. The effectiveness of the proposed method is demonstrated through quantitative simulation on standard IEEE 14 bus system for contingency condition.
Hsieh, P. A.; Shelly, D. R.; Hill, D. P.
2016-12-01
Rapid migration of hypocenters during the 2014 Long Valley Caldera, California, earthquake swarm suggests that the activity was initiated and sustained by fluids, composed primarily of water and carbon dioxide, exsolved from underlying magma (Shelly et al., 2016, JGR, doi:10.1002/2015JB012719). To investigate how fluid pressure and faulting may co-evolve, we develop a simulation model that couples fluid flow with mechanical stress, while treating earthquakes with the "static/dynamic friction" approach developed by McClure and Horne (2010, Geotherm. Resour. Counc. Trans., 34, 381-396). We set up the model to represent a vertical, permeable fault (divided into a grid of elements) bounded by impermeable rock in a strike-slip tectonic environment. At the start of the simulation, a high-pressure source is introduced at depth to represent the injection of magmatic-derived fluids along the fault. The fluid flow component of the model simulates diffusion of the source pressure into the fault, and halts when the pressure increase at a fault element is sufficient to cause failure (slip). Next, the mechanical stress component of the model calculates the stress changes that result from the slip. Because these stress changes could induce surrounding elements to slip, the model iterates through this chain-reaction process until a stable configuration is achieved (i.e., slipped elements do not induce further slips).The patch of slipped elements represents an earthquake in the model. The permeability of the patch is increased to represent the effect of shear displacement, and the pressure diffusion simulation resumes. Although this model implements a simplified representation of the earthquake mechanism, it is able to mimic (using reasonable rock properties) the overall character of a local swarm of approximately 1000 earthquakes (-0.9 < M < 2.8) that occurred during 7 July 2014 in Long Valley Caldera.
Particle Swarm Optimization for inverse modeling of solute transport in fractured gneiss aquifer.
Abdelaziz, Ramadan; Zambrano-Bigiarini, Mauricio
2014-08-01
Particle Swarm Optimization (PSO) has received considerable attention as a global optimization technique from scientists of different disciplines around the world. In this article, we illustrate how to use PSO for inverse modeling of a coupled flow and transport groundwater model (MODFLOW2005-MT3DMS) in a fractured gneiss aquifer. In particular, the hydroPSO R package is used as optimization engine, because it has been specifically designed to calibrate environmental, hydrological and hydrogeological models. In addition, hydroPSO implements the latest Standard Particle Swarm Optimization algorithm (SPSO-2011), with an adaptive random topology and rotational invariance constituting the main advancements over previous PSO versions. A tracer test conducted in the experimental field at TU Bergakademie Freiberg (Germany) is used as case study. A double-porosity approach is used to simulate the solute transport in the fractured Gneiss aquifer. Tracer concentrations obtained with hydroPSO were in good agreement with its corresponding observations, as measured by a high value of the coefficient of determination and a low sum of squared residuals. Several graphical outputs automatically generated by hydroPSO provided useful insights to assess the quality of the calibration results. It was found that hydroPSO required a small number of model runs to reach the region of the global optimum, and it proved to be both an effective and efficient optimization technique to calibrate the movement of solute transport over time in a fractured aquifer. In addition, the parallel feature of hydroPSO allowed to reduce the total computation time used in the inverse modeling process up to an eighth of the total time required without using that feature. This work provides a first attempt to demonstrate the capability and versatility of hydroPSO to work as an optimizer of a coupled flow and transport model for contaminant migration. Copyright © 2014 Elsevier B.V. All rights reserved.
Long, Zhili; Wang, Rui; Fang, Jiwen; Dai, Xufei; Li, Zuohua
2017-07-01
Piezoelectric actuators invariably exhibit hysteresis nonlinearities that tend to become significant under the open-loop condition and could cause oscillations and errors in nanometer-positioning tasks. Chaotic map modified particle swarm optimization (MPSO) is proposed and implemented to identify the Prandtl-Ishlinskii model for piezoelectric actuators. Hysteresis compensation is attained through application of an inverse Prandtl-Ishlinskii model, in which the parameters are formulated based on the original model with chaotic map MPSO. To strengthen the diversity and improve the searching ergodicity of the swarm, an initial method of adaptive inertia weight based on a chaotic map is proposed. To compare and prove that the swarm's convergence occurs before stochastic initialization and to attain an optimal particle swarm optimization algorithm, the parameters of a proportional-integral-derivative controller are searched using self-tuning, and the simulated results are used to verify the search effectiveness of chaotic map MPSO. The results show that chaotic map MPSO is superior to its competitors for identifying the Prandtl-Ishlinskii model and that the inverse Prandtl-Ishlinskii model can provide hysteresis compensation under different conditions in a simple and effective manner.
Long, Zhili; Wang, Rui; Fang, Jiwen; Dai, Xufei; Li, Zuohua
2017-07-01
Piezoelectric actuators invariably exhibit hysteresis nonlinearities that tend to become significant under the open-loop condition and could cause oscillations and errors in nanometer-positioning tasks. Chaotic map modified particle swarm optimization (MPSO) is proposed and implemented to identify the Prandtl-Ishlinskii model for piezoelectric actuators. Hysteresis compensation is attained through application of an inverse Prandtl-Ishlinskii model, in which the parameters are formulated based on the original model with chaotic map MPSO. To strengthen the diversity and improve the searching ergodicity of the swarm, an initial method of adaptive inertia weight based on a chaotic map is proposed. To compare and prove that the swarm's convergence occurs before stochastic initialization and to attain an optimal particle swarm optimization algorithm, the parameters of a proportional-integral-derivative controller are searched using self-tuning, and the simulated results are used to verify the search effectiveness of chaotic map MPSO. The results show that chaotic map MPSO is superior to its competitors for identifying the Prandtl-Ishlinskii model and that the inverse Prandtl-Ishlinskii model can provide hysteresis compensation under different conditions in a simple and effective manner.
Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids
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Alma Y. Alanis
2013-01-01
Full Text Available This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF improved using particle swarm optimization (PSO to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.
Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L
2017-02-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.
NATURAL-FOCAL DISEASES IN THE VLADIMIR REGION (RUSSIA
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Tatiana A. Trifonova
2015-01-01
Full Text Available The paper describes a study that monitored the epidemiological situation of a complex of natural-focal diseases in the Vladimir region (Russia, from 1958 to 2012. The morbidity rates of these natural-focal diseases have been differentiated by territory using ArcView 3.1 (GIS software. The activity of natural foci for each zooanthroponosis varied between administrative districts in the region. A schematic map has been compiled; the map reflectsthe danger of infection caused by natural-focal diseases in the Vladimir region. The paperdiscusses the role of the anthropogenic factor in natural-ecosystem development: it likelypromotes the transit and localization rates of carriers. Correlation and regression analysis ofthe data showed that climatic factors such as the average temperatures in July and September in the preceding year influence Lyme disease (Lyme borreliosis patterns. This is likely related to particular stages in the life cycle of Ixodidae ticks. Using multiple linear regression analysis, a mathematical model for the prediction of Lyme borreliosis patterns has been created.
Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization
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S. Sakinah S. Ahmad
2012-01-01
Full Text Available The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space, a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO as an optimization vehicle of forming a subset of features and data (instances to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances, we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.
Pusateri, Elise Noel
abruptly. The objective of the PhD research is to mitigate this effect by integrating a conduction electron model into CHAP-LA which can calculate the conduction current based on a non-equilibrium electron distribution. We propose to use an electron swarm model to monitor the time evolution of conduction electrons in the EMP environment which is characterized by electric field and pressure. Swarm theory uses various collision frequencies and reaction rates to study how the electron distribution and the resultant transport coefficients change with time, ultimately reaching an equilibrium distribution. Validation of the swarm model we develop is a necessary step for completion of the thesis work. After validation, the swarm model is integrated in the air chemistry model CHAP-LA employs for conduction electron simulations. We test high altitude EMP simulations with the swarm model option in the air chemistry model to show improvements in the computational capability of CHAP-LA. A swarm model has been developed that is based on a previous swarm model developed by Higgins, Longmire and O'Dell 1973, hereinafter HLO. The code used for the swarm model calculation solves a system of coupled differential equations for electric field, electron temperature, electron number density, and drift velocity. Important swarm parameters, including the momentum transfer collision frequency, energy transfer collision frequency, and ionization rate, are recalculated and compared to the previously reported empirical results given by HLO. These swarm parameters are found using BOLSIG+, a two term Boltzmann solver developed by Hagelaar and Pitchford 2005. BOLSIG+ utilizes updated electron scattering cross sections that are defined over an expanded energy range found in the atomic and molecular cross section database published by Phelps in the Phelps Database 2014 on the LXcat website created by Pancheshnyi et al. 2012. The swarm model is also updated from the original HLO model by including
Vladimir Panov : obvinenija nado dokazõvat / Vladimir Panov ; interv. Jana Toom
Panov, Vladimir, 1941-
2008-01-01
Pistisevõtmises süüdistatud Tallinna endine abilinnapea Vladimir Panov vastab küsimustele, mis puudutavad viieaastast kohtuprotsessi ning Äripäeva ja meedia suhtumist talle esitatud süüdistusse. Kommenteerivad: Tarmu Tammerk, Andrus Saar, Edgar Savisaar
Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction
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Jianzhou Wang
2014-01-01
Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.
Life and death of Vladimir Mikhailovich Bekhterev
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Péricles Maranhão Filho
2015-01-01
Full Text Available Vladimir Mikhailovich Bekhterev was a Russian innovative neuroscientist, extraordinary in the study, diagnosis, and research in the fields of neurology, psychology, morphology, physiology, and psychiatry. Considering the ample and multifaceted scientific feats, only some are touched in a very brief manner. However, it is necessary to highlight his contributions to neurology, with the description of structures, signs and syndromes, to physiology, including reflexology, which later underpinned behaviorism, to psychology, including objective psychology and suggestion. His accomplishments and legacy remained until the present days. Some comments about the scenery that involved his death are also presented.
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Michala Jakubcová
2015-01-01
Full Text Available The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.
An improved swarm optimization for parameter estimation and biological model selection.
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Afnizanfaizal Abdullah
Full Text Available One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete
An improved swarm optimization for parameter estimation and biological model selection.
Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail
2013-01-01
One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This
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Allison Kolpas
Full Text Available Schools of fish and flocks of birds are examples of self-organized animal groups that arise through social interactions among individuals. We numerically study two individual-based models, which recent empirical studies have suggested to explain self-organized group animal behavior: (i a zone-based model where the group communication topology is determined by finite interacting zones of repulsion, attraction, and orientation among individuals; and (ii a model where the communication topology is described by Delaunay triangulation, which is defined by each individual's Voronoi neighbors. The models include a tunable parameter that controls an individual's relative weighting of attraction and alignment. We perform computational experiments to investigate how effectively simulated groups transfer information in the form of velocity when an individual is perturbed. A cross-correlation function is used to measure the sensitivity of groups to sudden perturbations in the heading of individual members. The results show how relative weighting of attraction and alignment, location of the perturbed individual, population size, and the communication topology affect group structure and response to perturbation. We find that in the Delaunay-based model an individual who is perturbed is capable of triggering a cascade of responses, ultimately leading to the group changing direction. This phenomenon has been seen in self-organized animal groups in both experiments and nature.
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Hediyeh Karimi
2013-01-01
Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.
Du, Jinsong; Chen, Chao; Xiong, Xiong; Li, Yongdong; Liang, Qing
2016-04-01
Recently, because of continually accumulated magnetic measurements by CHAMP satellite and Swarm constellation of three satellites and well developed methodologies and techniques of data processing and geomagnetic field modeling etc., global lithospheric magnetic anomaly field models become more and more reliable. This makes the quantitative interpretation of lithospheric magnetic anomaly field possible for having an insight into large-scale magnetic structures in the crust and uppermost mantle. Many different approaches have been utilized to understand the magnetized sources, such as forward, inversion, statistics, correlation analysis, Euler deconvolution, signal transformations etc. Among all quantitative interpretation methods, the directly converting a magnetic anomaly map into a magnetic susceptibility anomaly map proposed by Arkani-Hamed & Strangway (1985) is, we think, the most fast quantitative interpretation tool for global studies. We just call this method AS85 hereinafter for short. Although Gubbins et al. (2011) provided a formula to directly calculate the apparent magnetic vector distribution, the AS85 method introduced constraints of magnetized direction and thus corresponding results are expected to be more robust especially in world-wide continents. Therefore, in this study, we first improved the AS85 method further considering non-axial dipolar inducing field using formulae by Nolte & Siebert (1987), initial model or priori information for starting coefficients in the apparent susceptibility conversion, hidden longest-wavelength components of lithospheric magnetic field and field contaminations from global oceanic remanent magnetization. Then, we used the vertically integrated susceptibility model by Hemant & Maus (2005) and vertically integrated remanent magnetization model by Masterton et al. (2013) to test the validity of our improved method. Subsequently, we applied the conversion method to geomagnetic field models by CHAMP and Swarm satellite
Pei, Zongrui; Eisenbach, Markus
2017-06-01
Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), the local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.
Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar
2017-08-01
Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.
Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model
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Lokesh Selvaraj
2014-01-01
Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.
Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.
Selvaraj, Lokesh; Ganesan, Balakrishnan
2014-01-01
Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.
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Shidrokh Goudarzi
2015-01-01
Full Text Available The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2 and mean square error (MSE based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.
SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan
2017-01-01
In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning. PMID:28287468
SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
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Xin Li
2017-03-01
Full Text Available In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.
SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots.
Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan
2017-03-11
In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.
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Fereydoun Naghibi
2016-12-01
Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was
Vladimir Putin : las koerad hauguvad, karavan liigub edasi / Kaivo Kopli
Kopli, Kaivo
2006-01-01
Venemaa presidendi Vladimir Putini nn. aasta pressikonverentsist, milles ta keskendust peamiselt majandusteemadele, kuid puudutas ka Venemaa osalemist G-8-s, rahutusi Usbekistanis jm. Lisa: Putin pole ärimees ja ärisse ei sukeldu
Rossija i Jevrosojuz nuzhnõ drug drugu / Vladimir Simonov
Simonov, Vladimir
2005-01-01
Venemaa esindajaks Euroopa Liidu juurde määrati edukas karjääridiplomaat Vladimir Tshizhov, esmasteks ülesanneteks viisarežiimi lihtsustamine venemaalastele, energeetilise dialoogi elavdamine, uue partnerlusleppe sõlmimine
Generalnaja uborka / Nina Kotel, Vladimir Salnikov ; interv. Nikolai Hrustaljov
Kotel, Nina
2005-01-01
Tallinnas Vene saatkonna galeriis oma töid näitavate Moskva kunstnike Nina Koteli ja Vladimir Salnikoviga nende loomingust. Salnikov eksponeerib naise vasakut kõrva kujutavaid akvarellmaale, Kotel massikultuuri vastast installatsiooni "Uborka"
Russia's foreign policy toward the Caucasus under Vladimir Putin
Farajirad, Abdolreza; Khezerzade, Asaad
2010-01-01
This paper seeks to examine Russia's foreign policy toward the Caucasus during Vladimir Putin's presidency. Moreover, the period between 2000 and 2008 was one of the most important eras for Russian-Caucasian relations under Vladimir Putin in Russia, since his presidency brought about significant changes in Russia's foreign policy. However, this study will not analyze pre-2000; instead, it will concentrate on certain facts and events that are important for understanding the period between 2000...
Daljokaja, kak otrazhenije... / Vladimir Fomitshev ; interv. Galina Balashova
Fomitshev, Vladimir
2002-01-01
Peterburi kunstnike Natalja Melnikova ja Vladimir Fomitshevi (rühmitus CoMELFO) meresõitjale F. Bellingshausenile pühendatud graafikanäituse avamisest Eesti Rahvusraamatukogus. Intervjuu Vladimir Fomitsheviga CoMELFO tööde Bellingshausenile pühendatud näitusest Arktikas 01.01. 2000 ja sellest, kuidas valmib nende maaliline graafika - nn. monotüüpiline joonistus
Morcuende, Jose A.; Stevens, Jeff W.; Scheetz, Todd E.; de Fatima Bonaldoc, Maria; Casavant, Thomas L.; Otero, Jesse E.; Soares, Marcelo B.
2012-01-01
Swarm rat chondrosarcoma cells have been used extensively for biochemical studies of extra-cellular matrix metabolism in cartilage. However, these cells also possess tumor-like behavior in vivo and are useful in investigation of chondrosarcoma biology. the current study was designed to develop a metastatic model using swarm rat chondrosarcoma cells, and to assess the effect of tissue-environment on tumor behavior in vivo. Tumors were implanted subcutaneously or into bone, and animals were assessed radiographically and microscopically for tumor growth and metastasis. The subcutaneous tumor grew to an average mass of 35 g, while tumor implanted into bone grew 75 mg. Transplantation of the cells into the bone led to extensive bone remodeling with invasion of the medullary cavity and destruction of the bone cortex. Light microscopy demonstrated no significant differences in the number of mitoses, cellular atypia or extracellular matrix staining between the two sites of tumor implantation. Interestingly, lung colonization was observed in none of the animals in the subcutaneous tumor injection group, while tumors colonized the lungs in 95% of the rats with tumor injected into bone. Analysis of cDNA libraries from subcutaneous and bone-transplanted tumors demonstrated a complex and diverse array of expressed transcripts, and there were significant differences in gene expression between tumors at different sites. The results of this study suggest swarm rat chondrosarcoma is a model that resembles human chondrosarcoma mimicking its ability to infiltrate and remodel local bone and to colonize the lungs. Furthermore, the interaction between host-tissue and tumor cells plays a major role in the tumor behavior in this model. Identifying these interactions will lead to further understanding of chondrosarcoma and contribute to therapeutic targets in the future. PMID:23576921
Pusateri, E. N.; Morris, H. E.; Nelson, E.; Ji, W.
2015-12-01
Electromagnetic pulse (EMP) events in the atmosphere are important physical phenomena that occur through both man-made and natural processes, such as lightning, and can be disruptive to surrounding electrical systems. Due to the disruptive nature of EMP, it is important to accurately predict EMP evolution and propagation with computational models. In EMP, low-energy conduction electrons are produced from Compton electron or photoelectron ionizations with air. These conduction electrons continue to interact with the surrounding air and alter the EMP waveform. Many EMP simulation codes use an equilibrium ohmic model for computing the conduction current. The equilibrium model works well when the equilibration time is short compared to the rise time or duration of the EMP. However, at high altitude, the conduction electron equilibration time can be comparable to or longer than the rise time or duration of the EMP. This matters, for example, when calculating the EMP propagating upward toward a satellite. In these scenarios, the equilibrium ionization rate becomes very large for even a modest electric field. The ohmic model produces an unphysically large number of conduction electrons that prematurely and abruptly short the EMP in the simulation code. An electron swarm model, which simulates the time evolution of conduction electrons, can be used to overcome the limitations exhibited by the equilibrium ohmic model. We have developed and validated an electron swarm model in an environment characterized by electric field and pressure previously in Pusateri et al. (2015). This swarm model has been integrated into CHAP-LA, a state-of-the-art EMP code developed by researchers at Los Alamos National Laboratory, which previously calculated conduction current using an ohmic model. We demonstrate the EMP damping behavior caused by the ohmic model at high altitudes and show improvements on high altitude EMP modeling obtained by employing the swarm model.
Time Optimal Reachability Analysis Using Swarm Verification
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
and planning problems, response time optimization etc. We propose swarm verification to accelerate time optimal reachability using the real-time model-checker Uppaal. In swarm verification, a large number of model checker instances execute in parallel on a computer cluster using different, typically randomized...... search strategies. We develop four swarm algorithms and evaluate them with four models in terms scalability, and time- and memory consumption. Three of these cooperate by exchanging costs of intermediate solutions to prune the search using a branch-and-bound approach. Our results show that swarm...
Towards CHAOS-5 - How can Swarm contribute?
DEFF Research Database (Denmark)
Finlay, Chris; Olsen, Nils; Tøffner-Clausen, Lars
2014-01-01
The launch of ESA's satellite trio Swarm in November 2013 opens an exciting new chapter in the observation and monitoring of Earth's magnetic field from space. We report preliminary results from an extension of the CHAOS series of geomagnetic field models to include both scalar and vector field...... observations from the three Swarm satellites, along with the most recent quasi-definitive ground observatory data. The fit of this new update CHAOS field model to the Swarm observations will be presented in detail providing useful insight the initial Swarm data. Enhancements of the CHAOS modelling scheme...
Swarming behavior in plant roots.
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Marzena Ciszak
Full Text Available Interactions between individuals that are guided by simple rules can generate swarming behavior. Swarming behavior has been observed in many groups of organisms, including humans, and recent research has revealed that plants also demonstrate social behavior based on mutual interaction with other individuals. However, this behavior has not previously been analyzed in the context of swarming. Here, we show that roots can be influenced by their neighbors to induce a tendency to align the directions of their growth. In the apparently noisy patterns formed by growing roots, episodic alignments are observed as the roots grow close to each other. These events are incompatible with the statistics of purely random growth. We present experimental results and a theoretical model that describes the growth of maize roots in terms of swarming.
Transport of Particle Swarms Through Fractures
Boomsma, E.; Pyrak-Nolte, L. J.
2011-12-01
The transport of engineered micro- and nano-scale particles through fractured rock is often assumed to occur as dispersions or emulsions. Another potential transport mechanism is the release of particle swarms from natural or industrial processes where small liquid drops, containing thousands to millions of colloidal-size particles, are released over time from seepage or leaks. Swarms have higher velocities than any individual colloid because the interactions among the particles maintain the cohesiveness of the swarm as it falls under gravity. Thus particle swarms give rise to the possibility that engineered particles may be transported farther and faster in fractures than predicted by traditional dispersion models. In this study, the effect of fractures on colloidal swarm cohesiveness and evolution was studied as a swarm falls under gravity and interacts with fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with either (1) a uniform aperture or (2) a converging aperture followed by a uniform aperture (funnel-shaped). The samples consisted of two blocks that measured 100 x 100 x 50 mm. The separation between these blocks determined the aperture (0.5 mm to 50 mm). During experiments, a fracture was fully submerged in water and swarms were released into it. The swarms consisted of dilute suspensions of either 25 micron soda-lime glass beads (2% by mass) or 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. In the uniform aperture fracture, the speed of the swarm prior to bifurcation increased with aperture up to a maximum at a fracture width of approximately 10 mm. For apertures greater than ~15 mm, the velocity was essentially constant with fracture width (but less than at 10 mm). This peak suggests that two competing mechanisms affect swarm velocity in fractures. The wall provides both drag, which
Molina, Mario Martínez; Moreno-Armendáriz, Marco A; Carlos Seck Tuoh Mora, Juan
2013-11-07
A two-dimensional lattice model based on Cellular Automata theory and swarm intelligence is used to study the spatial and population dynamics of a theoretical ecosystem. It is found that the social interactions among predators provoke the formation of clusters, and that by increasing the mobility of predators the model enters into an oscillatory behavior. © 2013 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Jie-sheng Wang
2014-01-01
Full Text Available For predicting the key technology index of electroslag remelting (ESR process (the melting rate and cone purification coefficient of the consumable electrode, a radial basis function (RBF neural network soft-sensor model optimized by the artificial fish swarm algorithm (AFSA is proposed. Based on the technique characteristics of ESR production process, the auxiliary variables of soft-sensor model are selected. Then the AFSA is adopted to train the RBF neural network prediction model in order to realize the nonlinear mapping between input and output variables. Simulation results show that the model has better generalization and prediction accuracy, which can meet the online soft sensing requirement of ESR process real-time control.
Olsen, Nils; Ravat, Dhananjay; Finlay, Christopher C.; Kother, Livia K.
2017-12-01
We derive a new model, named LCS-1, of Earth's lithospheric field based on four years (2006 September-2010 September) of magnetic observations taken by the CHAMP satellite at altitudes lower than 350 km, as well as almost three years (2014 April-2016 December) of measurements taken by the two lower Swarm satellites Alpha and Charlie. The model is determined entirely from magnetic 'gradient' data (approximated by finite differences): the north-south gradient is approximated by first differences of 15 s along-track data (for CHAMP and each of the two Swarm satellites), while the east-west gradient is approximated by the difference between observations taken by Swarm Alpha and Charlie. In total, we used 6.2 mio data points. The model is parametrized by 35 000 equivalent point sources located on an almost equal-area grid at a depth of 100 km below the surface (WGS84 ellipsoid). The amplitudes of these point sources are determined by minimizing the misfit to the magnetic satellite 'gradient' data together with the global average of |Br| at the ellipsoid surface (i.e. applying an L1 model regularization of Br). In a final step, we transform the point-source representation to a spherical harmonic expansion. The model shows very good agreement with previous satellite-derived lithospheric field models at low degree (degree correlation above 0.8 for degrees n ≤ 133). Comparison with independent near-surface aeromagnetic data from Australia yields good agreement (coherence >0.55) at horizontal wavelengths down to at least 250 km, corresponding to spherical harmonic degree n ≈ 160. The LCS-1 vertical component and field intensity anomaly maps at Earth's surface show similar features to those exhibited by the WDMAM2 and EMM2015 lithospheric field models truncated at degree 185 in regions where they include near-surface data and provide unprecedented detail where they do not. Example regions of improvement include the Bangui anomaly region in central Africa, the west African
DEFF Research Database (Denmark)
Sabaka, Terence J.; Olsen, Nils; Tyler, Robert H.
2015-01-01
A comprehensive magnetic field model named CM5 has been derived from CHAMP, Orsted and SAC-C satellite and observatory hourly-means data from 2000 August to 2013 January using the Swarm Level-2 Comprehensive Inversion (CI) algorithm. Swarm is a recently launched constellation of three satellites...... induction. SIVW has allowed for a much improved lithospheric field recovery over CM4 by exploiting CHAMP along-track difference data yielding resolution levels up to spherical harmonic degree 107, and has allowed for the successful extraction of the oceanic M2 tidal magnetic field from quiet, nightside data...
Handayani, D.; Nuraini, N.; Tse, O.; Saragih, R.; Naiborhu, J.
2016-04-01
PSO is a computational optimization method motivated by the social behavior of organisms like bird flocking, fish schooling and human social relations. PSO is one of the most important swarm intelligence algorithms. In this study, we analyze the convergence of PSO when it is applied to with-in host dengue infection treatment model simulation in our early research. We used PSO method to construct the initial adjoin equation and to solve a control problem. Its properties of control input on the continuity of objective function and ability of adapting to the dynamic environment made us have to analyze the convergence of PSO. With the convergence analysis of PSO we will have some parameters that ensure the convergence result of numerical simulations on this model using PSO.
Integrative modeling and novel particle swarm-based optimal design of wind farms
Chowdhury, Souma
To meet the energy needs of the future, while seeking to decrease our carbon footprint, a greater penetration of sustainable energy resources such as wind energy is necessary. However, a consistent growth of wind energy (especially in the wake of unfortunate policy changes and reported under-performance of existing projects) calls for a paradigm shift in wind power generation technologies. This dissertation develops a comprehensive methodology to explore, analyze and define the interactions between the key elements of wind farm development, and establish the foundation for designing high-performing wind farms. The primary contribution of this research is the effective quantification of the complex combined influence of wind turbine features, turbine placement, farm-land configuration, nameplate capacity, and wind resource variations on the energy output of the wind farm. A new Particle Swarm Optimization (PSO) algorithm, uniquely capable of preserving population diversity while addressing discrete variables, is also developed to provide powerful solutions towards optimizing wind farm configurations. In conventional wind farm design, the major elements that influence the farm performance are often addressed individually. The failure to fully capture the critical interactions among these factors introduces important inaccuracies in the projected farm performance and leads to suboptimal wind farm planning. In this dissertation, we develop the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology to model and optimize the performance of wind farms. The UWFLO method obviates traditional assumptions regarding (i) turbine placement, (ii) turbine-wind flow interactions, (iii) variation of wind conditions, and (iv) types of turbines (single/multiple) to be installed. The allowance of multiple turbines, which demands complex modeling, is rare in the existing literature. The UWFLO method also significantly advances the state of the art in wind farm optimization by
Swarm Products and Space Weather Applications
DEFF Research Database (Denmark)
Stolle, Claudia; Olsen, Nils; Martini, Daniel
The Swarm satellite constellation mission provides high precision magnetic field data and models and other observations that enable us to explore near Earth space for example in terms of in situ electron density and electric fields. On board GPS observables can be used for sounding ionospheric an...... in aeronomy and space weather. We will emphasize results from the Swarm mission....
CM5: A pre-Swarm magnetic field model based upon the comprehensive modeling approach
DEFF Research Database (Denmark)
Sabaka, T.; Olsen, Nils; Tyler, Robert
2014-01-01
We have developed a model based upon the very successful Comprehensive Modeling (CM) approach using recent CHAMP, Ørsted, SAC-C and observatory hourly-means data from September 2000 to the end of 2013. This CM, called CM5, was derived from the algorithm that will provide a consistent line of Leve...
Dynamic scaling in natural swarms
Cavagna, Andrea; Conti, Daniele; Creato, Chiara; Del Castello, Lorenzo; Giardina, Irene; Grigera, Tomas S.; Melillo, Stefania; Parisi, Leonardo; Viale, Massimiliano
2017-09-01
Collective behaviour in biological systems presents theoretical challenges beyond the borders of classical statistical physics. The lack of concepts such as scaling and renormalization is particularly problematic, as it forces us to negotiate details whose relevance is often hard to assess. In an attempt to improve this situation, we present here experimental evidence of the emergence of dynamic scaling laws in natural swarms of midges. We find that spatio-temporal correlation functions in different swarms can be rescaled by using a single characteristic time, which grows with the correlation length with a dynamical critical exponent z ~ 1, a value not found in any other standard statistical model. To check whether out-of-equilibrium effects may be responsible for this anomalous exponent, we run simulations of the simplest model of self-propelled particles and find z ~ 2, suggesting that natural swarms belong to a novel dynamic universality class. This conclusion is strengthened by experimental evidence of the presence of non-dissipative modes in the relaxation, indicating that previously overlooked inertial effects are needed to describe swarm dynamics. The absence of a purely dissipative regime suggests that natural swarms undergo a near-critical censorship of hydrodynamics.
Buyukada, Musa
2016-09-01
Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R(2) of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305°C, and heating rate of 49°Cmin(-1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5%±0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Ahmed R. Abas
2012-07-01
Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.
Rasmussen, Thomas Kiel; Krink, Thiemo
2003-11-01
Multiple sequence alignment (MSA) is one of the basic problems in computational biology. Realistic problem instances of MSA are computationally intractable for exact algorithms. One way to tackle MSA is to use Hidden Markov Models (HMMs), which are known to be very powerful in the related problem domain of speech recognition. However, the training of HMMs is computationally hard and there is no known exact method that can guarantee optimal training within reasonable computing time. Perhaps the most powerful training method is the Baum-Welch algorithm, which is fast, but bears the problem of stagnation at local optima. In the study reported in this paper, we used a hybrid algorithm combining particle swarm optimization with evolutionary algorithms to train HMMs for the alignment of protein sequences. Our experiments show that our approach yields better alignments for a set of benchmark protein sequences than the most commonly applied HMM training methods, such as Baum-Welch and Simulated Annealing.
Kamaruddin, Saadi Bin Ahmad; Marponga Tolos, Siti; Hee, Pah Chin; Ghani, Nor Azura Md; Ramli, Norazan Mohamed; Nasir, Noorhamizah Binti Mohamed; Ksm Kader, Babul Salam Bin; Saiful Huq, Mohammad
2017-03-01
Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.
Berezovski : Vladimir Putin tellis Litvinenko tapmise / Aadu Hiietamm
Hiietamm, Aadu, 1954-
2007-01-01
Seitse viimast aastat Londonis elanud Vene miljardär Boriss Berezovski ütles Prantsuse päevalehele Le Figaro antud intervjuus, et Aleksander Litvinenko mõrva tellis Venemaa president Vladimir Putin ja selle organiseeris Föderaalne Julgeolekuteenistus (FSB)
Vladimir Putini kaks nägu / Nina L. Hrushtshova
Hrushtshova, Nina L.
2005-01-01
Hoolimata sellest, et president Vladimir Putin püüab jätta endast muljet kui Venemaa moderniseerijast, usub ta sarnaselt eelkäijatele, et ainult autoritaarne valitsemine suudab riigi päästa lagunemisest ja anarhiast, leiab autor. Lisa: Nina L. Hrushtshova
Epidemiology of Primary Multidrug-Resistant Tuberculosis, Vladimir Region, Russia
Ershova, Julia V.; Volchenkov, Grigory V.; Kaminski, Dorothy A.; Somova, Tatiana R.; Kuznetsova, Tatiana A.; Kaunetis, Natalia V.; Cegielski, J. Peter; Kurbatova, Ekaterina V.
2015-01-01
We studied the epidemiology of drug-resistant tuberculosis (TB) in Vladimir Region, Russia, in 2012. Most cases of multidrug-resistant TB (MDR TB) were caused by transmission of drug-resistant strains, and >33% were in patients referred for testing after mass radiographic screening. Early diagnosis of drug resistance is essential for preventing transmission of MDR TB.
Pesok v rukomoinike / Vladimir Anshon, Nikolai Hrustaljov ; interv. Jevgeni Frolov
Anšon, Vladimir, 1963-
1999-01-01
Peterburi rahvusvahelisel mononäidendite festivalil "Monokkel 1999" käis ka Vene Draamateatri trupp lavastusega "Daam koerakesega" A. Tshehhovi jutustuse järgi. Etenduse tõid lavale lavastaja Eduard Toman, kunstnik Vladimir Anshon ja osatäitja Nikolai Hrustaljov. Lavastus ja kunstnikutöö märgiti ära eripreemiaga
Zov zhuravlinoi gorõ / Vladimir Anshon ; interv. Galina Balashova
Anšon, Vladimir, 1963-
2005-01-01
Eestimaa vene kunstnike ühendus valmistab Vladimir Anshoni initsiatiivil ette heategevuslikku kunstinäitust Vasknarva kiriku taastamise toetuseks. Näitus avatakse 11. mail Tallinna galeriis Atrium. Intervjuu V. Anshoniga Pühtitsa kloostrist ja sellega seotud Vasknarva kirikust
Empirical inference festschrift in honor of Vladimir N. Vapnik
Schölkopf, Bernhard; Vovk, Vladimir
2013-01-01
This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever.
Shabri, Ani; Samsudin, Ruhaidah
2014-01-01
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
Energy Technology Data Exchange (ETDEWEB)
Cappa, F.; Rutqvist, J.; Yamamoto, K.
2009-05-15
In Matsushiro, central Japan, a series of more than 700,000 earthquakes occurred over a 2-year period (1965-1967) associated with a strike-slip faulting sequence. This swarm of earthquakes resulted in ground surface deformations, cracking of the topsoil, and enhanced spring-outflows with changes in chemical compositions as well as carbon dioxide (CO{sub 2}) degassing. Previous investigations of the Matsushiro earthquake swarm have suggested that migration of underground water and/or magma may have had a strong influence on the swarm activity. In this study, employing coupled multiphase flow and geomechanical modelling, we show that observed crustal deformations and seismicity can have been driven by upwelling of deep CO{sub 2}-rich fluids around the intersection of two fault zones - the regional East Nagano earthquake fault and the conjugate Matsushiro fault. We show that the observed spatial evolution of seismicity along the two faults and magnitudes surface uplift, are convincingly explained by a few MPa of pressurization from the upwelling fluid within the critically stressed crust - a crust under a strike-slip stress regime near the frictional strength limit. Our analysis indicates that the most important cause for triggering of seismicity during the Matsushiro swarm was the fluid pressurization with the associated reduction in effective stress and strength in fault segments that were initially near critically stressed for shear failure. Moreover, our analysis indicates that a two order of magnitude permeability enhancement in ruptured fault segments may be necessary to match the observed time evolution of surface uplift. We conclude that our hydromechanical modelling study of the Matsushiro earthquake swarm shows a clear connection between earthquake rupture, deformation, stress, and permeability changes, as well as large-scale fluid flow related to degassing of CO{sub 2} in the shallow seismogenic crust. Thus, our study provides further evidence of the
Swarm Science objectives and challenges
DEFF Research Database (Denmark)
Friis-Christensen, Eigil; Lühr, Hermann; Hulot, Gauthier
Swarm is the fifth Earth Explorer mission in ESA’s Living Planet Programme to be launched in 2009. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution. The innovative constellation concept and a unique set of dedicated instrume...... instruments will provide the necessary observations that are required to separate and model the various sources of the geomagnetic field. This will provide new insights into the Earth system by improving our understanding of the Earth’s interior and Sun-Earth connection processes....
Vladimir Prelog i Zavod za organsku kemiju
Directory of Open Access Journals (Sweden)
Jakopčić, K.
2007-03-01
Full Text Available The Department of organic chemistry was founded on October 1st, 1922 as part of the Chemical Engineering Department of the High Technical School in Zagreb. The first-appointed professor of organic chemistry was Ivan Marek, formerly the professor at the School of Trade in Zagreb. The High Technical School with all its departments was soon (1926/27 incorporated into the University of Zagreb and became the nucleus of the Technical Faculty. The management of the faculty was fully aware of the necessity to engage the best scientists in the field as faculty professors. As far as organic chemistry was concerned, there was no better choice in the mid 1930's, than to invite young but already recognized organic chemist Dr. Vladimir Prelog to join the staff and to succeed professor Marek, who retired in 1935. Dr. Prelog did not hesitate to accept the invitation and was eager to continue his scientific work in an academic laboratory in Zagreb. At the time of the invitation, Dr. Prelog was engaged in a small laboratory synthesizing special samples of rare chemicals to be sold through the chemical store "Dřiza" in Prague. There he was provided the modest opportunity to carry on his own research, and together with Rudolf Lukeš and Emil Votoček, published a number of papers concerning organic synthesis and chemistry of natural products. Elected in Zagreb for the lectureship of the assistant professor in organic chemistry, Prelog started teaching as early as in the summer semester of 1934/35. The ingenious and bright lecturing of young professor Prelog inspired his students, and many of them were attracted to organic chemistry. On the other hand, the working conditions in the disposable laboratory of the Organic Chemistry Department were too modest to accept a number of students. The budget was so small that it only covered tuition, while there was no research fund at the faculty. Luckily, Dr. Prelog did not hesitate to accept an offer for scientific co
Safari, Mir Jafar Sadegh; Shirzad, Akbar; Mohammadi, Mirali
2017-08-01
May proposed two dimensionless parameters of transport (η) and mobility (Fs) for self-cleansing design of sewers with deposited bed condition. The relationships between those two parameters were introduced in conditional form for specific ranges of Fs, which makes it difficult to use as a practical tool for sewer design. In this study, using the same experimental data used by May and employing the particle swarm optimization algorithm, a unified equation is recommended based on η and Fs. The developed model is compared with original May relationships as well as corresponding models available in the literature. A large amount of data taken from the literature is used for the models' evaluation. The results demonstrate that the developed model in this study is superior to May and other existing models in the literature. Due to the fact that in May's dimensionless parameters more effective variables in the sediment transport process in sewers with deposited bed condition are considered, it is concluded that the revised May equation proposed in this study is a reliable model for sewer design.
Mäkitie, Hannu; Data, Gabriel; Isabirye, Edward; Mänttäri, Irmeli; Huhma, Hannu; Klausen, Martin B.; Pakkanen, Lassi; Virransalo, Petri
2014-09-01
source signatures. A model of how the LIP configuration formed, and especially its giant arcuate swarm, requires fortuitous pre-existing structures, an unusually large sub-crustal magma chamber, and/or some very intrinsic rift process. The LIP is apparently related to a global 1.4-1.2 Ga rifting event that led to the break-up of the Columbia/Nuna supercontinent.
behaved particle swarm optimization (QPSO)
African Journals Online (AJOL)
Administrator
2011-06-13
Jun 13, 2011 ... fermentation process, and consequently, it increased the yield of fermentation. Key words: Soft-sensing model, quantum-behaved particle swarm optimization algorithm, neural network. INTRODUCTION. In industrial production through fermentation, the main effect variables include physical variables (the ...
Directory of Open Access Journals (Sweden)
Tadikonda Venkata Bharat
2017-01-01
Full Text Available A precise estimation of isotherm model parameters and selection of isotherms from the measured data are essential for the fate and transport of toxic contaminants in the environment. Nonlinear least-square techniques are widely used for fitting the isotherm model on the experimental data. However, such conventional techniques pose several limitations in the parameter estimation and the choice of appropriate isotherm model as shown in this paper. It is demonstrated in the present work that the classical deterministic techniques are sensitive to the initial guess and thus the performance is impeded by the presence of local optima. A novel solver based on modified artificial bee-colony (MABC algorithm is proposed in this work for the selection and configuration of appropriate sorption isotherms. The performance of the proposed solver is compared with the other three solvers based on swarm intelligence for model parameter estimation using measured data from 21 soils. Performance comparison of developed solvers on the measured data reveals that the proposed solver demonstrates excellent convergence capabilities due to the superior exploration-exploitation abilities. The estimated solutions by the proposed solver are almost identical to the mean fitness values obtained over 20 independent runs. The advantages of the proposed solver are presented.
From hybrid swarms to swarms of hybrids
Stohlgren, Thomas J.; Szalanski, Allen L; Gaskin, John F.; Young, Nicholas E.; West, Amanda; Jarnevich, Catherine S.; Tripodi, Amber
2014-01-01
Science has shown that the introgression or hybridization of modern humans (Homo sapiens) with Neanderthals up to 40,000 YBP may have led to the swarm of modern humans on earth. However, there is little doubt that modern trade and transportation in support of the humans has continued to introduce additional species, genotypes, and hybrids to every country on the globe. We assessed the utility of species distributions modeling of genotypes to assess the risk of current and future invaders. We evaluated 93 locations of the genus Tamarix for which genetic data were available. Maxent models of habitat suitability showed that the hybrid, T. ramosissima x T. chinensis, was slightly greater than the parent taxa (AUCs > 0.83). General linear models of Africanized honey bees, a hybrid cross of Tanzanian Apis mellifera scutellata and a variety of European honey bee including A. m. ligustica, showed that the Africanized bees (AUC = 0.81) may be displacing European honey bees (AUC > 0.76) over large areas of the southwestern U.S. More important, Maxent modeling of sub-populations (A1 and A26 mitotypes based on mDNA) could be accurately modeled (AUC > 0.9), and they responded differently to environmental drivers. This suggests that rapid evolutionary change may be underway in the Africanized bees, allowing the bees to spread into new areas and extending their total range. Protecting native species and ecosystems may benefit from risk maps of harmful invasive species, hybrids, and genotypes.
Shidrokh Goudarzi; Wan Haslina Hassan; Mohammad Hossein Anisi; Seyed Ahmad Soleymani; Parvaneh Shabanzadeh
2015-01-01
The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) an...
Samareh, Hossein; Khoshrou, Seyed Hassan; Shahriar, Kourosh; Ebadzadeh, Mohammad Mehdi; Eslami, Mohammad
2017-09-01
When particle's wave velocity resulting from mining blasts exceeds a certain level, then the intensity of produced vibrations incur damages to the structures around the blasting regions. Development of mathematical models for predicting the peak particle velocity (PPV) based on the properties of the wave emission environment is an appropriate method for better designing of blasting parameters, since the probability of incurred damages can considerably be mitigated by controlling the intensity of vibrations at the building sites. In this research, first out of 11 blasting and geo-mechanical parameters of rock masses, four parameters which had the greatest influence on the vibrational wave velocities were specified using regression analysis. Thereafter, some models were developed for predicting the PPV by nonlinear regression analysis (NLRA) and artificial neural network (ANN) with correlation coefficients of 0.854 and 0.662, respectively. Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm. The values of PPV were estimated for 18 testing dataset in order to evaluate the accuracy of the prediction and performance of the developed models. By calculating statistical indices for the test recorded maps, it was found that the optimized model can predict the PPV with a lower error than the other two models. Furthermore, considering the correlation coefficient (0.75) between the values of the PPV measured and predicted by the optimized nonlinear model, it was found that this model possesses a more desirable performance for predicting the PPV than the other two models.
Analog Circuit Fault Diagnosis Approach Based on Improved Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Ming-Fang WANG
2014-07-01
Full Text Available The basic thought of particle swarm optimization is introduced firstly, then particle swarm optimization algorithm model is established. The application of the improved particle swarm optimization algorithm to power supply system fault diagnosis is analyzed in accordance with problem of the algorithm, and migration strategy is added to particle swarm optimization algorithm. Finally the parameters of the wide area damping controller are adjusted by the improved particle swarm optimization algorithm.
Antarctic krill swarm characteristics in the Southeast Atlantic sector of the Southern Ocean
Krafft, BA
2012-09-28
Knowledge about swarm dynamics and underlying causes is essential to understand the ecology and distribution of Antarctic krill Euphausia superba. We collected acoustic data and key environmental data continuously across extensive gradients in the little-studied Southeast Atlantic sector of the Southern Ocean. A total of 4791 krill swarms with swarm descriptors including swarm height and length, packing density, swimming depth and inter-swarm distance were extracted. Through multivariate statistics, swarms were categorized into 4 groups. Group 2 swarms were largest (median length 108 m and thickness 18 m), whereas swarms in both Groups 1 and 4 were on average small, but differed markedly in depth distribution (median: 52 m for Group 1 vs. 133 m for Group 4). There was a strong spatial autocorrelation in the occurrence of swarms, and an autologistic regression model found no prediction of swarm occurrence from environmental variables for any of the Groups 1, 2 or 4. Probability of occurrence of Group 3 swarms, however, increased with increasing depth and temperature. Group 3 was the most distinctive swarm group with an order of magnitude higher packing density (median: 226 ind. m−3) than swarms from any of the other groups and about twice the distance to nearest neighbor swarm (median: 493 m). The majority of the krill were present in Group 3 swarms, and the absence of association with hydrographic or topographic concentrating mechanisms strongly suggests that these swarms aggregate through their own locomotion, possibly associated with migration.
Components of Swarm Intelligence
Energy Technology Data Exchange (ETDEWEB)
David Bruemmer; Donald Dudenhoeffer; Matthew Anderson; Mark McKay
2004-03-01
This paper discusses the successes and failures over the past three years as efforts at the Idaho National Engineering and Environmental Laboratory (INEEL) have developed and evaluated robot behaviors that promote the emergence of swarm intelligence. Using a team of 12 small robots with the ability to respond to light and sound, the INEEL has investigated the fundamental advantages of swarm behavior as well as the limitations of this approach. The paper discusses the ways in which biology has inspired this work and the ways in which adherence to the biological model has proven to be both a benefit and hindrance to developing a fieldable system. The paper outlines how a hierarchical command and control structure can be imposed in order to permit human control at a level of group abstraction and discusses experimental results that show how group performance scales as different numbers of robots are utilized. Lastly, the paper outlines the applications for which the resulting capabilities have been applied and demonstrated.
Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw
2002-01-01
The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.
A surgeon to remember: notes about Vladimir Demikhov.
Shumacker, H B
1994-10-01
Vladimir Demikhov, first to transplant an auxiliary heart into the chest of a warm-blooded animal, first to replace the heart with a homograft, first to carry out a pulmonary transplantation, first to perform a complete heart and lung replacement, and first to perform a successful internal mammary-coronary anastomosis, deserves a place among the great experimental surgeons of all times. He has not had the widespread recognition he earned.
Gholami, Ali; Honarvar, Farhang; Abrishami Moghaddam, Hamid
2017-06-01
This paper presents an accurate and easy-to-implement algorithm for estimating the parameters of the asymmetric Gaussian chirplet model (AGCM) used for modeling echoes measured in ultrasonic nondestructive testing (NDT) of materials. The proposed algorithm is a combination of particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithms. PSO does not need an accurate initial guess and quickly converges to a reasonable output while LM needs a good initial guess in order to provide an accurate output. In the combined algorithm, PSO is run first to provide a rough estimate of the output and this result is consequently inputted to the LM algorithm for more accurate estimation of parameters. To apply the algorithm to signals with multiple echoes, the space alternating generalized expectation maximization (SAGE) is used. The proposed combined algorithm is robust and accurate. To examine the performance of the proposed algorithm, it is applied to a number of simulated echoes having various signal to noise ratios. The combined algorithm is also applied to a number of experimental ultrasonic signals. The results corroborate the accuracy and reliability of the proposed combined algorithm.
Directory of Open Access Journals (Sweden)
Rahmat Arab
2014-01-01
Full Text Available In this paper a novel, bi-objective mixed-integer mathematical programming has been proposed for a distribution network problem. One objective function minimizes the total purchasing, transportation and holding costs and the another objective minimizes the total amount of delayed or before time deliveries multiplied by respective durations, named "JIT distribution". Supplying the customer demand, holding and delivering products at warehouse are the most important constraints considered in this model. This model has been designed for a three-echelon distribution network consisting multiple suppliers, wholesalers and retailers to distribute multiple products with a deterministic amount of demand through either direct or indirect channels in a planning horizon. Since real-sized problems of the resulting bi-objective mixed-integer linear programming (MILP cannot be solved with exact methods, a multi objective particle swarm algorithm (MOPSO is designed of which, quality in small-sized problems is compared with the solutions obtained by the LINGO software. The computational results show that the proposed MOPSO algorithm finds good solutions in shorter times than LINGO and has acceptable running times in large-scale problems.
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Yulong Ying
2015-01-01
Full Text Available In the lifespan of a gas turbine engine, abrupt faults and performance degradation of its gas-path components may happen; however the performance degradation is not easily foreseeable when the level of degradation is small. Gas path analysis (GPA method has been widely applied to monitor gas turbine engine health status as it can easily obtain the magnitudes of the detected component faults. However, when the number of components within engine is large or/and the measurement noise level is high, the smearing effect may be strong and the degraded components may not be recognized. In order to improve diagnostic effect, a nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm (PSO-GPA has been proposed in this study. The proposed approach has been tested in ten test cases where the degradation of a model three-shaft marine engine has been analyzed. These case studies have shown that the approach can accurately search and isolate the degraded components and further quantify the degradation for major gas-path components. Compared with the typical GPA method, the approach has shown better measurement noise immunity and diagnostic accuracy.
The precursory earthquake swarm in Greece
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D. Rhoades
2000-06-01
Full Text Available The Hellenic subduction region displays the same precursory swarm phenomenon as has been found in comparable regions of New Zealand and Japan. In the earthquake catalogue of the Aristotle University of Thessaloniki, 10 past sequences of precursory swarms and related major mainshock events have been identified. These correlate, in respect of location, magnitude and time, with the 9 sequences previously identified in New Zealand, and 9 in Japan, bringing the total of sequences to 28, and the totals of related events (allowing for clustering to 56 precursory swarms and 42 mainshock events. The results add strength to the hypothesis that swarms are long-range predictors of mainshock events. A close similarity between the swarm and aftershock magnitudes in a given sequence is also confirmed in Greece, supporting the proposal that swarms are an integral part of the seismogenic process in subduction regions. Further, the modelling of swarms as part of an overall increase in seismicity, the onset of which marks the onset of seismogenesis, is well illustrated from past sequences in Greece. Formal tests are being carried out in Greece, in parallel with New Zealand and Japan, to ascertain the performance of the hypothesis as a basis for long-range synoptic forecasting.
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Caroline Peyrode
2011-01-01
Full Text Available Our lab developed 99mTc-NTP 15-5 radiotracer as targeting proteoglycans (PGs for the scintigraphic imaging of joint. This paper reports preclinical results of 99mTc-NTP 15-5 imaging of an orthotopic model of Swarm rat chondrosarcoma (SRC. 99mTc-NTP 15-5 imaging of SRC-bearing and sham-operated animals was performed and quantified at regular intervals after surgery and compared to bone scintigraphy and tumoural volume. Tumours were characterized by histology and PG assay. SRC exhibited a significant 99mTc-NTP 15-5 uptake at very early stage after implant (with tumour/muscle ratio of 1.61 ± 0.14, whereas no measurable tumour was evidenced. As tumour grew, mean tumour/muscle ratio was increased by 2.4, between the early and late stage of pathology. Bone scintigraphy failed to image chondrosarcoma, even at the later stage of study. 99mTc-NTP 15-5 imaging provided a suitable set of quantitative criteria for the in vivo characterization of chondrosarcoma behaviour in bone environment, useful for achieving a greater understanding of the pathology.
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Kun Zhang
2016-01-01
Full Text Available Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.
The Swarm Satellite Constellation Application and Research Facility (SCARF) and Swarm data products
DEFF Research Database (Denmark)
Olsen, Nils; Friis-Christensen, Eigil; Floberghagen, R.
2013-01-01
Swarm, a three-satellite constellation to study the dynamics of the Earth's magnetic field and its interactions with the Earth system, is expected to be launched in late 2013. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution......, in order to gain new insights into the Earth system by improving our understanding of the Earth's interior and environment. In order to derive advanced models of the geomagnetic field (and other higher-level data products) it is necessary to take explicit advantage of the constellation aspect of Swarm....... The Swarm SCARF (Satellite Constellation Application and Research Facility) has been established with the goal of deriving Level-2 products by combination of data from the three satellites, and of the various instruments. The present paper describes the Swarm input data products (Level-1b and auxiliary data...
Sabaka, Terence J.; Olsen, Nils; Tyler, Robert H.; Kuvshinov, Alexey
2014-01-01
A comprehensive magnetic field model named CM5 has been derived from CHAMP, Ørsted and SAC-C satellite and observatory hourly-means data from 2000 August to 2013 January using the Swarm Level-2 Comprehensive Inversion (CI) algorithm. Swarm is a recently launched constellation of three satellites to map the Earth's magnetic field. The CI technique includes several interesting features such as the bias mitigation scheme known as Selective Infinite Variance Weighting (SIVW), a new treatment for attitude error in satellite vector measurements, and the inclusion of 3-D conductivity for ionospheric induction. SIVW has allowed for a much improved lithospheric field recovery over CM4 by exploiting CHAMP along-track difference data yielding resolution levels up to spherical harmonic degree 107, and has allowed for the successful extraction of the oceanic M2 tidal magnetic field from quiet, nightside data. The 3-D induction now captures anomalous Solar-quiet features in coastal observatory daily records. CM5 provides a satisfactory, continuous description of the major magnetic fields in the near-Earth region over this time span, and its lithospheric, ionospheric and oceanic M2 tidal constituents may be used as validation tools for future Swarm Level-2 products coming from the CI algorithm and other dedicated product algorithms.
The Swarm Computing Approach to Business Intelligence
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Schumann Andrew
2015-07-01
Full Text Available We have proposed to use some features of swarm behaviours in modelling business processes. Due to these features we deal with a propagation of business processes in all accessible directions. This propagation is involved into our formalization instead of communicating sequential processes. As a result, we have constructed a business process diagram language based on the swarm behavior and an extension of that language in the form of reflexive management language.
Yang, Qidong; Zuo, Hongchao; Li, Weidong
2016-01-01
Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.
DEFF Research Database (Denmark)
Moretto, T.; Vennerstrøm, Susanne; Olsen, Nils
2006-01-01
We have used a global model of the solar wind magnetosphere interaction to model the high latitude part of the external contributions to the geomagnetic field near the Earth. The model also provides corresponding values for the electric field. Geomagnetic quiet conditions were modeled to provide...
Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin
2015-12-01
The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. Copyright © 2015 Elsevier Ltd. All rights reserved.
A REVIEW OF SWARMING UNMANNED AERIAL VEHICLES
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CORNEA Mihai
2016-11-01
Full Text Available This paper in if fact an overview of state of the art in mobile multi-robot systems as an initial part of our research in implementing a system based on swarm robotics concepts to be used in natural disaster search and rescue missions. The system is to be composed of a group of drones that can detect survivor mobile cell signals and exhibit some other features as well. This paper surveys the swarm robotics research landscape to provide a theoretical background to the implementation and help determine the techniques available to create the system. The Particle swarm optimization (PSO and Glowworm swarm optimization (GSO algorithms are briefly described and there is also insight into Bird flocking behavior and the model behind it
Synchronized rotation in swarms of magnetotactic bacteria
Belovs, M.; Livanovičs, R.; CÄ`bers, A.
2017-10-01
Self-organizing behavior has been widely reported in both natural and artificial systems, typically distinguishing between temporal organization (synchronization) and spatial organization (swarming). Swarming has been experimentally observed in systems of magnetotactic bacteria under the action of external magnetic fields. Here we present a model of ensembles of magnetotactic bacteria in which hydrodynamic interactions lead to temporal synchronization in addition to the swarming. After a period of stabilization during which the bacteria form a quasiregular hexagonal lattice structure, the entire swarm begins to rotate in a direction opposite to the direction of the rotation of the magnetic field. We thus illustrate an emergent mechanism of macroscopic motion arising from the synchronized microscopic rotations of hydrodynamically interacting bacteria, reminiscent of the recently proposed concept of swarmalators.
Survey of Methods and Algorithms of Robot Swarm Aggregation
E Shlyakhov, N.; Vatamaniuk, I. V.; Ronzhin, A. L.
2017-01-01
The paper considers the problem of swarm aggregation of autonomous robots with the use of three methods based on the analogy of the behavior of biological objects. The algorithms substantiating the requirements for hardware realization of sensor, computer and network resources and propulsion devices are presented. Techniques for efficiency estimation of swarm aggregation via space-time characteristics are described. The developed model of the robot swarm reconfiguration into a predetermined three-dimensional shape is presented.
From hybrid swarms to swarms of hybrids
The introgression of modern humans (Homo sapiens) with Neanderthals 40,000 YBP after a half-million years of separation, may have led to the best example of a hybrid swarm on earth. Modern trade and transportation in support of the human hybrids has continued to introduce additional species, genotyp...
MAGNAS - Magnetic Nanoprobe SWARM
DEFF Research Database (Denmark)
Lubberstedt, H.; Koebel, D.; Hansen, Flemming
2005-01-01
This paper presents the Magnetic Nano-Probe Swarm mission utilising a constellation of several swarms of nano-satellites in order to acquire simultaneous measurements of the geomagnetic field resolving the local field gradients. The space segment comprises of up to 4 S/C swarms each consisting...... of up to 6 nano-satellites (Nano-Probes) and 1 mother spacecraft (MSC) to be launched with a single launcher in polar low Earth orbits. The Nano-Probes. equipped with magnetometer payloads operate in the vicinity of the MSCs. The MSCs will eject the NPs after acquisition of the initial orbits. provide...
Adaptive cockroach swarm algorithm
Obagbuwa, Ibidun C.; Abidoye, Ademola P.
2017-07-01
An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.
Gene expression in Pseudomonas aeruginosa swarming motility
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Déziel Eric
2010-10-01
swarm center cells, tendril tip populations of a swarming colony displays general down-regulation of genes associated with virulence and up-regulation of genes involved in energy metabolism. These results allow us to propose a model where tendril tip cells function as «scouts» whose main purpose is to rapidly spread on uncolonized surfaces while swarm center population are in a state allowing a permanent settlement of the colonized area (biofilm-like.
Vladimir Anshon : "Tolko sostradanije mozhet uderzhat ot neobratimõh postupkov..." / Eteri Kekelidze
Kekelidze, Eteri, 1944-
2007-01-01
Teatrikunstnik Vladimir Anshon lavakujundusest Adolf Shapiro poolt Tallinna Linnateatris lavastatud Luigi Pirandello näidendile "Nii see on, kui teile nii näib". XI Praha lavastuskunstnike kvadriennaali žürii liige Vladimir Anshon žürii tööst. Eestit esindab näitusel Ene-Liis Semper
Numerical derivation of the drag force coefficient in bubble swarms using a Front Tracking model
Dijkhuizen, W.; Roghair, I.; van Sint Annaland, M.; Kuipers, J.A.M.
2008-01-01
Dispersed gas-liquid flows are often encountered in the chemical process industry. Large scale models which describe the overall behavior of these flows use closure relations to account for the interactions between the phases, such as the drag, lift and virtual mass forces. The closure relations for
The Swarm Magnetometry Package
DEFF Research Database (Denmark)
Merayo, José M.G.; Jørgensen, John Leif; Friis-Christensen, Eigil
2008-01-01
The Swarm mission under the ESA's Living Planet Programme is planned for launch in 2010 and consists of a constellation of three satellites at LEO. The prime objective of Swarm is to measure the geomagnetic field with unprecedented accuracy in space and time. The magnetometry package consists of ...... of an extremely accurate and stable vector magnetometer, which is co-mounted in an optical bench together with a start tracker system to ensure mechanical stability of the measurements....
Wang, S. C.; Lee, C. S.
2016-12-01
In recent five years, geothermal energy became one of the most prosperous renewable energy in the world, but produces only 0.5% of the global electricity. Why this great potential of green energy cannot replace the fuel and nuclear energy? The necessity of complicated exploration procedures and precious experts in geothermal field is similar to that of the oil and gas industry. The Yilan Plain (NE Taiwan) is one of the hot area for geothermal development and research in the second phase of National Energy Program (NEP-II). The geological and geophysical studies of the area indicate that the Yilan Plain is an extension of the Okinawa Trough back arc rifting which provide the geothermal resource. Based on the new constrains from properties of supercritical fluids and dissipative structure theory, the geophysical evidence give confident clues on how the geothermal system evolved at depth. The geothermal conceptual model in NEP-II indicates that the volcanic intrusion under the complicate fault system is possibly beneath the Yilan Plain. However, the bottom temperature of first deep drilling and geochemical evidence in NEP-II imply no volcanic intrusion. In contrast, our results show that seismic activities in geothermal field observed self-organization, and are consistent with the brittle-ductile / brittle-plastic transition, which indicates that supercritical fluids triggered earthquake swarms. The geothermal gradient and geochemical anomalies in Yilan Plain indicate an open system far from equilibrium. Mantle and crust exchange energy and materials through supercritical fluids to generate a dissipative structure in geothermal fields and promote water-rock interactions and fractures. Our initial studies have suggested a dissipative structure of geothermal system that could be identified by geochemical and geophysical data. The key factor is the tectonic setting that triggered supercritical fluids upwelling from deep (possibly from the mantle or the upper crust). Our
Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark
2017-04-05
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.
Zhang, Liping; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian
2014-06-01
In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. Copyright © 2014. Published by Elsevier Ltd.
Kun Zhang; Zhao Hu; Xiao-Ting Gan; Jian-Bo Fang
2016-01-01
Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The pa...
Modeling of urban growth using cellular automata (CA) optimized by Particle Swarm Optimization (PSO)
Khalilnia, M. H.; Ghaemirad, T.; Abbaspour, R. A.
2013-09-01
In this paper, two satellite images of Tehran, the capital city of Iran, which were taken by TM and ETM+ for years 1988 and 2010 are used as the base information layers to study the changes in urban patterns of this metropolis. The patterns of urban growth for the city of Tehran are extracted in a period of twelve years using cellular automata setting the logistic regression functions as transition functions. Furthermore, the weighting coefficients of parameters affecting the urban growth, i.e. distance from urban centers, distance from rural centers, distance from agricultural centers, and neighborhood effects were selected using PSO. In order to evaluate the results of the prediction, the percent correct match index is calculated. According to the results, by combining optimization techniques with cellular automata model, the urban growth patterns can be predicted with accuracy up to 75 %.
Extracting distinct behaviors from laboratory insect swarms
Puckett, James; Ouellette, Nicholas
2014-03-01
Throughout nature, self-organized collective motion in animal groups produces rich and complex behaviors. Many modeling approaches have been proposed from continuum to discrete agent based models which are capable of emulating the behavior observed in flocks and swarms. Most models assume uniformity in the way individuals interact and discard differences between individuals and changes of behavior with time. While in many cases individual differences may average out in large groups of animals, this is not likely the case for small groups. By measuring trajectories and kinematics of individual Chironomids in laboratory mating swarms, we assess the dynamics of individual behavior and discuss the impact of our results on current models.
Directory of Open Access Journals (Sweden)
Zhilong Wang
2014-01-01
Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.
Vladimir Nabokov: A Case Study of Multilingualism and Translation
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Paulina Rothermel
2014-11-01
Full Text Available This article explores the relationship between translation and multilingualism through an examination of Vladimir Nabokov’s works and views on the topic. The main idea of the article is that translation is one of the implications of multi-competence, as defined by Vivian Cook in 1991, and as such is reliant on the translator’s cultural grounding. In Nabokov’s case, multilingualism and multiculturalism resulted in some very specific approaches in his own translation, as well as in his setting of canons for other translators to follow. Advocacy of the literal style in transliteration which remains faithful to the original author constitutes evidence of the utmost appreciation for the broadening of mental horizons that such foreignization may bring. Some rendering of Nabokov’s works into Polish, and the following of his directives in those renditions, were also analyzed by the author of the article.
Balance of threat: The domestic insecurity of Vladimir Putin
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Robert Person
2017-01-01
Full Text Available During the 17 years that Vladimir Putin has ruled Russia, the country has become increasingly authoritarian. However, I argue that this rollback of democracy has not been motivated by Putin's blind desire to maximize his political power, as many have assumed. Rather, his anti-democratic policies have responded to perceived specific threats to his control. In applying theories originally developed in the field of international relations to individual leaders, we can understand Putin as a “defensive realist” who balances against threats in order to maintain security rather than maximize power. This is an essential distinction that produces important conclusions about what motives lie behind the increasingly authoritarian character of the Russian state and gives insights into the possible future trajectory of the regime.
Swarms of UAVs and fighter aircraft
Energy Technology Data Exchange (ETDEWEB)
Trahan, M.W.; Wagner, J.S.; Stantz, K.M.; Gray, P.C.; Robinett, R.
1998-11-01
This paper describes a method of modeling swarms of UAVs and/or fighter aircraft using particle simulation concepts. Recent investigations into the use of genetic algorithms to design neural networks for the control of autonomous vehicles (i.e., robots) led to the examination of methods of simulating large collections of robots. This paper describes the successful implementation of a model of swarm dynamics using particle simulation concepts. Several examples of the complex behaviors achieved in a target/interceptor scenario are presented.
Directory of Open Access Journals (Sweden)
Jie-Sheng Wang
2015-01-01
Full Text Available For predicting the key technology indicators (concentrate grade and tailings recovery rate of flotation process, a feed-forward neural network (FNN based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO algorithm and gravitational search algorithm (GSA is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.
Particle Swarm Optimization Toolbox
Grant, Michael J.
2010-01-01
The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry
A persistent homology approach to collective behavior in insect swarms
Sinhuber, Michael; Ouellette, Nicholas T.
Various animals from birds and fish to insects tend to form aggregates, displaying self-organized collective swarming behavior. Due to their frequent occurrence in nature and their implications for engineered, collective systems, these systems have been investigated and modeled thoroughly for decades. Common approaches range from modeling them with coupled differential equations on the individual level up to continuum approaches. We present an alternative, topology-based approach for describing swarming behavior at the macroscale rather than the microscale. We study laboratory swarms of Chironomus riparius, a flying, non-biting midge. To obtain the time-resolved three-dimensional trajectories of individual insects, we use a multi-camera stereoimaging and particle-tracking setup. To investigate the swarming behavior in a topological sense, we employ a persistent homology approach to identify persisting structures and features in the insect swarm that elude a direct, ensemble-averaging approach. We are able to identify features of sub-clusters in the swarm that show behavior distinct from that of the remaining swarm members. The coexistence of sub-swarms with different features resembles some non-biological systems such as active colloids or even thermodynamic systems.
Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.
Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi
2017-01-01
Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.
Capdevila Piro, Antonio
2012-01-01
Aquesta memòria presenta les línies generals que s'han seguit per tal d'implementar una aplicació anomenada SWARM. En aquest document es recullen les bases del nostre projecte utilitzant el llenguatge de programació C# i fent servir altres eines i frameworks per les diferents capes de què consta el projecte, com poden ser Silverlight o WCF. Esta memoria presenta las líneas generales que se han seguido para implementar una aplicación llamada SWARM. En este documento se recogen las bases de ...
Structural Preconditions of West Bohemia Earthquake Swarms
Novotný, M.; Špičák, A.; Weinlich, F. H.
2013-07-01
The West Bohemia and adjacent Vogtland are well known for quasi-periodical earthquake swarms persisting for centuries. The seismogenic area near Nový Kostel involved about 90 % of overall earthquake activity clustered here in space and time. The latest major earthquake swarm took place in August-September 2011. In 1994 and 1997, two minor earthquake swarms appeared in another location, near Lazy. Recently, the depth-recursive tomography yielded a velocity image with an improved resolution along the CEL09 refraction profile passing between these swarm areas. The resolution, achieved in the velocity image and its agreement with the inverse gravity modeling along the collateral 9HR reflection profile, enabled us to reveal the key structural background of these West Bohemia earthquake swarms. The CEL09 velocity image detected two deeply rooted high-velocity bodies adjacent to the Nový Kostel and Lazy focal zones. They correspond to two Variscan mafic intrusions influenced by the SE inclined slab of Saxothuringian crust that subducted beneath the Teplá-Barrandian terrane in the Devonian era. In their uppermost SE inclined parts, they roof both focal zones. The high P-wave velocities of 6,100-6,200 m/s, detected in both roofing caps, indicate their relative compactness and impermeability. The focal domains themselves are located in the almost gradient-free zones with the swarm foci spread near the axial planes of profound velocity depressions. The lower velocities of 5,950-6,050 m/s, observed in the upper parts of focal zones, are indicative of less compact rock complexes corrugated and tectonically disturbed by the SE bordering magma ascents. The high-velocity/high-density caps obviously seal the swarm focal domains because almost no magmatic fluids of mantle origin occur in the Nový Kostel and Lazy seismogenic areas of the West Bohemia/Vogtland territory, otherwise rich in the mantle-derived fluids. This supports the hypothesis of the fluid triggering of earthquake
National Research Council Canada - National Science Library
Pavlovic, Eduard
2009-01-01
Vladimir Hudolin was born in Ogulin in 1922 and died in Zagreb in 1996. He was one of the best students of the Susak grammar school and distinguished himself in a Catholic youth association Domagoj...
Eugene Onegin the Cold War Monument: How Edmund Wilson Quarreled with Vladimir Nabokov
National Research Council Canada - National Science Library
Conley, Tim
2014-01-01
The tale of how Edmund Wilson quarreled with Vladimir Nabokov over the latters 1964 translation of Eugene Onegin can be instructively read as a politically charged event, specifically a high culture...
A mystery of Vladimir P. Demikhov: the 50th anniversary of the first intrathoracic transplantation
National Research Council Canada - National Science Library
Konstantinov, I E
1998-01-01
Vladimir P. Demikhov was the first to perform intrathoracic transplantation of the heart alone, lung alone, and the heart and lungs in a warm-blooded animal and the first to perform an experimental coronary...
Ja nenavizhu etot marshrut : "London - Moskva!" / Vladimir Zhirinovski ; interv. Mihhail Petrov
Žirinovski, Vladimir, 1946-
2006-01-01
Vladimir Zhirinovski esinemisest partei kongressil, Oktoobrirevolutsioon ja süüdistused Lenini aadressil, Suur Isamaasõda, Venemaa vaenlased, perestroika, NSV Liidu likvideerimine, Afganistan, Iraak, Iraan, Venemaa eesmärgid uutes oludes
Anšon, Vladimir, 1963-
1999-01-01
Eesti teatrikunstnik Vladimir Anshon pälvis Prahas ülemaailmsel teatrikunstnike näitusel UNESCO preemia. Näitusel olid väljas maketid etendustele "Pianoola", "Tarelkini surm", "Kuritöö ja karistus" ja "Padaemand"
Investigating Ground Swarm Robotics Using Agent Based Simulation
2006-12-01
interesting to see how alternatives like MANA (and even Pythagoras 3 ) measure up to the calling. If indeed MANA has rarely been dedicated to model swarm... Pythagoras is an agent based simulation package developed by Northrop Grumman 5 Figure 2. Simulation packages used to models robot swarms... Pythagoras , an agent based software platform developed by Northrop Grumman. 93 As mentioned before, the model is not complete without modeling the
Transport of Particle Swarms Through Variable Aperture Fractures
Boomsma, E.; Pyrak-Nolte, L. J.
2012-12-01
Particle transport through fractured rock is a key concern with the increased use of micro- and nano-size particles in consumer products as well as from other activities in the sub- and near surface (e.g. mining, industrial waste, hydraulic fracturing, etc.). While particle transport is often studied as the transport of emulsions or dispersions, particles may also enter the subsurface from leaks or seepage that lead to particle swarms. Swarms are drop-like collections of millions of colloidal-sized particles that exhibit a number of unique characteristics when compared to dispersions and emulsions. Any contaminant or engineered particle that forms a swarm can be transported farther, faster, and more cohesively in fractures than would be expected from a traditional dispersion model. In this study, the effects of several variable aperture fractures on colloidal swarm cohesiveness and evolution were studied as a swarm fell under gravity and interacted with the fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with (1) a uniform aperture, (2) a converging region followed by a uniform region (funnel shaped), (3) a uniform region followed by a diverging region (inverted funnel), and (4) a cast of a an induced fracture from a carbonate rock. All of the samples consisted of two blocks that measured 100 x 100 x 50 mm. The minimum separation between these blocks determined the nominal aperture (0.5 mm to 20 mm). During experiments a fracture was fully submerged in water and swarms were released into it. The swarms consisted of a dilute suspension of 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. The variation in fracture aperture controlled swarm behavior. Diverging apertures caused a sudden loss of confinement that resulted in a rapid change in the swarm's shape as well as a sharp increase in its velocity
Vaingort, Vladimir, 1938-
2006-01-01
OÜ Kardis üks omanikke ja Eesti Maksumaksjate Liidu nõukoja liige oma tegevusest erinevates valdkondades, Eesti poliitikast, maksusüsteemist, muukeelse elanikkonna nõustamisest maksunduse vallas ning Eestisse tööle asumisest. Lisa: Vladimir Vaingort. Kommenteerivad: sotsioloog Aleksei Semjonov ja ajakirjanik Leivi Šer
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Yan Xiang
2017-01-01
Full Text Available Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization (PSO algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.
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Li Wang
2017-02-01
Full Text Available The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO algorithm that is based on random perturbation (RP-PSO. The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.
Effects of physical factors on the swarming motility of text itPseudomonas aeruginosa
Si, Tieyan; Ma, Zidong; Tang, Wai Shing; Yang, Alexander; Tang, Jay
Many species of bacteria can spread over a semi-solid surface via a particular form of collective motion known as surface swarming. Using Pseudomonas aeruginosa as a model organism, we investigate physical factors that either facilitate or restrict the swarming motility. The semi-solid surface is typically formed by 0.5-1% agar containing essential nutrients for the bacterial growth and proliferation. Most bacterial species, including P. aeruginosa, synthesize bio-surfactants to aid in swarming. We found addition of exogenous surfactants such as triton into the agar matrix enhances the swarming. In contrast, increasing agar percentage, infusing osmolites, and adding viscous agents all decrease swarming. We propose that the swarming speed is restricted by the rate of water supply from within the agar gel and by the line tension at the swarm front involving three materials in contact: the air, the bacteria propelled liquid film, and the agar substrate.
Vladimir P. Demikhov, a pioneer of organ transplantation.
Langer, R M
2011-05-01
Vladimir P. Demikhov was born in a Russian peasant family in 1916. As a biology student at The Moscow University in 1937, he constructed a metal artificial heart and maintained the circulation of a dog for 5.5 hours. From 1946, after his military service, he worked in the Surgical Institute of The Moscow Academy of Sciences performing heterotopic heart transplantations in dogs. In 1947, he performed the first orthotopic lung transplant. Later he performed complex cardiothoracic transplantations as well as renal and hepatic transplantations. He restarted his investigations with the artificial heart and performed coronary bypass operations in dogs. In 1954 he performed a head transplantation, for which he gained worldwide infamy. Stalinist propaganda advertised this fact as the superiority of Soviet science. In fact, it was the upper body of a smaller dog to the neck of a bigger one. The two heads could eat and drink separately. But he could not overcome the problems of rejection, so the longest survival was 1 month among 20 such operations. His influence on the pioneers of transplantation is unquestionable. He was an innovative creative man, and many pioneers of transplantation highly appreciated his work. Demikhov contributed to clinical heart and lung transplantation by demonstrating the possibility of their experimental realization; furthermore, he motivated the pioneers of coronary bypass operations with his work. He died in 1998, but before that was honored with a high state award and the "Pioneer Award" of the International Society for Heart and Lung Transplantation. Copyright © 2011 Elsevier Inc. All rights reserved.
Post-Soviet emptiness (Vladimir Makanin and Viktor Pelevin
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Hans Günther
2013-01-01
Full Text Available Emptiness is a key word in several post-Soviet Russian novels of the late 1990s. One can find it as well in Vladimir Makanin's “Underground” as in two novels written by Viktor Pelevin, “Generation ‘P’” and “Chapaev and Emptiness”. After the fall of Soviet power Pelevin's cynical hero from “Generation ‘P’” changes from literature into advertising business, and in his novel “Chapaev and Emptiness” the legendary Soviet Civil War hero Chapaev transforms into a preacher of quasi-Buddhist nothingness. Makanin's hero, the writer Petrovich, renounces of his profession in order to work as a watchman in shelters for the homeless. His self-abasement is in accordance with the tradition of kenoticism (derived from the Greek word kenós = empty which played an important part in the history of Russian religious and cultural life. Criticizing the hypermoralism of classical Russian literature Makanin outlines a new image of the writer which is opposed to the Russian literary myth but still propagates moral and religious values. Pelevin's novels which reflect the relativism of postmodern poetics focus on another issue – the blurring of the difference between reality and illusion. In “Generation ‘P’”, mass media and advertising produce deceitful simulacra of reality and in “Chapaev and Emptiness” the deconstruction of Soviet mythology assumes the shape of a nightmare. Unsurprisingly, among the imagery of emptiness Malevich's famous “Black Square” including its numerous equivalents as black holes or all sorts of empty spaces is rather frequent in the three novels. Emptiness may be considered to be a characteristic trait of the atmosphere of the 1990s when Russians felt to live in a cultural vacuum somewhere between state economy and unbridled capitalism, between Soviet order and “post-slave” (Makanin chaos.
Sharkey, Amanda J. C.
2007-09-01
Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralized local control and communication. Earlier studies went a step further in emphasizing the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions, and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes subareas of SR based on the emphases and justifications for minimalism and individual simplicity.
Koch, Tuuli
2010-01-01
Eesti Keskerakonna esimehe Edgar Savisaare sidemetest Venemaa Raudtee presidendi ja endise KGB kõrge ohvitseri Vladimir Jakuniniga, rahatehingutest ning Edgar Savisaare võimalikust ohust riigi julgeolekule
Role of tumbling in bacterial swarming
Sidortsov, Marina; Morgenstern, Yakov; Be'er, Avraham
2017-08-01
Typical wild-type bacteria swimming in sparse suspensions exhibit a movement pattern called "run and tumble," characterized by straight trajectories (runs) interspersed by shorter, random reorientation (tumbles). This is achieved by rotating their flagella counterclockwise, or clockwise, respectively. The chemotaxis signaling network operates in controlling the frequency of tumbles, enabling navigation toward or away from desired regions in the medium. In contrast, while in dense populations, flagellated bacteria exhibit collective motion and form large dynamic clusters, whirls, and jets, with intricate dynamics that is fundamentally different than trajectories of sparsely swimming cells. Although collectively swarming cells do change direction at the level of the individual cell, often exhibiting reversals, it has been suggested that chemotaxis does not play a role in multicellular colony expansion, but the change in direction stems from clockwise flagellar rotation. In this paper, the effects of cell rotor switching (i.e., the ability to tumble) and chemotaxis on the collective statistics of swarming bacteria are studied experimentally in wild-type Bacillus subtilis and two mutants—one that does not tumble and one that tumbles independently of the chemotaxis system. We show that while several of the parameters examined are similar between the strains, other collective and individual characteristics are significantly different. The results demonstrate that tumbling and/or flagellar directional rotor switching has an important role on the dynamics of swarming, and imply that swarming models of self-propelled rods that do not take tumbling and/or rotor switching into account may be oversimplified.
Colias: An Autonomous Micro Robot for Swarm Robotic Applications
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Farshad Arvin
2014-07-01
Full Text Available Robotic swarms that take inspiration from nature are becoming a fascinating topic for multi-robot researchers. The aim is to control a large number of simple robots in order to solve common complex tasks. Due to the hardware complexities and cost of robot platforms, current research in swarm robotics is mostly performed by simulation software. The simulation of large numbers of these robots in robotic swarm applications is extremely complex and often inaccurate due to the poor modelling of external conditions. In this paper, we present the design of a low-cost, open-platform, autonomous micro-robot (Colias for robotic swarm applications. Colias employs a circular platform with a diameter of 4 cm. It has a maximum speed of 35 cm/s which enables it to be used in swarm scenarios very quickly over large arenas. Long-range infrared modules with an adjustable output power allow the robot to communicate with its direct neighbours at a range of 0.5 cm to 2 m. Colias has been designed as a complete platform with supporting software development tools for robotics education and research. It has been tested in both individual and swarm scenarios, and the observed results demonstrate its feasibility for use as a micro-sized mobile robot and as a low-cost platform for robot swarm applications.
Directory of Open Access Journals (Sweden)
Marie Bouchet
2009-06-01
Full Text Available The terms “strange” and “stranger” derive from extraneus, a Latin word literally meaning “outside of”. This article proposes to examine the notions of distance and limit which shape the consciousness of a foreigner / outsider—Vladimir Nabokov. Nabokov lost his native land when he was 18, and was condemned to remain a stranger in a foreign land. In his fiction, many “estranged” characters are to be found, especially as main focalizers or first-person narrators. The present article thus demonstrates how the concept of frontier is crucial to interpreting such alien status: these characters are forever estranged, due to geographical borders, temporal distances or linguistic barriers. Nabokov, himself a trilingual writer, challenged the very idea of a foreign language. His English, which he claimed was merely “second rate”, is indeed quite different from the style of American classics, yet it has a unique poetic flavor. Maybe because he “did not think in any language, but in images”, his condition as a foreigner was not felt as alienating. Contrary to most of his characters, hybridization seems to have been a powerful element inspiring his artistic impulse, as illustrated in his extraordinary linguistic virtuosity and his constant playing on words. Similarly, the repeated inclusion of other semiotic codes (painting, photography, advertisements,… shows that by playing with limits and the interpenetration of familiar and strange(r elements, Nabokov found a creative alternative to the usually alienating condition of being a foreigner.
Xing, Haifeng; Hou, Bo; Lin, Zhihui; Guo, Meifeng
2017-10-13
MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/s, 0.00412°/s, and 0.00328°/s to 0.00065°/s, 0.00072°/s and 0.00061°/s, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.
Operating Small Sat Swarms as a Single Entity: Introducing SODA
Conn, Tracie; Dono Perez, Andres
2017-01-01
Swarm concepts are a growing topic of interest in the small satellite community. Compared to a small satellite constellation, a swarm has the distinction of being multiple spacecraft in close proximity, in approximately the same orbit. Furthermore, we envision swarms to have capabilities for cross-link communication and station-keeping. Of particular interest is a means to maintain operator-specified geometry, alignment, and/or separation.From NASA's decadal survey, it is clear that simultaneous measurements from a 3D volume of space are desired for a variety of Earth scientific studies. As this mission concept is ultimately extended to deep space, some degree of local control for the swarm to self-correct its configuration is required. We claim that the practicality of ground commanding each individual satellite in the swarm is simply not a feasible concept of operations. In other words, the current state-of-practice does not scale to very large swarms (e.g. 100 spacecraft or more) without becoming cost prohibitive. To contain the operations costs and complexity, a new approach is required: the swarm must be operated as a unit, responding to high-level specifications for relative position and velocity.The Mission Design Division at NASA Ames Research Center is looking to the near future for opportunities to develop satellite swarm technology. As part of this effort, we are developing SODA (Swarm Orbital Dynamics Advisor), a tool that provides the orbital maneuvers required to achieve a desired type of relative swarm motion. The purpose of SODA is two-fold. First, it encompasses the algorithms and orbital dynamics model to enable the desired relative motion of the swarm satellites. The process starts with the user specifying the properties of a swarm configuration. This could be as simple as varying in-track spacing of the swarm in one orbit, or as complex as maintaining a specified 3D geometrical orientation. We presume that science objectives will drive this
Swarm Robot Systems Based on the Evolution of Personality Traits
Jr., Sidney Nascimento GIVIGI; SCHWARTZ, Howard M.
2007-01-01
Game theory may be very useful in modeling and analyzing swarms of robots. Using game theory in conjunction with traits of personalities, we achieve intelligent swarm robots. Traits of personality are characteristics of each robot that define the robots' behaviours. The environment is represented as a game and due to the evolution of the traits through a learning process, we show how the robots may react intelligently to changes in the environment. A proof of convergence f...
Cui, Ying; Chen, Qinggang; Li, Yaxiao; Tang, Ling
2017-02-01
Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R(2)) of the whole training set (training and prediction) was 0.924, and the R(2) of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q(2) value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.
Directory of Open Access Journals (Sweden)
Xiujuan Zhao
Full Text Available The correct location of earthquake emergency shelters and their allocation to residents can effectively reduce the number of casualties by providing safe havens and efficient evacuation routes during the chaotic period of the unfolding disaster. However, diverse and strict constraints and the discrete feasible domain of the required models make the problem of shelter location and allocation more difficult. A number of models have been developed to solve this problem, but there are still large differences between the models and the actual situation because the characteristics of the evacuees and the construction costs of the shelters have been excessively simplified. We report here the development of a multi-objective model for the allocation of residents to earthquake shelters by considering these factors using the Chaoyang district, Beijing, China as a case study. The two objectives of this model were to minimize the total weighted evacuation time from residential areas to a specified shelter and to minimize the total area of all the shelters. The two constraints were the shelter capacity and the service radius. Three scenarios were considered to estimate the number of people who would need to be evacuated. The particle swarm optimization algorithm was first modified by applying the von Neumann structure in former loops and global structure in later loops, and then used to solve this problem. The results show that increasing the shelter area can result in a large decrease in the total weighted evacuation time from scheme 1 to scheme 9 in scenario A, from scheme 1 to scheme 9 in scenario B, from scheme 1 to scheme 19 in scenario C. If the funding were not a limitation, then the final schemes of each scenario are the best solutions, otherwise the earlier schemes are more reasonable. The modified model proved to be useful for the optimization of shelter allocation, and the result can be used as a scientific reference for planning shelters in the
PERFORMANCE OF PID CONTROLLER OF NONLINEAR SYSTEM USING SWARM INTELLIGENCE TECHNIQUES
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Neeraj Jain
2016-07-01
Full Text Available In this paper swarm intelligence based PID controller tuning is proposed for a nonlinear ball and hoop system. Particle swarm optimization (PSO, Artificial bee colony (ABC, Bacterial foraging optimization (BFO is some example of swarm intelligence techniques which are focused for PID controller tuning. These algorithms are also tested on perturbed ball and hoop model. Integral square error (ISE based performance index is used for finding the best possible value of controller parameters. Matlab software is used for designing the ball and hoop model. It is found that these swarm intelligence techniques have easy implementation & lesser settling & rise time compare to conventional methods.
portrait of the architect Vladimir Bukh (1935-2013
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Elena Grigoryeva
2014-01-01
Full Text Available Bukh, the maximalist, as some of his friends called him.Then a more appropriate word was found – perfectionist.He aimed at perfection in everything he did. Be it town planning, municipal administration, work for a non-governmental organization Union of Architects of Russia, or creating a journal.The headwater area has always been and remains the best residential area ever built in recent history of Irkutsk.Irkutsk has never seen such a clever and honest chief architect. He has done so much to develop the city in the right and progressive direction and to promote high quality architecture.Andrei Bokov’s words can be related to Vladimir Bukh’s activity: “Everything that excites a fair and natural envy, the best examples of the second heroic epoch of the Russian-Soviet architecture, belong to Irkutsk architecture of the 1970s: orientation towards the future (but not the past, bigness of volumes and ideas, restraint, strictness, honesty, courage, persistency and strength.Irkutsk experience… was accumulated by young people, who were energetic, ambitious, lucky, captivating, talented and highly professional.”For several decades Bukh was a member of the Board of the Irkutsk organization of the Union of Architects of the USSR and then of Russia. The expression “to work with feeling” can be fully applied to Bukh. To do your job the right way. To do it according to your conscience. When he quitted the Board after demolition of the House on the Legs, it became clear that he was indispensable. He took to heart all town planning mistakes of the 1990s and 2000s. Pavlov’s death, demolition of the House on the Legs, legal proceedings, Nina’s death left scars on his heart.His daughter Lesya, grandchildren Nikita and Nastya, and great grandson Matvey are continuation of his life. All we, his colleagues and friends, can do is try to keep to his understanding of what is right and what is wrong. And to keep memory
Karri, Rama Rao; Sahu, J N
2018-01-15
Zn (II) is one the common pollutant among heavy metals found in industrial effluents. Removal of pollutant from industrial effluents can be accomplished by various techniques, out of which adsorption was found to be an efficient method. Applications of adsorption limits itself due to high cost of adsorbent. In this regard, a low cost adsorbent produced from palm oil kernel shell based agricultural waste is examined for its efficiency to remove Zn (II) from waste water and aqueous solution. The influence of independent process variables like initial concentration, pH, residence time, activated carbon (AC) dosage and process temperature on the removal of Zn (II) by palm kernel shell based AC from batch adsorption process are studied systematically. Based on the design of experimental matrix, 50 experimental runs are performed with each process variable in the experimental range. The optimal values of process variables to achieve maximum removal efficiency is studied using response surface methodology (RSM) and artificial neural network (ANN) approaches. A quadratic model, which consists of first order and second order degree regressive model is developed using the analysis of variance and RSM - CCD framework. The particle swarm optimization which is a meta-heuristic optimization is embedded on the ANN architecture to optimize the search space of neural network. The optimized trained neural network well depicts the testing data and validation data with R2 equal to 0.9106 and 0.9279 respectively. The outcomes indicates that the superiority of ANN-PSO based model predictions over the quadratic model predictions provided by RSM. Copyright © 2017 Elsevier Ltd. All rights reserved.
Estimation of Valve Stiction Using Particle Swarm Optimization
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S. Sivagamasundari
2011-06-01
Full Text Available This paper presents a procedure for quantifying valve stiction in control loops based on particle swarm optimization. Measurements of the Process Variable (PV and Controller Output (OP are used to estimate the parameters of a Hammerstein system, consisting of connection of a non linear control valve stiction model and a linear process model. The parameters of the Hammerstein model are estimated using particle swarm optimization, from the input-output data by minimizing the error between the true model output and the identified model output. Using particle swarm optimization, Hammerstein models with known nonlinear structure and unknown parameters can be identified. A cost-effective optimization technique is adopted to find the best valve stiction models representing a more realistic valve behavior in the oscillating loop. Simulation and practical laboratory control system results are included, which demonstrates the effectiveness and robustness of the identification scheme.
Vladimir Prokopenko i jego utshenik stavjat spektakl bez slov / Irina Tokareva
Tokareva, Irina
2004-01-01
Peatselt algaval Narva teatrifestivalil "Kuldkalake" näitab Soldina gümnaasiumi "TeaterV" performace'it "Midgard. Aastaajad - elamise ajad", mis põhineb Skandinaavia müütidel. Lavastasid Vladimir Prokopenko ja tema õpilane Mihhail Kovalenko
The Mystery of the Conversion of the Prince Vladimir and His Covenants to Russian People
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Sekachev Vasilii
2016-06-01
Full Text Available The article is dedicated to the circumstances of Baptism of Holy Equal-to-the-Apostles Prince Vladimir. As is known, sometimes in Russian ancient records the circumstances of Baptism is expounded contradictorily. The comparison of records permits to explain external contradictions and to give the logical picture of Conversion and Baptism of the Great Prince.
Tallinn - Beograd liinil / Vladimir J. Konečni ; intervjueerinud Helen Arusoo
Konečni, Vladimir J.
2009-01-01
Intervjuu serbia-austria-tšehhi päritolu kosmopoliidist poeedi, fotograafi ja näitekirjaniku, hariduselt psühholoogiga Vladimir J. Konečniga, kel kodu nii Beogradis, Amsterdamis, San Diegos Californias ja Tallinnas, ning kes võrdleb eestlaste elukeskkonda Kesk-Euroopa ja ookeanitagusega.
Arnold, Vladimir I; Khesin, Boris; Marsden, Jerrold E; Varchenko, AN; Vassiliev, Victor A; Viro, Oleg Yanovich; Zakalyukin, Vladimir
2013-01-01
Vladimir Arnold was one of the great mathematical scientists of our time. He is famous for both the breadth and the depth of his work. At the same time he is one of the most prolific and outstanding mathematical authors. This second volume of his ""Collected Works"" focuses on hydrodynamics, bifurcation theory, and algebraic geometry.
Macroscopic definition of distributed swarm morphogenesis
Aznar, Fidel; Pujol, Mar; Rizo, Ramón
2012-12-01
In this paper, we present a system that will be able to obtain microscopic assembly behaviours for a robotic swarm to achieve an assembly target (macroscopic model). It will be designed taking into consideration the essential features of a self-assembling system needed to be implemented in a real robotic swarm. This system is composed of a typology of generative languages PD0L, and an algorithm for generating individual rules to be processed by the robots. The assembly process will be performed in a distributed manner, and will be also designed to require minimal communication capabilities between robots. Both the expressive capacities of language and the rule generation algorithm will be demonstrated by evaluating their performance with a core set of test morphologies widely used in self-assembly tasks. Furthermore, we compare the assembly time and the number of messages required between a classic controller (centralised) and our distributed approach.
Thermoregulation and adaptation in honeybee swarms
Ocko, Samuel; Mahadevan, L.
2012-11-01
Swarming is an essential part of honeybee behavior, wherein thousands of bees cling onto each other to form a dense cluster that is exposed to the environment for up to several days. This cluster has the ability to maintain its core temperature actively without a central controller raising the question of mechanism. Inspired by experimental observations, we treat the swarm cluster as an active porous structure with a variable metabolism that needs to adjust to outside conditions to control heat loss and regulate its core temperature. Using a continuum model that takes the form of a set of advection-diffusion equations for heat transfer in a mobile porous medium, we show that effective thermoregulation can result from the collective behavior of individual bees in the cluster.
Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah
2015-01-01
The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.
Directory of Open Access Journals (Sweden)
Sanjay Saini
Full Text Available The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO. However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO. The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF and Hierarchical Particle Swarm Optimization (HPSO. Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.
Holtkamp, S.; Brudzinski, M. R.
2011-12-01
For each Mw≥8.5 earthquake with a publicly available finite fault rupture model, we find slip is closely bounded along-strike by earthquake swarms, either prior or subsequent. These earthquake swarms tend to have much larger spatial extents than their cumulative moment would suggest, arguing against a static stress triggering mechanism. In Japan, Chile, Sumatra, and Alaska, earthquake swarms correlate with regions of the plate interface that exhibit low interseismic strain accumulation. This low fault coupling could be a result of aseismic slip during swarms or stress heterogeneity that leads to both swarm occurrence and great earthquake termination. Geodetic studies of earthquake swarms are limited but show several cases with no evidence for aseismic slip during swarms. Moreover, the 1964 Alaska and 2010 Maule earthquakes ruptured through regions with lower coupling than where they terminated, arguing that a factor other than small pre-stress controls where large earthquakes terminate. Large variations in coupling over small spatial scales could produce a fragmented set of small asperities conducive for generating a swarm of smaller earthquakes (Figure). Great earthquakes would be unlikely to rupture through that region as homogeneity of fault zone properties seems to be conducive for generating the largest megathrust earthquakes. Earthquake swarms are one of the better proxies for along-strike segmentation of subduction megathrusts, thereby potentially providing an new method for finding margins with the potential for devastating Mw~9 scale earthquakes. Figure: Cartoon illustrating our preferred hypothesis that increased stress heterogeneity causes earthquake swarm activity and stops large earthquake rupture propagation. Stress on the fault is in grayscale with black being high fault pre-stress. In this model, the heterogeneous stress distribution fosters swarm activity by limiting the size to which an earthquake can grow (leading to a high b
Taormina, Riccardo; Chau, Kwok-Wing
2015-10-01
Selecting an adequate set of inputs is a critical step for successful data-driven streamflow prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that employs Binary-coded discrete Fully Informed Particle Swarm optimization (BFIPS) and Extreme Learning Machines (ELM) to develop fast and accurate IVS algorithms. A scheme is employed to encode the subset of selected inputs and ELM specifications into the binary particles, which are evolved using single objective and multi-objective BFIPS optimization (MBFIPS). The performances of these ELM-based methods are assessed using the evaluation criteria and the datasets included in the comprehensive IVS evaluation framework proposed by Galelli et al. (2014). From a comparison with 4 major IVS techniques used in their original study it emerges that the proposed methods compare very well in terms of selection accuracy. The best performers were found to be (1) a MBFIPS-ELM algorithm based on the concurrent minimization of an error function and the number of selected inputs, and (2) a BFIPS-ELM algorithm based on the minimization of a variant of the Akaike Information Criterion (AIC). The first technique is arguably the most accurate overall, and is able to reach an almost perfect specification of the optimal input subset for a partially synthetic rainfall-runoff experiment devised for the Kentucky River basin. In addition, MBFIPS-ELM allows for the determination of the relative importance of the selected inputs. On the other hand, the BFIPS-ELM is found to consistently reach high accuracy scores while being considerably faster. By extrapolating the results obtained on the IVS test-bed, it can be concluded that the proposed techniques are particularly suited for rainfall-runoff modeling applications characterized by high nonlinearity in the catchment dynamics.
Directory of Open Access Journals (Sweden)
Xiaomin Xu
2015-11-01
Full Text Available The uncertainty and regularity of wind power generation are caused by wind resources’ intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP neural network, the results verify the superiority of the proposed method.
Collective motion of predictive swarms.
Directory of Open Access Journals (Sweden)
Nathaniel Rupprecht
Full Text Available Theoretical models of populations and swarms typically start with the assumption that the motion of agents is governed by the local stimuli. However, an intelligent agent, with some understanding of the laws that govern its habitat, can anticipate the future, and make predictions to gather resources more efficiently. Here we study a specific model of this kind, where agents aim to maximize their consumption of a diffusing resource, by attempting to predict the future of a resource field and the actions of other agents. Once the agents make a prediction, they are attracted to move towards regions that have, and will have, denser resources. We find that the further the agents attempt to see into the future, the more their attempts at prediction fail, and the less resources they consume. We also study the case where predictive agents compete against non-predictive agents and find the predictors perform better than the non-predictors only when their relative numbers are very small. We conclude that predictivity pays off either when the predictors do not see too far into the future or the number of predictors is small.
DEFF Research Database (Denmark)
Shrout, J.D.; Chopp, D.L.; Just, C.L.
2006-01-01
nutritionally conditional control of biofilm development through regulation of swarming motility. Examination of pilA and fliM mutant strains further supported the role of swarming motility in biofilm formation. These data led to a model proposing that the prevailing nutritional conditions dictate...
Luo, Yaqi; Zeng, Bi
2017-08-01
This paper researches the drainage routing problem in drainage pipe network, and propose an intelligent scheduling method. The method relates to the design of improved particle swarm optimization algorithm, the establishment of the corresponding model from the pipe network, and the process by using the algorithm based on improved particle swarm optimization to find the optimum drainage route in the current environment.
An iron detection system determines bacterial swarming initiation and biofilm formation
Lin, Chuan-Sheng; Tsai, Yu-Huan; Chang, Chih-Jung; Tseng, Shun-Fu; Wu, Tsung-Ru; Lu, Chia-Chen; Wu, Ting-Shu; Lu, Jang-Jih; Horng, Jim-Tong; Martel, Jan; Ojcius, David M.; Lai, Hsin-Chih; Young, John D.; Andrews, S. C.; Robinson, A. K.; Rodriguez-Quinones, F.; Touati, D.; Yeom, J.; Imlay, J. A.; Park, W.; Marx, J. J.; Braun, V.; Hantke, K.; Cornelis, P.; Wei, Q.; Vinckx, T.; Troxell, B.; Hassan, H. M.; Verstraeten, N.; Lewis, K.; Hall-Stoodley, L.; Costerton, J. W.; Stoodley, P.; Kearns, D. B.; Losick, R.; Butler, M. T.; Wang, Q.; Harshey, R. M.; Lai, S.; Tremblay, J.; Deziel, E.; Overhage, J.; Bains, M.; Brazas, M. D.; Hancock, R. E.; Partridge, J. D.; Kim, W.; Surette, M. G.; Givskov, M.; Rather, P. N.; Houdt, R. Van; Michiels, C. W.; Mukherjee, S.; Inoue, T.; Frye, J. G.; McClelland, M.; McCarter, L.; Silverman, M.; Matilla, M. A.; Wu, Y.; Outten, F. W.; Singh, P. K.; Parsek, M. R.; Greenberg, E. P.; Welsh, M. J.; Banin, E.; Vasil, M. L.; Wosten, M. M.; Kox, L. F.; Chamnongpol, S.; Soncini, F. C.; Groisman, E. A.; Laub, M. T.; Goulian, M.; Krell, T.; Lai, H. C.; Lin, C. S.; Soo, P. C.; Tsai, Y. H.; Wei, J. R.; Wyckoff, E. E.; Mey, A. R.; Leimbach, A.; Fisher, C. F.; Payne, S. M.; Livak, K. J.; Schmittgen, T. D.; Clarke, M. B.; Hughes, D. T.; Zhu, C.; Boedeker, E. C.; Sperandio, V.; Stintzi, A.; Clarke-Pearson, M. F.; Brady, S. F.; Drake, E. J.; Gulick, A. M.; Qaisar, U.; Rowland, M. A.; Deeds, E. J.; Garcia, C. A.; Alcaraz, E. S.; Franco, M. A.; Rossi, B. N. Passerini de; Mehi, O.; Skaar, E. P.; Visaggio, D.; Nishino, K.; Dietz, P.; Gerlach, G.; Beier, D.; Bustin, S. A.; Schwyn, B.; Neilands, J. B.
2016-01-01
Iron availability affects swarming and biofilm formation in various bacterial species. However, how bacteria sense iron and coordinate swarming and biofilm formation remains unclear. Using Serratia marcescens as a model organism, we identify here a stage-specific iron-regulatory machinery comprising
National Research Council Canada - National Science Library
Morris, William
2003-01-01
Over the last three and a half years Russian President Vladimir Putin and German Chancellor Gerhard Schroeder have developed a relationship that has brought the two countries closer foreign relation ties...
Loodav Vene natsiseadus ähvardab naabreid / Argo Ideon ; komment. Vladimir Juškin ja Marko Mihkelson
Ideon, Argo, 1966-
2009-01-01
Seaduseelnõust, millega tahetakse luua õiguslik raamistik võitluseks endisel Nõukogude Liidu alal väidetavalt vohava natsliku ideoloogia ja selle toetajatega. Kommenteerivad Vladimir Juškin ja Marko Mihkelson
Tuschi, Cyril, 1969-
2011-01-01
Dokumentaalfilmi "Hodorkovsky" režissöör Cyril Tuschi, Balti Vene Uuringute Keskuse direktor Vladimir Juškin ja suursaadik Mart Helme filmi peategelasest Mihhail Hodorkovskist ja Venemaa sisepoliitlisest olukorrast
DEFF Research Database (Denmark)
Erkut, Cumhur; Fehr, Jonas
2016-01-01
We present the design and evaluation of an interactive installation to be explored by movement and sound under Human-Activity Model. In the installation, movement qualities that are extracted from the motion tracking data excite a dynamical system (a synthetic flock of agents), which responds to ...
At the Cutting Edge of the Impossible: A Tribute to Vladimir P. Demikhov
Konstantinov, Igor E.
2009-01-01
Vladimir P. Demikhov (1916–1998) performed the world's first experimental intrathoracic transplantations and coronary artery bypass operation. His successes heralded the era of modern heart and lung transplantation and the surgical treatment of coronary artery disease. Even though he was one of the greatest experimental surgeons of the 20th century, his international isolation fueled speculation, suppositions, and myths. Ironically, his transplantation of a dog's head drew more publicity than...
Antipova, Anastasia
2015-01-01
Vol. 15 o f the "Jewish Encyclopedia" (St. Petersburg 1908-1913) contains an article on Freud, signed by Vladimir Zederbaum. The data for the article were provided by Max Eitingon. This paper addresses the question of whether Zederbaum himself was Eitingon's contact. Several archives produced a lot of information about Zederbaum's medical and journalistic activities in St. Petersburg. However, to date no connection between the two men could be established.
Avalik kiri Vladimir Iljaševitšile / Hannes Walter
Walter, Hannes, 1952-2004
2012-01-01
Vastukaja Olev Miili artiklile "Faktid väljamõeldiste vastu" (Õhtuleht, 22. juuni, 1988). Olev Miili pseudonüümi all kirjutas KGB töötaja Vladimir Iljaševitš, kes 1988. aasta aprillis avaldas üleliidulise uudisteagentuuri APN ajakirjas Globus artikli, milles õigustas ja põhjendas nõukogude võimu poolt Eestis sooritatud küüditamisi
A Novel Distributed Quantum-Behaved Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Yangyang Li
2017-01-01
Full Text Available Quantum-behaved particle swarm optimization (QPSO is an improved version of particle swarm optimization (PSO and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases.
A Review of Swarm-Based 1D/2D Signal Processing
Directory of Open Access Journals (Sweden)
Horia Mihail Teodorescu
2012-10-01
Full Text Available While swarming behavior, widely encountered in nature, has recently sparked numerous models and interest in domains as optimization, data clustering, and control, their application to signal processing remains sporadic. In this paper I provide a unitary treatment and a review of former results obtained in signal filtering and enhancement using swarms. General equations are presented for these procedures and stability issues are considered, with examples. The paper overviews several swarming model I introduced in previous papers and provides new evidence of the applicability of these models in signal processing. In all the models for 1D signal processing, the key idea is that the swarm hunts a prey that impersonates the filtered signal. In the 2D models, the signal (image represents the “landscape” over which the swarm moves at a distance, while the swarm interacts with the signal (landscape. I provide and discuss details of the underlying theory of the models for processing time-domain signals and images. While this paper partly follows and summarizes previous papers, it nevertheless includes supplementary theoretical and algorithmic considerations and new results for both 1D and 2D signal processing. Although following either biological models or physical models in swarm algorithms is not generally accepted for technical applications, we prefer to emphasize the analogies established by our biomimetic approach with these two groups of models.
A MÍSTICA RENANA E O PENSAMENTO DE VLADIMIR LOSSKY
Directory of Open Access Journals (Sweden)
Edrisi Fernandes
2015-07-01
Full Text Available Nossa reflexão consiste na reavaliação de algumas considerações originais de Vladimir Lossky em Théologie négative et connaissance de Dieu chez Maître Eckhart (publicação póstuma, Paris, 1960 quanto a uma aproximação entre a teologia apofática e a relação entre Essência e Energias divinas em escritos do Mestre Eckhart (1260-1328 e de Gregório Palamas (1296-1359, rememorando o projeto losskiano (inacabado de efetuar um estudo das convergências ou semelhanças entre a mística renana e o “palamismo”. Para isso, levamos em conta opiniões defendidas por Nikolai Gavryushin em "‘Istínnoe bogoslovie preobrazhaet metafiziku’: Zametki o Vladimire Losskom" ("‘A verdadeira teologia transforma a metafísica’: Notas sobre Vladimir Lossky", 2004, e analisamos o inacabado projeto de Lossky à luz da revisão das ideias de Gregório Palamas operada por pensadores da “escola russa de neoplatonismo”.
Particle Swarm Optimization with Double Learning Patterns.
Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian
2016-01-01
Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.
Particle Swarm Optimization with Double Learning Patterns
Directory of Open Access Journals (Sweden)
Yuanxia Shen
2016-01-01
Full Text Available Particle Swarm Optimization (PSO is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.
UAV Swarm Operational Risk Assessment System
2015-09-01
distribution is unlimited UAV SWARM OPERATIONAL RISK ASSESSMENT SYSTEM by Team CQ Alpha Cohort 311-141A September 2015 Project Advisors: Gregory......need for a UAV Swarm Risk Assessment Tool and how it can assist the Navy’s decision makers in assessing risk of UAV swarm threats in littoral
Directory of Open Access Journals (Sweden)
Rong Shan
2016-06-01
Full Text Available Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP. It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction.
Multispacecraft current estimates at swarm
DEFF Research Database (Denmark)
Dunlop, M. W.; Yang, Y.-Y.; Yang, J.-Y.
2015-01-01
During the first several months of the three-spacecraft Swarm mission all three spacecraft camerepeatedly into close alignment, providing an ideal opportunity for validating the proposed dual-spacecraftmethod for estimating current density from the Swarm magnetic field data. Two of the Swarm...... orbit the use oftime-shifted positions allow stable estimates of current density to be made and can verify temporal effects aswell as validating the interpretation of the current components as arising predominantly from field-alignedcurrents. In the case of four-spacecraft configurations we can resolve...... the full vector current and therefore cancheck the perpendicular as well as parallel current density components directly, together with the qualityfactor for the estimates directly (for the first time in situ at low Earth orbit)....
Jin, Xiuliang; Li, Zhenhai; Yang, Guijun; Yang, Hao; Feng, Haikuan; Xu, Xingang; Wang, Jihua; Li, Xinchuan; Luo, Juhua
2017-04-01
Timely and accurate estimation of winter wheat yield at a regional scale is crucial for national food policy and security assessments. Near-infrared reflectance is not sensitive to the leaf area index (LAI) and biomass of winter wheat at medium to high canopy cover (CC), and most of the vegetation indices displayed saturation phenomenon. However, LAI and biomass at medium to high CC can be efficiently estimated using imaging data from radar with stronger penetration, such as RADARSAT-2. This study had the following three objectives: (i) to combine vegetation indices based on our previous studies for estimating CC and biomass for winter wheat using HJ-1A/B and RADARSAT-2 imaging data; (ii) to combine HJ-1A/B and RADARSAT-2 imaging data with the AquaCrop model using the particle swarm optimization (PSO) algorithm to estimate winter wheat yield; and (iii) to compare the results from the assimilation of HJ-1A/B + RADARSAT-2 imaging data, HJ-1A/B imaging data, and RADARSAT-2 imaging data into the AquaCrop model using the PSO algorithm. Remote sensing data and concurrent LAI, biomass, and yield of sample fields were acquired in Yangling District, Shaanxi, China, during the 2014 winter wheat growing season. The PSO optimization algorithm was used to integrate the AquaCrop model and remote sensing data for yield estimation. The modified triangular vegetation index 2 (MTVI2) × radar vegetation index (RVI) and the enhanced vegetation index (EVI) × RVI had good relationships with CC and biomass, respectively. The results indicated that the predicted and measured yield (R2 = 0.31 and RMSE = 0.94 ton/ha) had agreement when the estimated CC from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model. When the estimated biomass from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model, the predicted yield showed agreement with the measured yield (R2 = 0.42 and RMSE = 0.81 ton/ha). These results show
A Diversity-Guided Particle Swarm Optimizer - the ARPSO
DEFF Research Database (Denmark)
Vesterstrøm, Jacob Svaneborg; Riget, Jacques
2002-01-01
The particle swarm optimization (PSO) algorithm is a new population based search strat- egy, which has exhibited good performance on well-known numerical test problems. How- ever, on strongly multi-modal test problems the PSO tends to suffer from premature convergence. This is due to a decrease......) in trying to overcome the problem of premature convergence. It uses a diversity measure to control the swarm. The result is an algorithm that alternates between phases of attraction and repulsion. The performance of the ARPSO is compared to a basic PSO (bPSO) and a genetic algorithm (GA). The results show...... that the ARPSO prevents premature convergence to a high degree, but still keeps a rapid convergence like the basic PSO. Thus, it clearly outperforms the basic PSO as well as the implemented GA in multi-modal optimization. Keywords Particle Swarm Optimization, Diversity-Guided Search 1 Introduction The PSO model...
Particle swarm optimization with composite particles in dynamic environments.
Liu, Lili; Yang, Shengxiang; Wang, Dingwei
2010-12-01
In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.
Swarm intelligence metaheuristics for enhanced data analysis and optimization.
Hanrahan, Grady
2011-09-21
The swarm intelligence (SI) computing paradigm has proven itself as a comprehensive means of solving complicated analytical chemistry problems by emulating biologically-inspired processes. As global optimum search metaheuristics, associated algorithms have been widely used in training neural networks, function optimization, prediction and classification, and in a variety of process-based analytical applications. The goal of this review is to provide readers with critical insight into the utility of swarm intelligence tools as methods for solving complex chemical problems. Consideration will be given to algorithm development, ease of implementation and model performance, detailing subsequent influences on a number of application areas in the analytical, bioanalytical and detection sciences.
Gravity inversion of a fault by Particle swarm optimization (PSO).
Toushmalani, Reza
2013-01-01
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. In this paper we introduce and use this method in gravity inverse problem. We discuss the solution for the inverse problem of determining the shape of a fault whose gravity anomaly is known. Application of the proposed algorithm to this problem has proven its capability to deal with difficult optimization problems. The technique proved to work efficiently when tested to a number of models.
Robotic swarm concept for efficient oil spill confrontation.
Kakalis, Nikolaos M P; Ventikos, Yiannis
2008-06-15
This paper examines the behaviour of a distributed system/robotic swarm concept for the effective confrontation of oil spills. The system described consists of a number of identical robotic units of high-power autonomy that recover oil mechanically and are able to communicate with each other. A mathematical model that accounts for a multitude of oil weathering processes and for the concerted action of the autonomous units is implemented for this investigation. Computational assessment of the robotic swarm in weathering oil spills indicates the potential effectiveness of the method.
Gold rush - A swarm dynamics in games
Zelinka, Ivan; Bukacek, Michal
2017-07-01
This paper is focused on swarm intelligence techniques and its practical use in computer games. The aim is to show how a swarm dynamics can be generated by multiplayer game, then recorded, analyzed and eventually controlled. In this paper we also discuss possibility to use swarm intelligence instead of game players. Based on our previous experiments two games, using swarm algorithms are mentioned briefly here. The first one is strategy game StarCraft: Brood War, and TicTacToe in which SOMA algorithm has also take a role of player against human player. Open research reported here has shown potential benefit of swarm computation in the field of strategy games and players strategy based on swarm behavior record and analysis. We propose new game called Gold Rush as an experimental environment for human or artificial swarm behavior and consequent analysis.
Epidemic Synchronization in Robotic Swarms
DEFF Research Database (Denmark)
Schiøler, Henrik; Nielsen, Jens Frederik Dalsgaard; Ngo, Trung Dung
2009-01-01
Clock synchronization in swarms of networked mobile robots is studied in a probabilistic, epidemic framework. In this setting communication and synchonization is considered to be a randomized process, taking place at unplanned instants of geographical rendezvous between robots. In combination wit...
Particle Swarm Optimization Based of the Maximum Photovoltaic ...
African Journals Online (AJOL)
A photovoltaic system including a solar panel and PSO MPP tracker is modelled and simulated, it has been has been carried out which has shown the effectiveness of PSO to draw much energy and fast response against change in working conditions. Keywords: Particle Swarm Optimization (PSO), photovoltaic system, ...
Pavlovic, Eduard
2009-01-01
Vladimir Hudolin was born in Ogulin in 1922 and died in Zagreb in 1996. He was one of the best students of the Susak grammar school and distinguished himself in a Catholic youth association Domagoj. In 1940, he moved to Zagreb to study medicine. In 1948 he graduated, and in 1951 specialised in psychiatry. His field of expertise was social psychiatry, alcohology in particular. In developing his own original preventive and remedial programmes, he much relied on the concepts of Community Psychiatry and alike, and managed to encourage their implementation on a variety of community levels, from local to national. His concept was recognised in a number of countries around the world; over 650 articles speak about how successful it was. This article focuses on Vladimir Hudolin's grammar school years in Susak, proposing that particular circumstances and figures from his formative years played a key role in his humanistic and scientific development. Early on it was his social activity in the Catholic youth association Domagoj and Bonifacije Perović, a theologist-sociologist who was a member of the Croatian Catholic Movement. The key figures who made him aware of the alcoholism issue were Fran Gudrum, Mirko Cunko, Maksimilijan Benković, Andrija Stampar, Josip Silović, and the Bishop of Senj Josip Marusić. Regardless of the controversies and controversial activities of some of the members of the Croatian Catholic Movement between the two world wars, there is no doubt that this movement has played a major role in the development of one of the most distinct figures in world alcohology, Vladimir Hudolin.
Cosmological parameter estimation using Particle Swarm Optimization
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Essays in mathematics and its applications in honor of Vladimir Arnold
Pardalos, Panos
2016-01-01
This volume, dedicated to the eminent mathematician Vladimir Arnold, presents a collection of research and survey papers written on a large spectrum of theories and problems that have been studied or introduced by Arnold himself. Emphasis is given to topics relating to dynamical systems, stability of integrable systems, algebraic and differential topology, global analysis, singularity theory and classical mechanics. A number of applications of Arnold’s groundbreaking work are presented. This publication will assist graduate students and research mathematicians in acquiring an in-depth understanding and insight into a wide domain of research of an interdisciplinary nature.
At the cutting edge of the impossible: a tribute to Vladimir P. Demikhov.
Konstantinov, Igor E
2009-01-01
Vladimir P. Demikhov (1916-1998) performed the world's first experimental intrathoracic transplantations and coronary artery bypass operation. His successes heralded the era of modern heart and lung transplantation and the surgical treatment of coronary artery disease. Even though he was one of the greatest experimental surgeons of the 20th century, his international isolation fueled speculation, suppositions, and myths. Ironically, his transplantation of a dog's head drew more publicity than did his pioneering thoracic surgical accomplishments, and he became an easy target for criticism. An account of Demikhov's life and work is presented herein.
Vladimir V. Lapin, Armiia Rossii v Kavkazskoi voine XVIII – XIX vv.
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Arsène Saparov
2009-12-01
Full Text Available Vladimir Lapin’s Armiia Rossii v Kavkazskoi voine XVIII-XIX vv. (The Russian Army in the Caucasian War XVIII – XIX centuries deals with the subject of the Russian involvement and conquest of the Caucasus in the course of 18th and 19th centuries. Unlike many traditional works on the subject the author does not present a chronological history of this conflict. Instead Lapin analyses the transformations the Russian Army underwent as a result of its prolonged interaction with the people and natu...
Phase Coexistence in Insect Swarms
Sinhuber, Michael; Ouellette, Nicholas T.
2017-10-01
Animal aggregations are visually striking, and as such are popular examples of collective behavior in the natural world. Quantitatively demonstrating the collective nature of such groups, however, remains surprisingly difficult. Inspired by thermodynamics, we applied topological data analysis to laboratory insect swarms and found evidence for emergent, material-like states. We show that the swarms consist of a core "condensed" phase surrounded by a dilute "vapor" phase. These two phases coexist in equilibrium, and maintain their distinct macroscopic properties even though individual insects pass freely between them. We further define a pressure and chemical potential to describe these phases, extending theories of active matter to aggregations of macroscopic animals and laying the groundwork for a thermodynamic description of collective animal groups.
Organic Computing and Swarm Intelligence
Merkle, Daniel; Middendorf, Martin; Scheidler, Alexander
The relations between swarm intelligence and organic computing are discussed in this chapter. The aim of organic computing is to design and study computing systems that consist of many autonomous components and show forms of collective behavior. Such organic computing systems (OC systems) should possess self-x properties (e.g., self-healing, self-managing, self-optimizing), have a decentralized control, and be adaptive to changing requirements of their user. Examples of OC systems are described in this chapter and two case studies are presented that show in detail that OC systems share important properties with social insect colonies and how methods of swarm intelligence can be used to solve problems in organic computing.
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Latin America and Indo-China (p. 27). It should be emphasised that Schmidt does not reduce the Cold War to American–Soviet confrontation, and writes about ... the contrary, caused the death of thousands of civilians. In the Conclusion of the book, Schmidt states, “during the period under consideration (1945–2010), foreign ...
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intervention at all” (p. xvi). The structure of the book is quite logical. The Introduction is followed by the first chapter, where Schmidt provides an analysis of nationalism and decolonisation during the Cold War (1945–1991). The introduction examines the ideologies, interests and practices of external powers involved in Africa.
The Cup of Vladimir Davydovich as an Evidence of Intercultural Contacts
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Medyntseva Albina A.
2012-03-01
Full Text Available The article is devoted to a unique find, a silver cup of the Chernigov Prince Vladimir Davydovich (1139-1151, found on the Tsarev (Sarai al-Jadid city site during the 1843 excavations. The cup is notable for its size (8 liters and especially remarkable for the toast inscription engraved on its upper edge. This discovery is widely discussed in a wide range of publications. In this article, the cup is treated as an evidence of cultural ties between Ancient Rus and the Steppe. The dialectal features of the inscription point to the Southern Russian origin of the craftsman. According to the author, the cup was made in 1139, when Vladimir Davydovich occupied the Chernigov throne. After the death of the Prince, his widow married the Polovtsian Khan, and it is owing to the Polovtsians that the cup could reach the Golden Horde capital. From The excavations on the Tsarev (Sarai al-Jadid city site have produced items of local origin, for which the cup under consideration could evidently serve as prototype.
Vladimir Levstik et la langue française avant 1941
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Florence Gacoin-Marks
2005-12-01
Full Text Available En Slovénie, Vladimir Levstik (1886-1957 est aujourd'hui surtout connu comme un traducteur des littératures anglaise, française et russe particulièrement fecond, ayant traduit au total près d'une centaine d'reuvres. Parmi toutes ces traductions ayant souvent connu plusieurs reimpressions ou rééditions, une soixantaine sont parues avant la seconde guerre mondiale, à la période où Levstik est également très actif en tant qu'écrivain. Cette activité de traducteur très intensive n'est done pas seulement intéressante en elle-même: transmetteur ayant permis aux lecteurs slovènes d'accéder aux plus grands chefs-d'reuvre du patrimoine littéraire (en parti culier romanesque européen, Vladimir Levstik doit être également envisager en tant que lecteur et récepteur privilégié des littératures européennes.
Collective behaviour without collective order in wild swarms of midges.
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Alessandro Attanasi
2014-07-01
Full Text Available Collective behaviour is a widespread phenomenon in biology, cutting through a huge span of scales, from cell colonies up to bird flocks and fish schools. The most prominent trait of collective behaviour is the emergence of global order: individuals synchronize their states, giving the stunning impression that the group behaves as one. In many biological systems, though, it is unclear whether global order is present. A paradigmatic case is that of insect swarms, whose erratic movements seem to suggest that group formation is a mere epiphenomenon of the independent interaction of each individual with an external landmark. In these cases, whether or not the group behaves truly collectively is debated. Here, we experimentally study swarms of midges in the field and measure how much the change of direction of one midge affects that of other individuals. We discover that, despite the lack of collective order, swarms display very strong correlations, totally incompatible with models of non-interacting particles. We find that correlation increases sharply with the swarm's density, indicating that the interaction between midges is based on a metric perception mechanism. By means of numerical simulations we demonstrate that such growing correlation is typical of a system close to an ordering transition. Our findings suggest that correlation, rather than order, is the true hallmark of collective behaviour in biological systems.
Reversals and collisions optimize protein exchange in bacterial swarms
Energy Technology Data Exchange (ETDEWEB)
Amiri, Aboutaleb; Harvey, Cameron; Buchmann, Amy; Christley, Scott; Shrout, Joshua D.; Aranson, Igor S.; Alber, Mark
2017-03-01
Swarming groups of bacteria coordinate their behavior by self-organizing as a population to move over surfaces in search of nutrients and optimal niches for colonization. Many open questions remain about the cues used by swarming bacteria to achieve this self-organization. While chemical cue signaling known as quorum sensing is well-described, swarming bacteria often act and coordinate on time scales that could not be achieved via these extracellular quorum sensing cues. Here, cell-cell contact-dependent protein exchange is explored as amechanism of intercellular signaling for the bacterium Myxococcus xanthus. A detailed biologically calibrated computational model is used to study how M. xanthus optimizes the connection rate between cells and maximizes the spread of an extracellular protein within the population. The maximum rate of protein spreading is observed for cells that reverse direction optimally for swarming. Cells that reverse too slowly or too fast fail to spread extracellular protein efficiently. In particular, a specific range of cell reversal frequencies was observed to maximize the cell-cell connection rate and minimize the time of protein spreading. Furthermore, our findings suggest that predesigned motion reversal can be employed to enhance the collective behavior of biological synthetic active systems.
Raghib, Michael; Levin, Simon; Kevrekidis, Ioannis
2010-05-01
Self-propelled particle models (SPP's) are a class of agent-based simulations that have been successfully used to explore questions related to various flavors of collective motion, including flocking, swarming, and milling. These models typically consist of particle configurations, where each particle moves with constant speed, but changes its orientation in response to local averages of the positions and orientations of its neighbors found within some interaction region. These local averages are based on `social interactions', which include avoidance of collisions, attraction, and polarization, that are designed to generate configurations that move as a single object. Errors made by the individuals in the estimates of the state of the local configuration are modeled as a random rotation of the updated orientation resulting from the social rules. More recently, SPP's have been introduced in the context of collective decision-making, where the main innovation consists of dividing the population into naïve and `informed' individuals. Whereas naïve individuals follow the classical collective motion rules, members of the informed sub-population update their orientations according to a weighted average of the social rules and a fixed `preferred' direction, shared by all the informed individuals. Collective decision-making is then understood in terms of the ability of the informed sub-population to steer the whole group along the preferred direction. Summary statistics of collective decision-making are defined in terms of the stochastic properties of the random walk followed by the centroid of the configuration as the particles move about, in particular the scaling behavior of the mean squared displacement (msd). For the region of parameters where the group remains coherent , we note that there are two characteristic time scales, first there is an anomalous transient shared by both purely naïve and informed configurations, i.e. the scaling exponent lies between 1 and
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S. R. McNutt
1996-06-01
Full Text Available Global data from 1979 to 1989 pertaining to volcanic earthquake swarms have been compiled into a custom-designed relational database. The database is composed of three sections: 1 a section containing general information on volcanoes, 2 a section containing earthquake swarm data (such as dates of swarm occurrence and durations, and 3 a section containing eruption information. The most abundant and reliable parameter, duration of volcanic earthquake swarms, was chosen for preliminary analysis. The distribution of all swarm durations was found to have a geometric mean of 5.5 days. Precursory swarms were then separated from those not associated with eruptions. The geometric mean precursory swarm duration was 8 days whereas the geometric mean duration of swarms not associated with eruptive activity was 3.5 days. Two groups of precursory swarms are apparent when duration is compared with the eruption repose time. Swarms with durations shorter than 4 months showed no clear relationship with the eruption repose time. However, the second group, lasting longer than 4 months, showed a significant positive correlation with the log10 of the eruption repose period. The two groups suggest that different suites of physical processes are involved in the generation of volcanic earthquake swarms.
Panov, Vladimir, 1941-
2002-01-01
Ilmunud ka: Stolitsa 30.august lk.3. AS Tallinna Vesi ja Tallinna linn leppisid kokku uues tariifi muutmise struktuuris järgmisteks aastateks. Tallinna abilinnapea Vladimir Panov vastab küsimustele, mis puudutavad läbirääkimisi Tallinna Vee ja Tallinna linna vahel
Monitoring a robot swarm using a data-driven fault detection approach
Khaldi, Belkacem
2017-06-30
Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).
Changes in Earth's core-generated magnetic field, as observed by Swarm
DEFF Research Database (Denmark)
Finlay, Chris; Olsen, Nils; Gillet, Nicolas
By far the largest part of the Earth's magnetic field is generated by motions taking place within our planet's liquid metal outer core. Variations of this core-generated field thus provide us with a unique means of probing the dynamics taking place in the deepest reaches of the Earth....... In this contribution, we will present the core-generated magnetic field, and its recent time changes, as seen by ESA's Earth explorer mission Swarm. We will present a new time-dependent geomagnetic field model, called CHAOS-6, derived from satellite data collected by the Swarm constellation, as well as data from...... the previous missions CHAMP and Oersted together with ground observatory data. Advantage is taken of the constellation aspect of the Swarm mission by ingesting field differences along track and across track between the lower pair of Swarm satellites. Evaluating the global field model at the outer boundary...
The k -Unanimity Rule for Self-Organized Decision-Making in Swarms of Robots.
Scheidler, Alexander; Brutschy, Arne; Ferrante, Eliseo; Dorigo, Marco
2016-05-01
In this paper, we propose a collective decision-making method for swarms of robots. The method enables a robot swarm to select, from a set of possible actions, the one that has the fastest mean execution time. By means of positive feedback the method achieves consensus on the fastest action. The novelty of our method is that it allows robots to collectively find consensus on the fastest action without measuring explicitly the execution times of all available actions. We study two analytical models of the decision-making method in order to understand the dynamics of the consensus formation process. Moreover, we verify the applicability of the method in a real swarm robotics scenario. To this end, we conduct three sets of experiments that show that a robotic swarm can collectively select the shortest of two paths. Finally, we use a Monte Carlo simulation model to study and predict the influence of different parameters on the method.
A Distributed Particle Swarm Optimization Zlgorithmfor Flexible Job-hop Scheduling Problem
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LIU Sheng--hui
2017-06-01
Full Text Available According to the characteristics of the Flexible job shop scheduling problem the minimum makespan as measures we proposed a distributed particle swarm optimization algorithm aiming to solve flexible job shop scheduling problem. The algorithm adopts the method of distributed ideas to solve problems and we are established for two multi agent particle swarm optimization model in this algorithm it can solve the traditional particle swarm optimization algorithm when making decisions in real time according to the emergencies. Finally some benthmark problems were experimented and the results are compared with the traditional algorithm. Experimental results proved that the developed distributed PSO is enough effective and efficient to solve the FJSP and it also verified the reasonableness of the multi}gent particle swarm optimization model.
Seismological mechanism analysis of 2015 Luanxian swarm, Hebei province,China
Tan, Yipei; Liao, Xu; Ma, Hongsheng; Zhou, Longquan; Wang, Xingzhou
2017-04-01
The seismological mechanism of an earthquake swarm, a kind of seismic burst activity, means the physical and dynamic process in earthquakes triggering in the swarm. Here we focus on the seismological mechanism of 2015 Luanxian swarm in Hebei province, China. The process of digital seismic waveform data processing is divided into four steps. (1) Choose the three components waveform of earthquakes in the catalog as templates, and detect missing earthquakes by scanning the continues waveforms with matched filter technique. (2) Recalibrate P and S-wave phase arrival time using waveform cross-correlation phase detection technique to eliminate the artificial error in phase picking in the observation report made by Hebei seismic network, and then we obtain a more complete catalog and a more precise seismic phase report. (3) Relocate the earthquakes in the swarm using hypoDD based on phase arrival time we recalibrated, and analyze the characteristics of swarm epicenter migration based on the earthquake relocation result. (4) Detect whether there are repeating earthquakes activity using both waveform cross-correlation standard and whether rupture areas can overlapped. We finally detect 106 missing earthquakes in the swarm, 66 of them have the magnitude greater than ML0.0, include 2 greater than ML1.0. Relocation result shows that the epicenters of earthquakes in the swarm have a strip distribution in NE-SW direction, which indicates the seismogenic structure may be a NE-SW trending fault. The spatial-temporal distribution variation of epicenters in the swarm shows a kind of two stages linear migration characteristics, in which the first stage has appeared with a higher migration velocity as 1.2 km per day, and the velocity of the second step is 0.0024 km per day. According to the three basic models to explain the seismological mechanism of earthquake swarms: cascade model, slow slip model and fluid diffusion model, repeating earthquakes activity is difficult to explain by
Goodarzi, Mohammad; Saeys, Wouter; Deeb, Omar; Pieters, Sigrid; Vander Heyden, Yvan
2013-12-01
Quantitative structure-activity relationship (QSAR) modeling was performed for imidazo[1,5-a]pyrido[3,2-e]pyrazines, which constitute a class of phosphodiesterase 10A inhibitors. Particle swarm optimization (PSO) and genetic algorithm (GA) were used as feature selection techniques to find the most reliable molecular descriptors from a large pool. Modeling of the relationship between the selected descriptors and the pIC50 activity data was achieved by linear [multiple linear regression (MLR)] and non-linear [locally weighted regression (LWR) based on both Euclidean (E) and Mahalanobis (M) distances] methods. In addition, a stepwise MLR model was built using only a limited number of quantum chemical descriptors, selected because of their correlation with the pIC50 . The model was not found interesting. It was concluded that the LWR model, based on the Euclidean distance, applied on the descriptors selected by PSO has the best prediction ability. However, some other models behaved similarly. The root-mean-squared errors of prediction (RMSEP) for the test sets obtained by PSO/MLR, GA/MLR, PSO/LWRE, PSO/LWRM, GA/LWRE, and GA/LWRM models were 0.333, 0.394, 0.313, 0.333, 0.421, and 0.424, respectively. The PSO-selected descriptors resulted in the best prediction models, both linear and non-linear. © 2013 John Wiley & Sons A/S.
Swarm Data Processing and First Scientific Results
DEFF Research Database (Denmark)
Olsen, Nils
2014-01-01
, accelerometer, plasma and electric field measurements. These observations will be distributed by ESA as Level-1b data, which are the calibrated and formatted time series of e.g. the magnetic field measurements taken by each of the three Swarm satellites. The talks presents a first scientific validation of Swarm...... Level-1b data products....
Alteri, Christopher J; Himpsl, Stephanie D; Engstrom, Michael D; Mobley, Harry L T
2012-10-30
observations that followed the invention of the microscope. A bacterium can swim through a fluid environment or coordinate motion with a group of bacteria and swarm across a surface. The flagellar motor, which propels the bacterium, is fueled by proton motive force. In contrast to the physiology that governs swimming motility, much less is known about the energy sources required for multicellular swarming on surfaces. In this study, we used Proteus mirabilis as a model organism to study vigorous swarming behavior and genetic and biochemical approaches to define energy pathways and central metabolism that contribute to multicellular motility. We found that swarming bacteria use a complete aerobic tricarboxylic acid (TCA) cycle but do not respire oxygen as the terminal electron acceptor, suggesting that multicellular cooperation during swarming reduces the amount of energy required by individual bacteria to achieve rapid motility.
Triarhou, Lazaros C; Verina, Tatyana
2016-11-01
In 1899 a landmark paper entitled "On the musical centers of the brain" was published in Pflügers Archiv, based on work carried out in the Anatomo-Physiological Laboratory of the Neuropsychiatric Clinic of Vladimir M. Bekhterev (1857-1927) in St. Petersburg, Imperial Russia. The author of that paper was Vladimir E. Larionov (1857-1929), a military doctor and devoted brain scientist, who pursued the problem of the localization of function in the canine and human auditory cortex. His data detailed the existence of tonotopy in the temporal lobe and further demonstrated centrifugal auditory pathways emanating from the auditory cortex and directed to the opposite hemisphere and lower brain centers. Larionov's discoveries have been largely considered as findings of the Bekhterev school. Perhaps this is why there are limited resources on Larionov, especially keeping in mind his military medical career and the fact that after 1917 he just seems to have practiced otorhinolaryngology in Odessa. Larionov died two years after Bekhterev's mysterious death of 1927. The present study highlights the pioneering contributions of Larionov to auditory neuroscience, trusting that the life and work of Vladimir Efimovich will finally, and deservedly, emerge from the shadow of his celebrated master, Vladimir Mikhailovich. Copyright © 2016 Elsevier B.V. All rights reserved.
PAGES OF LIFE OF PHYSICIAN-IN-ORDINARY IN THE FAMILY OF NICHOLAS II VLADIMIR NIKOLAYEVICH DEREVENKO
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Shevtsova Z.I. Shevtsova Z.I.
2013-08-01
Full Text Available Stages of life and professional activity of a scientist, professor, surgeon, citizen, Vladimir Derevenko have been provided. He had to endure hardships, exiles, arrests. The reason of persecution is the six-year work on rendering medical assistance to the tsar’s family and in particular to the heir to the throne Alexey.
Particle Swarm Transport in Fracture Networks
Pyrak-Nolte, L. J.; Mackin, T.; Boomsma, E.
2012-12-01
Colloidal particles of many types occur in fractures in the subsurface as a result of both natural and industrial processes (e.g., environmental influences, synthetic nano- & micro-particles from consumer products, chemical and mechanical erosion of geologic material, proppants used in gas and oil extraction, etc.). The degree of localization and speed of transport of such particles depends on the transport mechanisms, the chemical and physical properties of the particles and the surrounding rock, and the flow path geometry through the fracture. In this study, we investigated the transport of particle swarms through artificial fracture networks. A synthetic fracture network was created using an Objet Eden 350V 3D printer to build a network of fractures. Each fracture in the network had a rectangular cross-sectional area with a constant depth of 7 mm but with widths that ranged from 2 mm to 11 mm. The overall dimensions of the network were 132 mm by 166 mm. The fracture network had 7 ports that were used either as the inlet or outlet for fluid flow through the sample or for introducing a particle swarm. Water flow rates through the fracture were controlled with a syringe pump, and ranged from zero flow to 6 ml/min. Swarms were composed of a dilute suspension (2% by mass) of 3 μm fluorescent polystyrene beads in water. Swarms with volumes of 5, 10, 20, 30 and 60 μl were used and delivered into the network using a second syringe pump. The swarm behavior was imaged using an optical fluorescent imaging system illuminated by green (525 nm) LED arrays and captured by a CCD camera. For fracture networks with quiescent fluids, particle swarms fell under gravity and remained localized within the network. Large swarms (30-60 μl) were observed to bifurcate at shallower depths resulting in a broader dispersal of the particles than for smaller swarm volumes. For all swarm volumes studied, particle swarms tended to bifurcate at the intersection between fractures. These
Design and control of swarm dynamics
Bouffanais, Roland
2016-01-01
The book is about the key elements required for designing, building and controlling effective artificial swarms comprised of multiple moving physical agents. Therefore this book presents the fundamentals of each of those key elements in the particular frame of dynamic swarming, specifically exposing the profound connections between these elements and establish some general design principles for swarming behaviors. This scientific endeavor requires an inter-disciplinary approach: biomimetic inspiration from ethology and ecology, study of social information flow, analysis of temporal and adaptive signaling network of interaction, considerations of control of networked real-time systems, and lastly, elements of complex adaptive dynamical systems. This book offers a completely new perspective on the scientific understanding of dynamic collective behaviors thanks to its multi-disciplinary approach and its focus on artificial swarm of physical agents. Two of the key problems in understanding the emergence of swarm ...
Insular species swarm goes underground
DEFF Research Database (Denmark)
P. S. Reboleira, Ana Sofia; Enghoff, Henrik
2014-01-01
-group, an insular species swarm distributed in the archipelagos of Madeira and the Canary Islands. We discuss the differences between the new species and their relatives and present information on the subterranean environment of Madeira. An updated overview of the subterranean biodiversity of millipedes......Two new species of the genus Cylindroiulus Verhoeff, 1894, C. julesvernei and C. oromii, are described from the subterranean ecosystem of Madeira Island, Portugal. Species are illustrated with photographs and diagrammatic drawings. The new species belong to the Cylindroiulus madeirae...
Eugene Onegin the Cold War Monument: How Edmund Wilson Quarreled with Vladimir Nabokov
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Tim Conley
2014-01-01
Full Text Available The tale of how Edmund Wilson quarreled with Vladimir Nabokov over the latter’s 1964 translation of Eugene Onegin can be instructively read as a politically charged event, specifically a “high culture” allegory of the Cold War. Dissemination of anti-Communist ideals (often in liberal and literary guises was the mandate of the Congress for Cultural Freedom, whose funding and editorial initiatives included the publication of both pre-Revolution Russian literature and, more notoriously, the journal Encounter (1953-1990, where Nabokov’s fiery “Reply” to Wilson appeared. This essay outlines the propaganda value of the Onegin debate within and to Cold War mythology.
A mystery of Vladimir P. Demikhov: the 50th anniversary of the first intrathoracic transplantation.
Konstantinov, I E
1998-04-01
Vladimir P. Demikhov was the first to perform intrathoracic transplantation of the heart alone, lung alone, and the heart and lungs in a warm-blooded animal and the first to perform an experimental coronary artery bypass operation with success. He designed the first mechanical cardiac substitute and was one of the first to use the vascular stapling device in experiments. In 1960 Demikhov published the world's first monograph dealing with the subject of thoracic transplantation. This monograph, Experimental Transplantation of Vital Organs, became a "bible of intrathoracic transplantation" and deserves the recognition of everyone who is interested in organ transplantation. However, to say only that Demikhov was the first in the world who performed these operations is to say nothing. It is important to describe the circumstances under which these operations were done to appreciate the true scale of his innovations.
Nationalism and legitimation for authoritarianism: A comparison of Nicholas I and Vladimir Putin
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Sean Cannady
2014-01-01
Full Text Available This article draws parallels between Tsar Nicholas I and current Russian President Vladimir Putin with respect to their use of nationalism to justify statist policies and political authoritarianism. Building upon insights by Alexander Gerschenkron about the economic development of “backwards” states, it argues that both Nicholas and Putin have rhetorically used Western concepts such as nationalism and democracy to legitimize their rule but have modified them to give them more statist content. Under Nicholas, this was exemplified in the tripartite (Orthodoxy, Autocracy, and Nationality Official Nationality policy. Putin has emphasized patriotism, power, and statism to justify centralization of power and authoritarian policies. Putin's policies and rhetoric are strong analogs to those of Nicholas. Ultimately, the goal of this paper is to explain state-inspired Russian nationalism and how it has been aligned with authoritarian politics, as well as specifying similarities between present and past in Russia.
Il talento di Vladimir Spasovič tra realtà e finzione letteraria
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Alessandra Elisa Visinoni
2012-04-01
Full Text Available It is well known that F.M. Dostoevsky was fascinated by Vladimir Spasovich. In fact the Russian writer devoted to him a large section of his Diary of a writer, and consider him as a prototype for creating Fetjukovich, the Dmitry Karamazov’s attorney. The present analysis aims to investigate the nature of the reflections about Spasovich by the comparison, only worked on drafts of the Diary, between the lawyer and the Tacitean Vibulenus. Secondly, this research will show the presence of another literary Spasovich’s alter ego in Brothers Karamazov: Ippolit Kirillovich, who embodies the Spasovich’s passion for criminal psychology and physiology. Finally, they will be explained the reasons for the creation of two "doubles" and the contrasting mechanisms of what we might call a "moral lesson" given by the evocative devices of storytelling.
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Yegorov I.N.,
2017-08-01
Full Text Available The paper summarizes the many years of experience in methodological support and training at the Center of Professional Education for the Disabled and at the “Inclusive Education” Research and Educational Center of the Vladimir State University. At these centers the work is aimed at establishing a system of continuous higher inclusive education for persons with hearing and visual impairments. The paper focuses on the issues concerning the organization of educational process and the adaptation of learning materials for students with hearing and visual impairments; it addresses the problem of creating a system of academic support for students with disabilities and providing psychological and educational assistance to individuals with hearing and visual impairments in the system of higher education.
Directory of Open Access Journals (Sweden)
Christine Raguet
2017-09-01
Full Text Available The aims of this article are: i to analyze the relations of Vladimir Nabokov as a multilingual author with translation when faced with exile and with the publication of one of his novels in a language in which he could write; ii to explore Nabokov’s attitude towards the translation of one of his books, his desires to be recognized as an author and to polish his style in the new culture-language; iii to present factors like personal and cultural identity, and also financial needs as linked to exile and as significant elements in the translating process; iv to discuss the impact of rewriting in an author seeking international recognition and in an obvious quest for new aesthetic values. Nabokov is not a unique case, but his situation and reactions are quite representative of the difficulties raised when changing one’s language of composition.
An, Meiyan; Wang, Zhaokui; Zhang, Yulin
2017-01-01
The self-organizing control strategy for asteroid intelligent detection swarm, which is considered as a space application instance of intelligent swarm, is developed. The leader-follower model for the asteroid intelligent detection swarm is established, and the further analysis is conducted for massive asteroid and small asteroid. For a massive asteroid, the leader spacecraft flies under the gravity field of the asteroid. For a small asteroid, the asteroid gravity is negligible, and a trajectory planning method is proposed based on elliptic cavity virtual potential field. The self-organizing control strategy for the follower spacecraft is developed based on a mechanism of velocity planning and velocity tracking. The simulation results show that the self-organizing control strategy is valid for both massive asteroid and small asteroid, and the exploration swarm forms a stable configuration.
Assessment of agronomic homogeneity and compatibility of soils in the Vladimir Opolie region
Shein, E. V.; Kiryushin, V. I.; Korchagin, A. A.; Mazirov, M. A.; Dembovetskii, A. V.; Il'in, L. I.
2017-10-01
Complexes of gray forest soils of different podzolization degrees with the participation of gray forest podzolized soils with the second humus horizon play a noticeable role in the soil cover patterns of Vladimir Opolie. The agronomic homogeneity and agronomic compatibility of gray forest soils in automorphic positions ("plakor" sites) were assessed on the test field of the Vladimir Agricultural Research Institute. The term "soil homogeneity" implies in our study the closeness of crop yield estimates (scores) for the soil polygons; the term "soil compatibility" implies the possibility to apply the same technologies in the same dates for different soil polygons within a field. To assess the agronomic homogeneity and compatibility of soils, the statistical analysis of the yields of test crop (oats) was performed, and the spatial distribution of the particular parameters of soil hydrothermic regime was studied. The analysis of crop yields showed their high variability: the gray forest soils on microhighs showed the minimal potential fertility, and the maximal fertility was typical of the soils with the second humus horizon in microlows. Soils also differed significantly in their hydrothermic regime, as the gray forest soils with the second humus horizon were heated and cooled slower than the background gray forest soils; their temperature had a stronger lag effect and displayed a narrower amplitude in seasonal fluctuations; and these soils were wetter during the first weeks (40 days) of the growing season. Being colder and wetter, the soils with the second humus horizons reached their physical ripeness later than the gray forest soils. Thus, the soil cover of the test plot in the automorphic position is heterogeneous; from the agronomic standpoint, its components are incompatible.
Further east: eutrophication as a major threat to the flora of Vladimir Oblast, Russia.
Seregin, Alexey P
2014-11-01
Eutrophication remains a major threat to the flora of Western Europe despite measures to reduce nitrogen emissions. Although nutrient enrichment has been recorded for both inland waters and adjacent seas, there is almost no evidence from Russia for large-scale anthropogenic eutrophication of soils and its impact on terrestrial biota. I used the distribution grid data (337 grid squares, ca. 96 km(2)) on 1,384 vascular plants of Vladimir Oblast for two periods (1869-1999 vs. 2000-2012) to estimate the shifts in mean Ellenberg's indicator values for nitrogen and soil reaction. Decadal changes in the flora of acid sandy Meshchera Lowlands were observed directly during two grid surveys of 2002 and 2012 based on a coarser grid (50 squares, ca. 24 km(2)). Despite the spatial correlation of Ellenberg's indicator values for soil reaction and nitrogen, mean grid values for nitrogen are growing in areas with both acid and neutral soils. The changes in mean grid indicator values for nitrogen are caused by either local extinctions of species from nutrient-poor habitats or spread of nitrophilous plants. I found that oligotrophic habitats are declining rapidly within the eutrophic loamy landscapes. In contrast, changes in landscapes with acid sandy soils are caused by increasing number of records of nitrophilous species, both invasive and native. These two processes have different spatial patterns caused by varying levels of geochemical buffer capacity and should be considered separately. Fragmentary historical data on Vladimir Oblast flora agrees with the overall European picture of eutrophication in the twentieth century.
Cooperative Control of Swarms of Unmanned Aerial Vehicles
De Vries, E.; Subbarao, K.
2011-01-01
Potential function based swarm control is a technique using artificial potential functions to generate steering commands resulting in swarming behavior. This means that the vehicles in the swarm autonomously take care of flying in formation, resulting in steering the swarm, instead of all the
Swarming dynamics in bacterial colonies
Zhang, Hepeng; Be'Er, Avraham; Smith, Rachel; Florin, E.-L.; Swinney, Harry L.
2009-11-01
Swarming is a widespread phenomenon observed in both biological and non-biological systems. Large mammal herds, fish schools, and bird flocks are among the most spectacular examples. Many theoretical and numerical efforts have been made to unveil the general principles of the phenomenon, but systematic experimental studies have been very limited. We determine the characteristic velocity, length, and time scales for bacterial motion in swarming colonies of Paenibacillus dendritiformis growing on semi-solid agar substrates. The bacteria swim within a thin fluid layer, and they form long-lived jets and vortices. These coherent structures lead to anisotropy in velocity spatial correlations and to a two-step relaxation in velocity temporal correlations. The mean squared displacement of passive tracers exhibits a short-time regime with nearly ballistic transport and a diffusive long-time regime. We find that various definitions of the correlation length all lead to length scales that are, surprisingly, essentially independent of the mean bacterial speed, while the correlation time is linearly proportional to the ratio of the correlation length to the mean speed.
Creating Virtual Communities by Means of Swarm Intelligence
Directory of Open Access Journals (Sweden)
Lucian Hancu
2011-01-01
Full Text Available
During centuries, observing the behavior of wild species has always been fascinating and full of mysteries. Modeling human interactions based on those revealed on the wilderness conducts to innovative solutions, but also poses unexpected issues. In this article, we describe our approach of creating communities of virtual entities by means of swarm intelligence. We discuss the algorithm of creating the virtual communities along with the issues that arise when modeling business entities as individuals of the swarm.
Intrinsic Fluctuations and Driven Response of Insect Swarms
Ni, Rui; Puckett, James G.; Dufresne, Eric R.; Ouellette, Nicholas T.
2015-09-01
Animals of all sizes form groups, as acting together can convey advantages over acting alone; thus, collective animal behavior has been identified as a promising template for designing engineered systems. However, models and observations have focused predominantly on characterizing the overall group morphology, and often focus on highly ordered groups such as bird flocks. We instead study a disorganized aggregation (an insect mating swarm), and compare its natural fluctuations with the group-level response to an external stimulus. We quantify the swarm's frequency-dependent linear response and its spectrum of intrinsic fluctuations, and show that the ratio of these two quantities has a simple scaling with frequency. Our results provide a new way of comparing models of collective behavior with experimental data.
Optimized Landing of Autonomous Unmanned Aerial Vehicle Swarms
2012-06-01
in a few hours. xvii THIS PAGE INTENTIONALLY LEFT BLANK xviii CHAPTER 1: INTRODUCTION 1.1 Background “The fiercest serpent may be overcome by a swarm... languages . Though Python has optimization-related packages and modeling languages such as Coopr and Pyomo, respectively, the lack of familiarity prevented...data structures that stay resident in the generating language , and/or using a compiled language . 32 Figure 3.12: Graphical Depiction of Scenario One
Synthesizing Sierpinski Antenna by Genetic Algorithm and Swarm Optimization
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Z. Raida
2008-12-01
Full Text Available The paper discusses the synthesis of the Sierpinski antenna operating at three prescribed frequencies: 0.9 GHz, 1.8 GHz (both GSM and 2.4 GHz (Bluetooth. In order to synthesize the antenna, a genetic algorithm and a particle swarm optimization were used. The numerical model of the antenna was developed in Zeland IE3D, optimization scripts were programmed in MATLAB. Results of both the optimization methods are compared and experimentally verified.
Energy Technology Data Exchange (ETDEWEB)
Lin Li [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082 (China); Lin Weiqi [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082 (China); Jiang Jianhui [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082 (China); Zhou Yanping [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082 (China); Shen Guoli [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082 (China); Yu Ruqin [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082 (China)]. E-mail: rqyu@hnu.cn
2005-11-03
In the present work, we employed piecewise hyper-sphere modeling by particle swarm optimization (PHMPSO) which splits the dataset into subsets with desired linearity in each model for QSAR studies of a series of 2-aryl(heteroaryl)-2,5-dihydropyrazolo[4,3-c]quinolin-3-(3H)-ones (PQs) for their affinity to benzodiazepine receptor (BzR). The results were compared to those obtained by MLR modeling in a single model with the whole data set as well as in submodels based on K-means clustering analysis. It has been clearly shown that electronic descriptors and spatial descriptors play the important roles in the compounds' affinity to BzR. In addition, the molecular density, the Y component of the principal moment of inertia, the magnitude and the Y component of the dipole moment of the molecules can detrimentally affect PQ analogue BzR affinity, while the X component of the dipole moment of the molecules can favorably affect compounds' affinity.
Physiological processes related to the bee swarming
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Jiří Svoboda
2010-01-01
Full Text Available One of the essential genetically subjected behaviours of a bee-colony is swarming. However, in the time of queen breeding and technical approach to colony division, swarming constitutes a problem in the effectiveness of controlled beekeeping and subsequently in decreasing of the attainable economic profits. The intensity of swarming is a polyfactorial phenomenon whose characteristic feature is seasonality (the availability of breed, course of weather so the swarming intensity is different in particular years. This study is connected with the research carried out at the Department of Zoology, Fisheries, Hydrobiology and Apiculture at Mendel University in Brno. The experiment focused on the relationship between the swarming and biological state of bee-colony was realized in three seasons of the period 2003–2005. Experimental bee-colonies were stimulated to the swarming fever by zoo-technical practices, at the same time the biological status of given bee-colony was observed. Within the process of marking of newly emerged workers there was observed their number continuously during the particular season. The samples of 3- and 4-week-old workers were instrumental to the analysis of the development of their hypopharyngeal glands. The study has proved that a bee-colonies building higher number of queen cells are likely expected to be in swarming fever, b 3-week-old workers have hypopharyngeal glands in higher stage of development than 4-week-old workers, c higher stage of swarming fever is closely correlated with higher stage of development of hypopharyngeal glands. These facts can contribute to the comprehension of the reason and relationships of the swarming.
Swarm Intelligence Optimization and Its Applications
Ding, Caichang; Lu, Lu; Liu, Yuanchao; Peng, Wenxiu
Swarm Intelligence is a computational and behavioral metaphor for solving distributed problems inspired from biological examples provided by social insects such as ants, termites, bees, and wasps and by swarm, herd, flock, and shoal phenomena in vertebrates such as fish shoals and bird flocks. An example of successful research direction in Swarm Intelligence is ant colony optimization (ACO), which focuses on combinatorial optimization problems. Ant algorithms can be viewed as multi-agent systems (ant colony), where agents (individual ants) solve required tasks through cooperation in the same way that ants create complex social behavior from the combined efforts of individuals.
Centimeter Precise Orbit Determination for SWARM Satellite via Reduced-dynamic Method
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ZHANG Bingbing
2016-11-01
Full Text Available Combining dual-frequency satellite-borne GPS observations with reduced dynamic models, and introducing proper pseudo-stochastic pulse parameters into the satellite's motion equation, SWARM satellite precise orbit determination is implemented. The orbit accuracy is assessed using three methods, which include analysis satellite-borne GPS phase observation residuals, orbit overlaps and external orbit comparisons. The results indicate that the SWARM satellite-borne GPS phase observation residual RMS is in the range of 7 to 10 mm, radial, along-track and cross-track orbit overlap difference RMS of 6 hours are about 1 cm, three directions have no significant systematic errors, comparisons with orbits computed by European Space Agency (ESA, Radial orbit difference RMS is in the range of 2 to 5 cm, along-track orbit difference RMS is in the range of 2 to 5 cm, cross-track orbit difference RMS is in the range of 2 to 4 cm, 3D orbit difference RMS is in the range of 4 to 7 cm, SWARM-B orbit accuracy is better than SWARM-A and SWARM-C. This evaluations indicate that SWARM satellite precise orbit determination is practicable by using reduced-dynamic method and orbit determining strategy in the article, the orbit solution is well and stable, the orbit accuracy reaches centimeter level.
The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm
Zhao, Xinying; Hu, Yihua; Gu, Youlin; Li, Le
2015-10-01
Microorganism aggregated particle swarm, which is quite an important composition of complex media environment, can be developed as a new kind of infrared functional materials. Current researches mainly focus on the optical properties of single microorganism particle. As for the swarm, especially the microorganism aggregated particle swarm, a more accurate simulation model should be proposed to calculate its extinction effect. At the same time, certain parameters deserve to be discussed, which helps to better develop the microorganism aggregated particle swarm as a new kind of infrared functional materials. In this paper, take Aspergillus Niger spore as an example. On the one hand, a new calculation model is established. Firstly, the cluster-cluster aggregation (CCA) model is used to simulate the structure of Aspergillus Niger spore aggregated particle. Secondly, the single scattering extinction parameters for Aspergillus Niger spore aggregated particle are calculated by using the discrete dipole approximation (DDA) method. Thirdly, the transmittance of Aspergillus Niger spore aggregated particle swarm is simulated by using Monte Carlo method. On the other hand, based on the model proposed above, what influences can wavelength causes has been studied, including the spectral distribution of scattering intensity of Aspergillus Niger spore aggregated particle and the infrared spectral transmittance of the aggregated particle swarm within the range of 8～14μm incident infrared wavelengths. Numerical results indicate that the scattering intensity of Aspergillus Niger spore aggregated particle reduces with the increase of incident wavelengths at each scattering angle. Scattering energy mainly concentrates on the scattering angle between 0～40°, forward scattering has an obvious effect. In addition, the infrared transmittance of Aspergillus Niger spore aggregated particle swarm goes up with the increase of incident wavelengths. However, some turning points of the trend
Particle swarm optimization for programming deep brain stimulation arrays.
Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D
2017-02-01
Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies
Particle swarm optimization for programming deep brain stimulation arrays
Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.
2017-02-01
Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon
Locating multiple optima using particle swarm optimization
CSIR Research Space (South Africa)
Brits, R
2007-01-01
Full Text Available Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems. This paper presents a new particle swarm optimization (PSO) technique to locate and refine multiple...
Love as the transformation of personality in the religious-philosophical views of Vladimir Solovyov
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A. V. Skliar
2016-06-01
Full Text Available The purpose of this study is to explore the meaning of love in its impact on humans, according to religious-philosophical views of Vladimir Solovyov. The influence of love on human nature is studied. In this research we can find the answer to the question «What love make with people and what is the main purpose of love?». The article presents the research of interrelation between the existence of love and freedom from humans selfishness, the relation between loving heart and opportunities to discover the meaning of life. It was found that love rescues man from selfishness and thus from degradation. The main types of love which were discriminated by Vladimir Solovyov were discussed. A detailed analysis of the the love between man and woman has been carried out. It was concluded that the fullness of human existence is embodied in love. Love can change not only one person, but due to this society as a whole. Love, therefore, is able to bring humanity to the highest level. Special attention was paid to the fact that the man who filled with love is able to overcome his selfishness, he is able to unite with other people. Because of this love allows the person to feel the presence of the Kingdom of God already here on earth. It was discovered that love is a powerful force for the spiritual development of mankind. It can be concluded that the feeling that the person loves you and knowing that you love him, give meaning to your own existence. Thus love is an incredibly powerful force that encourages self-improvement, inspires the soul to do something significant. In philosophy of V. Solovyov meaning of love is a spiritual birth of person, the resurrection of his soul. According to religious views, each person embodies the image of God. According to the philosophy of V. Solovyov, this image specifically is learned in love. Vladimir Solovyov sees the person in the context of the formation of unity. For Solovyov God and humanity form a unity that is
A Tale of Two Seismic Swarms: Implications for different forcing mechanisms (Invited)
Thelen, W. A.; Gomberg, J. S.; Bodin, P.; Hartog, R.; Wright, A.; Rohay, A.
2009-12-01
Within the compressional tectonic regime of the Columbia Basin, swarms are the dominant source of seismicity. Not all swarms, however, are created equal. Two recent swarms, the 2007-2008 swarm near Maupin, Oregon and the 2009 Wooded Island earthquake sequence near Richland, Washington, displayed very different behavior despite being within the same tectonic regime. The Maupin earthquake swarm lasted 20 months with strike-slip earthquakes with magnitudes as large as M3.8. The swarm was 18 km deep. Nearly all of the earthquakes within the Maupin swarm were highly similar with maximum p-wave lags of about 0.05 seconds. This seismic evidence therefore suggests a single, repeatable, and very energetic source with dimensions of less than 750 m at mid-crustal levels. Swarms, apparently collocated, also occurred in 1987 and 1976. However the waveforms from these swarms are not similar to the 2007-2008 sequence, suggesting that the source geometry or orientation of the source has changed from 1976 to 1987 and 2007. In contrast, the 2009 Wooded Island earthquake sequence is currently (as of September 3) only 8 months in duration, but has already had nearly five times the number earthquakes as the Maupin swarm, with a maximum magnitude of 3. The total energy released in the Wooded Island earthquake sequence was less than in the Maupin earthquake sequence. The earthquakes in the Wooded Island sequence were shallow, with focal depths of less than 2 km, which made focal mechanisms unreliable. However, the earthquakes were associated with a deformation anomaly detected from InSAR. Based on the correlations between waveforms, earthquakes that comprise this swarm can be classified into many different families, suggesting slip along a network of distributed shallow faults and/or a heterogeneous stress field. Precise relocations show a dipping structure, consistent with a fault geometry that matches well with source models of the InSAR deformation anomaly. Swarms have been attributed
SWARM - An earth Observation Mission investigating Geospace
DEFF Research Database (Denmark)
Friis-Christensen, Eigil; Lühr, H.; Knudsen, D.
2008-01-01
The Swarm mission was selected as the 5th mission in ESA's Earth Explorer Programme in 2004. This mission aims at measuring the Earth's magnetic field with unprecedented accuracy. This will be done by a constellation of three satellites, where two will fly at lower altitude, measuring the gradient...... of the Swarm science objectives, the mission concept, the scientific instrumentation, and the expected contribution to the ILWS programme will be summarized. (C) 2007 Published by Elsevier Ltd on behalf of COSPAR....
Heterogeneous architecture to process swarm optimization algorithms
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Maria A. Dávila-Guzmán
2014-01-01
Full Text Available Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.
A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations
Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw
2005-01-01
A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
On Formal Specification of Emergent Behaviours in Swarm Robotic Systems
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Alan FT Winfield
2005-12-01
Full Text Available It is a characteristic of swarm robotics that specifying overall emergent swarm behaviours in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation we need such specifications. This paper explores the use of temporal logic to formally specify, and possibly also prove, the emergent behaviours of a robotic swarm. The paper makes use of a simplified wireless connected swarm as a case study with which to illustrate the approach. Such a formal approach could be an important step toward a disciplined design methodology for swarm robotics.
Desiderio e scrittura in "The Real Life of Sebastian Knight" di Vladimir Nabokov
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Valeria Invernizzi
2014-12-01
This paper examines, from a psychoanalytic standpoint, the intersection between identity and writing in the novel The Real Life of Sebastian Knight (1941 by Vladimir Nabokov. In particular, in the wake of Freud and Lacan, the following contribution will take the notions of desire, signifier, and the process of sublimation involved in artistic creation as interpretative devices. The main focus of the analysis will be the relationship between the two half-brothers of the novel, V (the narrator and Sebastian. First, I will show that Sebastian Knight holds the role of the object of desire for the characters who have established a close relationship with him and, in particular, for V. Secondly, the paper will be devoted to the analysis of the trauma experienced by the characters because of Sebastian's death, with a particular focus on the narrator's mourning through writing (the fictional biography we read in the novel; in the end, I will give evidence of the so-called signifier’s fallacy, crucial episodes in which the narrator can experience an insight into the truth of desire. Because of the not negligible question of the fictional paternity of The Real Life and the equally essential matter of V’s ‘stylistic touch’, metaliterary aspects of the novel will also be part of the following reading of the text.
Merging the fields of swarm robotics and new media: Perceiving swarm robotics as new media
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Monika O. Ivanova
2014-06-01
Full Text Available The aim of this paper is to provide evidence that swarm robotic systems can be perceived as new media objects. A thorough description of the five principles of new media proposed by Lev Manovich in “The Language of New Media” is presented. This is complemented by a state of the art on swarm robotics with an in-depth comparison of the characteristics of both fields. Also presented are examples of swarm robotics used in new media installations in order to illustrate the cutting-edge applications of robotics and artificial intelligence achieved through the unity of bothfields. The hypothesis of this research is that a novel point of view would be introduced by examining the field of swarm robotics through the scope of new media, which would benefit thework of both new media and swarm robotic researchers.
The Fate of Colloidal Swarms in Fractures
Pyrak-Nolte, L. J.; Olander, M. K.
2009-12-01
In the next 10-20 years, nano- and micro-sensor engineering will advance to the stage where sensor swarms could be deployed in the subsurface to probe rock formations and the fluids contained in them. Sensor swarms are groups of nano- or micro- sensors that are maintained as a coherent group to enable either sensor-to-sensor communication and/or coherent transmission of information as a group. The ability to maintain a swarm of sensors depends on the complexity of the flow paths in the rock, on the size and shape of the sensors and on the chemical interaction among the sensors, fluids, and rock surfaces. In this study, we investigate the effect of fracture aperture and fluid currents on the formation, evolution and break-up of colloidal swarms under gravity. Transparent cubic samples (100 mm x 100 mm x 100 mm) containing synthetic fractures with uniform and non-uniform aperture distributions were used to quantify the effect of aperture on swarm formation, swarm velocity, and swarm geometry using optical imaging. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A fracture with a non-uniform aperture distribution was created with a polished rectangular acrylic prism and an acrylic replica of an induced fracture surface from a carbonate rock. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass) . The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. A swam was created when approximately 0.01 g drop of the suspension was
Directory of Open Access Journals (Sweden)
Fei Wang
2017-11-01
Full Text Available The optimal dispatching model for a stand-alone microgrid (MG is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditions as storms or abnormally low or high temperatures. A new two-time scale multi-objective optimization model, including day-ahead cursory scheduling and real-time scheduling for finer adjustments, is proposed to optimize the operational cost, load shedding compensation and environmental benefit of stand-alone MG through controllable load (CL and multi-distributed generations (DGs. The main novelty of the proposed model is that the synergetic response of CL and energy storage system (ESS in real-time scheduling offset the operation uncertainty quickly. And the improved dispatch strategy for combined cooling-heating-power (CCHP enhanced the system economy while the comfort is guaranteed. An improved algorithm, Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy (SIP-CO-PSO-ERS algorithm with strong searching capability and fast convergence speed, was presented to deal with the problem brought by the increased errors between actual renewable generation and load and prior predictions. Four typical scenarios are designed according to the combinations of day types (work day or weekend and weather categories (sunny or rainy to verify the performance of the presented dispatch strategy. The simulation results show that the proposed two-time scale model and SIP-CO-PSO-ERS algorithm exhibit better performance in adaptability, convergence speed and search ability than conventional methods for the stand-alone MG’s operation.
Hybridization hotspots at bat swarming sites.
Directory of Open Access Journals (Sweden)
Wiesław Bogdanowicz
Full Text Available During late summer and early autumn in temperate zones of the Northern Hemisphere, thousands of bats gather at caves, mainly for the purpose of mating. We demonstrated that this swarming behavior most probably leads not only to breeding among bats of the same species but also interbreeding between different species. Using 14 nuclear microsatellites and three different methods (the Bayesian assignment approaches of STRUCTURE and NEWHYBRIDS and a principal coordinate analysis of pairwise genetic distances, we analyzed 375 individuals belonging to three species of whiskered bats (genus Myotis at swarming sites across their sympatric range in southern Poland. The overall hybridization rate varied from 3.2 to 7.2%. At the species level, depending on the method used, these values ranged from 2.1-4.6% in M. mystacinus and 3.0-3.7% in M. brandtii to 6.5-30.4% in M. alcathoe. Hybrids occurred in about half of the caves we studied. In all three species, the sex ratio of hybrids was biased towards males but the observed differences did not differ statistically from those noted at the population level. In our opinion, factors leading to the formation of these admixed individuals and their relatively high frequency are: i swarming behaviour at swarming sites, where high numbers of bats belonging to several species meet; ii male-biased sex ratio during the swarming period; iii the fact that all these bats are generally polygynous. The highly different population sizes of different species at swarming sites may also play some role. Swarming sites may represent unique hybrid hotspots, which, as there are at least 2,000 caves in the Polish Carpathians alone, may occur on a massive scale not previously observed for any group of mammal species in the wild. Evidently, these sites should be treated as focal points for the conservation of biodiversity and evolutionary processes.
Genetic Learning Particle Swarm Optimization.
Gong, Yue-Jiao; Li, Jing-Jing; Zhou, Yicong; Li, Yun; Chung, Henry Shu-Hung; Shi, Yu-Hui; Zhang, Jun
2016-10-01
Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.
Directory of Open Access Journals (Sweden)
A. A. Meleschuk
2016-11-01
Full Text Available The meaning of the concept of society’s organic unity has been investigated. This concept was an important part of Russian religious philosophy in the middle of the XIX century. It has been found that Schelling and Hegel idealistic philosophy and orthodox theology were the main sources of the concept of organic unity. The character and forms of the presentation of the organic unity’s concept in Vladimir Soloviev and the main representatives of early Slavophilism’s social philosophy and philosophy of history has been analyzed. By Solovyov, the subject of the historical development is mankind as holistic, organic phenomenon which tries to overcome the formal unity and to reach its spiritual level in the form of «God-manhood». Economic and legal unity is called the lowest form of unity by Solovyov, and the highest one, which reaches the organic and conscious level, is the spiritual unity. In the philosophy of Ivan Kireyevski, society’s organic unity is opposed to the formal, rational unity. The formal unity was inherited by Europe from ancient Rome. Cultural traditions of Greece and Orthodoxy were inherited by Slavs and saved the spiritual unity of society. According to Alexey Khomyakov, there are two contrasting models of culture: Iranian and Cushitic ones. Iranian model is based on freedom and conscious action. Iranian model creates a spiritual unity without suppression of individuals in society. Cushitic model creates a formal unity, it coercions and suppress individuals. The formality of the state association and the spiritual unity of the community have been opposed by Konstantin Aksakov. In tsarist Russia, all power was given to the monarch to protect the community. Therefore, Russia maintained an organic, internal unity of the people unlike Europe. In Europe, the state became stronger then the communities and state there suppressed communities. Rights and economy became to be more important than morality. European society became
Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Perc, Matjaž
2011-01-01
We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.
Seismic Swarms at Paricutin Volcano Area. Magmatic Intrusion or Tectonic Seismicity?
Pinzon, J. I.; Nunez-Cornu, F. J.; Escudero, C. R.; Rowe, C. A.
2014-12-01
We relocate a seismic swarm with more than 700 earthquakes that took place between May and June 2006 in the Paricutin volcano area, Mexico inside of the Michoacan monogenetic volcanic field. This seismic swarm was recorded by the project "Mapping the Riviera Subduction Zone" (MARS), a temporary seismic network that was installed in the states of Jalisco, Colima and Michoacán between January 2006 and June 2007. Previously seismic swarms in the area were reported in the years of 1997, 1999 and 2000. For one that took place in the year of 1997 the Servicio Sismologico Nacional deployed a local network in the area, they conclude that the source of the seismicity was tectonic with depths between 18 and 12 km. The episodes of 1999 and 2000 were reported as similar to the 1997 swarm. A previous analysis of the 2006 swarm concludes that the depth of seismicity migrates from 9 to 5 km and was originated by a magmatic intrusion. We did a relocation of this swarm reading all the events and using Hypo71 and the P-wave velocity model used by the Jalisco Seismic and Acelerometric Network; a waveform analysis using cross-correlation method was also carried out. We obtained 15 earthquakes families with a correlation factor equal or greater than 0.79 and composed focal mechanism for each family. These families present a migration in depth beginning at 16 km and ended at 9 km. Our results agrees with a magmatic intrusion, but not so shallow as the previous study of the 2006 swarm.
Chaotic Hopfield Neural Network Swarm Optimization and Its Application
Directory of Open Access Journals (Sweden)
Yanxia Sun
2013-01-01
Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.
Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach
Khaldi, Belkacem
2018-02-02
In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.
The role of neighbours selection on cohesion and order of swarms.
Directory of Open Access Journals (Sweden)
Angelo M Calvão
Full Text Available We introduce a multi-agent model for exploring how selection of neighbours determines some aspects of order and cohesion in swarms. The model algorithm states that every agents' motion seeks for an optimal distance from the nearest topological neighbour encompassed in a limited attention field. Despite the great simplicity of the implementation, varying the amplitude of the attention landscape, swarms pass from cohesive and regular structures towards fragmented and irregular configurations. Interestingly, this movement rule is an ideal candidate for implementing the selfish herd hypothesis which explains aggregation of alarmed group of social animals.
A distance weighted-based approach for self-organized aggregation in robot swarms
Khaldi, Belkacem
2017-12-14
In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.
Smart swarms of bacteria-inspired agents with performance adaptable interactions.
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Adi Shklarsh
2011-09-01
Full Text Available Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment--by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.
Application of particle swarm optimization algorithm in the heating system planning problem.
Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi
2013-01-01
Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.
Double Flight-Modes Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Wang Yong
2013-01-01
Full Text Available Getting inspiration from the real birds in flight, we propose a new particle swarm optimization algorithm that we call the double flight modes particle swarm optimization (DMPSO in this paper. In the DMPSO, each bird (particle can use both rotational flight mode and nonrotational flight mode to fly, while it is searching for food in its search space. There is a King in the swarm of birds, and the King controls each bird’s flight behavior in accordance with certain rules all the time. Experiments were conducted on benchmark functions such as Schwefel, Rastrigin, Ackley, Step, Griewank, and Sphere. The experimental results show that the DMPSO not only has marked advantage of global convergence property but also can effectively avoid the premature convergence problem and has good performance in solving the complex and high-dimensional optimization problems.
Lu, Shengtao; Liu, Fang; Xing, Bengang; Yeow, Edwin K. L.
2015-12-01
A monolayer of swarming B. subtilis on semisolid agar is shown to display enhanced resistance against antibacterial drugs due to their collective behavior and motility. The dynamics of swarming motion, visualized in real time using time-lapse microscopy, prevents the bacteria from prolonged exposure to lethal drug concentrations. The elevated drug resistance is significantly reduced when the collective motion of bacteria is judiciously disrupted using nontoxic polystyrene colloidal particles immobilized on the agar surface. The colloidal particles block and hinder the motion of the cells, and force large swarming rafts to break up into smaller packs in order to maneuver across narrow spaces between densely packed particles. In this manner, cohesive rafts rapidly lose their collectivity, speed, and group dynamics, and the cells become vulnerable to the drugs. The antibiotic resistance capability of swarming B. subtilis is experimentally observed to be negatively correlated with the number density of colloidal particles on the engineered surface. This relationship is further tested using an improved self-propelled particle model that takes into account interparticle alignment and hard-core repulsion. This work has pertinent implications on the design of optimal methods to treat drug resistant bacteria commonly found in swarming colonies.
Swarm Optimization Methods in Microwave Imaging
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Andrea Randazzo
2012-01-01
Full Text Available Swarm intelligence denotes a class of new stochastic algorithms inspired by the collective social behavior of natural entities (e.g., birds, ants, etc.. Such approaches have been proven to be quite effective in several applicative fields, ranging from intelligent routing to image processing. In the last years, they have also been successfully applied in electromagnetics, especially for antenna synthesis, component design, and microwave imaging. In this paper, the application of swarm optimization methods to microwave imaging is discussed, and some recent imaging approaches based on such methods are critically reviewed.
Software Engineering and Swarm-Based Systems
Hinchey, Michael G.; Sterritt, Roy; Pena, Joaquin; Rouff, Christopher A.
2006-01-01
We discuss two software engineering aspects in the development of complex swarm-based systems. NASA researchers have been investigating various possible concept missions that would greatly advance future space exploration capabilities. The concept mission that we have focused on exploits the principles of autonomic computing as well as being based on the use of intelligent swarms, whereby a (potentially large) number of similar spacecraft collaborate to achieve mission goals. The intent is that such systems not only can be sent to explore remote and harsh environments but also are endowed with greater degrees of protection and longevity to achieve mission goals.
PMSM Driver Based on Hybrid Particle Swarm Optimization and CMAC
Tu, Ji; Cao, Shaozhong
A novel hybrid particle swarm optimization (PSO) and cerebellar model articulation controller (CMAC) is introduced to the permanent magnet synchronous motor (PMSM) driver. PSO can simulate the random learning among the individuals of population and CMAC can simulate the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments and comparisons have been done in MATLAB/SIMULINK. Analysis among PSO, hybrid PSO-CMAC and CMAC feed-forward control is also given. The results prove that the electric torque ripple and torque disturbance of the PMSM driver can be reduced by using the hybrid PSO-CMAC algorithm.
A measurement-based fault detection approach applied to monitor robots swarm
Khaldi, Belkacem
2017-07-10
Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.
Directory of Open Access Journals (Sweden)
Shaolong Chen
2016-01-01
Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.
Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades
Li, H.; Chandrashekhara, K.
2015-09-01
Composite blade manufacturing for hydrokinetic turbine application is quite complex and requires extensive optimization studies in terms of material selection, number of layers, stacking sequence, ply thickness and orientation. To avoid a repetitive trial-and-error method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite lay-up optimization. Layer numbers, ply thickness and ply orientations were optimized using standard particle swarm optimization to minimize the weight of the composite blade while satisfying failure evaluation. To address the discrete combinatorial optimization problem of blade stacking sequence, a novel permutation discrete particle swarm optimization model was also developed to maximize the out-of-plane load-carrying capability of the composite blade. A composite blade design with significant material saving and satisfactory performance was presented. The proposed methodology offers an alternative and efficient design solution to composite structural optimization which involves complex loading and multiple discrete and combinatorial design parameters.
Thermospheric neutral densities derived from Swarm accelerometer and GPS data
DEFF Research Database (Denmark)
Doornbos, Eelco; Encarnacao, Joao; van den IJss, Jose
approach, affects the possibility of determining densities from the accelerometer measurements of the Swarm A and B satellites. We also investigate the possibility of determining crosswind speeds from Swarm data.In the meantime, we have investigated the possibility of deriving thermosphere neutral density...... data from the Swarm GPS observations only, with a much lower temporal resolution. We analyse the differences in the data between the three Swarm satellites as well as between the accelerometer-derived and GPS-only-derived densities for Swarm C....
Collective Behavior of Animals: Swarming and Complex Patterns
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Cañizo, J. A.
2010-12-01
Full Text Available In this short note we review some of the individual based models of the collective motion of agents, called swarming. These models based on ODEs (ordinary differential equations exhibit a complex rich asymptotic behavior in terms of patterns, that we show numerically. Moreover, we comment on how these particle models are connected to partial differential equations to describe the evolution of densities of individuals in a continuum manner. The mathematical questions behind the stability issues of these PDE (partial differential equations models are questions of actual interest in mathematical biology research.
En esta nota repasamos algunos modelos basados en individuos para describir el movimiento colectivo de agentes, a lo que nos referimos usando la voz inglesa swarming. Estos modelos se basan en EDOs (ecuaciones diferenciales ordinarias y muestran un comportamiento asintótico complejo y rico en patrones, que mostramos numéricamente. Además, comentamos cómo se conectan estos modelos de partículas con las ecuaciones en derivadas parciales para describir la evolución de densidades de individuos de forma continua. Las cuestiones matemáticas relacionadas con la estabilidad de de estos modelos de EDP's (ecuaciones en derivadas parciales despiertan gran interés en la investigación en biología matemática.
Üksnes endale mõeldes ei jõua energiaküsimustes kuhugi / Vladimir Putin
Putin, Vladimir, 1952-
2006-01-01
Ilmunud ka: Postimees : na russkom jazõke 22. märts lk. 7. Venemaa president Vladimir Putin selgitab, et kaheksat suurriiki ühendava ühenduse G8 üheks strateegiliseks eesmärgiks on usaldusväärse ja täieliku energiajulgeoleku süsteemi loomine. Presidendi arvates muudab energiasektori globaliseerumine energiajulgeoleku jagamatuks ning juhtivate riikide ühine tulevik energiaküsimustes tähendab nii jagatud vastutust, riske kui hüvesid
Huang, Liang; Peng, Pengfei; Luo, Bing; He, Chu
2017-08-01
Optoelectronic load installation error is the main factor affecting the passive positioning accuracy of photoelectric load equipped on the aerial mobile single platform. In order to solve the problem of complex modeling and low accuracy of traditional analytical methods, a particle swarm optimization (PSO) method is used to correct the installation error. The simulation results show that the particle swarm optimization method has high efficiency and good search effect, and it can be used to correct the installation error. The method proposed in the thesis provides a new simple and workable way for load installation error correction.
Solution to Electric Power Dispatch Problem Using Fuzzy Particle Swarm Optimization Algorithm
Chaturvedi, D. K.; Kumar, S.
2015-03-01
This paper presents the application of fuzzy particle swarm optimization to constrained economic load dispatch (ELD) problem of thermal units. Several factors such as quadratic cost functions with valve point loading, ramp rate limits and prohibited operating zone are considered in the computation models. The Fuzzy particle swarm optimization (FPSO) provides a new mechanism to avoid premature convergence problem. The performance of proposed algorithm is evaluated on four test systems. Results obtained by proposed method have been compared with those obtained by PSO method and literature results. The experimental results show that proposed FPSO method is capable of obtaining minimum fuel costs in fewer numbers of iterations.
Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.
Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima
2013-01-01
The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.
Rushan earthquake swarm in eastern China and its indications of fluid-triggered rupture
Zheng, Jian-Chang; Li, Dong-Mei; Li, Cui-Qin; Wang, Peng; Xu, Chang-Peng
2017-12-01
An extraordinary earthquake swarm occurred at Rushan on the Jiaodong Peninsula from October 1, 2013, onwards, and more than 12,000 aftershocks had been detected by December 31, 2015. All the activities of the whole swarm were recorded at the nearest station, RSH, which is located about 12 km from the epicenter. We examine the statistical characteristics of the Rushan swarm in this paper using RSH station data to assess the arrival time difference, t_{{{S} - {P}}} , of Pg and Sg phases. A temporary network comprising 18 seismometers was set up on May 6, 2014, within the area of the epicenter; based on the data from this network and use of the double difference method, we determine precise hypocenter locations. As the distribution of relocated sources reveals migration of seismic activity, we applied the mean-shift cluster method to perform clustering analysis on relocated catalogs. The results of this study show that there were at least 16 clusters of seismic activities between May 6, 2014, and June 30, 2014, and that each was characterized by a hypocenter spreading process. We estimated the hydraulic diffusivity, D, of each cluster using envelope curve fitting; the results show that D values range between 1.2 and 3.5 m2/d and that approximate values for clusters on the edge of the source area are lower than those within the central area. We utilize an epidemic-type aftershock sequence (ETAS) model to separate external triggered events from self-excited aftershocks within the Rushan swarm. The estimated parameters for this model suggest that α = 1.156, equivalent to sequences induced by fluid-injection, and that the forcing rate (μ) implies just 0.15 events per day. These estimates indicate that around 3% of the events within the swarm were externally triggered. The fact that variation in μ is synchronous with swarm activity implies that pulses in fluid pressure likely drove this series of earthquakes.
Swarm Level 2 Comprehensive Inversion, 2016 Production
DEFF Research Database (Denmark)
Tøffner-Clausen, Lars; Sabaka, Terence; Olsen, Nils
In the framework of the ESA Earth Observation Magnetic Mapping Mission Swarm, the Expert Support Laboratories (ESL) provides high quality Level 2 Products describing a.o. the magnetic fields of the Earth. This poster provides details of the Level 2 Products from the Comprehensive Inversion chain...
Novelty-driven Particle Swarm Optimization
DEFF Research Database (Denmark)
Galvao, Diana; Lehman, Joel Anthony; Urbano, Paulo
2015-01-01
Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higher-fitness areas. However...
Selectively-informed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
2015-03-19
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
Swarm controlled emergence for ant clustering
DEFF Research Database (Denmark)
Scheidler, Alexander; Merkle, Daniel; Middendorf, Martin
2013-01-01
implications: The particular finding, that certain behaviours of control agents can lead to stronger clustering, can help to design improved clustering algorithms by using heterogeneous swarms of agents. Originality/value: In general, the control of (unwanted) emergent effects in artificial systems...
Agent based Particle Swarm Optimization for Load Frequency Control of Distribution Grid
DEFF Research Database (Denmark)
Cha, Seung-Tae; Saleem, Arshad; Wu, Qiuwei
2012-01-01
This paper presents a Particle Swarm Optimization (PSO) based on multi-agent controller. Real-time digital simulator (RTDS) is used for modelling the power system, while a PSO based multi-agent LFC algorithm is developed in JAVA for communicating with resource agents and determines the scenario t...
Swarm Tactics and the Doctrinal Void: Lessons from the Chechen Wars
2008-06-01
swarms), technology and advanced research (computers, modeling and simulation, robotics , and reconnaissance and surveillance); and military...Vol. 83, No. 11, 88-89. Taber, Robert. War of the Flea : The Classic Study of Guerrilla Warfare. Washington, D.C.: Brassey’s, Inc., 2002. Tarle
ADAPTIVE DISTRIBUTION OF A SWARM OF HETEROGENEOUS ROBOTS
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Amanda Prorok
2016-02-01
Full Text Available We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type is defined by the traits (capabilities that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.
Effect of Correlations in Swarms on Collective Response.
Mateo, David; Kuan, Yoke Kong; Bouffanais, Roland
2017-09-04
Social interaction increases significantly the performance of a wide range of cooperative systems. However, evidence that natural swarms limit the number of interactions suggests potentially detrimental consequences of excessive interaction. Using a canonical model of collective motion, we find that the collective response to a dynamic localized perturbation-emulating a predator attack-is hindered when the number of interacting neighbors exceeds a certain threshold. Specifically, the effectiveness in avoiding the predator is enhanced by large integrated correlations, which are known to peak at a given level of interagent interaction. From the network-theoretic perspective, we uncover the same interplay between number of connections and effectiveness in group-level response for two distinct decision-making models of distributed consensus operating over a range of static networks. The effect of the number of connections on the collective response critically depends on the dynamics of the perturbation. While adding more connections improves the response to slow perturbations, the opposite is true for fast ones. These results have far-reaching implications for the design of artificial swarms or interaction networks.
Bio Inspired Swarm Algorithm for Tumor Detection in Digital Mammogram
Dheeba, J.; Selvi, Tamil
Microcalcification clusters in mammograms is the significant early sign of breast cancer. Individual clusters are difficult to detect and hence an automatic computer aided mechanism will help the radiologist in detecting the microcalcification clusters in an easy and efficient way. This paper presents a new classification approach for detection of microcalcification in digital mammogram using particle swarm optimization algorithm (PSO) based clustering technique. Fuzzy C-means clustering technique, well defined for clustering data sets are used in combination with the PSO. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model. This method is quite simple and valid, and it can avoid the minimum local value. The proposed classification approach is applied to a database of 322 dense mammographic images, originating from the MIAS database. Results shows that the proposed PSO-FCM approach gives better detection performance compared to conventional approaches.
Directory of Open Access Journals (Sweden)
Pei Du
2016-08-01
Full Text Available With the limitations of conventional energy becoming increasing distinct, wind energy is emerging as a promising renewable energy source that plays a critical role in the modern electric and economic fields. However, how to select optimization algorithms to forecast wind speed series and improve prediction performance is still a highly challenging problem. Traditional single algorithms are widely utilized to select and optimize parameters of neural network algorithms, but these algorithms usually ignore the significance of parameter optimization, precise searching, and the application of accurate data, which results in poor forecasting performance. With the aim of overcoming the weaknesses of individual algorithms, a novel hybrid algorithm was created, which can not only easily obtain the real and effective wind speed series by using singular spectrum analysis, but also possesses stronger adaptive search and optimization capabilities than the other algorithms: it is faster, has fewer parameters, and is less expensive. For the purpose of estimating the forecasting ability of the proposed combined model, 10-min wind speed series from three wind farms in Shandong Province, eastern China, are employed as a case study. The experimental results were considerably more accurately predicted by the presented algorithm than the comparison algorithms.
Directory of Open Access Journals (Sweden)
Xue-cun Yang
2015-01-01
Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.
McMahon, Paul; Jung, Hans-Juergen; Edwards, Jeff
2013-09-01
The Swarm programme consists of 3 magnetically clean satellites flying in close formation designed to measure the Earth's magnetic field using 2 Magnetometers mounted on a 4.3m long deployable boom.Deployment is initiated by releasing 3 HDRMs, once released the boom oscillates back and forth on a pair of pivots, similar to a restaurant kitchen door hinge, for around 120 seconds before coming to rest on 3 kinematic mounts which are used to provide an accurate reference location in the deployed position. Motion of the boom is damped through a combination of friction, spring hysteresis and flexing of the 120+ cables crossing the hinge. Considerable development work and accurate numerical modelling of the hinge motion was required to predict performance across a wide temperature range and ensure that during the 1st overshoot the boom did not damage itself, the harness or the spacecraft.Due to the magnetic cleanliness requirements of the spacecraft no magnetic materials could be used in the design of the hardware.
Cat swarm optimization based evolutionary framework for multi document summarization
Rautray, Rasmita; Balabantaray, Rakesh Chandra
2017-07-01
Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study.
Multivariable optimization of liquid rocket engines using particle swarm algorithms
Jones, Daniel Ray
Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.
Magnetic Investigations On The Okavango Giant Dyke Swarm (n Botswana)
Tshoso, G.; Dyment, J.; Aubourg, C.; Le Gall, B.; Tiercelin, J. J.; Féraud, G.; Bertrand, H.; Jourdan, F.; Kampunzu, H.
The Okavango Giant Dyke Swarm is one of the largest mafic dyke complex world- wide. It extends as a 1500 x 100 km intrusive system across the Karoo igneous province of E. Namibia, N. Botswana and W. Zimbabwe. It is marked by prominent magnetic anomalies on the many aeromagnetic surveys acquired by mining compa- nies. Beyond the analysis of these data, ground truth evidence has been collected along a 100 km continuous section nearly perpendicular to the dyke swarm on the Shashe River, which present excellent exposures of dykes and basement host-rocks. Samples have been cored on 15 dykes for paleomagnetic and rock magnetic analy- ses. The paleomagnetic poles determined from most of the dykes is consistent with a Karoo age on the Apparent Polar Wander path for Africa and confirm the radio- metric results obtained by Ar-Ar dating technique. A very different pole is obtained for one basement dyke dated at 880 Ma. Magnetic susceptibility and natural rema- nent magnetization have been compiled and used to constrain forward modeling of the aeromagnetic anomalies. The direction of magmatic flow within individual dykes is investigated through the analysis of magnetic susceptibility anisotropy.
Scaling and spatial complementarity of tectonic earthquake swarms
Passarelli, Luigi
2017-11-10
Tectonic earthquake swarms (TES) often coincide with aseismic slip and sometimes precede damaging earthquakes. In spite of recent progress in understanding the significance and properties of TES at plate boundaries, their mechanics and scaling are still largely uncertain. Here we evaluate several TES that occurred during the past 20 years on a transform plate boundary in North Iceland. We show that the swarms complement each other spatially with later swarms discouraged from fault segments activated by earlier swarms, which suggests efficient strain release and aseismic slip. The fault area illuminated by earthquakes during swarms may be more representative of the total moment release than the cumulative moment of the swarm earthquakes. We use these findings and other published results from a variety of tectonic settings to discuss general scaling properties for TES. The results indicate that the importance of TES in releasing tectonic strain at plate boundaries may have been underestimated.
Scaling and spatial complementarity of tectonic earthquake swarms
Passarelli, Luigi; Rivalta, Eleonora; Jónsson, Sigurjón; Hensch, Martin; Metzger, Sabrina; Jakobsdóttir, Steinunn S.; Maccaferri, Francesco; Corbi, Fabio; Dahm, Torsten
2018-01-01
Tectonic earthquake swarms (TES) often coincide with aseismic slip and sometimes precede damaging earthquakes. In spite of recent progress in understanding the significance and properties of TES at plate boundaries, their mechanics and scaling are still largely uncertain. Here we evaluate several TES that occurred during the past 20 years on a transform plate boundary in North Iceland. We show that the swarms complement each other spatially with later swarms discouraged from fault segments activated by earlier swarms, which suggests efficient strain release and aseismic slip. The fault area illuminated by earthquakes during swarms may be more representative of the total moment release than the cumulative moment of the swarm earthquakes. We use these findings and other published results from a variety of tectonic settings to discuss general scaling properties for TES. The results indicate that the importance of TES in releasing tectonic strain at plate boundaries may have been underestimated.
Glowworm swarm optimization theory, algorithms, and applications
Kaipa, Krishnanand N
2017-01-01
This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intellige...
A comprehensive review of swarm optimization algorithms.
Directory of Open Access Journals (Sweden)
Mohd Nadhir Ab Wahab
Full Text Available Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE and is closely followed by Particle Swarm Optimization (PSO, compared with other considered approaches.
A comprehensive review of swarm optimization algorithms.
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
A Game Theoretic Approach to Swarm Robotics
Directory of Open Access Journals (Sweden)
S. N. Givigi
2006-01-01
Full Text Available In this article, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced, and some simulations showing its potential are reported.
Countering A2/AD with Swarming
2016-04-01
inferior force can deny or delay a decisive engagement through time or attrition ultimately changing the political calculus.6 Using chess as an...deterrence in a non-nuclear scenario. Conventional deterrence is largely based on perceptions . Creating the wrong perception can cause conventional...necessarily defeat an enemy’s A2/AD strategy. Rather, the swarm needs to only create a perception that the U.S. is willing to fight within the A2/AD
Two Invariants of Human-Swarm Interaction
2018-01-16
often have formal attractors such as nest selection (Nevai & Passino, 2010) and many of the collective animal behaviors described by Sumpter (Sumpter...can be used to design swarm systems with desired fan-outs and workloads in mind . The key reason this is possible is that we are managing attractors...E., Giardina, I., et al. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a
INHIBITION OF SWARMING BY UREA AND ITS DIAGNOSTIC ...
African Journals Online (AJOL)
The anti-swarming property of urea and effects on antibiotic susceptibility among 52 uropathogenic Proteus strains from Lagos, Nigeria were investigated. Urea caused a reduction in swarming and number of swarmed cells at 0.5% (n = 42, DOCZ = 15.5mm), 0.75% (n= 24, DOCZ = 10.7mm), 1% (n = 17, DOCZ = 3.4mm) and ...
Foraging behavior analysis of swarm robotics system
Directory of Open Access Journals (Sweden)
Sakthivelmurugan E.
2018-01-01
Full Text Available Swarm robotics is a number of small robots that are synchronically works together to accomplish a given task. Swarm robotics faces many problems in performing a given task. The problems are pattern formation, aggregation, Chain formation, self-assembly, coordinated movement, hole avoidance, foraging and self-deployment. Foraging is most essential part in swarm robotics. Foraging is the task to discover the item and get back into the shell. The researchers conducted foraging experiments with random-movement of robots and they have end up with unique solutions. Most of the researchers have conducted experiments using the circular arena. The shell is placed at the centre of the arena and environment boundary is well known. In this study, an attempt is made to different strategic movements like straight line approach, parallel line approach, divider approach, expanding square approach, and parallel sweep approach. All these approaches are to be simulated by using player/stage open-source simulation software based on C and C++ programming language in Linux operating system. Finally statistical comparison will be done with task completion time of all these strategies using ANOVA to identify the significant searching strategy.
A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.
Sun, Tao; Xu, Ming-Hai
2017-01-01
Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.
Collective motion of a class of social foraging swarms
Energy Technology Data Exchange (ETDEWEB)
Liu Bo [Intelligent Control Laboratory, Center for Systems and Control, Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871 (China)], E-mail: boliu@mech.pku.edu.cn; Chu Tianguang [Intelligent Control Laboratory, Center for Systems and Control, Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871 (China)], E-mail: chutg@pku.edu.cn; Wang Long; Wang Zhanfeng [Intelligent Control Laboratory, Center for Systems and Control, Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871 (China)
2008-10-15
This paper considers a class of social foraging swarms with a nutrient profile (or an attractant/repellent) and an attraction-repulsion coupling function, which is chosen to guarantee collision avoidance between individuals. The paper also studies non-identical interaction ability or efficiency among different swarm individuals for different profiles. The swarm behavior is a result of a balance between inter-individual interplays as well as the interplays of the swarm individuals (agents) with their environment. It is proved that the individuals of a quasi-reciprocal swarm will aggregate and eventually form a cohesive cluster of finite size for different profiles. It is also shown that the swarm system is completely stable, that is, every solution converges to the set of equilibrium points of the system. Moreover, all the swarm individuals will converge to more favorable areas of the profile under certain conditions. For general non-reciprocal swarms, numerical simulations show that more complex self-organized rotation may occur in the swarms.
Can hydroseismicity explain recurring earthquake swarms in NW-Bohemia?
Heinicke, Jens; Woith, Heiko; Alexandrakis, Catherine; Buske, Stefan; Telesca, Luciano
2018-01-01
Fluid driven seismicity has been observed worldwide. The occurrence of intraplate seismicity triggered by pore pressure perturbations is a widely accepted process. Past analysis of earthquake swarms in the NW-Bohemia/Vogtland region provided evidence for the diffusion of pore pressure fronts during the migration of earthquakes within each swarm. Here, we test the hypothesis whether the diffusion of hydraulically induced pore pressure perturbations from the surface to the hypocentral depth could be a valid trigger mechanism. We test this hypothesis for the earthquake swarms in the Nový Kostel focal zone based on the analysis of 121 earthquake swarms and microswarms which occurred between 1992 and 2016.
Swarming behaviour and mass occurrences in the world's largest ...
African Journals Online (AJOL)
millipede species, Zoosphaerium neptunus, on Madagascar and its implication for conservation efforts (Diplopoda: Sphaerotheriida) ... Madagascar Conservation & Development ... KEY WORDS: Swarming behaviour, millipede, island gigantism.
Laboratory and Modeling Studies of Insect Swarms
2016-03-10
Excellence grant for Education , Research and Engineering: The number of undergraduates funded by your agreement who graduated during this period and...Interaction Rules The generally accepted picture of collective animal behavior is that low- level, local interactions between individuals percolate...upscale and lead to the macroscopic behavior of the aggregation. Animal aggregations are thus expected to be distinct from other distributed systems
Martinec, Zdeněk; Velímský, Jakub; Haagmans, Roger; Šachl, Libor
2018-02-01
This study deals with the analysis of Swarm vector magnetic field measurements in order to estimate the magnetic field of magnetospheric ring current. For a single Swarm satellite, the magnetic measurements are processed by along-track spectral analysis on a track-by-track basis. The main and lithospheric magnetic fields are modelled by the CHAOS-6 field model and subtracted from the along-track Swarm magnetic data. The mid-latitude residual signal is then spectrally analysed and extrapolated to the polar regions. The resulting model of the magnetosphere (model MME) is compared to the existing Swarm Level 2 magnetospheric field model (MMA_SHA_2C). The differences of up to 10 nT are found on the nightsides Swarm data from 2014 April 8 to May 10, which are due to different processing schemes used to construct the two magnetospheric magnetic field models. The forward-simulated magnetospheric magnetic field generated by the external part of model MME then demonstrates the consistency of the separation of the Swarm along-track signal into the external and internal parts by the two-step along-track spectral analysis.
DEFF Research Database (Denmark)
Ren, Jingzheng; Tan, Shiyu; Dong, Lichun
2010-01-01
the searching ability of basic particle swarm algorithm significantly. An example of utilizing the improved algorithm to solve the mathematical model was demonstrated; the result showed that it is efficient and convenient to optimize the reflux ratio for a distillation column by using the mathematical model......A mathematical model relating operation profits with reflux ratio of a stage distillation column was established. In order to optimize the reflux ratio by solving the nonlinear objective function, an improved particle swarm algorithm was developed and has been proved to be able to enhance...
Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search
Directory of Open Access Journals (Sweden)
Simon Fong
2013-01-01
Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.
Consensus reaching in swarms ruled by a hybrid metric-topological distance
Shang, Yilun; Bouffanais, Roland
2014-12-01
Recent empirical observations of three-dimensional bird flocks and human crowds have challenged the long-prevailing assumption that a metric interaction distance rules swarming behaviors. In some cases, individual agents are found to be engaged in local information exchanges with a fixed number of neighbors, i.e. a topological interaction. However, complex system dynamics based on pure metric or pure topological distances both face physical inconsistencies in low and high density situations. Here, we propose a hybrid metric-topological interaction distance overcoming these issues and enabling a real-life implementation in artificial robotic swarms. We use network- and graph-theoretic approaches combined with a dynamical model of locally interacting self-propelled particles to study the consensus reaching process for a swarm ruled by this hybrid interaction distance. Specifically, we establish exactly the probability of reaching consensus in the absence of noise. In addition, simulations of swarms of self-propelled particles are carried out to assess the influence of the hybrid distance and noise.
Artificial pheromone for path selection by a foraging swarm of robots.
Campo, Alexandre; Gutiérrez, Alvaro; Nouyan, Shervin; Pinciroli, Carlo; Longchamp, Valentin; Garnier, Simon; Dorigo, Marco
2010-11-01
Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.
Knowledge Management and Problem Solving in Real Time: The Role of Swarm Intelligence
Directory of Open Access Journals (Sweden)
Chris W Callaghan
2016-06-01
Full Text Available Knowledge management research applied to the development of real-time research capability, or capability to solve societal problems in hours and days instead of years and decades, is perhaps increasingly important, given persistent global problems such as the Zika virus and rapidly developing antibiotic resistance. Drawing on swarm intelligence theory, this paper presents an approach to real-time research problem-solving in the form of a framework for understanding the complexity of real-time research and the challenges associated with maximizing collaboration. The objective of this research is to make explicit certain theoretical, methodological, and practical implications deriving from new literature on emerging technologies and new forms of problem solving and to offer a model of real-time problem solving based on a synthesis of the literature. Drawing from ant colony, bee colony, and particle swarm optimization, as well as other population-based metaheuristics, swarm intelligence principles are derived in support of improved effectiveness and efficiency for multidisciplinary human swarm problem-solving. This synthesis seeks to offer useful insights into the research process, by offering a perspective of what maximized collaboration, as a system, implies for real-time problem solving.
Directory of Open Access Journals (Sweden)
Ion Cucui
2010-11-01
Full Text Available The awarding of the highest academic distinction to His Excellency Dr. Vladimir JARMOLENKO, the ambassador extraordinary and plenipotentiary of the Republic of Lithuania at Bucharest, is the major event marking the beginning of a new university year.We wanted to pay our homage to such an outstanding personality of the scientific and diplomatic world in equal measure. Mr. Ambassador proved to be, besides an excellent diplomat, an undeniable friend of our university. We cannot forget the interest and support extended whenever it was needed to the "Grigore Gafencu" Research Center for the History of International Relations and Cultural Studies and to the Romanian Association for Baltic and Nordic Studies whose honorary president has become in 2008.
Swarm intelligence. A whole new way to think about business.
Bonabeau, E; Meyer, C
2001-05-01
What do ants and bees have to do with business? A great deal, it turns out. Individually, social insects are only minimally intelligent, and their work together is largely self-organized and unsupervised. Yet collectively they're capable of finding highly efficient solutions to difficult problems and can adapt automatically to changing environments. Over the past 20 years, the authors and other researchers have developed rigorous mathematical models to describe this phenomenon, which has been dubbed "swarm intelligence," and they are now applying them to business. Their research has already helped several companies develop more efficient ways to schedule factory equipment, divide tasks among workers, organize people, and even plot strategy. Emulating the way ants find the shortest path to a new food supply, for example, has led researchers at Hewlett-Packard to develop software programs that can find the most efficient way to route phone traffic over a telecommunications network. South-west Airlines has used a similar model to efficiently route cargo. To allocate labor, honeybees appear to follow one simple but powerful rule--they seem to specialize in a particular activity unless they perceive an important need to perform another function. Using that model, researchers at Northwestern University have devised a system for painting trucks that can automatically adapt to changing conditions. In the future, the authors speculate, a company might structure its entire business using the principles of swarm intelligence. The result, they believe, would be the ultimate self-organizing enterprise--one that could adapt quickly and instinctively to fast-changing markets.
Agent-Based Simulation and Analysis of a Defensive UAV Swarm Against an Enemy UAV Swarm
2011-06-01
energy options” [10]. The research of swarm robotics derives much of its inspiration from natural systems. Social insects are known to coordinate their...Monterey, California 9. CPT. Francisco J. Hederra Direccion de Investigacion , Programas y Desarrollo de la Armada Armada de Chile CHILE 10. CAPT Jeffrey Kline, USN(ret.) Naval Postgraduate School Monterey, California 91
Swarms of repeating long-period earthquakes at Shishaldin Volcano, Alaska, 2001-2004
Petersen, Tanja
2007-01-01
During 2001–2004, a series of four periods of elevated long-period seismic activity, each lasting about 1–2 months, occurred at Shishaldin Volcano, Aleutian Islands, Alaska. The time periods are termed swarms of repeating events, reflecting an abundance of earthquakes with highly similar waveforms that indicate stable, non-destructive sources. These swarms are characterized by increased earthquake amplitudes, although the seismicity rate of one event every 0.5–5 min has remained more or less constant since Shishaldin last erupted in 1999. A method based on waveform cross-correlation is used to identify highly repetitive events, suggestive of spatially distinct source locations. The waveform analysis shows that several different families of similar events co-exist during a given swarm day, but generally only one large family dominates. A network of hydrothermal fractures may explain the events that do not belong to a dominant repeating event group, i.e. multiple sources at different locations exist next to a dominant source. The dominant waveforms exhibit systematic changes throughout each swarm, but some of these waveforms do reappear over the course of 4 years indicating repeatedly activated source locations. The choked flow model provides a plausible trigger mechanism for the repeating events observed at Shishaldin, explaining the gradual changes in waveforms over time by changes in pressure gradient across a constriction within the uppermost part of the conduit. The sustained generation of Shishaldin's long-period events may be attributed to complex dynamics of a multi-fractured hydrothermal system: the pressure gradient within the main conduit may be regulated by temporarily sealing and reopening of parallel flow pathways, by the amount of debris within the main conduit and/or by changing gas influx into the hydrothermal system. The observations suggest that Shishaldin's swarms of repeating events represent time periods during which a dominant source
Firooz Salaami; Nima Jamshidi; Mostafa Rostami; Siamak Najarian
2008-01-01
In this research, an athlete's body on sagittal plane in tension phase of snatch weightlifting has been modeled in two dimensions for calculating the generated torques in joints. The error back propagation multi-layer perceptrons has been used for modeling the torque through changing the angular velocity, angular acceleration and absolute angle of each segment. Finally, the torque in joints has been minimized by particle swarm optimization technique and the power of athlete has been maximized...
Distributed Pheromone-Based Swarming Control of Unmanned Air and Ground Vehicles for RSTA
2008-03-20
physics models 4, 5, and digital pheromones based on insect models 6-11. Digital pheromones are similar to potential fields, but they more naturally...fields that many social insects use to coordinate their behavior. Different “flavors” of pheromones convey different kinds of information. They have...Forthcoming in Proceedings of SPIE Defense & Security Conference, March 2008, Orlando, FL Distributed Pheromone -Based Swarming Control of Unmanned
Improved cuckoo search with particle swarm optimization for ...
Indian Academy of Sciences (India)
Content based image retrieval (CBIR); image compression; partial recurrent neural network (PRNN); particle swarm optimization (PSO); HAARwavelet; Cuckoo Search ... are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.
A new hybrid teaching–learning particle swarm optimization ...
Indian Academy of Sciences (India)
This paper proposes a novel hybrid teaching–learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teaching–learning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after ...
Swarming modulatory effects of some amino acids on Proteus ...
African Journals Online (AJOL)
Swarming motility, a multicellular behaviour characterized by periodic concentric growth on solid media has severally been reported as a constraint in the clinical investigation of mixed-culture infections involving Proteus and as a requirement for virulence. While media are being formulated to restrain swarming in this ...
Level-2 product generation for the Swarm satellite constellation mission
DEFF Research Database (Denmark)
Olsen, Poul Erik Holmdahl; Tøffner-Clausen, Lars; Olsen, Nils
In order to take advantage of the unique constellation aspect of ESA's Swarm constellation mission, considerably advanced data analysis tools have been developed. The Swarm ESL/SCARF (Satellite Constellation Application and Research Facility), a consortium of several research institutions, derives...
A novel particle swarm optimization based on population category
Wang, Jingying; Qu, Jianhua
2017-10-01
This paper raised a novel particle swarm optimization algorithm based on population category. Traditional particle swarm optimization algorithm is easily to trap in local optimum. In order to avoid standard algorithm appearing premature convergence, this novel algorithm use population category strategy to find new directions for particles. At last, computational results show that the new method is effective and has a high-performance.
Proteus mirabilis interkingdom swarming signals attract blow flies
Ma, Qun; Fonseca, Alicia; Liu, Wenqi; Fields, Andrew T; Pimsler, Meaghan L; Spindola, Aline F; Tarone, Aaron M; Crippen, Tawni L; Tomberlin, Jeffery K; Wood, Thomas K
2012-01-01
Flies transport specific bacteria with their larvae that provide a wider range of nutrients for those bacteria. Our hypothesis was that this symbiotic interaction may depend on interkingdom signaling. We obtained Proteus mirabilis from the salivary glands of the blow fly Lucilia sericata; this strain swarmed significantly and produced a strong odor that attracts blow flies. To identify the putative interkingdom signals for the bacterium and flies, we reasoned that as swarming is used by this bacterium to cover the food resource and requires bacterial signaling, the same bacterial signals used for swarming may be used to communicate with blow flies. Using transposon mutagenesis, we identified six novel genes for swarming (ureR, fis, hybG, zapB, fadE and PROSTU_03490), then, confirming our hypothesis, we discovered that fly attractants, lactic acid, phenol, NaOH, KOH and ammonia, restore swarming for cells with the swarming mutations. Hence, compounds produced by the bacterium that attract flies also are utilized for swarming. In addition, bacteria with the swarming mutation rfaL attracted fewer blow flies and reduced the number of eggs laid by the flies. Therefore, we have identified several interkingdom signals between P. mirabilis and blow flies. PMID:22237540
Swarming modulatory effects of some amino acids on Proteus ...
African Journals Online (AJOL)
Swarming motility, a multicellular behaviour characterized by periodic concentric growth on solid media has severally been reported as a constraint in the clinical ... The effects of 20 amino acids on swarming, extracellular protease activity, cellular RNA level and total protein concentration in 20 clinical Proteus strains from ...
New paleomagnetic results on 2367 Ma Dharwar giant dyke swarm ...
Indian Academy of Sciences (India)
N Ramesh Babu
2018-02-14
Feb 14, 2018 ... 2012). This direction is considered as primary magnetization of 2082 Ma radiating dyke swarm of. EDC (Kumar et al. 2015). Hence, we infer here that the source for component (B) is possibly the recently reported 2080 Ma spectacular radiating dyke swarm, which radiates beneath the Cudda- pah basin with ...
Swarming Robot Design, Construction and Software Implementation
Stolleis, Karl A.
2014-01-01
In this paper is presented an overview of the hardware design, construction overview, software design and software implementation for a small, low-cost robot to be used for swarming robot development. In addition to the work done on the robot, a full simulation of the robotic system was developed using Robot Operating System (ROS) and its associated simulation. The eventual use of the robots will be exploration of evolving behaviors via genetic algorithms and builds on the work done at the University of New Mexico Biological Computation Lab.
Collective motion in Proteus mirabilis swarms
Haoran, Xu
Proteus mirabilisis a Gram-negative, rod-shaped bacterium. It is widely distributed in soil and water, and it is well known for exhibiting swarming motility on nutrient agar surfaces. In our study, we focused on the collective motility of P. mirabilis and uncovered a range of interesting phenomena. Here we will present our efforts to understand these phenomena through experiments and simulation. Mailing address: Room 306 Science Centre North Block, The Chinese University of Hong Kong, Shatin, N.T. Hong Kong SAR. Phone: +852-3943-6354. Fax: +852-2603-5204. E-mail:xhrphx@gmail.com.
Human management of a robotic swarm
Salomons, N.; Kapellmann-Zafra, G.; Gross, R.
2016-01-01
This paper proposes a management algorithm that allows a human operator to organize a robotic swarm via a robot leader. When the operator requests a robot to become a leader, nearby robots suspend their activities. The operator can then request a count of the robots, and assign them into subgroups, one for each task. Once the operator releases the leader, the robots perform the tasks they were assigned to. We report a series of experiments conducted with up to 30 e-puck mobile robots. On aver...
Particle Swarm Optimization and Regression Analysis II
Mohanty, Soumya D.
2012-10-01
In the first part of this article, Particle Swarm Optimization (PSO) was applied to the problem of optimizing knot placement in the regression spline method. Although promising for broadband signals having smooth, but otherwise unknown, waveforms, this simple approach fails in the case of narrowband signals when the carrier frequency as well as the amplitude and phase modulations are unknown. A method is presented that addresses this challenge by using PSO based regression splines for the in-phase and quadrature amplitudes separately. It is thereby seen that PSO is an effective tool for regression analysis of a broad class of signals.
Test Frequency Selection Using Particle Swarm Optimization
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Zdenek Kincl
2013-01-01
Full Text Available The paper deals with the problem of test frequency selection for multi-frequency parametric fault diagnosis of analog linear circuits. An appropriate set of test frequencies is determined by minimizing the conditionality of the sensitivity matrix based on the system of fault equations using a global stochastic optimization. A novel method based on the Particle Swarm Optimization, which provides more accurate results and improves the convergence rate, is described. The paper provides several practical examples of its application to test frequency selection for active RC filters. A comparison of the results obtained by the proposed method and by the Genetic Algorithm is also presented.
Computational Swarming: A Cultural Technique for Generative Architecture
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Sebastian Vehlken
2014-11-01
Full Text Available After a first wave of digital architecture in the 1990s, the last decade saw some approaches where agent-based modelling and simulation (ABM was used for generative strategies in architectural design. By taking advantage of the self-organisational capabilities of computational agent collectives whose global behaviour emerges from the local interaction of a large number of relatively simple individuals (as it does, for instance, in animal swarms, architects are able to understand buildings and urbanscapes in a novel way as complex spaces that are constituted by the movement of multiple material and informational elements. As a major, zoo-technological branch of ABM, Computational Swarm Intelligence (SI coalesces all kinds of architectural elements – materials, people, environmental forces, traffic dynamics, etc. – into a collective population. Thereby, SI and ABM initiate a shift from geometric or parametric planning to time-based and less prescriptive software tools.Agent-based applications of this sort are used to model solution strategies in a number of areas where opaque and complex problems present themselves – from epidemiology to logistics, and from market simulations to crowd control. This article seeks to conceptualise SI and ABM as a fundamental and novel cultural technique for governing dynamic processes, taking their employment in generative architectural design as a concrete example. In order to avoid a rather conventional application of philosophical theories to this field, the paper explores how the procedures of such technologies can be understood in relation to the media-historical concept of Cultural Techniques.
Fluid Induced Earthquakes: From KTB Experiments to Natural Seismicity Swarms.
Shapiro, S. A.
2006-12-01
), both heterogeneously distributed in rocks. The results of the analysis of the most significant and best studied (year 2000) earthquake swarm support this concept. Using a numerical model, where spatially correlated diffusivity and criticalit y patches (where patches with higher diffusivity are assumed to be less stable) are considered, we successfully simulate a general seismicity pattern of the swarms, including the spatio-temporal clustering of events and the migration of seismic activity. Therefore, in some cases spontaneously triggered natural seismicity, like earthquake swarms, also shows diffusion-typical signatures mentioned above. However, it seems that there are also some principle differences. They are emphasized in this presentation.
Wagner, F.
2003-12-01
The Hannes Alfvén Prize of the European Physical Society for Outstanding Contributions to Plasma Physics (2003) has been awarded to Vladimir Evgenievitch Fortov `for his seminal contributions in the area of non-ideal plasmas and strongly coupled Coulomb systems, and for his pioneering work on the generation and investigation of plasmas under extreme conditions'. Vladimir Evgenievitch Fortov was born on 23 January 1946 in Noginsk, Russia. He studied physics at the Moscow Institute of Physics and Technology (PhD in 1976). In 1978 he was made a Professor and in 1991 he was awarded the Chair of the Moscow Institute of Physics and Technology. In the same year he became a Member of the Russian Academy of Sciences and was its vice-chairman from 1996 to 2001. From 1996 to 1998, Professor Fortov went into politics where he was just as successful, becoming Deputy Prime Minister of the Government of the Russian Federation and Minister of Science and Technology of the Russian Federation. Professor Fortov has made outstanding experimental and theoretical contributions to low temperature plasma physics. His pioneering work investigating non-ideal plasmas produced by intense shock waves initiated a new research field---the physical properties of highly compressed plasmas with strong inter-particle interactions. Under the leadership of Professor Fortov, experimental methods for generating and diagnosing these plasmas under extreme conditions were developed. To generate intense shock waves, a broad spectrum of drivers was used---chemical explosives, hypervelocity impact, lasers, relativistic electrons, heavy-ion and soft x-ray beams. Measurements of the equation of state, transport and optical properties of strongly coupled plasmas were carried out, including the interesting region lying between condensed matter and rarefied plasmas where specific plasma phase transitions and insulator--metal transitions were expected and explored. In another area of strongly coupled plasmas
Adaptive Gradient Multiobjective Particle Swarm Optimization.
Han, Honggui; Lu, Wei; Zhang, Lu; Qiao, Junfei
2017-10-09
An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO. Attributed to the stocktickerMOG method and the self-adaptive flight parameters mechanism, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy, but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front.
Particle Swarms in Fractures: Open Versus Partially Closed Systems
Boomsma, E.; Pyrak-Nolte, L. J.
2014-12-01
In the field, fractures may be isolated or connected to fluid reservoirs anywhere along the perimeter of a fracture. These boundaries affect fluid circulation, flow paths and communication with external reservoirs. The transport of drop like collections of colloidal-sized particles (particle swarms) in open and partially closed systems was studied. A uniform aperture synthetic fracture was constructed using two blocks (100 x 100 x 50 mm) of transparent acrylic placed parallel to each other. The fracture was fully submerged a tank filled with 100cSt silicone oil. Fracture apertures were varied from 5-80 mm. Partially closed systems were created by sealing the sides of the fracture with plastic film. The four boundary conditions study were: (Case 1) open, (Case 2) closed on the sides, (Case 3) closed on the bottom, and (Case 4) closed on both the sides and bottom of the fracture. A 15 μL dilute suspension of soda-lime glass particles in oil (2% by mass) were released into the fracture. Particle swarms were illuminated using a green (525 nm) LED array and imaged with a CCD camera. The presence of the additional boundaries modified the speed of the particle swarms (see figure). In Case 1, enhanced swarm transport was observed for a range of apertures, traveling faster than either very small or very large apertures. In Case 2, swarm velocities were enhanced over a larger range of fracture apertures than in any of the other cases. Case 3 shifted the enhanced transport regime to lower apertures and also reduced swarm speed when compared to Case 2. Finally, Case 4 eliminated the enhanced transport regime entirely. Communication between the fluid in the fracture and an external fluid reservoir resulted in enhanced swarm transport in Cases 1-3. The non-rigid nature of a swarm enables drag from the fracture walls to modify the swarm geometry. The particles composing a swarm reorganize in response to the fracture, elongating the swarm and maintaining its density. Unlike a
Optimization of Transformation Coefficients Using Direct Search and Swarm Intelligence
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Manusov V.Z.
2017-04-01
Full Text Available This research considers optimization of tap position of transformers in power systems to reduce power losses. Now, methods based on heuristic rules and fuzzy logic, or methods that optimize parts of the whole system separately, are applied to this problem. The first approach requires expert knowledge about processes in the network. The second methods are not able to consider all the interrelations of system’s parts, while changes in segment affect the entire system. Both approaches are tough to implement and require adjustment to the tasks solved. It needs to implement algorithms that can take into account complex interrelations of optimized variables and self-adapt to optimization task. It is advisable to use algorithms given complex interrelations of optimized variables and independently adapting from optimization tasks. Such algorithms include Swarm Intelligence algorithms. Their main features are self-organization, which allows them to automatically adapt to conditions of tasks, and the ability to efficiently exit from local extremes. Thus, they do not require specialized knowledge of the system, in contrast to fuzzy logic. In addition, they can efficiently find quasi-optimal solutions converging to the global optimum. This research applies Particle Swarm Optimization algorithm (PSO. The model of Tajik power system used in experiments. It was found out that PSO is much more efficient than greedy heuristics and more flexible and easier to use than fuzzy logic. PSO allows reducing active power losses from 48.01 to 45.83 MW (4.5%. With al, the effect of using greedy heuristics or fuzzy logic is two times smaller (2.3%.
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
Han, Gaining; Fu, Weiping; Wang, Wen
2016-01-01
In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.
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Zhou Feng
2013-09-01
Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.
Rayleigh wave dispersion curve inversion by using particle swarm optimization and genetic algorithm
Buyuk, Ersin; Zor, Ekrem; Karaman, Abdullah
2017-04-01
Inversion of surface wave dispersion curves with its highly nonlinear nature has some difficulties using traditional linear inverse methods due to the need and strong dependence to the initial model, possibility of trapping in local minima and evaluation of partial derivatives. There are some modern global optimization methods to overcome of these difficulties in surface wave analysis such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). GA is based on biologic evolution consisting reproduction, crossover and mutation operations, while PSO algorithm developed after GA is inspired from the social behaviour of birds or fish of swarms. Utility of these methods require plausible convergence rate, acceptable relative error and optimum computation cost that are important for modelling studies. Even though PSO and GA processes are similar in appearence, the cross-over operation in GA is not used in PSO and the mutation operation is a stochastic process for changing the genes within chromosomes in GA. Unlike GA, the particles in PSO algorithm changes their position with logical velocities according to particle's own experience and swarm's experience. In this study, we applied PSO algorithm to estimate S wave velocities and thicknesses of the layered earth model by using Rayleigh wave dispersion curve and also compared these results with GA and we emphasize on the advantage of using PSO algorithm for geophysical modelling studies considering its rapid convergence, low misfit error and computation cost.
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Alexander А. Valitov
2015-09-01
Full Text Available The visit of Grand Duke Vladimir Alexandrovich in Western Siberia in 1868, was not only a study tour by the representative of the Imperial Family with the marginal edge of the country. This visit was one of the forms of legimitacy power, which is a special ritual – including the official program, which was designed to reflect the unity of government and people. In General, the study period in the political culture of the country has developed specific scenarios of power, its functional embodiment they received in the form of various rituals, among which an important place was occupied by Imperial holidays and visit the highest personages. Thus, the visit of Grand Duke Vladimir, gave the chief Executive an opportunity to formally introduce myself filed for forming in the public mind about a particular scenario to the proximity of the public and the authorities, in the form of direct interaction between the Tsar and the Russian people.
Swarm of bees and particles algorithms in the problem of gradual failure reliability assurance
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M. F. Anop
2015-01-01
Full Text Available Probability-statistical framework of reliability theory uses models based on the chance failures analysis. These models are not functional and do not reflect relation of reliability characteristics to the object performance. At the same time, a significant part of the technical systems failures are gradual failures caused by degradation of the internal parameters of the system under the influence of various external factors.The paper shows how to provide the required level of reliability at the design stage using a functional model of a technical object. Paper describes the method for solving this problem under incomplete initial information, when there is no information about the patterns of technological deviations and degradation parameters, and the considered system model is a \\black box" one.To this end, we formulate the problem of optimal parametric synthesis. It lies in the choice of the nominal values of the system parameters to satisfy the requirements for its operation and take into account the unavoidable deviations of the parameters from their design values during operation. As an optimization criterion in this case we propose to use a deterministic geometric criterion \\reliability reserve", which is the minimum distance measured along the coordinate directions from the nominal parameter value to the acceptability region boundary rather than statistical values.The paper presents the results of the application of heuristic swarm intelligence methods to solve the formulated optimization problem. Efficiency of particle swarm algorithms and swarm of bees one compared with undirected random search algorithm in solving a number of test optimal parametric synthesis problems in three areas: reliability, convergence rate and operating time. The study suggests that the use of a swarm of bees method for solving the problem of the technical systems gradual failure reliability ensuring is preferred because of the greater flexibility of the
Particle Swarm Transport through Immiscible Fluid Layers in a Fracture
Teasdale, N. D.; Boomsma, E.; Pyrak-Nolte, L. J.
2011-12-01
Immiscible fluids occur either naturally (e.g. oil & water) or from anthropogenic processes (e.g. liquid CO2 & water) in the subsurface and complicate the transport of natural or engineered micro- or nano-scale particles. In this study, we examined the effect of immiscible fluids on the formation and evolution of particle swarms in a fracture. A particle swarm is a collection of colloidal-size particles in a dilute suspension that exhibits cohesive behavior. Swarms fall under gravity with a velocity that is greater than the settling velocity of a single particle. Thus a particle swarm of colloidal contaminants can potentially travel farther and faster in a fracture than expected for a dispersion or emulsion of colloidal particles. We investigated the formation, evolution, and break-up of colloidal swarms under gravity in a uniform aperture fracture as hydrophobic/hydrophyllic particle swarms move across an oil-water interface. A uniform aperture fracture was fabricated from two transparent acrylic rectangular prisms (100 mm x 50 mm x 100 mm) that are separated by 1, 2.5, 5, 10 or 50 mm. The fracture was placed, vertically, inside a glass tank containing a layer of pure silicone oil (polydimethylsiloxane) on distilled water. Along the length of the fracture, 30 mm was filled with oil and 70 mm with water. Experiments were conducted using silicone oils with viscosities of 5, 10, 100, or 1000 cSt. Particle swarms (5 μl) were comprised of a 1% concentration (by mass) of 25 micron glass beads (hydrophilic) suspended in a water drop, or a 1% concentration (by mass) of 3 micron polystyrene fluorescent beads (hydrophobic) suspended in a water drop. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera and by green (525 nm) LED arrays for illumination. Swarms were spherical and remained coherent as they fell through the oil because of the immiscibility of oil and water. However, as a swarm approached the oil-water interface, it
Water surface tension modulates the swarming mechanics of Bacillus subtilis.
Ke, Wan-Ju; Hsueh, Yi-Huang; Cheng, Yu-Chieh; Wu, Chih-Ching; Liu, Shih-Tung
2015-01-01
Many Bacillus subtilis strains swarm, often forming colonies with tendrils on agar medium. It is known that B. subtilis swarming requires flagella and a biosurfactant, surfactin. In this study, we find that water surface tension plays a role in swarming dynamics. B. subtilis colonies were found to contain water, and when a low amount of surfactin is produced, the water surface tension of the colony restricts expansion, causing bacterial density to rise. The increased density induces a quorum sensing response that leads to heightened production of surfactin, which then weakens water surface tension to allow colony expansion. When the barrier formed by water surface tension is breached at a specific location, a stream of bacteria swarms out of the colony to form a tendril. If a B. subtilis strain produces surfactin at levels that can substantially weaken the overall water surface tension of the colony, water floods the agar surface in a thin layer, within which bacteria swarm and migrate rapidly. This study sheds light on the role of water surface tension in regulating B. subtilis swarming, and provides insight into the mechanisms underlying swarming initiation and tendril formation.
Particle Swarm Optimization With Interswarm Interactive Learning Strategy.
Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui
2016-10-01
The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.
Middleware Design for Swarm-Driving Robots Accompanying Humans.
Kim, Min Su; Kim, Sang Hyuck; Kang, Soon Ju
2017-02-17
Research on robots that accompany humans is being continuously studied. The Pet-Bot provides walking-assistance and object-carrying services without any specific controls through interaction between the robot and the human in real time. However, with Pet-Bot, there is a limit to the number of robots a user can use. If this limit is overcome, the Pet-Bot can provide services in more areas. Therefore, in this study, we propose a swarm-driving middleware design adopting the concept of a swarm, which provides effective parallel movement to allow multiple human-accompanying robots to accomplish a common purpose. The functions of middleware divide into three parts: a sequence manager for swarm process, a messaging manager, and a relative-location identification manager. This middleware processes the sequence of swarm-process of robots in the swarm through message exchanging using radio frequency (RF) communication of an IEEE 802.15.4 MAC protocol and manages an infrared (IR) communication module identifying relative location with IR signal strength. The swarm in this study is composed of the master interacting with the user and the slaves having no interaction with the user. This composition is intended to control the overall swarm in synchronization with the user activity, which is difficult to predict. We evaluate the accuracy of the relative-location estimation using IR communication, the response time of the slaves to a change in user activity, and the time to organize a network according to the number of slaves.
Chaotic particle swarm optimization with mutation for classification.
Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza
2015-01-01
In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.
Couceiro, Micael
2015-01-01
This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, suc
Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer
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Yu-Jun Zheng
2012-01-01
Full Text Available Particle swarm optimization (PSO is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO, which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.
Frog Swarms: Earthquake Precursors or False Alarms?
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Rachel A. Grant
2013-10-01
Full Text Available In short-term earthquake risk forecasting, the avoidance of false alarms is of utmost importance to preclude the possibility of unnecessary panic among populations in seismic hazard areas. Unusual animal behaviour prior to earthquakes has been reported for millennia but has rarely been scientifically documented. Recently large migrations or unusual behaviour of amphibians have been linked to large earthquakes, and media reports of large frog and toad migrations in areas of high seismic risk such as Greece and China have led to fears of a subsequent large earthquake. However, at certain times of year large migrations are part of the normal behavioural repertoire of amphibians. News reports of “frog swarms” from 1850 to the present day were examined for evidence that this behaviour is a precursor to large earthquakes. It was found that only two of 28 reported frog swarms preceded large earthquakes (Sichuan province, China in 2008 and 2010. All of the reported mass migrations of amphibians occurred in late spring, summer and autumn and appeared to relate to small juvenile anurans (frogs and toads. It was concluded that most reported “frog swarms” are actually normal behaviour, probably caused by juvenile animals migrating away from their breeding pond, after a fruitful reproductive season. As amphibian populations undergo large fluctuations in numbers from year to year, this phenomenon will not occur on a yearly basis but will depend on successful reproduction, which is related to numerous climatic and geophysical factors. Hence, most large swarms of amphibians, particularly those involving very small frogs and occurring in late spring or summer, are not unusual and should not be considered earthquake precursors. In addition, it is likely that reports of several mass migration of small toads prior to the Great Sichuan Earthquake in 2008 were not linked to the subsequent M = 7.9 event (some occurred at a great distance from the epicentre
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Mehdi Neshat
2015-11-01
Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.
Loving More, Being Less: Reflections on Vladimir Jankélévitch’s Le paradoxe de la morale
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Jennifer Rosato
2014-12-01
Full Text Available In Le paradoxe de la morale, Vladimir Jankélévitch proposes that the moral life is a matter of balancing the demands of love, which call us to give without limit, and our natural, egoistical attachment to self, which he terms 'being'. This balancing act is ultimately paradoxical since love must both depend on and overcome being. The vision of moral life as a paradoxical balancing act of love and being, however, is implicitly challenged by another, "supernatural" vision of ethics that Jankélévitch proposes near the end of the text. In these passages, the egoism of being that marks human nature is not merely balanced but genuinely transformed by the passionate care for an other. In this paper, these two visions of the ethical life offered by Jankélévitch's text are discussed in turn, and a way to read them as complementary rather than contradictory is proposed.
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Natalija Vid
2008-06-01
Full Text Available In the article the author focuses on Vladimir Nabokov’s translation of Lewis Carroll’s novel Alice’s Adventures in Wonderland, made in 1923. The main intention of the article is to analyze Nabokov’s translation strategies of domestication, realized in the text as substitution and localization, and to explain possible reasons for his decision in favour of almost complete Russification of the original. It is possible that Nabokov considered children’s attitude towards the final result as the most important part of the translation process. Thus, he used domesticated strategies to transfer for Russian children the humour, the originality and brightness of the paradoxical and attractive world of Lewis Carroll, his sense of the absurd and his amazing gift for games of logic and language, providing a recognizable and familiar atmosphere for the readers. Undoubtedly, his young Russian readers were able to identify themselves with the story and to comprehend the complex world created by Lewis Carroll. On the other hand, Nabokov refuses to oversimplify his translation or to patronize its young audience through simplistic translation solutions.
Un análisis de los aspectos comunicativos del pianista Vladimir Horowitz con el público
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Andrés, Carlos
2006-06-01
Full Text Available This work addresses the artistic communication in general, and the musicalcommunication in particular, focusing on one of the most exceptional figures in the historyof musical interpretation: the pianist Vladimir Horowitz. First, we provide a dualperspective on when and how the intrepreter intermediates the communication between thecomposer and the audience: one perspective is that of the artist who shapes hisinterpretation based on his knowledge and experiences, and the other perspective is of thelistener, whose way of listening is affected by emotional, cognitive and socio-culturalfactors. In the empirical part we have analyzed the different types of perceptions reportedby an audience, that have been selected along a number of characteristics. We related the body language of Horowitz with his way of playing, distinguishing between the perceptions of the audience in those pieces where they could only listen to the music, and those inwhich they could see the artist performing. In addition, we have investigated therelationship among all the subjects according to their profiles, and analyzed the technical resources employed by Horowitz in capturing the audience's attention, thus evaluating his ability to communicate. Finally, we have sought to advance a definition of the degree of communication between Horowitz and the selected audience, based on their reported sensations and emotions.
Extracting Ocean-Generated Tidal Magnetic Signals from Swarm Data through Satellite Gradiometry
DEFF Research Database (Denmark)
Sabaka, Terence J.; Tyler, Robert H.; Olsen, Nils
2016-01-01
Ocean-generated magnetic field models of the Principal Lunar, M2, and the Larger Lunar elliptic, N2, semi-diurnal tidal constituents were estimated through a “Comprehensive Inversion" of the first 20.5 months of magnetic measurements from ESA's Swarm satellite constellation mission. While the con...
Directory of Open Access Journals (Sweden)
Oihane Irazoki
2016-10-01
Full Text Available In addition to its role in DNA damage repair and recombination, the RecA protein, through its interaction with CheW, is involved in swarming motility, a form of flagella-dependent movement across surfaces. In order to better understand how SOS response modulates swarming, in this work the location of RecA and CheW proteins within the swarming cells has been studied by using super-resolution microscopy. Further, and after in silico docking studies, the specific RecA and CheW regions associated with the RecA-CheW interaction have also been confirmed by site-directed mutagenesis and immunoprecipitation techniques. Our results point out that the CheW distribution changes, from the cell poles to foci distributed in a helical pattern along the cell axis when SOS response is activated or RecA protein is overexpressed. In this situation, the CheW presents the same subcellular location as that of RecA, pointing out that the previously described RecA storage structures may be modulators of swarming motility. Data reported herein not only confirmed that the RecA-CheW pair is essential for swarming motility but it is directly involved in the CheW distribution change associated to SOS response activation. A model explaining not only the mechanism by which DNA damage modulates swarming but also how both the lack and the excess of RecA protein impair this motility is proposed.
Pan, Qing; Wang, Ruofan; Reglin, Bettina; Fang, Luping; Pries, Axel R; Ning, Gangmin
2014-01-01
Estimation of the boundary condition is a critical problem in simulating hemodynamics in microvascular networks. This paper proposed a boundary estimation strategy based on a particle swarm optimization (PSO) algorithm, which aims to minimize the number of vessels with inverted flow direction in comparison to the experimental observation. The algorithm took boundary values as the particle swarm and updated the position of the particles iteratively to approach the optimization target. The method was tested in a real rat mesenteric network. With random initial boundary values, the method achieved a minimized 9 segments with an inverted flow direction in the network with 546 vessels. Compared with reported literature, the current work has the advantage of a better fit with experimental observations and is more suitable for the boundary estimation problem in pulsatile hemodynamic models due to the experiment-based optimization target selection.
DEFF Research Database (Denmark)
Amm, O.; Vanhamäki, H.; Kauristie, K.
2015-01-01
The European Space Agency (ESA) Swarm spacecraft mission is the first multisatellite ionospheric mission with two low-orbiting spacecraft that are flying in parallel at a distance of ~100–140 km, thus allowing derivation of spatial gradients of ionospheric parameters not only along the orbits...... but also in the direction perpendicular to them. A third satellite with a higher orbit regularly crosses the paths of the lower spacecraft. Using the Swarmmagnetic and electric field instruments,we present a novel technique that allows derivation of two-dimensional (2-D) maps of ionospheric conductances......, currents, and electric field in the area between the trajectories of the two lower spacecraft, and even to some extent outside of it. This technique is based on Spherical Elementary Current Systems. We present test cases of modeled situations from which we calculate virtual Swarm data and show...
Directory of Open Access Journals (Sweden)
Lina Hamouche
2017-02-01
Full Text Available Bacteria adopt social behavior to expand into new territory, led by specialized swarmers, before forming a biofilm. Such mass migration of Bacillus subtilis on a synthetic medium produces hyperbranching dendrites that transiently (equivalent to 4 to 5 generations of growth maintain a cellular monolayer over long distances, greatly facilitating single-cell gene expression analysis. Paradoxically, while cells in the dendrites (nonswarmers might be expected to grow exponentially, the rate of swarm expansion is constant, suggesting that some cells are not multiplying. Little attention has been paid to which cells in a swarm are actually multiplying and contributing to the overall biomass. Here, we show in situ that DNA replication, protein translation and peptidoglycan synthesis are primarily restricted to the swarmer cells at dendrite tips. Thus, these specialized cells not only lead the population forward but are apparently the source of all cells in the stems of early dendrites. We developed a simple mathematical model that supports this conclusion.
Desmarais, Jacques K.; Spiteri, Raymond J.
2017-12-01
A parallelized implementation of the particle swarm optimization algorithm is developed. We use the optimization procedure to speed up a previously published algorithm for airborne electromagnetic data interpretation. This algorithm is the only parametrized automated procedure for extracting the three-dimensionally varying geometrical parameters of conductors embedded in a resistive environment, such as igneous and metamorphic terranes. When compared to the original algorithm, the new optimization procedure is faster by two orders of magnitude (factor of 100). Synthetic model tests show that for the chosen system architecture and objective function, the particle swarm optimization approach depends very weakly on the rate of communication of the processors. Optimal wall-clock times are obtained using three processors. The increased performance means that the algorithm can now easily be used for fast routine interpretation of airborne electromagnetic surveys consisting of several anomalies, as is displayed by a test on MEGATEM field data collected at the Chibougamau site, Québec.
Multi-Objective Optimization of Wire Antennas: Genetic Algorithms Versus Particle Swarm Optimization
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Z. Raida
2005-12-01
Full Text Available The paper is aimed to the multi-objective optimization of wiremulti-band antennas. Antennas are numerically modeled using time-domainintegral-equation method. That way, the designed antennas can becharacterized in a wide band of frequencies within a single run of theanalysis. Antennas are optimized to reach the prescribed matching, toexhibit the omni-directional constant gain and to have the satisfactorypolarization purity. Results of the design are experimentally verified. The multi-objective cost function is minimized by the genetic algorithmand by the particle swarm optimization. Results of the optimization byboth the multi-objective methods are in detail compared. The combination of the time domain analysis and global optimizationmethods for the broadband antenna design and the detailed comparison ofthe multi-objective particle swarm optimization with themulti-objective genetic algorithm are the original contributions of thepaper.
Semisupervised Particle Swarm Optimization for Classification
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Xiangrong Zhang
2014-01-01
Full Text Available A semisupervised classification method based on particle swarm optimization (PSO is proposed. The semisupervised PSO simultaneously uses limited labeled samples and large amounts of unlabeled samples to find a collection of prototypes (or centroids that are considered to precisely represent the patterns of the whole data, and then, in principle of the “nearest neighborhood,” the unlabeled data can be classified with the obtained prototypes. In order to validate the performance of the proposed method, we compare the classification accuracy of PSO classifier, k-nearest neighbor algorithm, and support vector machine on six UCI datasets, four typical artificial datasets, and the USPS handwritten dataset. Experimental results demonstrate that the proposed method has good performance even with very limited labeled samples due to the usage of both discriminant information provided by labeled samples and the structure information provided by unlabeled samples.
Particle Swarm Optimization and regression analysis I
Mohanty, Souyma D.
2012-04-01
Particle Swarm Optimization (PSO) is now widely used in many problems that require global optimization of high-dimensional and highly multi-modal functions. However, PSO has not yet seen widespread use in astronomical data analysis even though optimization problems in this field have become increasingly complex. In this two-part article, we first provide an overview of the PSO method in the concrete context of a ubiquitous problem in astronomy, namely, regression analysis. In particular, we consider the problem of optimizing the placement of knots in regression based on cubic splines (spline smoothing). The second part will describe an in-depth investigation of PSO in some realistic data analysis challenges.
Kladis, Georgios P.; Menon, Prathyush P.; Edwards, Christopher
2016-12-01
This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi-Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.
Particle swarm optimization with random keys applied to the nuclear reactor reload problem
Energy Technology Data Exchange (ETDEWEB)
Meneses, Anderson Alvarenga de Moura [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear; Fundacao Educacional de Macae (FUNEMAC), RJ (Brazil). Faculdade Professor Miguel Angelo da Silva Santos; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mails: ameneses@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; schirru@lmp.ufrj.br
2007-07-01
In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)
DeÂ Michelis, Paola; Consolini, Giuseppe; Tozzi, Roberta; Marcucci, Maria Federica
2017-10-01
This paper attempts to explore the statistical scaling features of high-latitude geomagnetic field fluctuations at Swarm altitude. Data for this study are low-resolution (1 Hz) magnetic data recorded by the vector field magnetometer on board Swarm A satellite over 1 year (from 15 April 2014 to 15 April 2015). The first- and second-order structure function scaling exponents and the degree of intermittency of the fluctuations of the intensity of the horizontal component of the magnetic field at high northern latitudes have been evaluated for different interplanetary magnetic field orientations in the GSM Y-Z plane and seasons. In the case of the first-order structure function scaling exponent, a comparison between the average spatial distributions of the obtained values and the statistical convection patterns obtained using a Super Dual Auroral Radar Network dynamic model (CS10 model) has been also considered. The obtained results support the idea that the knowledge of the scaling features of the geomagnetic field fluctuations can help in the characterization of the different ionospheric turbulence regimes of the medium crossed by Swarm A satellite. This study shows that different turbulent regimes of the geomagnetic field fluctuations exist in the regions characterized by a double-cell convection pattern and in those regions near the border of the convective structures.
Dempsey, E. D.; Holdsworth, R. E.; Di Toro, G.; Bistacchi, A.
2012-12-01
Earthquake swarms are often characterised by clusters of seismic events with highly variable earthquake focal mechanisms, irrespective of whether or not they are associated with a main shock event. Our current understanding of how such multiple seismic events manifest themselves in the geological record is based largely on the Hill (1977) and Sibson (1996) 'fracture mesh' models. Whilst these simple models are theoretically sound for homogeneous isotropic rock masses, they do not account for the effects of variably oriented pre-existing mechanical anisotropies and how these may lead to a more complex fracture evolution and geologic strain. Interconnected networks of faults and veins filled with zeolites and other hydrothermal minerals are widespread in many orogenic terrains, including deformed granitic plutons and regions of metamorphic basement. Typically the fracture fills formed late in the tectonic history, at relatively low temperatures (e.g. stress loading and reactivation of widely distributed pre-existing structures (contacts, joints, shear zone fabrics, faults). The differing orientations of the pre-existing structures relative to the far-field and near-field stresses lead to the simultaneous development of interlinked reverse, strike-slip and extensional faults. The kinematic complexity and cyclic nature of the hydraulically-induced fracturing provides compelling evidence that the mineralised fracture systems represent a geologic manifestation of foreshock-aftershock swarm development. Our proposal highlights the key role of crustal fluids during earthquake swarm development and the inherent geometrical complexities that may result from the reactivation of pre-existing anisotropies in rocks.
Dempsey, Eddie; Holdsworth, Bob; DiToro, Giulio; Bistacchi, Andrea
2013-04-01
Earthquake swarms are often characterised by clusters of seismic events with highly variable earthquake focal mechanisms, irrespective of whether or not they are associated with a main shock event. Our current understanding of how such events manifest themselves in the geological record is based largely on the Hill (1977) and Sibson (1996) 'fracture mesh' models. Whilst these simple models are theoretically sound for homogeneous isotropic rock masses, they do not account for the effects of variably oriented pre-existing mechanical anisotropies and how these may lead to a more complex fracture evolution and geologic strain. Interconnected networks of faults and veins filled with zeolites and other hydrothermal minerals are widespread in many orogenic terrains, including deformed granitic plutons and regions of metamorphic basement. Typically the fracture fills formed late in the tectonic history, at relatively low temperatures (e.g. stress loading and reactivation of widely distributed pre-existing structures (contacts, joints, shear zone fabrics, faults). The differing orientations of the pre-existing structures relative to the far-field and near-field stresses lead to the simultaneous development of interlinked reverse, strike-slip and extensional faults. The kinematic complexity and cyclic nature of the hydraulically-induced fracturing provides compelling evidence that the mineralised fracture systems represent a geologic manifestation of foreshock-aftershock swarm development. Our proposal highlights the key role of crustal fluids during earthquake swarm development and the inherent geometrical complexities that may result from the reactivation of pre-existing anisotropies in rocks.
Incorporating the Avoidance Behavior to the Standard Particle Swarm Optimization 2011
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ALTINOZ, O. T.
2015-05-01
Full Text Available Inspired from social and cognitive behaviors of animals living as swarms; particle swarm optimization (PSO provides a simple but very powerful tool for researchers who are dealing with collective intelligence. The algorithm depends on modeling the very basic random behavior (i.e. exploration capability of individuals in addition to their tendency to revisit positions of good memories (cognitive behavior and tendency to keep an eye on and follow the majority of swarm members (social behavior. The balance among these three major behaviors is the key of success of the algorithm. On the other hand, there are other social and cognitive phenomena, which might be useful for improvement of the algorithm. In this paper, we particularly investigate avoidance from the bad behavior. We propose modifications about modeling the Standard PSO 2011 formulation, and we test performance of our proposals at each step via benchmark functions, and compare the results of the proposed algorithms with well-known algorithms. Our results show that incorporation of Social Avoidance behavior into SPSO11 improves the performance. It is also shown that in case the Social Avoidance behavior is applied in an adaptive manner at the very first iterations of the algorithm, there might be further improvements.
Investigating the auroral electrojets using Swarm
Smith, Ashley; Macmillan, Susan; Beggan, Ciaran; Whaler, Kathy
2016-04-01
The auroral electrojets are large horizontal currents that flow within the ionosphere in ovals around the polar regions. They are an important aspect of space weather and their position and intensity vary with solar wind conditions and geomagnetic activity. The electrojet positions are also governed by the Earth's main magnetic field. During more active periods, the auroral electrojets typically move equatorward and become more intense. This causes a range of effects on Earth and in space, including geomagnetically induced currents in power transmission networks, disturbance to radio communications and increased drag on satellites due to expansion of the atmosphere. They are also indicative of where the aurora are visible. Monitoring of the auroral electrojets in the pre-satellite era was limited to the network of ground-based magnetic observatories, from which the traditional AE activity indices are produced. These suffer in particular from the stations' poor distribution in position and so this motivates the use of satellite-based measurements. With polar low-Earth orbit satellites carrying magnetometers, all latitudes can be sampled with excellent resolution. This poster presents an investigation using Swarm's magnetometer data to detect the electrojets as the spacecraft move above them. We compare and contrast two approaches, one which uses vector data and the other which uses scalar data (Hamilton and Macmillan 2013, Vennerstrom and Moretto, 2013). Using ideas from both approaches we determine the oval positions and intensities from Swarm and earlier satellites. The variation in latitude and intensity with solar wind conditions, geomagnetic activity and secular variation of the main field is investigated. We aim to elucidate the relative importance of these factors. Hamilton, B. and Macmillan, S., 2013. Investigation of decadal scale changes in the auroral oval positions using Magsat and CHAMP data. Poster at IAGA 12th Scientific Assembly, 2013. http
Mahadwar, G; Chauhan, K R; Bhagavathy, G V; Murphy, C; Smith, A D; Bhagwat, A A
2015-04-01
Controlling spread of human pathogens on fresh produce is a top priority for public health reasons. Isolation of compounds from agricultural waste that would control spread of human pathogens was explored using Salmonella enterica serovar Typhimurium as a model organism. In the environment, micro-organisms migrate as a 'community' especially when they move on moist surfaces. This type of motility is characterized as swarming motility. We examined extracts from agricultural waste such as soya bean husk, peels of orange, pineapple, avocado and pomegranate for antiswarming activity. Avocado and pineapple peels showed moderate (~40%) inhibition of swarming motility while pomegranate peel extract had high antiswarming activity (~85% inhibition) and was examined in further detail. Although the pomegranate peel extract was acidic, swarm-inhibitory activity was not due to low pH and the peel extract did not inhibit growth of Salmonella. Among the key swarm motility regulatory genes, class II (fliF, fliA, fliT and fliZ) and class III (fliC and fliM) regulators were downregulated upon exposure to pomegranate peel extract. Pomegranate peels offer great potential as a bioactive repellent for pathogenic micro-organisms on moist surfaces. Controlling the spread of food-borne pathogens in moist environments is an important microbial food safety issue. Isolation of compounds from agricultural waste (such as fruit peels) that would control spread of human pathogens was explored using Salmonella enterica serovar Typhimurium as a model organism. Pomegranate peels offer great potential as a bioactive repellent for pathogenic micro-organisms. © 2014 The Society for Applied Microbiology.
Generating a Multiphase Equation of State with Swarm Intelligence
Cox, Geoffrey
2017-06-01
Hydrocode calculations require knowledge of the variation of pressure of a material with density and temperature, which is given by the equation of state. An accurate model needs to account for discontinuities in energy, density and properties of a material across a phase boundary. When generating a multiphase equation of state the modeller attempts to balance the agreement between the available data for compression, expansion and phase boundary location. However, this can prove difficult because minor adjustments in the equation of state for a single phase can have a large impact on the overall phase diagram. Recently, Cox and Christie described a method for combining statistical-mechanics-based condensed matter physics models with a stochastic analysis technique called particle swarm optimisation. The models produced show good agreement with experiment over a wide range of pressure-temperature space. This talk details the general implementation of this technique, shows example results, and describes the types of analysis that can be performed with this method.
Self-Assembling Wireless Autonomous Reconfigurable Modules (SWARM) Project
National Aeronautics and Space Administration — Payload Systems Inc. and the MIT Space Systems Laboratory propose Self-assembling, Wireless, Autonomous, Reconfigurable Modules (SWARM) as an innovative approach to...
Algorithmic requirements for swarm intelligence in differently coupled collective systems.
Stradner, Jürgen; Thenius, Ronald; Zahadat, Payam; Hamann, Heiko; Crailsheim, Karl; Schmickl, Thomas
2013-05-01
Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments.
SCARF - The Swarm Satellite Constellation Application and Research Facility
DEFF Research Database (Denmark)
Olsen, Nils
2014-01-01
Swarm, a three-satellite constellation to study the dynamics of the Earth's magnetic field and its interactions with the Earth system, has been launched in November 2013. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution, which...... will bring new insights into the Earth system by improving our understanding of the Earth's interior and environment. In order to take advantage of the unique constellation aspect of Swarm, considerably advanced data analysis tools have been developed. Scientific users will also benefit significantly from...... derived products, the so-called Level-2 products, that take into account the features of the constellation. The Swarm SCARF (Satellite Constellation Application and Research Facility), a consortium of several research institutions, has been established with the goal of deriving Level-2 products...
An apparatus to measure electrical charge of bubble swarms.
Uddin, S; Jin, L; Mirnezami, M; Finch, J A
2013-01-01
An apparatus has been developed to characterize bubble charge by measuring the swarm potential of gas bubbles. The technique allows in-process measurement of all system variables associated with bubble surface electrical charge: swarm potential, solution conductivity, gas holdup, pH and bubble size distribution. The method was validated by comparing with literature iso-electric point (iep) values. Bubble swarm potential was measured as a function of concentration and pH for a series of non-ionic surfactant frothers, ionic surfactant collectors and multivalent metal ions. Results showed good agreement with established theory and prior experimental findings. The setup is a step towards measurement of charge on flotation size range of bubble swarms. Copyright © 2012 Elsevier Inc. All rights reserved.
Formation Control of Robotic Swarm Using Bounded Artificial Forces
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Long Qin
2013-01-01
Full Text Available Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
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Trung Dung Ngo
2011-08-01
Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
DEFF Research Database (Denmark)
Ngo, Trung Dung
2012-01-01
optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm......A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One...... of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...
A Markov Chain Approach to Probabilistic Swarm Guidance
Acikmese, Behcet; Bayard, David S.
2012-01-01
This paper introduces a probabilistic guidance approach for the coordination of swarms of autonomous agents. The main idea is to drive the swarm to a prescribed density distribution in a prescribed region of the configuration space. In its simplest form, the probabilistic approach is completely decentralized and does not require communication or collabo- ration between agents. Agents make statistically independent probabilistic decisions based solely on their own state, that ultimately guides the swarm to the desired density distribution in the configuration space. In addition to being completely decentralized, the probabilistic guidance approach has a novel autonomous self-repair property: Once the desired swarm density distribution is attained, the agents automatically repair any damage to the distribution without collaborating and without any knowledge about the damage.
Particle swarm inspired optimization algorithm without velocity equation
Directory of Open Access Journals (Sweden)
Mahmoud Mostafa El-Sherbiny
2011-03-01
Full Text Available This paper introduces Particle Swarm Without Velocity equation optimization algorithm (PSWV that significantly reduces the number of iterations required to reach good solutions for optimization problems. PSWV algorithm uses a set of particles as in particle swarm optimization algorithm but a different mechanism for finding the next position for each particle is used in order to reach a good solution in a minimum number of iterations. In PSWV algorithm, the new position of each particle is determined directly from the result of linear combination between its own best position and the swarm best position without using velocity equation. The results of PSWV algorithm and the results of different variations of particle swarm optimizer are experimentally compared. The performance of PSWV algorithm and the solution quality prove that PSWV is highly competitive and can be considered as a viable alternative to solve optimization problems.
Femto-satellite Swarm State and Density Estimation Project
National Aeronautics and Space Administration — NASA is planning future missions involving fleets of small satellites in LEO and GEO that can exhibit autonomous collective behavior. Such a "swarm of...
Stable swarming using adaptive long-range interactions
Gorbonos, Dan; Gov, Nir S.
2017-04-01
Sensory mechanisms in biology, from cells to humans, have the property of adaptivity, whereby the response produced by the sensor is adapted to the overall amplitude of the signal, reducing the sensitivity in the presence of strong stimulus, while increasing it when it is weak. This property is inherently energy consuming and a manifestation of the nonequilibrium nature of living organisms. We explore here how adaptivity affects the effective forces that organisms feel due to others in the context of a uniform swarm, in both two and three dimensions. The interactions between the individuals are taken to be attractive and long-range and of power-law form. We find that the effects of adaptivity inside the swarm are dramatic, where the effective forces decrease (or remain constant) with increasing swarm density. Linear stability analysis demonstrates how this property prevents collapse (Jeans instability), when the forces are adaptive. Adaptivity therefore endows swarms with a natural mechanism for self-stabilization.
Extending Particle Swarm Optimisers with Self-Organized Criticality
DEFF Research Database (Denmark)
Løvbjerg, Morten; Krink, Thiemo
2002-01-01
Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.......Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions....
Information flow principles for plasticity in foraging robot swarms
Pitonakova, Lenka; Crowder, Richard; Bullock, Seth
2016-01-01
An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. For example, a swarm of foraging robots should be able to repeatedly reorganise in order to exploit resource deposits that appear intermittently in different locations throughout their environment. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that a...
Instantaneous movement of krill swarms in the Antarctic Circumpolar Current
Tarling, Geraint A.; Thorpe, Sally E.
2014-01-01
Antarctic krill are known to have strong swimming capabilities, but direct observations of the speed and direction of krill-swarm movement within their natural environment are rare. We identified and examined 4060 swarms within the main flow of the Antarctic Circumpolar Current (Scotia Sea) using a combination of an EK60 echosounder, a 153.6 kHz acoustic Doppler current profiler, and ground-truthing nets. Net displacement magnitude (m) and net angle of deviation (d) were determined by vector ...
Multi-Robot Motion Planning Using Swarm Intelligence
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Gerasimos G. Rigatos
2008-11-01
Full Text Available Swarm intelligence theory is proposed for motion planning of multi-robot systems. Multiple particles start from different points in the solutions space and interact to each other while moving towards the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robotcooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The method's performance is evaluated through simulation tests.
Multi-Robot Motion Planning Using Swarm Intelligence
Directory of Open Access Journals (Sweden)
Gerasimos G. Rigatos
2008-06-01
Full Text Available Swarm intelligence theory is proposed for motion planning of multi-robot systems. Multiple particles start from different points in the solutions space and interact to each other while moving towards the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robotcooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The method's performance is evaluated through simulation tests.
Proterozoic Geomagnetic Field Geometry from Mafic Dyke Swarms
Panzik, J. E.; Evans, D. A.
2012-12-01
Pre-Mesozoic continental reconstructions and paleoclimatic inferences from paleomagnetism rely critically upon the assumption of a time-averaged geocentric axial dipole (GAD) magnetic field. We have been testing the GAD assumption empirically, by compiling paleomagnetic remanence directional variations among coeval volcanic rock suites distributed over large areas of the Earth's surface. We compute virtual geomagnetic poles (VGPs) from site-mean remanence data using either a pure GAD model, or alternative models with varying amounts of zonal quadrupole or octupole fields. Rastering through quadrupole vs. octupole space, we produce contour plots of precision/dispersion for the mean of VGPs in each computation. Using the 0-5 Ma volcanics global database as a test, our method shows results consistent with the compilations of Schneider & Kent (1990, Rev. Geophys. 28, 71-96) and McElhinny et al. (1996, JGR 101, 25007-25027), notably reproducing the reversal asymmetry in a significant (order 3%) quadrupolar contribution. Performing the same test on ancient mafic dyke swarms, the Karoo-Ferrar large igneous province (ca. 0.18 Ga) and the central Atlantic magmatic province (CAMP) (0.20 Ga) datasets are consistent with a range of models, including both GAD and independent estimates of non-GAD contributions derived from global tectonic reconstructions (Torsvik & Van der Voo, 2002, GJI 151, 771-794). The method is limited by paleolongitudinal restriction of ancient LIPs, for similarly restrictive sub-sampling of the 0-5 Ma volcanic data can generate results that differ dramatically from the global mean (e.g., the far-sided offset of VGPs relative to the spin axis). Analysis of pre-Pangean datasets is limited by the uncertainty of tectonic reconstructions, but within solely the intact North American (Laurentian) craton, the Franklin (ca. 0.72), Mackenzie (ca. 1.27) and Matachewan (2.45 Ga) dyke swarms are used as Pre-Mesozoic targets that have large areal coverage. None of the
Chicken Swarm Optimization Based on Elite Opposition-Based Learning
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Chiwen Qu
2017-01-01
Full Text Available Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.
Parallel particle swarm optimization algorithm in nuclear problems
Energy Technology Data Exchange (ETDEWEB)
Waintraub, Marcel; Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)], e-mail: marcel@ien.gov.br, e-mail: cmnap@ien.gov.br; Schirru, Roberto [Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Lab. de Monitoracao de Processos], e-mail: schirru@lmp.ufrj.br
2009-07-01
Particle Swarm Optimization (PSO) is a population-based metaheuristic (PBM), in which solution candidates evolve through simulation of a simplified social adaptation model. Putting together robustness, efficiency and simplicity, PSO has gained great popularity. Many successful applications of PSO are reported, in which PSO demonstrated to have advantages over other well-established PBM. However, computational costs are still a great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time consuming objective functions. To overcome such difficulty, parallel computation has been used. The default advantage of parallel PSO (PPSO) is the reduction of computational time. Master-slave approaches, exploring this characteristic are the most investigated. However, much more should be expected. It is known that PSO may be improved by more elaborated neighborhood topologies. Hence, in this work, we develop several different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by communication strategies in multiprocessor architectures. The proposed PPSOs have been applied to two complex and time consuming nuclear engineering problems: reactor core design and fuel reload optimization. After exhaustive experiments, it has been concluded that: PPSO still improves solutions after many thousands of iterations, making prohibitive the efficient use of serial (non-parallel) PSO in such kind of realworld problems; and PPSO with more elaborated communication strategies demonstrated to be more efficient and robust than the master-slave model. Advantages and peculiarities of each model are carefully discussed in this work. (author)
Application of particle swarm optimization to interpret Rayleigh wave dispersion curves
Song, Xianhai; Tang, Li; Lv, Xiaochun; Fang, Hongping; Gu, Hanming
2012-09-01
Rayleigh waves have been used increasingly as an appealing tool to obtain near-surface shear (S)-wave velocity profiles. However, inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this study, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme based on particle swarm optimization (PSO). PSO is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate calculation efficiency and stability of PSO to inversion of surface wave data, we first inverted three noise-free and three noise-corrupted synthetic data sets. Then, we made a comparative analysis with genetic algorithms (GA) and a Monte Carlo (MC) sampler and reconstructed a histogram of model parameters sampled on a low-misfit region less than 15% relative error to further investigate the performance of the proposed inverse procedure. Finally, we inverted a real-world example from a waste disposal site in NE Italy to examine the applicability of PSO on Rayleigh wave dispersion curves. Results from both synthetic and field data demonstrate that particle swarm optimization can be used for quantitative interpretation of Rayleigh wave dispersion curves. PSO seems superior to GA and MC in terms of both reliability and computational efforts. The great advantages of PSO are fast in locating the low misfit region and easy to implement. Also there are only three parameters to tune (inertia weight or constriction factor, local and global acceleration constants). Theoretical results exist to explain how to tune these parameters.
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Dao-Wei Bi
2007-05-01
Full Text Available The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and DijkstraÃ¢Â€Â™s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications.
Mixed reality framework for collective motion patterns of swarms with delay coupling
Szwaykowska, Klementyna; Schwartz, Ira
The formation of coherent patterns in swarms of interacting self-propelled autonomous agents is an important subject for many applications within the field of distributed robotic systems. However, there are significant logistical challenges associated with testing fully distributed systems in real-world settings. In this paper, we provide a rigorous theoretical justification for the use of mixed-reality experiments as a stepping stone to fully physical testing of distributed robotic systems. We also model and experimentally realize a mixed-reality large-scale swarm of delay-coupled agents. Our analyses, assuming agents communicating over an Erdos-Renyi network, demonstrate the existence of stable coherent patterns that can be achieved only with delay coupling and that are robust to decreasing network connectivity and heterogeneity in agent dynamics. We show how the bifurcation structure for emergence of different patterns changes with heterogeneity in agent acceleration capabilities and limited connectivity in the network as a function of coupling strength and delay. Our results are verified through simulation as well as preliminary experimental results of delay-induced pattern formation in a mixed-reality swarm. K. S. was a National Research Council postdoctoral fellow. I.B.S was supported by the U.S. Naval Research Laboratory funding (N0001414WX00023) and office of Naval Research (N0001414WX20610).
Revisiting the South Atlantic Anomaly after 3 years of Swarm satellite mission
Pavón-Carrasco, F. Javier; Campuzano, Saioa A.; De Santis, Angelo
2017-04-01
Covering part of Southern America and the South Atlantic Ocean, the South Atlantic Anomaly (SAA) is nowadays one of the most important and largest features of the geomagnetic field at the Earth's surface. It is characterized by lower intensity values than expected for those geomagnetic latitudes. Thanks to the global geomagnetic models, the spatial and temporal geometry of the Earth's magnetic field can be defined at the core-mantle boundary, showing the origin of the SAA as a reversal polarity patch that is growing with a pronounced rate of -2.54ṡ105 nT per century and with western drift. Since the Swarm satellite mission of the European Space Agency was launched at the end of 2013, the three twin satellites are picking up the most accurate values of the geomagnetic field up to now. In this work, we use the satellite magnetic data from Swarm mission along with the observatory ground data of surrounding areas to evaluate the spatial and temporal evolution of the SAA during the Swarm-life.
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Hao Yin
2014-01-01
Full Text Available For SLA-aware service composition problem (SSC, an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
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Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-11
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Adaptive feature selection using v-shaped binary particle swarm optimization.
Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong
2017-01-01
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.
Janaki, Sathya D.; Geetha, K.
2017-06-01
Interpreting Dynamic Contrast-Enhanced (DCE) MR images for signs of breast cancer is time consuming and complex, since the amount of data that needs to be examined by a radiologist in breast DCE-MRI to locate suspicious lesions is huge. Misclassifications can arise from either overlooking a suspicious region or from incorrectly interpreting a suspicious region. The segmentation of breast DCE-MRI for suspicious lesions in detection is thus attractive, because it drastically decreases the amount of data that needs to be examined. The new segmentation method for detection of suspicious lesions in DCE-MRI of the breast tissues is based on artificial fishes swarm clustering algorithm is presented in this paper. Artificial fish swarm optimization algorithm is a swarm intelligence algorithm, which performs a search based on population and neighborhood search combined with random search. The major criteria for segmentation are based on the image voxel values and the parameters of an empirical parametric model of segmentation algorithms. The experimental results show considerable impact on the performance of the segmentation algorithm, which can assist the physician with the task of locating suspicious regions at minimal time.
Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction
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Chao-Hong Chen
2011-01-01
Full Text Available We analyze the convergence time of particle swarm optimization (PSO on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.
SWARMS Early Trials Management for The SWARMs ECSEL-H2020 Project
Alcaraz, Daniel; Morales, Tania; Castro, Ayoze; Barrera, Carlos; Hernández, Joaquín; Llinás, Octavio
2017-04-01
The work presented on this paper is aimed to explain how the Early Trials of the Project SWARMS were managed in order to complete the first field demonstrations on real environment. SWARMs aims to reduce the operational cost in the use of maritime robots and vehicles, in order to increase the safety of tasks and reduce profesional divers risks. This will be achieved enabling the AUVs/ROVs to work in a cooperative mesh. The challenge is to design and develop an integrated platform (a set of Software/Hardware components), incorporated into the current generation of underwater vehicles in order to improve autonomy, cooperation, robustness, cost-effectiveness, and reliability of the offshore operations. The first demonstration of the project has been performed at PLOCAN (The Oceanic Platform of the Canary Islands) where these technologies were validated on its first stage. The Early Trials have represented the first in situ deployment and test of the novel technologies developed during the initial 14 months of the Project. Going into the sea supposed a huge challenge also in terms of management. The 32 partners of SWARMS had very different requirements (logistics, technical needs, software/computation needs, etc.), and a limited time frame to test and prove their individual developments. In order to fullfill the project objectives, all these tests were divided in 7 missions that were aimed to cover this early demonstration requiements. From PLOCAN, a management protocol was designed in order to cover all the partners needs and make an efficient resource asignment from the begining. These results will be extended to other two demonstrations of the project that forseen to be held in Romania (2017) and Norway (2018).
Particle Swarm Optimization for Adaptive Resource Allocation in Communication Networks
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Gheitanchi Shahin
2010-01-01
Full Text Available A generalized model of particle swarm optimization (PSO technique is proposed as a low complexity method for adaptive centralized and distributed resource allocation in communication networks. The proposed model is applied to adaptive multicarrier cooperative communications (MCCC technique which utilizes the subcarriers in deep fade using a relay node in order to improve the bandwidth efficiency. Centralized PSO, based on virtual particles (VPs, is introduced for single layer and cross-layer subcarrier allocation to improve the bit error rate performance in multipath frequency selective fading channels. In the single layer strategy, the subcarriers are allocated based on the channel gains. In the cross-layer strategy, the subcarriers are allocated based on a joint measure of channel gains and distance provided by the physical layer and network layer to mitigate the effect of path loss. The concept of training particles in distributed PSO is proposed and then is applied for relay node selection. The computational complexity and traffic of the proposed techniques are investigated, and it is shown that using PSO for subcarrier allocation has a lower complexity than the techniques in the literature. Significant reduction in the traffic overhead of PSO is demonstrated when using trained particles in distributed optimizations.
Quantum particle swarm approaches applied to combinatorial problems
Energy Technology Data Exchange (ETDEWEB)
Nicolau, Andressa dos S.; Schirru, Roberto; Lima, Alan M.M. de, E-mail: andressa@lmp.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Programa de Engenharia Nuclear
2017-07-01
Quantum Particle Swarm Optimization (QPSO) is a global convergence algorithm that combines the classical PSO philosophy and quantum mechanics to improve performance of PSO. Different from PSO it only has the 'measurement' of the position equation for all particles. The process of 'measurement' in quantum mechanics, obey classic laws while the particle itself follows the quantum rules. QPSO works like PSO in search ability but has fewer parameters control. In order to improve the QPSO performance, some strategies have been proposed in the literature. Weighted QPSO (WQPSO) is a version of QPSO, where weight parameter is insert in the calculation of the balance between the global and local searching of the algorithm. It has been shown to perform well in finding the optimal solutions for many optimization problems. In this article random confinement was introduced in WQPSO. The WQPSO with random confinement was tested in two combinatorial problems. First, we execute the model on Travelling Salesman Problem (TSP) to find the parameters' values resulting in good solutions in general. Finally, the model was tested on Nuclear Reactor Reload Problem, and the performance was compared with QPSO standard. (author)
Amiri, Aboutaleb; Tierra, Giordano; Xu, Zhiliang; Shrout, Joshua; Alber, Mark
Collective motion has been observed by several bacterial species including the pathogenic bacterium P. aeruginosa. A flagellum at the pole is known to generate a self-propulsion motion. However, the role of type IV pili (TFP), distributed on the cell membrane, during swarming needs to be investigated in more details. In this work we introduce a model that combines the hydrodynamic and biophysical interactions in order to study the impact of the TFP interactions on swarming behavior of the colony. The model describes the motion and interactions of rod-shaped self propelled bacteria inside a thin liquid film. It also includes the equations describing the production and diffusion of surfactant rhamnolipids that is responsible for extraction of water from substrate, and Marangoni driven expansion of the thin liquid film by altering the surface tension. We show that TFP interactions are responsible for slower expansion rate of colonies of TFP deficient mutants compared to wild type. Experimental observations were used to calibrate the model and verify the model assumptions and predictions.
Middleware Design for Swarm-Driving Robots Accompanying Humans
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Min Su Kim
2017-02-01
Full Text Available Research on robots that accompany humans is being continuously studied. The Pet-Bot provides walking-assistance and object-carrying services without any specific controls through interaction between the robot and the human in real time. However, with Pet-Bot, there is a limit to the number of robots a user can use. If this limit is overcome, the Pet-Bot can provide services in more areas. Therefore, in this study, we propose a swarm-driving middleware design adopting the concept of a swarm, which provides effective parallel movement to allow multiple human-accompanying robots to accomplish a common purpose. The functions of middleware divide into three parts: a sequence manager for swarm process, a messaging manager, and a relative-location identification manager. This middleware processes the sequence of swarm-process of robots in the swarm through message exchanging using radio frequency (RF communication of an IEEE 802.15.4 MAC protocol and manages an infrared (IR communication module identifying relative location with IR signal strength. The swarm in this study is composed of the master interacting with the user and the slaves having no interaction with the user. This composition is intended to control the overall swarm in synchronization with the user activity, which is difficult to predict. We evaluate the accuracy of the relative-location estimation using IR communication, the response time of the slaves to a change in user activity, and the time to organize a network according to the number of slaves.
Binary particle swarm optimization for operon prediction.
Chuang, Li-Yeh; Tsai, Jui-Hung; Yang, Cheng-Hong
2010-07-01
An operon is a fundamental unit of transcription and contains specific functional genes for the construction and regulation of networks at the entire genome level. The correct prediction of operons is vital for understanding gene regulations and functions in newly sequenced genomes. As experimental methods for operon detection tend to be nontrivial and time consuming, various methods for operon prediction have been proposed in the literature. In this study, a binary particle swarm optimization is used for operon prediction in bacterial genomes. The intergenic distance, participation in the same metabolic pathway, the cluster of orthologous groups, the gene length ratio and the operon length are used to design a fitness function. We trained the proper values on the Escherichia coli genome, and used the above five properties to implement feature selection. Finally, our study used the intergenic distance, metabolic pathway and the gene length ratio property to predict operons. Experimental results show that the prediction accuracy of this method reached 92.1%, 93.3% and 95.9% on the Bacillus subtilis genome, the Pseudomonas aeruginosa PA01 genome and the Staphylococcus aureus genome, respectively. This method has enabled us to predict operons with high accuracy for these three genomes, for which only limited data on the properties of the operon structure exists.
Galactic Building Blocks Seen Swarming Around Andromeda
2004-02-01
Green Bank, WV - A team of astronomers using the National Science Foundation's Robert C. Byrd Green Bank Telescope (GBT) has made the first conclusive detection of what appear to be the leftover building blocks of galaxy formation -- neutral hydrogen clouds -- swarming around the Andromeda Galaxy, the nearest large spiral galaxy to the Milky Way. This discovery may help scientists understand the structure and evolution of the Milky Way and all spiral galaxies. It also may help explain why certain young stars in mature galaxies are surprisingly bereft of the heavy elements that their contemporaries contain. Andromeda Galaxy This image depicts several long-sought galactic "building blocks" in orbit of the Andromeda Galaxy (M31). The newfound hydrogen clouds are depicted in a shade of orange (GBT), while gas that comprises the massive hydrogen disk of Andromeda is shown at high-resolution in blue (Westerbork Sythesis Radio Telescope). CREDIT: NRAO/AUI/NSF, WSRT (Click on Image for Larger Version) "Giant galaxies, like Andromeda and our own Milky Way, are thought to form through repeated mergers with smaller galaxies and through the accretion of vast numbers of even lower mass 'clouds' -- dark objects that lack stars and even are too small to call galaxies," said David A. Thilker of the Johns Hopkins University in Baltimore, Maryland. "Theoretical studies predict that this process of galactic growth continues today, but astronomers have been unable to detect the expected low mass 'building blocks' falling into nearby galaxies, until now." Thilker's research is published in the Astrophysical Journal Letters. Other contributors include: Robert Braun of the Netherlands Foundation for Research in Astronomy; Rene A.M. Walterbos of New Mexico State University; Edvige Corbelli of the Osservatorio Astrofisico di Arcetri in Italy; Felix J. Lockman and Ronald Maddalena of the National Radio Astronomy Observatory (NRAO) in Green Bank, West Virginia; and Edward Murphy of the
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Yannicke Chupin
2009-06-01
Full Text Available This article focuses on the figure of displaced writers in Vladimir Nabokov’s fiction, and more specifically in Lolita and Pnin. Living the life of exiles affected by permanent instability, the two heroes, Humbert and Pnin, find personal outlets in the writing process. The eccentric language used by both characters displays a form of inventiveness which derives from linguistic disjunction. This article aims at showing in which ways displacement is what sustains the linguistic creativity of these two intellectual exiles.
Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization
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Ya-zhong Luo
2014-01-01
Full Text Available A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.
Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization
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Moncef Gabbouj
2009-01-01
Full Text Available Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO for finding optimal (number of dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis- similarities over HSV (or HSL color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.
Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices
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Naser El-Sheimy
2012-09-01
Full Text Available Inertial Navigation Systems (INS consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS applications.
Particle swarm optimization algorithm based low cost magnetometer calibration
Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.
2011-12-01
Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments
Inherent noise can facilitate coherence in collective swarm motion.
Yates, Christian A; Erban, Radek; Escudero, Carlos; Couzin, Iain D; Buhl, Jerome; Kevrekidis, Ioannis G; Maini, Philip K; Sumpter, David J T
2009-04-07
Among the most striking aspects of the movement of many animal groups are their sudden coherent changes in direction. Recent observations of locusts and starlings have shown that this directional switching is an intrinsic property of their motion. Similar direction switches are seen in self-propelled particle and other models of group motion. Comprehending the factors that determine such switches is key to understanding the movement of these groups. Here, we adopt a coarse-grained approach to the study of directional switching in a self-propelled particle model assuming an underlying one-dimensional Fokker-Planck equation for the mean velocity of the particles. We continue with this assumption in analyzing experimental data on locusts and use a similar systematic Fokker-Planck equation coefficient estimation approach to extract the relevant information for the assumed Fokker-Planck equation underlying that experimental data. In the experiment itself the motion of groups of 5 to 100 locust nymphs was investigated in a homogeneous laboratory environment, helping us to establish the intrinsic dynamics of locust marching bands. We determine the mean time between direction switches as a function of group density for the experimental data and the self-propelled particle model. This systematic approach allows us to identify key differences between the experimental data and the model, revealing that individual locusts appear to increase the randomness of their movements in response to a loss of alignment by the group. We give a quantitative description of how locusts use noise to maintain swarm alignment. We discuss further how properties of individual animal behavior, inferred by using the Fokker-Planck equation coefficient estimation approach, can be implemented in the self-propelled particle model to replicate qualitatively the group level dynamics seen in the experimental data.
Inherent noise can facilitate coherence in collective swarm motion
Yates, C. A.
2009-03-31
Among the most striking aspects of the movement of many animal groups are their sudden coherent changes in direction. Recent observations of locusts and starlings have shown that this directional switching is an intrinsic property of their motion. Similar direction switches are seen in self-propelled particle and other models of group motion. Comprehending the factors that determine such switches is key to understanding the movement of these groups. Here, we adopt a coarse-grained approach to the study of directional switching in a self-propelled particle model assuming an underlying one-dimensional Fokker-Planck equation for the mean velocity of the particles. We continue with this assumption in analyzing experimental data on locusts and use a similar systematic Fokker-Planck equation coefficient estimation approach to extract the relevant information for the assumed Fokker-Planck equation underlying that experimental data. In the experiment itself the motion of groups of 5 to 100 locust nymphs was investigated in a homogeneous laboratory environment, helping us to establish the intrinsic dynamics of locust marching bands. We determine the mean time between direction switches as a function of group density for the experimental data and the self-propelled particle model. This systematic approach allows us to identify key differences between the experimental data and the model, revealing that individual locusts appear to increase the randomness of their movements in response to a loss of alignment by the group. We give a quantitative description of how locusts use noise to maintain swarm alignment. We discuss further how properties of individual animal behavior, inferred by using the Fokker-Planck equation coefficient estimation approach, can be implemented in the self-propelled particle model to replicate qualitatively the group level dynamics seen in the experimental data.
SwarmDock and the Use of Normal Modes in Protein-Protein Docking
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Paul A. Bates
2010-09-01
Full Text Available Here is presented an investigation of the use of normal modes in protein-protein docking, both in theory and in practice. Upper limits of the ability of normal modes to capture the unbound to bound conformational change are calculated on a large test set, with particular focus on the binding interface, the subset of residues from which the binding energy is calculated. Further, the SwarmDock algorithm is presented, to demonstrate that the modelling of conformational change as a linear combination of normal modes is an effective method of modelling flexibility in protein-protein docking.
Swarm, genetic and evolutionary programming algorithms applied to multiuser detection
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Paul Jean Etienne Jeszensky
2005-02-01
Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.
Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance
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Nabil M. Hewahi
2017-07-01
Full Text Available The robots science has been developed over the past few years, where robots have become used to accomplish difficult, repetitive or accurate tasks, which are very hard for humans to carry out. In this paper, we propose an algorithm to control the motion of a swarm of robots and make them able to avoid obstacles. The proposed solution is based on forming the robots in circular fashion. A group set of robots consists of multiple groups of robots, each group of robots consists of robots forming a circular shape and each group set is a circular form of robots. The proposed algorithm is concerned with first locating the randomly generated robots in groups and secondly with the swarm robot motion and finally with the swarm obstacle avoidance and swarm reorganization after crossing the obstacle. The proposed algorithm has been simulated with five different obstacles with various numbers of randomly generated robots. The results show that the swarm in the circular form can deal with the obstacles very effectively by passing the obstacles smoothly. The proposed algorithm has been compared with flocking algorithm and it is shown that the circular formation algorithm does not need extensive computation after obstacle avoidance whereas the flocking algorithm needs extensive computation. In addition, the circular formation algorithm maintains every robot in its group after avoiding the obstacles whereas with flocking algorithm does not.
Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem
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S Sarathambekai
2017-03-01
Full Text Available Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.
Herceg, M.; Jørgensen, P. S.; Jørgensen, J. L.
2017-08-01
Launched into orbit on November 22, 2013, the Swarm constellation of three satellites precisely measures magnetic signal of the Earth. To ensure the high accuracy of magnetic observation by vector magnetometer (VFM), its inertial attitude is precisely determined by μASC (micro Advanced Stellar Compass). Each of the three Swarm satellites is equipped with three μASC Camera Head Units (CHU) mounted on a common optical bench (OB), which has a purpose of transference of the attitude from the star trackers to the magnetometer measurements. Although substantial pre-launch analyses were made to maximize thermal and mechanical stability of the OB, significant signal with thermal signature is discovered when comparing relative attitude between the three CHU's (Inter Boresight Angle, IBA). These misalignments between CHU's, and consequently geomagnetic reference frame, are found to be correlated with the period of angle between Swarm orbital plane and the Sun (ca. 267 days), which suggests sensitivity of optical bench system on temperature variation. In this paper, we investigate the propagation of thermal effects into the μASC attitude observations and demonstrate how thermally induced attitude variation can be predicted and corrected in the Swarm data processing. The results after applying thermal corrections show decrease in IBA RMS from 6.41 to 2.58″. The model significantly improves attitude determination which, after correction, meets the requirements of Swarm satellite mission. This study demonstrates the importance of the OB pre-launch analysis to ensure minimum thermal gradient on satellite optical system and therefore maximum attitude accuracy.
Cornelissen, B.; Gerritsen, L.J.M.
2006-01-01
In 2004 and 2005 experiments were carried out to test the efficacy and efficiency of Varroa control combined with swarm prevention methods in spring. Honey bee colonies were split in an artificial swarm and a brood carrier. Hereafter the swarms were treated with oxalic acid and the brood carriers
Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence
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Alejandro Carrasco Elizalde
2008-01-01
Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.
Composite Particle Swarm Optimizer With Historical Memory for Function Optimization.
Li, Jie; Zhang, JunQi; Jiang, ChangJun; Zhou, MengChu
2015-10-01
Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.
Recent advances in swarm intelligence and evolutionary computation
2015-01-01
This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference f...
Multi-objective swarm intelligence theoretical advances and applications
Jagadev, Alok; Panda, Mrutyunjaya
2015-01-01
The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.
A Novel Particle Swarm Optimization Algorithm for Global Optimization.
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.
Implementasi Algoritma Particle Swarm untuk Menyelesaikan Sistem Persamaan Nonlinear
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Ardiana Rosita
2012-09-01
Full Text Available Penyelesaian sistem persamaan nonlinear merupakan salah satu permasalahan yang sulit pada komputasi numerik dan berbagai aplikasi teknik. Beberapa metode telah dikembangkan untuk menyelesaikan sistem persamaan ini dan metode Newton merupakan metode yang paling sering digunakan. Namun metode ini memerlukan perkiraan solusi awal dan memilih perkiraan solusi awal yang baik untuk sebagian besar sistem persamaan nonlinear tidaklah mudah. Pada makalah ini, algoritma Particle Swarm yang diusulkan oleh Jaberipour dan kawan-kawan[1] diimplementasikan. Algoritma ini merupakan pengembangan dari algoritma Particle Swarm Optimization (PSO. Algoritma ini meyelesaikan sistem persamaan nonlinear yang sebelumnya telah diubah menjadi permasalahan optimasi. Uji coba dilakukan terhadap beberapa fungsi dan sistem persamaan nonlinear untuk menguji kinerja dan efisiensi algoritma. Berdasarkan hasil uji coba, beberapa fungsi dan sistem persamaan nonlinear telah konvergen pada iterasi ke 10 sampai 20 dan terdapat fungsi yang konvergen pada iterasi ke 200. Selain itu, solusi yang dihasilkan algoritma Particle Swarm mendekati solusi eksak.
Modified Particle Swarm Optimization using Nonlinear Decreased Inertia Weight
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Alrijadjis .
2016-04-01
Full Text Available Particle Swarm Optimization (PSO has demonstrated great performance in various optimization problems. However, PSO has weaknesses, namely premature convergence and easy to get stuck or fall into local optima for complex multimodal problems. One of the causes of these weaknesses is unbalance between exploration and exploitation ability in PSO. This paper proposes a Modified Particle Swarm Optimization (MPSO using nonlinearly decreased inertia weight called MPSO-NDW to improve the balance. The key idea of the proposed method is to control the period and decreasing rate of exploration-exploitation ability. The investigation with three famous benchmark functions shows that the accuracy, success rate, and convergence speed of the proposed MPSO-NDW is better than the common used PSO with linearly decreased inertia weight or called PSO-LDW Keywords: particle swarm optimization (PSO, premature convergence, local optima, exploration ability, exploitation ability.
Sprashivajem? Otvetshajem... / Vladimir Evve
Evve, Vladimir
2002-01-01
Autor kommenteerib Res Publica reklaamartiklit kohalikeks valimisteks, kus esitati kaks küsimust - mida teha, et me elaksime nagu inimesed ja kes on süüdi, et Kohtla-Järvel on halb? Vt. ka 20. sep. Infopressi
MARTIAN SWARM EXPLORATION AND MAPPING USING LASER SLAM
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S. Nowak
2013-08-01
Full Text Available In order to explore planet Mars in detail and search for extra-terrestrial life the observation from orbit is not sufficient. To realize complex exploration tasks the use of automatic operating robots with a robust fault-tolerant method of navigation, independent of any infrastructure is a possibility. This work includes a concept of rotary-wing Unmanned Aerial Vehicles (UAVs and Unmanned Ground Vehicles (UGVs for Martian exploration in a swarm. Besides the scenario of Martian surrounding, with a small number of distinctive landmarks, the challenge consists of a Simultaneous Localization and Mapping (SLAM concept using laser data of all swarm members.
Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem
Rahmalia, Dinita
2017-08-01
Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.
Parameter estimation for chaotic systems using improved bird swarm algorithm
Xu, Chuangbiao; Yang, Renhuan
2017-12-01
Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.
Optimal PMU Placement By Improved Particle Swarm Optimization
DEFF Research Database (Denmark)
Rather, Zakir Hussain; Liu, Leo; Chen, Zhe
2013-01-01
This paper presents an improved method of binary particle swarm optimization (IBPSO) technique for optimal phasor measurement unit (PMU) placement in a power network for complete system observability. Various effective improvements have been proposed to enhance the efficiency and convergence rate...... of conventional particle swarm optimization method. The proposed method of IBPSO ensures optimal PMU placement with and without consideration of zero injection measurements. The proposed method has been applied to standard test systems like 17 bus, IEEE 24-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus system...
Nonlinear Adaptive Filters based on Particle Swarm Optimization
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Faten BEN ARFIA
2009-07-01
Full Text Available This paper presents a particle swarm optimization (PSO algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
Welding Diagnostics by Means of Particle Swarm Optimization and Feature Selection
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J. Mirapeix
2012-01-01
Full Text Available In a previous contribution, a welding diagnostics approach based on plasma optical spectroscopy was presented. It consisted of the employment of optimization algorithms and synthetic spectra to obtain the participation profiles of the species participating in the plasma. A modification of the model is discussed here: on the one hand the controlled random search algorithm has been substituted by a particle swarm optimization implementation. On the other hand a feature selection stage has been included to determine those spectral windows where the optimization process will take place. Both experimental and field tests will be shown to illustrate the performance of the solution that improves the results of the previous work.
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V. Sedenka
2010-09-01
Full Text Available The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II and a novel multi-objective Particle Swarm Optimization (PSO. The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM. The hit rate and the quality of the Pareto front distribution are classified.
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Jhon E. González-Pérez
2017-01-01
Full Text Available In this paper, a methodology for design of electrical field relaxing electrodes is shown. This design methodology is based in an optimization process carried out by particle swarm optimization technique. The objective function of the optimization process, include the electro statics model of the high voltage equipment that is solved by the finite element method. The proposed methodology was implemented using the computational tools Matlab and Comsol. This methodology was validated by designing the electric fields relaxing electrodes in a high voltage resistive divider, which used in measurement of lightning impulse waves.
Optimisation of thin shell parts by using particle swarm optimisation (PSO) method
Hidayah, M. H. N.; Shayfull, Z.; Nasir, S. M.; Sazli, S. M.; Fathullah, M.
2017-09-01
This paper proposes an optimization model of process parameters in Plastic Injection Moulding (PIM) in which the quality characteristics for the plastic injection product that been study are warpage. In this study, plastic dispenser of dental floss (thin shell part) has been analysed with thermoplastic material of Polypropylene (PP) used as the moulded material. Design of Experiment (DOE), Response surface methodology (RSM) and Particle Swarm Optimisation (PSO) method were used to analyse the optimal process parameters setting. From optimal processing parameter, the value of warpage inx, y and z-axis have been optimised by 22.1%, 27.34% and 23.81%, respectively.
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Muhammad Asif Zahoor Raja
2012-01-01
Full Text Available A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method.
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Kim Wook
2003-01-01
Full Text Available Salmonella enterica serovar Typhimurium is capable of swarming over semi-solid surfaces. Although its swarming behavior shares many readily observable similarities with other swarming bacteria, the phenomenon remains somewhat of an enigma in this bacterium since some attributes skew away from the better characterized systems. Swarming is quite distinct from the classic swimming motility, as there is a prerequisite for cells to first undergo a morphological transformation into swarmer cells. In some organisms, swarming is controlled by quorum sensing, and in others, swarming has been shown to be coupled to increased expression of important virulence factors. Swarming in serovar Typhimurium is coupled to elevated resistance to a wide variety of structurally and functionally distinct classes of antimicrobial compounds. As serovar Typhimurium differentiates into swarm cells, the pmrHFIJKLM operon is up-regulated, resulting in a more positively charged LPS core. Furthermore, as swarm cells begin to de-differentiate, the pmr operon expression is down-regulated, rapidly reaching the levels observed in swim cells. This is one potential mechanism which confers swarm cells increased resistance to antibiotics such as the cationic antimicrobial peptides. However, additional mechanisms are likely associated with the cells in the swarm state that confer elevated resistance to such a broad spectrum of antimicrobial agents.
Energy Technology Data Exchange (ETDEWEB)
Castellano, T.; De Palma, L.; Laneve, D.; Strippoli, V.; Cuccovilllo, A.; Prudenzano, F. [Electrical and Information Engineering Department (DEI), Polytechnic Institute of Bari, 4 Orabona Street, CAP 70125, Bari, (Italy); Dimiccoli, V.; Losito, O.; Prisco, R. [ITEL Telecomunicazioni, 39 Labriola Street, CAP 70037, Ruvo di Puglia, Bari, (Italy)
2015-07-01
A homemade computer code for designing a Side- Coupled Linear Accelerator (SCL) is written. It integrates a simplified model of SCL tanks with the Particle Swarm Optimization (PSO) algorithm. The computer code main aim is to obtain useful guidelines for the design of Linear Accelerator (LINAC) resonant cavities. The design procedure, assisted via the aforesaid approach seems very promising, allowing future improvements towards the optimization of actual accelerating geometries. (authors)