Conferences related to Self Evolution

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE 70th Electronic Components and Technology Conference (ECTC)

ECTC is the premier international conference sponsored by the IEEE Components, Packaging and Manufacturing Society. ECTC paper comprise a wide spectrum of topics, including 3D packaging, electronic components, materials, assembly, interconnections, device and system packaging, optoelectronics, reliability, and simulation.


2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


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Periodicals related to Self Evolution

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Antennas and Propagation, IEEE Transactions on

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.


Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


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Most published Xplore authors for Self Evolution

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Xplore Articles related to Self Evolution

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Self-evolution of a flexible job shop in the knowledgeable manufacturing environment

2013 Ninth International Conference on Natural Computation (ICNC), 2013

In this paper, we introduced a new concept `self-evolution' to improve production performance of manufacturing systems through adjusting system paramerters. Taking a flexible job shop with dynamic jobs arrival as an example, the self-evolution problem is studied. The principle of self- evolution is applied to the flexible job shop. A mathematical model of the static decision problem at each decision ...


Deep Self-Evolution Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, None

Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is recast as a binary ...


Self-evolution fuzzy chaotic neural network and its application

2011 IEEE International Conference on Mechatronics and Automation, 2011

In this paper, a novel neural network model called self-evolution fuzzy chaotic neural network is proposed. This model is constructing on the basis of fuzzy Hopfield neural network and self-evolution neural network. Self- evolution neural network has been confirmed of contain chaos characteristic under certain conditions. The new established self-evolution fuzzy chaotic neural network is proved to have both fuzzy ...


Self-evolution in knowledge bases

IEEE Autotestcon, 2005., 2005

From the volumes of data that can be obtained today, information extraction has been a very challenging task. An organized set of information such that it can be considered as knowledge is yet another level of abstraction that puts these pieces of information in place, in space and time, so that when combined, they make sense, thus forming what is ...


Memorial self evolution algorithm to solve JIT machine scheduling problem

2008 6th IEEE International Conference on Industrial Informatics, 2008

The just-in-time (JIT) concept is of great importance in many manufacturing processes. JIT scheduling problems affects the performance of the whole production procedure, because early in job completion causes inventory cost while delay in job completion raises penalties paid to customers. In this paper, a memorial self evolution algorithm is proposed to solve the problem of total earliness and tardiness ...


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Educational Resources on Self Evolution

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IEEE-USA E-Books

  • Self-evolution of a flexible job shop in the knowledgeable manufacturing environment

    In this paper, we introduced a new concept `self-evolution' to improve production performance of manufacturing systems through adjusting system paramerters. Taking a flexible job shop with dynamic jobs arrival as an example, the self-evolution problem is studied. The principle of self- evolution is applied to the flexible job shop. A mathematical model of the static decision problem at each decision moment is established according to the bi-level programming theory. A bi-level genetic algorithm (bi-level GA) is proposed to solve the model. Simulation results demonstrate the effectiveness and feasiblity of the model and algorithm. Through a comparative research, self-evolution operations can improve the performance of the system.

  • Deep Self-Evolution Clustering

    Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is recast as a binary pairwise-classification problem to estimate whether pairwise patterns are similar. Specifically, similarities between pairwise patterns are defined by the dot product between indicator features which are generated by a deep neural network (DNN). To learn informative representations for clustering, clustering constraints are imposed on the indicator features to represent specific concepts with specific representations. Since the ground-truth similarities are unavailable in clustering, an alternating iterative algorithm called Self-Evolution Clustering Training (SECT) is presented to select similar and dissimilar pairwise patterns and to train the DNN alternately. Consequently, the indicator features tend to be one-hot vectors and the patterns can be clustered by locating the largest response of the learned indicator features. Extensive experiments strongly evidence that DSEC outperforms current models on twelve popular image, text and audio datasets consistently.

  • Self-evolution fuzzy chaotic neural network and its application

    In this paper, a novel neural network model called self-evolution fuzzy chaotic neural network is proposed. This model is constructing on the basis of fuzzy Hopfield neural network and self-evolution neural network. Self- evolution neural network has been confirmed of contain chaos characteristic under certain conditions. The new established self-evolution fuzzy chaotic neural network is proved to have both fuzzy clustering function and chaotic associate memory ability throughout theory analysis and simulation.

  • Self-evolution in knowledge bases

    From the volumes of data that can be obtained today, information extraction has been a very challenging task. An organized set of information such that it can be considered as knowledge is yet another level of abstraction that puts these pieces of information in place, in space and time, so that when combined, they make sense, thus forming what is commonly known as a Knowledgebase (KB). A 'self-evolution' process in a KB is meant to handle such information related issues as incorrect information, missing information, and incomplete information. Other maintenance issues are related to data organization that results in incorrect or inefficient information retrieval, and removal of unnecessary data. The concepts presented in this paper are inspired by the overall vision for an asset readiness decision-making system

  • Memorial self evolution algorithm to solve JIT machine scheduling problem

    The just-in-time (JIT) concept is of great importance in many manufacturing processes. JIT scheduling problems affects the performance of the whole production procedure, because early in job completion causes inventory cost while delay in job completion raises penalties paid to customers. In this paper, a memorial self evolution algorithm is proposed to solve the problem of total earliness and tardiness penalties on a machine unit with a common due date. Up to now, researches on this problem have paid no specific attention to straddling V-shaped schedules, which may be better than pure V-shaped schedules for early due date cases; and no specific discussions have been made on the start time setting of the first job in a schedule. Thus, efforts have been made on searching good straddling V-shaped schedules, and optimizing start time setting of schedules. A GHRM approach is proposed to create the initial solution for memorial self evolution. Meanwhile a database which keeps the memories of the elite solutions is introduced to deliver better initial solutions for similar problems. The performance of the proposed algorithm has been tested on 280 benchmark instances ranging from 10 to 1000 jobs. The results show that the proposed memorial self evolution algorithm delivers better results in in finding optimal or near-optimal solutions than previous researches.

  • Self evolution algorithm for common due date scheduling problem

    Inventory cost and delay penalty are two kinds of annoying spendings in manufactory industry. Accordingly, earliness and tardiness penalties are proposed to simulate such scheduling problems where the popular just-in-time (JIT) concept is considered to be of significant importance. In this paper, a self evolution algorithm is proposed to solve the problem of single machine total earliness and tardiness penalties with a common due date. Up to now, such problem has been solved without specific consideration of straddling V-shaped schedules, which may be better than pure V-shaped schedules for early due date problems; without specific discussions on g improving, where g refers to the idle time before the start of the first job; and the many individuals in all so far proposed GA-like algorithms become the bottleneck of execution time reduction. Therefore, in this research, efforts have been made on digging out the straddling V-shaped schedules, improving the efficiency of g improving, and reducing the execution time. In addition, a new RHRM approach is proposed to create the initial solution for evolution, which helps achieve the fast contingency of the algorithm. The performance of the proposed algorithm has been tested on 280 benchmark instances ranging from 10 to 1000 jobs from the OR Library, the results showing that the proposed self evolution algorithm delivers much higher efficiency in finding optimal or near-optimal solutions with both better results in total penalties and significant execution time reduction.

  • Etissue: A bio-inspired match-based reconfigurable hardware architecture supporting hierarchical self-healing and self-evolution

    This paper presents the concept of a match-based biological inspired reconfigurable hardware architecture named electronic tissue (eTissue), which supports hierarchical self-healing and self-evolution. In existing multicellular array architecture, data and cells are tightly-coupled, that is, each data can be processed only by a certain cell. In eTissue, we imitate the match-based recognition mechanism in protein sorting to loosely couple data and cells, i.e., each data can be recognized and processed by any homogeneous cell. This loosely-coupled relationship makes the replacement of cells more flexible, equipping eTissue with more powerful self-healing and self-evolution capabilities. Substitution among homogeneous cells, differentiation of adult stem cells, and conversion between heterogeneous cells endow eTissue with the capability of hierarchical self-healing. The self-evolution of eTissue is derived from differentiation of adult stem cells and conversion between heterogeneous cells. Our fault-injection experiments show that eTissue has promising self-healing and self-evolution capabilities.

  • Self-Evolution in a Constructive Binary String System

    We examine the qualitative dynamics of a catalytic self-organizing system of binary strings that is inspired by the chemical information processing metaphor. A string is interpreted in two different ways: either (a) as raw data or (b) as a machine that is able to process another string as data in order to produce a third one. This article focuses on the phenomena of evolution whose appearance is notable because no explicit mutation, recombination, or artificial selection operators are introduced. We call the system self-evolving because every variation is performed by the objects themselves in their machine form.

  • Numerical analysis and basic algorithm of self-organization of cellular robotics 'CEBOT'

    The authors propose a novel distributed intelligence system which is based on the concept of a cellular robotic system (CEBOT). This proposed intelligent system has the abilities of learning, reasoning, and self-organizing. This system consists of various kinds of intelligence units called knowledge-cells. By using the knowledge cells and the proposed algorithms, which should be stored in each cell genetically, the system realizes self-organization and self-evolution. In addition to proposing the basic genetic algorithm, the organization of a cell-structured system is numerically analyzed. Some simulations are also shown to demonstrate the self-organizing and self- evolution abilities of the distributed intelligence system.<<ETX>>

  • Mapping Route Optimization in Warehousing Environment Based on Improved Genetic Algorithm

    In large and structured warehousing environment, mapping route optimization based on mobile robot platform has been a challenging problem. To make the length of route shorter and form closed-loop earlier are the two goals of this optimization problem. Shorter route length could decrease the time and resource consumption during mapping. Earlier closed-loop could increase the accuracy of map and reduce closure detection errors. Because warehousing environment is structured, we could build an undirected graph in which the edges stand for passageways and the nodes represent the crossroads. With the two goals and the graph, we could transform the problem to a multi-objective Chinese postman problem. Genetic algorithm was used to solve this problem. We applied tournament selection operation and parthenogenesis. To improve the speed of evolution, the self-evolution process was introduced into the genetic algorithm. The results of experiments show the efficiency and validity of the proposed algorithm.



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