WorldWideScience

Sample records for artificial life models

  1. Artificial life and Piaget.

    Science.gov (United States)

    Mueller, Ulrich; Grobman, K H.

    2003-04-01

    Artificial life provides important theoretical and methodological tools for the investigation of Piaget's developmental theory. This new method uses artificial neural networks to simulate living phenomena in a computer. A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework. We contrast artificial life with traditional cognitivist approaches, discuss the role of innateness in development, and examine the relation between physiological and psychological explanations of intelligent behaviour.

  2. Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-04-01

    Full Text Available This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation. Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
    Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of processed cheese as 28.25 days, which is very close to experimental shelf life of 30 days.

  3. Artificial life and life artificialization in Tron

    Directory of Open Access Journals (Sweden)

    Carolina Dantas Figueiredo

    2012-12-01

    Full Text Available Cinema constantly shows the struggle between the men and artificial intelligences. Fiction, and more specifically fiction films, lends itself to explore possibilities asking “what if?”. “What if”, in this case, is related to the eventual rebellion of artificial intelligences, theme explored in the movies Tron (1982 and Tron Legacy (2010 trat portray the conflict between programs and users. The present paper examines these films, observing particularly the possibility programs empowering. Finally, is briefly mentioned the concept of cyborg as a possibility of response to human concerns.

  4. Application of artificial neural engineering and regression models for forecasting shelf life of instant coffee drink

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2011-07-01

    Full Text Available Coffee as beverage is prepared from the roasted seeds (beans of the coffee plant. Coffee is the second most important product in the international market in terms of volume trade and the most important in terms of value. Artificial neural engineering and regression models were developed to predict shelf life of instant coffee drink. Colour and appearance, flavour, viscosity and sediment were used as input parameters. Overall acceptability was used as output parameter. The dataset consisted of experimentally developed 50 observations. The dataset was divided into two disjoint subsets, namely, training set containing 40 observations (80% of total observations and test set comprising of 10 observations (20% of total observations. The network was trained with 500 epochs. Neural network toolbox under Matlab 7.0 software was used for training the models. From the investigation it was revealed that multiple linear regression model was superior over radial basis model for forecasting shelf life of instant coffee drink.

  5. Disease spread models in wild and feral animal populations: application of artificial life models.

    Science.gov (United States)

    Ward, M P; Laffan, S W; Highfield, L D

    2011-08-01

    The role that wild and feral animal populations might play in the incursion and spread of important transboundary animal diseases, such as foot and mouth disease (FMD), has received less attention than is warranted by the potential impacts. An artificial life model (Sirca) has been used to investigate this issue in studies based on spatially referenced data sets from southern Texas. An incursion of FMD in which either feral pig or deer populations were infected could result in between 698 and 1557 infected cattle and affect an area of between 166 km2 and 455 km2 after a 100-day period. Although outbreak size in deer populations can be predicted bythe size of the local deer population initially infected, the resulting outbreaks in feral pig populations are less predictable. Also, in the case of deer, the size of potential outbreaks might depend on the season when the incursion occurs. The impact of various mitigation strategies on disease spread has also been investigated. The approach used in the studies reviewed here explicitly incorporates the spatial distribution and relationships between animal populations, providing a new framework to explore potential impacts, costs, and control strategies.

  6. Use of Artificial Neural Network for Testing Effectiveness of Intelligent Computing Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    GOYAL Kumar Gyanendra

    2012-10-01

    Full Text Available This paper presents the suitability of artificial neural network (ANN models for predicting the shelf life of processed cheese stored at 7-8ºC. Soluble nitrogen, pH; standard plate count, yeast & mould count, and spore count were input variables, and sensory score was output variable. Mean square error, root mean square error, coefficient of determination and Nash - sutcliffo coefficient were used in order to test the effectiveness of the developed ANN models. Excellent agreement was found between experimental results and these mathematical parameters, thus confirming that ANN models are very effective in predicting the shelf life of processed cheese.

  7. Time – Delay Simulated Artificial Neural Network Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-05-01

    Full Text Available This paper highlights the significance of Time-Delay ANN models for predicting shelf life of processed cheese stored at 7-8o^C. Bayesian regularization algorithm was selected as training function. Number of neurons in single and multiple hidden layers varied from 1 to 20. The network was trained with up to 100 epochs. Mean square error, root mean square error, coefficient of determination and nash - Sutcliffe coefficient were used for calculating the prediction capability of the developed models. Time-Delay ANN models with multilayer are quite efficient in predicting the shelf life of processed cheese stored at 7-8o^C.

  8. Artificial life: The coming evolution

    Energy Technology Data Exchange (ETDEWEB)

    Farmer, J.D. (Los Alamos National Lab., NM (USA) Santa Fe Inst., NM (USA)); Belin, A.d' A. (Shute, Mihaly, and Weinberger, Santa Fe, NM (USA))

    1990-01-01

    Within fifty to a hundred years a new class of organisms is likely to emerge. These organisms will be artificial in the sense that they will originally be designed by humans. However, they will reproduce, and will evolve into something other than their initial form; they will be alive'' under any reasonable definition of the word. These organisms will evolve in a fundamentally different manner than contemporary biological organisms, since their reproduction will be under at least partial conscious control, giving it a Lamarckian component. The pace of evolutionary change consequently will be extremely rapid. The advent of artificial life will be the most significant historical event since the emergence of human beings. The impact on humanity and the biosphere could be enormous, larger than the industrial revolution, nuclear weapons, or environmental pollution. We must take steps now to shape the emergence of artificial organisms; they have potential to be either the ugliest terrestrial disaster, or the most beautiful creation of humanity. 22 refs., 3 figs.

  9. 公司战略的人工生命建模%Artificial Life Modeling in Corporate Strategy

    Institute of Scientific and Technical Information of China (English)

    阎静; 吕胜富; 钟宁

    2007-01-01

    针对商业公司的竞争建立人工生命的模型,通过遗传算法,有效地洞察到公司政策制定的关键营销因素.将软计算应用在商业竞争中,即在可控的人工环境中,创造出被观察的个体行为.同时,开发了软件AL-GAE,用于模拟个体公司竞争演化中优选基因的特征.实验结果表明,ALGAE可以挖掘出事先未知的一些突发基因和行为特征.经过ALGAE分析,市场优势的预测可以从基因数据中获得,表明该模型的有效性.%Applying soft computing methods to a business problem is an innovative application in artificial intelligence. In this paper, Artificial Life (AL) modeling is used to optimize corporate strategies. This model adapted an AL concept where artificial environments could be created to inspect behaviors of subjects under controlled conditions. This Genetic Algorithm application extracts key marketing factors used in corporate decision-making, and market dominance can be predicted by the ALGAE program developed specially for this research. Evolution of hypothetical business entities demonstrates significant results in optimizing genetic characteristics in competitive environments. Analysis shows emergent genotypes and behaviors which cannot be discovered in advance.

  10. The artificial pancreas : From logic to life

    NARCIS (Netherlands)

    Kropff, J.

    2017-01-01

    In this thesis we investigated the efficacy of real-life use of an artificial pancreas starting with use of these systems in a hotel setting and finally 24/7 long-term use at home. We investigated the accuracy of continuous glucose monitoring (CGM) systems that act as input for the artificial pancre

  11. A simple artificial life model explains irrational behavior in human decision-making.

    Directory of Open Access Journals (Sweden)

    Carolina Feher da Silva

    Full Text Available Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

  12. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  13. Artificial Life - Why Should Musicians Bother?

    DEFF Research Database (Denmark)

    Berry, Rodney; Dahlstedt, Palle

    2003-01-01

    for artistic expression. Artists serve to prepare society for the invisible changes going on within it by producing artworks in response to the mechanisms of change. This paper discusses the authors' approaches to using concepts from artificial life in their musical works, which are basically of two kinds...... - artificial worlds producing music as an output, and interactive compositional tools using evolutionary algorithms to generate music and sound. It also provides a brief cultural context for these works....

  14. The Biological Relevance of Artificial Life: Lessons from Artificial Intelligence

    Science.gov (United States)

    Colombano, Silvano

    2000-01-01

    There is no fundamental reason why A-life couldn't simply be a branch of computer science that deals with algorithms that are inspired by, or emulate biological phenomena. However, if these are the limits we place on this field, we miss the opportunity to help advance Theoretical Biology and to contribute to a deeper understanding of the nature of life. The history of Artificial Intelligence provides a good example, in that early interest in the nature of cognition quickly was lost to the process of building tools, such as "expert systems" that, were certainly useful, but provided little insight in the nature of cognition. Based on this lesson, I will discuss criteria for increasing the biological relevance of A-life and the probability that this field may provide a theoretical foundation for Biology.

  15. An artificial life approach to language.

    Science.gov (United States)

    Parisi, D

    1997-08-01

    The aim of the paper is to show that an Artificial Life approach to language tends to change the research agenda on language which has been shared by both the symbolic paradigm and classical connectionism. Artificial Life Neural Networks (ALNNs) are different from classical connectionist networks because they interact with an independent physical environment; are subject to evolutionary, developmental, and cultural change, and not only to learning; and are part of organisms that have a physical body, have a life (are born, develop, and die), and are members of genetic and sometimes, cultural populations. Using ALNNs to study language shifts the emphasis from research on linguistic forms and laboratory-like tasks to the investigation of the emergence and transmission of language, the use of language, its role in cognition, and language as a populational rather than as an individual phenomenon.

  16. Generalized Artificial Life Structure for Time-dependent Problems

    Institute of Scientific and Technical Information of China (English)

    TSAU Minhe; KAO Weiwen; CHANG Albert

    2009-01-01

    In recent years, more attention has been paid on artificial life researches. Artificial life(AL) is a research on regulating gene parameters of digital organisms under complicated problematic environments through natural selections and evolutions to achieve the final emergence of intelligence. Most recent studies focused on solving certain real problems by artificial life methods, yet without much address on the AL life basic mechanism. The real problems are often very complicated, and the proposed methods sometimes seem too simple to handle those problems. This study proposed a new approach in AL research, named "generalized artificial life structure(GALS)", in which the traditional "gene bits" in genetic algorithms is first replaced by "gene parameters", which could appear anywhere in GALS. A modeling procedure is taken to normalize the input data, and AL "tissue" is innovated to make AL more complex. GALS is anticipated to contribute significantly to the fitness of AL evolution. The formation of"tissue" begins with some different AL basic cells, and then tissue is produced by the casual selections of one or several of these cells. As a result, the gene parameters, represented by "tissues", could become highly diversified. This diversification should have obvious effects on improving gene fitness. This study took the innovative method of GALS in a stock forecasting problem under a carefully designed manipulating platform. And the researching results verify that the GALS is successful in improving the gene evolution fitness.

  17. The Ultimate Future of Artificial Life: Towards Artificial Cosmogenesis

    CERN Document Server

    Vidal, Clement

    2008-01-01

    This philosophical paper tries to tackle the question of what could be the ultimate future of ALife from a cosmic viewpoint. We first argue that the natural direction of ALife is a simulation of an entire universe. Two new challenges naturally arise. The first is to simulate open-ended evolution at all levels in a single simulation; i.e. not only in biology, but also to link it up a level below (physical evolution) and a level above (cultural evolution). The second challenge is to probe what would happen if we would "replay the tape of the universe". Assuming that intelligent life would indeed simulate an entire universe, this leads to two tentative hypotheses. Following the soft-ALife program, some authors argued that we could be in a simulation run by an intelligent entity. Following the hard/wet-ALife program, this would lead to an artificial cosmogenesis. This last direction is argued with a careful speculative philosophical approach, emphasizing the imperative to find a solution to the heat death problem...

  18. A comprehensive overview of the applications of artificial life.

    Science.gov (United States)

    Kim, Kyung-Joong; Cho, Sung-Bae

    2006-01-01

    We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

  19. The Past, Present, and Future of Artificial Life

    Directory of Open Access Journals (Sweden)

    Wendy eAguilar

    2014-10-01

    Full Text Available For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into fourteen themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning, ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields.

  20. Synthetic biology and the moral significance of artificial life

    DEFF Research Database (Denmark)

    Christiansen, Andreas

    2016-01-01

    I discuss the moral significance of artificial life within synthetic biology via a discussion of Douglas, Powell and Savulescu's paper 'Is the creation of artificial life morally significant’. I argue that the definitions of 'artificial life’ and of 'moral significance’ are too narrow. Douglas......, Powell and Savulescu's definition of artificial life does not capture all core projects of synthetic biology or the ethical concerns that have been voiced, and their definition of moral significance fails to take into account the possibility that creating artificial life is conditionally acceptable....... Finally, I show how several important objections to synthetic biology are plausibly understood as arguing that creating artificial life in a wide sense is only conditionally acceptable....

  1. PATTERN MINING AND DISCOVERY ORIENTED TO ARTIFICIAL LIFE

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    The nano-technology requires new methodology to handle difficult problems that involve the information processing, material technology and life phenomena in the nano-world. Concentrating on the synthesis of techniques in scientific frontier fields such as KDD(Knowledge Discovery in Database), evolutionary computation, rough set and logic, a new artificial life model for pattern mining and discovery has been proposed and the corresponding emergent algorithm has been built and implemented. The original contribution of the research work can be summarized in the following two principal respects: (a) pattern mining and discovery for genomic dynamics within the theoretic framework of artificial life; (b) information fusion of multiparadigm for modeling and building of evolutionary KDD system with rough pattern inference. Through computer experiments the artificial sequence generated by computational processes has matched the evidence convinced by the latest scientific reality. The work is helpful to analyze and build the next generation of bio-nonferrous metal materials in the level of genomics and nano-technology.

  2. KINETIC MODELLING AND HALF LIFE STUDY OF ADSORPTIVE BIOREMEDIATION OF SOIL ARTIFICIALLY CONTAMINATED WITH BONNY LIGHT CRUDE OIL

    Directory of Open Access Journals (Sweden)

    Samuel Enahoro Agarry

    2015-06-01

    Full Text Available In this study, comparative potential effects of commercial activated carbon (CAC and plantain peel-derived biochar (PPBC of different particle sizes and dosage to stimulate petroleum hydrocarbon biodegradation in soil were investigated. Microcosms containing soil were spiked with weathered Bonny light crude oil (WBLCO (10% w/w and amended with different particle sizes (0.02, 0.07 and 0.48 mm and dosage (20, 30 and 40 g of CAC and PPBC, respectively. The bioremediation experiments were carried out for a period of 28 days under laboratory conditions. The results showed that there was a positive relationship between the rate of petroleum hydrocarbons reduction and presence of the CAC and PPBC in crude oil contaminated soil microcosms. The WBLCO biodegradation data fitted well to the first-order kinetic model. The model revealed that WBLCO contaminated-soil microcosms amended with CAC and PPBC had higher biodegradation rate constants (k as well as lower half-life times (t1/2 than unamended soil (natural attenuation remediation system. The rate constants increased while half-life times decreased with decreased particle size and increased dosage of amendment agents. ANOVA statistical analysis revealed that WBLCO biodegradation in soil was significantly (p = 0.05 influenced by the addition of CAC and biochar amendment agents, respectively. However, Tukey’s post hoc test (at p = 0.05 showed that there was no significant difference in the bioremediation efficiency of CAC and PPBC. Thus, amendment of soils with biochar has the potential to be an inexpensive, efficient, environmentally friendly and relatively novel strategy to mitigate organic compound-contaminated soil.

  3. Artificial Intelligence Software Engineering (AISE) model

    Science.gov (United States)

    Kiss, Peter A.

    1990-01-01

    The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

  4. Creating and maintaining chemical artificial life by robotic symbiosis

    DEFF Research Database (Denmark)

    Hanczyc, Martin M.; Parrilla, Juan M.; Nicholson, Arwen;

    2015-01-01

    We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior of the d...... confluence of chemical, artificial intelligence, and robotic approaches to artificial life.......We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior...

  5. Creating and maintaining chemical artificial life by robotic symbiosis

    DEFF Research Database (Denmark)

    Hanczyc, Martin M.; Parrilla, Juan M.; Nicholson, Arwen

    2015-01-01

    We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior of the d......We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior...... confluence of chemical, artificial intelligence, and robotic approaches to artificial life....

  6. Artificial Life Model Based on Bionic Fish%基于仿生机器鱼的人工生命模型

    Institute of Scientific and Technical Information of China (English)

    金旭东; 薛方正

    2012-01-01

    为了构建具有自学习、自适应、自协调的人工生命体,结合对鱼类生理结构和行为机理的研究和分析,提出了基于人工生命的仿生机器鱼系统。将非接触式供电技术应用于水下供电,模拟鱼类生理活动,在仿生鱼上实现了动态、无连接、不间断的电能供给,并且基于鱼类行为的层次结构,实现了"动机-行为-动作"的3层行为决策系统,引入强化学习算法,模拟鱼类与环境交互的后天学习过程。通过实物仿生鱼的游姿优化实验,证明了研究和设计工作的有效性。%In order to build self-learning, adaptive, self-coordination of intelligent agent, and combining with the research and analysis on physiological structure and behavior mechanism of natural fish, we proposed the artificial life system model of bionic fish. The paper applied non-contact power supply technology in underwater electrical supply, simulated physiological activity of natural fish, and real- ized dynamic, no-connection, uninterrupted power supply in bionic fish; Based on fish behavior hierarchy, the paper achieved the "motive-behavior-action" three layers decision-making system, and introduced the reinforcement learning algorithm to simulate environmental interaction learning process. The experiment of swim posture optimization verified the effectiveness of the research and design work.

  7. "Life is a verb": inflections of artificial life in cultural context.

    Science.gov (United States)

    Helmreich, Stefan

    2007-01-01

    This review essay surveys recent literature in the history of science, literary theory, anthropology, and art criticism dedicated to exploring how the artificial life enterprise has been inflected by--and might also reshape--existing social, historical, cognitive, and cultural frames of thought and action. The piece works through various possible interpretations of Kevin Kelly's phrase "life is a verb," in order to track recent shifts in cultural studies of artificial life from an aesthetic of critique to an aesthetic of conversation, discerning in the process different styles of translating between the concerns of the humanities, social sciences, natural sciences, and sciences of the artificial.

  8. Creating and maintaining chemical artificial life by robotic symbiosis.

    Science.gov (United States)

    Hanczyc, Martin M; Parrilla, Juan M; Nicholson, Arwen; Yanev, Kliment; Stoy, Kasper

    2015-01-01

    We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior of the droplet that it creates. The robot can then use this categorization to autonomously detect the current state of the droplet and respond. The robot is programmed to visually track the droplet and either inject more chemical fuel to sustain a motile state or introduce a new chemical component that results in a state change (e.g., division). Coupling inexpensive open source hardware with sensing and feedback allows for replicable real-time manipulation and monitoring of nonequilibrium systems that would be otherwise tedious, expensive, and error-prone. This system is a first step towards the practical confluence of chemical, artificial intelligence, and robotic approaches to artificial life.

  9. Artificial Life Art, Creativity, and Techno-hybridization (editor's introduction).

    Science.gov (United States)

    Dorin, Alan

    2015-01-01

    Artists and engineers have devised lifelike technology for millennia. Their ingenious devices have often prompted inquiry into our preferences, prejudices, and beliefs about living systems, especially regarding their origins, status, constitution, and behavior. A recurring fabrication technique is shared across artificial life art, science, and engineering. This involves aggregating representations or re-creations of familiar biological parts-techno-hybridization-but the motives of practitioners may differ markedly. This article, and the special issue it introduces, explores how ground familiar to contemporary artificial life science and engineering has been assessed and interpreted in parallel by (a) artists and (b) theorists studying creativity explicitly. This activity offers thoughtful, alternative perspectives on artificial life science and engineering, highlighting and sometimes undermining the fields' underlying assumptions, or exposing avenues that are yet to be explored outside of art. Additionally, art has the potential to engage the general public, supporting and exploring the findings of scientific research and engineering. This adds considerably to the maturity of a culture tackling the issues the discipline of artificial life raises.

  10. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  11. Economic modeling using artificial intelligence methods

    CERN Document Server

    Marwala, Tshilidzi

    2013-01-01

    This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.

  12. Imitation of Life: Advanced system for native Artificial Evolution

    CERN Document Server

    Sperl, Thomas

    2011-01-01

    A model for artificial evolution in native x86 Windows systems has been developed at the end of 2010. In this text, further improvements and additional analogies to natural microbiologic processes are presented. Several experiments indicate the capability of the system - and raise the question of possible countermeasures.

  13. Artificial Nutrition (Food) and Hydration (Fluids) at the End of Life

    Science.gov (United States)

    Artificial Nutrition (Food) and Hydration (Fluids) at the End of Life It is very common for doctors to provide ... or recovering from surgery. This is called “artificial nutrition and hydration” and like all medical treatments, it ...

  14. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

    Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian

  15. The dilemma of the symbols: analogies between philosophy, biology and artificial life.

    Science.gov (United States)

    Spadaro, Salvatore

    2013-01-01

    This article analyzes some analogies going from Artificial Life questions about the symbol-matter connection to Artificial Intelligence questions about symbol-grounding. It focuses on the notion of the interpretability of syntax and how the symbols are integrated in a unity ("binding problem"). Utilizing the DNA code as a model, this paper discusses how syntactic features could be defined as high-grade characteristics of the non syntactic relations in a material-dynamic structure, by using an emergentist approach. This topic furnishes the ground for a confutation of J. Searle's statement that syntax is observer-relative, as he wrote in his book "Mind: A Brief Introduction". Moreover the evolving discussion also modifies the classic symbol-processing doctrine in the mind which Searle attacks as a strong AL argument, that life could be implemented in a computational mode. Lastly, this paper furnishes a new way of support for the autonomous systems thesis in Artificial Life and Artificial Intelligence, using, inter alia, the "adaptive resonance theory" (ART).

  16. Synthetic Biology and the Moral Significance of Artificial Life: A Reply to Douglas, Powell and Savulescu.

    Science.gov (United States)

    Christiansen, Andreas

    2016-06-01

    I discuss the moral significance of artificial life within synthetic biology via a discussion of Douglas, Powell and Savulescu's paper 'Is the creation of artificial life morally significant'. I argue that the definitions of 'artificial life' and of 'moral significance' are too narrow. Douglas, Powell and Savulescu's definition of artificial life does not capture all core projects of synthetic biology or the ethical concerns that have been voiced, and their definition of moral significance fails to take into account the possibility that creating artificial life is conditionally acceptable. Finally, I show how several important objections to synthetic biology are plausibly understood as arguing that creating artificial life in a wide sense is only conditionally acceptable.

  17. Psychometric Measurement Models and Artificial Neural Networks

    Science.gov (United States)

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  18. Potential of artificial neural network technology for predicting shelf life of processed cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    Full Text Available Radial basis (fewer neurons artificial neural network (ANN models were developed for predicting the shelf life of processed cheese stored at 7-8o C. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient were applied in order to compare the prediction ability of the developed models. Soluble nitrogen, pH; standard plate count, yeast & mouldcount, and spore count were the input parameters, while sensory score was output parameter for the developed model. The developed model showed very good correlation between actual data and predicted data with high coefficient of determination and nash - sutcliffo coefficient besides low root mean square error, suggesting that the developed model is quite efficient in predicting the shelf life of processed cheese.

  19. EXPLORATION OF STYLIZED FACTS IN THE ARTIFICIAL LIFE SYSTEM AVIDA

    Directory of Open Access Journals (Sweden)

    Shinta Koyano

    2016-09-01

    Full Text Available Population dynamics in the evolution, extinction, and re-evolution of various logic-function performing organisms is studied in the artificial life system, Avida. Following the work of Yedid (2009, we design an experiment involving two extinction regimes, pulse-extinction (corresponding to a random-kill event and press-extinction (corresponding to a prolonged episode of rare resources. In addition, we study the effect of environmental topology (toroidal grid and clique graph. In the study of population dynamics, logarithmic returns are generally applied. The resulting distributions display a fat tail form of the power law: the more complex the logic function (in terms of NAND components, the broader the full width at half a maximum of the histogram. The power law exponents were in sound agreement with those of “real-life” populations and distributions. The distributions of evolutionary times, as well as post-extinction recovery periods, were very broad, and presumably had no standard deviations. Using 100 runs of 200,000 updates for each of the four cases (about 1 month of central processing unit time, we established the dynamics of the average population, with the effect of world topology.

  20. Reactively and Anticipatory Behaving Agents for Artificial Life Simulations

    Science.gov (United States)

    Kohout, Karel; Nahodil, Pavel

    2010-11-01

    Reactive behavior is still considered and the exact opposite for the anticipatory one. Despite the advances on the field of anticipation there are little thoughts on relation with the reactive behavior, the similarities and where the boundary is. In this article we will present our viewpoint and we will try to show that reactive and anticipatory behavior can be combined. This is the basic ground of our unified theory for anticipatory behavior architecture. We still miss such compact theory, which would integrate multiple aspects of anticipation. My multi-level anticipatory behavior approach is based on the current understanding of anticipation from both the artificial intelligence and biology point of view. As part of the explanation we will also elaborate on the topic of weak and strong artificial life. Anticipation is not matter of a single mechanism in a living organism. It was noted already that it happens on many different levels even in the very simple creatures. What we consider to be important for our work and what is our original though is that it happens even without voluntary control. We believe that this is novelty though for the anticipation theory. Naturally research of anticipation was in the beginning of this decade focused on the anticipatory principles bringing advances on the field itself. This allowed us to build on those, look at them from higher perspective, and use not one but multiple levels of anticipation in a creature design. This presents second original though and that is composition of the agent architecture that has anticipation built in almost every function. In this article we will focus only on first two levels within the 8-factor anticipation framework. We will introduce them as defined categories of anticipation and describe them from theory and implementation algorithm point of view. We will also present an experiment conducted, however this experiment serves more as explanatory example. These first two levels may seem trivial

  1. Modelling natural and artificial hands with synergies.

    Science.gov (United States)

    Bicchi, Antonio; Gabiccini, Marco; Santello, Marco

    2011-11-12

    We report on recent work in modelling the process of grasping and active touch by natural and artificial hands. Starting from observations made in human hands about the correlation of degrees of freedom in patterns of more frequent use (postural synergies), we consider the implications of a geometrical model accounting for such data, which is applicable to the pre-grasping phase occurring when shaping the hand before actual contact with the grasped object. To extend applicability of the synergy model to study force distribution in the actual grasp, we introduce a modified model including the mechanical compliance of the hand's musculotendinous system. Numerical results obtained by this model indicate that the same principal synergies observed from pre-grasp postural data are also fundamental in achieving proper grasp force distribution. To illustrate the concept of synergies in the dual domain of haptic sensing, we provide a review of models of how the complexity and heterogeneity of sensory information from touch can be harnessed in simplified, tractable abstractions. These abstractions are amenable to fast processing to enable quick reflexes as well as elaboration of high-level percepts. Applications of the synergy model to the design and control of artificial hands and tactile sensors are illustrated.

  2. The Essential Turing Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life Plus the Secrets of Enigma

    CERN Document Server

    2004-01-01

    The ideas that gave birth to the computer age. Alan Turing, pioneer of computing and WWII codebreaker, was one of the most important and influential thinkers of the twentieth century. In this volume for the first time his key writings are made available to a broad, non-specialist readership. They make fascinating reading both in their own right and for their historic significance: contemporary computational theory, cognitive science, artificial intelligence, and artificial life all spring from this ground-breaking work, which is also rich. in philosophical and logical insight. An introduction

  3. Mathematical problems in modeling artificial heart

    Directory of Open Access Journals (Sweden)

    Ahmed N. U.

    1995-01-01

    Full Text Available In this paper we discuss some problems arising in mathematical modeling of artificial hearts. The hydrodynamics of blood flow in an artificial heart chamber is governed by the Navier-Stokes equation, coupled with an equation of hyperbolic type subject to moving boundary conditions. The flow is induced by the motion of a diaphragm (membrane inside the heart chamber attached to a part of the boundary and driven by a compressor (pusher plate. On one side of the diaphragm is the blood and on the other side is the compressor fluid. For a complete mathematical model it is necessary to write the equation of motion of the diaphragm and all the dynamic couplings that exist between its position, velocity and the blood flow in the heart chamber. This gives rise to a system of coupled nonlinear partial differential equations; the Navier-Stokes equation being of parabolic type and the equation for the membrane being of hyperbolic type. The system is completed by introducing all the necessary static and dynamic boundary conditions. The ultimate objective is to control the flow pattern so as to minimize hemolysis (damage to red blood cells by optimal choice of geometry, and by optimal control of the membrane for a given geometry. The other clinical problems, such as compatibility of the material used in the construction of the heart chamber, and the membrane, are not considered in this paper. Also the dynamics of the valve is not considered here, though it is also an important element in the overall design of an artificial heart. We hope to model the valve dynamics in later paper.

  4. Applications of Artificial Life and Digital Organisms in the Study of Genetic Evolution

    Directory of Open Access Journals (Sweden)

    Maurice HT Ling

    Full Text Available Testing evolutionary hypothesis in experimental setting is expensive, time consuming, and unlikely to recapitulate evolutionary history if evolution is repeated. Computer simulations of virtual organisms, also known as artificial life or digital organisms ...

  5. Artificial hydration at the end of life in an oncology ward in Singapore

    Directory of Open Access Journals (Sweden)

    Lalit Kumar Radha Krishna

    2010-01-01

    Conclusion: Artificial hydration during the last 48 h of life did not have any significant impact on symptoms related to hydration status, medication use or on survival in terminally ill cancer patients under palliative care.

  6. An entropy model for artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Emmanuel Pothos

    2010-06-01

    Full Text Available A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL. In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy.

  7. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  8. Evolvable mathematical models: A new artificial Intelligence paradigm

    Science.gov (United States)

    Grouchy, Paul

    We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

  9. Microbial growth modelling with artificial neural networks.

    Science.gov (United States)

    Jeyamkonda, S; Jaya, D S; Holle, R A

    2001-03-20

    There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.

  10. Artificial Spin-Ice and Vertex Models

    Science.gov (United States)

    Cugliandolo, Leticia F.

    2017-01-01

    In classical and quantum frustrated magnets the interactions in combination with the lattice structure impede the spins to order in optimal configurations at zero temperature. The theoretical interest in their classical realisations has been boosted by the artificial manufacture of materials with these properties, that are of flexible design. This note summarises work on the use of vertex models to study bidimensional spin-ices samples, done in collaboration with R. A. Borzi, M. V. Ferreyra, L. Foini, G. Gonnella, S. A. Grigera, P. Guruciaga, D. Levis, A. Pelizzola and M. Tarzia, in recent years. It is an invited contribution to a J. Stat. Mech. special issue dedicated to the memory of Leo P. Kadanoff.

  11. Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

    Directory of Open Access Journals (Sweden)

    Ye Tian

    2014-01-01

    Full Text Available An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL of lithium-ion (Li-ion batteries based on artificial fish swarm algorithm (AFSA and particle filter (PF, which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.

  12. Interaction model of artificial fish in virtual environment

    Institute of Scientific and Technical Information of China (English)

    Meng Xiangsong; Ban Xiaojuan; Yin Yixin

    2008-01-01

    Conventional artificial fish has some shortages on the interaction with environment,other fish,and the animator.This article proposes a multi-tier interaction control model of artificial fish,realizes the interaction model through integration of virtual reality technology and Markov sequence,and provides a virtual marine world to describe the interaction between artificial fish and the virtual environment and the interaction between the artificial fish and the animator.Simulation results show that the interaction model owns not only the basic characteristics of virtual biology,but also has high trueness interaction function.

  13. Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology

    Directory of Open Access Journals (Sweden)

    Hossam Meshref

    2013-01-01

    Full Text Available In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful models to overcome its complexity such as the artificial neural networks. Common concepts between the two systems were investigated to design a simple, yet a robust, model of artificial immune networks. Three artificial neural networks learning models were available to choose from in the research design: supervised, unsupervised, and reinforcement learning models. However, it was found that the reinforcement model is the most suitable model. Research results examined network parameters, and appropriate relations between concentration ranges and their dependent parameters as well as the expected reward during network learning. In conclusion, it is recommended the use of the designed model by other researchers in different applications such as controlling robots in hazardous environment to save human lives as well as using it on image retrieval in general to help the police department identify suspects.

  14. Credit Scoring Model Hybridizing Artificial Intelligence with Logistic Regression

    Directory of Open Access Journals (Sweden)

    Han Lu

    2013-01-01

    Full Text Available Today the most commonly used techniques for credit scoring are artificial intelligence and statistics. In this paper, we started a new way to use these two kinds of models. Through logistic regression filters the variables with a high degree of correlation, artificial intelligence models reduce complexity and accelerate convergence, while these models hybridizing logistic regression have better explanations in statistically significance, thus improve the effect of artificial intelligence models. With experiments on German data set, we find an interesting phenomenon defined as ‘Dimensional interference’ with support vector machine and from cross validation it can be seen that the new method gives a lot of help with credit scoring.

  15. Stochastic Differential Equations in Artificial Pancreas Modelling

    DEFF Research Database (Denmark)

    Duun-Henriksen, Anne Katrine

    to assess the timing, intensity and duration of physical exercise in advance, to adjust the insulin dose accordingly. Additionally, several uncontrollable and unpredictable factors such as stress, hormonal cycles and sickness changing the metabolic state make this task even more difficult. The development...... of the insulin pump and the CGM has paved the way for a fully automatic treatment regime, the artificial pancreas. The idea is to connect the CGM with the insulin pump via a control algorithm running on e.g. the patients smart phone. The CGM observations are sent to the smart phone and based on this information...... of the system directly. The purpose of this PhD-project was to investigate the potential of SDEs in the artificial pancreas development. Especially, the emerging continuous monitoring of glucose levels makes SDEs highly applicable to this field. The current thesis aims at demonstrating and discussing...

  16. Natural - synthetic - artificial!

    DEFF Research Database (Denmark)

    Nielsen, Peter E

    2010-01-01

    The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life.......The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life....

  17. A Suitable Artificial Intelligence Model for Inventory Level Optimization

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-05-01

    Full Text Available Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings of the research show that artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction.. Conclusions resulting from the paper are beneficial for further research. It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise.

  18. Accelerated life models modeling and statistical analysis

    CERN Document Server

    Bagdonavicius, Vilijandas

    2001-01-01

    Failure Time DistributionsIntroductionParametric Classes of Failure Time DistributionsAccelerated Life ModelsIntroductionGeneralized Sedyakin's ModelAccelerated Failure Time ModelProportional Hazards ModelGeneralized Proportional Hazards ModelsGeneralized Additive and Additive-Multiplicative Hazards ModelsChanging Shape and Scale ModelsGeneralizationsModels Including Switch-Up and Cycling EffectsHeredity HypothesisSummaryAccelerated Degradation ModelsIntroductionDegradation ModelsModeling the Influence of Explanatory Varia

  19. Dynamic Model for Life History of Scyphozoa.

    Directory of Open Access Journals (Sweden)

    Congbo Xie

    Full Text Available A two-state life history model governed by ODEs is formulated to elucidate the population dynamics of jellyfish and to illuminate the triggering mechanism of its blooms. The polyp-medusa model admits trichotomous global dynamic scenarios: extinction, polyps survival only, and both survival. The population dynamics sensitively depend on several biotic and abiotic limiting factors such as substrate, temperature, and predation. The combination of temperature increase, substrate expansion, and predator diminishment acts synergistically to create a habitat that is more favorable for jellyfishes. Reducing artificial marine constructions, aiding predator populations, and directly controlling the jellyfish population would help to manage the jellyfish blooms. The theoretical analyses and numerical experiments yield several insights into the nature underlying the model and shed some new light on the general control strategy for jellyfish.

  20. The LifeCycle model

    DEFF Research Database (Denmark)

    Krink, Thiemo; Løvbjerg, Morten

    2002-01-01

    Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the LifeCycle model that simultaneously applies...

  1. Neuro-Based Artificial Intelligence Model for Loan Decisions

    Directory of Open Access Journals (Sweden)

    Shorouq F. Eletter

    2010-01-01

    Full Text Available Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an enabling tool for evaluating credit applications to support loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpropagation learning algorithm was used to build up the proposed model. Results: Different representative cases of loan applications were considered based on the guidelines of different banks in Jordan, to validate the neural network model. Conclusion: The results indicated that artificial neural networks are a successful technology that can be used in loan application evaluation in the Jordanian commercial banks.

  2. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  3. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  4. Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

    Directory of Open Access Journals (Sweden)

    Aleksandar Sabljic

    2004-12-01

    Full Text Available This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

  5. Natural vs. artificial groundwater recharge, quantification through inverse modeling

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2013-02-01

    Full Text Available Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady- and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncertainty, and model sensitivity were analyzed in all modeling steps. The results showed that in a normal year without extreme events, the floodwater spreading system is the main contributor to recharge with 80% and the ephemeral river channel with 20% of total recharge in the studied area. Uncertainty analysis revealed that the river channel recharge estimation represents relatively more uncertainty in comparison to the artificial recharge zones. The model is also less sensitive to the river channel. The results show that by expanding the artificial recharge system, the recharge volume can be increased even for small flood events, while the recharge through the river channel increases only for major flood events.

  6. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  7. Pegylated polystyrene particles as a model system for artificial cells

    NARCIS (Netherlands)

    Meng, Fenghua; Engbers, Gerard H.M.; Gessner, Andrea; Müller, Reiner H.; Feijen, Jan

    2004-01-01

    Pegylated polystyrene particles (PS-PEG) were prepared as a model system for artificial cells, by modification of carboxyl polystyrene particles (PS-COOH) with homo- and hetero-bifunctional polyethylene glycols (PEG, MW 1500, 3400, and 5000) containing an amino end group for immobilization and an am

  8. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  9. Modeling Advance Life Support Systems

    Science.gov (United States)

    Pitts, Marvin; Sager, John; Loader, Coleen; Drysdale, Alan

    1996-01-01

    Activities this summer consisted of two projects that involved computer simulation of bioregenerative life support systems for space habitats. Students in the Space Life Science Training Program (SLSTP) used the simulation, space station, to learn about relationships between humans, fish, plants, and microorganisms in a closed environment. One student complete a six week project to modify the simulation by converting the microbes from anaerobic to aerobic, and then balancing the simulation's life support system. A detailed computer simulation of a closed lunar station using bioregenerative life support was attempted, but there was not enough known about system restraints and constants in plant growth, bioreactor design for space habitats and food preparation to develop an integrated model with any confidence. Instead of a completed detailed model with broad assumptions concerning the unknown system parameters, a framework for an integrated model was outlined and work begun on plant and bioreactor simulations. The NASA sponsors and the summer Fell were satisfied with the progress made during the 10 weeks, and we have planned future cooperative work.

  10. Proposal of a system of function-discovery using a bug type of artificial life

    Energy Technology Data Exchange (ETDEWEB)

    Serikawa, S.; Shimomura, T. [Kyushu Institute of Technology, Kitakyushu (Japan)

    1998-02-01

    The effectiveness of artificial life is investigated from an engineering point of view. A system (named S-system) of function-discovery using a bug type of artificial life is proposed in this study. Some functions are extracted by the system. The chromosome of a bug consists of functions, constants and variables. A tree structure is used for the expression of the chromosome. Some observation data are provided for the bugs. After obtaining the data, they reproduce. The concept of sexual / asexual reproduction is introduced in this study. The number of homogeneous bugs is limited for a variety of species. These ideas are very effective for a function-search. A part of the chromosome changes by mutation. As the generation proceeds, the bugs with the function in agreement with the observation data survive selectively, and finally determine the true function. For the validity of this system, some data which obey the known laws have been given for the bugs. The bugs have evolved and discovered some functions in agreement with the laws. As for an unknown function, observation data on glossiness have been provided. They have also discovered the function. In addition, they have determined the multiple curves included in the image data. The S-system has the characteristics that the solution tends to converge and stabilizes in comparison with Genetic Programming. Moreover, the form of the function is relatively simple. 11 refs., 14 figs., 3 tabs.

  11. Biology and fertility life table of Agrotis ipsilon on artificial diet; Biologia e tabela de vida de fertilidade de Agrotis ipsilon em dieta artificial

    Energy Technology Data Exchange (ETDEWEB)

    Bento, Flavia de Moura Manoel; Fortes, Priscila; Zerio, Neide Graciano; Parra, Jose Roberto Postali [Universidade de Sao Paulo (USP), Piracicaba, SP (Brazil). Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Dept. Entomologia, Fitopatologia e Zoologia Agricola]. E-mail: flaviam@esalq.usp.br, pfortes@esalq.usp.br, ngzerio@esalq.usp.br, jrpparra@esalq.usp.br; Magro, Sandra Regina [Universidade Camilo Castelo Branco, Fernandopolis, SP (Brazil)]. E-mail: sandra.magro@gmail.com

    2007-10-15

    The objective of this work was to develop an artificial diet to rear Agrotis ipsilon in laboratory using biological parameters and fertility life table. The artificial diet was prepared with bean, casein, soybean protein, yeast and wheat germ as protein sources. The biological aspects duration and viability of larval and pupal stages, pupal weight, sex ratio, life span of adults, preoviposition period, egg laying capacity and fertility life table were evaluated. Six larval instars were observed comprising larval duration of 28,4 days and 93% of viability mean pupal duration of 12,4 days and viability of 96%. The total viability of the life cycle was 72%. The pupae weight was 387 mg for males and 484 mg for females. The sex ratio was 0,46 and the preoviposition period lasted one day and egg laying was 1,806 eggs per female. The net reproductive rate per generation and increase finite rate were 616,9 and 1,14, respectively. Artificial diet is adequate for rearing A. ipsilon in laboratory. (author)

  12. Artificial intelligence support for scientific model-building

    Science.gov (United States)

    Keller, Richard M.

    1992-01-01

    Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

  13. Runoff forecasting by artificial neural network and conventional model

    Directory of Open Access Journals (Sweden)

    A.R. Ghumman

    2011-12-01

    Full Text Available Rainfall runoff models are highly useful for water resources planning and development. In the present study rainfall–runoff model based on Artificial Neural Networks (ANNs was developed and applied on a watershed in Pakistan. The model was developed to suite the conditions in which the collected dataset is short and the quality of dataset is questionable. The results of ANN models were compared with a mathematical conceptual model. The cross validation approach was adopted for the generalization of ANN models. The precipitation used data was collected from Meteorological Department Karachi Pakistan. The results confirmed that ANN model is an important alternative to conceptual models and it can be used when the range of collected dataset is short and data is of low standard.

  14. Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Morteza Vadood; Majid Safar Johari; Ali Reza Rahai

    2015-01-01

    While various kinds of fibers are used to improve the hot mix asphalt (HMA) performance, a few works have been undertaken on the hybrid fiber-reinforced HMA. Therefore, the fatigue life of modified HMA samples using polypropylene and polyester fibers was evaluated and two models namely regression and artificial neural network (ANN) were used to predict the fatigue life based on the fibers parameters. As ANN contains many parameters such as the number of hidden layers which directly influence the prediction accuracy, genetic algorithm (GA) was used to solve optimization problem for ANN. Moreover, the trial and error method was used to optimize the GA parameters such as the population size. The comparison of the results obtained from regression and optimized ANN with GA shows that the two-hidden-layer ANN with two and five neurons in the first and second hidden layers, respectively, can predict the fatigue life of fiber-reinforced HMA with high accuracy (correlation coefficient of 0.96).

  15. Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

    Science.gov (United States)

    Eskinazi, Ilan; Fregly, Benjamin J.

    2016-01-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  16. Artificial Neural Network Model for Optical Fiber Direction Coupler Design

    Institute of Scientific and Technical Information of China (English)

    李九生; 鲍振武

    2004-01-01

    A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time-saving. So it is promising in the field of both investigation and application.

  17. Artificial Neural Network Model of Hydrocarbon Migration and Accumulation

    Institute of Scientific and Technical Information of China (English)

    刘海滨; 吴冲龙

    2002-01-01

    Based on the dynamic simulation of the 3-D structure the sedimentary modeling, the unit entity model has been adopted to transfer the heterogeneous complex pas sage system into limited simple homogeneous entity, and then the traditional dyn amic simulation has been used to calculate the phase and the drive forces of the hyd rocarbon , and the artificial neural network(ANN) technology has been applied to resolve such problems as the direction, velocity and quantity of the hydrocarbo n migration among the unit entities. Through simulating of petroleum migration a nd accumulation in Zhu Ⅲ depression, the complex mechanism of hydrocarbon migra tion and accumulation has been opened out.

  18. Effects of artificial lighting on the detection of plant stress with spectral reflectance remote sensing in bioregenerative life support systems

    Science.gov (United States)

    Schuerger, Andrew C.; Richards, Jeffrey T.

    2006-09-01

    Plant-based life support systems that utilize bioregenerative technologies have been proposed for long-term human missions to both the Moon and Mars. Bioregenerative life support systems will utilize higher plants to regenerate oxygen, water, and edible biomass for crews, and are likely to significantly lower the ‘equivalent system mass’ of crewed vehicles. As part of an ongoing effort to begin the development of an automatic remote sensing system to monitor plant health in bioregenerative life support modules, we tested the efficacy of seven artificial illumination sources on the remote detection of plant stresses. A cohort of pepper plants (Capsicum annuum L.) were grown 42 days at 25 °C, 70% relative humidity, and 300 μmol m-2 s-1 of photosynthetically active radiation (PAR; from 400 to 700 nm). Plants were grown under nutritional stresses induced by irrigating subsets of the plants with 100, 50, 25, or 10% of a standard nutrient solution. Reflectance spectra of the healthy and stressed plants were collected under seven artificial lamps including two tungsten halogen lamps, plus high pressure sodium, metal halide, fluorescent, microwave, and red/blue light emitting diode (LED) sources. Results indicated that several common algorithms used to estimate biomass and leaf chlorophyll content were effective in predicting plant stress under all seven illumination sources. However, the two types of tungsten halogen lamps and the microwave illumination source yielded linear models with the highest residuals and thus the highest predictive capabilities of all lamps tested. The illumination sources with the least predictive capabilities were the red/blue LEDs and fluorescent lamps. Although the red/blue LEDs yielded the lowest residuals for linear models derived from the remote sensing data, the LED arrays used in these experiments were optimized for plant productivity and not the collection of remote sensing data. Thus, we propose that if adjusted to optimize the

  19. Searching for turbulence models by artificial neural network

    CERN Document Server

    Gamahara, Masataka

    2016-01-01

    Artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. ANN is used to establish a functional relation between the grid-scale (GS) flow field and the SGS stress without any assumption of the form of function. Data required for training and test of ANN are provided by direct numerical simulation (DNS) of a turbulent channel flow. It is shown that ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model.

  20. Landslide susceptibility analysis using an artificial neural network model

    Science.gov (United States)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  1. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    Ali Aytek; M Asce; Murat Alp

    2008-04-01

    This study proposes an application of two techniques of artificial intelligence (AI) for rainfall–runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.

  2. Influence of adjustments to amputation and artificial limb on quality of life in patients following lower limb amputation

    NARCIS (Netherlands)

    Sinha, Richa; van den Heuvel, Wim J. A.; Arokiasamy, Perianayagam; van Dijk, Jitse P.

    2014-01-01

    The objectives of this study are to investigate the relationship between adjustments to amputation and artificial limb, and quality of life (QoL), and to analyse the influence of sociodemographic, medical and amputation-related factors on this relationship. Patients with unilateral and noncongenital

  3. Transport modeling of sorbing tracers in artificial fractures

    Energy Technology Data Exchange (ETDEWEB)

    Keum, Dong Kwon; Baik, Min Hoon; Park, Chung Kyun; Cho, Young Hwan; Hahn, Phil Soo

    1998-02-01

    This study was performed as part of a fifty-man year attachment program between AECL (Atomic Energy Canada Limited) and KAERI. Three kinds of computer code, HDD, POMKAP and VAMKAP, were developed to predict transport of contaminants in fractured rock. MDDM was to calculate the mass transport of contaminants in a single fracture using a simple hydrodynamic dispersion diffusion model. POMKAP was to predict the mass transport of contaminants by a two-dimensional variable aperture model. In parallel with modeling, the validation of models was also performed through the analysis of the migration experimental data obtained in acrylic plastic and granite artificial fracture system at the Whiteshell laboratories, AECL, Canada. (author). 34 refs., 11 tabs., 76 figs.

  4. Modeling biodegradation and kinetics of glyphosate by artificial neural network.

    Science.gov (United States)

    Nourouzi, Mohsen M; Chuah, Teong G; Choong, Thomas S Y; Rabiei, F

    2012-01-01

    An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (glyphosate on bacteria growth. The value of K(i)/K(s) increased when the mixed inoculum size was increased from 10(4) to 10(6) bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed.

  5. Application of Artificial Intelligence for Bridge Deterioration Model.

    Science.gov (United States)

    Chen, Zhang; Wu, Yangyang; Li, Li; Sun, Lijun

    2015-01-01

    The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  6. Application of Artificial Intelligence for Bridge Deterioration Model

    Directory of Open Access Journals (Sweden)

    Zhang Chen

    2015-01-01

    Full Text Available The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  7. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.

  8. Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle costing of construction projects

    Directory of Open Access Journals (Sweden)

    Ayedh Alqahtani

    2013-09-01

    Full Text Available Industrial application of life-cycle cost analysis (LCCA is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client the advantages to be gained from objective (LCCA comparison of (subcomponent material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs to compute the whole-cost(s of construction and uses the concept of cost significant items (CSI to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE; and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver. The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver and 2% (via back-propagation respectively.

  9. Can interbreeding of wild and artificially propagated animals be prevented by using broodstock selected for a divergent life history?

    Science.gov (United States)

    Seamons, Todd R; Hauser, Lorenz; Naish, Kerry A; Quinn, Thomas P

    2012-11-01

    TWO STRATEGIES HAVE BEEN PROPOSED TO AVOID NEGATIVE GENETIC EFFECTS OF ARTIFICIALLY PROPAGATED INDIVIDUALS ON WILD POPULATIONS: (i) integration of wild and captive populations to minimize domestication selection and (ii) segregation of released individuals from the wild population to minimize interbreeding. We tested the efficacy of the strategy of segregation by divergent life history in a steelhead trout, Oncorhynchus mykiss, system, where hatchery fish were selected to spawn months earlier than the indigenous wild population. The proportion of wild ancestry smolts and adults declined by 10-20% over the three generations since the hatchery program began. Up to 80% of the naturally produced steelhead in any given year were hatchery/wild hybrids. Regression model selection analysis showed that the proportion of hatchery ancestry smolts was lower in years when stream discharge was high, suggesting a negative effect of flow on reproductive success of early-spawning hatchery fish. Furthermore, proportions of hybrid smolts and adults were higher in years when the number of naturally spawning hatchery-produced adults was higher. Divergent life history failed to prevent interbreeding when physical isolation was ineffective, an inadequacy that is likely to prevail in many other situations.

  10. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  11. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  12. An Application of Finite Element Modelling to Pneumatic Artificial Muscle

    Directory of Open Access Journals (Sweden)

    R. Ramasamy

    2005-01-01

    Full Text Available The purpose of this article was to introduce and to give an overview of the Pneumatic Artificial Muscles (PAMs as a whole and to discuss its numerical modelling, using the Finite Element (FE Method. Thus, more information to understand on its behaviour in generating force for actuation was obtained. The construction of PAMs was mainly consists of flexible, inflatable membranes which having orthotropic material behaviour. The main properties influencing the PAMs will be explained in terms of their load-carrying capacity and low weight in assembly. Discussion on their designs and capacity to function as locomotion device in robotics applications will be laid out, followed by FE modelling to represent PAMs overall structural behaviour under any potential operational conditions.

  13. Modelling the SOFC behaviours by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Milewski, Jaroslaw; Swirski, Konrad [Institute of Heat Engineering, Warsaw University of Technology, 25 Nowowiejska Street, 00-665 Warsaw (Poland)

    2009-07-15

    The Artificial Neural Network (ANN) can be applied to simulate an object's behaviour without an algorithmic solution merely by utilizing available experimental data. The ANN is used for modelling singular cell behaviour. The optimal network architecture is shown and commented. The error backpropagation algorithm was used for an ANN training procedure. The ANN based SOFC model has the following input parameters: current density, temperature, fuel volume flow density (ml min{sup -1} cm{sup -2}), and oxidant volume flow density. Based on these input parameters, cell voltage is predicted by the model. Obtained results show that the ANN can be successfully used for modelling the singular solid oxide fuel cell. The self-learning process of the ANN provides an opportunity to adapt the model to new situations (e.g. certain types of impurities at inlet streams etc.). Based on the results from this study it can be concluded that, by using the ANN, an SOFC can be modelled with relatively high accuracy. In contrast to traditional models, the ANN is able to predict cell voltage without knowledge of numerous physical, chemical, and electrochemical factors. (author)

  14. Artificial emotional model based on finite state machine

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-mei; WU Wei-guo

    2008-01-01

    According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

  15. Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

    Directory of Open Access Journals (Sweden)

    Christopher Gan

    2005-01-01

    Full Text Available Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN, a special class of neural networks and a MLFN with a logistic model on consumers’ choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers’ use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

  16. Constitutive models of artificial muscles:a review

    Institute of Scientific and Technical Information of China (English)

    Hui-ming WANG; Shao-xing QU

    2016-01-01

    Artificial muscles are materials which possess muscle-like characteristics; they have many promising applications and many materials have been exploited as artificial muscles. In this review, the artificial muscles discussed are confined to die-lectric elastomers and responsive gels. We focus on their constitutive models based on free energy function theory. For dielectric elastomers, both hyperelastic and visco-hyperelastic models are involved. For responsive gels, we consider different kinds of gels, such as hydrogel, pH-sensitive gel, temperature-sensitive gel, polyelectrolyte gel, reactive gel, etc. With an accurate, relia-ble, and powerful constitutive model, exact theoretical analysis can be achieved and the important intrinsic characteristics of artificial muscle based systems can be revealed.%中文概要题目:人工肌肉本构模型的综述人工肌肉是指具有类似肌肉特性的材料,这些材料在外界激励下,可以实现大变形,且响应速度快。本文总结两类人工肌肉本构模型的研究成果:一类是介电高弹体,另一类是响应性凝胶。本文中提到的本构模型仅限于用自由能函数导出的情形。对于介电高弹体材料,分别综述超弹性模型和粘性超弹性模型。在超弹性模型中,列出目前研究中使用较多的一些本构模型的自由能函数具体表达式;比较 neo-Hookean、Gent、Arruda-Boyce和 Ogden四种模型在单轴拉伸和等双轴拉伸两种情形下的名义应力-伸长曲线;给出了考虑一些重要因素的研究模型,这些因素包括材料可压缩性、取向极化、变介电常数、热耦合、受纤维约束、流体耦合以及空气耦合等。对于响应性凝胶,分别综述水凝胶、pH 敏感性凝胶、温度敏感性凝胶、聚电解质凝胶以及反应性凝胶等的本构模型。这些精确、可靠和有效的本构模型,将有助于开展人工肌肉系统的性能分析和预测,甚至揭示

  17. Phosphorus fate, management, and modeling in artificially drained systems.

    Science.gov (United States)

    Kleinman, Peter J A; Smith, Douglas R; Bolster, Carl H; Easton, Zachary M

    2015-03-01

    Phosphorus (P) losses in agricultural drainage waters, both surface and subsurface, are among the most difficult form of nonpoint source pollution to mitigate. This special collection of papers on P in drainage waters documents the range of field conditions leading to P loss in drainage water, the potential for drainage and nutrient management practices to control drainage losses of P, and the ability of models to represent P loss to drainage systems. A review of P in tile drainage and case studies from North America, Europe, and New Zealand highlight the potential for artificial drainage to exacerbate watershed loads of dissolved and particulate P via rapid, bypass flow and shorter flow path distances. Trade-offs are identified in association with drainage intensification, tillage, cover crops, and manure management. While P in drainage waters tends to be tied to surface sources of P (soil, amendments or vegetation) that are in highest concentration, legacy sources of P may occur at deeper depths or other points along drainage flow paths. Most startling, none of the major fate-and-transport models used to predict management impacts on watershed P losses simulate the dominant processes of P loss to drainage waters. Because P losses to drainage waters can be so difficult to manage and to model, major investment are needed (i) in systems that can provide necessary drainage for agronomic production while detaining peak flows and promoting P retention and (ii) in models that can adequately describe P loss to drainage waters.

  18. Modelling in life insurance a management perspective

    CERN Document Server

    Norberg, Ragnar; Planchet, Frédéric

    2016-01-01

    Focussing on life insurance and pensions, this book addresses various aspects of modelling in modern insurance: insurance liabilities; asset-liability management; securitization, hedging, and investment strategies. With contributions from internationally renowned academics in actuarial science, finance, and management science and key people in major life insurance and reinsurance companies, there is expert coverage of a wide range of topics, for example: models in life insurance and their roles in decision making; an account of the contemporary history of insurance and life insurance mathematics; choice, calibration, and evaluation of models; documentation and quality checks of data; new insurance regulations and accounting rules; cash flow projection models; economic scenario generators; model uncertainty and model risk; model-based decision-making at line management level; models and behaviour of stakeholders. With author profiles ranging from highly specialized model builders to decision makers at chief ex...

  19. Promising synergies of simulation model management, software engineering, artificial intelligence, and general system theories

    Energy Technology Data Exchange (ETDEWEB)

    Oren, T.I.

    1982-01-01

    Simulation is viewed within the model management paradigm. Major components of simulation systems as well as elements of model management are outlined. Possible synergies of simulation model management, software engineering, artificial intelligence, and general system theories are systematized. 21 references.

  20. Artificial Neural Network L* from different magnetospheric field models

    Science.gov (United States)

    Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

    2011-12-01

    The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

  1. Applying artificial vision models to human scene understanding

    Directory of Open Access Journals (Sweden)

    Elissa Michele Aminoff

    2015-02-01

    Full Text Available How do we understand the complex patterns of neural responses that underlie scene understanding? Studies of the network of brain regions held to be scene-selective – the parahippocampal/lingual region (PPA, the retrosplenial complex (RSC, and the occipital place area (TOS – have typically focused on single visual dimensions (e.g., size, rather than the high-dimensional feature space in which scenes are likely to be neurally represented. Here we leverage well-specified artificial vision systems to explicate a more complex understanding of how scenes are encoded in this functional network. We correlated similarity matrices within three different scene-spaces arising from: 1 BOLD activity in scene-selective brain regions; 2 behavioral measured judgments of visually-perceived scene similarity; and 3 several different computer vision models. These correlations revealed: 1 models that relied on mid- and high-level scene attributes showed the highest correlations with the patterns of neural activity within the scene-selective network; 2 NEIL and SUN – the models that best accounted for the patterns obtained from PPA and TOS – were different from the GIST model that best accounted for the pattern obtained from RSC; 3 The best performing models outperformed behaviorally-measured judgments of scene similarity in accounting for neural data. One computer vision method – NEIL (Never-Ending-Image-Learner, which incorporates visual features learned as statistical regularities across web-scale numbers of scenes – showed significant correlations with neural activity in all three scene-selective regions and was one of the two models best able to account for variance in the PPA and TOS. We suggest that these results are a promising first step in explicating more fine-grained models of neural scene understanding, including developing a clearer picture of the division of labor among the components of the functional scene-selective brain network.

  2. Network modeling of membrane-based artificial cellular systems

    Science.gov (United States)

    Freeman, Eric C.; Philen, Michael K.; Leo, Donald J.

    2013-04-01

    Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.

  3. Modelling artificial sea salt emission in large eddy simulations

    Science.gov (United States)

    Maalick, Z.; Korhonen, H.; Kokkola, H.; Kühn, T.; Romakkaniemi, S.

    2014-01-01

    We study the dispersion of sea salt particles from artificially injected sea spray at a cloud-resolving scale. Understanding of how different aerosol processes affect particle dispersion is crucial when designing emission sources for marine cloud brightening. Compared with previous studies, we include for the first time an explicit treatment of aerosol water, which takes into account condensation, evaporation and their effect on ambient temperature. This enables us to capture the negative buoyancy caused by water evaporation from aerosols. Additionally, we use a higher model resolution to capture aerosol loss through coagulation near the source point. We find that, with a seawater flux of 15 kg s−1, the cooling due to evaporation can be as much as 1.4 K, causing a delay in particle dispersion of 10–20 min. This delay enhances particle scavenging by a factor of 1.14 compared with simulations without aerosol water. We further show that both cooling and particle dispersion depend on the model resolution, with a maximum particle scavenging efficiency of 20% within 5 h after emission at maximum resolution of 50 m. Based on these results, we suggest further regional high-resolution studies which model several injection periods over several weeks. PMID:25404679

  4. Modelling artificial sea salt emission in large eddy simulations.

    Science.gov (United States)

    Maalick, Z; Korhonen, H; Kokkola, H; Kühn, T; Romakkaniemi, S

    2014-12-28

    We study the dispersion of sea salt particles from artificially injected sea spray at a cloud-resolving scale. Understanding of how different aerosol processes affect particle dispersion is crucial when designing emission sources for marine cloud brightening. Compared with previous studies, we include for the first time an explicit treatment of aerosol water, which takes into account condensation, evaporation and their effect on ambient temperature. This enables us to capture the negative buoyancy caused by water evaporation from aerosols. Additionally, we use a higher model resolution to capture aerosol loss through coagulation near the source point. We find that, with a seawater flux of 15 kg s(-1), the cooling due to evaporation can be as much as 1.4 K, causing a delay in particle dispersion of 10-20 min. This delay enhances particle scavenging by a factor of 1.14 compared with simulations without aerosol water. We further show that both cooling and particle dispersion depend on the model resolution, with a maximum particle scavenging efficiency of 20% within 5 h after emission at maximum resolution of 50 m. Based on these results, we suggest further regional high-resolution studies which model several injection periods over several weeks.

  5. COMPUTER MODELING IN THE DEVELOPMENT OF ARTIFICIAL VENTRICLES OF HEART

    Directory of Open Access Journals (Sweden)

    L. V. Belyaev

    2011-01-01

    Full Text Available In article modern researches of processes of development of artificial ventricles of heart are described. Advanta- ges of application computer (CAD/CAE technologies are shown by development of artificial ventricles of heart. The systems developed with application of the given technologies are submitted. 

  6. Estimation of storage time of yogurt with artificial neural network modeling.

    Science.gov (United States)

    Sofu, A; Ekinci, F Y

    2007-07-01

    Changes in the physical, chemical, and microbiological structure of yogurt determine the storage and shelf life of the product. In this study, microbial counts and pH values of yogurt during storage were determined at d 1, 7, and 14. Simultaneously, image processing of yogurt was digitized by using a machine vision system (MVS) to determine color changes during storage, and the obtained data were modeled with an artificial neural network (ANN) for prediction of shelf life of set-type whole-fat and low-fat yogurts. The ANN models were developed using back-propagation networks with a single hidden layer and sigmoid activation functions. The input variables of the network were pH; total aerobic, yeast, mold, and coliform counts; and color analysis values measured by the machine vision system. The output variable was the storage time of the yogurt. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.9996) showing that the developed model was able to analyze nonlinear multivariant data with very good performance, fewer parameters, and shorter calculation time. The model might be an alternative method to control the expiration date of yogurt shown in labeling and provide consumers with a safer food supply.

  7. Predicting the Need to Perform Life-Saving Interventions in Trauma Patients by Using New Vital Signs and Artificial Neural Networks

    Science.gov (United States)

    2009-07-01

    Patients by Using New Vital Signs and Artificial Neural Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...to Perform Life-Saving Interventions in Trauma Patients Using New Vital Signs and Artificial Neural Networks P R O J E C T - S U S T A IN IN

  8. Biology and fertility life table of Hypercompe indecisa on artificial diet; Biologia e tabela de vida de fertilidade de Hypercompe indecisa em dieta artificial

    Energy Technology Data Exchange (ETDEWEB)

    Nava, Dori Edson; Diez-Rodriguez, Gabriela Ines; Melo, Mirtes; Afonso, Ana Paula Schneid [EMBRAPA Clima Temperado, Pelotas, RS (Brazil)]. E-mail: nava@cpact.embrapa.br; gidiez@gmail.com; mirtes@cpact.embrapa.br; anapaula@cpact.embrapa.br

    2008-12-15

    The objective of this work was to study the biology of Hypercompe indecisa and construct a life table of fertility on an artificial diet, aiming at insect rearing in the laboratory. The following variables were determined: duration and survival of egg, larval and pupal phases; number of larval stages; sexual ratio; pupal weight; longevity; fecundity; and the period of pre-oviposition and oviposition. The embrionary, larval and pupal phases were 6, 25.4 and 64.3 days, with survival of 92.7, 92 and 71.9%, respectively. Six larval stages with variable durations were observed. Pupal weight was 1.04 g for females and 0.726 g for males. The biological cycle was 95.6 days, with 61.3% total survival. Females laid in average 1,531 eggs, during 8.3 days, with a pre-oviposition period of two days. The average longevity of males and females was 21.9 and 21.8 days, respectively, and the sexual ratio of males to females was 0.54. Increase of H. indecisa was 283 times through each generation, with the generation average duration of 98 days, and the fi nite ratio of increase of 1.0593. The used artificial diet was adequate for rearing H. indecisa, in the laboratory. (author)

  9. An Agent-Based Model of Institutional Life-Cycles

    Directory of Open Access Journals (Sweden)

    Manuel Wäckerle

    2014-08-01

    Full Text Available We use an agent-based model to investigate the interdependent dynamics between individual agency and emergent socioeconomic structure, leading to institutional change in a generic way. Our model simulates the emergence and exit of institutional units, understood as generic governed social structures. We show how endogenized trust and exogenously given leader authority influences institutional change, i.e., diversity in institutional life-cycles. It turns out that these governed institutions (destructure in cyclical patterns dependent on the overall evolution of trust in the artificial society, while at the same time, influencing this evolution by supporting social learning. Simulation results indicate three scenarios of institutional life-cycles. Institutions may, (1 build up very fast and freeze the artificial society in a stable but fearful pattern (ordered system; (2 exist only for a short time, leading to a very trusty society (highly fluctuating system; and (3 structure in cyclical patterns over time and support social learning due to cumulative causation of societal trust (complex system.

  10. Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Armando J. Pinho

    2013-08-01

    Full Text Available It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of 17.2 x 109 memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model and FCM-Mx (mixture of finite-context models , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.

  11. Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

    Science.gov (United States)

    Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

  12. Modeling Common-Sense Decisions in Artificial Intelligence

    Science.gov (United States)

    Zak, Michail

    2010-01-01

    A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

  13. Forecasting Financial Time-Series using Artificial Market Models

    CERN Document Server

    Gupta, N; Johnson, N F; Gupta, Nachi; Hauser, Raphael; Johnson, Neil F.

    2005-01-01

    We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat/0105303 and cond-mat/0105258 and was based on some encouraging preliminary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices. However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset' - which is equivalent to sayi...

  14. Cellular automata-based artificial life system of horizontal gene transfer

    Directory of Open Access Journals (Sweden)

    Ji-xin Liu

    2016-02-01

    Full Text Available Mutation and natural selection is the core of Darwin's idea about evolution. Many algorithms and models are based on this idea. However, in the evolution of prokaryotes, more and more researches have indicated that horizontal gene transfer (HGT would be much more important and universal than the authors had imagined. Owing to this mechanism, the prokaryotes not only become adaptable in nearly any environment on Earth, but also form a global genetic bank and a super communication network with all the genes of the prokaryotic world. Under this background, they present a novel cellular automata model general gene transfer to simulate and study the vertical gene transfer and HGT in the prokaryotes. At the same time, they use Schrodinger's life theory to formulate some evaluation indices and to discuss the intelligence and cognition of prokaryotes which is derived from HGT.

  15. Artificial Reefs

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — An artificial reef is a human-made underwater structure, typically built to promote marine life in areas with a generally featureless bottom, control erosion, block...

  16. An artificial blood vessel implanted three-dimensional microsystem for modeling transvascular migration of tumor cells.

    Science.gov (United States)

    Wang, Xue-Ying; Pei, Ying; Xie, Min; Jin, Zi-He; Xiao, Ya-Shi; Wang, Yang; Zhang, Li-Na; Li, Yan; Huang, Wei-Hua

    2015-02-21

    Reproducing a tumor microenvironment consisting of blood vessels and tumor cells for modeling tumor invasion in vitro is particularly challenging. Here, we report an artificial blood vessel implanted 3D microfluidic system for reproducing transvascular migration of tumor cells. The transparent, porous and elastic artificial blood vessels are obtained by constructing polysaccharide cellulose-based microtubes using a chitosan sacrificial template, and possess excellent cytocompatibility, permeability, and mechanical characteristics. The artificial blood vessels are then fully implanted into the collagen matrix to reconstruct the 3D microsystem for modeling transvascular migration of tumor cells. Well-defined simulated vascular lumens were obtained by proliferation of the human umbilical vein endothelial cells (HUVECs) lining the artificial blood vessels, which enables us to reproduce structures and functions of blood vessels and replicate various hemodynamic parameters. Based on this model, the adhesion and transvascular migration of tumor cells across the artificial blood vessel have been well reproduced.

  17. Evaluating portland cement concrete degradation by sulphate exposure through artificial neural networks modeling

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Douglas Nunes de; Bourguignon, Lucas Gabriel Garcia; Tolentino, Evandro, E-mail: tolentino@timoteo.cefetmg.br [Centro Federal de Educacao Tecnologica de Minas Gerais (CEFET-MG), Timoteo, MG (Brazil); Costa, Rodrigo Moyses, E-mail: rodrigo@moyses.com.br [Universidade de Itauna, Itauna, MG (Brazil); Tello, Cledola Cassia Oliveira de, E-mail: tellocc@cdtn.br [Centro de Desenvolvimento da Tecnologia Nucelar (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

    2015-07-01

    A concrete is durable if it has accomplished the desired service life in the environment in which it is exposed. The durability of concrete materials can be limited as a result of adverse performance of its cement-paste matrix or aggregate constituents under either chemical or physical attack. Among other aggressive chemical exposures, the sulphate attack is an important concern. Water, soils and gases, which contain sulphate, represent a potential threat to the durability of concrete structures. Sulphate attack in concrete leads to the conversion of the hydration products of cement to ettringite, gypsum, and other phases, and also it leads to the destabilization of the primary strength generating calcium silicate hydrate (C-S-H) gel. The formation of ettringite and gypsum is common in cementitious systems exposed to most types of sulphate solutions. The present work presents the application of the neural networks for estimating deterioration of various concrete mixtures due to exposure to sulphate solutions. A neural networks model was constructed, trained and tested using the available database. In general, artificial neural networks could be successfully used in function approximation problems in order to approach the data generation function. Once data generation function is known, artificial neural network structure is tested using data not presented to the network during training. This paper is intent to provide the technical requirements related to the production of a durable concrete to be used in the structures of the Brazilian near-surface repository of radioactive wastes. (author)

  18. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A;

    1999-01-01

    part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...

  19. On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model

    Science.gov (United States)

    Paiva, Juliana Pereira Lisboa Mohallem; Paiva, Henrique Mohallem; Esposito, Elisa; Morais, Michelle Manfrini

    2016-01-01

    This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following elements to characterize the artificial feeding scenario: a function to model the preference of the bees for natural food over artificial food; parameters to quantify the quality and palatability of artificial diets; a function to account for the efficiency of the foragers in gathering food under different environmental conditions; and a function to represent different approaches used by the beekeeper to feed the hive with artificial food. Simulated results are presented to illustrate the main characteristics of the model and its behavior under different scenarios. The model results are validated with experimental data from the literature involving four different artificial diets. A good match between simulated and experimental results was achieved. PMID:27875589

  20. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  1. NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Tian Sheping; Ding Guoqing; Yan Detian; Lin Liangming

    2004-01-01

    The pneumatic artificial muscles are widely used in the fields of medical robots,etc.Neural networks are applied to modeling and controlling of artificial muscle system.A single-joint artificial muscle test system is designed.The recursive prediction error (RPE) algorithm which yields faster convergence than back propagation (BP) algorithm is applied to train the neural networks.The realization of RPE algorithm is given.The difference of modeling of artificial muscles using neural networks with different input nodes and different hidden layer nodes is discussed.On this basis the nonlinear control scheme using neural networks for artificial muscle system has been introduced.The experimental results show that the nonlinear control scheme yields faster response and higher control accuracy than the traditional linear control scheme.

  2. Early life exposure to artificial light at night affects the physiological condition: An experimental study on the ecophysiology of free-living nestling songbirds.

    Science.gov (United States)

    Raap, Thomas; Casasole, Giulia; Pinxten, Rianne; Eens, Marcel

    2016-11-01

    Light pollution or artificial light at night (ALAN) is increasingly recognised to be an important anthropogenic environmental pressure on wildlife, affecting animal behaviour and physiology. Early life experiences are extremely important for the development, physiological status and health of organisms, and as such, early exposure to artificial light may have detrimental consequences for organism fitness. We experimentally manipulated the light environment of free-living great tit nestlings (Parus major), an important model species in evolutionary and environmental research. Haptoglobin (Hp) and nitric oxide (NOx), as important indicators of immunity, health, and physiological condition, were quantified in nestlings at baseline (13 days after hatching) and after a two night exposure to ALAN. We found that ALAN increased Hp and decreased NOx. ALAN may increase stress and oxidative stress and reduce melatonin which could subsequently lead to increased Hp and decreased NOx. Haptoglobin is part of the immune response and mounting an immune response is costly in energy and resources and, trade-offs are likely to occur with other energetically demanding tasks, such as survival or reproduction. Acute inhibition of NOx may have a cascading effect as it also affects other physiological aspects and may negatively affect immunocompetence. The consequences of the observed effects on Hp and NOx remain to be examined. Our study provides experimental field evidence that ALAN affects nestlings' physiology during development and early life exposure to ALAN could therefore have long lasting effects throughout adulthood.

  3. Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation.

    Energy Technology Data Exchange (ETDEWEB)

    Saffer, Shelley (Sam) I.

    2014-12-01

    This is a final report of the DOE award DE-SC0001132, Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation. This document describes the achievements of the goals, and resulting research made possible by this award.

  4. Model systems for life processes on Mars

    Science.gov (United States)

    Mitz, M. A.

    1974-01-01

    In the evolution of life forms nonphotosynthetic mechanisms are developed. The question remains whether a total life system could evolve which is not dependent upon photosynthesis. In trying to visualize life on other planets, the photosynthetic process has problems. On Mars, the high intensity of light at the surface is a concern and alternative mechanisms need to be defined and analyzed. In the UV search for alternate mechanisms, several different areas may be identified. These involve activated inorganic compounds in the atmosphere, such as the products of photodissociation of carbon dioxide and the organic material which may be created by natural phenomena. In addition, a life system based on the pressure of the atmospheric constituents, such as carbon dioxide, is a possibility. These considerations may be important for the understanding of evolutionary processes of life on another planet. Model systems which depend on these alternative mechanisms are defined and related to presently planned and future planetary missions.

  5. Influence of adjustments to amputation and artificial limb on quality of life in patients following lower limb amputation.

    Science.gov (United States)

    Sinha, Richa; van den Heuvel, Wim J A; Arokiasamy, Perianayagam; van Dijk, Jitse P

    2014-03-01

    The objectives of this study are to investigate the relationship between adjustments to amputation and artificial limb, and quality of life (QoL), and to analyse the influence of sociodemographic, medical and amputation-related factors on this relationship. Patients with unilateral and noncongenital lower limb amputation who were using artificial limb were interviewed (n=368) using structured questionnaires. The Trinity Amputation and Prosthesis Experience Scales (TAPES) were used to assess adjustments to amputation and artificial limb and the MOS Short-Form Health Survey (SF-36) was used to assess the physical (PCS) and mental (MCS) component summary of QoL. Absence of comorbidity and residual stump pain, being employed, young age, less functional restriction, being more adjusted to limitation, increased social adjustment and less restriction in athletic activity were related to better PCS scores. Absence of comorbidity and phantom limb pain, nonuse of assistive device, being more adjusted to limitation, increased social adjustment and being less functionally restricted were related to higher MCS scores. Comorbidity had a modifying effect on both PCS and MCS scores. In addition, age, being employed and residual stump pain had a modifying influence on PCS, whereas assistive device use and phantom limb pain had a modifying influence on MCS. Our findings show that TAPES subscales have a modifying effect on the associations between several background (sociodemographic and amputation characteristics) and QoL (PCS and MCS). This indicates that adjustments to amputation and artificial limb are the key determinants of QoL in individuals following lower limb amputation.

  6. HIV lipodystrophy case definition using artificial neural network modelling

    DEFF Research Database (Denmark)

    Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew

    2003-01-01

    OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS...

  7. A simple, closed-form, mathematical model for gas exchange in microchannel artificial lungs.

    Science.gov (United States)

    Potkay, Joseph A

    2013-06-01

    Microfabrication techniques are attractive for constructing artificial lungs due to the ability to create features similar in size to those in the natural lung. However, a simple and intuitive mathematical model capable of accurately predicting the gas exchange performance of microchannel artificial lungs does not currently exist. Such a model is critical to understanding and optimizing these devices. Here, we describe a simple, closed-form mathematical model for gas exchange in microchannel artificial lungs and qualify it through application to experimental data from several research groups. We utilize lumped parameters and several assumptions to obtain a closed-form set of equations that describe gas exchange. This work is intended to augment computational models by providing a more intuitive, albeit potentially less accurate, understanding of the operation and trade-offs inherent in microchannel artificial lung devices.

  8. Life Insurance Actuarial Model with Returnable Premium

    Institute of Scientific and Technical Information of China (English)

    YanhuaiLang

    2004-01-01

    Insurance is the important aspect of finance. It has been fully developed in the western developed countries. With the fast development of market economy in our country,it is necessary to introduce the alien modern theories and techniques, and at the same time,adapt them to our concrete situation. The model of general life insurance product is to be established in this paper, including deferred life annuities, increasing whole life insurance and returnable premiums. Then through the regulation of various parameters, we can obtain various insurance products.

  9. Bed bugs, their blood sources and life history parameters: a comparison of artificial and natural feeding.

    Science.gov (United States)

    Aak, A; Rukke, B A

    2014-03-01

    A blood-feeding system that utilizes a small amount of whole heparinized human blood in parafilm bags is described in detail, and similarities and differences between artificially fed and naturally rodent-fed bed bugs (Hemiptera: Cimicidae) are discussed. Blood with high levels of heparin (10%) was unsuitable for artificial colony rearing, whereas bed bugs fed on 1% heparinized blood and those that naturally ingested rat blood completed their lifecycle with similar stage structures over time, with no significant differences in mortality. No differences in feeding efficiency or fertility were found in a direct comparison of bed bugs maintained under each of these two treatments, but analysis of the full lifecycle revealed that artificially fed bed bugs became significantly smaller and laid fewer eggs than rodent-fed bed bugs. The level of membrane stretching regulated the number of bed bugs that fed. When the membrane was stretched to twice its length and width, 96% of bed bugs successfully fed through the parafilm. Whole heparinized blood that was stored at 6 °C for ≥ 14 days failed to retain its nutritional value and the amount of blood consumed and number of consecutive moults were significantly reduced.

  10. PERCEPTION OF MEDICAL STUDENTS TOWARDS ARTIFICIAL BONES AND POP MODELS OF VISCERA

    OpenAIRE

    Sumit Tulshidas Patil; Nazia Quadir; Rashmi Deopujari; Vivekanand Gajbhiye

    2015-01-01

    Background: In learning of anatomy, bones and viscera are very important. Now days, artificial bones are replacing the original bones for study purpose due to unavailability. Original viscera are available for students only at dissection hours. So we have tried to find out perception of medical students towards artificial bones and POP models of viscera. Materials and Methods: We had prepared a questionnaire consisting of 20 questions, 10 related to bones and 10 related to the POP models o...

  11. A Community Membership Life Cycle Model

    CERN Document Server

    Sonnenbichler, Andreas C

    2010-01-01

    Web 2.0 is transforming the internet: Information consumers become information producers and consumers at the same time. In virtual places like Facebook, Youtube, discussion boards and weblogs diversificated topics, groups and issues are propagated and discussed. Today an internet user is a member of lots of communities at different virtual places. "Real life" group membership and group behavior has been analyzed in science intensively in the last decades. Most interestingly, to our knowledge, user roles and behavior have not been adapted to the modern internet. In this work, we give a short overview of traditional community roles. We adapt those models and apply them to virtual online communities. We suggest a community membership life cycle model describing roles a user can take during his membership in a community. Our model is systematic and generic; it can be adapted to concrete communities in the web. The knowledge of a community's life cycle allows influencing the group structure: Stage transitions can...

  12. Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN model for exchange rate prediction

    Directory of Open Access Journals (Sweden)

    Pradyot Ranjan Jena

    2015-10-01

    Full Text Available This paper presents a new adaptive forecasting model using a knowledge guided artificial neural network (KGANN structure for efficient prediction of exchange rate. The new structure has two parallel systems. The first system is a least mean square (LMS trained adaptive linear combiner, whereas the second system employs an adaptive FLANN model to supplement the knowledge base with an objective to improve its performance value. The output of a trained LMS model is added to an adaptive FLANN model to provide a more accurate exchange rate compared to that predicted by either a simple LMS or a FLANN model. This finding has been demonstrated through an exhausting computer simulation study and using real life data. Thus the proposed KGANN is an efficient forecasting model for exchange rate prediction.

  13. STUDY ON THERMODYNAMIC MODEL OF A COMPRESSOR WITH ARTIFICIAL NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    A new compressor thermodynamic model is set up. Artificial neural networks(ANN) which have self-adjusting functions are adopted to calculate volumetric efficiency and electrical efficiency of a compressor. The new compressor model composed of the theoretical model and ANN reaches more precise results than traditional ones. Furthermore, the new compressor model is of better flexibility in a large scale.

  14. An exemplar model of performance in the artificial grammar task: holographic representation.

    Science.gov (United States)

    Jamieson, Randall K; Hauri, Brian R

    2012-06-01

    We apply a multitrace model of memory to explain performance in the artificial grammar task. The model blends the convolution method for representation from Jones and Mewhort's BEAGLE model (Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37) of semantic memory with the multitrace storage and retrieval model from Hintzman's MINERVA 2 model (Hintzman, D. L. (1986). "Schema abstraction" in a multiple-trace memory model. Psychological Review, 93, 411-428) of episodic memory. We report an artificial grammar experiment, and we fit the model to those data at the level of individual items. We argue that performance in the artificial grammar task is best understood as a process of retrospective inference from memory.

  15. MODELLING OF THIN LAYER DRYING KINETICS OF COCOA BEANS DURING ARTIFICIAL AND NATURAL DRYING

    Directory of Open Access Journals (Sweden)

    C.L. HII

    2008-04-01

    Full Text Available Drying experiments were conducted using air-ventilated oven and sun dryer to simulate the artificial and natural drying processes of cocoa beans. The drying data were fitted with several published thin layer drying models. A new model was introduced which is a combination of the Page and two-term drying model. Selection of the best model was investigated by comparing the determination of coefficient (R2, reduced chi-square (2 and root mean square error (RMSE between the experimental and predicted values. The results showed that the new model was found best described the artificial and natural drying kinetics of cocoa under the conditions tested.

  16. Microbial Life in Soil - Linking Biophysical Models with Observations

    Science.gov (United States)

    Or, Dani; Tecon, Robin; Ebrahimi, Ali; Kleyer, Hannah; Ilie, Olga; Wang, Gang

    2015-04-01

    Microbial life in soil occurs within fragmented aquatic habitats formed in complex pore spaces where motility is restricted to short hydration windows (e.g., following rainfall). The limited range of self-dispersion and physical confinement promote spatial association among trophically interdepended microbial species. Competition and preferences for different nutrient resources and byproducts and their diffusion require high level of spatial organization to sustain the functioning of multispecies communities. We report mechanistic modeling studies of competing multispecies microbial communities grown on hydrated surfaces and within artificial soil aggregates (represented by 3-D pore network). Results show how trophic dependencies and cell-level interactions within patchy diffusion fields promote spatial self-organization of motile microbial cells. The spontaneously forming patterns of segregated, yet coexisting species were robust to spatial heterogeneities and to temporal perturbations (hydration dynamics), and respond primarily to the type of trophic dependencies. Such spatially self-organized consortia may reflect ecological templates that optimize substrate utilization and could form the basic architecture for more permanent surface-attached microbial colonies. Hydration dynamics affect structure and spatial arrangement of aerobic and anaerobic microbial communities and their biogeochemical functions. Experiments with well-characterized artificial soil microbial assemblies grown on porous surfaces provide access to community dynamics during wetting and drying cycles detected through genetic fingerprinting. Experiments for visual observations of spatial associations of tagged bacterial species with known trophic dependencies on model porous surfaces are underway. Biophysical modeling provide a means for predicting hydration-mediated critical separation distances for activation of spatial self-organization. The study provides new modeling and observational tools

  17. MODEL OF TRAINING OF SUCCESS IN LIFE

    Directory of Open Access Journals (Sweden)

    Екатерина Александровна Лежнева

    2014-04-01

    Full Text Available The article explains the importance of the development of motive to succeed in adolescence. It is determined the value of the motive to achieve success in the further development of the teenager: a motive to achieve effective internal forces mobilized for the implementation of successful operation ensures the active involvement of teenagers in social and interpersonal relationships. As the primary means of motive development success is considered training. The author provides a definition of "training for success in life," creates a model of training for success in life, and describes its units (targeted, informative, technological, productive, reveals the successful development of the technology life strategy used during the training (self-presentation, targets, incentives, subject-orientation. The author pays attention to the need for a future psychologist to develop teenagers’ motive to achieve success through the mastery of competence in constructing a model of training for success in life, and its implementation in the course of professional activities. The main means of training students of psychology to the use of training success in life identified the additional educational programs and psychological section.DOI: http://dx.doi.org/10.12731/2218-7405-2013-9-77

  18. NEW ANTIMICROBIAL SENSITIVITY TESTS OF BIOFILM OF STREPTOCOCCUS MUTANS IN ARTIFICIAL MOUTH MODEL

    Institute of Scientific and Technical Information of China (English)

    李鸣宇; 汪俊; 刘正; 朱彩莲

    2004-01-01

    Objective To develop a new antimicrobial sensitivity test model for oral products in vitro.Methods A biofilm artificial mouth model for antimicrobial sensitivity tests was established by modifying the LKI chromatography chamber. Using sodium fluoride and Tea polyphenol as antimicrobial agent and Streptococcus mutans as target, sensitivity tests were studied. Results The modeling biofilm assay resulted in a MIC of 1.28mg/ml for fluoride against S. mutans, which was 32 times the MIC for broth maco-dilution method. The differential resistance of bacteria bioflim to antimicrobial agent relative to planktonic cells was also demonstrated. Conclusion The biofilm artificial mouth model may be useful in oral products test.

  19. RBF-Type Artificial Neural Network Model Applied in Alloy Design of Steels

    Institute of Scientific and Technical Information of China (English)

    YOU Wei; LIU Ya-xiu; BAI Bing-zhe; FANG Hong-sheng

    2008-01-01

    RBF model, a new type of artificial neural network model was developed to design the content of carbon in low-alloy engineering steels. The errors of the ANN model are. MSE 0. 052 1, MSRE 17. 85%, and VOF 1. 932 9. The results obtained are satisfactory. The method is a powerful aid for designing new steels.

  20. Chain modeling for life cycle systems engineering

    Energy Technology Data Exchange (ETDEWEB)

    Rivera, J.J. [Sandia National Lab., Albuquerque, NM (United States); Shapiro, V. [Univ. of Wisconsin, Madison, WI (United States). Spatial Automation Lab.

    1997-12-01

    Throughout Sandia`s history, products have been represented by drawings. Solid modeling systems have recently replaced drawings as the preferred means for representing product geometry. These systems are used for product visualization, engineering analysis and manufacturing planning. Unfortunately, solid modeling technology is inadequate for life cycle systems engineering, which requires maintenance of technical history, efficient management of geometric and non-geometric data, and explicit representation of engineering and manufacturing characteristics. Such information is not part of the mathematical foundation of solid modeling. The current state-of-the-art in life cycle engineering is comprised of painstakingly created special purpose tools, which often are incompatible. New research on {open_quotes}chain modeling{close_quotes} provides a method of chaining the functionality of a part to the geometric representation. Chain modeling extends classical solid modeling to include physical, manufacturing, and procedural information required for life cycle engineering. In addition, chain modeling promises to provide the missing theoretical basis for Sandia`s parent/child product realization paradigm. In chain modeling, artifacts and systems are characterized in terms of their combinatorial properties: cell complexes, chains, and their operators. This approach is firmly rooted in algebraic topology and is a natural extension of current technology. The potential benefits of this approach include explicit hierarchical and combinatorial representation of physics, geometry, functionality, test, and legacy data in a common computational framework that supports a rational decision process and partial design automation. Chain modeling will have a significant impact on design preservation, system identification, parameterization, system reliability, and design simplification.

  1. Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

    Science.gov (United States)

    Güreşen, Erkam; Kayakutlu, Gülgün

    Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

  2. Wirklich wie im wirklichen Leben. Sarah Kember untersucht die (Re-Produktion des kleinen Unterschieds und seiner großen Folgen in Theorie und Praxis von Artificial Life Real as in real life. Sarah Kember examines the (reproduction of ‘the small difference and its big consequences’ in the theory and practice of artificial life.

    Directory of Open Access Journals (Sweden)

    Verena Kuni

    2004-03-01

    Full Text Available Wie greifen diese ‚virtuellen Realitäten‘ in unsere Körper- und Identitätskonzepte, unsere Subjekt- und Geschlechtervorstellungen ein? Sarah Kembers Buch verspricht, erhellende Schneisen durch das Dickicht der definitionsmächtigen Diskurse, Konzepte und Konstruktionen zu schlagen und neue Wege für feministische Interventionen in die Auseinandersetzungen um ‚Artificial Life‘ aufzuzeigen.ow a way through the labyrinth of discourse, concepts and constructions, and to open the door for feminist interpretations in debates about artificial life.

  3. Simulation of large-scale tropical tuna movements in relation with daily remote sensing data: the artificial life approach.

    Science.gov (United States)

    Dagorn, L; Petit, M; Stretta, J M

    1997-01-01

    Tunas are known to be able to travel long distances. The aim of this paper is to propose new ethological models which reproduce some tuna movements using the dynamics of their environment. We use sea surface temperature animations (from remote sensing data) to model the South West Indian Ocean, and French purse seiners data are used to estimate movements of fish. The objective of the models will be to find a northern movement from the Mozambique Channel to the Seychelles Islands at the appropriate time (May-July). The initial model uses our ecological knowledge of tunas, i.e. the search behavior for high concentrations of food commonly associated with thermal fronts. In some cases, this simple model creates some northern movements from the Mozambique Channel, but it cannot be used to reproduce large-scale movements between the Mozambique Channel and the Seychelles Islands. The next generation model is created where tuna behaviors are modeled by an artificial neural network, using a genetic algorithm to adjust the connection weights. The tuna school-network receives daily information from its local environment and chooses the best actions in order to be able to pass from the Mozambique Channel to the Seychelles Islands at the appropriate time. One neural network emerges and represents an adaptive behavior able to interpret daily sea surface temperatures to mimic large-scale tuna movements. This artificial behavior can be generalized to each possible departure position from the Mozambique Channel. This modelling represents a new tool to study large-scale movements of pelagic fish, and is a first step towards real-time management of fisheries.

  4. Using chaotic artificial neural networks to model memory in the brain

    Science.gov (United States)

    Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh

    2017-03-01

    In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.

  5. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

    Directory of Open Access Journals (Sweden)

    Onur Satir

    2016-09-01

    Full Text Available Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN method was used to map forest fire probability in Upper Seyhan Basin (USB in Turkey. Multi-layer perceptron (MLP approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43, tree cover (R = 0.93 and temperature (R = 0.42 were strongly correlated with forest fire probability in the USB region.

  6. A Parallelized Pumpless Artificial Placenta System Significantly Prolonged Survival Time in a Preterm Lamb Model.

    Science.gov (United States)

    Miura, Yuichiro; Matsuda, Tadashi; Usuda, Haruo; Watanabe, Shimpei; Kitanishi, Ryuta; Saito, Masatoshi; Hanita, Takushi; Kobayashi, Yoshiyasu

    2016-05-01

    An artificial placenta (AP) is an arterio-venous extracorporeal life support system that is connected to the fetal circulation via the umbilical vasculature. Previously, we published an article describing a pumpless AP system with a small priming volume. We subsequently developed a parallelized system, hypothesizing that the reduced circuit resistance conveyed by this modification would enable healthy fetal survival time to be prolonged. We conducted experiments using a premature lamb model to test this hypothesis. As a result, the fetal survival period was significantly prolonged (60.4 ± 3.8 vs. 18.2 ± 3.2 h, P < 0.01), and circuit resistance and minimal blood lactate levels were significantly lower in the parallel circuit group, compared with our previous single circuit group. Fetal physiological parameters remained stable until the conclusion of the experiments. In summary, parallelization of the AP system was associated with reduced circuit resistance and lactate levels and allowed preterm lamb fetuses to survive for a significantly longer period when compared with previous studies.

  7. Artificial intelligence in process control: Knowledge base for the shuttle ECS model

    Science.gov (United States)

    Stiffler, A. Kent

    1989-01-01

    The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

  8. Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development.

    Science.gov (United States)

    Thomas, Michael S C; Forrester, Neil A; Ronald, Angelica

    2016-01-01

    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene

  9. Applications of artificial neural networks for microbial water quality modeling

    Energy Technology Data Exchange (ETDEWEB)

    Brion, G.M.; Lingireddy, S. [Univ. of Kentucky, Dept. of Civil Engineering, Lexington, Kentucky (United States)]. E-mail: gbrion@engr.uky.edu

    2002-06-15

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  10. Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models

    Science.gov (United States)

    Moosavi, Vahid; Malekinezhad, Hossein; Shirmohammadi, Bagher

    2014-04-01

    This study was carried out to evaluate the wavelet-artificial intelligence hybrid models to produce fractional snow cover maps. At first, cloud cover was removed from MODIS data and cloud free images were produced. SVM-based binary classified ETM+ imagery were then used as reference maps in order to obtain train and test data for sub-pixel classification models. ANN and ANFIS-based modeling were performed using raw data (without wavelet-based preprocessing). In the next step, several mother wavelets and levels were used in order to decompose the original data to obtain wavelet coefficients. Then, the decomposed data were used for further modeling processes. ANN, ANFIS, wavelet-ANN and wavelet-ANFIS models were compared to evaluate the effect of wavelet transformation on the ability of artificial intelligence models. It was demonstrated that wavelet transformation as a preprocessing approach can significantly enhance the performance of ANN and ANFIS models. This study indicated an overall accuracy of 92.45% for wavelet-ANFIS model, 86.13% for wavelet-ANN, 72.23% for ANFIS model and 66.78% for ANN model. In fact, hybrid wavelet-artificial intelligence models can extract the characteristics of the original signals (i.e. model inputs) accurately through decomposing the non-stationary and complex signals into several stationary and simpler signals. The positive effect of fuzzification as well as wavelet transformation in the wavelet-ANFIS model was also confirmed.

  11. Vadose zone-attenuated artificial recharge for input to a ground water model.

    Science.gov (United States)

    Nichols, William E; Wurstner, Signe K; Eslinger, Paul W

    2007-01-01

    Accurate representation of artificial recharge is requisite to calibration of a ground water model of an unconfined aquifer for a semiarid or arid site with a vadose zone that imparts significant attenuation of liquid transmission and substantial anthropogenic liquid discharges. Under such circumstances, artificial recharge occurs in response to liquid disposal to the vadose zone in areas that are small relative to the ground water model domain. Natural recharge, in contrast, is spatially variable and occurs over the entire upper boundary of a typical unconfined ground water model. An improved technique for partitioning artificial recharge from simulated total recharge for inclusion in a ground water model is presented. The improved technique is applied using data from the semiarid Hanford Site. From 1944 until the late 1980s, when Hanford's mission was the production of nuclear materials, the quantities of liquid discharged from production facilities to the ground vastly exceeded natural recharge. Nearly all hydraulic head data available for use in calibrating a ground water model at this site were collected during this period or later, when the aquifer was under the diminishing influence of the massive water disposals. The vadose zone is typically 80 to 90 m thick at the Central Plateau where most production facilities were located at this semiarid site, and its attenuation of liquid transmission to the aquifer can be significant. The new technique is shown to improve the representation of artificial recharge and thereby contribute to improvement in the calibration of a site-wide ground water model.

  12. Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

    Science.gov (United States)

    Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun

    2013-02-15

    This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

  13. Forecasting TRY/USD Exchange Rate with Various Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cagatay Bal

    2017-02-01

    Full Text Available Exchange rate forecasting is one of the most common subjects among the forecasting problem field. Researchers and academicians from many different disciplines proposed various approaches for better exchange rate forecasting. In recent years, for solving the stated forecasting problem artificial neural networks have become successful tool to obtain solutions. Many different artificial neural networks have been used, developed and still developing for even better and trustable forecasts. In this study, TRY/USD exchange rate forecasting is modeled with different learning algorithms, activations functions and performance measures. Various Artificial Neural Network (ANN models for better forecasting were investigated, compared and the obtained forecasting results interpreted respectively. The results of the application show that Variable Learning Rate Backpropagation learning algorithm with tan-sigmoid activation function has the best performance for TRY/USD exchange rate forecasting.

  14. Actuarial models of life insurance with stochastic interest rate

    Science.gov (United States)

    Wei, Xiang; Hu, Ping

    2009-07-01

    On the basis of general actuarial model of life insurance, this article has carried on research to continuous life insurance actuarial models under the stochastic interest rate separately. And it provide net single premium for life insurance and life annuity due over a period based on that de Moivre law of mortality and Makeham's law of mortality separately.

  15. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel [Pamukkale University, Mechanical Engineering Department, Denizli (Turkey); Ceylan, Halim [Pamukkale University, Civil Engineering Department, Denizli (Turkey)

    2009-11-15

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  16. Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    R. S.M.N. Malar

    2009-01-01

    Full Text Available Continuous Stirred Tank Reactor (CSTR is one of the common reactors in chemical plant. Problem statement: Developing a model incorporating the nonlinear dynamics of the system warrants lot of computation. An efficient control of the product concentration can be achieved only through accurate model. Approach: In this study, attempts were made to alleviate the above mentioned problem using “Artificial Intelligence” (AI techniques. One of the AI techniques namely Artificial Neural Networks (ANN was used to model the CSTR incorporating its non-linear characteristics. Two nonlinear models based control strategies namely internal model control and direct inverse control were designed using the neural networks and applied to the control of isothermal CSTR. Results: The simulation results for the above control schemes with set point tracking were presented. Conclusion: Results indicated that neural networks can learn accurate models and give good non-linear control when model equations are not known.

  17. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  18. Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

    NARCIS (Netherlands)

    Vos, de N.J.; Rientjes, T.H.M.

    2005-01-01

    The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of

  19. PERCEPTION OF MEDICAL STUDENTS TOWARDS ARTIFICIAL BONES AND POP MODELS OF VISCERA

    Directory of Open Access Journals (Sweden)

    Sumit Tulshidas Patil

    2015-03-01

    Full Text Available Background: In learning of anatomy, bones and viscera are very important. Now days, artificial bones are replacing the original bones for study purpose due to unavailability. Original viscera are available for students only at dissection hours. So we have tried to find out perception of medical students towards artificial bones and POP models of viscera. Materials and Methods: We had prepared a questionnaire consisting of 20 questions, 10 related to bones and 10 related to the POP models of viscera and asked 150 students of 1st year MBBS to answer it. Results: All the 150 students agreed that bones are necessary for study but only 36 students have bone set (14 original & 22 artificial. Maximum students get the bones only when made available from department. While 107 (71% students said they would prefer original specimen of viscera, over POP models, for studying; but still 126 (84% wanted to keep the POP models of viscera while studying its relations from text book. Conclusions: Good quality artificial bones should be promoted for students, if original bones are not available. It will be better than having nothing. POP models of viscera cannot replace original viscera but due to its handy quality will be helpful for understanding.

  20. A multiscale approach for modeling actuation response of polymeric artificial muscles.

    Science.gov (United States)

    Sharafi, Soodabeh; Li, Guoqiang

    2015-05-21

    Artificial muscles are emerging materials in the field of smart materials with applications in aerospace, robotic, and biomedical industries. Despite extensive experimental investigations in this field, there is a need for numerical modeling techniques that facilitate cutting edge research and development. This work aims at studying an artificial muscle made of twisted Nylon 6.6 fibers that are highly cold-drawn. A computationally efficient phenomenological thermo-mechanical constitutive model is developed in which several physical properties of the artificial muscles are incorporated to minimize the trial-and-error numerical curve fitting processes. Two types of molecular chains are considered at the micro-scale level that control training and actuation processes viz. (a) helically oriented chains which are structural switches that store a twisted shape in their low temperature phase and restore their random configuration during the thermal actuation process, and (b) entropic chains which are highly drawn chains that could actuate as soon as the muscle heats up, and saturates when coil contact temperature is reached. The thermal actuation response of the muscle over working temperatures has been elaborated in the Modeling section. The performance of the model is validated by available experiments in the literature. The model may provide a design platform for future artificial muscle developments.

  1. A self-taught artificial agent for multi-physics computational model personalization.

    Science.gov (United States)

    Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

    2016-12-01

    Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.

  2. Artificial Intelligence for Constructing Accurate, Low-Cost Models and

    Science.gov (United States)

    2005-01-01

    0.5 1 1.5 2 2.5 -12 -7 -2 3 8 13 AOA C l Equation Model Human Predicition Machine Learning Test Data Figure C-14 NACA 4421 Model Comparison...Natrajan, Anand, and Srinivasan, Sudhir, (1997) “Consistency Maintenance in Multiresolution Simulation”, ACM Transactions on Modeling and

  3. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  4. Models of Non-Life Insurance Mathematics

    Directory of Open Access Journals (Sweden)

    Constanta Nicoleta BODEA

    2008-01-01

    Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter q. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. Mathematics Subject Classification: 62P05.

  5. Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

    OpenAIRE

    Abdelkrim Moussaoui; Yacine Selaimia; Hadj A. Abbassi

    2006-01-01

    The authors discuss the combination of an Artificial Neural Network (ANN) with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capa...

  6. Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian - River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple-variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

  7. Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

    OpenAIRE

    Khalil, Khaled M.; Abdel-Aziz, M.; Nazmy, Taymour T.; Salem, Abdel-Badeeh M.

    2010-01-01

    Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, a...

  8. Artificial intelligence and finite element modelling for monitoring flood defence structures

    NARCIS (Netherlands)

    Pyayt, A.L.; Mokhov, I.I.; Kozionov, A.; Kusherbaeva, V.; Melnikova, N.B.; Krzhizhanovskaya, V.V.; Meijer, R.J.

    2011-01-01

    We present a hybrid approach to monitoring the stability of flood defence structures equipped with sensors. This approach combines the finite element modelling with the artificial intelligence for real-time signal processing and anomaly detection. This combined method has been developed for the Urba

  9. Multiobjective training of artificial neural networks for rainfall-runoff modeling

    NARCIS (Netherlands)

    De Vos, N.J.; Rientjes, T.H.M.

    2008-01-01

    This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for thi

  10. Modeling of human colonic blood flow for a novel artificial anal sphincter system

    Institute of Scientific and Technical Information of China (English)

    Peng ZAN; Guo-zheng YAN; Hua LIU

    2008-01-01

    A novel artificial anal sphincter system has been developed to simulate the normal physiology of the human anorectum. With the goal of engineering a safe and reliable device, the model of human colonic blood flow has been built and the relationship between the colonic blood flow rate and the operating occlusion pressure of the anorectum is achieved. The tissue ischemia is analyzed based on constitutive relations for human anorectum. The results suggest that at the planned operating occlusion pressure of less than 4 kPa the artificial anal sphincter should not risk the vaseularity of the human colon.

  11. The influence of artificial-thunderstorm cell polarity on discharge initiation by model hydrometeor arrays

    Science.gov (United States)

    Temnikov, A. G.; Chernenskii, L. L.; Orlov, A. V.; Lysov, N. Yu.; Belova, O. S.; Kalugina, I. E.; Gerastenok, T. K.; Zhuravkova, D. S.

    2017-02-01

    The initiation of discharge by model hydrometeors between an artificial-thunderstorm cell (aerosol cloud) of negative or positive polarity and ground has been experimentally studied. It is established for the first time that the conditions of cloud-ground spark discharge initiation by hydrometeors, as well as the characteristics of discharge significantly depend on the polarity of charged cloud. The effect of hydrometeor arrays can be manifested by the cloud-ground lightning initiated in a thundercloud and used for developing scientific principles of artificial lightning discharge.

  12. Artificial intelligence techniques for modeling database user behavior

    Science.gov (United States)

    Tanner, Steve; Graves, Sara J.

    1990-01-01

    The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model.

  13. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    Science.gov (United States)

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-03

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

  14. 基于LifeMOD的个性化人工膝关节设计中的生物力学分析%A biomechanical analysis based on LifeMOD for individualized artificial knee joint design

    Institute of Scientific and Technical Information of China (English)

    马妮; 肖丽英

    2011-01-01

    Objective: To investigate the biomechanical performanre of individualized design artificial knee joint after surgical operation.Method: Based on the digital biomechanical simulation software LifeMOD, human biomechanical model for simulating the motions of healthy human and total knee replacement(TKR) patient in normal gait was designed and generated, where the motion and internal forces could be explored.Result: The outputs included joint angles, joint contact forces and ligament / muscle forces. Through comparative analysis, the individualized artificial knee joint could replicate the normal knee ideally in gait characteristics.Conclusion: This study provided an effective access for further research of dynamic knee biomechanics, as well as a reference to artificial knee joint design, performance evaluation and clinical application.%目的:探讨个性化设计的人工膝关节植入人体后的生物力学响应.方法:基于生物力学数字仿真软件LifeMOD,建立健康人体及人工膝关节置换术后人体步行的运动/动力学模型,对人体在步行过程中的运动和内部受力情况进行分析.结果:仿真得到整个步态周期中膝关节的运动的角位移、关节接触力以及韧带和肌肉的载荷曲线,通过对比分析,可知本文所设计的人工膝关节在步态特性方面较理想地复制了正常的膝关节.结论:本研究对探索人工膝关节的动态生物力学特性和关节似体的设计、性能评价以及临床应用等有重要意义.

  15. DUAL RANDOM MODEL OF INCREASING LIFE INSURANCE FOR MULTIPLE-LIFE STATUS

    Institute of Scientific and Technical Information of China (English)

    ZhangYi; HeWenjiong

    2002-01-01

    In this paper the dual random model of increasing life insurance for multiple-life status is discussed. The rnth moment of the present value of benefits are calculated and the respective expressions of the moments under joint life status or last- survivor status are presented.Fur-thermore,the limiting distribution of average cost of a portfolio of increasing life insurance for multiple-life status is studied.

  16. Application of artificial intelligence models in water quality forecasting.

    Science.gov (United States)

    Yeon, I S; Kim, J H; Jun, K W

    2008-06-01

    The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.

  17. A top-down multi-scale modeling for actuation response of polymeric artificial muscles

    Science.gov (United States)

    Yang, Qianxi; Li, Guoqiang

    2016-07-01

    A class of innovative artificial muscles made of high-strength polymeric fibers such as fishing lines or sewing threads have been discovered recently. These muscles are fabricated by a simple "twist-insertion" procedure, which have attracted increasing attention due to their low cost and readily availability, giant tensile stroke, record energy density, and easy controllability. In the present paper, we established a multi-scale modeling framework for the thermomechanical actuation responses by a top-down strategy, spanning from macro-scale helical spring analysis down to molecular level chain interaction study. Comparison between modeling results and experimental results exhibited excellent agreement. The effect of the micro-, meso- and macro-scale parameters on the actuation responses of the artificial muscle was further discussed through a parametric study per the validated model. This work helps understand the physical origin behind the remarkable tensile actuation behavior of the twisted-then-coiled polymeric artificial muscles and also provides inspirations for optimal design of advanced artificial muscles made by twist-insertion procedure.

  18. Methods of artificial enlargement of the training set for statistical shape models.

    Science.gov (United States)

    Koikkalainen, Juha; Tölli, Tuomas; Lauerma, Kirsi; Antila, Kari; Mattila, Elina; Lilja, Mikko; Lötjönen, Jyrki

    2008-11-01

    Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.

  19. Proton exchange membrane fuel cells modeling based on artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Yudong Tian; Xinjian Zhu; Guangyi Cao

    2005-01-01

    To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

  20. Developing energy forecasting model using hybrid artificial intelligence method

    Institute of Scientific and Technical Information of China (English)

    Shahram Mollaiy-Berneti

    2015-01-01

    An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

  1. Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.

    Science.gov (United States)

    Honarmand, M; Mousavi, S M

    2014-04-01

    Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.

  2. Comparative analysis of regression and artificial neural network models for wind speed prediction

    Science.gov (United States)

    Bilgili, Mehmet; Sahin, Besir

    2010-11-01

    In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.

  3. Modeling, Optimization and simulation of Rotary Furnace using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Dr. R, K. Jain,

    2011-04-01

    Full Text Available This paper deals with modeling and simulation of LDO fired rotary furnace using feed forward modeling method of artificial neural network (ANN.The authors conducted experimental investigations onfuel consumption in a rotary furnace in an industry. It was observed that 6% oxygen enrichment of the air preheated up to 4600C simultaneously with reduction of air volume to 75% of its theoretical requirement lowered the specific fuel consumption to 0.260 lit/kg..The compact heat exchanger with 533 fins was used for preheating the air. Accordingly the emission level was also considerably reduced. The feed forward modeling method of artificial neural network contained in MAT LAB software was used for modeling andoptimization of specific fuel consumption. The percentage variation, between actual experimental data and same data when simulated is +1.730%, and other feasible simulated datas is +6.192%,-3.038%,-5.692%,and+0.115%which is fairly acceptable.

  4. Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks

    NARCIS (Netherlands)

    B.L.K. Vroomen (Björn); Ph.H.B.F. Franses (Philip Hans); J.E.M. van Nierop

    2001-01-01

    textabstractThe concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model f

  5. Modelling of Surface Ships using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Jensen, F. M.; Thoft-Christensen, Palle

    are costly and time consuming - at least in the context of sampling sufficient data for statistical analyses purposes. Furthermore, a modelling of the process of navigating could be used in a vessel autopilot to guide a vessel on a fixed heading (course - keeping) or a new heading (course -changing)....

  6. Mathematical modeling of the growth and development of the mussel Mytilus galloprovincialis on artificial substrates

    Science.gov (United States)

    Vasechkina, E. F.; Kazankova, I. I.

    2014-11-01

    A mathematical model simulating the growth and development of the mussel Mytilus galloprovincialis Lam. on artificial substrates has been constructed. The model is based on experimental data and contains mathematical descriptions of the filtration, respiration, excretion, spawning, and growth of an individual during its ontogenesis from the moment it attaches to a solid substrate to the attainment of a marketable size. The test computations have been compared to the available observation data for mussel farms.

  7. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Zhehuang Huang

    2015-01-01

    Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  8. Proximal caries detection using digital subtraction radiography in the artificial caries activity model

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jong Hoon; Lee, Gi Ja; Choi, Sam Jin; Park, Young Ho; Kim, Kyung Soo; Jin, Hyun Seok; Hong, Kyung Won; Oh, Berm Seok; Park, Hun Kuk [Department of Biomedical Engineering, School of Medicine, Kyung Hee University, Seoul (Korea, Republic of); Choi, Yong Suk; Hwang, Eui Hwan [Department of Oral and Maxillofacial Radiology, Institute of Oral Biology, School of Dentistry, Kyung Hee University, Seoul (Korea, Republic of)

    2009-03-15

    The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Digital radiographs of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size no.2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. In artificial caries activity model, the range of lesional depth was 572-1,374 {mu}m and the range of lesional area was 36.95-138.52 mm{sup 2}. The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

  9. Log-linear model based behavior selection method for artificial fish swarm algorithm.

    Science.gov (United States)

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

    Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  10. Modeling and optoelectronic realization of an artificial cortex

    Science.gov (United States)

    Pashaie, Ramin

    Cortex, the outermost layer of the cerebrum, is recognized as the most developed part of the brain. It is believed that the higher-level functionality of the brain, the operations such as perception, cognition, and learning of both static and dynamic sensory information, originates from the dynamics of the massively interconnected gray cells of cortex. Because of the compact three-dimensional architecture of this biological computational paradigm, realization of bio-inspired machines that imitate such functionalities, including all the cellular details, is prohibitively difficult even if we consider the available nano-fabrication technologies. Based on this logical deduction, instead of considering each single neuron, an intriguing conjecture is to build aggregate level models that mimic the behavior of a population of neurons with collective emergent properties. In our approach, which is presented in this dissertation, cortex is assumed to be a composition of a sequence of discernable interconnected cortical patches. Each concerned patch is a network of asymmetrically coupled complex processing elements whose dynamics contain not only fixed-point and periodic attractors but also bifurcation and chaos. Dynamics of the complex processing elements, in this dissertation, is mathematically modeled by a slight modification of the time evolution of netlets adapted from computational neuroscience. Regarding this modification, the dynamics of a netlet is approximated by that of a quadratic return map. Studying the previous experimental observations demonstrates that a smart way of coupling such processing units is to couple them through their bifurcation parameters. Putting all pieces of this puzzle together, we model each cortical patch by a network of parametrically coupled quadratic return maps. Our simulations prove the ability of this network to emulate many salient features of cortical information processing, such as clustering, classification, generation of sparse

  11. The prediction of brick wall strengths with artificial neural networks model

    Science.gov (United States)

    Demir, Ali; Kumanlioglu, Ahmet Ali

    2017-01-01

    The aim of this study is to predict with Artificial Neural Networks (ANN) shear strength of brick masonry walls. Shear strength of the walls is determined with diagonal shear tests. It is very difficult to determine strengths of brick masonry walls with experimental procedures. Therefore, an Artificial Neural Networks model is developed with data obtained by investigating many papers from literature and experiments carried out by the authors. Finally, a good degree of coherency is obtained between the experimental and predicted data. The model that is developed makes it possible to easily predict shear strength of the masonry walls. Additionally, this model can be continuously trained with new data and its applicability range can easily be expanded.

  12. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Pezeshki

    2016-02-01

    Full Text Available Background Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  13. Generalized in vitro-in vivo relationship (IVIVR model based on artificial neural networks

    Directory of Open Access Journals (Sweden)

    Mendyk A

    2013-03-01

    Full Text Available Aleksander Mendyk,1 Pawel Tuszynski,1 Sebastian Polak,2 Renata Jachowicz1 1Department of Pharmaceutical Technology and Biopharmaceutics, 2Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland Background: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC/IVIVR. Methods: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. Keywords: artificial neural networks

  14. Modeling Reality - How Computers Mirror Life

    Science.gov (United States)

    Bialynicki-Birula, Iwo; Bialynicka-Birula, Iwona

    2005-01-01

    The bookModeling Reality covers a wide range of fascinating subjects, accessible to anyone who wants to learn about the use of computer modeling to solve a diverse range of problems, but who does not possess a specialized training in mathematics or computer science. The material presented is pitched at the level of high-school graduates, even though it covers some advanced topics (cellular automata, Shannon's measure of information, deterministic chaos, fractals, game theory, neural networks, genetic algorithms, and Turing machines). These advanced topics are explained in terms of well known simple concepts: Cellular automata - Game of Life, Shannon's formula - Game of twenty questions, Game theory - Television quiz, etc. The book is unique in explaining in a straightforward, yet complete, fashion many important ideas, related to various models of reality and their applications. Twenty-five programs, written especially for this book, are provided on an accompanying CD. They greatly enhance its pedagogical value and make learning of even the more complex topics an enjoyable pleasure.

  15. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Science.gov (United States)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  16. Artificial intelligence and exponential technologies business models evolution and new investment opportunities

    CERN Document Server

    Corea, Francesco

    2017-01-01

    Artificial Intelligence is a huge breakthrough technology that is changing our world. It requires some degrees of technical skills to be developed and understood, so in this book we are going to first of all define AI and categorize it with a non-technical language. We will explain how we reached this phase and what historically happened to artificial intelligence in the last century. Recent advancements in machine learning, neuroscience, and artificial intelligence technology will be addressed, and new business models introduced for and by artificial intelligence research will be analyzed. Finally, we will describe the investment landscape, through the quite comprehensive study of almost 14,000 AI companies and we will discuss important features and characteristics of both AI investors as well as investments. This is the “Internet of Thinks” era. AI is revolutionizing the world we live in. It is augmenting the human experiences, and it targets to amplify human intelligence in a future not so distant from...

  17. A BOD-DO coupling model for water quality simulation by artificial neural network

    Institute of Scientific and Technical Information of China (English)

    郭劲松; LONG; Tengrui; 等

    2002-01-01

    A one-dimensional BOD-DO coupling model for water quality simulation is presented,which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network.The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model.The result shows that such model is feasible for water quality simulation and is more accurate than traditional models.

  18. Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

    CERN Document Server

    Khalil, Khaled M; Nazmy, Taymour T; Salem, Abdel-Badeeh M

    2010-01-01

    Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, and intensity-based learning) for crisis response operations. In addition, the proposed response model is applied on the spread of pandemic influenza in Egypt as a case study.

  19. Artificial neural network models for biomass gasification in fluidized bed gasifiers

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles

    2013-01-01

    bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published......Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine...

  20. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  1. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

    Directory of Open Access Journals (Sweden)

    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

  2. Pilot biomechanical design of biomaterials for artificial nucleus prosthesis using 3D finite-element modeling

    Institute of Scientific and Technical Information of China (English)

    Qijin Huang; Guoquan Liu; Yong Li; Jin Gao; Zhengqiu Gu; Yuanzheng Ma; Haibin Xue

    2004-01-01

    Pilot biomechanical design of biomaterials for artificial nucleus prosthesis was carried out based on the 3D finite-element method. Two 3D models of lumbar intervertebral disc respectively with a real human nucleus and with the nucleus removed were developed and validated using published experimental and clinical data. Then the models with a stainless steel nucleus prosthesis implanted and with polymer nucleus prostheses of various properties implanted were used for the 3D finite-element biomechanical analysis. All the above simulation and analysis were carried out for the L4/L5 disc under a human worst-daily compression load of 2000 N. The results show that the polymer materials with Young's modulus of elasticity E = 0.1-100 MPa and Poisson's ratio v=0.35-0.5 are suitable to produce artificial nucleus prosthesis in view of biomechanical consideration.

  3. Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

    Science.gov (United States)

    Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

    Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

  4. Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network.

    Science.gov (United States)

    Cinar, Ozer; Hasar, Halil; Kinaci, Cumali

    2006-05-17

    A submerged membrane bioreactor receiving cheese whey was modeled by artificial neural network and its performance over a period of 100 days at different solids retention times was evaluated with this robust tool. A cascade-forward network was used to model the membrane bioreactor and normalization was used as a preprocessing method. The network was fed with two subsets of operational data, with two-thirds being used for training and one-third for testing the performance of the artificial neural network. The training procedure for effluent chemical oxygen demand (COD), ammonia, nitrate and total phosphate concentrations was very successful and a perfect match was obtained between the measured and the calculated concentrations. The results of the confirmation (or testing) procedure for effluent ammonia and nitrate concentrations were very successful; however, the results of the confirmation procedure for effluent COD and total phosphate concentrations were only satisfactory.

  5. Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Jiang XU; Xishan XIE; Zhong XU

    2003-01-01

    A model is developed for predicting the correlation between processing parameters and the technical target of double glowby applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiecevoltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three importanttechnical targets, namely the gross element content, the thickness of surface alloying layer and the absorption rate (the ratioof the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surfacealloying. The processing parameters and technical target are then used as a training set for an artificial neural network. Themodel is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and thecalculated results are in good agreement with the experimental ones.

  6. Modelling home equity conversion loans with life insurance models

    Directory of Open Access Journals (Sweden)

    Baškot Bojan

    2013-01-01

    Full Text Available Home equity represents a reserve that can be used for providing additional money for its owners during their retirement. Life insurance models can be successfully applied to model home equity conversion loans. The home equity conversion loan is a financial product that provides a certain flexibility by using home equity as a resource for a quality life during retirement. Home equity conversion loans do not have a predetermined maturity date, as do conventional loans. But, like every loan, it must be repaid. One potential advantage of using a home equity conversion loan during tough financial times instead of some types of need-based assistance is that eligibility is straightforward. Home equity conversion loans can be useful tools in the process of pension system reform.

  7. Droplets: Unconventional Protocell Model with Life-Like Dynamics and Room to Grow

    Directory of Open Access Journals (Sweden)

    Martin M Hanczyc

    2014-12-01

    Full Text Available Over the past few decades, several protocell models have been developed that mimic certain essential characteristics of living cells. These protocells tend to be highly reductionist simplifications of living cells with prominent bilayer membrane boundaries, encapsulated metabolisms and/or encapsulated biologically-derived polymers as potential sources of information coding. In parallel with this conventional work, a novel protocell model based on droplets is also being developed. Such water-in-oil and oil-in-water droplet systems can possess chemical and biochemical transformations and biomolecule production, self-movement, self-division, individuality, group dynamics, and perhaps the fundamentals of intelligent systems and evolution. Given the diverse functionality possible with droplets as mimics of living cells, this system has the potential to be the first true embodiment of artificial life that is an orthologous departure from the one familiar type of biological life. This paper will synthesize the recent activity to develop droplets as protocell models.

  8. Prediction Model of Antibacterial Activities for Inorganic Antibacterial Agents Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    刘雪峰; 张利; 涂铭旌

    2004-01-01

    Quantitatively evaluation of antibacterial activities of inorganic antibacterial agents is an urgent problem to be solved. Using experimental data by an orthogonal design, a prediction model of the relation between conditions of preparing inorganic antibacterial agents and their antibacterial activities has been developed. This is accomplished by introducing BP artificial neural networks in the study of inorganic antibacterial agents..It provides a theoretical support for the development and research on inorganic antibacterial agents.

  9. Analysis of multicriteria models application for selection of an optimal artificial lift method in oil production

    OpenAIRE

    Crnogorac, Miroslav P.; Danilović, Dušan Š.; Karović-Maričić, Vesna D.; Leković, Branko A.

    2016-01-01

    In the world today for the exploitation of oil reservoirs by artificial lift methods are applied different types of deep pumps (piston, centrifugal, screw, hydraulic), water jet pumps and gas lift (continuous, intermittent and plunger). Maximum values of oil production achieved by these exploitation methods are significantly different. In order to select the optimal exploitation method of oil well, the multicriteria analysis models are used. In this paper is presented an analysis of the multi...

  10. Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

    Science.gov (United States)

    Bakanovskaya, L. N.

    2016-08-01

    The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

  11. Modeling Fall Run Chinook Salmon Populations in the San Joaquin River Basin Using an Artificial Neural Network

    Science.gov (United States)

    Keyantash, J.; Quinn, N. W.; Hidalgo, H. G.; Dracup, J. A.

    2002-12-01

    The number of chinook salmon returning to spawn during the fall run (September-November) were separately modeled for three San Joaquin River tributaries-the Stanislaus, Tuolumne, and Merced Rivers-to determine the sensitivity of salmon populations to hydrologic alterations associated with potential climate change. The modeling was accomplished using a feed-forward artificial neural network (ANN) with error backpropagation. Inputs to the ANN included modeled monthly river temperature and streamflow data for each tributary, and were lagged multiple years to include the effects of antecedent environmental conditions upon populations of salmon throughout their life histories. Temperature and streamflow conditions at downstream locations in each tributary were computed using the California Dept. of Water Resources' DSM-2 model. Inputs to the DSM-2 model originated from regional climate modeling under a CO2 doubling scenario. Annual population data for adult chinook salmon (1951-present) were provided by the California Dept. of Fish and Game, and were used for supervised training of the ANN. It was determined that Stanislaus, Tuolumne and Merced River chinook runs could be impacted by alterations to the hydroclimatology of the San Joaquin basin.

  12. An artificial pancreas provided a novel model of blood glucose level variability in beagles.

    Science.gov (United States)

    Munekage, Masaya; Yatabe, Tomoaki; Kitagawa, Hiroyuki; Takezaki, Yuka; Tamura, Takahiko; Namikawa, Tsutomu; Hanazaki, Kazuhiro

    2015-12-01

    Although the effects on prognosis of blood glucose level variability have gained increasing attention, it is unclear whether blood glucose level variability itself or the manifestation of pathological conditions that worsen prognosis. Then, previous reports have not been published on variability models of perioperative blood glucose levels. The aim of this study is to establish a novel variability model of blood glucose concentration using an artificial pancreas. We maintained six healthy, male beagles. After anesthesia induction, a 20-G venous catheter was inserted in the right femoral vein and an artificial pancreas (STG-22, Nikkiso Co. Ltd., Tokyo, Japan) was connected for continuous blood glucose monitoring and glucose management. After achieving muscle relaxation, total pancreatectomy was performed. After 1 h of stabilization, automatic blood glucose control was initiated using the artificial pancreas. Blood glucose level varied for 8 h, alternating between the target blood glucose values of 170 and 70 mg/dL. Eight hours later, the experiment was concluded. Total pancreatectomy was performed for 62 ± 13 min. Blood glucose swings were achieved 9.8 ± 2.3 times. The average blood glucose level was 128.1 ± 5.1 mg/dL with an SD of 44.6 ± 3.9 mg/dL. The potassium levels after stabilization and at the end of the experiment were 3.5 ± 0.3 and 3.1 ± 0.5 mmol/L, respectively. In conclusion, the results of the present study demonstrated that an artificial pancreas contributed to the establishment of a novel variability model of blood glucose levels in beagles.

  13. Examining a Model of Life Satisfaction among Unemployed Adults

    Science.gov (United States)

    Duffy, Ryan D.; Bott, Elizabeth M.; Allan, Blake A.; Torrey, Carrie L.

    2013-01-01

    The present study examined a model of life satisfaction among a diverse sample of 184 adults who had been unemployed for an average of 10.60 months. Using the Lent (2004) model of life satisfaction as a framework, a model was tested with 5 hypothesized predictor variables: optimism, job search self-efficacy, job search support, job search…

  14. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks.

    Science.gov (United States)

    Pirrung, Silvia M; van der Wielen, Luuk A M; van Beckhoven, Ruud F W C; van de Sandt, Emile J A X; Eppink, Michel H M; Ottens, Marcel

    2017-01-05

    Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 2017.

  15. Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

    Science.gov (United States)

    Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

    2016-10-01

    An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

  16. MODEL IMPROVEMENT AND EXPERI-MENT VALIDATION OF PNEUMATIC ARTIFICIAL MUSCLES

    Institute of Scientific and Technical Information of China (English)

    Zhou Aiguo; Shi Guanglin; Zhong Tingxiu

    2004-01-01

    According to the deficiency of the present model of pneumatic artificial muscles (PAM), a serial model is built up based on the PAM's essential working principle with the elastic theory, it is validated by the quasi-static and dynamic experiment results, which are gained from two experiment systems.The experiment results and the simulation results illustrate that the serial model has made a great success compared with Chou's model, which can describe the force characteristics of PAM more precisely.A compensation item considering the braid's elasticity and the coulomb damp is attached to the serial model based on the analysis of the experiment results.The dynamic experiment proves that the viscous damp of the PAM could be ignored in order to simplify the model of PAM.Finally, an improved serial model of PAM is obtained.

  17. Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

    Science.gov (United States)

    Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

    2014-06-01

    This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

  18. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    is by active truncated models. In these models only the very top part of the system is represented by a physical model whereas the behavior of the part below the truncation is calculated by numerical models and accounted for in the physical model by active actuators applying relevant forces to the physical...... model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run...... simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...

  19. Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method

    CERN Document Server

    Quiza, Ramón; Davim, J Paulo

    2012-01-01

    Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

  20. Artificial neural network model of constitutive relations for shock-prestrained copper

    Institute of Scientific and Technical Information of China (English)

    杨扬; 朱远志; 李正华; 张新明; 杨立斌; 陈志永

    2001-01-01

    Data from the deformation on Split-Hopkinson Bar were used for constructing an artificial neural network model. When putting the thermodynamic parameters of the metals into the trained network model, the corresponding yielding stress can be predicted. The results show that the systematic error is small when the objective function is 0.5, the number of the nodes in the hidden layer is 6 and the learning rate is about 0.1, and the accuracy of the rate-error is less than 3%.

  1. Modeling of steam distillation mechanism during steam injection process using artificial intelligence.

    Science.gov (United States)

    Daryasafar, Amin; Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

  2. Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

    Science.gov (United States)

    Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods. PMID:24883365

  3. Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

    Directory of Open Access Journals (Sweden)

    Abdelkrim Moussaoui

    2006-01-01

    Full Text Available The authors discuss the combination of an Artificial Neural Network (ANN with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.

  4. Artificial Neural Network Modeling of Microstructure During C-Mn and HSLA Plate Rolling

    Institute of Scientific and Technical Information of China (English)

    TAN Wen; LIU Zhen-yu; WU Di; WANG Guo-dong

    2009-01-01

    An artificial neural network (ANN) model for predicting transformed mierostrueture in conventional roll-ing process and thermomechanieal controlled process (TMCP) is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstrueture evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted re-sults show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, mierostructure and properties in TMCP.

  5. Improving peak flow estimates in artificial neural network river flow models

    Science.gov (United States)

    Sudheer, K. P.; Nayak, P. C.; Ramasastri, K. S.

    2003-02-01

    In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation.

  6. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  7. Towards artificial tissue models: past, present, and future of 3D bioprinting.

    Science.gov (United States)

    Arslan-Yildiz, Ahu; El Assal, Rami; Chen, Pu; Guven, Sinan; Inci, Fatih; Demirci, Utkan

    2016-03-01

    Regenerative medicine and tissue engineering have seen unprecedented growth in the past decade, driving the field of artificial tissue models towards a revolution in future medicine. Major progress has been achieved through the development of innovative biomanufacturing strategies to pattern and assemble cells and extracellular matrix (ECM) in three-dimensions (3D) to create functional tissue constructs. Bioprinting has emerged as a promising 3D biomanufacturing technology, enabling precise control over spatial and temporal distribution of cells and ECM. Bioprinting technology can be used to engineer artificial tissues and organs by producing scaffolds with controlled spatial heterogeneity of physical properties, cellular composition, and ECM organization. This innovative approach is increasingly utilized in biomedicine, and has potential to create artificial functional constructs for drug screening and toxicology research, as well as tissue and organ transplantation. Herein, we review the recent advances in bioprinting technologies and discuss current markets, approaches, and biomedical applications. We also present current challenges and provide future directions for bioprinting research.

  8. Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2013-01-01

    Full Text Available Artificial neural network (ANN models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M, MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

  9. Implementing an Agent Based Artificial Stock Market Model in JADE – An Illustration

    Directory of Open Access Journals (Sweden)

    PN Kumar

    2013-06-01

    Full Text Available Agent-based approach to economic and financial analysis is a suitable research methodolgy for developing and understanding the complex patterns and phenomena that are observed in economic systems. In agent-based financial market models, prices can be endogenously formed by the system itself as the result of interaction of market participants. By using agents for the study, heterogeneous, boundedly rational, and adaptive behaviour of market participants can be analysed and its impact assessed. The collective behaviour of such groups is determined by the interaction of individual behaviours distributed across the group. This being the scenario prevailing in stock markets, agent based models are suitable for the study. Through this paper, we have attempted to illustrate a detailed implementation of multi agents in an artificial stock market invoking the agent-based methodology on Java Agent Development (JADE environment, a platform to develop multi-agent systems. The Extended Glosten and Milgrom Model, an agent based artificial stock market model, has been chosen to depict the multi-agent environment model in JADE.

  10. A conceptual and computational model of moral decision making in human and artificial agents.

    Science.gov (United States)

    Wallach, Wendell; Franklin, Stan; Allen, Colin

    2010-07-01

    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent's selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we

  11. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  12. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C. K. M.; Mishra, B. N.

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500. PMID:26368924

  13. Connectionist models of artificial grammar learning: what type of knowledge is acquired?

    Science.gov (United States)

    Kinder, Annette; Lotz, Anja

    2009-09-01

    Two experiments are presented that test the predictions of two associative learning models of Artificial Grammar Learning. The two models are the simple recurrent network (SRN) and the competitive chunking (CC) model. The two experiments investigate acquisition of different types of knowledge in this task: knowledge of frequency and novelty of stimulus fragments (Experiment 1) and knowledge of letter positions, of small fragments, and of large fragments up to entire strings (Experiment 2). The results show that participants acquired all types of knowledge. Simulation studies demonstrate that the CC model explains the acquisition of all types of fragment knowledge but fails to account for the acquisition of positional knowledge. The SRN model, by contrast, accounts for the entire pattern of results found in the two experiments.

  14. A Hybrid Fresh Apple Export Volume Forecasting Model Based on Time Series and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Yang

    2015-04-01

    Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.

  15. Artificial Neural Network Turbulent Modeling for Predicting the Pressure Drop of Nanofluid

    Directory of Open Access Journals (Sweden)

    M. S. Youssef

    2013-10-01

    Full Text Available An Artificial Neural Network (ANN model was developed to predict the pressure drop of titanium dioxide-water (TiO2-water. The model was developed based on experimentally measured data. Experimental measurements of fully developed turbulent flow in pipe at different particle volumetric concentrations, nanoparticle diameters, nanofluid temperature and Reynolds number were used to construct the proposed model. The ANN model was validated by comparing the predicted results with the experimental measured data at different experimental conditions. It was shown that, the present ANN model performed well in predicting the pressure drop of TiO2-water nanofluid under different flow conditions with a high degree of accuracy.

  16. Explaining the internal behaviour of artificial neural network river flow models

    Science.gov (United States)

    Sudheer, K. P.; Jain, Ashu

    2004-03-01

    A novel method of visualizing and understanding the internal functional behaviour of an artificial neural network (ANN) river flow model is presented. The method hypothesizes that an ANN is able to map a function similar to the flow duration curve while modelling the river flow. A mathematical analysis of the hypothesis is presented, and a case study of an ANN river flow model confirms its significance. The proposed approach is also useful within other models that improve the performance of an ANN. The reasons why these models improve a raw ANN can be clearly understood using this approach. While the field of ANN knowledge-extraction is one that continues to attract considerable interest, it is anticipated that the current approach will initiate further research and make ANNs more useful to the hydrologic community.

  17. Implementation of recurrent artificial neural networks for nonlinear dynamic modeling in biomedical applications.

    Science.gov (United States)

    Stošovic, Miona V Andrejevic; Litovski, Vanco B

    2013-11-01

    Simulation is indispensable during the design of many biomedical prostheses that are based on fundamental electrical and electronic actions. However, simulation necessitates the use of adequate models. The main difficulties related to the modeling of such devices are their nonlinearity and dynamic behavior. Here we report the application of recurrent artificial neural networks for modeling of a nonlinear, two-terminal circuit equivalent to a specific implantable hearing device. The method is general in the sense that any nonlinear dynamic two-terminal device or circuit may be modeled in the same way. The model generated was successfully used for simulation and optimization of a driver (operational amplifier)-transducer ensemble. This confirms our claim that in addition to the proper design and optimization of the hearing actuator, optimization in the electronic domain, at the electronic driver circuit-to-actuator interface, should take place in order to achieve best performance of the complete hearing aid.

  18. Chance of Necessity: Modeling Origins of Life

    Science.gov (United States)

    Pohorille, Andrew

    2006-01-01

    The fundamental nature of processes that led to the emergence of life has been a subject of long-standing debate. One view holds that the origin of life is an event governed by chance, and the result of so many random events is unpredictable. This view was eloquently expressed by Jacques Monod in his book Chance or Necessity. In an alternative view, the origin of life is considered a deterministic event. Its details need not be deterministic in every respect, but the overall behavior is predictable. A corollary to the deterministic view is that the emergence of life must have been determined primarily by universal chemistry and biochemistry rather than by subtle details of environmental conditions. In my lecture I will explore two different paradigms for the emergence of life and discuss their implications for predictability and universality of life-forming processes. The dominant approach is that the origin of life was guided by information stored in nucleic acids (the RNA World hypothesis). In this view, selection of improved combinations of nucleic acids obtained through random mutations drove evolution of biological systems from their conception. An alternative hypothesis states that the formation of protocellular metabolism was driven by non-genomic processes. Even though these processes were highly stochastic the outcome was largely deterministic, strongly constrained by laws of chemistry. I will argue that self-replication of macromolecules was not required at the early stages of evolution; the reproduction of cellular functions alone was sufficient for self-maintenance of protocells. In fact, the precise transfer of information between successive generations of the earliest protocells was unnecessary and could have impeded the discovery of cellular metabolism. I will also show that such concepts as speciation and fitness to the environment, developed in the context of genomic evolution also hold in the absence of a genome.

  19. Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

    Directory of Open Access Journals (Sweden)

    Pouraria Hassan

    2016-01-01

    Full Text Available In this study, artificial neural networks (ANNs have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D, the ratio of the cold outlet diameter to the tube diameter(d/D, inlet pressure(P, and cold mass fraction (Y on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y. The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

  20. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  1. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    Science.gov (United States)

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols.

  2. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram

    2016-05-01

    Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

  3. Adaptive life simulator: A novel approach to modeling the cardiovascular system

    Energy Technology Data Exchange (ETDEWEB)

    Kangas, L.J.; Keller, P.E.; Hashem, S. [and others

    1995-06-01

    In this paper, an adaptive life simulator (ALS) is introduced. The ALS models a subset of the dynamics of the cardiovascular behavior of an individual by using a recurrent artificial neural network. These models are developed for use in applications that require simulations of cardiovascular systems, such as medical mannequins, and in medical diagnostic systems. This approach is unique in that each cardiovascular model is developed from physiological measurements of an individual. Any differences between the modeled variables and the actual variables of an individual can subsequently be used for diagnosis. This approach also exploits sensor fusion applied to biomedical sensors. Sensor fusion optimizes the utilization of the sensors. The advantage of sensor fusion has been demonstrated in applications including control and diagnostics of mechanical and chemical processes.

  4. The Evolution of a Malignancy Risk Prediction Model for Thyroid Nodules Using the Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Shahram Paydar

    2016-01-01

    Full Text Available Background: Clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the United States. In the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. A considerable fact is that sensitivity and specificity of the fine needle aspiration varies significantly as it remains highly dependent on the operator as well as the cytologist’s skills. Practically, in the above group of patients, thyroid lobectomy/isthmusectomy becomes mandatory for attaining a definitive diagnosis where the majority (70%-80% have a benign surgical pathology. The scattered nature of clinically gathered data and analysis of their relevant variables need a compliant statistical method. The artificial neural network is a branch of artificial intelligence. We have hypothesized that conduction of an artificial neural network applied to certain clinical attributes could develop a malignancy risk assessment tool to help physicians interpret the fine needle aspiration biopsy results of thyroid nodules in a context composed of patient’s clinical variables, known as malignancy related risk factors. Methods: We designed and trained an artificial neural network on a prospectively formed cohort gathered over a four year period (2007-2011. The study population comprised 345 subjects who underwent thyroid resection at Nemazee and Rajaee hospitals, tertiary care centers of Shiraz University of Medical Sciences, and Rajaee Hospital as a level I trauma center in Shiraz, Iran after having undergone thyroid fine needle aspiration. Histopathological results of the fine needle aspirations and surgical specimens were analyzed and compared by experienced, board-certified pathologists who lacked knowledge of the fine needle aspiration results for thyroid malignancy. Results: We compared the preoperative

  5. A 3D model describing the initial structure of an artificial hydrological catchment

    Science.gov (United States)

    Maurer, T.; Schneider, A.; Buczko, U.; Gerke, H. H.

    2009-04-01

    The initial development stages of artificially constructed hydrologic catchments are characterized by the absence of vegetation, soil organic matter and soil horizons. This results in increased surface runoff and favors erosion processes that dominate the initial phase. Hydraulic conditions on artificial catchments thus are governed by rapidly changing surface structures as well as by the primary internal structural framework. Contemporary hydrological modeling does not consider any dynamic change of relevant structural features but rather assumes a stable, invariant landscape. The objective of this study was the digital visualization and quantitative description of the initial state and its early structural dynamics, exemplified for the small artificial hydrological catchment "Huehnerwasser" near Cottbus, Germany. Photogrammetric surveys of surface and internal structural units (clay basis liner) during the construction phase provided spatially and temporally resolved data for digital elevation models (DEM). Interpolated physical and chemical soil properties obtained at a borehole grid (e.g., texture) are used for the visualization of spatial distribution of relevant (hydraulic) parameters. The data are merged in a database and visualized in the 3D-GIS application GoCAD. The specific technological construction processes determines the internal structure of the artificial catchment. Resulting differences in bulk density and texture are supposed to have considerable impact on hydraulic properties. A structure generator program was implemented to reproduce the initial structure of the sediment layer as closely as possible. Results of the digital structure generation are checked with non-invasive geophysical measurements, on-site bore holes data and off-site 2D vertical spoil exploration. The accuracy of structure generator results will be compared with predictions of different interpolation methods. Thus, the structure model will serve as a basis for deriving the 3D

  6. An Artificial Neural Network Model for the Wholesale Company Order's Cycle Management

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-06-01

    Full Text Available The purpose of this article is to verify the possibility of using artificial neural networks (ANN in business management processes, primarily in the area of supply chain management. The author has designed several neural network models featuring different architectures to optimize the level of the company’s inventory. The results of the research show that ANN can be used for managing a company’s order cycle and lead to reduced levels of goods purchased and storage costs. Optimal neural networks show suitable results for subsequent prediction of the amount of items to be ordered and for achieving reduced inventory purchase and keeping costs down.

  7. Modeling and prediction of retardance in citric acid coated ferrofluid using artificial neural network

    Science.gov (United States)

    Lin, Jing-Fung; Sheu, Jer-Jia

    2016-06-01

    Citric acid coated (citrate-stabilized) magnetite (Fe3O4) magnetic nanoparticles have been conducted and applied in the biomedical fields. Using Taguchi-based measured retardances as the training data, an artificial neural network (ANN) model was developed for the prediction of retardance in citric acid (CA) coated ferrofluid (FF). According to the ANN simulation results in the training stage, the correlation coefficient between predicted retardances and measured retardances was found to be as high as 0.9999998. Based on the well-trained ANN model, the predicted retardance at excellent program from Taguchi method showed less error of 2.17% compared with a multiple regression (MR) analysis of statistical significance. Meanwhile, the parameter analysis at excellent program by the ANN model had the guiding significance to find out a possible program for the maximum retardance. It was concluded that the proposed ANN model had high ability for the prediction of retardance in CA coated FF.

  8. Modeling Spammer Behavior: Na\\"ive Bayes vs. Artificial Neural Networks

    CERN Document Server

    Islam, Md Saiful; Farhan, Khalid; Rahman, Md Abdur; Rahman, Joy

    2010-01-01

    Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Na\\"ive Bayes and Artificial Neural Networks (ANN) based modeling of spammer behavior. Keyword-based spam email filtering techniques fall short to model spammer behavior as the spammer constantly changes tactics to circumvent these filters. The evasive tactics that the spammer uses are themselves patterns that can be modeled to combat spam. It has been observed that both Na\\"ive Bayes and ANN are best suitable for modeling spammer common patterns. Experimental results demonstrate that both of them achieve a promising detection rate of around 92%, which is considerably an improvement of performance compared to the keyword-based contemporary filtering approaches.

  9. Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network

    Institute of Scientific and Technical Information of China (English)

    LIN Qi-quan; PENG Da-shu; ZHU Yuan-zhi

    2005-01-01

    An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.

  10. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  11. Behavioral Modeling of a C-Band Ring Hybrid Coupler Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    E. Demircioglu

    2010-12-01

    Full Text Available Artificial Neural Networks (ANNs gained importance on the RF microwave (MW design area and behavioral modeling of MW components in the past few decades. This paper presents a cost effective neural network (NN approach to overcome design, modeling and optimization problems of an 180deg ring hybrid coupler operating in C-Band. The proposed NN model is trained by data sets obtained from electromagnetic (EM simulators and neural test results are compared with simulator findings to determine the network accuracy. Moreover, necessary trade-offs are applied to improve the networks’ performance. Finally correlation factors, which are defined as comparison criteria between EM-simulator and proposed neural models, are calculated for each trade-off case.

  12. Rolling force prediction for strip casting using theoretical model and artificial intelligence

    Institute of Scientific and Technical Information of China (English)

    CAO Guang-ming; LI Cheng-gang; ZHOU Guo-ping; LIU Zhen-yu; WU Di; WANG Guo-dong; LIU Xiang-hua

    2010-01-01

    Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of+7.0%,which indicates that the predicting accuracy of this model is very high.

  13. Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

    2004-01-01

    Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

  14. Artificial neural network models for prediction of intestinal permeability of oligopeptides

    Directory of Open Access Journals (Sweden)

    Kim Min-Kook

    2007-07-01

    Full Text Available Abstract Background Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. Results The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC curve (the ROC score. The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence. Conclusion We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.

  15. The Life-Cycle Policy model

    NARCIS (Netherlands)

    Anciaux, Nicolas; Bouganim, Luc; Heerde, van Harold; Pucheral, Philippe; Apers, Peter M.G.

    2008-01-01

    Our daily life activity leaves digital trails in an increasing number of databases (commercial web sites, internet service providers, search engines, location tracking systems, etc). Personal digital trails are commonly exposed to accidental disclosures resulting from negligence or piracy and to ill

  16. Modeling light-driven proton pumps in artificial photosynthetic reaction centers.

    Science.gov (United States)

    Ghosh, Pulak Kumar; Smirnov, Anatoly Yu; Nori, Franco

    2009-07-21

    We study a model of a light-induced proton pump in artificial reaction centers. The model contains a molecular triad with four electron states (i.e., one donor state, two photosensitive group states, and one acceptor state) as well as a molecular shuttle having one electron and one proton-binding sites. The shuttle diffuses between the sides of the membrane and translocates protons energetically uphill: from the negative side to the positive side of the membrane, harnessing for this purpose the energy of the electron-charge separation produced by light. Using the methods of quantum transport theory we calculate the range of light intensity and transmembrane potentials that maximize both the light-induced proton current and the energy transduction efficiency. We also study the effect of temperature on proton pumping. The light-induced proton pump in our model gives a quantum yield of proton translocation of about 55%. Thus, our results explain previous experiments on these artificial photosynthetic reaction centers.

  17. Modeling of frost crystal growth over a flat plate using artificial neural networks and fractal geometries

    Science.gov (United States)

    Tahavvor, Ali Reza

    2016-06-01

    In the present study artificial neural network and fractal geometry are used to predict frost thickness and density on a cold flat plate having constant surface temperature under forced convection for different ambient conditions. These methods are very applicable in this area because phase changes such as melting and solidification are simulated by conventional methods but frost formation is a most complicated phase change phenomenon consists of coupled heat and mass transfer. Therefore conventional mathematical techniques cannot capture the effects of all parameters on its growth and development because this process influenced by many factors and it is a time dependent process. Therefore, in this work soft computing method such as artificial neural network and fractal geometry are used to do this manner. The databases for modeling are generated from the experimental measurements. First, multilayer perceptron network is used and it is found that the back-propagation algorithm with Levenberg-Marquardt learning rule is the best choice to estimate frost growth properties due to accurate and faster training procedure. Second, fractal geometry based on the Von-Koch curve is used to model frost growth procedure especially in frost thickness and density. Comparison is performed between experimental measurements and soft computing methods. Results show that soft computing methods can be used more efficiently to determine frost properties over a flat plate. Based on the developed models, wide range of frost formation over flat plates can be determined for various conditions.

  18. Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor

    Energy Technology Data Exchange (ETDEWEB)

    Garg, A. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Sastry, P.S. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Pandey, M. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India)]. E-mail: manmohan@iitg.ac.in; Dixit, U.S. [Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam (India); Gupta, S.K. [Atomic Energy Regulatory Board, Mumbai 400085 (India)

    2007-02-15

    Numerical simulation of natural circulation boiling water reactor is important in order to study its performance for different designs and under various off-design conditions. Numerical simulations can be performed by using thermal-hydraulic codes. Very fast numerical simulations, useful for extensive parametric studies and for solving design optimization problems, can be achieved by using an artificial neural network (ANN) model of the system. In the present work, numerical simulations of natural circulation boiling water reactor have been performed with RELAP5 code for different values of design parameters and operational conditions. Parametric trends observed have been discussed. The data obtained from these simulations have been used to train artificial neural networks, which in turn have been used for further parametric studies and design optimization. The ANN models showed error within {+-}5% for all the simulated data. Two most popular methods, multilayer perceptron (MLP) and radial basis function (RBF) networks, have been used for the training of ANN model. Sequential quadratic programming (SQP) has been used for optimization.

  19. Modeling of frost crystal growth over a flat plate using artificial neural networks and fractal geometries

    Science.gov (United States)

    Tahavvor, Ali Reza

    2017-03-01

    In the present study artificial neural network and fractal geometry are used to predict frost thickness and density on a cold flat plate having constant surface temperature under forced convection for different ambient conditions. These methods are very applicable in this area because phase changes such as melting and solidification are simulated by conventional methods but frost formation is a most complicated phase change phenomenon consists of coupled heat and mass transfer. Therefore conventional mathematical techniques cannot capture the effects of all parameters on its growth and development because this process influenced by many factors and it is a time dependent process. Therefore, in this work soft computing method such as artificial neural network and fractal geometry are used to do this manner. The databases for modeling are generated from the experimental measurements. First, multilayer perceptron network is used and it is found that the back-propagation algorithm with Levenberg-Marquardt learning rule is the best choice to estimate frost growth properties due to accurate and faster training procedure. Second, fractal geometry based on the Von-Koch curve is used to model frost growth procedure especially in frost thickness and density. Comparison is performed between experimental measurements and soft computing methods. Results show that soft computing methods can be used more efficiently to determine frost properties over a flat plate. Based on the developed models, wide range of frost formation over flat plates can be determined for various conditions.

  20. Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge

    Directory of Open Access Journals (Sweden)

    M. Mohan Raju

    2011-01-01

    Full Text Available The present study demonstrates the application of artificial neural networks (ANNs in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (, determination coefficient, or Nash Sutcliff's efficiency (DC. Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

  1. MODELS OF FATIGUE LIFE CURVES IN FATIGUE LIFE CALCULATIONS OF MACHINE ELEMENTS – EXAMPLES OF RESEARCH

    Directory of Open Access Journals (Sweden)

    Grzegorz SZALA

    2014-03-01

    Full Text Available In the paper there was attempted to analyse models of fatigue life curves possible to apply in calculations of fatigue life of machine elements. The analysis was limited to fatigue life curves in stress approach enabling cyclic stresses from the range of low cycle fatigue (LCF, high cycle fatigue (HCF, fatigue limit (FL and giga cycle fatigue (GCF appearing in the loading spectrum at the same time. Chosen models of the analysed fatigue live curves will be illustrated with test results of steel and aluminium alloys.

  2. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  3. Enhancing Wireless Sensor Network Security using Artificial Neural Network based Trust Model

    Directory of Open Access Journals (Sweden)

    Adwan Yasin

    2016-09-01

    Full Text Available Wireless sensor network (WSN is widely used in environmental conditions where the systems depend on sensing and monitoring approach. Water pollution monitoring system depends on a network of wireless sensing nodes which communicate together depending on a specific topological order. The nodes distributed in a harsh environment to detect the polluted zones within the WSN range based on the sensed data. WSN exposes several malicious attacks as a consequence of its presence in such open environment, so additional techniques are needed alongside with the existing cryptography approach. In this paper an enhanced trust model based on the use of radial base artificial neural network (RBANN is presented to predict the future behavior of each node based on its weighted direct and indirect behaviors, in order to provide a comprehensive trust model that helps to detect and eliminate malicious nodes within the WSN. The proposed model considered the limited power, storage and processing capabilities of the system.

  4. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

    Directory of Open Access Journals (Sweden)

    Nevenka Djurovic

    2015-01-01

    Full Text Available Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS and an artificial neural network (ANN model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

  5. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

    Science.gov (United States)

    Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

    2015-01-01

    Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models. PMID:26759830

  6. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  7. ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

  8. Artificial neural network approach to modelling of metal contents in different types of chocolates.

    Science.gov (United States)

    Podunavac-Kuzmanović, Sanja; Jevrić, Lidija; Švarc-Gajić, Jaroslava; Kovačević, Strahinja; Vasiljević, Ivana; Kecojević, Isidora; Ivanović, Evica

    2015-01-01

    The relationships between the contents of various metals in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations, that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate.

  9. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    Science.gov (United States)

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.

  10. Battery Calendar Life Estimator Manual Modeling and Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Jon P. Christophersen; Ira Bloom; Ed Thomas; Vince Battaglia

    2012-10-01

    The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.

  11. Prediction of human skin permeability using artificial neural network (ANN)modeling

    Institute of Scientific and Technical Information of China (English)

    Long-jian CHEN; Guo-ping LIAN; Lu-jia HAN

    2007-01-01

    Aim: To develop an artificial neural network (ANN) model for predicting skin permeability (log Kp) of new chemical entities. Methods: A large dataset of 215experimental data points was compiled from the literature. The dataset was subdi-vided into 5 subsets and 4 of them were used to train and validate an ANN model.The same 4 datasets were also used to build a multiple linear regression (MLR)model. The remaining dataset was then used to test the 2 models. Abraham descriptors were employed as inputs into the 2 models. Model predictions were compared with the experimental results. In addition, the relationship between log Kp and Abraham descriptors were investigated. Results: The regression re-sults of the MLR model were n=215, determination coefficient (R2)=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R2=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log Kp and Abraham descriptors is non-linear. Conclusion:The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.

  12. Residential Saudi load forecasting using analytical model and Artificial Neural Networks

    Science.gov (United States)

    Al-Harbi, Ahmad Abdulaziz

    In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

  13. Response surface and artificial neural network prediction model and optimization for surface roughness in machining

    Directory of Open Access Journals (Sweden)

    Ashok Kumar Sahoo

    2015-04-01

    Full Text Available The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity. It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

  14. Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Han, In-Su; Shin, Hyun Khil [GS Caltex Corp, Daejeon (Korea, Republic of)

    2015-04-15

    We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

  15. Motility at the origin of life: its characterization and a model.

    Science.gov (United States)

    Froese, Tom; Virgo, Nathaniel; Ikegami, Takashi

    2014-01-01

    Due to recent advances in synthetic biology and artificial life, the origin of life is currently a hot topic of research. We review the literature and argue that the two traditionally competing replicator-first and metabolism-first approaches are merging into one integrated theory of individuation and evolution. We contribute to the maturation of this more inclusive approach by highlighting some problematic assumptions that still lead to an ximpoverished conception of the phenomenon of life. In particular, we argue that the new consensus has so far failed to consider the relevance of intermediate time scales. We propose that an adequate theory of life must account for the fact that all living beings are situated in at least four distinct time scales, which are typically associated with metabolism, motility, development, and evolution. In this view, self-movement, adaptive behavior, and morphological changes could have already been present at the origin of life. In order to illustrate this possibility, we analyze a minimal model of lifelike phenomena, namely, of precarious, individuated, dissipative structures that can be found in simple reaction-diffusion systems. Based on our analysis, we suggest that processes on intermediate time scales could have already been operative in prebiotic systems. They may have facilitated and constrained changes occurring in the faster- and slower-paced time scales of chemical self-individuation and evolution by natural selection, respectively.

  16. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    Science.gov (United States)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

  17. The application of artificial neural network model in the non-invasive diagnosis of liver fibrosis

    Directory of Open Access Journals (Sweden)

    Bo LI

    2012-12-01

    Full Text Available Objective  To construct and evaluate an artificial neural network (ANN model as a new non-invasive diagnostic method for clinical assessment of liver fibrosis at early stage. Methods  The model was set up and tested among 683 chronic hepatitis B (CHB patients, with authentic positive clinical biopsy results, proved to have liver fibrosis or cirrhosis, admitted to 302 Hospital of PLA from May 2008 to March 2011. Among 683 samples, 504 samples were diagnosed as cirrhosis as a result of CHB, and 179 liver fibrosis due to other liver diseases. 134 out of 683 patients were included in training group by stratified sampling, and the others for verification. Six items (age, AST, PTS, PLT, GGT and DBil were selected as input layer indexes to set up the model for evaluation. Results  The ANN model for diagnosis of liver fibrosis was set up. The diagnostic accuracy was 77.4%, sensitivity was 76.8%, and specificity was 77.8%. Its Kappa concordance tests showed the diagnosis result of the model was consistent with biopsy result (Kappa index=0.534. The accuracy, sensitivity and specificity of CHB patients were 80.4%, 79.9% and 80.7% (Kappa index=0.598 respectively, and those for other liver diseases were 67.9%, 64.3% and 69.7% (Kappa index=0.316. Conclusion  The artificial neural network model established by the authors demonstrates its high sensitivity and specificity as a new non-invasive diagnostic method for liver fibrosis induced by HBV infection. However, it shows limited diagnostic reliability to fibrosis as a result of other liver diseases.

  18. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    Energy Technology Data Exchange (ETDEWEB)

    Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

    2011-07-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  19. Artificial snowmaking possibilities and climate change based on regional climate modeling in the Southern Black Forest

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, Philipp; Matzarakis, Andreas [Freiburg Univ. (Germany). Meteorological Inst.; Steiger, Robert [alpS - Centre for Climate Change Adaptation Technologies, Innsbruck (Austria)

    2012-04-15

    Winter sport, especially ski tourism - is one of those sectors of tourism that will be affected by climate change. Ski resorts across the Alps and in the adjacent low mountain ranges react to warm winter seasons by investing in artificial snowmaking. But snowmaking in warm winter seasons is fraught with risk, because sufficiently low air temperature will become less frequent in the future. The present study deals with the ski resort Feldberg, which has 14 ski lifts and 16 ski slopes which is the biggest ski resort in the German Federal state Baden-Wuerttemberg. The impact of climate change in this region is extraordinary important because winter tourism is the main source of revenue for the whole area around the ski resort. The study area is in altitudinal range of 850 to 1450 meters above sea level. At the moment, it is possible to supply one third of the whole area with artificial snow, but there is plan for artificial snowmaking of the whole Feldberg area by the year 2020. Based on this, more detailed investigations of season length and the needed volume of produced snow are necessary. A ski season simulation model (SkiSim 2.0) was applied in order to assess potential impacts of climate change on the Feldberg ski area for the A1B and B1 emission scenarios based on the ECHAM5 GCM downscaled by the REMO RCM. SkiSim 2.0 calculates daily snow depth (natural and technically produced snow) and the required amount of artificial snow for 100 m altitudinal bands. Analysing the development of the number of potential skiing days, it can be assessed whether ski operation is cost covering or not. Model results of the study show a more pronounced and rapid shortening of the ski season in the lower ranges until the year 2100 in each climate scenario. In both the A1B and B1 scenario runs of REMO, a cost-covering ski season of 100 days cannot be guaranteed in every altitudinal range even if snowmaking is considered. In this context, the obtained high-resolution snow data can

  20. Predicting life satisfaction of the Angolan elderly: a structural model.

    Science.gov (United States)

    Gutiérrez, M; Tomás, J M; Galiana, L; Sancho, P; Cebrià, M A

    2013-01-01

    Satisfaction with life is of particular interest in the study of old age well-being because it has arisen as an important component of old age. A considerable amount of research has been done to explain life satisfaction in the elderly, and there is growing empirical evidence on best predictors of life satisfaction. This research evaluates the predictive power of some aging process variables, on Angolan elderly people's life satisfaction, while including perceived health into the model. Data for this research come from a cross-sectional survey of elderly people living in the capital of Angola, Luanda. A total of 1003 Angolan elderly were surveyed on socio-demographic information, perceived health, active engagement, generativity, and life satisfaction. A Multiple Indicators Multiple Causes model was built to test variables' predictive power on life satisfaction. The estimated theoretical model fitted the data well. The main predictors were those related to active engagement with others. Perceived health also had a significant and positive effect on life satisfaction. Several processes together may predict life satisfaction in the elderly population of Angola, and the variance accounted for it is large enough to be considered relevant. The key factor associated to life satisfaction seems to be active engagement with others.

  1. The cognitive life of mechanical molecular models.

    Science.gov (United States)

    Charbonneau, Mathieu

    2013-12-01

    The use of physical models of molecular structures as research tools has been central to the development of biochemistry and molecular biology. Intriguingly, it has received little attention from scholars of science. In this paper, I argue that these physical models are not mere three-dimensional representations but that they are in fact very special research tools: they are cognitive augmentations. Despite the fact that they are external props, these models serve as cognitive tools that augment and extend the modeler's cognitive capacities and performance in molecular modeling tasks. This cognitive enhancement is obtained because of the way the modeler interacts with these models, the models' materiality contributing to the solving of the molecule's structure. Furthermore, I argue that these material models and their component parts were designed, built and used specifically to serve as cognitive facilitators and cognitive augmentations.

  2. 3D artificial bones for bone repair prepared by computed tomography-guided fused deposition modeling for bone repair.

    Science.gov (United States)

    Xu, Ning; Ye, Xiaojian; Wei, Daixu; Zhong, Jian; Chen, Yuyun; Xu, Guohua; He, Dannong

    2014-09-10

    The medical community has expressed significant interest in the development of new types of artificial bones that mimic natural bones. In this study, computed tomography (CT)-guided fused deposition modeling (FDM) was employed to fabricate polycaprolactone (PCL)/hydroxyapatite (HA) and PCL 3D artificial bones to mimic natural goat femurs. The in vitro mechanical properties, in vitro cell biocompatibility, and in vivo performance of the artificial bones in a long load-bearing goat femur bone segmental defect model were studied. All of the results indicate that CT-guided FDM is a simple, convenient, relatively low-cost method that is suitable for fabricating natural bonelike artificial bones. Moreover, PCL/HA 3D artificial bones prepared by CT-guided FDM have more close mechanics to natural bone, good in vitro cell biocompatibility, biodegradation ability, and appropriate in vivo new bone formation ability. Therefore, PCL/HA 3D artificial bones could be potentially be of use in the treatment of patients with clinical bone defects.

  3. Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

    Directory of Open Access Journals (Sweden)

    Sungwon Kim

    2015-06-01

    Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

  4. Forecasting model for the incidence of hepatitis A based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Peng Guan; De-Sheng Huang; Bao-Sen Zhou

    2004-01-01

    AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon.METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA)model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into [0,1] intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and verifying sets and the data from 1998 to 2001 were made up into the test set.STATISTICA neural network (ST NN) was used to construct,train and simulate the artificial neural network.RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlatior, was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9 090.21, the sum square error of the training set and testing set was 8 377.52 and 712.69,respectively, they were all less than that of ARIMA model.The corresponding value of ARIMA was 12 291.79, 8 944.95and 3 346.84, respectively. The correlation coefficient of nonlinear regression (RNL) of ANN was 0.71, while the RNL of ARIMA linear autoregression model was 0.66.CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.

  5. Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

    Directory of Open Access Journals (Sweden)

    SASSAN MOHAMMADY

    2013-01-01

    Full Text Available Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR is equal to 88.91%. Percent Correct Match (PCM and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively.

  6. Multi-criteria validation of artificial neural network rainfall-runoff modeling

    Directory of Open Access Journals (Sweden)

    R. Modarres

    2008-12-01

    Full Text Available In this study we propose a comprehensive multi-criteria validation test for rainfall-runoff modeling by artificial neural networks. This study applies 17 global statistics and 3 additional non-parametric tests to evaluate the ANNs. The weakness of global statistics for validation of ANN is demonstrated by rainfall-runoff modeling of the Plasjan Basin in the western region of the Zayandehrud watershed, Iran. Although the global statistics showed that the multi layer perceptron with 4 hidden layers (MLP4 is the best ANN for the basin comparing with other MLP networks and empirical regression model, but the non-parametric tests illustrate that neither the ANNs nor the regression model are able to reproduce the probability distribution of observed runoff in validation phase. However, the MLP4 network is the best network to reproduce the mean and variance of the observed runoff based on non-parametric tests. The performance of ANNs and empirical model was also demonstrated for low-medium and high flows. Although the MLP4 network gives the best performance among ANNs for low-medium and high flows based on different statistics but the empirical model shows better results. However, none of the models is able to simulate the frequency distribution of low-medium and high flows according to non-parametric tests. This study illustrates that the modelers should select appropriate and relevant evaluation measures from the set of existing metrics based on the particular requirements of each individual applications.

  7. Analytical modeling of gas production rate in tight channel sand formation and optimization of artificial fracture.

    Science.gov (United States)

    Wang, Ruifei; Song, Hongqing; Tang, Hewei; Wang, Yuhe; Killough, John; Huang, Gang

    2016-01-01

    Permeability variation in tight channel sand formation makes an important role in gas production. Based on the features of channel sand formation, a mathematical model has been established considering anisotropy of permeability. The analytical solutions were derived for productivity of both vertical wells and vertically fractured wells. Simulation results show that, gas production rate of anisotropic channel sand formation is less than that of isotropic formation. For vertically fractured well, artificial fracture direction, drainage radius, permeability ratio and fracture half-length have considerable influence on production rate. The optimum fracture direction should be deviated less than π/8 from the maximum permeability direction (or the channel direction). In addition, the analytical model was verified by in situ measured data. The research provides theoretical basis for the development of tight channel sand gas reservoirs.

  8. Back-Propagation Artificial Neural Networks for Water Supply Pipeline Model

    Institute of Scientific and Technical Information of China (English)

    朱东海; 张土乔; 毛根海

    2002-01-01

    Water supply pipelines are the lifelines of a city. When pipelines burst, the burst site is difficult to locate by traditional methods such as manual tools or only by watching. In this paper, the burst site was identified using back-propagation (BP) artificial neural networks (ANN). The study is based on an indoor urban water supply model experiment. The key to appling BP ANN is to optimize the ANN's topological structure and learning parameters. This paper presents the optimizing method for a 3-layer BP neural network's topological structure and its learning parameters-learning ratio and the momentum factor. The indoor water supply pipeline model experimental results show that BP ANNs can be used to locate the burst point in urban water supply systems. The topological structure and learning parameters were optimized using the experimental results.

  9. MaNN: Multiple Artificial Neural Networks for modelling the Interstellar Medium

    CERN Document Server

    Grassi, T; Piovan, L; Buonomo, U; Chiosi, C

    2011-01-01

    Modelling the complex physics of the Interstellar Medium (ISM) in the context of large-scale numerical simulations is a challenging task. A number of methods have been proposed to embed a description of the ISM into different codes. We propose a new way to achieve this task: Artificial Neural Networks (ANNs). The ANN has been trained on a pre-compiled model database, and its predictions have been compared to the expected theoretical ones, finding good agreement both in static and in dynamical tests run using the Padova Tree-SPH code \\textsc{EvoL}. A neural network can reproduce the details of the interstellar gas evolution, requiring limited computational resources. We suggest that such an algorithm can replace a real-time calculation of mass elements chemical evolution in hydrodynamical codes.

  10. Discussion of using artificial neural nets to identify the well-test interpretation model

    Energy Technology Data Exchange (ETDEWEB)

    Yeung, K. (Univ. of Alberta, Edmonton, Alberta (Canada)); Chakrabarty, C. (Golder Associates, Nottingham (United Kingdom)); Wu, S. (Univ. of Melbourne (Australia))

    1994-09-01

    Use of artificial neural nets (ANN's) to identify noisy and apparently unrecognizable patterns is common for many real-world problems, ranging from applications such as speech recognition to stock market prediction. ANN approaches are often good candidates for recognizing patterns when rigid mathematical models do not exist or are insufficient to meet a full-scale identification requirement. Al-Kaabi and Lee's proposal of using ANN's to identify the well-test interpretation model is appropriate because well-test data is often highly nonlinear and noisy. The purpose of this discussion is to present some of the authors results in a similar study and to suggest a simple technique that would enhance the use of ANN's in Al-Kaabi and Lee's approach.

  11. Artificial Neural Network based Diagnostic Model For Causes of Success and Failures

    CERN Document Server

    Kaur, Bikrampal

    2010-01-01

    In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited to the problems that are well-understood and known solution(s). On the other hand the ANNs have learning by example and processing capabilities similar to that of a human brain. ANN has been followed due to its inherent advantage over conversion algorithmic like approaches and having capabilities, training and human like intuitive decision making capabilities. Therefore, this ANN based approach is likely to help researchers and organizations to reach a better solution to the problem of managing the human resource. The stud...

  12. A Comparison of the Monetary Model and Artificial Neural Networks in Exchange Rate Forecasting

    Directory of Open Access Journals (Sweden)

    Filiz Ozkan

    2012-01-01

    Full Text Available Exchange value is one of the significant tools for investors in decision making. Since exchange values are volatile and they change within short periods, investors need an effective method to minimize the risk. This study compares the prediction performances of artificial neural networks, which is recently being used as an effective tool of prediction, and the monetary model, which is one of the methods to predict structural exchange rates. In this study exchange rates of Turkish Lira against US Dollar and Euro are predicted. In models, inflation levels for domestic and foreign countries, money supply, interest rates and economic indicators are used. The time period between 1986 and 2010 is covered for the USD and the time period between 1999 and 2010 is covered for the EU. Results of this study show that ANN, which is recently being used for the prediction problems, reached a high level prediction performance.

  13. A simple numerical model for membrane oxygenation of an artificial lung machine

    Science.gov (United States)

    Subraveti, Sai Nikhil; Sai, P. S. T.; Viswanathan Pillai, Vinod Kumar; Patnaik, B. S. V.

    2015-11-01

    Optimal design of membrane oxygenators will have far reaching ramification in the development of artificial heart-lung systems. In the present CFD study, we simulate the gas exchange between the venous blood and air that passes through the hollow fiber membranes on a benchmark device. The gas exchange between the tube side fluid and the shell side venous liquid is modeled by solving mass, momentum conservation equations. The fiber bundle was modelled as a porous block with a bundle porosity of 0.6. The resistance offered by the fiber bundle was estimated by the standard Ergun correlation. The present numerical simulations are validated against available benchmark data. The effect of bundle porosity, bundle size, Reynolds number, non-Newtonian constitutive relation, upstream velocity distribution etc. on the pressure drop, oxygen saturation levels etc. are investigated. To emulate the features of gas transfer past the alveoli, the effect of pulsatility on the membrane oxygenation is also investigated.

  14. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

  15. Pollutant intrusion modeling in water distribution networks using artificial neural networks.

    Science.gov (United States)

    Singh, Raj Mohan; Rahul, Akhouri Ishan

    2011-07-01

    The development and implementation of water quality models for water distribution systems have been growing interest for both environment and hydraulic researchers. It is imperative that the system is able to distribute disinfectants and/or chemicals efficiently for specified quality standards and recover the actual quality of water in case of intrusion of a pollutant into the distribution network. The present work presents hydraulic and quality analysis in a typical water distribution system to obtain the concentration at the sources (pumping station or tanks) affected by typical pollutants utilizing water quality at monitoring points as inputs to artificial neural network (ANN) model. The universal function approximation property of the ANN architecture is being employed for inverse mapping to predict the water quality at the source using the water quality at arbitrary monitoring locations in the distribution system. The optimal monitoring points are identified by water age analysis. The performance evaluation results are encouraging and demonstrate the potential applicability of the methodology.

  16. Modelling the spectral energy distribution of galaxies: introducing the artificial neural network

    CERN Document Server

    Silva, L; Granato, G L; Almeida, C; Baugh, C M; Frenk, C S; Lacey, C G; Paoletti, L; Petrella, A; Selvestrel, D

    2010-01-01

    The spectral energy distribution of galaxies is a complex function of the star formation history and geometrical arrangement of stars and gas in galaxies. The computation of the radiative transfer of stellar radiation through the dust distribution is time-consuming. This aspect becomes unacceptable in particular when dealing with the predictions by semi-analytical galaxy formation models populating cosmological volumes, to be then compared with multi-wavelength surveys. Mainly for this aim, we have implemented an artificial neural network algorithm into the spectro-photometric and radiative transfer code GRASIL in order to compute the spectral energy distribution of galaxies in a short computing time. This allows to avoid the adoption of empirical templates that may have nothing to do with the mock galaxies output by models. The ANN has been implemented to compute the dust emission spectrum (the bottleneck of the computation), and separately for the star-forming molecular clouds and the diffuse dust (due to t...

  17. An Additive-Multiplicative Restricted Mean Residual Life Model

    DEFF Research Database (Denmark)

    Mansourvar, Zahra; Martinussen, Torben; Scheike, Thomas H.

    2016-01-01

    mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline....... For the suggested model, some covariate effects are allowed to be time-varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness...... of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial....

  18. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Directory of Open Access Journals (Sweden)

    Lakshmi Pathak

    Full Text Available Cholesterol oxidase (COD is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM, artificial neural network (ANN and genetic algorithm (GA have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  19. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Soteris A. Kalogirou, [Higher Technical Institute, Nicosia (Cyprus). Department of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. AI systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how AI techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of AI as a design tool in many areas of combustion engineering. 109 refs., 31 figs., 11 tabs.

  20. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Kalogirou, S.A. [Higher Technical Inst., Nicosia, Cyprus (Greece). Dept. of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. Al systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how Al techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of Al as a design tool in many areas of combustion engineering. (author)

  1. A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems.

    Science.gov (United States)

    Merrick, Kathryn E; Shafi, Kamran

    2013-01-01

    An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players' optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

  2. A Game Theoretic Framework for Incentive-Based Models of Intrinsic Motivation in Artificial Systems

    Directory of Open Access Journals (Sweden)

    Kathryn Elizabeth Merrick

    2013-10-01

    Full Text Available An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players’ optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

  3. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    Directory of Open Access Journals (Sweden)

    Hongshan Zhao

    2012-05-01

    Full Text Available Short-term solar irradiance forecasting (STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV, and the Levenberg-Marquardt algorithm (LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

  4. Chemometric Amylose Modeling and Sample Selection for Global Calibration Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    SHIMIZU N; OKADOME H; WADA D; KIMURA T; OHTSUBO K

    2008-01-01

    Chemometric arnylose modeling for global calibration, using whole grain near infrared transmittance spectra andsample selection, was used in an artificial neural network (ANN), to assess the global and local models generated, based onsamples of newly bred Indica, Japonica and rice. Global sample sets had a wide range of sample variation for amylose content(0 to 25.9%). The local sample set, Japonica sample, had relatively low amylose content and a narrow sample variation(amylose; 12.3% to 21.0%). For sample selection the CENTER algorithm was applied to generate calibration, validation andstop sample sets. Spectral preprocessing was found to reduce the optimum number of partial least squares (PLS) componentsfor amylose content and thus enhance the robustness of the local calibration. The best model was found to be an ANN globalcalibration with spectral preprocessing; the next was a PLS global calibration using standard spectra. These results pose thequestion whether an ANN algorithm with spectral preprocessing could be developed for global and local calibration models orwhether PLS without spectral preprocessing should be developed for global calibration models. We suggest that global calibra-tion models incorporating an ANN may be used as a universal calibration model.

  5. An artificial neural network model for rainfall forecasting in Bangkok, Thailand

    Directory of Open Access Journals (Sweden)

    N. Q. Hung

    2008-01-01

    Full Text Available The present study developed an artificial neural network (ANN model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, and the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input for training of the model was found most satisfactory in forecasting rainfall in Bangkok, Thailand. The developed ANN model was applied to derive rainfall forecast from 1 to 6 h ahead at 75 rain gauge stations in the study area as forecast point from the data of 3 consecutive years (1997–1999. Results were highly satisfactory for rainfall forecast 1 to 3 h ahead. Sensitivity analysis indicated that the most important input parameter beside rainfall itself is the wet bulb temperature in forecasting rainfall. Based on these results, it is recommended that the developed ANN model can be used for real-time rainfall forecasting and flood management in Bangkok, Thailand.

  6. An artificial neural network model for rainfall forecasting in Bangkok, Thailand

    Directory of Open Access Journals (Sweden)

    N. Q. Hung

    2009-08-01

    Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

  7. Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

    Science.gov (United States)

    Prilianti, K. R.; Hariyanto, S.; Natali, F. D. D.; Indriatmoko, Adhiwibawa, M. A. S.; Limantara, L.; Brotosudarmo, T. H. P.

    2016-04-01

    The development of rapid and automatic pigment characterization method become an important issue due to the fact that there are only less than 1% of plant pigments in the earth have been explored. In this research, a mathematical model based on artificial intelligence approach was developed to simplify and accelerate pigment characterization process from HPLC (high-performance liquid chromatography) procedure. HPLC is a widely used technique to separate and identify pigments in a mixture. Input of the model is chromatographic data from HPLC device and output of the model is a list of pigments which is the spectrum pattern is discovered in it. This model provides two dimensional (retention time and wavelength) fingerprints for pigment characterization which is proven to be more accurate than one dimensional fingerprint (fixed wavelength). Moreover, by mimicking interconnection of the neuron in the nervous systems of the human brain, the model have learning ability that could be replacing expert judgement on evaluating spectrum pattern. In the preprocessing step, principal component analysis (PCA) was used to reduce the huge dimension of the chromatographic data. The aim of this step is to simplify the model and accelerate the identification process. Six photosynthetic pigments i.e. zeaxantin, pheophytin a, α-carotene, β-carotene, lycopene and lutein could be well identified by the model with accuracy up to 85.33% and processing time less than 1 second.

  8. An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

    Directory of Open Access Journals (Sweden)

    Muhammed Yasin Çodur

    2015-06-01

    Full Text Available This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012. The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km, annual average daily traffic (AADT, the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage, and traffic accidents that occurred in summer (percentage. In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE, the root mean square error (RMSE, and the coefficient of determination (R2. The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.

  9. Artificial Neural Network Model for Discrimination of Stability of Ancient Landslide in Impounding Area of Three Gorges Project, China

    Institute of Scientific and Technical Information of China (English)

    Zhou Pinggen

    2003-01-01

    The factors of geomorphology, geological setting, effect of ground water and environment dynamic factors (e. g. rainfall and artificial water recharge) should be integrated in the discrimination of the stability of the ancient landslide. As the criterion of landslide stability has been studied, the artificial neural network model was then applied to discriminate the stability of the ancient landslide in the impounding area of the Three Gorges project on the Yangtze River, China. The model has the property of self-adaptive identifying and integrating complex qualitative factors and quantitative factors. The results of the artificial neural network model are coincided well with what were gained by classical limit equilibrinm analysis (the Bishop method and Janbu method) and by other comprehensive discrimination methods.

  10. A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

    Science.gov (United States)

    Yorek, Nurettin; Ugulu, Ilker

    2015-01-01

    In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

  11. A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word-Order Universal

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…

  12. Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

    This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource’s behaviour under...

  13. Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

    This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource’s behaviour under c...

  14. Using artificial neural networks to model extrusion processes for the manufacturing of polymeric micro-tubes

    Science.gov (United States)

    Mekras, N.; Artemakis, I.

    2012-09-01

    In this paper a methodology and an application example are presented aiming to show how Artificial Neural Networks (ANNs) can be used to model manufacturing processes when mathematical models are missing or are not applicable e.g. due to the micro- & nano-scaling, due to non-conventional processes, etc. Besides the ANNs methodology, the results of a Software System developed will be presented, which was used to create ANNs models for micro & nano manufacturing processes. More specifically results of a specific application example will be presented, concerning the modeling of extrusion processes for polymeric micro-tubes. ANNs models are capable for modeling manufacturing processes as far as adequate experimental and/or historical data of processes' inputs & outputs are available for their training. The POLYTUBES ANNs models have been trained and tested with experimental data records of process' inputs and outputs concerning a micro-extrusion process of polymeric micro-tubes for several materials such as: COC, PC, PET, PETG, PP and PVDF. The main ANN model of the extrusion application example has 3 inputs and 9 outputs. The inputs are: tube's inner & outer diameters, and the material density. The model outputs are 9 process parameters, which correspond to the specific inputs e.g. process temperature, die inner & outer diameters, extrusion pressure, draw speed etc. The training of the ANN model was completed, when the errors for the model's outputs, which expressed the difference between the training target values and the ANNs outputs, were minimized to acceptable levels. After the training, the micro-extrusion ANN is capable to simulate the process and can be used to calculate model's outputs, which are the process parameters for any new set of inputs. By this way a satisfactory functional approximation of the whole process is achieved. This research work has been supported by the EU FP7 NMP project POLYTUBES.

  15. Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach.

    Science.gov (United States)

    Stamenković, Lidija J; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2015-12-01

    Ammonia emissions at the national level are frequently estimated by applying the emission inventory approach, which includes the use of emission factors, which are difficult and expensive to determine. Emission factors are therefore the subject of estimation, and as such they contribute to inherent uncertainties in the estimation of ammonia emissions. This paper presents an alternative approach for the prediction of ammonia emissions at the national level based on artificial neural networks and broadly available sustainability and economical/agricultural indicators as model inputs. The Multilayer Perceptron (MLP) architecture was optimized using a trial-and-error procedure, including the number of hidden neurons, activation function, and a back-propagation algorithm. Principal component analysis (PCA) was applied to reduce mutual correlation between the inputs. The obtained results demonstrate that the MLP model created using the PCA transformed inputs (PCA-MLP) provides a more accurate prediction than the MLP model based on the original inputs. In the validation stage, the MLP and PCA-MLP models were tested for ammonia emission predictions for up to 2 years and compared with a principal component regression model. Among the three models, the PCA-MLP demonstrated the best performance, providing predictions for the USA and the majority of EU countries with a relative error of less than 20%.

  16. Comparison of Different Artificial Neural Network (ANN) Architectures in Modeling of Chlorella sp. Flocculation.

    Science.gov (United States)

    Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

    2017-01-03

    Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae specie, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network (ANN). Neural network architectures of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

  17. Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Anson Chui Yan Tang

    2012-01-01

    Full Text Available In view of lacking a quantifiable traditional Chinese medicine (TCM pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group. A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

  18. Microwave-vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks.

    Science.gov (United States)

    Motavali, Ali; Najafi, Gholam Hassan; Abbasi, Solayman; Minaei, Saeid; Ghaderi, Abdurrahman

    2013-08-01

    Drying characteristics of sour cherries were determined using microwave vacuum drier at various microwave powers (360, 600, 840, 1200 W) and absolute pressures (200, 400, 600, 800 mbars). In addition, using the artificial neural networks (ANN), trained by standard Back-Propagation algorithm, the effects of microwave power, pressure and drying time on moisture ratio (MR) and drying rate (DR) were investigated Based on the evaluation of experimental data fitting with semi-theoretical and empirical models, the Midilli et al. model was selected as the most appropriate one. Furthermore, the ANN model was able to predict the moisture ratio and drying rate quite well with determination coefficients (R(2)) of 0.9996, 0.9961 and 0.9958 for training, validation and testing, respectively. The prediction Mean Square Error of ANN was about 0.0003, 0.0071 and 0.0053 for training, validation and testing, respectively. This parameter signifies the difference between the desired outputs (as measured values) and the simulated values by the model. The good agreement between the experimental data and ANN model leads to the conclusion that the model adequately describes the drying behavior of sour cherries, in the range of operating conditions tested.

  19. Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents

    Energy Technology Data Exchange (ETDEWEB)

    Shokir, Eissa Mohamed El-Moghawry; El-Midany, Ayman Abdel-Hamid [Cairo University, Giza (Egypt); Al-Homadhi, Emad Souliman; Al-Mahdy, Osama [King Saud University, Riyadh (Saudi Arabia)

    2014-08-15

    This paper presents the application of artificial neural networks (ANN) to develop new models of liquid solvent dissolution of supercritical fluids with solutes in the presence of cosolvents. The neural network model of the liquid solvent dissolution of CO{sub 2} was built as a function of pressure, temperature, and concentrations of the solutes and cosolvents. Different experimental measurements of liquid solvent dissolution of supercritical fluids (CO{sub 2}) with solutes in the presence of cosolvents were collected. The collected data are divided into two parts. The first part was used in building the models, and the second part was used to test and validate the developed models against the Peng- Robinson equation of state. The developed ANN models showed high accuracy, within the studied variables range, in predicting the solubility of the 2-naphthol, anthracene, and aspirin in the supercritical fluid in the presence and absence of co-solvents compared to (EoS). Therefore, the developed ANN models could be considered as a good tool in predicting the solubility of tested solutes in supercritical fluid.

  20. Grope in the Dark - Hydrological modelling of the artificial Chicken Creek catchment without validation possibilities

    Science.gov (United States)

    Hölzel, H.; Rössler, O.; Diekkrüger, B.

    Environmental modelling is often reduced to a calibration-validation exercise in which a thorough understanding of the processes involved is less important than sophisticated parameter estimation tools. A problem occurs when no data are available for calibration and validation. To compare the ability of different models and modellers to describe the hydrological behaviour of ungauged catchments, a model comparison was initiated by Transregional Collaboration Research Centre 38 (SFB/TRR 38). The comparison was conducted in several consecutive stages, in which knowledge of the investigated artificial catchment of approximately 6 ha was enhanced stepwise. For modelling purposes, all basic input data were given, but neither discharge nor soil moisture measurements were provided. This hard information was supplemented stepwise by soft (qualitative) information mostly obtained by a field inspection after the first modelling stage. In this study, the process-based distributed Water balance Simulation Model (WaSiM-ETH) was applied. An initial perceptual model was developed without site-specific knowledge. Significant modifications to the perceptual model were conducted using the available soft information. These modifications resulted in more reliable water fluxes. The main changes, such as the importance of a sealed topsoil layer, vegetation development, initial conditions, and the effects of a small pond on discharge behaviour, were related to process understanding. Although hard information on the water balance was still missing, the qualitative behaviour of the model seemed to be realistic. This underlines the importance of soft information for successful modelling, which is especially important if ungauged catchments are to be investigated.

  1. Modeling a thick unsaturated zone at San Gorgonio Pass, California: lessons learned after five years of artificial recharge

    Science.gov (United States)

    Flint, Alan L.; Ellett, Kevin M.; Christensen, Allen H.; Martin, Peter

    2012-01-01

    The information flow among the tasks of framework assessment, numerical modeling, model forecasting and hind casting, and system-performance monitoring is illustrated. Results provide an understanding of artificial recharge in high-altitude desert settings where large vertical distances may separate application ponds from their target aquifers. Approximately 3.8 million cubic meters of surface water was applied to spreading ponds from 2003–2007 to artificially recharge the underlying aquifer through a 200-meter thick unsaturated zone in the San Gorgonio Pass area in southern California. A study was conducted between 1997 and 2003, and a numerical model was developed to help determine the suitability of the site for artificial recharge. Ongoing monitoring results indicated that the existing model needed to be modified and recalibrated to more accurately predict artificial recharge at the site. The objective of this work was to recalibrate the model by using observation of the application rates, the rise and fall of the water level above a perching layer, and the approximate arrival time to the water table during the 5-yr monitoring period following initiation of long-term artificial recharge. Continuous monitoring of soil-matric potential, temperature, and water levels beneath the site indicated that artificial recharge reached the underlying water table between 3.75 and 4.5 yr after the initial application of the recharge water. The model was modified to allow the simulation to more adequately match the perching layer dynamics and the time of arrival at the water table. The instrumentation also showed that the lag time between changes in application of water at the surface and the response at the perching layer decreased from about 4 mo to less than 1 mo due to the wet-up of the unsaturated zone and the increase in relative permeability. The results of this study demonstrate the importance of iteratively monitoring and modeling the unsaturated zone in layered

  2. Artificial Neural Network Integrated Artificial Endocrine System Model%一种与人工神经网络结合的人工内分泌系统模型

    Institute of Scientific and Technical Information of China (English)

    林广栋; 王煦法; 尤海峰

    2012-01-01

    Endocrine system of human body is highly self-adaptive and self-regulated. In endocrine system, thyroid hormone plays an important role. It enables humans to react violently in emergence, thus increases the chance of survival. In this work, a new artificial endocrine model called TAES (Thyroid hormone inspired Artificial Endocrine System) is proposed. TAES is inspired by the secretion, storage, and release mechanism of thyroid hormone. It can increase system's adaptability towards dynamic environment. In this work, TAES is applied to robot control system. Simulated experiments are carried out and the experimental results prove the TAES's validity.%人体的内分泌系统具有强大的自适应、自调节能力.在人体的内分泌系统中,甲状腺激素能使人类在紧急情况下产生异常迅速的反应,从而增加人类的生存概率.本文借鉴甲状腺激素的分泌,储存和释放机制,提出一种新的与人工神经网络结合的人工内分泌模型,称为TAES(Thyroid hormone inspired Artificial Endocrine System)模型.TAES模型可以增加控制系统对动态环境的适应性.本文将TAES模型应用于机器人避障实验中.实验结果表明,该系统可以增加机器人在紧急情况下的避障能力.

  3. Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce

    Directory of Open Access Journals (Sweden)

    Wei-Chin Lin

    2009-04-01

    Full Text Available Greenhouse-grown butter lettuce (Lactuca sativa L. can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN models to predict the remaining shelf life (RSL under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.

  4. The Extent of the Crack on Artificial Simulation Models with CBCT and Periapical Radiography

    Science.gov (United States)

    Wang, Shuang; Xu, Yiran; Shen, Zhengyan; Li, Minghua; Wu, Ligeng

    2017-01-01

    Background The aim of this study was to investigate the extent of the crack of a cracked tooth on an artificial simulation model with Periapical Radiography (PR) and cone beam computed tomography (CBCT) in vitro, providing the basis for early diagnosis and an appropriate treatment plan. Methods Forty-four teeth with different extents of artificial cracks, created by exposure to liquid nitrogen after hot water at 100°C, were collected. They were subjected to PR and CBCT. Micro-computed tomography (micro-CT) examination, regarded as a relatively more accurate measurement than others, was used to measure and record the crack depth. Three observers, an endodontic graduate student, an experienced endodontist, and an experienced radiologist, examined the PR and CBCT results independently, and the presence or absence of cracks with PR and CBCT were respectively recorded. The external consistency ICC with 95% confidence interval (95% CI) was used to analyze the consistency among the graduate student, endodontist, and radiologist; ROC curves were used for the analysis of diagnostic performance of both radiographic modalities for tooth cracks with crack depth. Results For the interpretation of the PR results, there were statistically significant differences among the three different observers (P < 0.001), and the interpretation of the CBCT results (P < 0.001). In the group of results read by the graduate student, the sensitivity of diagnosis with CBCT and PR was 77.27% and 22.73%, respectively (P < 0.001). In the group of results read by the endodontist, the sensitivity of diagnosis with CBCT and PR was 81.81% and 8.19%, respectively (P < 0.001). In the group of results read by the radiologist, the sensitivity of diagnosis with CBCT and PR was 88.64% and 11.36%, respectively (P < 0.001). As for CBCT diagnosis, the critical value for the graduate, endodontist, and radiologist was 3.20 mm, 2.06 mm, and 1.24 mm, respectively. For the PR diagnosis, the critical value for the

  5. Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Priya Selvanathan Shanmuga

    2016-01-01

    Full Text Available The advent of solar energy as the best alternative to traditional energy sources has led to an extensive study on the measurement and prediction of solar radiation. Devices such as pyranometer, pyrrheliometer, global UV radiometer are used for the measurement of solar radiation. The solar radiation measuring instruments available at Innovation Center, MIT Manipal were integrated with a Raspberry Pi to allow remote access to the data through the university Local Area Network. The connections of the data loggers and the Raspberry Pi were enclosed in a plastic box to prevent damage from the rainfall and humidity in Manipal. The solar radiation data was used to validate an Artificial Neural Network model which was developed using various meterological data from 2011-2015.

  6. Computational Approaches to Modeling Artificial Emotion -– An overview of the Proposed Solutions

    Directory of Open Access Journals (Sweden)

    Zdzislaw eKOWALCZUK

    2016-04-01

    Full Text Available Cybernetic approach to modeling artificial emotion through the use of different theories of psychology is considered in this paper, presenting a review of twelve proposed solutions: ActAffAct, FLAME, EMA, ParleE, FearNot!, FAtiMA, WASABI, Cathexis, KARO, MAMID, FCM, and xEmotion. The main motivation for this study is founded on the hypothesis that emotions can play a definite utility role of scheduling variables in the construction of intelligent autonomous systems, agents and mobile robots. In this review we also include an innovative and panoptical, comprehensive system, referred to as the Intelligent System of Decision-making (ISD, which has been employed in practical applications of various autonomous units, and which applies as its part the xEmotion, taking into consideration the personal aspects of emotions, affects (short term emotions and mood (principally, long term emotions.

  7. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    Science.gov (United States)

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  8. Back stepping-Based-PID-Controller Designed for an Artificial Pancreas model

    Directory of Open Access Journals (Sweden)

    ShaimaMahmou Mahdi

    2011-01-01

    Full Text Available Artificial pancreas is simulated to handle Type I diabetic patients under intensive care by automatically controlling the insulin infusion rate. A Backstepping technique is used to apply the effect of PID controller to blood glucose level since there is no direct relation between insulin infusion (the manipulated variable and glucose level in Bergman’s system model subjected to an oral glucose tolerance test by applying a meal translated into a disturbance. Backstepping technique is usually recommended to stabilize and control the states of Bergman's class of nonlinear systems. The results showed a very satisfactory behavior of glucose deviation to a sudden rise represented by the meal that increase the blood glucose

  9. An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

    Directory of Open Access Journals (Sweden)

    Karina Di Scala

    2013-09-01

    Full Text Available In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C and at three air flow-rates (0.5, 1, and 1.5 m/s. Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

  10. Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks

    CERN Document Server

    Barthélemy, Johan; Collier, Louise; Hallet, Vincent; Moriamé, Marie; Sartenaer, Annick

    2016-01-01

    Groundwater and rock are intensively exploited in the world. When a quarry is deepened the water table of the exploited geological formation might be reached. A dewatering system is therefore installed so that the quarry activities can continue, possibly impacting the nearby water catchments. In order to recommend an adequate feasibility study before deepening a quarry, we propose two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. The levels of each index (low, medium, high, very high) correspond to the potential impact of the quarry on the regional hydrogeology. The first index is based on a discrete choice modelling methodology while the second is relying on an artificial neural network. It is shown that these two complementary approaches (the former being probabilistic while the latter fully deterministic) are able to predict accurately the level of interaction. Their use is finally illustrate...

  11. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis

    Science.gov (United States)

    Nian, Rui; Liu, Fang; He, Bo

    2013-01-01

    Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA) framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs). PMID:23863855

  12. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis

    Directory of Open Access Journals (Sweden)

    Bo He

    2013-07-01

    Full Text Available Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs.

  13. Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model

    Directory of Open Access Journals (Sweden)

    A. Papagera

    2014-12-01

    Full Text Available Lake Koronia is located in the North part of Greece and is protected by the Ramsar Convention of wetlands. A deficit in the water balance has been presented at the last twenty years due to the excessive water consumption for agricultural uses. This research is an attempt to simulate water flow with MIKE SHE model in order to observe how the water is allocated in the study area. The results of water flow module used for the estimation of Lake’s water balance for 4 hydrological years (2008-2012. Furthermore the Artificial Neural Networks (ANNs was used for the prediction of water flow in two sub-catchments. The coefficient correlation (R was found for Bogdanas (0.9 and Kolxikos (0.86. The Root Mean Square Error (RMSE and the Mean Absolute Percentages Error (MAPE were also calculated in order to evaluate the quality of the ANNs results.

  14. Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

    Science.gov (United States)

    Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

    2016-11-01

    This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production.

  15. Modeling of useful operating life of radioelectronics

    Directory of Open Access Journals (Sweden)

    Nevlyudova V. V.

    2014-08-01

    Full Text Available The author considers the possibility of using the laws of nonequilibrium thermodynamics to determine the relationship between controlled parameters of radioelectronics and the displayed environment, as well as the construction of a deterministic model of the processes of manufacturing defects development. This possibility is based on the observed patterns of change in the amount of content area, in accordance with the principles of behavior of the thermodynamic parameters characterizing the state of the real environment (entropy, the quantity of heat, etc.. The equation for the evolution of the technical state of radioelectronics is based on the deterministic kinetic model of the processes occurring in the multi-component environment, and on the observation model, which takes into account the errors caused by external influences instability and uncertainty.

  16. Fast radiative transfer of dust reprocessing in semi-analytic models with artificial neural networks

    CERN Document Server

    Silva, Laura; Granato, Gian Luigi

    2012-01-01

    A serious concern for semi-analytical galaxy formation models, aiming to simulate multi-wavelength surveys and to thoroughly explore the model parameter space, is the extremely time consuming numerical solution of the radiative transfer of stellar radiation through dusty media. To overcome this problem, we have implemented an artificial neural network algorithm in the radiative transfer code GRASIL, in order to significantly speed up the computation of the infrared SED. The ANN we have implemented is of general use, in that its input neurons are defined as those quantities effectively determining the shape of the IR SED. Therefore, the training of the ANN can be performed with any model and then applied to other models. We made a blind test to check the algorithm, by applying a net trained with a standard chemical evolution model (i.e. CHE_EVO) to a mock catalogue extracted from the SAM MORGANA, and compared galaxy counts and evolution of the luminosity functions in several near-IR to sub-mm bands, and also t...

  17. Three-dimensional artificial neural network model of the dayside magnetopause

    CERN Document Server

    Dmitriev, A V

    2013-01-01

    Artificial Neural Networks (ANN) from package NeuroShell 2 are applied for modeling of dayside magnetopause. The model data set is based on the magnetopause crossings and on the corresponding hour-averaged measurements of solar wind plasma and interplanetary magnetic field. ANN model represents 3D shape and size of the dayside magnetopause in dependence on solar wind dynamic pressure, Bz and By components of interplanetary magnetic field. The model permits firstly to describe dynamics of the magnetic cusp and global asymmetry of the dayside magnetopause under wide range of the external conditions. Slightly change of the magnetopause size with change of By absolute value is presented quantitatively in the form of analytical expression. ANN model shows three regimes of the magnetopause dynamics that controlled by Bz component: pressure balance regime (Bz>0 nT), reconnection regime (0>Bz>-10 nT) and regime of reconnection saturation (Bz<-10 nT). Three different regimes of magnetopause dynamics at the subsolar...

  18. Artificial neural network modelling of continuous wet granulation using a twin-screw extruder.

    Science.gov (United States)

    Shirazian, Saeed; Kuhs, Manuel; Darwish, Shaza; Croker, Denise; Walker, Gavin M

    2017-04-15

    Computational modelling of twin-screw granulation was conducted by using an artificial neural network (ANN) approach. Various ANN configurations were considered with changing hidden layers, nodes and activation functions to determine the optimum model for the prediction of the process. The neural networks were trained using experimental data obtained for granulation of pure microcrystalline cellulose using a 12mm twin-screw extruder. The experimental data were obtained for various liquid binder (water) to solid ratios, screw speeds, material throughputs, and screw configurations. The granulate particle size distribution, represented by d-values (d10, d50, d90) were considered the response in the experiments and the ANN model. Linear and non-linear activation functions were taken into account in the simulations and more accurate results were obtained for non-linear function in terms of prediction. Moreover, 2 hidden layers with 2 nodes per layer and 3-Fold cross-validation method gave the most accurate simulation. The results revealed that the developed ANN model is capable of predicting granule size distribution in high-shear twin-screw granulation with a high accuracy in different conditions, and can be used for implementation of model predictive control in continuous pharmaceutical manufacturing.

  19. Offline and online detection of damage using autoregressive models and artificial neural networks

    Science.gov (United States)

    Omenzetter, Piotr; de Lautour, Oliver R.

    2007-04-01

    Developed to study long, regularly sampled streams of data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring. In this research, Autoregressive (AR) models are used in conjunction with Artificial Neural Networks (ANNs) for damage detection, localisation and severity assessment. In the first reported experimental exercise, AR models were used offline to fit the acceleration time histories of a 3-storey test structure in undamaged and various damaged states when excited by earthquake motion simulated on a shake table. Damage was introduced into the structure by replacing the columns with those of a thinner thickness. Analytical models of the structure in both damaged and undamaged states were also developed and updated using experimental data in order to determine structural stiffness. The coefficients of AR models were used as damage sensitive features and input into an ANN to build a relationship between them and the remaining structural stiffness. In the second, analytical exercise, a system with gradually progressing damage was numerically simulated and acceleration AR models with exogenous inputs were identified recursively. A trained ANN was then required to trace the structural stiffness online. The results for the offline and online approach showed the efficiency of using AR coefficient as damage sensitive features and good performance of the ANNs for damage detection, localization and quantification.

  20. Use of artificial neural networks for electrical conductivity modeling in Asi River

    Science.gov (United States)

    Ghorbani, Mohammad Ali; Aalami, Mohammad Taghi; Naghipour, Leila

    2015-10-01

    This study aims to model monthly electrical conductivity (EC) values in the Asi River using artificial neural networks (ANNs) to evaluate water quality conditions using pH, temperature, water discharge, sodium, sum of calcium and magnesium concentrations. The results are compared using multiple linear regression (MLR). Recorded data are available at a gauging site in Antakya, Turkey, for the period from 1984 to 2008. Comparing the modeled values by ANNs with the experimental data indicates that neural network model with seven neurons in hidden layer provides accurate results (R 2 = 0.968, RMSE = 46.927 µS/cm, MAE = 32.462 µS/cm and MRSE = 0.0029 for the training data and R 2 = 0.965, RMSE = 50.810 µS/cm, MAE = 37.495 µS/cm and MRSE = 0.0024 for the testing data). The Garson method of the connection weights of the network was used to study the relative % contribution of each of the input variables. It was found that the sum of calcium and magnesium concentration and temperature had the most effect on the predicted EC. The results indicate that two proposed models were able to approximate the EC parameter reasonably well; however, the ANN was found to perform better than the MLR model.

  1. Modeling of methane oxidation in landfill cover soil using an artificial neural network.

    Science.gov (United States)

    Abushammala, Mohammed F M; Basri, Noor Ezlin Ahmad; Elfithri, Rahmah; Younes, Mohammad K; Irwan, Dani

    2014-02-01

    Knowing the fraction of methane (CH4) oxidized in landfill cover soils is an important step in estimating the total CH4 emissions from any landfill. Predicting CH4 oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH4 oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O2) concentration at a depth of 10 cm in cover soil, and CH4 concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH4 oxidation with a MSE of 0.0082, a coefficient of determination (R2) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

  2. Towards a Humanized Mouse Model of Liver Stage Malaria Using Ectopic Artificial Livers

    Science.gov (United States)

    Ng, Shengyong; March, Sandra; Galstian, Ani; Gural, Nil; Stevens, Kelly R.; Mota, Maria M.; Bhatia, Sangeeta N.

    2017-01-01

    The malaria liver stage is an attractive target for antimalarial development, and preclinical malaria models are essential for testing such candidates. Given ethical concerns and costs associated with non‐human primate models, humanized mouse models containing chimeric human livers offer a valuable alternative as small animal models of liver stage human malaria. The best available human liver chimeric mice rely on cellular transplantation into mice with genetically engineered liver injury, but these systems involve a long and variable humanization process, are expensive, and require the use of breeding-challenged mouse strains which are not widely accessible. We previously incorporated primary human hepatocytes into engineered polyethylene glycol (PEG)-based nanoporous human ectopic artificial livers (HEALs), implanted them in mice without liver injury, and rapidly generated human liver chimeric mice in a reproducible and scalable fashion. By re-designing the PEG scaffold to be macroporous, we demonstrate the facile fabrication of implantable porous HEALs that support liver stage human malaria (P. falciparum) infection in vitro, and also after implantation in mice with normal liver function, 60% of the time. This proof-of-concept study demonstrates the feasibility of applying a tissue engineering strategy towards the development of scalable preclinical models of liver stage malaria infection for future applications. PMID:28361899

  3. APPLICATION OF THE OVERLAY WEIGHTED MODEL AND BOOLEAN LOGIC TO DETERMINE THE BEST LOCATIONS FOR ARTIFICIAL RECHARGE OF GROUNDWATER

    Directory of Open Access Journals (Sweden)

    Peter H.S. Riad

    2011-01-01

    Full Text Available With population increase, lack of conventional fresh water resources and uncertainties due to climate change, there is growing interest in the arid and semi-arid areas to increase groundwater recharge with recycled water. Finding the best locations for artificial recharge of groundwater in such areas is one of the most crucial design steps to guarantee the long life and the sustainability of these projects. This study presents two ways to go about performing analysis; creating a suitability map to find out the suitability of every location on the map and another way is querying the created data sets to obtain a Boolean result of true or false map. These techniques have been applied on Sadat Industrial City which is located in a semi arid area in the western desert fringes of The Nile delta in the north west of Egypt. Thematic layers for number of parameters were prepared from some maps and satellite images and they have been classified, weighted and integrated in ArcGIS environment. By the means of the overlay weighted model in ArcGIS a suitability map which is classified into number of priority zones was obtained and it could be compared with the obtained true-false map of Boolean logic. Both methods suggested mostly the northern parts of the city for groundwater recharge; however the weighted model could give more accurate suitability map while Boolean logic suggested wider ranges of areas. This study recommends Boolean logic as a first estimator for locating the best locations as it is easier and not time consuming, while the overlay weighted model for more accurate results.

  4. APPLICATION OF THE OVERLAY WEIGHTED MODEL AND BOOLEAN LOGIC TO DETERMINE THE BEST LOCATIONS FOR ARTIFICIAL RECHARGE OF GROUNDWATER

    Directory of Open Access Journals (Sweden)

    Max Billib

    2011-12-01

    Full Text Available With population increase, lack of conventional fresh water resources and uncertainties due to climate change, there is growing interest in the arid and semi-arid areas to increase groundwater recharge with recycled water. Finding the best locations for artificial recharge of groundwater in such areas is one of the most crucial design steps to guarantee the long life and the sustainability of these projects. This study presents two ways to go about performing analysis; creating a suitability map to find out the suitability of every location on the map and another way is querying the created data sets to obtain a Boolean result of true or false map. These techniques have been applied on Sadat Industrial City which is located in a semi arid area in the western desert fringes of The Nile delta in the north west of Egypt. Thematic layers for number of parameters were prepared from some maps and satellite images and they have been classified, weighted and integrated in ArcGIS environment. By the means of the overlay weighted model in ArcGIS a suitability map which is classified into number of priority zones was obtained and it could be compared with the obtained true-false map of Boolean logic. Both methods suggested mostly the northern parts of the city for groundwater recharge; however the weighted model could give more accurate suitability map while Boolean logic suggested wider ranges of areas. This study recommends Boolean logic as a first estimator for locating the best locations as it is easier and not time consuming, while the overlay weighted model for more accurate results.

  5. Discrimination-based Artificial Immune System: Modeling the Learning Mechanism of Self and Non-self Discrimination for Classification

    Directory of Open Access Journals (Sweden)

    Kazushi Igawa

    2007-01-01

    Full Text Available This study presents a new artificial immune system for classification. It was named discrimination-based artificial immune system (DAIS and was based on the principle of self and non-self discrimination by T cells in the human immune system. Ability of a natural immune system to distinguish between self and non-self molecules was applicable for classification in a way that one class was distinguished from others. We model this and the mechanism of the education in a thymus for classification. Especially, we introduce the method to decide the recognition distance threshold of the artificial lymphocyte, as the negative selection algorithm. We apply DAIS to real world datasets and show its performance to be comparable to that of other classifier systems. We conclude that this modeling was appropriate and DAIS was a useful classifier.

  6. Modeling Operating Modes during Plant Life Cycle

    DEFF Research Database (Denmark)

    Jørgensen, Sten Bay; Lind, Morten

    2012-01-01

    Modelling process plants during normal operation requires a set a basic assumptions to define the desired functionalities which lead to fullfillment of the operational goal(-s) for the plant. However during during start-up and shut down as well as during batch operation an ensemble of interrelate...

  7. Artificial Life Animation Engine Key Technology Research Based on Embedded System%基于嵌入式系统的人工生命动画引擎关键技术研究

    Institute of Scientific and Technical Information of China (English)

    王丽丽; 李国义; 孟先宇; 李晗; 王飞

    2012-01-01

    The traditional animation using keyframe animation design is the lack of autonomy,a single plot,to a certain extent,can not meet the needs of users.This article uses the method based on artificial life animation engine design.Taking into account the limited hardware performance in embedded systems,artificial life animation new methods to adapt to embedded systems,using the hierarchical algorithm for constructing artificial life model,in order to improve the animation speed,running efficiency.In addition,because 50% of the CPU processing time spent on the top of the rendering engine,focusing on several key technologies in the graphics rendering,and on the basis of a combination of theory and practice,a simple graphics rendering engine.%传统动画采用关键帧技术,设计出的动画缺乏自主性、情节单一、在一定程度上不能满足用户的需求。因此本文采用基于人工生命的方法,进行动画引擎的设计。考虑到嵌入式系统中硬件性能有限的问题,提出了适应嵌入式系统的人工生命动画新方法,即采用分层算法构建人工生命模型,以提高动画运行速度、运行效率。另外,由于50%的CPU处理时间花费在渲染引擎上面,重点分析了图形渲染中的几个关键技术,并在理论与实践相结合的基础上实现了一个简单的图形渲染引擎。

  8. Evaluation Model of Life Loss Due to Dam Failure

    Science.gov (United States)

    Huang, Dongjing

    2016-04-01

    Dam failure poses a serious threat to human life, however there is still lack of systematic research on life loss which due to dam failure in China. From the perspective of protecting human life, an evaluation model for life loss caused by dam failure is put forward. The model building gets three progressive steps. Twenty dam failure cases in China are preferably chosen as the basic data, considering geographical location and construction time of dams, as well as various conditions of dam failure. Then twelve impact factors of life loss are selected, including severity degree of flood, population at risk, understanding of dam failure, warning time, evacuation condition, number of damaged buildings, water temperature, reservoir storage, dam height, dam type, break time and distance from flood area to dam. And through principal component analysis, it gets four principal components consisting of the first flood character principle component, the second warning system principle component, the third human character principle component and the fourth space-time impact principle component. After multivariate nonlinear regression and ten-fold validation in combination, the evaluation model for life loss is finally established. And the result of the proposed model is closer to the true value and better in fitting effect in comparison with the results of RESCDAM method and M. Peng method. The proposed model is not only applied to evaluate life loss and its rate under various kinds of dam failure conditions in China, but also provides reliable cause analysis and prediction approach to reduce the risk of life loss.

  9. 人造生命技术及其伦理学讨论综述%The Review of Artificial Life Technology and its Ethics Discussion

    Institute of Scientific and Technical Information of China (English)

    张翮

    2011-01-01

    生命科学的迅猛发展不可避免地涉及人类自身,随着后基因组时代的到来,合成生物学、人造生命技术的发展必将广泛地应用于能源、环境、材料、医药等诸多领域,因此科学技术进步与传统伦理之间的矛盾也必将出现,从而影响和改变人类未来的生活。%The rapid development of life science inevitably involve human itself,along with post genome era,synthetic biology,artificial life technology development will be widely used in energy,environment,material,medicine and so on many domains and so on scientific and technological progress and the contradiction between the traditional ethics,which will also appear to influence change and human life in the future.

  10. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

    Science.gov (United States)

    Qiu, Mingyue; Song, Yu

    2016-01-01

    In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

  11. Prediction of soil temperature using regression and artificial neural network models

    Science.gov (United States)

    Bilgili, Mehmet

    2010-12-01

    In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the "best" regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.

  12. Predicting Free Flow Speed and Crash Risk of Bicycle Traffic Flow Using Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cheng Xu

    2015-01-01

    Full Text Available Free flow speed is a fundamental measure of traffic performance and has been found to affect the severity of crash risk. However, the previous studies lack analysis and modelling of impact factors on bicycles’ free flow speed. The main focus of this study is to develop multilayer back propagation artificial neural network (BPANN models for the prediction of free flow speed and crash risk on the separated bicycle path. Four different models with considering different combinations of input variables (e.g., path width, traffic condition, bicycle type, and cyclists’ characteristics were developed. 459 field data samples were collected from eleven bicycle paths in Hangzhou, China, and 70% of total samples were used for training, 15% for validation, and 15% for testing. The results show that considering the input variables of bicycle types and characteristics of cyclists will effectively improve the accuracy of the prediction models. Meanwhile, the parameters of bicycle types have more significant effect on predicting free flow speed of bicycle compared to those of cyclists’ characteristics. The findings could contribute for evaluation, planning, and management of bicycle safety.

  13. The influence of an antitumor lipid - erucylphosphocholine - on artificial lipid raft system modeled as Langmuir monolayer.

    Science.gov (United States)

    Wnętrzak, Anita; Łątka, Kazimierz; Makyła-Juzak, Katarzyna; Zemla, Joanna; Dynarowicz-Łątka, Patrycja

    2015-01-01

    Outer layer of cellular membrane contains ordered domains enriched in cholesterol and sphingolipids, called 'lipid rafts', which play various biological roles, i.e., are involved in the induction of cell death by apoptosis. Recent studies have shown that these domains may constitute binding sites for selected drugs. For example alkylphosphocholines (APCs), which are new-generation antitumor agents characterized by high selectivity and broad spectrum of activity, are known to have their molecular targets located at cellular membrane and their selective accumulation in tumor cells has been hypothesized to be linked with the alternation of biophysical properties of lipid rafts. To get a deeper insight into this issue, interactions between representative APC: erucylphosphocholine, and artificial lipid raft system, modeled as Langmuir monolayer (composed of cholesterol and sphingomyelin mixed in 1:2 proportion) were investigated. The Langmuir monolayer experiments, based on recording surface pressure-area isotherms, were complemented with Brewster angle microscopy results, which enabled direct visualization of the monolayers structure. In addition, the investigated monolayers were transferred onto solid supports and studied with AFM. The interactions between model raft system and erucylphosphocholine were analyzed qualitatively (with mean molecular area values) as well as quantitatively (with ΔG(exc) function). The obtained results indicate that erucylphosphocholine introduced to raft-mimicking model membrane causes fluidizing effect and weakens the interactions between cholesterol and sphingomyelin, which results in phase separation at high surface pressures. This leads to the redistribution of cholesterol molecules in model raft, which confirms the results observed in biological studies.

  14. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    Science.gov (United States)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

  15. Linear and nonlinear ARMA model parameter estimation using an artificial neural network

    Science.gov (United States)

    Chon, K. H.; Cohen, R. J.

    1997-01-01

    This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

  16. Artificial neural network modeling for fission gas release in LWR UO 2 fuel under RIA conditions

    Science.gov (United States)

    Koo, Yang-Hyun; Oh, Jae-Yong; Lee, Byung-Ho; Tahk, Young-Wook; Song, Kun-Woo

    2010-10-01

    A fission gas release (FGR) model was developed by using an artificial neural network method to predict fission gas release in UO 2 fuel under reactivity initiated accident (RIA) conditions. Based on the test data obtained in the CABRI test reactor and nuclear safety research reactor, the model takes into account the effect of the five parameters: pellet average burnup, peak fuel enthalpy, the ratio of peak fuel enthalpy to pulse width, fission gas release during base-irradiation, and grain size of a fuel pellet. The parametric study of the model, producing a physically reasonable trend of FGR for each parameter, shows that the pellet average burnup and the ratio of peak fuel enthalpy to pulse width are two of the most important parameters. Depending on the combination of input values for the five parameters, the application of the model to a fuel rod under typical RIA conditions of light water reactor produces 1.7-14.0% of FGR for the pellet average burnup ranging from 20 to 70 MW d/kg U.

  17. HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    AbdoulAhad Validi

    2012-01-01

    Full Text Available A Dynamic model of Homogeneous Charge Compression Ignition (HCCI, based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC. A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA, so that combustion initiates and SOC timing posits in the desired crank angle. The best SOC timing to achieve higher performance and efficiency in HCCI engines is between 5 and 15 degrees crank angle (CAD after top dead center (TDC. To achieve this SOC timing, in the first case, the inlet temperature and equivalence ratio are optimized simultaneously and in the second case, compression ratio is optimized by GA. The model’s results are validated with previous works. The SOC timing can be predicted in less than 0.01 second and the CPU time savings are encouraging. This model can successfully be used for real engine control applications.

  18. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Ahmed Ramadan Suleiman

    2017-02-01

    Full Text Available This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN. A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c, type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components on the self-healing performance in cement-based materials.

  19. Artificial Neural Network Model to Estimate the Viscosity of Polymer Solutions for Enhanced Oil Recovery

    Directory of Open Access Journals (Sweden)

    Pan-Sang Kang

    2016-06-01

    Full Text Available Polymer flooding is now considered a technically- and commercially-proven method for enhanced oil recovery (EOR. The viscosity of the injected polymer solution is the key property for successful polymer flooding. Given that the viscosity of a polymer solution has a non-linear relationship with various influential parameters (molecular weight, degree of hydrolysis, polymer concentration, cation concentration of polymer solution, shear rate, temperature and that measurement of viscosity based on these parameters is a time-consuming process, the range of solution samples and the measurement conditions need to be limited and precise. Viscosity estimation of the polymer solution is effective for these purposes. An artificial neural network (ANN was applied to the viscosity estimation of FlopaamTM 3330S, FlopaamTM 3630S and AN-125 solutions, three commonly-used EOR polymers. The viscosities measured and estimated by ANN and the Carreau model using Lee’s correlation, the only method for estimating the viscosity of an EOR polymer solution in unmeasured conditions, were compared. Estimation accuracy was evaluated by the average absolute relative deviation, which has been widely used for accuracy evaluation of the results of ANN models. In all conditions, the accuracy of the ANN model is higher than that of the Carreau model using Lee’s correlation.

  20. Incorporating Artificial Neural Networks in the dynamic thermal-hydraulic model of a controlled cryogenic circuit

    Science.gov (United States)

    Carli, S.; Bonifetto, R.; Savoldi, L.; Zanino, R.

    2015-09-01

    A model based on Artificial Neural Networks (ANNs) is developed for the heated line portion of a cryogenic circuit, where supercritical helium (SHe) flows and that also includes a cold circulator, valves, pipes/cryolines and heat exchangers between the main loop and a saturated liquid helium (LHe) bath. The heated line mimics the heat load coming from the superconducting magnets to their cryogenic cooling circuits during the operation of a tokamak fusion reactor. An ANN is trained, using the output from simulations of the circuit performed with the 4C thermal-hydraulic (TH) code, to reproduce the dynamic behavior of the heated line, including for the first time also scenarios where different types of controls act on the circuit. The ANN is then implemented in the 4C circuit model as a new component, which substitutes the original 4C heated line model. For different operational scenarios and control strategies, a good agreement is shown between the simplified ANN model results and the original 4C results, as well as with experimental data from the HELIOS facility confirming the suitability of this new approach which, extended to an entire magnet systems, can lead to real-time control of the cooling loops and fast assessment of control strategies for heat load smoothing to the cryoplant.

  1. Numerical model of total artificial heart hemodynamics and the effect of its size on stress accumulation.

    Science.gov (United States)

    Marom, Gil; Chiu, Wei-Che; Slepian, Marvin J; Bluestein, Danny

    2014-01-01

    The total artificial heart (TAH) is a bi-ventricular mechanical circulatory support device that replaces the heart in patients with end-stage congestive heart failure. The device acts as blood pump via pneumatic activation of diaphragms altering the volume of the ventricular chambers. Flow in and out of the ventricles is controlled by mechanical heart valves. The aim of this study is to evaluate the flow regime in the TAH and to estimate the thrombogenic potential during systole. Toward that goal, three numerical models of TAHs of differing sizes, that include the deforming diaphragm and the blood flow from the left chamber to the aorta, are introduced. A multiphase model with injection of platelet particles is employed to calculate their trajectories. The shear stress accumulation in the three models are calculated along the platelets trajectories and their probability density functions, which represent the `thrombogenic footprint' of the device are compared. The calculated flow regime successfully captures the mitral regurgitation and the flows that open and close the aortic valve during systole. Physiological velocity magnitudes are found in all three models, with higher velocities and increased stress accumulation predicted for smaller devices.

  2. Modelling of an ICS solar water heater using artificial neural networks and TRNSYS

    Energy Technology Data Exchange (ETDEWEB)

    Souliotis, M.; Tripanagnostopoulos, Y. [Physics Department, University of Patras, Patras 26504, ACHAIA (Greece); Kalogirou, S. [Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, Limassol 3603 (Cyprus)

    2009-05-15

    A study, in which a suitable artificial neural network (ANN) and TRNSYS are combined in order to predict the performance of an Integrated Collector Storage (ICS) prototype, is presented. Experimental data that have been collected from outdoor tests of an ICS solar water heater with cylindrical water storage tank inside a CPC reflector trough were used to train the ANN. The ANN is then used through the Excel interface (Type 62) in TRNSYS to model the annual performance of the system by running the model with the values of a typical meteorological year for Athens, Greece. In this way the specific capabilities of both approaches are combined, i.e., use of the radiation processing and modelling power of TRNSYS together with the 'black box' modelling approach of ANNs. The details of the calculation steps of both methods that aim to perform an accurate prediction of the system performance are presented and it is shown that this new method can be used effectively for such predictions. (author)

  3. Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

    Science.gov (United States)

    Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

    2015-11-01

    Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The

  4. Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine

    Directory of Open Access Journals (Sweden)

    Mojdeh Piran

    2014-01-01

    Full Text Available In this research, manage the Internal Combustion (IC engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, multi inputs-multi outputs and time variant. One purpose of accurate modeling is to save development costs of real engines and minimizing the risks of damaging an engine when validating controller designs. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. A baseline estimator with varying parameter gain is designed with guaranteed stability to allow implementation of the proposed state feedback sliding mode methodology into a MATLAB simulation environment, where the sliding mode strategy is implemented into a model engine control module (“software”. To estimate the dynamic model of IC engine fuzzy inference engine is applied to baseline sliding mode methodology. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.

  5. Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process

    Directory of Open Access Journals (Sweden)

    Hossein Jafari Mansoorian

    2017-01-01

    Full Text Available Background & Aims of the Study: A feed forward artificial neural network (FFANN was developed to predict the efficiency of total petroleum hydrocarbon (TPH removal from a contaminated soil, using soil washing process with Tween 80. The main objective of this study was to assess the performance of developed FFANN model for the estimation of   TPH removal. Materials and Methods: Several independent repressors including pH, shaking speed, surfactant concentration and contact time were used to describe the removal of TPH as a dependent variable in a FFANN model. 85% of data set observations were used for training the model and remaining 15% were used for model testing, approximately. The performance of the model was compared with linear regression and assessed, using Root of Mean Square Error (RMSE as goodness-of-fit measure Results: For the prediction of TPH removal efficiency, a FANN model with a three-hidden-layer structure of 4-3-1 and a learning rate of 0.01 showed the best predictive results. The RMSE and R2 for the training and testing steps of the model were obtained to be 2.596, 0.966, 10.70 and 0.78, respectively. Conclusion: For about 80% of the TPH removal efficiency can be described by the assessed regressors the developed model. Thus, focusing on the optimization of soil washing process regarding to shaking speed, contact time, surfactant concentration and pH can improve the TPH removal performance from polluted soils. The results of this study could be the basis for the application of FANN for the assessment of soil washing process and the control of petroleum hydrocarbon emission into the environments.

  6. Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification.

    Science.gov (United States)

    Asadi, Somayeh; Hassan, Marwa; Nadiri, Ataallah; Dylla, Heather

    2014-01-01

    In recent years, the application of titanium dioxide (TiO₂) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO₂ on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO₂ solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

  7. Lucilia sericata strain from Colombia: Experimental colonization, life tables and evaluation of two artificial diets of the blowfly Lucilia sericata (Meigen) (Diptera: Calliphoridae), Bogotá, Colombia strain.

    Science.gov (United States)

    Rueda, Luis C; Ortega, Luis G; Segura, Nidya A; Acero, Víctor M; Bello, Felio

    2010-01-01

    The objective of this work was to establish, under experimental laboratory conditions, a colony of Lucilia sericata, Bogotá-Colombia strain, to build life tables and evaluate two artificial diets. This blowfly is frequently used in postmortem interval studies and in injury treatment. The parental adult insects collected in Bogotá were maintained in cages at 22°C±1 average temperature, 60%±5 relative humidity and 12 h photoperiodicity. The blowflies were fed on two artificial diets that were evaluated over seven continuous generations. Reproductive and population parameters were assessed. The life cycle of the species was expressed in the number of days of the different stages: egg = 0.8±0.1, larvae I = 1.1±0.02, larvae II = 1.94±0.16, larvae III = 3.5±0.54, pupae = 6.55±0.47, male adult = 28.7±0.83 and female adult = 33.5±1.0. Total survival from egg stage to adult stage was 91.2% for diet 1, while for diet 2 this parameter was 40.5%. The lifetime reproductive output was 184.51±11.2 eggs per female. The population parameters, as well as the reproductive output of the blowflies that were assessed, showed relatively high values, giving evidence of the continuous increase of the strain over the different generations and making possible its maintenance as a stable colony that has lasted for more than two years.

  8. Models of life: epigenetics, diversity and cycles

    Science.gov (United States)

    Sneppen, Kim

    2017-04-01

    This review emphasizes aspects of biology that can be understood through repeated applications of simple causal rules. The selected topics include perspectives on gene regulation, phage lambda development, epigenetics, microbial ecology, as well as model approaches to diversity and to punctuated equilibrium in evolution. Two outstanding features are repeatedly described. One is the minimal number of rules to sustain specific states of complex systems for a long time. The other is the collapse of such states and the subsequent dynamical cycle of situations that restitute the system to a potentially new metastable state.

  9. A predictive fatigue life model for anodized 7050 aluminium alloy

    OpenAIRE

    Chaussumier, Michel; Mabru, Catherine; Shahzad, Majid; Chieragatti, Rémy; Rezaï-Aria, Farhad

    2013-01-01

    International audience; The objective of this study is to predict fatigue life of anodized 7050 aluminum alloy specimens. In the case of anodized 7050-T7451 alloy, fractographic observations of fatigue tested specimens showed that pickling pits were the predominant sites for crack nucleation and subsequent failure. It has been shown that fatigue failure was favored by the presence of multiple cracks. From these experimental results, a fatigue life predictive model has been developed including...

  10. Service life modeling for fly ash in concrete

    Energy Technology Data Exchange (ETDEWEB)

    Copeland, K. [Boral Material Technologies Inc. (United States)

    2006-07-01

    Because virtually all durability aspects of concrete are improved with a reduction in permeability, the use of fly ash improves concrete durability. Concrete water demand is typically reduced when using fly ash leading to a lower water cementitious ratio. The Life 365 model is one example of a life cycle cost prediction model. It was originally created as a industry standard model to predict ingress of chloride in structures and mitigation technologies for controlling corrosion. This model allows users to input the actual diffusion data for a given mix design. Users can also select pre-set modifications such as fly ash to produce high performance concrete, or such corrosion inhibitors as stainless steel, epoxy coated reinforcement, or application of sealers or membranes. Other more recent computer based service life models attempt to improve on some of the perceived shortcomings of Life 365. One such model, STADIUM, predicts the movement of other ions in addition to chloride, including sulfate, hydroxyl, sodium, potassium, magnesium, and others, as well as several solid phases. The transport mechanism in this model is not limited to diffusion of ions in saturated conditions. One notable feature of STADIUM is its ability to account for movement of ions, and to adjust for coupling due to changes in concentration of various ions. This model will adjust the amount of predicted ions in solution in keeping with maximum solubilities of the respective compounds. 2 figs.

  11. The LAILAPS search engine: a feature model for relevance ranking in life science databases.

    Science.gov (United States)

    Lange, Matthias; Spies, Karl; Colmsee, Christian; Flemming, Steffen; Klapperstück, Matthias; Scholz, Uwe

    2010-03-25

    Efficient and effective information retrieval in life sciences is one of the most pressing challenge in bioinformatics. The incredible growth of life science databases to a vast network of interconnected information systems is to the same extent a big challenge and a great chance for life science research. The knowledge found in the Web, in particular in life-science databases, are a valuable major resource. In order to bring it to the scientist desktop, it is essential to have well performing search engines. Thereby, not the response time nor the number of results is important. The most crucial factor for millions of query results is the relevance ranking. In this paper, we present a feature model for relevance ranking in life science databases and its implementation in the LAILAPS search engine. Motivated by the observation of user behavior during their inspection of search engine result, we condensed a set of 9 relevance discriminating features. These features are intuitively used by scientists, who briefly screen database entries for potential relevance. The features are both sufficient to estimate the potential relevance, and efficiently quantifiable. The derivation of a relevance prediction function that computes the relevance from this features constitutes a regression problem. To solve this problem, we used artificial neural networks that have been trained with a reference set of relevant database entries for 19 protein queries. Supporting a flexible text index and a simple data import format, this concepts are implemented in the LAILAPS search engine. It can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. LAILAPS is publicly available for SWISSPROT data at http://lailaps.ipk-gatersleben.de.

  12. THE ROLE OF POROUS MEDIA IN MODELING FLUID FLOW WITHIN HOLLOW FIBER MEMBRANES OF THE TOTAL ARTIFICIAL LUNG.

    Science.gov (United States)

    Khanafer, Khalil; Cook, Keith; Marafie, Alia

    2012-01-01

    A numerical study was conducted to analyze fluid flow within hollow fiber membranes of the artificial lungs. The hollow fiber bundle was approximated using a porous media model. In addition, the transport equations were solved using the finite-element formulation based on the Galerkin method of weighted residuals. Comparisons with previously published work on the basis of special cases were performed and found to be in excellent agreement. A Newtonian viscous fluid model for the blood was used. Different flow models for porous media, such as the Brinkman-extended Darcy model, Darcy's law model, and the generalized flow model, were considered. Results were obtained in terms of streamlines, velocity vectors, and pressure distribution for various Reynolds numbers and Darcy numbers. The results from this investigation showed that the pressure drop across the artificial lung device increased with an increase in the Reynolds number. In addition, the pressure drop was found to increase significantly for small Darcy numbers.

  13. Fatigue Life Analysis of Rolling Bearings Based on Quasistatic Modeling

    Directory of Open Access Journals (Sweden)

    Wei Guo

    2015-01-01

    Full Text Available Rolling bearings are widely used in aeroengine, machine tool spindles, locomotive wheelset, and so forth. Rolling bearings are usually the weakest components that influence the remaining life of the whole machine. In this paper, a fatigue life prediction method is proposed based on quasistatic modeling of rolling bearings. With consideration of radial centrifugal expansion and thermal deformations on the geometric displacement in the bearings, the Jones’ bearing model is updated, which can predict the contact angle, deformation, and load between rolling elements and bearing raceways more accurately. Based on Hertz contact theory and contact mechanics, the contact stress field between rolling elements and raceways is calculated. A coupling model of fatigue life and damage for rolling bearings is given and verified through accelerated life test. Afterwards, the variation of bearing life is investigated under different working conditions, that is, axial load, radial load, and rotational speed. The results suggested that the working condition had a great influence on fatigue life of bearing parts and the order in which the damage appears on bearing parts.

  14. Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

    Science.gov (United States)

    Chappell, Jackie; Hawes, Nick

    2012-10-05

    Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended.

  15. Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

    Science.gov (United States)

    Chappell, Jackie; Hawes, Nick

    2012-01-01

    Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended. PMID:22927571

  16. Mathematical Modeling of the Origins of Life

    Science.gov (United States)

    Pohorille, Andrew

    2006-01-01

    The emergence of early metabolism - a network of catalyzed chemical reactions that supported self-maintenance, growth, reproduction and evolution of the ancestors of contemporary cells (protocells) was a critical, but still very poorly understood step on the path from inanimate to animate matter. Here, it is proposed and tested through mathematical modeling of biochemically plausible systems that the emergence of metabolism and its initial evolution towards higher complexity preceded the emergence of a genome. Even though the formation of protocellular metabolism was driven by non-genomic, highly stochastic processes the outcome was largely deterministic, strongly constrained by laws of chemistry. It is shown that such concepts as speciation and fitness to the environment, developed in the context of genomic evolution, also held in the absence of a genome.

  17. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  18. A comparative study between nonlinear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

    Science.gov (United States)

    Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...

  19. Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

    Science.gov (United States)

    Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

    2013-01-01

    As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

  20. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  1. URBAN GROWTH MODELING USING AN ARTIFICIAL NEURAL NETWORK A CASE STUDY OF SANANDAJ CITY, IRAN

    Directory of Open Access Journals (Sweden)

    S. Mohammady

    2014-10-01

    Full Text Available Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognized as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related with the type of inappropriate urban development such as increased traffic and demand for mobility, reduced landscape attractively, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study is to use the Artificial Neural Network (ANN to make a powerful tool for simulating urban growth patterns. Our study area is Sanandaj city located in the west of Iran. Landsat imageries acquired at 2000 and 2006 are used. Dataset were used include distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centers. In this study an appropriate methodology for urban growth modelling using satellite remotely sensed data is presented and evaluated. Percent Correct Match (PCM and Figure of Merit were used to evaluate ANN results.

  2. Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective.

    Science.gov (United States)

    Casagranda, Ivo; Costantino, Giorgio; Falavigna, Greta; Furlan, Raffaello; Ippoliti, Roberto

    2016-01-01

    The primary goal of Emergency Department (ED) physicians is to discriminate between individuals at low risk, who can be safely discharged, and patients at high risk, who require prompt hospitalization. The problem of correctly classifying patients is an issue involving not only clinical but also managerial aspects, since reducing the rate of admission of patients to EDs could dramatically cut costs. Nevertheless, a trade-off might arise due to the need to find a balance between economic interests and the health conditions of patients. This work considers patients in EDs after a syncope event and presents a comparative analysis between two models: a multivariate logistic regression model, as proposed by the scientific community to stratify the expected risk of severe outcomes in the short and long run, and Artificial Neural Networks (ANNs), an innovative model. The analysis highlights differences in correct classification of severe outcomes at 10 days (98.30% vs. 94.07%) and 1 year (97.67% vs. 96.40%), pointing to the superiority of Neural Networks. According to the results, there is also a significant superiority of ANNs in terms of false negatives both at 10 days (3.70% vs. 5.93%) and at 1 year (2.33% vs. 10.07%). However, considering the false positives, the adoption of ANNs would cause an increase in hospital costs, highlighting the potential trade-off which policy makers might face.

  3. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Science.gov (United States)

    Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

    2016-02-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  4. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  5. Comparisons of prediction models of quality of life after laparoscopic cholecystectomy: a longitudinal prospective study.

    Directory of Open Access Journals (Sweden)

    Hon-Yi Shi

    Full Text Available BACKGROUND: Few studies of laparoscopic cholecystectomy (LC outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility of the artificial neural network (ANN, support vector machine (SVM, Gaussian process regression (GPR and multiple linear regression (MLR models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR. METHODOLOGY/PRINCIPAL FINDINGS: A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE and mean absolute percentage error (MAPE. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC. CONCLUSIONS/SIGNIFICANCE: Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

  6. EASEWASTE-life cycle modeling capabilities for waste management technologies

    DEFF Research Database (Denmark)

    Bhander, Gurbakhash Singh; Christensen, Thomas Højlund; Hauschild, Michael Zwicky

    2010-01-01

    Background, Aims and Scope The management of municipal solid waste and the associated environmental impacts are subject of growing attention in industrialized countries. EU has recently strongly emphasized the role of LCA in its waste and resource strategies. The development of sustainable solid...... waste management systems applying a life-cycle perspective requires readily understandable tools for modelling the life cycle impacts of waste management systems. The aim of the paper is to demonstrate the structure, functionalities and LCA modelling capabilities of the PC-based life cycle oriented...... waste management model EASEWASTE, developed at the Technical University of Denmark specifically to meet the needs of the waste system developer with the objective to evaluate the environmental performance of the various elements of existing or proposed solid waste management systems. Materials...

  7. Externalities in a life cycle model with endogenous survival☆

    Science.gov (United States)

    Kuhn, Michael; Wrzaczek, Stefan; Prskawetz, Alexia; Feichtinger, Gustav

    2011-01-01

    We study socially vs individually optimal life cycle allocations of consumption and health, when individual health care curbs own mortality but also has a spillover effect on other persons’ survival. Such spillovers arise, for instance, when health care activity at aggregate level triggers improvements in treatment through learning-by-doing (positive externality) or a deterioration in the quality of care through congestion (negative externality). We combine an age-structured optimal control model at population level with a conventional life cycle model to derive the social and private value of life. We then examine how individual incentives deviate from social incentives and how they can be aligned by way of a transfer scheme. The age-patterns of socially and individually optimal health expenditures and the transfer rate are derived. Numerical analysis illustrates the working of our model.

  8. Life course models: improving interpretation by consideration of total effects.

    Science.gov (United States)

    Green, Michael J; Popham, Frank

    2016-12-28

    Life course epidemiology has used models of accumulation and critical or sensitive periods to examine the importance of exposure timing in disease aetiology. These models are usually used to describe the direct effects of exposures over the life course. In comparison with consideration of direct effects only, we show how consideration of total effects improves interpretation of these models, giving clearer notions of when it will be most effective to intervene. We show how life course variation in the total effects depends on the magnitude of the direct effects and the stability of the exposure. We discuss interpretation in terms of total, direct and indirect effects and highlight the causal assumptions required for conclusions as to the most effective timing of interventions.

  9. Development of a totally implantable artificial larynx

    NARCIS (Netherlands)

    Verkerke, GJ; Veenstra, Aalze; de Vries, MP; Schutte, HK; Busscher, HJ; Herrmann, IF; Van der Mei, HC; Rakhorst, G; Clements, MP

    1996-01-01

    Background. The consequences of a life-saving laryngectomy are still very distressing. The Eureka project "Artificial Larynx" aims at realization of an implantable artificial larynx to eliminate all drawbacks. Methods. The artificial larynx will consist of artificial vocal folds of adjustable pitch

  10. New Approaches in Usable Booster System Life Cycle Cost Modeling

    Science.gov (United States)

    2012-01-01

    Lean NPD practices (many) • Lean Production & Operations Practices (many) • Supply Chain Operations Reference ( SCOR ) Model , Best Practices Make Deliver...NEW APPROACHES IN REUSABLE BOOSTER SYSTEM LIFE CYCLE COST MODELING Edgar Zapata National Aeronautics and Space Administration Kennedy Space Center...Kennedy Space Center (KSC) and the Air Force Research Laboratory (AFRL). The work included the creation of a new cost estimating model and an LCC

  11. Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

    Science.gov (United States)

    Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2016-05-01

    This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

  12. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model

    Science.gov (United States)

    Panda, Rabindra K.; Pramanik, Niranjan; Bala, Biplab

    2010-06-01

    Simulation of water levels at different sections of a river using physically based flood routing models is quite cumbersome, because it requires many types of data such as hydrologic time series, river geometry, hydraulics of existing control structures and channel roughness coefficients. Normally in developing countries like India it is not easy to collect these data because of poor monitoring and record keeping. Therefore, an artificial neural network (ANN) technique is used as an effective alternative in hydrologic simulation studies. The present study aims at comparing the performance of the ANN technique with a widely used physically based hydrodynamic model in the MIKE 11 environment. The MIKE 11 hydrodynamic model was calibrated and validated for the monsoon periods (June-September) of the years 2006 and 2001, respectively. Feed forward neural network architecture with Levenberg-Marquardt (LM) back propagation training algorithm was used to train the neural network model using hourly water level data of the period June-September 2006. The trained ANN model was tested using data for the same period of the year 2001. Simulated water levels by the MIKE 11HD were compared with the corresponding water levels predicted by the ANN model. The results obtained from the ANN model were found to be much better than that of the MIKE 11HD results as indicated by the values of the goodness of fit indices used in the study. The Nash-Sutcliffe index ( E) and root mean square error (RMSE) obtained in case of the ANN model were found to be 0.8419 and 0.8939 m, respectively, during model testing, whereas in case of MIKE 11HD, the values of E and RMSE were found to be 0.7836 and 1.00 m, respectively, during model validation. The difference between the observed and simulated peak water levels obtained from the ANN model was found to be much lower than that of MIKE 11HD. The study reveals that the use of Levenberg-Marquardt algorithm with eight hidden neurons in the hidden layer

  13. Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

    Science.gov (United States)

    Taylan, Osman

    2017-02-01

    High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

  14. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    Directory of Open Access Journals (Sweden)

    Pudjo Sukarno

    2007-05-01

    Full Text Available Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields.

  15. Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model

    Science.gov (United States)

    Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya

    2015-10-01

    The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.

  16. Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

    Science.gov (United States)

    Rodrigues, Pedro L.; Moreira, António H. J.; Rodrigues, Nuno F.; Pinho, ACM; Fonseca, Jaime C.; Correia-Pinto, Jorge; Vilaça, João. L.

    2014-03-01

    Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82+/-5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7+/-4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

  17. An artificial neural network model of the landscape pattern in Shanghai metropolitan region,China

    Institute of Scientific and Technical Information of China (English)

    ZHANG Liquan; ZHEN Yu

    2006-01-01

    To characterize the urbanization pattern quantitatively,a study on the mechanisms of the landscape pattern formation could facilitate the understanding on urban landscape patterns and processes,the ecological and socioeconomic consequences of urbanization,as well as the establishment of more effective strategies for landscape management.In this study,we integrated a Geographic Information System (GIS)-based analysis on landscape pattern with an artificial neural network (ANN) to quantitatively characterize the urbanization pattern of the metropolitan area of Shanghai,China,and to establish an ANN model that could preferably simulate the responses of urban landscape pattern to the natural and socioeconomic factors such as residence area,road density,population density,urban development history and the Huangpu River as an element of economic change.Our results showed that the ANN model seems appropriate for studying the nonlinear relationship among the forcing factors of urbanization and the urban landscape patterns,which provided an effective and practical approach for further understanding the mechanisms of the landscape formation pattern and the reciprocal relationship between landscape spatial pattern and ecological process.

  18. ARTIFICIAL NEURAL NETWORK MODEL OF CONSTITUTIVE RELATIONSHIP FOR 2A70 ALUMINUM ALLOY

    Institute of Scientific and Technical Information of China (English)

    F. Liu; D.B. Shan; Y. Lu; Y.Y. Yang

    2005-01-01

    The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over a wide range of temperatures 360-480℃ with strain rates of 0.01-1s-1 and the largest deformation of 60%, and the true stress of the material was obtained under the above-mentioned conditions. The experimental results shows that 2A70 aluminum alloy is a kind of aluminum alloy with the property of dynamic recovery; its flow stress declines with the increase of temperature, while its flow stress increases with the increase of strain rates. On the basis of experiments, the constitutive relationship of the 2A70 aluminum alloy was constructed using a BP artificial neural network. Comparison of the predicted values with the experimental data shows that the relative error of the trained model is less than ±3% for the sampled data while it is less than ±6% for the nonsampled data. It is evident that the model constructed by BP ANN can accurately predict the flow stress of the 2A70 alloy.

  19. Numerical model of nonhydrostatic ocean dynamics based on methods of artificial compressibility and multicomponent splitting

    Science.gov (United States)

    Zalesny, V. B.; Gusev, A. V.; Fomin, V. V.

    2016-11-01

    An algorithm is proposed for solving three-dimensional ocean hydrodynamics equations without hydrostatic approximation and traditional simplification of Coriolis acceleration. It is based on multicomponent splitting of the modified model with artificial compressibility. The original system of equations is split into two subsystems describing the transport of three velocity components and adjustment of the density and velocity fields. At the adjustment stage, the horizontal velocity components are represented as a sum of the depth means and deviations; the two corresponding subsystems are derived. For barotropic dynamics, the compressibility effect is represented as the boundary condition at the free surface, while for the baroclinic subsystem, it is introduced as ɛ-regularization of the continuity equation. Then, the baroclinic equations are split into two subsystems describing the hydrostatic and nonhydrostatic dynamics. The nonhydrostatic dynamics is computed at a separate splitting stage. The algorithm is included into the Institute of Numerical Mathematics of the Russian Academy of Sciences model based on "primitive" equations and verified by solving the hydrodynamics problem for the Sea of Marmara.

  20. Modeling of CVI process in fabrication of carbon/carbon composites by an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    李爱军; 李贺军; 李克智; 顾正彬

    2003-01-01

    The chemical vapor infiltration(CVI) process in fabrication of carbon-carbon composites is very complex and highly inefficient, which adds considerably to the cost of fabrication and limits the application of the material. This paper tries to use a supervised artificial neural network(ANN) to model the nonlinear relationship between parameters of isothermal CVI(ICVI) processes and physical properties of C/C composites. A model for preprocessing dataset and selecting its topology is developed using the Levenberg-Marquardt training algorithm and trained with comprehensive dataset of tubal C/C components collected from experimental data and abundant simulated data obtained by the finite element method. A basic repository on the domain knowledge of CVI processes is established via sufficient data mining by the network. With the help of the repository stored in the trained network, not only the time-dependent effects of parameters in CVI processes but also their coupling effects can be analyzed and predicted. The results show that the ANN system is effective and successful for optimizing CVI processes in fabrication of C/C composites.

  1. Biological model of vision for an artificial system that learns to perceive its environment

    Energy Technology Data Exchange (ETDEWEB)

    Blackburn, M.R.; Nguyen, H.G.

    1989-06-01

    The objective is to design an artificial vision system for use in robotics applications. Because the desired performance is equivalent to that achieved by nature, the authors anticipate that the objective will be accomplished most efficiently through modeling aspects of the neuroanatomy and neurophysiology of the biological visual system. Information enters the biological visual system through the retina and is passed to the lateral geniculate and optic tectum. The lateral geniculate nucleus (LGN) also receives information from the cerebral cortex and the result of these two inflows is returned to the cortex. The optic tectum likewise receives the retinal information in a context of other converging signals and organizes motor responses. A computer algorithm is described which implements models of the biological visual mechanisms of the retina, thalamic lateral geniculate and perigeniculate nuclei, and primary visual cortex. Motion and pattern analyses are performed in parallel and interact in the cortex to construct perceptions. We hypothesize that motion reflexes serve as unconditioned pathways for the learning and recall of pattern information. The algorithm demonstrates this conditioning through a learning function approximating heterosynaptic facilitation.

  2. Potential Habitat Modelling of Ferula ovina Using Artificial Neural Network in Fereydunshahr Region, Isfahan Province

    Directory of Open Access Journals (Sweden)

    Z. Rahmati

    2015-06-01

    Full Text Available Species distribution maps have been widely developed based on ecological niche theory together with statistical and geographical information system in plant ecology. The current study aimed to evaluate Artificial Neural Network (ANN in mapping potential habitat of Ferula ovina Boiss in Ferydunshar rangelands, Isfahan. This is known as valuable forage and medicinal species. Environmental data (independent variables and species occurrence data (dependent variable were required to determine potential habitat of a given species. Some physical and chemical soil properties, climate and physiographic variables were mapped for the entire studied area using krigging and inverse distance weighting methods. F. ovina occurrence data were collected from 278 sites including 137 presence and 141 absence sites. The relationships between the studied environmental variables and F. ovina occurrence data were explored using ANN method. According to the sensitivity analysis, occurrence of F. ovina mostly correlated with silt and sand percentage, elevation slope, and organic matter. Model evaluation based on Kappa coefficient (0.66 and Receiver operating characteristic (ROC=0.9 showed good model fitness in relation to reality on local scales. The ANN technique enables managers to identify appropriate areas for rehabilitation practices such as direct seeding and planting.                       

  3. Development of distributed topographical forecasting model for wind resource assessment using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Narayana, P.B. [Green Life Energy Solutions LLP, Secunderabad (India); Rao, S.S. [National Institute of Technology. Dept. of Mechanical Engineering, Warangal (India); Reddy, K.H. [JNT Univ.. Dept. of Mechanical Engineering, Anantapur (India)

    2012-07-01

    Economics of wind power projects largely depend on the availability of wind power density. Wind resource assessment is a study estimating wind speeds and wind power densities in the region under consideration. The accuracy and reliability of data sets comprising of wind speeds and wind power densities at different heights per topographic region characterized by elevation or mean sea level, is important for wind power projects. Indian Wind Resource Assessment program conducted in 80's consisted of wind data measured by monitoring stations at different topographies in order to measure wind power density values at 25 and 50 meters above the ground level. In this paper, an attempt has been made to assess wind resource at a given location using artificial neural networks. Existing wind resource data has been used to train the neural networks. Location topography (characterized by longitude, latitude and mean sea level), air density, mean annual wind speed (MAWS) are used as inputs to the neural network. Mean annual wind power density (MAWPD) in watt/m{sup 2} is predicted for a new topographic location. Simple back propagation based neural network has been found to be sufficient for predicting these values with suitable accuracy. This model is closely linked to the problem of wind energy forecasting considering the variations of specific atmospheric variables with time horizons. This model will help the wind farm developers to have an initial estimation of the wind energy potential at a particular topography. (Author)

  4. An Improved Artificial Colony Algorithm Model for Forecasting Chinese Electricity Consumption and Analyzing Effect Mechanism

    Directory of Open Access Journals (Sweden)

    Jingmin Wang

    2016-01-01

    Full Text Available Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC algorithm which combined with multivariate linear regression (MLR for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption.

  5. The impact of antimicrobial photodynamic therapy on Streptococcus mutans in an artificial biofilm model

    Science.gov (United States)

    Schneider, Martin; Kirfel, Gregor; Krause, Felix; Berthold, Michael; Brede, Olivier; Frentzen, Matthias; Braun, Andreas

    2010-02-01

    The aim of the study was to assess the impact of laser induced antimicrobial photodynamic therapy on the viability of Streptococcus mutans cells employing an aritificial biofilm model. Employing sterile chambered coverglasses, a salivary pellicle layer formation was induced in 19 chambers. Streptococcus mutans cells were inoculated in a sterile culture medium. Using a live/dead bacterial viability kit, bacteria with intact cell membranes stain fluorescent green. Test chambers containing each the pellicle layer and 0.5 ml of the bacterial culture were analyzed using a confocal laser scan microscope within a layer of 10 μm at intervals of 1 μm from the pellicle layer. A photosensitizer was added to the test chambers and irradiated with a diode laser (wavelength: 660 nm, output power: 100 mW, Helbo) for 2 min each. Comparing the baseline fluorescence (median: 13.8 [U], min: 3.7, max: 26.2) with the values after adding the photosensitizer (median: 3.7, min: 1.1, max: 9), a dilution caused decrease of fluorescence could be observed (p0.05). The present study indicates that antimicrobial photodynamic therapy can reduce living bacteria within a layer of 10 μm in an artificial biofilm model. Further studies have to evaluate the maximum biofilm thickness that still allows a toxic effect on microorganisms.

  6. Metabolic model reconstruction and analysis of an artificial microbial ecosystem for vitamin C production.

    Science.gov (United States)

    Ye, Chao; Zou, Wei; Xu, Nan; Liu, Liming

    2014-07-20

    An artificial microbial ecosystem (AME) consisting of Ketogulonicigenium vulgare and Bacillus megaterium is currently used in a two-step fermentation process for vitamin C production. In order to obtain a comprehensive understanding of the metabolic interactions between the two bacteria, a two-species stoichiometric metabolic model (iWZ-KV-663-BM-1055) consisting of 1718 genes, 1573 metabolites, and 1891 reactions (excluding exchange reactions) was constructed based on separate genome-scale metabolic models (GSMMs) of K. vulgare and B. megaterium. These two compartments (k and b) of iWZ-KV-663-BM-1055 shared 453 reactions and 548 metabolites. Compartment b was richer in subsystems than compartment k. In minimal media with glucose (MG), metabolite exchange between compartments was assessed by constraint-based analysis. Compartment b secreted essential amino acids, nucleic acids, vitamins and cofactors important for K. vulgare growth and biosynthesis of 2-keto-l-gulonic acid (2-KLG). Further research showed that when co-cultured with B. megaterium in l-sorbose-CSLP medium, the growth rate of K. vulgare and 2-KLG production were increased by 111.9% and 29.42%, respectively, under the constraints employed. Our study demonstrated that GSMMs and constraint-based methods can be used to decode the physiological features and inter-species interactions of AMEs used in industrial biotechnology, which will be of benefit for improving regulation and refinement in future industrial processes.

  7. Cardiopulmonary values in dogs with artificial model of caval syndrome in heartworm disease.

    Science.gov (United States)

    Kuwahara, Y; Kitagawa, H; Sasaki, Y; Ishihara, K

    1991-02-01

    Cardiopulmonary values were determined in dogs with an artificial model of heartworm caval syndrome, which was produced by insertion of heartworm-like silicone tubes into the tricuspid valve orifice and right atrium. Fifteen to 25 tubes with some knots were inserted in 6 dogs (knot group), and 7 to 11 tubes (small-number group) or 29 to 37 tubes (large-number group) without a knot in 3 dogs, respectively. After tube insertion, angiographic contrast medium infused into the right ventricle regurgitated to the right atrium in all cases, and the regurgitation was the most severe in the large-number group. On electrocardiographic findings, the atrial and/or ventricular premature beat developed. The height of a- and v-wave of right atrial pressure curves elevated in all groups. The elevation in v-wave was obvious in the large-number group. The pulmonary arterial pressure tended to fall or to elevate slightly, and total pulmonary resistance increased in all groups. The right cardiac output decreased significantly in all cases. The right heart hemodynamics of the model might resemble those in spontaneous cases without disturbed pulmonary circulation.

  8. Mathematical Modeling of Pneumatic Artificial Muscle Actuation via Hydrogen Driving Metal Hydride-LaNi5

    Institute of Scientific and Technical Information of China (English)

    Thananchai Leephakpreeda

    2012-01-01

    Quantitative understanding of mechanical actuation of intricate Pneumatic Artificial Muscle (PAM) actuators is technically required in control system design for effective real-time implementation.This paper presents mathematical modeling of the PAM driven by hydrogen-gas pressure due to absorption and desorption of metal hydride.Empirical models of both mechanical actuation of industrial PAM and chemical reaction of the metal hydride-LaNi5 are derived systematically where their interactions comply with the continuity principle and energy balance in describing actual dynamic behaviors of the PAM actuator (PAM and hydriding/dehydriding-reaction bed).Simulation studies of mechanical actuation under various loads are conducted so as to present dynamic responses of the PAM actuators.From the promising results,it is intriguing that the heat input for the PAM actuator can be supplied to,or pumped from the reaction bed,in such a way that absorption and desorption of hydrogen gas take place,respectively,in controlling the pressure of hydrogen gas within the PAM actuator.Accordingly,this manipulation results in desired mechanical actuation of the PAM actuator in practical uses.

  9. Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

    Directory of Open Access Journals (Sweden)

    Young Min Kim

    2016-06-01

    Full Text Available The current Building Energy Performance Simulation (BEPS tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings—mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert’s effort, a data-driven approach (so-called “inverse” approach has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN, Support Vector Machine (SVM, and Gaussian Process (GP for predicting a chiller’s energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS, and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.

  10. Generalized fish life-cycle poplulation model and computer program

    Energy Technology Data Exchange (ETDEWEB)

    DeAngelis, D. L.; Van Winkle, W.; Christensen, S. W.; Blum, S. R.; Kirk, B. L.; Rust, B. W.; Ross, C.

    1978-03-01

    A generalized fish life-cycle population model and computer program have been prepared to evaluate the long-term effect of changes in mortality in age class 0. The general question concerns what happens to a fishery when density-independent sources of mortality are introduced that act on age class 0, particularly entrainment and impingement at power plants. This paper discusses the model formulation and computer program, including sample results. The population model consists of a system of difference equations involving age-dependent fecundity and survival. The fecundity for each age class is assumed to be a function of both the fraction of females sexually mature and the weight of females as they enter each age class. Natural mortality for age classes 1 and older is assumed to be independent of population size. Fishing mortality is assumed to vary with the number and weight of fish available to the fishery. Age class 0 is divided into six life stages. The probability of survival for age class 0 is estimated considering both density-independent mortality (natural and power plant) and density-dependent mortality for each life stage. Two types of density-dependent mortality are included. These are cannibalism of each life stage by older age classes and intra-life-stage competition.

  11. Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Pirmoradi

    2012-04-01

    Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

  12. Artificial neural networks versus proportional hazards Cox models to predict 45-year all-cause mortality in the Italian Rural Areas of the Seven Countries Study

    Directory of Open Access Journals (Sweden)

    Puddu Paolo

    2012-07-01

    Full Text Available Abstract Background Projection pursuit regression, multilayer feed-forward networks, multivariate adaptive regression splines and trees (including survival trees have challenged classic multivariable models such as the multiple logistic function, the proportional hazards life table Cox model (Cox, the Poisson’s model, and the Weibull’s life table model to perform multivariable predictions. However, only artificial neural networks (NN have become popular in medical applications. Results We compared several Cox versus NN models in predicting 45-year all-cause mortality (45-ACM by 18 risk factors selected a priori: age; father life status; mother life status; family history of cardiovascular diseases; job-related physical activity; cigarette smoking; body mass index (linear and quadratic terms; arm circumference; mean blood pressure; heart rate; forced expiratory volume; serum cholesterol; corneal arcus; diagnoses of cardiovascular diseases, cancer and diabetes; minor ECG abnormalities at rest. Two Italian rural cohorts of the Seven Countries Study, made up of men aged 40 to 59 years, enrolled and first examined in 1960 in Italy. Cox models were estimated by: a forcing all factors; b a forward-; and c a backward-stepwise procedure. Observed cases of deaths and of survivors were computed in decile classes of estimated risk. Forced and stepwise NN were run and compared by C-statistics (ROC analysis with the Cox models. Out of 1591 men, 1447 died. Model global accuracies were extremely high by all methods (ROCs > 0.810 but there was no clear-cut superiority of any model to predict 45-ACM. The highest ROCs (> 0.838 were observed by NN. There were inter-model variations to select predictive covariates: whereas all models concurred to define the role of 10 covariates (mainly cardiovascular risk factors, family history, heart rate and minor ECG abnormalities were not contributors by Cox models but were so by forced NN. Forced expiratory volume and arm

  13. Preliminary results of Physiological plant growth modelling for human life support in space

    Science.gov (United States)

    Sasidharan L, Swathy; Dussap, Claude-Gilles; Hezard, Pauline

    2012-07-01

    Human life support is fundamental and crucial in any kind of space explorations. MELiSSA project of European Space Agency aims at developing a closed, artificial ecological life support system involving human, plants and micro organisms. Consuming carbon dioxide and water from the life support system, plants grow in one of the chambers and convert it into food and oxygen along with potable water. The environmental conditions, nutrient availability and its consumption of plants should be studied and necessarily modeled to predict the amount of food, oxygen and water with respect to the environmental changes and limitations. The reliability of a completely closed system mainly depends on the control laws and strategies used. An efficient control can occur, only if the system to control is itself well known, described and ideally if the responses of the system to environmental changes are predictable. In this aspect, the general structure of plant growth model has been designed together with physiological modelling.The physiological model consists of metabolic models of leaves, stem and roots, of which concern specific metabolisms of the associated plant parts. On the basis of the carbon source transport (eg. sucrose) through stem, the metabolic models (leaf and root) can be interconnected to each other and finally coupled to obtain the entire plant model. For the first step, leaf metabolic model network was built using stoichiometric, mass and energy balanced metabolic equations under steady state approach considering all necessary plant pathways for growth and maintenance of leaves. As the experimental data for lettuce plants grown in closed and controlled environmental chambers were available, the leaf metabolic model has been established for lettuce leaves. The constructed metabolic network is analyzed using known stoichiometric metabolic technique called metabolic flux analysis (MFA). Though, the leaf metabolic model alone is not sufficient to achieve the

  14. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2016-10-20

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  15. Comparative evaluation of the use of artificial neural networks for modelling the epidemiology of schistosomiasis mansoni.

    Science.gov (United States)

    Hammad, T A; Abdel-Wahab, M F; DeClaris, N; El-Sahly, A; El-Kady, N; Strickland, G T

    1996-01-01

    There has been a marked increase in the application of approaches based on artificial intelligence (AI) in the field of computer science and medical diagnosis, but AI is still relatively unused in epidemiological settings. In this study we report results of the application of neural networks (NN; a special category of AI) to schistosomiasis. It was possible to design an NN structure which can process and fit epidemiological data collected from 251 schoolchildren in Egypt using the first year's data to predict second and third years' infection rates. Data collected over 3 years included age, gender, exposure to canal water and agricultural activities, medical history and examination, and stool and urine parasitology. Schistosoma mansoni infection rates were 50%, 78% and 66% at the baseline and the 2 follow-up periods, respectively. NN modelling was based on the standard back-propagation algorithm, in which we built a suitable configuration of the network, using the first year's data, that optimized performance. It was implemented on an IBM compatible computer using commercially available software. The performance of the NN model in the first year compared favourably with logistic regression (NN sensitivity = 83% (95% confidence interval [CI] 78-88%) and positive predictive value (PPV) = 63% (95% CI 57-69%); logistic regression sensitivity = 66% (95% CI 60%-72%) and PPV = 59% (95% CI 53%-65%). The NN model generalized the criteria for predicting infection over time better than logistic regression and showed more stability over time, as it retained its sensitivity and specificity and had better false positive and negative profiles than logistic regression. The potential of NN-based models to analyse and predict wide-scale control programme data, which are inevitably based on unstable egg excretion rates and insensitive laboratory techniques, is promising but still untapped.

  16. An artificial neural network prediction model of congenital heart disease based on risk factors

    Science.gov (United States)

    Li, Huixia; Luo, Miyang; Zheng, Jianfei; Luo, Jiayou; Zeng, Rong; Feng, Na; Du, Qiyun; Fang, Junqun

    2017-01-01

    Abstract An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women. This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1. The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio  = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0

  17. Data Assimilation using Artificial Neural Networks for the global FSU atmospheric model

    Science.gov (United States)

    Cintra, Rosangela; Cocke, Steven; Campos Velho, Haroldo

    2015-04-01

    Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. Uncertainty is the characteristic of the atmosphere, coupled with inevitable inadequacies in observations and computer models and increase errors in weather forecasts. Data assimilation is a technique to generate an initial condition to a weather or climate forecasts. This paper shows the results of a data assimilation technique using artificial neural networks (ANN) to obtain the initial condition to the atmospheric general circulation model (AGCM) for the Florida State University in USA. The Local Ensemble Transform Kalman filter (LETKF) is implemented with Florida State University Global Spectral Model (FSUGSM). The ANN data assimilation is made to emulate the initial condition from LETKF to run the FSUGSM. LETKF is a version of Kalman filter with Monte-Carlo ensembles of short-term forecasts to solve the data assimilation problem. The model FSUGSM is a multilevel (27 vertical levels) spectral primitive equation model with a vertical sigma coordinate. All variables are expanded horizontally in a truncated series of spherical harmonic functions (at resolution T63) and a transform technique is applied to calculate the physical processes in real space. The LETKF data assimilation experiments are based in synthetic observations data (surface pressure, absolute temperature, zonal component wind, meridional component wind and humidity). For the ANN data assimilation scheme, we use Multilayer Perceptron (MLP-DA) with supervised training algorithm where ANN receives input vectors with their corresponding response or target output from LETKF scheme. An automatic tool that finds the optimal representation to these ANNs configures the MLP-DA in this experiment. After the training process, the scheme MLP-DA is seen as a function of data assimilation where the inputs are observations and a short-range forecast to

  18. Experimental reconstruction of cervical esophageal defect with artificial esophagus made of polyurethane in a dog model.

    Science.gov (United States)

    Jiang, H; Cui, Y; Ma, K; Gong, M; Chang, D; Wang, T

    2016-01-01

    The defect of esophagus after surgical excision in patients is usually replaced by autologous stomach, jejunum, or colon. The operation brings severe trauma and complications. Using artificial esophagus to replace the defect in situ can reduce the operative trauma, simplify the operative procedures, and decrease the influence to digestive function. A variety of experiments have been designed for developing a practical artificial esophagus. Nevertheless, a safe and reliable artificial esophagus is not yet available. The objective is to evaluate the possibility of the artificial esophagus made of non-degradable polyurethane materials being used in reconstruction of the segmental defect of cervical esophagus in beagles, observe the regeneration of esophageal tissue, and gather experience for future study. The cervical esophageal defects in 13 beagles were designed to 2-cm long and were constructed by the artificial esophagus made of non-degradable polyurethane materials. Nutrition supports were given after the operation. The operative mortality, anastomotic leakage, migration of artificial esophagus, and dysphagia were followed up. The regeneration of the esophageal tissues was evaluated by histopathology and immunohistochemical labeled streptavidin-biotin method. The surgical procedures were successfully completed in all beagles, and 12-month follow-ups were done. Only one beagle died of severe infection, and all others survived until being killed. The anastomotic leakage occurred in nine beagles, most of them (8/9) were cured after supportive therapy. The migration of artificial esophagus occurred in all 12 surviving beagles, and one artificial esophagus stayed in situ after migration. All 12 surviving beagles showed dysphagia with taking only fluid or soft food. No beagle died of malnutrition. The neo-esophagus was composed of granulation tissue, and the inner surface was covered by epithelium in 2-3 months completely. But the inner surface of neo-esophagus with

  19. THE ROLE OF POROUS MEDIA IN MODELING FLUID FLOW WITHIN HOLLOW FIBER MEMBRANES OF THE TOTAL ARTIFICIAL LUNG

    OpenAIRE

    2010-01-01

    A numerical study was conducted to analyze fluid flow within hollow fiber membranes of the artificial lungs. The hollow fiber bundle was approximated using a porous media model. In addition, the transport equations were solved using the finite-element formulation based on the Galerkin method of weighted residuals. Comparisons with previously published work on the basis of special cases were performed and found to be in excellent agreement. A Newtonian viscous fluid model for the blood was use...

  20. Modeling Root Length Density of Field Grown Potatoes under Different Irrigation Strategies and Soil Textures Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Ahmadi, Seyed Hamid; Sepaskhah, A R; Andersen, Mathias Neumann

    2014-01-01

    Root length density (RLD) is a highly wanted parameter for use in crop growth modeling but difficult to measure under field conditions. Therefore, artificial neural networks (ANNs) were implemented to predict the RLD of field grown potatoes that were subject to three irrigation strategies and three...... under a range of soil physical conditions with a high degree of accuracy and may be used in crop growth modeling....

  1. Physics of Life: A Model for Non-Newtonian Properties of Living Systems

    Science.gov (United States)

    Zak, Michail

    2010-01-01

    This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if

  2. STUDY ON ARTIFICIAL CHANGING OF PROGRAM LIFE CYCLE IN ANDROID SYSTEM%Android中人为改变程序生命周期的研究

    Institute of Scientific and Technical Information of China (English)

    李伟

    2013-01-01

    Android平台下程序的生命周期控制是由系统控制而非程序自身直接控制的特点导致了在Android中程序是否关闭不能由程序界面是否关闭而决定。程序会在界面关闭后的某个时间由系统关闭。这种不确定性在很多应用中会导致混乱。人为强制关闭程序在很多应用中都是非常必要的。分析Android 2.2中强制关闭程序的困难,提出几种人为强制关闭程序的方法,并以其中最有效的一种利用引发未处理异常强制关闭程序为例,给出实现原理及具体解决方案。%Program life cycle control in Android system is decided by the system rather than the programs themselves , this property results in that in Android whether to shut down the program interfaces can not decide the programs close .The program will be closed by the system at certain time after the interface closed , such uncertainty will cause chaos in many applications .Artificially forcible close of the program is very necessary in many applications .In this paper, we analyse the difficulty of forcible close of applications in Android 2.2, and propose some solutions on artificial forcible close of applications .Moreover , we take a most effective one among them as the example , which makes use of triggering an unhandled exception to forcibly close the application , to present the implementation principle and the specific solution .

  3. Environmental sustainability modeling with exergy methodology for building life cycle

    Institute of Scientific and Technical Information of China (English)

    刘猛; 姚润明

    2009-01-01

    As an important human activity,the building industry has created comfortable space for living and work,and at the same time brought considerable pollution and huge consumption of energy and recourses. From 1990s after the first building environmental assessment model-BREEAM was released in the UK,a number of assessment models were formulated as analytical and practical in methodology respectively. This paper aims to introduce a generic model of exergy assessment on environmental impact of building life cycle,taking into consideration of previous models and focusing on natural environment as well as building life cycle,and three environmental impacts will be analyzed,namely energy embodied exergy,resource chemical exergy and abatement exergy on energy consumption,resource consumption and pollutant discharge respectively. The model of exergy assessment on environmental impact of building life cycle thus formulated contains two sub-models,one from the aspect of building energy utilization,and the other from building materials use. Combining theories by ecologists such as Odum,building environmental sustainability modeling with exergy methodology is put forward with the index of exergy footprint of building environmental impacts.

  4. Thermal Modeling and Feedback Requirements for LIFE Neutronic Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Seifried, J E

    2009-07-15

    An initial study is performed to determine how temperature considerations affect LIFE neutronic simulations. Among other figures of merit, the isotopic mass accumulation, thermal power, tritium breeding, and criticality are analyzed. Possible fidelities of thermal modeling and degrees of coupling are explored. Lessons learned from switching and modifying nuclear datasets is communicated.

  5. Service Life and Maintenance Modelling of Reinforced Concrete Bridge Decks

    DEFF Research Database (Denmark)

    Thoft-Christensen, Palle

    Recent research in the area of assessment and maintenance of reinforced concrete bridge decks is presented in this paper. Three definitions of service lifetime are introduced and the difficult problem of assessing the service life is discussed. A stochastic modelling of corrosion and corrosion...

  6. Graded Response Modeling of the Quality of Life Interview.

    Science.gov (United States)

    Uttaro, Thomas; Lehman, Anthony

    1999-01-01

    Outlined a graded response model and applied it to an aggregated data set from four studies involving subjective items from the Quality of Life Interview (QOLI) (A. Lehman, 1988). Used the results to create customized QOLI scales. Discusses the use of this methodology for scales involving ordered, graded categories. (SLD)

  7. Effect of Head Position on Cerebrospinal Fluid Pressure in Cats: Comparison with Artificial Model

    Science.gov (United States)

    Klarica, Marijan; Radoš, Milan; Draganić, Pero; Erceg, Gorislav; Orešković, Darko; Maraković, Jurica; Bulat, Marin

    2006-01-01

    Aim To demonstrate that changes in the cerebrospinal fluid (CSF) pressure in the cranial cavity and spinal canal after head elevation from the horizontal level occur primarily due to the biophysical characteristics of the CSF system, ie, distensibility of the spinal dura. Methods Experiments in vivo were performed on cats and a new artificial model of the CSF system with dimensions similar to the CSF system in cats, consisting of non-distensible cranial and distensible spinal part. Measurements of the CSF pressure in the cranial and spinal spaces were performed in chloralose-anesthetized cats (n = 10) in the horizontal position on the base of a stereotaxic apparatus (reference zero point) and in the position in which the head was elevated to 5 cm and 10 cm above that horizontal position. Changes in the CSF pressure in the cranial and spinal part of the model were measured in the cranial part positioned in the same way as the head in cats (n = 5). Results When the cat was in the horizontal position, the values of the CSF pressure in the cranial (11.9 ± 1.1 cm H2O) and spinal (11.8 ± 0.6 cm H2O) space were not significantly different. When the head was elevated 5 cm or 10 cm above the reference zero point, the CSF pressure in the cranium significantly decreased to 7.7 ± 0.6 cm H2O and 4.7 ± 0.7 cm H2O, respectively, while the CSF pressure in the spinal space significantly increased to 13.8 ± 0.7 cm H2O and 18.5 ± 1.6 cm H2O, respectively (P<0.001 for both). When the artificial CSF model was positioned in the horizontal level and its cranial part elevated by 5 cm and 10 cm, the changes in the pressure were the same as those in the cats when in the same hydrostatic position. Conclusions The new model of the CSF system used in our study faithfully mimicked the changes in the CSF pressure in cats during head elevation in relation to the body. Changes in the pressure in the model were not accompanied by the changes in fluid volume in

  8. Modeling of a corrugated dielectric elastomer actuator for artificial muscle applications

    Science.gov (United States)

    Kadooka, Kevin; Taya, Minoru; Naito, Keishi; Saito, Makoto

    2015-04-01

    Dielectric elastomer actuators have many advantages, including light weight, simplicity, high energy density, and silent operation. These features make them suitable to replace conventional actuators and transducers, especially in artificial muscle applications where large contractile strains are necessary for lifelike motions. This paper will introduce the concept of a corrugated dielectric elastomer actuator (DEA), which consists of dielectric elastomer (DE) laminated to a thin elastic layer to induce bending motion at each of the corrugations, resulting in large axial deformation. The location of the DE and elastic layers can be configured to provide tensile or compressive axial strain. Such corrugated DE actuators are also highly scalable: linking multiple actuators in series results in greater deformation, whereas multiple actuators in parallel results in larger force output. Analytical closed-form solutions based on linear elasticity were derived for the displacement and force output of curved unimorph and corrugated DEA, both consisting of an arbitrary number of lamina. A total strain energy analysis and Castigiliano's theorem were used to predict the nonlinear force-displacement behavior of the corrugated actuator. Curved unimorph and corrugated DEA were fabricated using VHB F9469PC as the DE material. Displacement of the actuators observed during testing agreed well with the modeling results. Large contractile strain (25.5%) was achieved by the corrugated DEA. Future work includes investigating higher performance DE materials such as plasticized PVDF terpolymers, processed by thin film deposition methods.

  9. Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

    Science.gov (United States)

    Olawoyin, Richard

    2016-10-01

    The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants.

  10. Algorithm for Modeling Wire Cut Electrical Discharge Machine Parameters using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    G.Sankara Narayanan

    2014-03-01

    Full Text Available Unconventional machining process finds lot of application in aerospace and precision industries. It is preferred over other conventional methods because of the advent of composite and high strength to weight ratio materials, complex parts and also because of its high accuracy and precision. Usually in unconventional machine tools, trial and error method is used to fix the values of process parameters which increase the production time and material wastage. A mathematical model functionally relating process parameters and operating parameters of a wire cut electric discharge machine (WEDM is developed incorporating Artificial neural network (ANN and the work piece material is SKD11 tool steel. This is accomplished by training a feed forward neural network with back propagation learning Levenberg-Marquardt algorithm. The required data used for training and testing the ANN are obtained by conducting trial runs in wire cut electric discharge machine in a small scale industry from South India. The programs for training and testing the neural network are developed, using matlab 7.0.1 package. In this work, we have considered the parameters such as thickness, time and wear as the input values and from that the values of the process parameters are related and a algorithm is arrived. Hence, the proposed algorithm reduces the time taken by trial runs to set the input process parameters of WEDM and thus reduces the production time along with reduction in material wastage. Thus the cost of machining processes is reduced and thereby increases the overall productivity.

  11. Development of artificial model of caval syndrome in canine heartworm disease.

    Science.gov (United States)

    Kitagawa, H; Sasaki, Y; Ishihara, K; Kuwahara, Y

    1990-10-01

    In order to develop an artificial model of caval syndrome (dirofilarial hemoglobinuria), heartworm-like silicone tubes were inserted into the tricuspid valve orifice and right atrium of dogs. Fifteen to 25 tubes with some knots were inserted through the posterior vena cava in 6 dogs (knot-tube group), 7 to 12 tubes without knot (small-number group) through the jugular vein in another 5 dogs, or 25 to 35 tubes (large-number group) in yet another 5 dogs. The tubes remained in the right atrium, and a part of them protruded into the tricuspid valve orifice. The number of tubes at the tricuspid valve orifice was the greatest in the large-number group. After tube insertion, the signs of so-called "caval syndrome", such as systolic cardiac murmur, jugular pulse, anemia, and so on, were observed in almost all cases of the 3 groups, the signs were severest in the large-number group. Urine hemoglobin was detected in almost all cases of the knot-tube and large-number groups, and in 1 case in the small-number group. Ascites was observed in 1 case of the knot-tube group at 6 weeks, in 1 case of the small-number group at 7 days and in 3 cases of the large-number group at 7 days after insertion.

  12. Daylighting analysis of rooflights through model measurements in the artificial sky

    Energy Technology Data Exchange (ETDEWEB)

    Filetoht, Levente I. [University of Technnology and Economics, Budapest, Hungary (Hungary)

    2000-07-01

    In investigating the daylighting of rooflights, it can be assumed that they are behaving as luminaires-having a major role in the lighting features of the interior space. Until now there is not enough precise information on these daylighting systems, and because of this, it is difficult to predict precisely the quality and quantity of illuminance and the light distribution in the interior. The exact behavior of the rooflights are complex and sophisticated. Their features are based on their geometry, reflectance and transmittance of their non-transparent, transparent and translucent surfaces. The effect of these features can not be predicted of calculated using the traditional mathematical methods. I am investigating roof-lighting systems taking into account the complex effects of their parts, using a completely new method based on a series of model measurements under artificial sky. Investigating the roof-light as a complex system which consist of relatively large surfaces and the effects of surfaces on other surfaces. Considering the different illuminance fields of the different types of roof-lighting systems. I will be able to determine the light distribution and efficiency of the system as well. The results of this research will lead to the development of a new computer software which is beyond the scope of this paper. [Spanish] Al investigar la iluminacion de luz de dia de luces de techo puede suponerse que se estan comportando como luminarias teniendo un papel principal en las caracteristicas de iluminacion del espacio interior. Hasta ahora no existe ninguna informacion precisa de estos sistemas de iluminacion de dia y a causa de ello es dificil predecir con precision la calidad y cantidad de iluminancia y la distribucion de la luz en el interior. El comportamiento exacto de las luces de techo es complejo y sofisticado. Sus caracteristicas estan basadas en su geometria, reflectancia y trasmitancia de sus superficies no transparentes, transparentes y

  13. Synthesis of TiO2 nanoparticles in different thermal conditions and modeling its photocatalytic activity with artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Fatemeh Ghanbary; Nasser Modirshahla; Morteza Khosravi; Mohammad Ali Behnajady

    2012-01-01

    Titanium dioxide (TiO2) nanoparticles were prepared by sol gel route.The preparation parameters were optimized in the removal of 4-nitrophenol (4-NP).All catalysts were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM).An artificial neural network model (ANN) was developed to predict the photocatalytic removal of 4-NP in the presence of TiO2 nanoparticles prepared under desired conditions.The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.

  14. Application of artificial neural networks for modelling correlations in age hardenable aluminium alloys

    Directory of Open Access Journals (Sweden)

    F. Musharavati

    2010-07-01

    Full Text Available Purpose: This paper discusses some of the preliminary results of an ongoing research on the applications of artificial neural networks (ANNs in modelling, predicting and simulating correlations between mechanical properties of age hardenable aluminium alloys as a function of alloy composition.Design/methodology/approach: Appropriate combinations of inputs and outputs were selected for neural network modelling. Multilayer feedforward networks were created and trained using datasets from public literature. Influences of alloying elements, alloy composition and processing parameters on mechanical properties of aluminium alloys were predicted and simulated using ANNs models.Two sample t-tests were used to analyze the prediction accuracy of the trained ANNs.Findings: Good performances of the neural network models were achieved. The models were able to predict mechanical properties within acceptable margins of error and were able to provide relevant simulated data for correlating alloy composition and processing parameters with mechanical properties. Therefore, ANNs models are convenient and powerful tools that can provide useful information which can be used to identify desired properties in new aluminium alloys for practical applications in new and/or improved aluminium products.Research limitations/implications: Few public data bases are available for modelling properties. Minor contradictions on the experimental values of properties and alloy compositions were also observed. Future work will include further development of simulated data into property charts.Practical implications: Correlations between mechanical properties and alloy compositions can help in identifying a suitable alloy for a new or improved aluminum product application. In addition, availability of simulated structure-process-property data or charts assists in reducing the time and costs of trial and error experimental approaches by providing near-optimal values that can be used

  15. Life cycle Prognostic Model Development and Initial Application Results

    Energy Technology Data Exchange (ETDEWEB)

    Jeffries, Brien; Hines, Wesley; Nam, Alan; Sharp, Michael; Upadhyaya, Belle [The University of Tennessee, Knoxville (United States)

    2014-08-15

    In order to obtain more accurate Remaining Useful Life (RUL) estimates based on empirical modeling, a Lifecycle Prognostics algorithm was developed that integrates various prognostic models. These models can be categorized into three types based on the type of data they process. The application of multiple models takes advantage of the most useful information available as the system or component operates through its lifecycle. The Lifecycle Prognostics is applied to an impeller test bed, and the initial results serve as a proof of concept.

  16. Models for waste life cycle assessment: Review of technical assumptions

    DEFF Research Database (Denmark)

    Gentil, Emmanuel; Damgaard, Anders; Hauschild, Michael Zwicky;

    2010-01-01

    , such as the functional unit, system boundaries, waste composition and energy modelling. The modelling assumptions of waste management processes, ranging from collection, transportation, intermediate facilities, recycling, thermal treatment, biological treatment, and landfilling, are obviously critical when comparing......A number of waste life cycle assessment (LCA) models have been gradually developed since the early 1990s, in a number of countries, usually independently from each other. Large discrepancies in results have been observed among different waste LCA models, although it has also been shown that results...

  17. Impact of the quality of life of inseminators on the results of artificial insemination programs in beef cattle Impacto da qualidade de vida dos inseminadores nos resultados de programas de inseminação artificial em bovinos de corte

    Directory of Open Access Journals (Sweden)

    Lívia dos Santos Russi

    2010-07-01

    Full Text Available The objective of the present study was to evaluate the effect of factors of personal life and work conditions on the results obtained by inseminators in conventional and fixed-timed artificial insemination programs in beef cattle. Inseminators from three farms (21 in the total were interviewed and evaluated according to the general obtained pregnancy rates. The differences among the pregnancy rates obtained in the farms, motivation and its association with the obtained pregnancy rate and the effect of each variable of the groups of needs on the pregnancy rate at first insemination were evaluated. The open questions were grouped by similarity and then analyzed by frequency of the answers. Pregnancy rates obtained by the inseminators ranged from 12 to 57%, with a mean service index of 3.10 ± 1.62 doses/pregnancy. It was also observed that the satisfaction of biological, financial, and training needs was more intimately related to the pregnancy rate than the satisfaction of the other needs, although none had shown an antagonistic relationship with it. Factors concerned to life quality and to the work can be important in determining the performance of these professionals.Objetivou-se avaliar o efeito de fatores ligados à vida pessoal e ao trabalho sobre os resultados obtidos por inseminadores em programas de inseminação artificial convencional e em tempo fixo em bovinos de corte. Inseminadores de três propriedades rurais (21 no total foram entrevistados e avaliados quanto à taxa de gestação geral obtida. Foram avaliadas as diferenças entre as taxas de gestação obtidas nas fazendas, a motivação e sua associação com a taxa de gestação obtida e o efeito de cada variável dos grupos de necessidades sobre a taxa de gestação à primeira inseminação. As questões abertas, depois de agrupadas por similaridade, foram analisadas por frequência de respostas. A taxa de gestação obtida pelos inseminadores variou entre 12 e 57%, com

  18. Artificial Neural Networks

    OpenAIRE

    Chung-Ming Kuan

    2006-01-01

    Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.

  19. The study of the life cycle of technology assessment model

    Institute of Scientific and Technical Information of China (English)

    SHEN Yu-Zhi; HUANG Xun-jiang

    2001-01-01

    The life cycle of technology is one of the most important indexes to weigh up the risk of the investment to neo-tech. There are so many uncertainties because it is conditioned by a lot of factors, we can not make a rational forecasting by traditional assessment method. So this paper gives a conprehensive consideration to the factors that influence production and makes some modification to production function, and establishes the life cycle of technology assessmet model by the method of fuzzy mathematics. So it quantifies the risk of investment. We can take it as one foundational index for the decision making of the investment.

  20. Artificial Intelligence.

    Science.gov (United States)

    Waltz, David L.

    1982-01-01

    Describes kinds of results achieved by computer programs in artificial intelligence. Topics discussed include heuristic searches, artificial intelligence/psychology, planning program, backward chaining, learning (focusing on Winograd's blocks to explore learning strategies), concept learning, constraint propagation, language understanding…

  1. Life cycle optimization of automobile replacement: model and application.

    Science.gov (United States)

    Kim, Hyung Chul; Keoleian, Gregory A; Grande, Darby E; Bean, James C

    2003-12-01

    Although recent progress in automotive technology has reduced exhaust emissions per mile for new cars, the continuing use of inefficient, higher-polluting old cars as well as increasing vehicle miles driven are undermining the benefits of this progress. As a way to address the "inefficient old vehicle" contribution to this problem, a novel life cycle optimization (LCO) model is introduced and applied to the automobile replacement policy question. The LCO model determines optimal vehicle lifetimes, accounting for technology improvements of new models while considering deteriorating efficiencies of existing models. Life cycle inventories for different vehicle models that represent materials production, manufacturing, use, maintenance, and end-of-life environmental burdens are required as inputs to the LCO model. As a demonstration, the LCO model was applied to mid-sized passenger car models between 1985 and 2020. An optimization was conducted to minimize cumulative carbon monoxide (CO), non-methane hydrocarbon (NMHC), oxides of nitrogen (NOx), carbon dioxide (CO2), and energy use over the time horizon (1985-2020). For CO, NMHC, and NOx pollutants with 12000 mi of annual mileage, automobile lifetimes ranging from 3 to 6 yr are optimal for the 1980s and early 1990s model years while the optimal lifetimes are expected to be 7-14 yr for model year 2000s and beyond. On the other hand, a lifetime of 18 yr minimizes cumulative energy and CO2 based on driving 12000 miles annually. Optimal lifetimes are inversely correlated to annual vehicle mileage, especially for CO, NMHC, and NOx emissions. On the basis of the optimization results, policies improving durability of emission controls, retiring high-emitting vehicles, and improving fuel economies are discussed.

  2. Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

    Science.gov (United States)

    Grandjean, Bernard; Maier, Marc A

    2017-02-01

    Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

  3. A model to discriminate malignant from benign thyroid nodules using artificial neural network.

    Directory of Open Access Journals (Sweden)

    Lu-Cheng Zhu

    Full Text Available OBJECTIVE: This study aimed to construct a model for using in differentiating benign and malignant nodules with the artificial neural network and to increase the objective diagnostic accuracy of US. MATERIALS AND METHODS: 618 consecutive patients (528 women, 161 men with 689 thyroid nodules (425 malignant and 264 benign nodules were enrolled in the present study. The presence and absence of each sonographic feature was assessed for each nodule - shape, margin, echogenicity, internal composition, presence of calcifications, peripheral halo and vascularity on color Doppler. The variables meet the following criteria: important sonographic features and statistically significant difference were selected as the input layer to build the ANN for predicting the malignancy of nodules. RESULTS: Six sonographic features including shape (Taller than wide, p<0.001, margin (Not Well-circumscribed, p<0.001, echogenicity (Hypoechogenicity, p<0.001, internal composition (Solid, p<0.001, presence of calcifications (Microcalcification, p<0.001 and peripheral halo (Absent, p<0.001 were significantly associated with malignant nodules. A three-layer 6-8-1 feed-forward ANN model was built. In the training cohort, the accuracy of the ANN in predicting malignancy of thyroid nodules was 82.3% (AURO  = 0.818, the sensitivity and specificity was 84.5% and 79.1%, respectively. In the validation cohort, the accuracy, sensitivity and specificity was 83.1%, 83.8% and 81.8%, respectively. The AUROC was 0.828. CONCLUSION: ANN constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy.

  4. Predicting the fibre diameter of melt blown nonwovens: comparison of physical, statistical and artificial neural network models

    Science.gov (United States)

    Chen, Ting; Li, Liqing; Huang, Xiubao

    2005-06-01

    Physical, statistical and artificial neural network (ANN) models are established for predicting the fibre diameter of melt blown nonwovens from the processing parameters. The results show that the ANN model yields a very accurate prediction (average error of 0.013%), and a reasonably good ANN model can be achieved with relatively few data points. Because the physical model is based on the inherent physical principles of the phenomena of interest, it can yield reasonably good prediction results when experimental data are not available and the entire physical procedure is of interest. This area of research has great potential in the field of computer assisted design in melt blowing technology.

  5. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process.

    Science.gov (United States)

    Witek-Krowiak, Anna; Chojnacka, Katarzyna; Podstawczyk, Daria; Dawiec, Anna; Pokomeda, Karol

    2014-05-01

    A review on the application of response surface methodology (RSM) and artificial neural networks (ANN) in biosorption modelling and optimization is presented. The theoretical background of the discussed methods with the application procedure is explained. The paper describes most frequently used experimental designs, concerning their limitations and typical applications. The paper also presents ways to determine the accuracy and the significance of model fitting for both methodologies described herein. Furthermore, recent references on biosorption modelling and optimization with the use of RSM and the ANN approach are shown. Special attention was paid to the selection of factors and responses, as well as to statistical analysis of the modelling results.

  6. Theory, modelling and simulation in origins of life studies.

    Science.gov (United States)

    Coveney, Peter V; Swadling, Jacob B; Wattis, Jonathan A D; Greenwell, H Christopher

    2012-08-21

    Origins of life studies represent an exciting and highly multidisciplinary research field. In this review we focus on the contributions made by theory, modelling and simulation to addressing fundamental issues in the domain and the advances these approaches have helped to make in the field. Theoretical approaches will continue to make a major impact at the "systems chemistry" level based on the analysis of the remarkable properties of nonlinear catalytic chemical reaction networks, which arise due to the auto-catalytic and cross-catalytic nature of so many of the putative processes associated with self-replication and self-reproduction. In this way, we describe inter alia nonlinear kinetic models of RNA replication within a primordial Darwinian soup, the origins of homochirality and homochiral polymerization. We then discuss state-of-the-art computationally-based molecular modelling techniques that are currently being deployed to investigate various scenarios relevant to the origins of life.

  7. Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

    Science.gov (United States)

    Chattopadhyay, Surajit; Chattopadhyay, Goutami

    2012-10-01

    In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

  8. Theoretical and experimental decisions in the creation of an artificial ecosystem for human life support in space.

    Science.gov (United States)

    Kirensky, L V; Gitelson, I I; Terskov, I A; Kovrov, B G; Lisovsky, G M; Okladnikov, Y N

    1971-01-01

    All of man's former space flights were not real ventures into space in the biological sense, as his life was supported with unregenerated earth supplies. The coming stage of space exploration requires man's long existence in the cosmos and on the other planets. This stage of man's activity outside the earth become possible only by creating small man-made ecosystems, permitting the support of his metabolism by the recycling of substances of the terrestrial biosphere. Creation of such systems is a new scientific and technical task. Man-made ecosystems are a new product of man's activity, which have no complete analogy, either in nature, or in technology. Stochastic mechanisms, which stabilize biogeocenosis, cannot be effective in small ecosystems. A technique of parametric control over biosynthesis made it possible to calculate, and put to practice, an ecosystem for man with a cyclic regeneration of the atmosphere, water and, partially, food. The specific bio-technological properties of small man-made ecosystems are being analysed. The possibility of their application for man's excursions into space and for the settlement of other planets is being considered.

  9. An overview of methods for developing bioenergetic and life history models for rare and endangered species

    Science.gov (United States)

    Petersen, J.H.; DeAngelis, D.L.; Paukert, C.P.

    2008-01-01

    Many fish species are at risk to some degree, and conservation efforts are planned or underway to preserve sensitive populations. For many imperiled species, models could serve as useful tools for researchers and managers as they seek to understand individual growth, quantify predator-prey dynamics, and identify critical sources of mortality. Development and application of models for rare species however, has been constrained by small population sizes, difficulty in obtaining sampling permits, limited opportunities for funding, and regulations on how endangered species can be used in laboratory studies. Bioenergetic and life history models should help with endangered species-recovery planning since these types of models have been used successfully in the last 25 years to address management problems for many commercially and recreationally important fish species. In this paper we discuss five approaches to developing models and parameters for rare species. Borrowing model functions and parameters from related species is simple, but uncorroborated results can be misleading. Directly estimating parameters with laboratory studies may be possible for rare species that have locally abundant populations. Monte Carlo filtering can be used to estimate several parameters by means of performing simple laboratory growth experiments to first determine test criteria. Pattern-oriented modeling (POM) is a new and developing field of research that uses field-observed patterns to build, test, and parameterize models. Models developed using the POM approach are closely linked to field data, produce testable hypotheses, and require a close working relationship between modelers and empiricists. Artificial evolution in individual-based models can be used to gain insight into adaptive behaviors for poorly understood species and thus can fill in knowledge gaps. ?? Copyright by the American Fisheries Society 2008.

  10. Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption

    Institute of Scientific and Technical Information of China (English)

    Soofastaei Ali; Aminossadati Saiied M.; Arefi Mohammad M.; Kizil Mehmet S.

    2016-01-01

    The mining industry annually consumes trillions of British thermal units of energy, a large part of which is saveable. Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source. Gross vehicle weight, truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption. In this paper, an artificial neural net-work model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight, truck velocity and total resistance. The network was trained and tested using real data collected from a surface mining operation. The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage param-eters considered in this study.

  11. Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression

    Directory of Open Access Journals (Sweden)

    Neela Deshpande

    2014-12-01

    Full Text Available In the recent past Artificial Neural Networks (ANN have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC along with two other data driven techniques namely Model Tree (MT and Non-linear Regression (NLR. Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data. The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cement ratio, Water–Cement ratio and Replacement ratio of natural aggregates by recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete.

  12. Environmental insults in early life and submissiveness later in life in mouse models

    Directory of Open Access Journals (Sweden)

    Seico eBenner

    2015-03-01

    Full Text Available Dominant and subordinate dispositions are not only determined genetically but also nurtured by environmental stimuli during neuroendocrine development. However, the relationship between early life environment and dominance behavior remains elusive. Using the IntelliCage-based competition task for group-housed mice, we have previously described two cases in which environmental insults during the developmental period altered the outcome of dominance behavior later in life. First, mice that were repeatedly isolated from their mother and their littermates (early deprivation; ED, and second, mice perinatally exposed to an environmental pollutant, dioxin, both exhibited subordinate phenotypes, defined by decreased occupancy of limited resource sites under highly competitive circumstances. Similar alterations found in the cortex and limbic area of these two models are suggestive of the presence of neural systems shared across generalized dominance behavior.

  13. Assessment of the Need to Perform Life-Saving Interventions Using Comprehensive Analysis of the Electrocardiogram and Artificial Neural Networks

    Science.gov (United States)

    2010-04-01

    physiological interpretation and clinical use. Task force of the european society of cardiology and the north american society of pacing and...rate variation in conscious dogs . Am J Physiol 274:H1099-1105, 1998. 38. Abu-Hanna A and Lucas PJ. Prognostic models in medicine. Ai and statistical

  14. Determination of Pole and Rotation Period of not Stabilized Artificial Satellite by Use of Model "diffuse Cylinder"

    Science.gov (United States)

    Kolesnik, S. Ya.; Dobrovolsky, A. V.; Paltsev, N. G.

    The algorithm of determination of orientation of rotation axis (pole) and rotation period of satellite, simulated by a cylinder, which is precessing around of vector of angular moment of pulse with constant nutation angle is offered. The Lambert's law of light reflection is accepted. Simultaneously, dependence of light reflection coefficient versus phase angle is determined. The model's simulation confirm applicability of this method. Results of the calculations for artificial satellite No 28506 are carried out.

  15. Study on Artificial Neural Network Model for Crop EVapotranspiration%作物蒸发蒸腾量的人工神经网络模型研究

    Institute of Scientific and Technical Information of China (English)

    冯雪; 潘英华; 张振华

    2007-01-01

    Based on potted plant experiment,BP-artificial neural network was used to simulate crop evapotranspimtion and 3 kinds of artificial neural network models wero constructed as ET1(meteomlogical factors),ET2(meteorological factors and sowing days)and ET3(meteomlogical factors,sowing days and water content). And the predicted result was compared with actual value ET that Was obtained by weishing method.The results showed that the ET3 model had higher calculation precision and an optimum BP-artificial neural network model for calculating crop evapotranspiration.

  16. A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Tamer Khatib

    2015-01-01

    Full Text Available This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that the proposed model has better prediction accuracy compared to some empirical and statistical models. Two error statistics are used in this research to evaluate the proposed model, namely, mean absolute percentage error and root mean square error. These values for the proposed model are 11.8% and −3.1%, respectively. Finally, the proposed model shows better ability in overcoming the sophistic nature of the solar radiation data.

  17. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

    Science.gov (United States)

    Kalteh, Aman Mohammad

    2013-04-01

    Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.

  18. Software life cycle dynamic simulation model: The organizational performance submodel

    Science.gov (United States)

    Tausworthe, Robert C.

    1985-01-01

    The submodel structure of a software life cycle dynamic simulation model is described. The software process is divided into seven phases, each with product, staff, and funding flows. The model is subdivided into an organizational response submodel, a management submodel, a management influence interface, and a model analyst interface. The concentration here is on the organizational response model, which simulates the performance characteristics of a software development subject to external and internal influences. These influences emanate from two sources: the model analyst interface, which configures the model to simulate the response of an implementing organization subject to its own internal influences, and the management submodel that exerts external dynamic control over the production process. A complete characterization is given of the organizational response submodel in the form of parameterized differential equations governing product, staffing, and funding levels. The parameter values and functions are allocated to the two interfaces.

  19. Advances in Intelligent Modelling and Simulation Artificial Intelligence-Based Models and Techniques in Scalable Computing

    CERN Document Server

    Khan, Samee; Burczy´nski, Tadeusz

    2012-01-01

    One of the most challenging issues in today’s large-scale computational modeling and design is to effectively manage the complex distributed environments, such as computational clouds, grids, ad hoc, and P2P networks operating under  various  types of users with evolving relationships fraught with  uncertainties. In this context, the IT resources and services usually belong to different owners (institutions, enterprises, or individuals) and are managed by different administrators. Moreover, uncertainties are presented to the system at hand in various forms of information that are incomplete, imprecise, fragmentary, or overloading, which hinders in the full and precise resolve of the evaluation criteria, subsequencing and selection, and the assignment scores. Intelligent scalable systems enable the flexible routing and charging, advanced user interactions and the aggregation and sharing of geographically-distributed resources in modern large-scale systems.   This book presents new ideas, theories, models...

  20. Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network

    Directory of Open Access Journals (Sweden)

    Esfahanian Mehri

    2013-01-01

    Full Text Available In this study, the capabilities of response surface methodology (RSM and artificial neural networks (ANN for modeling and optimization of ethanol production from glucoseusing Saccharomyces cerevisiae in batch fermentation process were investigated. Effect of three independent variables in a defined range of pH (4.2-5.8, temperature (20-40ºC and glucose concentration (20-60 g/l on the cell growth and ethanol production was evaluated. Results showed that prediction accuracy of ANN was apparently similar to RSM. At optimum condition of temperature (32°C, pH (5.2 and glucose concentration (50 g/l suggested by the statistical methods, the maximum cell dry weight and ethanol concentration obtained from RSM were 12.06 and 16.2 g/l whereas experimental values were 12.09 and 16.53 g/l, respectively. The present study showed that using ANN as fitness function, the maximum cell dry weight and ethanol concentration were 12.05 and 16.16 g/l, respectively. Also, the coefficients of determination for biomass and ethanol concentration obtained from RSM were 0.9965 and 0.9853 and from ANN were 0.9975 and 0.9936, respectively. The process parameters optimization was successfully conducted using RSM and ANN; however prediction by ANN was slightly more precise than RSM. Based on experimental data maximum yield of ethanol production of 0.5 g ethanol/g substrate (97 % of theoretical yield was obtained.

  1. Triplet excitons in natural photosynthetic and artificial light harvesting systems: Measurement and modeling

    Science.gov (United States)

    Hartzler, Daniel Allen

    artificial (B)Chl and porphyrin dimers by experimental and computational methods. This data set obtained allowed for the development of an empirical model for prediction of the triplet state site energy from a given singlet site energy and for development and calibration of a T-T coupling model. Use of these models shows that triplet state lowering by pigment-protein interaction provides photoprotection to the FMO complex, while triplet state lowering by triplet exciton formation is insufficient to provide protection to the chlorosome antenna. Additionally, the T-T coupling model shows that in dimers and other aggregates, the coupling is highly sensitive to relative monomer orientation and position, contrary to what was previously assumed. The simple exponential models used to estimate T-T couplings miss this orientation sensitivity, thus in systems with significant contact between adjacent monomers a more accurate approach is required.

  2. Biomembrane Frontiers Nanostructures, Models, and the Design of Life

    CERN Document Server

    Faller, Roland; Risbud, Subhash H; Jue, Thomas

    2009-01-01

    HANDBOOK OF MODERN BIOPHYSICS Series Editor Thomas Jue, PhD Handbook of Modern Biophysics brings current biophysics topics into focus, so that biology, medical, engineering, mathematics, and physical-science students or researchers can learn fundamental concepts and the application of new techniques in addressing biomedical challenges. Chapters explicate the conceptual framework of the physics formalism and illustrate the biomedical applications. With the addition of problem sets, guides to further study, and references, the interested reader can continue to explore independently the ideas presented. Volume II: Biomembrane Frontiers: Nanostructures, Models, and the Design of Life Editors: Roland Faller, PhD, Thomas Jue, PhD, Marjorie L. Longo, PhD, and Subhash H. Risbud, PhD In Biomembrane Frontiers: Nanostructures, Models, and the Design of Life, prominent researchers have established a foundation for the study of biophysics related to the following topics: Perspectives: Complexes in Liquids, 1900–2008 Mol...

  3. Spatiotemporal realization of an artificial retina model and performance evaluation through ISI- and spike count-based image reconstruction methods

    OpenAIRE

    2014-01-01

    Development of an artificial retina model that can mimic the biologic retina is a highly challenging task and this task is an important step in the development of a visual prosthesis. The receptive field structure of the retina layer is usually modeled as a 2D difference of Gaussian (DOG) filter profile. In the present study, as a different approach, a retina model including a 3D 2-stage DOG filter (3D-ADOG) that has an adaptively changing bandwidth with respect to the local image stati...

  4. How can the total artificial heart (TAH) patient be mobile and enjoy his life with an air driven system?

    Science.gov (United States)

    Atsumi, K; Fujimasa, I; Imachi, K; Nakajima, M

    1984-01-01

    Two air driven VAD and TAH driving and control units were developed for clinical use, one to be installed at the bedside and the other to be installed in an electric wheelchair. The reliability and safety of the bedside unit were shown by long-term TAH experiments using animal models and by clinical application in conjunction with VAD. A TAH goat was safety taken on a 12 hr trip to a destination 550 km away while attached to a bedside unit. The driving and control functions of the wheelchair unit were found to be practically identical to those of the bedside unit.

  5. Developing an artificial neural network model for predicting concrete’s compression strength and electrical resistivity

    Directory of Open Access Journals (Sweden)

    Juan Manuel Lizarazo Marriaga

    2010-04-01

    Full Text Available The present study was conducted for predicting the compressive strength of concrete based on unit weight ultrasonic and pulse velocity (UPV for 41 different concrete mixtures. This research emerged from the need for a rapid test for predicting concrete’s compressive strength. The research was also conducted for predicting concrete’s electrical resistivity based on unit weight ultrasonic, pulse velocity (UPV and compressive strength with the same mixes. The prediction was made using simple regression analysis and artificial neural networks. The results revealed that artificial neural networks can be used for effectively predicting compressive strength and electrical resistivity.

  6. Adaptive Artificial Intelligence Based Model Base Controller: Applied to Surgical Endoscopy Telemanipulator

    Directory of Open Access Journals (Sweden)

    Farzin Piltan

    2013-08-01

    Full Text Available This research involved developing a surgical robot assistant using an articulated PUMA robot running on a linear or nonlinear axis. The research concentrated on studying the artificial intelligence based switching computed torque controller to localization of an endoscopic tool. Results show that the switching artificial nonlinear control algorithm is capable to design a stable controller. For this system, error was used as the performance metric. Positioning of the endoscopic manipulator relative to the world coordinate frame was possible to within 0.05 inch. Error in maintaining a constant point in space is evident during repositioning however this was caused by limitations in the robot arm.

  7. Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Cheng Zhi-Qun; Hu Sha; Liu Jun; Zhang Qi-Jun

    2011-01-01

    In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor (HEMT) with an artificial neural network (ANN). The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping (neuro-SM) technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AlGaN/GaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of AlGaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.

  8. Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

    Directory of Open Access Journals (Sweden)

    Musztyfaga-Staszuk M.

    2015-09-01

    Full Text Available This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace and unconventional (2. Selective Laser Sintering. Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM. Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

  9. Probabilistic Life Cycle Cost Model for Repairable System

    Science.gov (United States)

    Nasir, Meseret; Chong, H. Y.; Osman, Sabtuni

    2015-04-01

    Traditionally, Life cycle cost (LCC) has been predicted in a deterministic approach, however; this method is not capable to consider the uncertainties in the input variables. In this paper, a probabilistic approach using Adaptive network-based fuzzy inference system (ANFIS) is proposed to estimate the LCC of repairable systems. The developed model could handle the uncertainties of input variables in the estimation of LCC. The numerical analysis shows that the acquisition and downtime cost could have a high effect towards the LCC compared to repair cost. The developed model could also provide more precise quantitative information for decision making process.

  10. Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

    Science.gov (United States)

    Everson, Howard T.; And Others

    This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

  11. Capabilities For Modelling Of Conversion Processes In Life Cycle Assessment

    DEFF Research Database (Denmark)

    Damgaard, Anders; Zarrin, Bahram; Tonini, Davide

    Life cycle assessment was traditionally used for modelling of product design and optimization. This is also seen in the conventional LCA software which is optimized for the modelling of single materials streams of a homogeneous nature that is assembled into a final product. There has therefore been...... considering how the biochemical parameters change through a process chain. A good example of this is bio-refinery processes where different residual biomass products are converted through different steps into the final energy product. Here it is necessary to know the stoichiometry of the different products...... processes can be modelled and then integrated into the overall LCA model. This allows for flexible modules which automatically will adjust the material flows and the conversion takes places in processes on basis of its chemical information, which can be set for multiple input materials at the same time...

  12. Phenolics extraction from sweet potato peels: modelling and optimization by response surface modelling and artificial neural network.

    Science.gov (United States)

    Anastácio, Ana; Silva, Rúben; Carvalho, Isabel S

    2016-12-01

    Sweet potato peels (SPP) are a major waste generated during root processing and currently have little commercial value. Phenolics with free radical scavenging activity from SPP may represent a possible added-value product for the food industry. The aqueous extraction of phenolics from SPP was studied using a Central Composite Design with solvent to solid ratio (30-60 mL g(-1)), time (30-90 min) and temperature (25-75 °C) as independent variables. The comparison of response surface methodology (RSM) and artificial neural network (ANN) analysis on extraction modelling and optimising was performed. Temperature and solvent to solid ratio, alone and in interaction, presented a positive effect in TPC, ABTS and DPPH assays. Time was only significant for ABTS assay with a negative influence both as main effect and in interaction with other independent variables. RSM and ANN models predicted the same optimal extraction conditions as 60 mL g(-1) for solvent to solid ratio, 30 min for time and 75 °C for temperature. The obtained responses in the optimized conditions were as follow: 11.87 ± 0.69 mg GAE g(-1) DM for TPC, 12.91 ± 0.42 mg TE g(-1) DM for ABTS assay and 46.35 ± 3.08 mg TE g(-1) DM for DPPH assay. SPP presented similar optimum extraction conditions and phenolic content than peels of potato, tea fruit and bambangan. Predictive models and the optimized extraction conditions offers an opportunity for food processors to generate products with high potential health benefits.

  13. Research on Business Models in their Life Cycle

    Directory of Open Access Journals (Sweden)

    Adam Jabłoński

    2016-04-01

    Full Text Available The paper presents the results of theoretical discussions and research findings in the field of designing sustainable business models that support the creation of value at various stages of the business life cycle. The paper presents selected findings of extensive research into the business models of Polish companies listed on the Warsaw Stock Exchange. Companies which are at various stages of development should build and adapt their business models in order to maintain the ability to create value for stakeholders. Characteristics of business models at the early stages of development are different than at mature stages. The paper highlights the differences in business models in the context of the life cycle of companies and sustainability criteria. The paper presents research findings which show that the company’s development can be seen from the point of view of the business model. Research on business models concentrated on identifying the key attributes and the configuration of the business models appropriate for the early stage of development as well as the maturity stage. It was found that the business models of companies at an early stage of the development of companies listed on the Warsaw Stock Exchange are oriented primarily to how the company shapes, delivers, and captures value from the market in order to generate profits for shareholders and increase the value of the company, while the business models of mature companies include the intentions of management used to balance objectives with respect to different groups of stakeholders, and to carefully formulate and implement business objectives with particular attention paid to preserving the sustainability of the business. The assessment of business models from the point of view of the life cycle proves that managers change their approach to configuring business models over time; at some point, they include management intentions aimed at a broader range of goals than merely

  14. Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

    Science.gov (United States)

    Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

    2008-01-01

    In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

  15. Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte

    Directory of Open Access Journals (Sweden)

    Ahmad Danial Azzahari

    2016-01-01

    Full Text Available A gel polymer electrolyte system based on phthaloylchitosan was prepared. The effects of process variables, such as lithium iodide, caesium iodide, and 1-butyl-3-methylimidazolium iodide were investigated using a distance-based ternary mixture experimental design. A comparative approach was made between response surface methodology (RSM and artificial neural network (ANN to predict the ionic conductivity. The predictive capabilities of the two methodologies were compared in terms of coefficient of determination R2 based on the validation data set. It was shown that the developed ANN model had better predictive outcome as compared to the RSM model.

  16. Economy Analysis of a Recycle Model of End-of-Life Vehicle

    Institute of Scientific and Technical Information of China (English)

    QIN Ye; WANG Xiang; CHEN Ming; WANG Cheng-tao

    2005-01-01

    In order to evaluate recycle economy of end-of-life vehicles quantitatively,an economy model based on a recycle model of end-of-life vehicles and recycle cost analysis. With a practical example of recycling engines of endof-life vehicles ,the validity of the recycle economy model and good recycle economy of the end-of-life vehicle engine tors to raise the recycle economy of the end-of-life vehicle.

  17. The Velocity of Money in a Life-Cycle Model

    CERN Document Server

    Wang, Y; Wang, Yougui; Qiu, Hanqing

    2005-01-01

    The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is governed by behavior patterns of economic agents and open a way to constructing micro-foundation of it. It is found that time pattern of income and expense for a representative individual can be obtained from a simple version of life-cycle model, and average holding time of money resulted from the individual's optimal choice depends on the expected length of relevant planning periods.

  18. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients.

    Science.gov (United States)

    Zhu, Lucheng; Luo, Wenhua; Su, Meng; Wei, Hangping; Wei, Juan; Zhang, Xuebang; Zou, Changlin

    2013-09-01

    The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.

  19. The Life Span Model of Suicide and Its Neurobiological Foundation

    Science.gov (United States)

    Ludwig, Birgit; Roy, Bhaskar; Wang, Qingzhong; Birur, Badari; Dwivedi, Yogesh

    2017-01-01

    The very incomprehensibility of the suicidal act has been occupying the minds of researchers and health professionals for a long time. Several theories of suicide have been proposed since the beginning of the past century, and a myriad of neurobiological studies have been conducted over the past two decades in order to elucidate its pathophysiology. Both neurobiology and psychological theories tend to work in parallel lines that need behavioral and empirical data respectively, to confirm their hypotheses. In this review, we are proposing a “Life Span Model of Suicide” with an attempt to integrate the “Stress-Diathesis Model” and the “Interpersonal Model of Suicide” into a neurobiological narrative and support it by providing a thorough compilation of related genetic, epigenetic, and gene expression findings. This proposed model comprises three layers, forming the capability of suicide: genetic factors as the predisposing Diathesis on one side and Stress, characterized by epigenetic marks on the other side, and in between gene expression and gene function which are thought to be influenced by Diathesis and Stress components. The empirical evidence of this model is yet to be confirmed and further research, specifically epigenetic studies in particular, are needed to support the presence of a life-long, evolving capability of suicide and identify its neurobiological correlates. PMID:28261051

  20. Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models

    Directory of Open Access Journals (Sweden)

    Chih-Chieh Young

    2015-01-01

    Full Text Available Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN model (back propagation neural network, BPNN, a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations was collected for model calibration (training and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.

  1. Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction

    Directory of Open Access Journals (Sweden)

    Mohammed Abo-Zahhad

    2014-07-01

    Full Text Available Human Genome Project has led to a huge inflow of genomic data. After the completion of human genome sequencing, more and more effort is being put into identification of splicing sites of exons and introns (donor and acceptor sites. These invite bioinformatics to analysis the genome sequences and identify the location of exon and intron boundaries or in other words prediction of splicing sites. Prediction of splice sites in genic regions of DNA sequence is one of the most challenging aspects of gene structure recognition. Over the last two decades, artificial neural networks gradually became one of the essential tools in bioinformatics. In this paper artificial neural networks with different numerical mapping techniques have been employed for building integrated model for splice site prediction in genes. An artificial neural network is trained and then used to find splice sites in human genes. A comparison between different mapping methods using trained neural network in terms of their precision in prediction of donor and acceptor sites will be presented in this paper. Training and measuring performance of neural network are carried out using sequences of the human genome (GRch37/hg19- chr21. Simulation results indicate that using Electron-Ion Interaction Potential numerical mapping method with neural network yields to the best performance in prediction.

  2. Model of environmental life cycle assessment for coal mining operations.

    Science.gov (United States)

    Burchart-Korol, Dorota; Fugiel, Agata; Czaplicka-Kolarz, Krystyna; Turek, Marian

    2016-08-15

    This paper presents a novel approach to environmental assessment of coal mining operations, which enables assessment of the factors that are both directly and indirectly affecting the environment and are associated with the production of raw materials and energy used in processes. The primary novelty of the paper is the development of a computational environmental life cycle assessment (LCA) model for coal mining operations and the application of the model for coal mining operations in Poland. The LCA model enables the assessment of environmental indicators for all identified unit processes in hard coal mines with the life cycle approach. The proposed model enables the assessment of greenhouse gas emissions (GHGs) based on the IPCC method and the assessment of damage categories, such as human health, ecosystems and resources based on the ReCiPe method. The model enables the assessment of GHGs for hard coal mining operations in three time frames: 20, 100 and 500years. The model was used to evaluate the coal mines in Poland. It was demonstrated that the largest environmental impacts in damage categories were associated with the use of fossil fuels, methane emissions and the use of electricity, processing of wastes, heat, and steel supports. It was concluded that an environmental assessment of coal mining operations, apart from direct influence from processing waste, methane emissions and drainage water, should include the use of electricity, heat and steel, particularly for steel supports. Because the model allows the comparison of environmental impact assessment for various unit processes, it can be used for all hard coal mines, not only in Poland but also in the world. This development is an important step forward in the study of the impacts of fossil fuels on the environment with the potential to mitigate the impact of the coal industry on the environment.

  3. Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran

    Science.gov (United States)

    Kashani, Mahsa H.; Ghorbani, Mohammad Ali; Dinpashoh, Yagob; Shahmorad, Sedaghat

    2016-09-01

    Rainfall-runoff simulation is an important task in water resources management. In this study, an integrated Volterra model with artificial neural networks (IVANN) was presented to simulate the rainfall-runoff process. The proposed integrated model includes the semi-distributed forms of the Volterra and ANN models which can explore spatial variation in rainfall-runoff process without requiring physical characteristic parameters of the catchments, while taking advantage of the potential of Volterra and ANNs models in nonlinear mapping. The IVANN model was developed using hourly rainfall and runoff data pertaining to thirteen storms to study short-term responses of a forest catchment in northern Iran; and its performance was compared with that of semi-distributed integrated ANN (IANN) model and lumped Volterra model. The Volterra model was applied as a nonlinear model (second-order Volterra (SOV) model) and solved using the ordinary least square (OLS) method. The models performance were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge and error of time for peak to arrive. Results showed that the IVANN model performs well than the other semi-distributed and lumped models to simulate the rainfall-runoff process. Comparing to the integrated models, the lumped SOV model has lower precision to simulate the rainfall-runoff process.

  4. A Simulation Model for the Waterfall Software Development Life Cycle

    CERN Document Server

    Bassil, Youssef

    2012-01-01

    Software development life cycle or SDLC for short is a methodology for designing, building, and maintaining information and industrial systems. So far, there exist many SDLC models, one of which is the Waterfall model which comprises five phases to be completed sequentially in order to develop a software solution. However, SDLC of software systems has always encountered problems and limitations that resulted in significant budget overruns, late or suspended deliveries, and dissatisfied clients. The major reason for these deficiencies is that project directors are not wisely assigning the required number of workers and resources on the various activities of the SDLC. Consequently, some SDLC phases with insufficient resources may be delayed; while, others with excess resources may be idled, leading to a bottleneck between the arrival and delivery of projects and to a failure in delivering an operational product on time and within budget. This paper proposes a simulation model for the Waterfall development proce...

  5. A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems

    OpenAIRE

    Kathryn Elizabeth Merrick; Kamran eShafi

    2013-01-01

    An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivati...

  6. Simulation of a pulsatile total artificial heart: Development of a partitioned Fluid Structure Interaction model

    Science.gov (United States)

    Sonntag, Simon J.; Kaufmann, Tim A. S.; Büsen, Martin R.; Laumen, Marco; Linde, Torsten; Schmitz-Rode, Thomas; Steinseifer, Ulrich

    2013-04-01

    Heart disease is one of the leading causes of death in the world. Due to a shortage in donor organs artificial hearts can be a bridge to transplantation or even serve as a destination therapy for patients with terminal heart insufficiency. A pusher plate driven pulsatile membrane pump, the Total Artificial Heart (TAH) ReinHeart, is currently under development at the Institute of Applied Medical Engineering of RWTH Aachen University.This paper presents the methodology of a fully coupled three-dimensional time-dependent Fluid Structure Interaction (FSI) simulation of the TAH using a commercial partitioned block-Gauss-Seidel coupling package. Partitioned coupling of the incompressible fluid with the slender flexible membrane as well as a high fluid/structure density ratio of about unity led inherently to a deterioration of the stability (‘artificial added mass instability’). The objective was to conduct a stable simulation with high accuracy of the pumping process. In order to achieve stability, a combined resistance and pressure outlet boundary condition as well as the interface artificial compressibility method was applied. An analysis of the contact algorithm and turbulence condition is presented. Independence tests are performed for the structural and the fluid mesh, the time step size and the number of pulse cycles. Because of the large deformation of the fluid domain, a variable mesh stiffness depending on certain mesh properties was specified for the fluid elements. Adaptive remeshing was avoided. Different approaches for the mesh stiffness function are compared with respect to convergence, preservation of mesh topology and mesh quality. The resulting mesh aspect ratios, mesh expansion factors and mesh orthogonalities are evaluated in detail. The membrane motion and flow distribution of the coupled simulations are compared with a top-view recording and stereo Particle Image Velocimetry (PIV) measurements, respectively, of the actual pump.

  7. Artificial neural network model to predict slag viscosity over a broad range of temperatures and slag compositions

    Energy Technology Data Exchange (ETDEWEB)

    Duchesne, Marc A. [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada); Macchi, Arturo [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); Lu, Dennis Y.; Hughes, Robin W.; McCalden, David; Anthony, Edward J. [CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada)

    2010-08-15

    Threshold slag viscosity heuristics are often used for the initial assessment of coal gasification projects. Slag viscosity predictions are also required for advanced combustion and gasification models. Due to unsatisfactory performance of theoretical equations, an artificial neural network model was developed to predict slag viscosity over a broad range of temperatures and slag compositions. This model outperforms other slag viscosity models, resulting in an average error factor of 5.05 which is lower than the best obtained with other available models. Genesee coal ash viscosity predictions were made to investigate the effect of adding Canadian limestone and dolomite. The results indicate that magnesium in the fluxing agent provides a greater viscosity reduction than calcium for the threshold slag tapping temperature range. (author)

  8. Mineral Bionization - Surface Chemical Modeling of the Emergence of Life

    Science.gov (United States)

    Arrhenius, G.

    2001-12-01

    The earliest stages in entering an RNA-world require natural mechanisms that are capable of selective concentration of simple aldehydes from dilute solution in the environment (4), furthermore phosphorylation of the sequestered aldehydes (2) and their catalytic condensation to form, selectively, tetrose- (threose) or pentose- (ribose) phosphate (3); the latter representing the R in RNA. A variety of common positively charged sheet structure minerals (mixed valence double layer metal hydroxide minerals such as hydrotalcite and green rust) have proven to be remarkably capable of performing these crucial tasks under simplified natural conditions (1). These prebiotic model reactions have demonstrated plausible closure of the gap, previously thought to preclude the natural formation of nucleoside phosphates, the backbone components of the information carrying genetic material. Pioneering research by other workers (5) has demonstrated the feasibility of necessary further steps in the chain toward functional RNA; mineral (montmorillonite) catalyzed oligomerization of nucleotides, the formation of complementary RNA strands (6) and the enzymatic activity of RNA (ribozymes). These contributions have placed the initially conjectural concept of an initial RNA-world on an experimental footing. Remaining problems include the initial transfer of information to spontaneously forming RNA, sufficient to convey biofunctionallity (7). Also in this central problem mineral surface interactions may be speculated to play a natural role; a question that is open to experimental verification. References. 1. Pitsch, S.; Eschenmoser, A.; Gedulin, B.; Hui, S. and Arrhenius, G. Origins Life Evol. Biosphere, 1994, 24 (5), 389. 2. Kolb, V.; Zhang, S.; Xu, Y.; Arrhenius, G. Origins Life Evol. Biosphere, 1997, 27, 485. 3. Krishnamurthy, R.; Pitsch, S.; Arrhenius, G. Origins Life Evol. Biosphere, Origins Life Evol. Biosphere 1999, 29, 139 4. Pitsch, S.; Krishnamurthy, R.; Arrhenius, G. Helv. Chim

  9. Life Story of Chinese College Students with Perfectionism Personality: A Qualitative Study Based on a Life Story Model

    Science.gov (United States)

    Ma, Min; Zi, Fei

    2015-01-01

    This manuscript aims to explore and delineate the common characteristics of college students with perfectionism, to promote an in-depth understanding of dynamic personality development of perfectionists from the views of life story model proposed by McAdams (1985). The researchers adopted a narrative qualitative research method. The life stories…

  10. The Volterra series as special case of artificial neural network model

    Science.gov (United States)

    Napiorkowski, J.; O Kane, J. P.

    2003-04-01

    !1! (2) In the above equation x is the input to the system, y is the output from the model, is the first order kernel which reflects the linear properties of the system, is the second order kernel which reflects the quadratic properties , and so on. Modelling of hydrological processes by means of a Volterra series has been developed independently of other methods of describing dynamic systems, in particular by state equation formulation. The problem of series identification has been solved by numerical methods applied to input record and its corresponding output by means of kernel expansion in orthonormal polynomials. It can be shown (Napiórkowski and Strupczewski, 1979; Napiórkowski and O'Kane, 1984) that if the function f in eq.(1) is differentiable as many times as required the state-space equation (1) can be approximated by the Volterra series (2) by means of Taylor series expansion for operators. The structure of the first two kernels was shown to be h_1 (τ ) = aH_n (τ ) % MathType!End!2!1! (3) h_2 (τ _1 ,τ _2 ) = bleft\\{ {H_n (τ _1 )sumlimitsk = 1^n {H_k (τ _2 ) + } H_n (τ _2 )sumlimitsk = 1^n {H_k (τ _1 ) - H_n [max (τ _1 ,τ _1 )]} } right\\} % MathType!End!2!1! (4) where Y(i) = sumlimitsk = 1N_s {H_1 (k)X(i - k) + sumlimitsk = 1N_s {sumlimitsl = 1N_s {H_2 (k,l)X(i - k)X(i - l)} } } + ... % MathType!End!2!1! (6) where Ns is the number representing the memory of t he system i.e. k, l =1,...Ns, NT is the number of observations, i.e. i=1,2,...NT. It is easy to see that discrete Volterra series described by eq.(6) is a special case of artificial network model that is called in resent publications as Volterra net. In the paper we focus our attention on of the existence and uniqueness of the solution of the described above identification problem. Literature Napiórkowski J.J., O'Kane P., 1984. A new nonlinear conceptual model of flood waves. Journal of Hydrology, 69, 43 58. Napiórkowski J.J., Strupczewski W.G., 1979. The analytical determination of the

  11. A New Model for the Organizational Knowledge Life Cycle

    CERN Document Server

    Lella, Luigi

    2010-01-01

    Actual organizations, in particular the ones which operate in evolving and distributed environments, need advanced frameworks for the management of the knowledge life cycle. These systems have to be based on the social relations which constitute the pattern of collaboration ties of the organization. We demonstrate here, with the aid of a model taken from the theory of graphs, that it is possible to provide the conditions for an effective knowledge management. A right way could be to involve the actors with the highest betweeness centrality in the generation of discussion groups. This solution allows the externalization of tacit knowledge, the preservation of knowledge and the raise of innovation processes.

  12. Life sciences research in space: The requirement for animal models

    Science.gov (United States)

    Fuller, C. A.; Philips, R. W.; Ballard, R. W.

    1987-01-01

    Use of animals in NASA space programs is reviewed. Animals are needed because life science experimentation frequently requires long-term controlled exposure to environments, statistical validation, invasive instrumentation or biological tissue sampling, tissue destruction, exposure to dangerous or unknown agents, or sacrifice of the subject. The availability and use of human subjects inflight is complicated by the multiple needs and demands upon crew time. Because only living organisms can sense, integrate and respond to the environment around them, the sole use of tissue culture and computer models is insufficient for understanding the influence of the space environment on intact organisms. Equipment for spaceborne experiments with animals is described.

  13. Design of Multithreaded Software The Entity-Life Modeling Approach

    CERN Document Server

    Sandén, Bo I

    2011-01-01

    This book assumes familiarity with threads (in a language such as Ada, C#, or Java) and introduces the entity-life modeling (ELM) design approach for certain kinds of multithreaded software. ELM focuses on "reactive systems," which continuously interact with the problem environment. These "reactive systems" include embedded systems, as well as such interactive systems as cruise controllers and automated teller machines.Part I covers two fundamentals: program-language thread support and state diagramming. These are necessary for understanding ELM and are provided primarily for reference. P

  14. Life cycle models of conventional and alternative-fueled automobiles

    Science.gov (United States)

    Maclean, Heather Louise

    This thesis reports life cycle inventories of internal combustion engine automobiles with feasible near term fuel/engine combinations. These combinations include unleaded gasoline, California Phase 2 Reformulated Gasoline, alcohol and gasoline blends (85 percent methanol or ethanol combined with 15 percent gasoline), and compressed natural gas in spark ignition direct and indirect injection engines. Additionally, I consider neat methanol and neat ethanol in spark ignition direct injection engines and diesel fuel in compression ignition direct and indirect injection engines. I investigate the potential of the above options to have a lower environmental impact than conventional gasoline-fueled automobiles, while still retaining comparable pricing and consumer benefits. More broadly, the objective is to assess whether the use of any of the alternative systems will help to lead to the goal of a more sustainable personal transportation system. The principal tool is the Economic Input-Output Life Cycle Analysis model which includes inventories of economic data, environmental discharges, and resource use. I develop a life cycle assessment framework to assemble the array of data generated by the model into three aggregate assessment parameters; economics, externalities, and vehicle attributes. The first step is to develop a set of 'comparable cars' with the alternative fuel/engine combinations, based on characteristics of a conventional 1998 gasoline-fueled Ford Taurus sedan, the baseline vehicle for the analyses. I calculate the assessment parameters assuming that these comparable cars can attain the potential thermal efficiencies estimated by experts for each fuel/engine combination. To a first approximation, there are no significant differences in the assessment parameters for the vehicle manufacture, service, fixed costs, and the end-of-life for any of the options. However, there are differences in the vehicle operation life cycle components and the state of technology

  15. A Bayesian model of biases in artificial language learning: the case of a word-order universal.

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word-order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross-linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners' inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization-Greenberg's Universal 18-which bans a particular word-order pattern relating nouns, adjectives, and numerals.

  16. Artificial blood

    Directory of Open Access Journals (Sweden)

    Sarkar Suman

    2008-01-01

    Full Text Available Artificial blood is a product made to act as a substitute for red blood cells. While true blood serves many different functions, artificial blood is designed for the sole purpose of transporting oxygen and carbon dioxide throughout the body. Depending on the type of artificial blood, it can be produced in different ways using synthetic production, chemical isolation, or recombinant biochemical technology. Development of the first blood substitutes dates back to the early 1600s, and the search for the ideal blood substitute continues. Various manufacturers have products in clinical trials; however, no truly safe and effective artificial blood product is currently marketed. It is anticipated that when an artificial blood product is available, it will have annual sales of over $7.6 billion in the United States alone.

  17. Artificial blood.

    Science.gov (United States)

    Sarkar, Suman

    2008-07-01

    Artificial blood is a product made to act as a substitute for red blood cells. While true blood serves many different functions, artificial blood is designed for the sole purpose of transporting oxygen and carbon dioxide throughout the body. Depending on the type of artificial blood, it can be produced in different ways using synthetic production, chemical isolation, or recombinant biochemical technology. Development of the first blood substitutes dates back to the early 1600s, and the search for the ideal blood substitute continues. Various manufacturers have products in clinical trials; however, no truly safe and effective artificial blood product is currently marketed. It is anticipated that when an artificial blood product is available, it will have annual sales of over $7.6 billion in the United States alone.

  18. Comparison of Artificial Neural Network And M5 Model Tree Technique In Water Level Forecasting of Solo River

    Science.gov (United States)

    Lasminto, Umboro; Hery Mularta, Listya

    2010-05-01

    Flood events along the Solo River flow at the end of December 2007 has caused lose of properties and lives. Floods occurred in the city of Ngawi, Madiun, Bojonegoro, Babat and surrounding areas. To reduce future losses, one of the important efforts that will occur during a flood is to get information about the magnitude and time will be floods, so that people can make an effort to reduce its impact. Flood forecasting model can provide information of water level in the river some time before the incident. This paper will compare the flood forecasting model at Bojonegoro City was built using the technique of Artificial Neural Network (ANN) and M5 Model Tree (M5MT). The model will forecast the water level of 1, 3 and 6 hours ahead at the point of water level recorders in the City of Bojonegoro using input from the water level at some point water level recorders in the upstream such as Karangnongko, Sekayu, Jurug and Wonogiri. The same data set of hourly water level records are used to build the model of ANN and M5MT technique. The selection of parameters and setup of ANN and M5MT technique is done to obtain the best result. The results of the model are evaluated by calculating the Root Mean Square Error (RMSE) between the predictions and observations. RMSE produced by the water level forecasting model 1, 3 and 6 hours ahead with M5MT technique are 0.2723, 0.6279 and 0.7176 meters. While the ANN technique are 0.1829, 0.3192 and 0517 meters. ANN technique has a better ability in predicting low flow, whereas M5 Model Tree technique has a better ability in predicting high flow. Keywords : Water level forecasting, Solo River, M5 Model Tree, Artificial Neural Network

  19. Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

    Science.gov (United States)

    Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

    2015-01-01

    Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

  20. Author's reply to discussion of using artificial neural nets to identify the well-test interpretation model

    Energy Technology Data Exchange (ETDEWEB)

    Al-Kaabi, A.U.; Lee, W.J. (Texas A and M Univ., College Station, TX (United States))

    1994-09-01

    The authors thank Yeung et al. for their discussion about their original paper. They agree with Yeung et al. that their proposed scaling method, when applied to patterns with distinct subparts such as the one shown, represents an improvement on the method they proposed. This is particularly true because Yeung et al.'s method eliminates the need to train the artificial neural networks (ANN's) on different sizes (scales) of the same pattern of a specific interpretation model. This paper presents the following comments for discussion and suggestions for further improvement.