207 resources related to Biogeography
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To promote awareness, understanding, advancement and application of ocean engineering and marine technology. This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.
2019 IEEE 58th Conference on Decision and Control (CDC)
The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).
2019 IEEE International Conference on Industrial Technology (ICIT)
The scope of the conference will cover, but will not be limited to, the following topics: Robotics; Mechatronics; Industrial Automation; Autonomous Systems; Sensing and artificial perception, Actuators and Micro-nanotechnology; Signal/Image Processing and Computational Intelligence; Control Systems; Electronic System on Chip and Embedded Control; Electric Transportation; Power Electronics; Electric Machines and Drives; Renewable Energy and Smart Grid; Data and Software Engineering, Communication; Networking and Industrial Informatics.
2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC2019) will be held in the south of Europe in Bari, one of the most beautiful and historical cities in Italy. The Bari region’s nickname is “Little California” for its nice weather and Bari's cuisine is one of Italian most traditional , based of local seafood and olive oil. SMC2019 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report up-to-the-minute innovations and developments, summarize stateof-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems and cybernetics. Advances have importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience, and thereby improve quality of life.
International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
Physics, medicine, astronomy—these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering (CiSE) presents scientific and computational contributions in a clear and accessible format. ...
Papers on application, design, and theory of evolutionary computation, with emphasis given to engineering systems and scientific applications. Evolutionary optimization, machine learning, intelligent systems design, image processing and machine vision, pattern recognition, evolutionary neurocomputing, evolutionary fuzzy systems, applications in biomedicine and biochemistry, robotics and control, mathematical modelling, civil, chemical, aeronautical, and industrial engineering applications.
Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Biogeography-Based Optimization (BBO) is a new bio-inspired and population based optimization algorithm. The convergence of original BBO to the optimum value is slow. Intelligent Biogeography-Based Optimization (IBBO) technique is a hybrid version of BBO with Bacterial Foraging algorithm (BFA). In this paper, authors integrate the bacterial intelligence feature of BFA to decide the valid emigration in migration process of BBO. ...
2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015
Biogeography based optimization is a nature oriented concept. It extracts the idea of optimization from the way how species are distributed in various geographical areas. Various habitats are differentiated on the basis of habitat suitability index which determines strength of organisms in particular habitat. So habitat having highest habitat suitability index is known as best habitat. The same concept is ...
2016 IEEE 6th International Conference on Power Systems (ICPS), 2016
This paper aim to optimize network reconfiguration of a distribution network to improve the voltage profile at the buses and minimize the distribution line losses. Network reconfiguration is the procedure of changing the topological structure of feeders by altering the open or close status of lines which include sectionalizing and tie lines. Biogeography Based Optimization (BBO) algorithm is proposed to ...
Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469), 1999
Phylogenetic analysis is an integral part of many biological research programs. In essence, it is the study of gene genealogy. It is the study of gene mutation and the generational relationships. Phylogenetic analysis is being used in many diverse areas such as human epidemiology, viral transmission, biogeography, and systematics. Researchers are now commonly generating many DNA sequences from many individuals, ...
2013 7th European Conference on Antennas and Propagation (EuCAP), 2013
In this communication some variations of the Biogeography Based Optimization, named MmCn-BBO are introduced. They present new features with respect to the standard BBO, aimed to improve the performances of this last algorithm. The comparison among the different schemes, when applied to both benchmark functions and real-world electromagnetic problems, shows the improvement of the here introduced BBO variations and serves ...
Biogeography-Based Optimization (BBO) is a new bio-inspired and population based optimization algorithm. The convergence of original BBO to the optimum value is slow. Intelligent Biogeography-Based Optimization (IBBO) technique is a hybrid version of BBO with Bacterial Foraging algorithm (BFA). In this paper, authors integrate the bacterial intelligence feature of BFA to decide the valid emigration in migration process of BBO. This increases the ability of BBO to escape from getting trapped in local minima and converges to near- global optimum. This makes faster convergence to the optimum value. To validate the performance of IBBO, experiments have been conducted on unimodal and multimodal benchmark functions of discrete variables. IBBO and BBO are initialized with integer versions of population.
Biogeography based optimization is a nature oriented concept. It extracts the idea of optimization from the way how species are distributed in various geographical areas. Various habitats are differentiated on the basis of habitat suitability index which determines strength of organisms in particular habitat. So habitat having highest habitat suitability index is known as best habitat. The same concept is used to derive the biogeography based optimization. A solution having the highest habitat suitability index is considered to be best and rest of all candidate solutions are modified according to the best solution. Original BBO has very limitations such as applicability in continuous domain, slow convergence speed etc which have been improved in the later years. This paper represents basic biogeography based optimization and its modified versions which aims at improving the original BBO.
This paper aim to optimize network reconfiguration of a distribution network to improve the voltage profile at the buses and minimize the distribution line losses. Network reconfiguration is the procedure of changing the topological structure of feeders by altering the open or close status of lines which include sectionalizing and tie lines. Biogeography Based Optimization (BBO) algorithm is proposed to solve the optimal network reconfiguration problem. Simulations are carried out on 33-bus radial distribution systems, in MATLAB Programming Environment.
Phylogenetic analysis is an integral part of many biological research programs. In essence, it is the study of gene genealogy. It is the study of gene mutation and the generational relationships. Phylogenetic analysis is being used in many diverse areas such as human epidemiology, viral transmission, biogeography, and systematics. Researchers are now commonly generating many DNA sequences from many individuals, thus creating very large data sets. However, our ability to analyze the data has not kept pace with data generation, and phylogenetics has now reached a crossroads where we cannot effectively analyze the data we generate. The chief challenge of phylogenetic systematics in the next century will be to develop algorithms and search strategies to effectively analyze large data sets. The crux of the computational problem is that the actual landscape of possible topologies can be extraordinarily difficult to evaluate with large data sets. The parsimony ratchet is actually a family of iterative tree search methods that use a statistical approach to sampling tree islands and ultimately finding the most parsimonious trees for a data set. Each iteration of the parsimony ratchet may occur in parallel as there is no direct dependency between iterations. The authors' implementation of the parallel ratchet is masterworker based. A master process is launched in the DOGMA system which then launches worker tasks on available nodes. Each worker task is simply wrapper code that is used to interact with the newest release version of NONA.
In this communication some variations of the Biogeography Based Optimization, named MmCn-BBO are introduced. They present new features with respect to the standard BBO, aimed to improve the performances of this last algorithm. The comparison among the different schemes, when applied to both benchmark functions and real-world electromagnetic problems, shows the improvement of the here introduced BBO variations and serves to define the most performing among the different proposed variation.
Distribution network reactive power optimization, which can effectively reduce distribution network wastage, can not only improve voltage distribution network quality, but also realize the distribution network security control. Stable operation provides solid foundation for the electric power enterprise economy. Unlike genetic algorithm that shares the feature information between the chromosomes, the unique migration pattern of BBO algorithm makes the good feature in the habitats broadcast widely among habitats, thus, the optimization process would be accelerated and the reactive power compensation location could be confirmed quickly. And by using BBO algorithm in the example analysis and conducting simulation through PSASP, it proves that biogeography optimization algorithm is quite effective when applied in distribution network reactive power optimization.
Sentiment classification of social media has recently become popular among scientists due to the emergence of product reviews, blogs and social networking sites. A large number of reviews are difficult to evaluate personally. Moreover due to variable nature of reviews it becomes difficult, to compile overall result of reviews, to know which product is better than other. Researchers have already implemented machine learning techniques to analyze sentiment present in the given document. But execution time for these techniques increases due to the increase in feature set of data. Also irrelevant features participate in determining the sentiment of the given document, thereby varying the accuracy of the algorithm. In order to get much better classification, we propose a Biogeography based optimization algorithm to select optimal features set from given data. Then by using Naïve Bayes and Support Vector Machine techniques, we perform sentiment classification of product reviews. The proposed technique can be applied to other classification problems where feature set is large.
The Evolutionary algorithm (EA) for researching parameters of nonlinear system is a rapidly growing field of identification. This can owe to the importance of EA for both the theoretical field and the engineering community. However, the identification of the nonlinear system is still a knotty problem, especially when heavy-tailed noises exists. Compared to classical identification methods, EA has more advantages as its fast searching speed and low complexity. In this paper, based on Biogeography Based Optimization (BBO) algorithm which was presented in recent years, a modified BBO (MBBO) is presented to overcome the premature drawback of BBO. A computing method of mutation ratio is added in MBBO to enhance exploration ability. Then, the MBBO algorithm is used for a Hammerstein model with a heavy-tailed perturbation. Experiment results verify that MBBO has much higher accuracy than BBO.
This paper presents biogeography-based optimization (BBO) technique for solving constrained economic dispatch problems in power system. Many nonlinear characteristics of generators, like valve point loading, ramp rate limits, prohibited zone, and multiple fuels cost functions are considered. Two Economic Load Dispatch (ELD) problems with different characteristics are applied and compared its solution quality and computation efficiency to genetic algorithm (GA), particle swarm optimization (PSO), and other optimization techniques. The simulation results show that the proposed algorithm outperforms previous optimization methods.
In recent years the remote sensing image classification has become a global research area for acquiring the geo-spatial information from satellite data. In this paper we have tried to explore the behavior of Biogeography-Based Optimization (BBO) over different terrain features of a satellite image. The findings of recent studies are showing strong evidence to the fact that various classifiers perform differently when applied to images having different natural terrain features. BBO has a way of sharing information between solutions depending on the migration mechanisms of ecosystem. The motivation of this paper is to use this feature of BBO for finding more accurate results. The approach followed involves making the clusters of different land cover features. The results indicate that highly accurate Homogeneous land-cover features are extracted when BBO is used, instead of other conventional classifiers.
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