WorldWideScience

Sample records for artificial life models

  1. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    OpenAIRE

    Atris Suyantohadi; Mochamad Hariadi; Mauridhi Hery Purnomo

    2010-01-01

    The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling...

  2. Ab Initio Modeling of Ecosystems with Artificial Life

    OpenAIRE

    Adami, C.

    2002-01-01

    Artificial Life provides the opportunity to study the emergence and evolution of simple ecosystems in real time. We give an overview of the advantages and limitations of such an approach, as well as its relation to individual-based modeling techniques. The Digital Life system Avida is introduced and prospects for experiments with ab initio evolution (evolution "from scratch"), maintenance, as well as stability of ecosystems are discussed.

  3. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Atris Suyantohadi

    2010-03-01

    Full Text Available The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN and Lindenmayer System (L-System methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N, Phosphor (P and Potassium (K. The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied.

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

    OpenAIRE

    Sumit Goyal; Gyanendra Kumar Goyal

    2012-01-01

    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...

  5. 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.

  6. Cascade Artificial Neural Network Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Gyanendra Kumar Goyal

    2013-05-01

    Full Text Available The purpose of this study is to develop artificial neural network (ANN models for predicting shelf life of processed cheese stored at 7-8ºC. Body & texture, aroma & flavour, moisture and free fatty acids were taken as input parameters, and sensory score as output parameter for developing the models. The developed Cascade single layer ANN models were compared with each other. Bayesian regularization was used for training ANN models. Network was trained with 100 epochs, and neurons in each hidden layer(s varied from 3 to 20.  Cascade ANN models very well predicted the shelf life of processed cheese.

  7. Smart Artificial Intelligence Computerized Models for Shelf Life Prediction of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-06-01

    Full Text Available Linear Layer (Design and multiple linear regression artificial intelligence computerized models were developed for predicting shelf life of processed cheese stored at 7-8ºC. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied for comparing the prediction ability of the developed models. The modelling results showed excellent agreement between the experimental data and predicted values with a high determination coefficient, suggesting that the Linear Layer (Design and MLR models are very efficient in predicting the shelf life of processed cheese stored at 7-8oC.

  8. Artificial Life in Quantum Technologies

    Science.gov (United States)

    Alvarez-Rodriguez, Unai; Sanz, Mikel; Lamata, Lucas; Solano, Enrique

    2016-02-01

    We develop a quantum information protocol that models the biological behaviours of individuals living in a natural selection scenario. The artificially engineered evolution of the quantum living units shows the fundamental features of life in a common environment, such as self-replication, mutation, interaction of individuals, and death. We propose how to mimic these bio-inspired features in a quantum-mechanical formalism, which allows for an experimental implementation achievable with current quantum platforms. This study paves the way for the realization of artificial life and embodied evolution with quantum technologies.

  9. 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.

  10. Artificial Life Meets Computational Creativity?

    OpenAIRE

    McMullin, Barry

    2009-01-01

    I (briefly) review the history of work in Artificial Life on the problem of the open-ended evolutionary growth of complexity in computational worlds. This is then put into the context of evolutionary epistemology and human creativity.

  11. Artificial life, the new paradigm

    International Nuclear Information System (INIS)

    A chronological synthesis of the most important facts is presented in the theoretical development and computational simulation that they have taken to the formation of a new paradigm that is known as artificial life; their characteristics and their main investigation lines are analyzed. Finally, a description of its work is made in the National University of Colombia

  12. 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.

  13. Abundance Distributions in Artificial Life and Stochastic Models: "Age and Area" revisited

    OpenAIRE

    Adami, C.; Brown, C. T.; Haggerty, M.

    1995-01-01

    Using an artificial system of self-replicating strings, we show a correlation between the age of a genotype and its abundance that reflects a punctuated rather than gradual picture of evolution, as suggested long ago by Willis. In support of this correlation, we measure genotype abundance distributions and find universal coefficients. Finally, we propose a simple stochastic model which describes the dynamics of equilibrium periods and which correctly predicts most of the observed distributions.

  14. Abundance distributions in artificial life and stochastic models "age and area" revisited

    CERN Document Server

    Adami, C; Haggerty, M; Brown, C T; Haggerty, M

    1995-01-01

    Using an artificial system of self-replicating strings, we show a correlation between the age of a genotype and its abundance that reflects a punctuated rather than gradual picture of evolution, as suggested long ago by Willis. In support of this correlation, we measure genotype abundance distributions and find universal coefficients. Finally, we propose a simple stochastic model which describes the dynamics of equilibrium periods and which correctly predicts most of the observed distributions.

  15. 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.

  16. 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.

  17. 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.

  18. 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. .

  19. Artificial Life:Visual Model with Simulation of a Plant Pet

    Institute of Scientific and Technical Information of China (English)

    JI Chen; QI Shen

    2004-01-01

    This paper introduces an L-system model, which simulates a plant pet. The plant, named Wilson, is able to respond to the change of its virtual environment and to user's behaviors. However it does not speak or move. What users can see are only the changes of its appearance. The connection between the plant's appearance and external inputs is implemented with some parameters embedded in the L-system.

  20. 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.

  1. Artificial Life - Why Should Musicians Bother?

    DEFF Research Database (Denmark)

    Berry, Rodney; Dahlstedt, Palle

    2003-01-01

    No new technology appears in a society without causing changes to that society. Artists instinctively respond to these changes and the potential meanings of the technologies that bring them about. In this way, artists' interest in technology goes beyond the simple desire to possess new tools for ...... - 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....... 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...

  2. Estimating Processed Cheese Shelf Life with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-05-01

    Full Text Available Cascade multilayer artificial neural network (ANN models were developed for estimating the shelf life of processed cheese stored at 7-8oC.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.The developed model with a combination of 5à16à16à1 showed excellent agreement between the actual and the predicted data , thus confirming that multilayer cascade models are good in estimating the shelf life of processed cheese.

  3. 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. PMID:16393455

  4. 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.

  5. 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.

  6. Levels of Functional Equivalence in Reverse Bioengineering: The Darwinian Turing Test for Artificial Life

    OpenAIRE

    Harnad, Stevan

    1994-01-01

    Both Artificial Life and Artificial Mind are branches of what Dennett has called "reverse engineering": Ordinary engineering attempts to build systems to meet certain functional specifications, reverse bioengineering attempts to understand how systems that have already been built by the Blind Watchmaker work. Computational modelling (virtual life) can capture the formal principles of life, perhaps predict and explain it completely, but it can no more be alive than a virtual forest fi...

  7. 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.

  8. Convergence of Media in Artificial Life --- Dilemma or Human's Nature

    OpenAIRE

    Lund, Henrik Hautop; Vucic, Vedran

    1997-01-01

    In this short essay we want to question the basic preconceptions about our perception and rigid mental maps about semantics of meaning in arts. This essay is based on our personal artistic experience in composing music and creating computer images using software packages based on ideas which are at the foundation of Artificial Life and in some way of Artificial Intelligence. Our idea is not to give final answers but, rather to provoke discussion about new ways of thinking and perception which...

  9. 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.

  10. Artificial nutrition at the end of life: ethical issues.

    Science.gov (United States)

    van de Vathorst, Suzanne

    2014-04-01

    Artificial nutrition is a medical treatment that first of all needs a sound scientific base before prescribing it. This base is absent for dying patients and patients in the end stage of dementia. Because feeding is a very emotional and symbolical issue, patient and family may request this treatment despite the lack of evidence. These issues should be addressed in good communication with patient and relatives. For comatose patients and patients in a persistent vegetative state artificial nutrition is a necessary support to bridge the time until either recovery is imminent or improbable. At that moment artificial nutrition no longer contributes to the life of the patient and should be ceased. Artificial nutrition has no place in patients that voluntary decide to stop eating and drinking in order to die. PMID:24810185

  11. "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. PMID:17355191

  12. 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 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...... confluence of chemical, artificial intelligence, and robotic approaches to artificial life....

  13. Shelf Life Estimation of Processed Cheese by Artificial Neural Network Expert Systems

    Directory of Open Access Journals (Sweden)

    SUMIT GOYAL

    2012-03-01

    Full Text Available Time –delay artificial neural network (ANN single layer and multilayer artificial models were developed for predicting the shelf life of processed cheese stored at 7-8o C. Soluble nitrogen, pH; standard plate count, yeast & mould count, and spore count were input variables, and sensory score was output variable. The results showed excellent agreement between training and validation data with high coefficient of determination and nash - sutcliffo coefficient, thus suggesting that the developed models are good for predicting the shelf life of processed cheese.

  14. A graph grammar approach to artificial life.

    Science.gov (United States)

    Kniemeyer, Ole; Buck-Sorlin, Gerhard H; Kurth, Winfried

    2004-01-01

    We present the high-level language of relational growth grammars (RGGs) as a formalism designed for the specification of ALife models. RGGs can be seen as an extension of the well-known parametric Lindenmayer systems and contain rule-based, procedural, and object-oriented features. They are defined as rewriting systems operating on graphs with the edges coming from a set of user-defined relations, whereas the nodes can be associated with objects. We demonstrate their ability to represent genes, regulatory networks of metabolites, and morphologically structured organisms, as well as developmental aspects of these entities, in a common formal framework. Mutation, crossing over, selection, and the dynamics of a network of gene regulation can all be represented with simple graph rewriting rules. This is demonstrated in some detail on the classical example of Dawkins' biomorphs and the ABC model of flower morphogenesis: other applications are briefly sketched. An interactive program was implemented, enabling the execution of the formalism and the visualization of the results. PMID:15479546

  15. Artificial-life researchers try to create social reality.

    Science.gov (United States)

    Flam, F

    1994-08-12

    Some scientists, among them cosmologist Stephen Hawking, argue that computer viruses are alive. A better case might be made for many of the self-replicating silicon-based creatures featured at the fourth Conference on Artificial Life, held on 5 to 8 July in Boston. Researchers from computer science, biology, and other disciplines presented computer programs that, among other things, evolved cooperative strategies in a selfish world and recreated themselves in ever more complex forms. PMID:17782127

  16. 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.

  17. 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.

  18. LIFE2 conference: the Life model

    OpenAIRE

    Wheatley, P.

    2008-01-01

    Summary and aims: Summary: Introduction to the LIFE Model and a recap of LIFE Model v1.0; Development of the LIFE Model v1.1; Looking ahead to LIFE Model v2.0; Scope of lifecycle costs and aims of costing; LIFE Methodology; Generic Preservation Model; Future work: LIFE3? Aims: Summarise recent developments in the LIFE approach and costing tools; Provide a foundation for the Case Study discussions later today; Highlight some outstanding issues for discussio...

  19. An artificial ecosystem model used in the study of social, economic and technological dynamics: An artificial electrical energy market

    International Nuclear Information System (INIS)

    This paper will present the artificial ecosystem as a tool, in the development of multi agent models for the simulation of economic and technological dynamics (as well as other possible applications). This tool is based on the mechanics of an artificial society and consists of autonomous artificial agents that interact with individuals that have different characteristics and behavior and other that have a similar conduct to their own. Initial conditions are assumed not to be controllable, however they can be influenced. The importance of the concept of the ecosystem is in understanding great units in the light of their own components which are relevant for the analysis and become interdependent among themselves and with other essential components that hold the total operation of the system. Ideas for the development of a simulation model based on autonomous intelligent agents are presented. These agents will have a brain that is based on artificial intelligence technologies. The Sand Kings Simulation Model, an artificial ecosystem model developed by the author, is described as well as the application of artificial intelligence to this artificial life model. An application to a real life problem is also offered as an artificial energy market that is currently being developed by the author is described

  20. 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.

  1. A Novel Method for Shelf Life Detection of Processed Cheese Using Cascade Single and Multi Layer Artificial Neural Network Computing Models

    OpenAIRE

    Sumit Goyal; Gyanendra Kumar Goyal

    2012-01-01

    This paper presents the potential of Cascade Backpropagation algorithm based ANN models in detecting the shelf life of processed cheese stored at 30o C. Processed cheese is a dairy product made from ripened Cheddar cheese and sometimes a part of ripened cheese is replaced by fresh cheese; plus emulsifiers, extra salt, spices and food colorings. The cascade backpropagation algorithm (CBA) is the basis of a conceptual design for accelerating learning in ANNs. In this research input parameters w...

  2. A Novel Method for Shelf Life Detection of Processed Cheese Using Cascade Single and Multi Layer Artificial Neural Network Computing Models

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-02-01

    Full Text Available This paper presents the potential of Cascade Backpropagation algorithm based ANN models in detecting the shelf life of processed cheese stored at 30o C. Processed cheese is a dairy product made from ripened Cheddar cheese and sometimes a part of ripened cheese is replaced by fresh cheese; plus emulsifiers, extra salt, spices and food colorings. The cascade backpropagation algorithm (CBA is the basis of a conceptual design for accelerating learning in ANNs. In this research input parameters were texture, aroma and flavour, moisture, free fatty acids.Sensory score was taken as output parameter. Bayesian regularization algorithm was used for training the network. Neurons in each hidden layers varied from 1 to 50. The network was trained with 200 epochs with single and multiple hidden layers. Transfer function for hidden layers was tangent sigmoid and pure linear was output function. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient performance measures were used to test the prediction potential of the developed CBA model. CBA model detected 29.13 daysshelf life which is quite close to experimentally obtained shelf life of 30 days suggesting that the product is acceptable.

  3. Ground water modelling in artificial recharge projects

    International Nuclear Information System (INIS)

    Decision-making is strongly based on modelling in two of our water supply projects concerning artificial ground water. The model used is a 2-dimensional flow and transport model using the element method. Due to sharp variations in Finnish soils, the modelling process is based on careful and detailed evaluation of basic hydrogeological data before and during model calibration

  4. 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 ...

  5. 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

  6. 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). PMID:24109563

  7. 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. PMID:26833578

  8. 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…

  9. Creative Agency: A Clearer Goal for Artificial Life in the Arts

    OpenAIRE

    Bown, Oliver; McCormack, Jon

    2009-01-01

    One of the goals of artificial life in the arts is to develop systems that exhibit creativity. We argue that creativity {it per se} is a confusing goal for artificial life systems because of the complexity of the relationship between the system, its designers and users, and the creative domain. We analyse this confusion in terms of factors affecting individual human motivation in the arts, and the methods used to measure the success of artificial creative systems. We argue that an attempt to ...

  10. 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.

  11. The autonomy of biological individuals and artificial models.

    Science.gov (United States)

    Moreno, Alvaro; Etxeberria, Arantza; Umerez, Jon

    2008-02-01

    This paper aims to offer an overview of the meaning of autonomy for biological individuals and artificial models rooted in a specific perspective that pays attention to the historical and structural aspects of its origins and evolution. Taking autopoiesis and the recursivity characteristic of its circular logic as a starting point, we depart from some of its consequences to claim that the theory of autonomy should also take into account historical and structural features. Autonomy should not be considered only in internal or constitutive terms, the largely neglected interactive aspects stemming from it should be equally addressed. Artificial models contribute to get a better understanding of the role of autonomy for life and the varieties of its organization and phenomenological diversity. PMID:17719170

  12. 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

  13. Artificial intelligence model for sustain ability measurement

    International Nuclear Information System (INIS)

    The article analyses the main dimensions of organizational sustain ability, their possible integrations into artificial neural network. In this article authors performing analyses of organizational internal and external environments, their possible correlations with 4 components of sustain ability, and the principal determination models for sustain ability of organizations. Based on the general principles of sustainable development organizations, a artificial intelligence model for the determination of organizational sustain ability has been developed. The use of self-organizing neural networks allows the identification of the organizational sustain ability and the endeavour to explore vital, social, antropogenical and economical efficiency. The determination of the forest enterprise sustain ability is expected to help better manage the sustain ability. (Authors)

  14. α-decay half-life calculations of superheavy nuclei using artificial neural networks

    International Nuclear Information System (INIS)

    Investigations of superheavy elements (SHE) have received much attention in the last two decades, due to the successful syntheses of SHE. In particular, α-decay of SHEs has a great importance because most synthesized SHE have a-decay and the experimentalists have evaluated the theoretical predictions of the a-decay half-life during the experimental design. Because of this, the correct prediction of α-decay half-life is important to investigate superheavy nuclei as well as heavy nuclei. In this work, artificial neural networks (ANN) have been employed on experimental a-decay half-lives of superheavy nuclei. Statistical modeling of a-decay half-life of superheavy nuclei have been found as to be successful

  15. Artificial diets for life tables bioassays of TPB in Mississippi

    Science.gov (United States)

    Two artificial diets for mass rearing and bioassay of the tarnished plant bug, (TPB), Lygus lineolaris Palisot de Beauvois, (Hemiptera: Miridae) were modified and developed, respectively. The first diet is a modification of a semisolid artificial diet (NI diet), which permits large scale rearing of ...

  16. 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

  17. WebAL-1: Workshop on Artificial Life and the Web 2014 Proceedings

    OpenAIRE

    Taylor, Tim

    2014-01-01

    Proceedings of WebAL-1: Workshop on Artificial Life and the Web 2014, held at the 14th International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14), New York, NY, 31 July 2014.

  18. 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.

  19. 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 ...

  20. 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.

  1. 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.

  2. Artificial Exo-Society Modeling: a New Tool for SETI Research

    Science.gov (United States)

    Gardner, James N.

    2002-01-01

    One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected

  3. 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.

  4. Artificial neural network models for image understanding

    Science.gov (United States)

    Kulkarni, Arun D.; Byars, P.

    1991-06-01

    In this paper we introduce a new class of artificial neural network (ANN) models based on transformed domain feature extraction. Many optical and/or digital recognition systems based on transformed domain feature extraction are available in practice. Optical systems are inherently parallel in nature and are preferred for real time applications, whereas digital systems are more suitable for nonlinear operations. In our ANN models we combine advantages of both digital and optical systems. Many transformed domain feature extraction techniques have been developed during the last three decades. They include: the Fourier transform (FT), the Walsh Hadamard transform (WHT), the discrete cosine transform (DCT), etc. As an example, we have developed ANN models using the FT and WHT domain features. The models consist of two stages, the feature extraction stage and the recognition stage. We have used back-propagation and competitive learning algorithms in the recognition stage. We have used these ANN models for invariant object recognition. The models have been used successfully to recognize various types of aircraft, and also have been tested with test patterns. ANN models based on other transforms can be developed in a similar fashion.

  5. 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.

  6. 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.

  7. Design and Characterisation of a Novel Artificial Life System Incorporating Hierarchical Selection

    OpenAIRE

    Kelly, Ciarán

    2010-01-01

    In this thesis, a minimal artificial chemistry system is presented, which is inspired by the RNA World hypothesis and is loosely based on Holland's Learning Classier Systems. The Molecular Classier System (MCS) takes a bottom-up, individual-based approach to building artificial bio-chemical networks. The MCS has been developed to demonstrate the effects of hierarchical selection. Hierarchical selection appears to have been critical for the evolution of complexity in life as we know it yet, ...

  8. 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.

  9. Artificial Intelligence Techniques for Steam Generator Modelling

    CERN Document Server

    Wright, Sarah

    2008-01-01

    This paper investigates the use of different Artificial Intelligence methods to predict the values of several continuous variables from a Steam Generator. The objective was to determine how the different artificial intelligence methods performed in making predictions on the given dataset. The artificial intelligence methods evaluated were Neural Networks, Support Vector Machines, and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian and committee techniques were applied to these neural networks. Each of the AI methods considered was simulated in Matlab. The results of the simulations showed that all the AI methods were capable of predicting the Steam Generator data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system out performed the other methods in terms of accuracy and ease of implementation, while still achieving a fast execution time as well as a reasonable training time.

  10. Artificial neural network based modelling of internal combustion engine performance

    OpenAIRE

    Boruah, Dibakor; Thakur, Pintu Kumar; Baruah, Dipal

    2016-01-01

    The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create...

  11. Closure to efficient causation, computability and artificial life

    OpenAIRE

    Cárdenas, M.L. María Luz; Letelier, Juan-Carlos; Gutiérrez, Claudio; Cornish-Bowden, Athel; Soto-Andrade, Jorge

    2010-01-01

    Abstract The major insight in Robert Rosen's view of a living organism as an (M,R)-system was the realization that an organism must be ?closed to efficient causation?, which means that the catalysts needed for its operation must be generated internally. This aspect is not controversial, but there has been confusion and misunderstanding about the logic Rosen used to achieve this closure. In addition, his corollary that an organism is not a mechanism and cannot have simulable models ...

  12. Biology and fertility life table of Agrotis ipsilon on artificial diet

    International Nuclear Information System (INIS)

    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)

  13. Using Artificial Intelligence Models in System Identification

    OpenAIRE

    Elshamy, Wesam

    2013-01-01

    Artificial Intelligence (AI) techniques are known for its ability in tackling problems found to be unyielding to traditional mathematical methods. A recent addition to these techniques are the Computational Intelligence (CI) techniques which, in most cases, are nature or biologically inspired techniques. Different CI techniques found their way to many control engineering applications, including system identification, and the results obtained by many researchers were encouraging. However, most...

  14. 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....

  15. Comparing Neural Networks and ARMA Models in Artificial Stock Market

    Czech Academy of Sciences Publication Activity Database

    Krtek, Jiří; Vošvrda, Miloslav

    2011-01-01

    Roč. 18, č. 28 (2011), s. 53-65. ISSN 1212-074X R&D Projects: GA ČR GD402/09/H045 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * vector ARMA * artificial market Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/krtek-comparing neural networks and arma models in artificial stock market.pdf

  16. A Study on Artificial Intelligence IQ and Standard Intelligent Model

    OpenAIRE

    Liu, Feng; Shi, Yong

    2015-01-01

    Currently, potential threats of artificial intelligence (AI) to human have triggered a large controversy in society, behind which, the nature of the issue is whether the artificial intelligence (AI) system can be evaluated quantitatively. This article analyzes and evaluates the challenges that the AI development level is facing, and proposes that the evaluation methods for the human intelligence test and the AI system are not uniform; and the key reason for which is that none of the models ca...

  17. Neuro-Based Artificial Intelligence Model for Loan Decisions

    OpenAIRE

    Shorouq F. Eletter; Saad G. Yaseen; Ghaleb A. Elrefae

    2010-01-01

    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 backpr...

  18. A new model of artificial neuron: cyberneuron and its use

    CERN Document Server

    Polikarpov, S V; Rumyantsev, K E; Golubchikov, D M

    2009-01-01

    This article describes a new type of artificial neuron, called the authors "cyberneuron". Unlike classical models of artificial neurons, this type of neuron used table substitution instead of the operation of multiplication of input values for the weights. This allowed to significantly increase the information capacity of a single neuron, but also greatly simplify the process of learning. Considered an example of the use of "cyberneuron" with the task of detecting computer viruses.

  19. 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

  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. ON GÖDEL'S INCOMPLETENESS THEOREM(S), ARTIFICIAL INTELLIGENCE/LIFE, AND HUMAN MIND

    OpenAIRE

    CHRISTIANTO, V.; FLORENTIN SMARANDACHE

    2015-01-01

    In the present paper we have discussed concerning Gödel’s incompleteness theorem(s) and plausible implications to artificial intelligence/life and human mind. Perhaps we should agree with Sullins III, that the value of this finding is not to discourage certain types of research in AL, but rather to help move us in a direction where we can more clearly define the results of that research.

  2. Stochastic Differential Equations in Artificial Pancreas Modelling

    DEFF Research Database (Denmark)

    Duun-Henriksen, Anne Katrine

    Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump and a contin......Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump...... 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...

  3. Metaplasticity Artificial Neural Networks Model Application to Radar Detection

    Directory of Open Access Journals (Sweden)

    Diego Andina

    2007-12-01

    Full Text Available Many Artificial Neural Networks design algorithms or learning methods imply the minimization of an error objective function. During learning, weight values are updated following a strategy that tends to minimize the final mean error in the Network performance. Weight values are classically seen as a representation of the synaptic weights in biological neurons and their ability to change its value could be interpreted as artificial plasticity inspired by this biological property of neurons. In such a way, metaplasticity is interpreted in this paper as the ability to change the efficiency of artificial plasticity giving more relevance to weight updating of less frequent activations and resting relevance to frequent ones. Modeling this interpretation in the training phase, the hypothesis of an improved training is tested in the Multilayer Perceptron with Backpropagation case. The results show a much more efficient training maintaining the Artificial Neural Network performance.

  4. 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.

  5. 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

  6. 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)

  7. Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

    2003-01-01

    Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

  8. Artificial Neural Network Model for Friction Stir Processing

    Directory of Open Access Journals (Sweden)

    Syed Muhammed Fahd

    2014-06-01

    Full Text Available Friction stir processing (FSP is an effective means of refining grain size of aluminum alloys. An artificial neural network model (ANN is made for predicting the grain size of alloys which are processed by FSP. The simulated results from the model show how grain size varies with the process parameters.

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2012-08-01

    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.

  11. 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.

  12. MACHINE LEARNING MODELS FOR PREDICTING SHELF LIFE OF PROCESSED CHEESE

    OpenAIRE

    Sumit, Goyal; Gyanendra, Goyal

    2013-01-01

    Feedforward multilayer machine learning artificial neural network (ANN) models were established for predicting shelf life of processed cheese stored at 7-8o C. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were input variables, and sensory score was the output variable. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash–Sutcliffe Coefficient were used for comparing the prediction ability of the developed models. Feedforward ...

  13. A psychoanalyst artificial intelligence model in a computer game

    OpenAIRE

    Muñoz Fernández, Enrique

    2012-01-01

    Projecte realitzat en el marc d'un programa de mobilitat amb la Vienna University of Technology. [ANGLÈS] Implementation of an artificial intelligence model based on the psychoanalytic theory of the ID-Ego-SuperEgo of Sigmund Freud into the computer game Unreal Tournament 2004. [CASTELLÀ] Implementación de un modelo de inteligencia artificial basado en la teoría psicoanalítica del ID-Ego-SuperEgo de Sigmund Freud en el videojuego Unreal Tournament 2004. [CATALÀ] Implementació d'un mo...

  14. 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…

  15. 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.

  16. Using artificial neural networks to predict the fatigue life of carbon and low-alloy steels

    International Nuclear Information System (INIS)

    The ASME boiler and pressure vessel code contains rules for the construction of nuclear power plant components. Figures I-9.1 through I-9.6 of Appendix I to Section III of the Code specify fatigue design curves for structural materials. However, the effects of light water reactor (LWR) coolant environments are not explicitly addressed by the code design curves. Recent test data indicate significant decreases in the fatigue lives of carbon and low-alloy steels in LWR environments when five conditions are satisfied simultaneously. When applied strain range, temperature, dissolved oxygen in the water, and sulfur content of the steel are above a minimum threshold level, and the loading strain rate is below a threshold value, environmentally assisted fatigue occurs. For this study, a data base of 1036 fatigue tests was used to train an artificial neural network (ANN). Once the optimal ANN was designed, ANN were trained and used to predict fatigue life for specified sets of loading and environmental conditions. By finding patterns and trends in the data, the ANN can find the fatigue life for any set of conditions. Artificial neural networks show great potential for predicting environmentally assisted corrosion. Their main benefits are that the fit of the data is based purely on data and not on preconceptions and that the network can interpolate effects by learning trends and patterns when data are not available. (orig.)

  17. The LIFE Model v1.1

    OpenAIRE

    P. Wheatley(University of Warwick, United Kingdom); Ayris, P.; R Davies; McLeod, R; Shenton, H.

    2007-01-01

    Extract: This document draws together feedback, discussion and review of the LIFE Model from a number of sources: 1. The LIFE and LIFE2 Project Teams, and the staff of their institutions 2. Feedback from review by independent economics expert 3. The LIFE Project Conference 4. Early adopters of the Life Model (particularly the Royal Danish Library, State Archives and the State and University Library, Denmark) The result is a revision of the LIFE Model which was first publ...

  18. 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)

  19. Modelling of artificial radioactivity migration in environment: a survey

    International Nuclear Information System (INIS)

    The aim of this report is to present a compilation and description of models to assess the environmental behaviour and effects of accidental and routine releases of artificial radioactivity from nuclear power facilities. About 60 models are described and a card is given for each one, to indicate in summarized form its features and data content. This collection is intended to help in developing specific personal models by assembling different parts chosen among the most suitable ones of different models of various degrees of sophistication

  20. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  1. 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).

  2. Biology and fertility life table of Hypercompe indecisa on artificial diet

    International Nuclear Information System (INIS)

    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)

  3. 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.

  4. Artificial crawler model for texture analysis on silk fibroin scaffolds

    International Nuclear Information System (INIS)

    Texture plays an important role in computer vision tasks. Several methods of texture analysis are available. However, these methods are not capable of extracting rich detail in images. This paper presents a novel approach to image texture classification based on the artificial crawler model. Here, we propose a new rule of movement that moves artificial crawler agents not only toward higher intensities but also toward lower ones. This strategy is able of capturing more detail because the agents explore the peaks as well as the valleys. Thus, compared with the state-of-the-art method, this approach shows an increased discriminatory power. Experiments on the most well known benchmark demonstrate the superior performance of our approach. We also tested our approach on silk fibroin scaffold analysis, and results indicate that our method is consistent and can be applied in real-world situations. (paper)

  5. Super capacitor modeling with artificial neural network (ANN)

    Energy Technology Data Exchange (ETDEWEB)

    Marie-Francoise, J.N.; Gualous, H.; Berthon, A. [Universite de Franche-Comte, Lab. en Electronique, Electrotechnique et Systemes (L2ES), UTBM, INRETS (LRE T31) 90 - Belfort (France)

    2004-07-01

    This paper presents super-capacitors modeling using Artificial Neural Network (ANN). The principle consists on a black box nonlinear multiple inputs single output (MISO) model. The system inputs are temperature and current, the output is the super-capacitor voltage. The learning and the validation of the ANN model from experimental charge and discharge of super-capacitor establish the relationship between inputs and output. The learning and the validation of the ANN model use experimental results of 2700 F, 3700 F and a super-capacitor pack. Once the network is trained, the ANN model can predict the super-capacitor behaviour with temperature variations. The update parameters of the ANN model are performed thanks to Levenberg-Marquardt method in order to minimize the error between the output of the system and the predicted output. The obtained results with the ANN model of super-capacitor and experimental ones are in good agreement. (authors)

  6. 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

  7. A model for estimating the life of electrical submersible pumps

    International Nuclear Information System (INIS)

    This paper reports on a good mathematical model of failure rate or life expectancy of an electrical submersible pump (ESP) that would be valuable in defining areas for study and improvement of run times. Also the expected life of any artificial lift method would be a great asset in economic justification and maintenance planning. Often average pump life is used, however, certain statistical techniques can provide additional insight into failure modes and causes. The model considered here is the Poisson pure death process. It was developed to describe the phenomenon of radioactive decay. The method makes it possible to calculate the probability that failure will occur in a given time period. It contains only a single parameter that can be obtained from relatively sparse data. The model is applied to ESP's in several west Texas and Michigan fields

  8. 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.

  9. 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.

  10. An analysis of urban collisions using an artificial intelligence model.

    Science.gov (United States)

    Mussone, L; Ferrari, A; Oneta, M

    1999-11-01

    Traditional studies on road accidents estimate the effect of variables (such as vehicular flows, road geometry, vehicular characteristics), and the calculation of the number of accidents. A descriptive statistical analysis of the accidents (those used in the model) over the period 1992-1995 is proposed. The paper describes an alternative method based on the use of artificial neural networks (ANN) in order to work out a model that relates to the analysis of vehicular accidents in Milan. The degree of danger of urban intersections using different scenarios is quantified by the ANN model. Methodology is the first result, which allows us to tackle the modelling of urban vehicular accidents by the innovative use of ANN. Other results deal with model outputs: intersection complexity may determine a higher accident index depending on the regulation of intersection. The highest index for running over of pedestrian occurs at non-signalised intersections at night-time. PMID:10487346

  11. 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.

  12. 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.

  13. Artificial light at night causes diapause inhibition and sex-specific life history changes in a moth.

    Science.gov (United States)

    van Geffen, Koert G; van Grunsven, Roy H A; van Ruijven, Jasper; Berendse, Frank; Veenendaal, Elmar M

    2014-06-01

    Rapidly increasing levels of light pollution subject nocturnal organisms to major alterations of their habitat, the ecological consequences of which are largely unknown. Moths are well-known to be attracted to light at night, but effects of light on other aspects of moth ecology, such as larval development and life-history, remain unknown. Such effects may have important consequences for fitness and thus for moth population sizes. To study the effects of artificial night lighting on development and life-history of moths, we experimentally subjected Mamestra brassicae (Noctuidae) caterpillars to low intensity green, white, red or no artificial light at night and determined their growth rate, maximum caterpillar mass, age at pupation, pupal mass and pupation duration. We found sex-specific effects of artificial light on caterpillar life-history, with male caterpillars subjected to green and white light reaching a lower maximum mass, pupating earlier and obtaining a lower pupal mass than male caterpillars under red light or in darkness. These effects can have major implications for fitness, but were absent in female caterpillars. Moreover, by the time that the first adult moth from the dark control treatment emerged from its pupa (after 110 days), about 85% of the moths that were under green light and 83% of the moths that were under white light had already emerged. These differences in pupation duration occurred in both sexes and were highly significant, and likely result from diapause inhibition by artificial night lighting. We conclude that low levels of nocturnal illumination can disrupt life-histories in moths and inhibit the initiation of pupal diapause. This may result in reduced fitness and increased mortality. The application of red light, instead of white or green light, might be an appropriate measure to mitigate negative artificial light effects on moth life history. PMID:25360250

  14. 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.

  15. 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. PMID:27297046

  16. Artificial Neural Network Modeling of Forest Tree Growth

    CERN Document Server

    Gordon, C

    1999-01-01

    The problem of modeling forest tree growth curves with an artificial neural network (NN) is examined. The NN parametric form is shown to be a suitable model if each forest tree plot is assumed to consist of several differently growing sub-plots. The predictive Bayesian approach is used in estimating the NN output. Data from the correlated curve trend (CCT) experiments are used. The NN predictions are compared with those of one of the best parametric solutions, the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate whether any observed differences are statistically significant. From a Frequentist perspective the differences between the Schnute and NN approach are found not to be significant. However, a Bayesian ANOVA indicates that there is a 93% probability of the NN approach producing better predictions on average.

  17. 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

  18. 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.

  19. Optimization of the artificial urinary sphincter: modelling and experimental validation

    International Nuclear Information System (INIS)

    The artificial urinary sphincter should be long enough to prevent strangulation effects of the urethral tissue and short enough to avoid the improper dissection of the surrounding tissue. To optimize the sphincter length, the empirical three-parameter urethra compression model is proposed based on the mechanical properties of the urethra: wall pressure, tissue response rim force and sphincter periphery length. In vitro studies using explanted animal or human urethras and different artificial sphincters demonstrate its applicability. The pressure of the sphincter to close the urethra is shown to be a linear function of the bladder pressure. The force to close the urethra depends on the sphincter length linearly. Human urethras display the same dependences as the urethras of pig, dog, sheep and calf. Quantitatively, however, sow urethras resemble best the human ones. For the human urethras, the mean wall pressure corresponds to (-12.6 ± 0.9) cmH2O and (-8.7 ± 1.1) cmH2O, the rim length to (3.0 ± 0.3) mm and (5.1 ± 0.3) mm and the rim force to (60 ± 20) mN and (100 ± 20) mN for urethra opening and closing, respectively. Assuming an intravesical pressure of 40 cmH2O, and an external pressure on the urethra of 60 cmH2O, the model leads to the optimized sphincter length of (17.3 ± 3.8) mm

  20. Engineering Computational Emotion - A Reference Model for Emotion in Artificial Systems

    OpenAIRE

    Sanchez Escribano, Guadalupe

    2016-01-01

    Emotion is generally argued to be an influence on the behavior of life systems, largely concerning flexibility and adaptivity. The way in which life systems acts in response to a particular situations of the environment, has revealed the decisive and crucial importance of this feature in the success of behaviors. And this source of inspiration has influenced the way of thinking artificial systems. During the last decades, artificial systems have undergone such an evolution that each day m...

  1. Artificial neural network modeling of dissolved oxygen in reservoir.

    Science.gov (United States)

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan. PMID:24078053

  2. Application of artificial neural networks for modeling localized corrosion

    International Nuclear Information System (INIS)

    Artificial neural networks (ANN) were applied to modeling localized corrosion of Incoloy Alloy 825 in simulated J - 13 well water. ANN as a non linear models can represent accurately localized corrosion phenomena caused by an environment containing chlorides, nitrates, fluorides and sulfates at various temperature ranges. Although the nature of the dependent variable of the ANN models, the visual rating of the localized corrosion is qualitative, a good correspondence between the output of the model and the actual indications is determined. Accurate ANN modeling has been carried out by using the visual inspection of the specimen surface, in contrast to linear modeling where in order to get a sound correlation between the system variables, a complex dependent parameter, having no clear physical meaning has been chosen. It has also been found that one can extrapolate to a certain extent, beyond the ability to interpolate (as with linear models). The ANN model predicted with a low relative error the visual rating of the corrosion rate of records which where part of the testing set of the ANN and belonging to the original full factorial design experiment. Thus, such models can be used for detailed analysis procedures as sensitivity, knowledge acquisition and optimization. (author). 7 refs, 9 figs

  3. Variable recruitment fluidic artificial muscles: modeling and experiments

    International Nuclear Information System (INIS)

    We investigate taking advantage of the lightweight, compliant nature of fluidic artificial muscles to create variable recruitment actuators in the form of artificial muscle bundles. Several actuator elements at different diameter scales are packaged to act as a single actuator device. The actuator elements of the bundle can be connected to the fluidic control circuit so that different groups of actuator elements, much like individual muscle fibers, can be activated independently depending on the required force output and motion. This novel actuation concept allows us to save energy by effectively impedance matching the active size of the actuators on the fly based on the instantaneous required load. This design also allows a single bundled actuator to operate in substantially different force regimes, which could be valuable for robots that need to perform a wide variety of tasks and interact safely with humans. This paper proposes, models and analyzes the actuation efficiency of this actuator concept. The analysis shows that variable recruitment operation can create an actuator that reduces throttling valve losses to operate more efficiently over a broader range of its force–strain operating space. We also present preliminary results of the design, fabrication and experimental characterization of three such bioinspired variable recruitment actuator prototypes. (paper)

  4. An artificial vector model for generating abnormal electrocardiographic rhythms

    International Nuclear Information System (INIS)

    We present generalizations of our previously published artificial models for generating multi-channel ECG to provide simulations of abnormal cardiac rhythms. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are specified either as perturbations to the normal dipole or as new dipole trajectories. Switching between normal and abnormal beat types is achieved using a first-order Markov chain. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time- and frequency-domain heart rate (HR) and heart rate variability characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by introducing a rotation matrix couple to the respiratory frequency. We demonstrate an example of the use of this model by simulating HR-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy. Application of our model also reveals previously unreported effects of common TWA estimation methods

  5. 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.

  6. 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.

  7. Artificial neural network model for earthquake prediction with radon monitoring

    International Nuclear Information System (INIS)

    Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process in the East Anatolian Fault System (EAFS). The proposed ANN system employs individual training strategy with fixed-weight and supervised models leading to estimations. The average relative error between the magnitudes of the earthquakes acquired by ANN and measured data is about 2.3%. The relative error between the test and earthquake data varies between 0% and 12%. In addition, the factor analysis was applied on all data and the model output values to see the statistical variation. The total variance of 80.18% was explained with four factors by this analysis. Consequently, it can be concluded that ANN approach is a potential alternative to other models with complex mathematical operations

  8. A Hybrid Artificial Neural Network Model for Forecasting Short Time Series

    OpenAIRE

    Mohan, Anil

    2012-01-01

    Forecasting has long been the domain of traditional statistical models. Recent research has shown that novel and complex forecasting models do not necessarily outperform simpler models. These include in particular Artificial Neural Networks (ANNs). Even though claims of superior forecasting performance were made by Neural Network researchers, these claims were often unsubstantiated. Artificial neural networks are information processing paradigms motivated by the information ...

  9. Artificial light at night causes diapause inhibition and sex-specific life history changes in a moth

    OpenAIRE

    Geffen, van, J; Grunsven, van, ECE; Ruijven, van, L.J.; Berendse, F.; Veenendaal, E. M.

    2014-01-01

    Rapidly increasing levels of light pollution subject nocturnal organisms to major alterations of their habitat, the ecological consequences of which are largely unknown. Moths are well-known to be attracted to light at night, but effects of light on other aspects of moth ecology, such as larval development and life-history, remain unknown. Such effects may have important consequences for fitness and thus for moth population sizes. To study the effects of artificial night lighting on developme...

  10. Hybrid Systems Knowledge Representation Using Modelling Environment System Techniques Artificial Intelligence

    OpenAIRE

    Latif, Kamran

    2014-01-01

    Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are re...

  11. 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...

  12. Modelling urban air quality using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nagendra, S.M. Shiva; Khare, Mukesh [Indian Institute of Technology Delhi, Department of Civil Engineering, New Delhi (India)

    2005-05-01

    This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been used in the models' formulation. Three scenarios were considered - considering both meteorological and traffic characteristics input parameters; only meteorological inputs; and only traffic characteristics input data. The performance of all the developed models was evaluated on the basis of index of agreement (d) and other statistical parameters, viz. the mean and the deviations of the observed and predicted concentrations, mean bias error, mean square error, systematic and unsystematic root mean square error, coefficient of determination and linear best fit constant and gradient (Willmott in B Am Meteorol Soc 63:1309, 1982). The forecast performance of the developed models, with meteorological and traffic characteristics (d=0.78 for AQCR1 and d=0.69 for AQCR2) and with only meteorological inputs (d=0.77 for AQCR1 and d=0.67 for AQCR2), were comparable with the measured data. (orig.)

  13. The "artificial artery" as in vitro perfusion model.

    Directory of Open Access Journals (Sweden)

    Doreen Janke

    . Taken together, the "artificial artery" provides a solid in vitro model to test pharmacological active compounds for their impact on arterio-damaging or arterio-protective properties on vascular response.

  14. VOICE RECOGNITION USING ARTIFICIAL NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS

    Directory of Open Access Journals (Sweden)

    AARON NICHIE

    2013-05-01

    Full Text Available The ability of recognition systems to correctly recognize speakers based on their speech waveform distribution depends largely on how the recognition system can train the model parameters so as to provide the best class of discrimination. This paper presents the results of an effort to recognize the voice of individual speakers based on their continuous speech waveform distribution using the combined frameworks of artificial neural networks (ANN and statistical Gaussian mixture models (GMM. A feed-forward multilayer ANN architecture with 30 hidden neurons was implemented for discriminative classification and training and the statistical GMM model computed scores that were transferred to best match the speech features. The decision system determines the recognized speakers using correlation coefficient analysis to measure the goodness of match of speech feature frames of the detected speaker from the ANN and GMM frameworks. To validate performance of the system, experiments were conducted using speech utterances from 30 different speakers (20 males and 10 females. System performance showed average recognition rates of 77% for 5-word utterances and 43% when the lengths of the utterances were increased to 20-word utterances for cases of trained speech utterances. With unknown utterances, recognition rate of 18% achieved for 20-word utterances.

  15. Spatiotemporal modeling of monthly soil temperature using artificial neural networks

    Science.gov (United States)

    Wu, Wei; Tang, Xiao-Ping; Guo, Nai-Jia; Yang, Chao; Liu, Hong-Bin; Shang, Yue-Feng

    2013-08-01

    Soil temperature data are critical for understanding land-atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.

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

    Directory of Open Access Journals (Sweden)

    L. V. Belyaev

    2011-12-01

    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. 

  17. 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.

  18. Soil NO emissions modelling using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Delon, Claire. e-mail: delc@aero.obs-mip.fr; Serca, Dominique; Dupont, Richard; Delmas, Robert [Laboratoire d' Aerologie, 14 avenue E. Belin, 31400 Toulouse (France); Boissard, Christophe; Dutot, Alain [Laboratoire Interuniversitaire des Systemes Atmospheriques, 94010 Creteil (France); Laville, Patricia [INRA, EGC, 78830 Thiverval-Grignon (France); Rosnay, Patricia de [CESBIO, 31400 Toulouse (France)

    2007-07-15

    Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the development of a relationship for NO flux emission from soil with pertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linear regression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature, surface water filled pore space, soil temperature at depth (20-30 cm), fertilisation rate, sand percentage in the soil, pH and wind speed. The network performance is evaluated each time a new variable is introduced in the network, i.e. each variable is justified and evaluated in improving the network performance. A resulting equation linking NO flux from soil and the seven variables is proposed, and shows to perform well with measurements (R2 = 0.71), whereas other regression models give a poor correlation coefficient between calculation and measurements (R2 = 0.12 for known algorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemical and large-scale models, for future application at regional scale.

  19. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    Directory of Open Access Journals (Sweden)

    Hooman Aghaebrahimi Samani

    2012-03-01

    The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA, based on the psychology of love, Artificial Endocrine System (AES, based on the physiology of love, and Affective State Transition (AST, based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN. The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

  20. 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.

  1. 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.

  2. 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

  3. 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 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. PMID:26644374

  4. 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...

  5. 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...

  6. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    OpenAIRE

    Hooman Aghaebrahimi Samani; Elham Saadatian

    2012-01-01

    A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelli...

  7. Metaplasticity Artificial Neural Networks Model Application to Radar Detection

    OpenAIRE

    Diego Andina; Juan Fombellida

    2007-01-01

    Many Artificial Neural Networks design algorithms or learning methods imply the minimization of an error objective function. During learning, weight values are updated following a strategy that tends to minimize the final mean error in the Network performance. Weight values are classically seen as a representation of the synaptic weights in biological neurons and their ability to change its value could be interpreted as artificial plasticity inspired by this biological property of neurons. In...

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

    International Nuclear Information System (INIS)

    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)

  9. 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)

  10. Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling

    Science.gov (United States)

    Abrahart, R. J.

    2004-05-01

    This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and

  11. Testing normality in bivariate probit models : a simple artificial regression based LM test

    OpenAIRE

    Anthony Murphy

    1994-01-01

    A simple and convenient LM test of normality in the bivariate probit model is derived. The alternative hypothesis is based on a form of truncated Gram Charlier Type series. The LM test may be calculated as an artificial regression. However, the proposed artificial regression does not use the outer product gradient form. Thus it is likely to perform reasonably well in small samples.

  12. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    Energy Technology Data Exchange (ETDEWEB)

    Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  13. Life Table and Laboratory Rearing of Nezara viridula (L.) (Heteroptera: Pentatomidae) on two Artificial Diets

    Science.gov (United States)

    The southern green stink bug, Nezara viridula (Linnaeus), is a rather destructive pest and has been reported responsible for an estimated $ 6.5 millions dollars in costs associated with crop loss and insecticide costs across the US. A great deal of interest exists in the potential for using artifici...

  14. Training Groups: A Basic Life Cycle Model.

    Science.gov (United States)

    Chadbourne, Joan

    1980-01-01

    Describes group training model that differs from the traditional T-group model in structure, leadership, and assumptions about learning. The life-cycle model is based on situational leadership, differential structures based on group maturity, and integration of conceptual and experiential learning. (Author)

  15. Water Turbidity Modelling During Water Treatment Processes Using Artificial Neural Networks

    OpenAIRE

    Rak, Adam

    2013-01-01

    Artificial neural networks are increasingly being used in the research and analysis of unit and technical processes related to water treatment. An artificial neural network model was created to predict the turbidity of treated water in a newly operating water treatment system for surface and retention water at the Sosnówka reservoir, Poland. To model water turbidity during the water treatment process for a selected system, a flexible Bayesian model of neural networks, Gaussian processes a...

  16. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  17. 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.

  18. Evolutionary Ecology Models of Weed Life History

    OpenAIRE

    Dekker, Jack

    2014-01-01

    The limitations of demographic models as well as the opportunities of evolutionary models are reviewed. Flaws associated with demographic models include the confounding effects of plant architecture, representation of heterogeneous individuals in populations, and changes in deme membership confounding covariance structure. Trait-based evolutionary models include FoxPatch which represents weedy Setaria spp. seed behavior with explicit life history process prediction rules and algorithms.

  19. 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.

  20. 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...

  1. A model for life cycle records management

    Energy Technology Data Exchange (ETDEWEB)

    Tayfun, A.C.; Gibson, S.

    1996-10-01

    The primary objective of this paper is to update an old Records Management concept; the management of records according to the records life cycle. Accordingly, the authors are presenting a new version of the Records Management life cycle model and its associated elements. The basic concept is that every record progresses through three phases; a record is created, is used and maintained, and dispositioned. In this presentation, the authors update the very old straight line model and the more current circular model with a new model that essentially combines the two. The model portrays Records Management as having a distinct straight-line beginning, a circular use and maintenance phase, and a distinct straight-line end. The presentation maps Records Management Program elements and activities against the phases depicted in the model. The authors believe that this new records life cycle model is an enhanced physical representation of the process. This presentation is designed to help put all of the specialized Records Management topics that participants have heard about during the conference in the perspective of the records life cycle.

  2. 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...

  3. Modeling of Relative Humidity Using Artificial Neural Network

    OpenAIRE

    Samer AlSadi; Tamer Khatib

    2012-01-01

    This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE are used to evaluate the neural networks. Based on results, the ...

  4. Modelling artificial sea salt emission in large eddy simulations

    OpenAIRE

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

    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 buo...

  5. Economic modelling for life extension decision making

    International Nuclear Information System (INIS)

    This paper presents a methodology for the economic and financial analysis of nuclear plant life extension under uncertainty and demonstrates its use in a case analysis. While the economic and financial evaluation of life extension does not require new analytical tools, such studies should be based on the following three premises. First, the methodology should examine effects at the level of the company or utility system, because the most important economic implications of life extension relate to the altered generation system expansion plan. Second, it should focus on the implications of uncertainty in order to understand the factors that most affect life extension benefits and identify risk management efforts. Third, the methodology should address multiple objectives, at a minimum, both economic and financial objectives. An analysis of the role of life extension for Virginia Power's generating system was performed using the MIDAS model, developed by the Electric Power Research Institute. MIDAS is particularly well suited to this type of study because of its decision analysis framework. The model incorporates modules for load analysis, capacity expansion, production costing, financial analysis, and rates. The decision tree structure facilitates the multiple-scenario analysis of uncertainty. The model's output includes many economic and financial measures, including capital expenditures, fuel and purchases power costs, revenue requirements, average rates, external financing requirements, and coverage ratio. Based on findings for Virginia Power's Surry 1 plant, nuclear plant life extension has economic benefits for a utility's customers and financial benefits for the utility's investors. These benefits depend on a number of economic, technical and regulatory factors. The economic analysis presented in this paper identifies many of the key factors and issues relevant to life extension planning

  6. 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. PMID:22686158

  7. Artificial neural networks and the effects of loading conditions on fatigue life of carbon and low-alloy steels

    International Nuclear Information System (INIS)

    The ASME Boiler and Pressure Vessel Code contains rules for the construction of nuclear power plant components. Figure 1-90 of Appendix I to Section III of the Code specifies fatigue design curves for structural materials. However, the effects of light water reactor (LWR) coolant environments are not explicitly addressed by the Code design curves. Recent test data indicate significant decreases in the fatigue lives of carbon and low-alloy steels in LWR environments when five conditions are satisfied simultaneously. When applied strain range, temperature, dissolved oxygen in the water, and sulfur content of the steel are above a minimum threshold level, and the loading strain rate is below a threshold value, environmentally assisted fatigue occurs. For this study, a data base of 1036 fatigue tests was used to train an artificial neural network (ANN). Once the optimal ANN was designed, ANN were trained and used to predict fatigue life for specified sets of loading and environmental conditions. By finding patterns and trends in the data, the ANN can find the fatigue lifetime for any set of conditions. Artificial neural networks show great potential for predicting environmentally assisted corrosion. Their main benefits are that the fit of the data is based purely on data and not on preconceptions and that the network can interpolate effects by learning trends and patterns when data are not available

  8. 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

  9. Testing normality in bivariate probit models : a simple artificial regression based LM test

    OpenAIRE

    Murphy, Anthony

    1994-01-01

    A simple and convenient LM test of normality in the bivariate probit model is derived. The alternative hypothesis is based on a form of truncated Gram Charlier Type series. The LM test may be calculated as an artificial regression. However, the proposed artificial regression does not use the outer product gradient form. Thus it is likely to perform reasonably well in small samples. non-peer-reviewed

  10. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  11. Life cycle modelling for tourism areas

    OpenAIRE

    Alvares, Daniela; Lourenço, Júlia

    2005-01-01

    Modelling tourism development cycles associated with planning and investment cycles intends to be a contribution to the understanding of the tourism activity within a continuum process. It allows a better apprehension of the sequence of interdependencies that exist and can be addressed enlarging the well-known concept of tourism product and its life cycle. The proposed model can contribute for monitoring the tourism activity and improve its development in a sustainable way. If the tourist sit...

  12. 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.

  13. 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.

  14. A Multilayered Model for Artificial Intelligence of Game Character as Agent Architecture

    OpenAIRE

    Miyake, Youichiro; Miyake, Yoichiro

    2015-01-01

    As all mathematics have a beautiful structure, an inner mind model of Artificial Intelligence has a grand architecture. It consists of information flow and software modules. In this twenty years, an agent's inner intelligence model has been researched and developed by many game AI programmers in game titles. A whole image of an agent's intelligent model is explained.

  15. The evolution of CSR life-history strategies in a plant model with explicit physiology and architecture

    OpenAIRE

    Bornhofen, S.; Barot, Sébastien; Lattaud, C.

    2011-01-01

    This paper introduces a functional-structural plant model based on artificial life concepts and L-systems. This model takes into account realistic physiological rules, the architecture of the plants and their demography. An original benefit of this approach is that it allows the simulation of plant evolution at both functional and life-history levels implementing mutations to the L-systems and a set of genetic parameter values. The conducted experiments focus on the evolutionary emergence of ...

  16. 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.

  17. 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.

  18. Characterisation of the plasma density with two artificial neural network models

    International Nuclear Information System (INIS)

    This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron: the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work. (general)

  19. 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; Holstein-Rathlou, N H; Marsh, D J

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... 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...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  20. 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.

  1. 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

  2. Modeling the Probability of Surface Artificialization in Zêzere Watershed (Portugal Using Environmental Data

    Directory of Open Access Journals (Sweden)

    Bruno M. Meneses

    2016-07-01

    Full Text Available The land use and land cover (LUC of the Zêzere watershed (Portugal have undergone major changes in recent decades, with the increase of artificial surfaces. This trend is quantified in some studies, but the probability of the increase of this type of LUC, nor the places where the next transitions or land use/cover changes (LUCC for artificial surfaces will have high probability of occurrence has not yet been assessed. This research presents an evaluation of these two aspects, by means of bivariate statistical models (fuzzy logic and information value and environmental data. The artificialization probability by sectors within the same watershed is also evaluated, to further understand which areas will require greater attention, taking into account the environmental conditions favorable to the occurrence of this process and bearing in mind the conditions under which this process took place in the past. The results obtained using these models were assessed independently, through curves of success, noting that the modeling through the fuzzy gamma presents slightly better efficiency in determining the probability of artificialization surfaces in the study area. The area with the highest probability of artificialization is mostly located in the SW of this watershed, but high probabilities are also present in the upstream sector, being those areas that require further preventive measures once they have influence on the water quality and quantity in the main reservoirs of this watershed.

  3. 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)

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

    International Nuclear Information System (INIS)

    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)

  5. A life span model of successful aging.

    Science.gov (United States)

    Schulz, R; Heckhausen, J

    1996-07-01

    To lay the foundation for our model, we first describe existing conceptions of successful aging, underlying assumptions of development, and criteria for success. The model presented extends the discourse on this topic in three directions: (a) It frames the discussion of successful aging in the broader context of life course development; (b) it accounts for both normative and nonnormative (i.e., exceptional) success; and (c) it integrates motivational processes into a theory of successful aging. Successful aging is equated with the development and maintenance of primary control throughout the life course, which is achieved through control-related processes that optimize selection and failure compensation functions. Selection processes regulate the choice of action goals so that diversity is maintained and positive and negative trade-offs between performance domains and life stages are taken into account. Compensation mechanisms serve to maintain, enhance, and remediate competencies and motivational resources after failure experiences. Both compensation and selection processes are motivated by desires for primary control and can be characterized in terms of primary and secondary control processes. PMID:8694390

  6. Bayesian model selection applied to artificial neural networks used for water resources modeling

    Science.gov (United States)

    Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

    2008-04-01

    Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

  7. 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.

  8. 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

    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 determin...... experimental data used R2 > 0.98. Furthermore a sensitivity analysis has been applied in each ANN model showing that all studied input variables are important....

  9. 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.

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

    International Nuclear Information System (INIS)

    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.

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

    International Nuclear Information System (INIS)

    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)

  12. Transport energy demand modeling of South Korea using artificial neural network

    International Nuclear Information System (INIS)

    Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

  13. 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.

  14. Adversarial life testing: A Bayesian negotiation model

    International Nuclear Information System (INIS)

    Life testing is a procedure intended for facilitating the process of making decisions in the context of industrial reliability. On the other hand, negotiation is a process of making joint decisions that has one of its main foundations in decision theory. A Bayesian sequential model of negotiation in the context of adversarial life testing is proposed. This model considers a general setting for which a manufacturer offers a product batch to a consumer. It is assumed that the reliability of the product is measured in terms of its lifetime. Furthermore, both the manufacturer and the consumer have to use their own information with respect to the quality of the product. Under these assumptions, two situations can be analyzed. For both of them, the main aim is to accept or reject the product batch based on the product reliability. This topic is related to a reliability demonstration problem. The procedure is applied to a class of distributions that belong to the exponential family. Thus, a unified framework addressing the main topics in the considered Bayesian model is presented. An illustrative example shows that the proposed technique can be easily applied in practice

  15. COMPARING OF DEPOSIT MODEL AND LIFE INSURANCE MODEL IN MACEDONIA

    Directory of Open Access Journals (Sweden)

    TATJANA ATANASOVA-PACHEMSKA

    2016-02-01

    Full Text Available In conditions of the continuous decline of the interest rates for bank deposits, and at a time when uncertainty about the future is increasing, physical and legal persons have doubts how to secure their future or how and where to invest their funds and thus to “fertilize” and increase their savings. Individuals usually choose to put their savings in the bank for a certain period, and for that period to receive certain interest, or decide to invest their savings in different types of life insurance and thus to "take care" of their life, their future and the future of their families. In mathematics are developed many models that relate to the compounding and the insurance. This paper is a comparison of the deposit model and the model of life insurance

  16. 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.

  17. A classical Master equation approach to modeling an artificial protein motor

    International Nuclear Information System (INIS)

    Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors.

  18. Modeling Expectations with GENEFER -- an Artificial Intelligence Approach

    OpenAIRE

    Eric Ringhut; Stefan Kooths

    2003-01-01

    Economic modeling of financial markets attempts to model highly complex systems in which expectations can be among the dominant driving forces. It is necessary, then, to focus on how agents form expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. Agents' bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule Bases. For example if a single agent believes the exchange rate is determined by a set of possible input...

  19. 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.

  20. 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.

  1. 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.

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

    OpenAIRE

    AbdoulAhad Validi; Jyh-Yuan Chen; Akbar Ghafourian

    2012-01-01

    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 ...

  3. Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

    OpenAIRE

    Murat Yildizoglu

    2002-01-01

    In this methodological work I explore the possibility of explicitly modelling expectations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of “internal model”. The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982).

  4. Validation of computer models of an artificial hip joint

    Directory of Open Access Journals (Sweden)

    S. Ziemba

    2007-05-01

    Full Text Available Purpose: Problems of the modelling of the surgical cement behaviour during implantation have been presented in the paper. The purpose was to validate the FEM model describing the temperature fields in the bone during the surgery treatment.Design/methodology/approach: The physical laboratory modelling has been used to perform validation of the model that makes it possible to predict the temperature influence on the bone tissue during polymerization process.Findings: Due to its non-invasive nature, the computer models’ validation method applied in the study seems to be the right solution for the research on surgical procedures of endoprosthesis implantation. However, a particular emphasis should be placed on a correct selection of thermophysical properties of the designed laboratory models. Relying on the calculations and research results, similar local values of maximum temperatures were obtained. Practical implications: The computer modelling methods presented in the paper together with the analytical approach are of great importance to both forecasting the implants’ behaviour during a surgical procedure and in their operational conditions, as well as in the selection and modification process of surgical cements’ material properties. The analysis carried out makes it possible to determine the location of zones most threatened with an adverse effect of an elevated temperature. They are located in the vicinity of the top of the endoprosthesis stem.Originality/value: The work presents the own method of validation of the FEM model used for heat flow modelling.

  5. Artificial neural network modelling in heavy ion collisions

    International Nuclear Information System (INIS)

    The neural network (NN) model and parton two fireball model (PTFM) have been used to study the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28 and S 32 on nuclear emulsion. The trained NN shows a better fitting with experimental data than the PTFM calculations. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The NN simulation results prove a strong presence modeling in heavy ion collisions

  6. MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

    OpenAIRE

    Stefan Kooths; Eric Ringhut

    2000-01-01

    Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and...

  7. Modeling of artificial stiction in steam turbine control valve

    International Nuclear Information System (INIS)

    The steam turbine control valves play a pivotal role in regulating the output power of the turbine in a commercial nuclear power plant. In this paper the turbine system refers to Ulchin units 3 and 4. The modeling of friction in steam turbine control valve is presented. Instead of a detailed physical model of the control valve friction, the data-driven models are adopted for modeling the friction to obtain an easier friction identification and faster calculation time. Some computational results by using the MARS thermal hydraulic analysis code are presented to show the effect of friction on the total mass flow at the inlet of the high pressure turbine. The computational results demonstrate that the friction will initiate fluctuations on the total mass flow at the turbine inlet

  8. From agent-based models to artificial economies

    OpenAIRE

    Teglio, Andrea

    2011-01-01

    The aim of this thesis is to propose and illustrate an alternative approach to economic modeling and policy design that is grounded in the innovative field of agent-based computational economics (ACE). The recent crisis pointed out the fundamental role played by macroeconomic policy design in order to preserve social welfare, and the consequent necessity of understanding the effects of coordinated policy measures on the economic system. Classic approaches to macroeconomic modeling, mainly rep...

  9. Weather Radar Estimations Feeding an Artificial Neural Network Model

    OpenAIRE

    Gustavo Cerda Villafaña; Sergio Ledesma; Dawei Han

    2008-01-01

    The application of ANNs (Artifi cial Neural Networks) has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwe...

  10. Modelling of solar energy potential in Nigeria using an artificial neural network model

    International Nuclear Information System (INIS)

    In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14oN, log. 2-15oE) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

  11. MODELLING STUDIES BY APPLICATION OF ARTIFICIAL NEURAL NETWORK USING MATLAB

    Directory of Open Access Journals (Sweden)

    K. S. ARJUN

    2015-11-01

    Full Text Available Four ANN models to estimate Bubble point pressure (Pb, Oil Formation Volume Factor (Bob, Bubble point solution Gas Oil Ratio (Rsob and Stock Tank Vent GOR (RST in the absence of Pressure, Volume and Temperature (PVT analysis, were proposed as a function of readily available field data. The estimated Rsob and RST values from the proposed models can be used as a basic input variable in many PVT correlations in order to estimate other fluid properties such as the Pb and Bob. Another proposed ANN model has the ability to predict and interpolate average reservoir pressure accurately by employing oil, water and gas production rates and number of producers are used as four inputs for the proposed model without the wells having to be closed. Another ANN model proposed is to predict the performance of oil production within water injection reservoirs, which can be utilized to find the most economical scenario of water injection to maximize ultimate oil recovery. It has reasonable accuracy, requires little data and can forecast quickly. ANN approach to solving the identified pipeline damage problem gives satisfactory results as the error between the ANN output and the target is very tolerable. The results conclusively proved with error 0.0027 that it has the ability to accurately predict the pipeline damage probability by employing the model data obtained in this study.

  12. 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.

  13. Development of Improved Artificial Neural Network Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    PRATAP KISHORE PADHIARY

    2011-02-01

    Full Text Available In recent year’s prediction of stock market returns is a hottest field of research in finance. Artificial Neural Network (ANN is a technique that is heavily researched and widely used in applications for engineering and scientific fields for various purposes ranging from control systems to artificial intelligence. This paper surveys key issues in financial forecasting and propose an ANN methodologywhich could be better for long term (one month, two month as well as short term (one day prediction of stock price of any leading stock market indices. Survey of existing literature reveals that adaptivelearning rate will give more accurate result than fixed learning rate parameter for ANN models. Many researchers noted that slight parameter changed causes major variations in the behavior of the network. So there is no theory which could be guideline for finding best network topology. The proposed trigonometric functional link artificial neural network (FLANN model employs standard least meansquare (LMS algorithm with search-then-converge scheduling which could effectively calculate learning rate parameter that changes with time and may require less experiments to train the model. The objective of this paper is to introduce a functional link single layer artificial neural network (FLANN for long term as well as short term stock market prediction.

  14. 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.

  15. Artificial Immune Danger Theory Based Model for Network Security Evaluation

    Directory of Open Access Journals (Sweden)

    Feixian Sun

    2011-02-01

    Full Text Available Inspired by the principles of immune danger theory, a danger theory based model for network security risk assessment is presented in this paper. Firstly, the principle of the danger theory is introduced. And then, with the improved concepts and formal definitions of antigen, antibody, danger signal, and detection lymphocyte for network security risk assessment presented, the distributed architecture of the proposed model is described. Following that, the principle of network intrusion detection is expounded. Finally, the method of network security risk assessment is given. Theoretical analysis and simulation results show that the proposed model can evaluate the network attack threats in real time. Thus, it provides an effective risk evaluation solution to network security.

  16. 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.

  17. Cognitive model of image interpretation for artificial intelligence applications

    International Nuclear Information System (INIS)

    A cognitive model of imaging diagnosis was devised to aid in the development of expert systems that assist in the interpretation of diagnostic images. In this cognitive model, a small set of observations that are strongly predictive of a particular diagnosis lead to a search for other observations that would support this diagnosis but are not necessarily specific for it. Then a set of alternative diagnoses is considered. This is followed by a search for observations that might allow differentiation of the primary diagnostic consideration from the alternatives. The production rules needed to implement this model can be classified into three major categories, each of which have certain general characteristics. Knowledge of these characteristics simplifies the development of these expert systems

  18. Modelling of Surface Ships using Artificial Neural Networks

    DEFF Research Database (Denmark)

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

    For various design and planning purposes there is at present an increasing interest and a need for numerical modelling of the process of navigating a vessel (or a floating body in general). The reasons for this is that experiments in "full mission" simulators with human navigators at the handles...

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

    OpenAIRE

    Kaur, Bikrampal; Aggarwal, Himanshu

    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 t...

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

    OpenAIRE

    Hongshan Zhao; Shi Su; Zengqiang Mi; Fei Wang

    2012-01-01

    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 t...

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

    OpenAIRE

    Sungwon Kim; Singh, Vijay P.

    2015-01-01

    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...

  2. An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

    OpenAIRE

    Tumer, Abdullah Erdal; Edebali, Serpil

    2015-01-01

    In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and...

  3. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    OpenAIRE

    Vinay Chandwani; Vinay Agrawal; Ravindra Nagar

    2014-01-01

    Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated de...

  4. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    OpenAIRE

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

    2011-01-01

    Artificially-created hydrological catchments are characterized by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial sediment distribution patterns dependi...

  5. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    OpenAIRE

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

    2011-01-01

    Artificially-created hydrological catchments are characterised by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial distribution patterns of dumped sedime...

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

    International Nuclear Information System (INIS)

    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 μm and the range of lesional area was 36.95-138.52 mm2. 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.

  7. 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.

  8. Artificial Life: Visual Model with Simulation of a Plant Pet

    Institute of Scientific and Technical Information of China (English)

    JIChen; QIShen

    2004-01-01

    This paper introduces an L-system model, which simulates a plant pet. The plant, named Wilson, is able to respond to the change of its virtual environment and to user's behaviors. However it does not speak or move. What users can see are only the changes of its appearance. The connection between the plant's appearance and external inputs is implemented with some parameters embedded in the L-system.

  9. The design and optimization for light-algae bioreactor controller based on Artificial Neural Network-Model Predictive Control

    Science.gov (United States)

    Hu, Dawei; Liu, Hong; Yang, Chenliang; Hu, Enzhu

    As a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO2 and releasing O2, so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network-Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.

  10. Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling

    Directory of Open Access Journals (Sweden)

    N. J. de Vos

    2013-01-01

    Full Text Available Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall–runoff modelling.

  11. Echo state networks as an alternative to traditional artificial neural networks in rainfall-runoff modelling

    Science.gov (United States)

    de Vos, N. J.

    2013-01-01

    Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.

  12. 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.

  13. 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.

  14. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

  15. 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

  16. INTEGRATED CORPORATE STRUCTURE LIFE CYCLE MANAGEMENT MODELING AND ORGANIZATION

    OpenAIRE

    Naumenko, M.; Morozova, L.

    2011-01-01

    Integrated business structure presented as complementary pool of its participants skills. The methodical approach to integrated business structure life cycle modeling proposed. Recommendations of enterprises life cycles stages correlate are submitted.

  17. An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images

    International Nuclear Information System (INIS)

    An optimized artificial neural network ensemble model is built to estimate daily global solar radiation over large areas. The model uses clear-sky estimates and satellite images as input variables. Unlike most studies using satellite imagery based on visible channels, our model also exploits all information within infrared channels of the Meteosat 9 satellite. A genetic algorithm is used to optimize selection of model inputs, for which twelve are selected – eleven 3-km Meteosat 9 channels and one clear-sky term. The model is validated in Andalusia (Spain) from January 2008 through December 2008. Measured data from 83 stations across the region are used, 65 for training and 18 independent ones for testing the model. At the latter stations, the ensemble model yields an overall root mean square error of 6.74% and correlation coefficient of 99%; the generated estimates are relatively accurate and errors spatially uniform. The model yields reliable results even on cloudy days, improving on current models based on satellite imagery. - Highlights: • Daily solar radiation data are generated using an artificial neural network ensemble. • Eleven Meteosat channels observations and a clear sky term are used as model inputs. • Model exploits all information within infrared Meteosat channels. • Measured data for a year from 83 ground stations are used. • The proposed approach has better performance than existing models on daily basis

  18. 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...

  19. Modeling of biodistribution of 90 Y-DOTA-hR3 by using artificial intelligence techniques

    International Nuclear Information System (INIS)

    In this work the biodistribution of radioimmunoconjugate 90Y-DOTA-hR3 was modeled by using an artificial neural network. In vivo stability of 90Y-DOTA-hR3 was determined in healthy male Wistar rats at 4, 24 and 48 hours, in different organs. A model describing the relationship between, by one hand, the incorporated dose and, by the other hand, organ and time was developed by using a multilayer perceptron neural network. Adjusted model was analyzed by several statistical tests. Outcomes shown that proposed neural model describes the relationship between the studied variables in a proper way. (Author)

  20. 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.

  1. Study on the Overfitting of the Artificial Neural Network Forecasting Model

    Institute of Scientific and Technical Information of China (English)

    JIN Long; KUANG Xueyuan; HUANG Haihong; QIN Zhinian; WANG Yehong

    2005-01-01

    Because of overfitting and the improvement of generalization capability (GC) available in the construction of forecasting models using artificial neural network (ANN), a new method is proposed for model establishment by means of making a low-dimension ANN learning matrix through principal component analysis (PCA). The results show that the PCA is able to construct an ANN model without the need of finding an optimal structure with the appropriate number of hidden-layer nodes, thus avoids overfitting by condensing forecasting information, reducing dimension and removing noise, and GC is greatly raised compared to the traditional ANN and stepwise regression techniques for model establishment.

  2. 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.

  3. Numerical modeling of artificial ionospheric layers driven by high-power HF-heating

    Science.gov (United States)

    Milikh, G. M.; Eliasson, B.; Shao, X.; Mishin, E. V.; Papadopoulos, K.

    2012-12-01

    We present a multi-scale dynamic model for the creation and propagation of artificial plasma layers in the ionosphere observed during high-power high frequency heating experiments at HAARP. Ordinary mode electromagnetic waves excite parametric instabilities and strong Langmuir turbulence near the reflection point. The coupling between high frequency electromagnetic and Langmuir waves and low-frequency ion acoustic waves is numerically simulated using a generalized Zakharov equation. The acceleration of plasma electrons is described by a Fokker-Planck model with an effective diffusion coefficient constructed using the simulated Langmuir wave spectrum. The propagation of the accelerated electrons through the non-uniform ionosphere is simulated by a kinetic model accounting for elastic and inelastic collisions with neutrals. The resulting ionization of neutral gas increases the plasma density below the acceleration region, so that the pump wave is reflected at a lower altitude. This leads to a new turbulent layer at the lower altitude, resulting in a descending artificial ionized layer, that moves from near 230 km to about 150 km. At the terminal altitude, ionization, recombination, and ambipolar diffusion reach equilibrium, so the descent stops. The modeling results reproduce artificial ionospheric layers produced for similar sets of parameters during the high-power HF experiments at HAARP.

  4. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    Science.gov (United States)

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. PMID:19880241

  5. 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.

  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. Artificial neural networks in models of specialization, guild evolution and sympatric speciation

    OpenAIRE

    Holmgren, Noél M A; Norrström, Niclas; Getz, Wayne M.

    2007-01-01

    Sympatric speciation can arise as a result of disruptive selection with assortative mating as a pleiotropic by-product. Studies on host choice, employing artificial neural networks as models for the host recognition system in exploiters, illustrate how disruptive selection on host choice coupled with assortative mating can arise as a consequence of selection for specialization. Our studies demonstrate that a generalist exploiter population can evolve into a guild of specialists with an ‘ideal...

  9. Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

    OpenAIRE

    Valente, Eduardo; Rocha, Miguel; Ferreira, E.C.; Rocha, I

    2009-01-01

    In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested...

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

    OpenAIRE

    Filiz Ozkan

    2012-01-01

    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...

  11. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    Science.gov (United States)

    Krishnan, G. S.

    1997-01-01

    A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

  12. Generalized Gap Model for Bacterial Artificial Chromosome Clone Fingerprint Mapping and Shotgun Sequencing

    OpenAIRE

    Wendl, Michael C; Robert H Waterston

    2002-01-01

    We develop an extension to the Lander-Waterman theory for characterizing gaps in bacterial artificial chromosome fingerprint mapping and shotgun sequencing projects. It supports a larger set of descriptive statistics and is applicable to a wider range of project parameters. We show that previous assertions regarding inconsistency of the Lander-Waterman theory at higher coverages are incorrect and that another well-known but ostensibly different model is in fact the same. The apparent paradox ...

  13. Investigation of a New Artificial Immune System Model Applied to Pattern Recognition

    OpenAIRE

    Alexandrino, Jos&#; Lima; Zanchettin, Cleber; Filho, Edson C. de B. Carvalho

    2008-01-01

    This chapter describes an application based on Artificial Immune System (AIS) with biologically inspired characteristics, such as the grouping of similar antibodies and memory antibodies were studied to allow the evolution of the AIS. The focus of this chapter was to evaluate the quality of this model to recognize handwritten digits patterns and evolving its performance and comparing it to other technique. This architecture combined different techniques to form a hybrid immune algorithm. It w...

  14. Modelling an artificial stock market: When cognitive institutions influence market dynamics

    OpenAIRE

    Lavigne, Stéphanie

    2004-01-01

    The paper presents an artificial financial market designed to analyse market dynamics from the behaviour of investors. The model especially allows highlighting the role of a particular kind of institution in the orientation of market dynamics. The information – delivered by financial intermediaries as rating agencies and considered as a cognitive institution – directs the decisions of investors who are heterogeneous agents endowed with capabilities of learning in a changing environment. We de...

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

    OpenAIRE

    Parvaneh Shabanzadeh; Norazak Senu; Kamyar Shameli; Maryam Mohaghegh Tabar

    2013-01-01

    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 pro...

  16. A nonlinear full model of switched reluctance motor with artificial neural network

    International Nuclear Information System (INIS)

    This paper presents a novel nonlinear full model developed by using artificial neural networks (ANNs) for switched reluctance motors (SRMs). The proposed ANN based nonlinear model consists of two different models, namely forward and inverse model. The purpose of the forward model is to estimate the flux linkage and torque of the SRM as a function of stator current and rotor position. And, the purpose of the inverse model is to estimate stator current and flux linkage of the SRM as a function of torque and rotor position. Also conversions can be achieved between torque, stator current and flux linkage with these models. Computational load of the processor has been considered and minimized to use the developed model in real industrial applications. The experimental tests are realized to verify the accuracy and feasibility of the proposed model.

  17. A comparison of production system life cycle models

    Science.gov (United States)

    Attri, Rajesh; Grover, Sandeep

    2012-09-01

    Companies today need to keep up with the rapidly changing market conditions to stay competitive. The main issues in this paper are related to a company's market and its competitors. The prediction of market behavior is helpful for a manufacturing enterprise to build efficient production systems. However, these predictions are usually not reliable. A production system is required to adapt to changing markets, but such requirement entails higher cost. Hence, analyzing different life cycle models of the production system is necessary. In this paper, different life cycle models of the production system are compared to evaluate the distinctive features and the limitations of each model. Furthermore, the difference between product life cycle and production life cycle is summarized, and the effect of product life cycle on production life cycle is explained. Finally, a production system life cycle model, along with key activities to be performed in each stage, is proposed specifically for the manufacturing sector.

  18. An artificial economy based on reinforcement learning and agent based modeling

    OpenAIRE

    Fernando Lozano; Jaime Lozano; Mario García

    2007-01-01

    In this paper we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on convention. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions meaning that a firm is likely to behave as it neighbors if it observes that their actions lead to a good pay-off. On the other hand, we propose the use of reinfo...

  19. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.

    Science.gov (United States)

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J; Marmarelis, V Z

    1998-01-01

    Volterra models have been increasingly popular in modeling studies of nonlinear physiological systems. In this paper, feedforward artificial neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks (sigmoidal and polynomial) and the Volterra models are comparable in terms of normalized mean-square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. Nonetheless, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general, since they may exhibit different strengths and weaknesses depending on the specific characteristics of each application. PMID:18252466

  20. RELIABILITY AND HAZARD RATE ESTIMATION OF A LIFE TESTING MODEL

    Directory of Open Access Journals (Sweden)

    Vinod Kumar

    2010-01-01

    Full Text Available The present paper deals with the reliability and hazard rate estimation of a Weibulltype life testing model. Its use as a life testing model has also been illustrated. The proposedmodel has been found better then exponential for several sets of lifetime data. Somecharacteristics of the model have also been investigated.

  1. Modeling Reality: How Computers Mirror Life

    International Nuclear Information System (INIS)

    Modeling Reality: How Computers Mirror Life covers a wide range of modern subjects in complex systems, suitable not only for undergraduate students who want to learn about modelling 'reality' by using computer simulations, but also for researchers who want to learn something about subjects outside of their majors and need a simple guide. Readers are not required to have specialized training before they start the book. Each chapter is organized so as to train the reader to grasp the essential idea of simulating phenomena and guide him/her towards more advanced areas. The topics presented in this textbook fall into two categories. The first is at graduate level, namely probability, statistics, information theory, graph theory, and the Turing machine, which are standard topics in the course of information science and information engineering departments. The second addresses more advanced topics, namely cellular automata, deterministic chaos, fractals, game theory, neural networks, and genetic algorithms. Several topics included here (neural networks, game theory, information processing, etc) are now some of the main subjects of statistical mechanics, and many papers related to these interdisciplinary fields are published in Journal of Physics A: Mathematical and General, so readers of this journal will be familiar with the subject areas of this book. However, each area is restricted to an elementary level and if readers wish to know more about the topics they are interested in, they will need more advanced books. For example, on neural networks, the text deals with the back-propagation algorithm for perceptron learning. Nowadays, however, this is a rather old topic, so the reader might well choose, for example, Introduction to the Theory of Neural Computation by J Hertz et al (Perseus books, 1991) or Statistical Physics of Spin Glasses and Information Processing by H Nishimori (Oxford University Press, 2001) for further reading. Nevertheless, this book is worthwhile

  2. Artificial Neural Network Modeling to Evaluate the Dynamic Flow Stress of 7050 Aluminum Alloy

    Science.gov (United States)

    Quan, Guo-zheng; Wang, Tong; Li, Yong-le; Zhan, Zong-yang; Xia, Yu-feng

    2016-02-01

    The flow stress data have been obtained by a set of isothermal hot compression tests, which were carried out in the temperature range of 573-723 K and strain rates of 0.01, 0.1, 1, and 10 s-1 with a reduction of 60% on a Gleeble-1500 thermo-mechanical simulator. On the basis of the experimental data, constitutive equation and an artificial neural network model were developed for the analysis and simulation of the flow behavior of the 7050 aluminum alloy. After training with standard back-propagation learning algorithm, the artificial neural network model has the ability to present the intrinsic relationship between the flow stress and the processing variables. In the present model, the temperature, strain, and strain rate were chosen as inputs, and the flow stress was chosen as output. By comparing the values of correlation coefficient and average absolute relative error, the prediction accuracy of the model and the improved Arrhenius-type model can be evaluated. The results indicated that the well-trained artificial neural network model is more accurate than the improved Arrhenius-type model in predicting the hot compressive behavior of the as-extruded 7050 aluminum alloy. Based on the predicted stress data and experimental stress data, the 3D continuous stress-strain maps at different strains, temperatures, and strain rates were plotted subsequently. Besides, the flow stress values at arbitrary temperature, strain rate, and strain are explicit on the 3D continuous stress-strain maps, which would be beneficial to articulate working processes more validly.

  3. Theoretical and computation modelling of polymer seal life

    OpenAIRE

    Ho, Tsz Hang Emily Ting

    1993-01-01

    Elastomer seals are widely used in the petroleum industry. Seal failure can be very expensive, due to losses in production and high maintenance costs. Another aspect of this problem is the difficulty in predicting the working life, of a specific elastomeric seal in a specific application, at the design stage. The objective of the present work is to develop the theoretical and computational seal life model to assist reliable prediction of seal life. Seal life computer software h...

  4. 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.

  5. 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.

  6. 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.

  7. 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. PMID:24883365

  8. Economic modeling for life extension decision making

    International Nuclear Information System (INIS)

    This paper presents a methodology for the economic and financial analysis of nuclear plant life extension under uncertainty and demonstrates its use in a case analysis. While the economic and financial evaluation of life extension does not require new analytical tools, such studies should be based on the following three premises. First, the methodology should examine effects at the level of the company or utility system, because the most important economic implications of life extension relate to the altered generation system expansion plan. Second, it should focus on the implications of uncertainty in order to understand the factors that most affect life extension benefits and identify risk management efforts. Third, the methodology should address multiple objectives, at a minimum, both economic and financial objectives

  9. 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.

  10. Fault diagnosis using noise modeling and a new artificial immune system based algorithm

    Science.gov (United States)

    Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad

    2015-12-01

    A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

  11. 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. PMID:26930133

  12. Experimental infection of Phlebotomus perniciosus by bioluminescent Leishmania infantum using murine model and artificial feeder

    Science.gov (United States)

    Cannet, Arnaud; Akhoundi, Mohammad; Michel, Gregory; Marty, Pierre; Delaunay, Pascal

    2016-01-01

    Leishmaniasis is a vector-borne disease that is transmitted by sandflies and caused by obligate intracellular protozoa of the genus Leishmania. In the present study, we carried out a screening on the experimental infection of Phlebotomus pernioucus by bioluminescent Leishmania infantum using murine model and artificial feeder. We developed a real-time polymerase chain reaction (RT-PCR)-based method to determine individually the number of Leishmania promastigotes fed by infected flies. Among 1840 new emerged female sand flies, 428 were fed on the infected mice. After their death, they were analysed individually by RT-PCR. Our results demonstrated just a single Leishmania positive female at sixth day post meal. A total of 1070 female sand flies were exposed in contact with artificial feeder containing the human blood with two different quantities of Leishmania parasites: 2.106/mL and 1.107/mL. A blood meal including 1.107/mL LUC-promastigotes was proposed to 270 females and 75 (28%) flies were engorged. Among them, 44 (59%) were positive by RT-PCR analysis, with a relative average of 50551 Leishmania parasites. In case of blood feeding of females with 2.106/mL promastigotes, 57 out of 800 (7%) females succeed to feed from artificial feeder which 22 (39%) were positive with a relative average of 6487 parasites. PMID:27439032

  13. Application of artificial neural networks in modelling of quenched and tempered structural steels mechanical properties

    Directory of Open Access Journals (Sweden)

    L.A. Dobrzański

    2010-05-01

    Full Text Available Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of structuralal steels after quenching and tempering processes.Design/methodology/approach: On the basis of input parameters, which are chemical composition, parameters of mechanical and heat treatment and dimensions of elements, steels’ mechanical properties : yield stress, tensile strength stress, elongation, area reduction, impact strength and hardness are predicted.Findings: Results obtained in the given ranges of input parameters indicates on very good ability of artificial neural networks to values prediction of described mechanical properties for steels after quenching and tempering processes. The uniform distribution of descriptive vectors in all, training, validation and testing sets, indicates on good ability of the networks to results generalisation.Practical implications: Artificial neural networks, created during modelling, allows easy prediction of steels properties and allows the better selection of both chemical composition and the processing parameters of investigated materials. It’s possible to obtain steels, which are qualitatively better, cheaper and more optimised under customers needs.Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It allows the preservation of customers quality requirements and brings also measurable financial advantages

  14. 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.

  15. Life Skills Literacy: An Intervention Model to Alleviate Family Poverty

    Science.gov (United States)

    Johnson, Lee N.; Carswell, Andrew T.; Palmer, Lance; Sweaney, Annie L.; Mullis, Rebecca M.; Leonas, Karen K.; Moss, Joan Koonce; Mauldin, Teresa

    2005-01-01

    Life Skills Literacy (LSL) is a multidisciplinary intervention model that helps families living with limited resources (including poverty) achieve sustainable well-being. This model, based on ecological theory and a readiness for change framework, prepares people to learn from the program and teaches necessary life skills. The LSL project…

  16. 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

  17. 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.

  18. A Nonlinear Elastic Model for Triaxial Compressive Properties of Artificial Methane-Hydrate-Bearing Sediment Samples

    Directory of Open Access Journals (Sweden)

    Tsutomu Yamaguchi

    2012-10-01

    Full Text Available A constitutive model for marine sediments containing natural gas hydrate is essential for the simulation of the geomechanical response to gas extraction from a gas-hydrate reservoir. In this study, the triaxial compressive properties of artificial methane-hydrate-bearing sediment samples reported in an earlier work were analyzed to examine the applicability of a nonlinear elastic constitutive model based on the Duncan-Chang model. The presented model considered the dependences of the mechanical properties on methane hydrate saturation and effective confining pressure. Some parameters were decided depending on the type of sand forming a specimen. The behaviors of lateral strain versus axial strain were also formulated as a function of effective confining pressure. The constitutive model presented in this study will provide a basis for an elastic analysis of the geomechanical behaviors of the gas-hydrate reservoir in the future study, although it is currently available to a limited extent.

  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. Investigation and modeling on protective textiles using artificial neural networks for defense applications

    International Nuclear Information System (INIS)

    Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25-40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

  2. Anticipatory Artificial Autopoiesis

    OpenAIRE

    DuBois, Daniel; Holmberg, Stig C.

    2010-01-01

    In examining relationships between autopoiesis and anticipation in artificial life (Alife) systems it is demonstrated that anticipation may increase efficiency and viability in artificial autopoietic living systems. This paper, firstly, gives a review of the Varela et al [1974] automata algorithm of an autopoietic living cell. Some problems in this algorithm must be corrected. Secondly, a new and original anticipatory artificial autopoiesis algorithm for automata is presented. ...

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. Improved simultaneous estimation of tracer kinetic models with artificial immune network based optimization method.

    Science.gov (United States)

    Liu, L; Ding, H; Huang, H B

    2016-01-01

    Tracer kinetic modeling (TKM) is a promising quantitative method for physiological and biochemical processes in vivo. In this paper, we investigated the applications of an immune-inspired method to better address the issues of Simultaneous Estimation (SIME) of TKM with multimodal optimization. Experiments of dynamic FDG PET imaging experiments and simulation studies were carried out. The proposed artificial immune network (TKM_AIN) shows more scalable and effective when compared with the gradient-based Marquardt-Levenberg algorithm and the scholastic-based simulated annealing method. PMID:26433131

  8. Research on WNN modeling for gold price forecasting based on improved artificial bee colony algorithm.

    Science.gov (United States)

    Li, Bai

    2014-01-01

    Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme. PMID:24744773

  9. Calculation of artificial radionuclides in the ocean by an ocean general circulation model

    International Nuclear Information System (INIS)

    The concentrations of three artificial radionuclides (90Sr, 137Cs and 239+240Pu) introduced into seawaters from global fallout were simulated from 1957 to 1994 by using an ocean general circulation model. The distributions of the calculated 137Cs and 90Sr concentrations were in good agreement with the observed concentrations. The vertical distribution of the calculated 239+240Pu concentration in the South Pacific also agreed with the observed data. However, the calculated 239+240Pu concentrations in the North Pacific water columns were significantly underestimated in comparison with the observed data, which strongly suggests the presence of additional sources of plutonium input to the North Pacific. (author)

  10. A New Method for Cardiovascular Disease Clinical Diagnosis Based on Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Huang Zhao-Ming

    2013-01-01

    Full Text Available Diagnosis. In order to improve the accuracy of Clinical Diagnosis for Cardiovascular Disease, ANN(Artificial Neural Network is introduced in this paper. 200 cases of cardiovascular disease which have similar symptom and different diagnosis are sampled from our database. BP Network model in Matlab environment is created for these cases. Simulation results show that the Diagnosis after training is much better than that with the doctor’ diagnosis opinion. It can be concluded that this new method is of important value for realistic scale in clinical.

  11. Ruthenium (II) polypyridyl complexes as models for artificial photosynthesis: synthesis and characterisation

    OpenAIRE

    Killeen, Jonathan Scott

    2001-01-01

    This thesis presents a study on the synthesis and characterisation of a series of novel ruthenium (II) polypyridyl complexes, which are potentially models for artificial photosynthesis. Chapter 1, the introduction, highlights the literature relevant to the topic. In Chapter 2, the experimental conditions of the various methods of characterisation are described. The synthesis and characterisation of a range of novel 5-phenyI-3-(2- pyridyl)-1,2,4-triazoles, their Ru(bpy)2 and their Ru(dg-bp...

  12. Artificial neural network modeling of fixed bed biosorption using radial basis approach

    Science.gov (United States)

    Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha

    2010-04-01

    In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.

  13. 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.

  14. 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.

  15. Direct, inverse, and combined problems in complex engineered system modeling by artificial neural networks

    Science.gov (United States)

    Terekhoff, Serge A.

    1997-04-01

    This paper summarizes theoretical findings and applications of artificial neural networks to modeling of complex engineered system response in the abnormal environments. The thermal fire impact on the industrial container for waste and fissile materials was investigated using model and experimental data. Solutions for the direct problem show that the generalization properties of neural network based model are significantly better than those for standard interpolation methods. Minimal amount of data required for good prediction of system response is estimated in computer experiments with MLP network. It was shown that Kohonen's self-organizing map with counterpropagation may also estimate local accuracy of regularized solution for inverse and combined problems. Feature space regions of partial correctness of the inverse model can be automatically extracted using adaptive clustering. Practical findings include time strategy recommendations for fire-safe services when industrial or transport accidents occur.

  16. Modeling mechanical properties of cast aluminum alloy using artificial neural network

    International Nuclear Information System (INIS)

    Modeling is widely used to investigate the mechanical properties of engineering materials due to increasing demand of low cost and high strength to weight ratio for many engineering applications. The aluminum casting alloys are cost competitive material and possess the desired properties. The mechanical properties largely depend upon composition of alloys and their processing method. Alloy design involves controlling mechanical properties via optimization of the composition and processing parameters. For optimization the possible root is empirical modeling and its more refined version is the analysis of the wide range of data using ANN (Artificial Neural Networks) modeling. The modeling of mechanical properties of the aluminum alloys are the main objective of present work. For this purpose, some data were collected and experimentally prepared using conventional casting method. A MLP (Multilayer Perceptron) network was developed, which is trained by using the error back propagation algorithm. (author)

  17. 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.

  18. 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.

  19. Optimization of geometry and modeling parameters of artificial neural networks using genetic algorithms

    Science.gov (United States)

    Sahoo, G. B.

    2007-12-01

    In recent years, artificial neural networks (ANNs) appear to be viable alternative to models that use phenomenological hypotheses (i.e. knowledge based models) for cases (1) the available data are not detailed and sufficient for using a process based model and (2) the detailed complex physics of the system is partially understood. ANNs have been widely used in many fields such as chemical and environmental engineering, hydrology, and water resources applications for optimum prediction of system parameters and variables. However, in most cases, parameters and system variables were forecasted employing suboptimal ANNs. The geometry and modeling parameters of an artificial neural network (ANN) and the training dataset have significant effects on its predictive performance efficiency. The combination of ANN modeling parameter and geometry arranged in the modeling domain (i.e. lower and upper bounds of each modeling parameter and geometry) is large enough (i.e. greater than 100000) that it is difficult to examine all cases using trial and error approach for the selection of an optimum set. Thus, one could easily end up with finding a set of suboptimal values. This study presents the use of genetic algorithms (GAs) to search for the optimal geometry and values of modeling parameters of a multilayer feedforward backpropagation neural network (BPNN) and a radial basis function network (RBFN). The predictive performance efficiency of the GA and ANN combination is examined using two datasets derived from the same population for training. It is illustrated that (1) the GA optimized ANN outperforms to the ANN using a trial and error approach, and (2) ANN predictive performance and geometry depend on the number of samples and the characteristics of samples included in the training dataset.

  20. 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.

  1. 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.

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

    International Nuclear Information System (INIS)

    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

  3. Artificial neural network and multiple regression model for nickel(II) adsorption on powdered activated carbons.

    Science.gov (United States)

    Hema, M; Srinivasan, K

    2011-07-01

    Nickel removal efficiency of powered activated carbons of coconut oilcake, neem oilcake and commercial carbon was investigated by using artificial neural network. The effective parameters for the removal of nickel (%R) by adsorption process, which included the pH, contact time (T), distinctiveness of activated carbon (Cn), amount of activated carbon (Cw) and initial concentration of nickel (Co) were investigated. Levenberg-Marquardt (LM) Back-propagation algorithm is used to train the network. The network topology was optimized by varying number of hidden layer and number of neurons in hidden layer. The model was developed in terms of training; validation and testing of experimental data, the test subsets that each of them contains 60%, 20% and 20% of total experimental data, respectively. Multiple regression equation was developed for nickel adsorption system and the output was compared with both simulated and experimental outputs. Standard deviation (SD) with respect to experimental output was quite higher in the case of regression model when compared with ANN model. The obtained experimental data best fitted with the artificial neural network. PMID:23029923

  4. 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.

  5. Battery Life Estimator Manual Linear Modeling and Simulation

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-08-01

    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.

  6. Modeling Tritium Life cycle in Nuclear Plants

    International Nuclear Information System (INIS)

    The mathematical development of a tritium model for nuclear power plants is presented. The model requires that the water and tritium material balance be satisfied throughout normal operations and shutdown. The model results obtained at the time of publishing include the system definitions and comparison of the model predictions of tritium generations compared to the observed plant data of the Braidwood station. A scenario that models using ion exchange resin to remove coolant boron demonstrates the tritium concentration levels are manageable. (authors)

  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. Application of artificial neural networks to infer subcriticality level through kinetic models - 173

    International Nuclear Information System (INIS)

    The paper presents some recent advances in the study of the inverse kinetics for subcritical systems. A neural-based approach is adopted to predict the reactivity of the multiplying medium through the analysis of the reactor response to a source pulse. An artificial neural network is designed to infer the subcriticality level through the analysis of power evolution. The training set is computed using an approximate model and its performances are then tested directly on experimental measures, here simulated through a detailed space-energy kinetic model. In order to improve the accuracy of the reactivity estimation, various strategies are proposed and compared, including a multi-transient inversion and the use of different kinetic models for the training. The issue of robustness of the inversion scheme to experimental noise is also addressed (authors)

  9. Studying electromagnetic interference spectrum in antenna under aircraft radome using models with artificial charged aerosol clouds

    Science.gov (United States)

    Temnikov, A. G.; Gilyazov, M. Z.; Matveev, D. A.; Voronkova, A. Yu.; Chernenskii, L. L.; Orlov, A. V.

    2011-09-01

    The spectrum of electromagnetic interference that is induced by discharges in an antenna arranged under an aircraft radome in an artificial charged aqueous aerosol cloud has been experimentally studied. It is established that, among different possible variants of lightning-arrest radomes, the minimum level of the spectral density of interference signals in the antenna is provided by vertical stripe electrodes on the radome surface. The maximum characteristic frequencies of signals in a model spherical antenna are several times lower than those in model lightning diverters, while the flat model antennas of disk or rectangular shapes exhibit the opposite trend. It has been suggested that a significant role in determining the characteristics of the electromagnetic interference spectrum in weather radar antennas is played by currents of discharges generated by charges accumulated on the dielectric radome surface.

  10. 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.

  11. 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.

  12. Equivalent Circuit Model for Designing of Jerusalem Cross-Based Artificial Magnetic Conductors

    Directory of Open Access Journals (Sweden)

    M. Hosseinipanah

    2009-12-01

    Full Text Available This paper introduces an equivalent circuit model to estimate the resonant frequency and reflection phase of Jerusalem cross-based artificial magnetic conductor (JC-AMC structures for waves normally incident on the structure. The JC-AMC structure is composed of an array of Jerusalem cross-shaped frequency selective surfaces (JC-FSSs printed on a metal-backed dielectric slab without vias. This approach is based on the coupled microstrip lines and transmission line theories. Analytical expressions for the resonant frequency and reflection phase resulting from our model are thoroughly verified by full-wave simulations for different samples reported in the open literature. A comprehensive parametric study of the design parameters of JC-AMC structure and the influence of these parameters on the reflection phase properties are presented. This model can easily be extended to complex-shaped AMCs.

  13. 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.

  14. Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

    Science.gov (United States)

    Wilson, Eric Lee

    Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

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

    Science.gov (United States)

    Djurovic, Nevenka; 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

  16. 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

    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...... 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...... reduction in computational effort when performing time domain simulation of mooring lines. The hybrid method uses a classical numerical model to generate simulation data, which are then subsequently used to train the ANN. After successful training the ANN is able to take over the simulation at a speed two...

  17. A physically consistent model for artificial dissipation in transonic potential flow computations

    Science.gov (United States)

    Dulikravich, George S.; Mortara, Karl W.; Marraffa, Lionel

    1988-01-01

    The effect that artificial dissipation has on numerical solutions of the transonic Full Potential Equation (FPE) are investigated by comparing the artificially dissipative FPE to a Physically Dissipative Potential (PDP) equation. Analytic expressions were derived from the variables C and M sub c that are used in the artificial density formulation. It was shown that these new values generate artificial dissipation which is equivalent to the physical dissipation existing in the PDP equation. The new expression for the variables C and M sub c can easily be incorporated into the existing full potential codes which are based either on the artificial density or on the artificial viscosity formulation. A comparison of Physically Dissipative Potential (PDP), Artificial Density or Viscosity (ADV), Artificial Mass Flux (AMF), and ADV with variable C and M sub c formulation (MCC) is also presented.

  18. 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.

  19. 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.

  20. 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.

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

    International Nuclear Information System (INIS)

    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

  2. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement. PMID:24061237

  3. 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)

  4. Deterministic and Stochastic Modeling of an Artificial Bistable Switch in E. coli

    Science.gov (United States)

    Finkelstein, Daniel; Buchler, Nicolas; Karapetyan, Sargis

    Networks of mutually interacting genes are common in natural regulatory networks. To better understand these interactions, scientists have recently been constructing artificial genetic networks. Much of the effort is focused on creating genetic oscillators and bistable switches. In this project, we analyzed the possibility to create a bistable switch in E. coli. In this realization of the switch, the Repressor (basic leucine zipper CEBP/alpha) represses the transcription of the Inhibitor (artificial dominant negative 3HF). The Inhibitor, in turn, sequesters the Repressor by binding to it. Using deterministic modeling we identified a range of parameters suitable for bistability. We then analyzed the resulting solutions with the full model taking the reaction rates corresponding to E. coli and the including stochastic nature of gene expression. We have shown that the bistability in not destroyed by stochastic fluctuations if several copies of genes are present. Specifically, taking a realistic number of plasmids (10) we show that the number of proteins in the systems undergoes sizable fluctuations; however, the two states with low and high concentrations of inhibitor stay distinct in the relevant range of parameters.

  5. 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.

  6. 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.

  7. 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

    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...... and methods The EASEWASTE model supports a full life cycle assessment of any user defined residential, bulky waste or garden waste management system. The model focuses on the major components of the waste and reviews each component in terms of the available waste management options, including bio...

  8. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  9. 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.

  10. Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

    Science.gov (United States)

    Buch, A. M.; Narain, A.; Pandey, P. C.

    1994-01-01

    The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

  11. 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.

  12. An experimental artificial-neural-network-based modeling of magneto-rheological fluid dampers

    International Nuclear Information System (INIS)

    A static model for a magneto-rheological (MR) damper based on artificial neural networks (ANNs) is proposed, and an intensive and experimental study is presented for designing the ANN structure. The ANN model does not require time delays in the input vector. Besides the electric current signal, only one additional sensor is used to achieve a reliable MR damper structure. The model is experimentally validated with two commercial MR dampers of different characteristics: MR1 damper with continuous actuation and MR2 damper with two levels of actuation. The error to signal ratio (ESR) index is used to measure the model accuracy; for both MR dampers, an average value of 6.03% of total error is obtained from different experiments, which are designed to explore the nonlinearities of the MR phenomenon at different frequencies by including the impact of the electric current fluctuations. The proposed ANN model is compared with other well known parametric models; the qualitative and quantitative comparison among the models highlights the advantages of the ANN for representing a commercial MR damper. The ESR index was reduced by the ANN-based model by up to 29% with respect to the parametric models for the MR1 damper and up to 40% for the MR2 damper. The force–velocity diagram is used to compare the modeling properties of each approach: (1) the Bingham model cannot describe the hysteresis of both MR dampers and the distribution function of the modeled force varies from the experimental data, (2) the algebraic models have complications in representing the nonlinear behavior of the asymmetric damper (MR2) and, (3) the ANN-based MR damper can model the nonlinearities of both MR dampers and presents good scalability; the accuracy of the results supports the use of this model for the validation of semi-active suspension control systems for a vehicle, by using nonlinear simulations. (paper)

  13. A computer model of the artificially ventilated human respiratory system in adult intensive care.

    Science.gov (United States)

    Wilson, A J; Murphy, C M; Brook, B S; Breen, D; Miles, A W; Tilley, D G

    2009-11-01

    A multi-technique approach to modelling artificially ventilated patients on the adult general intensive care unit (ICU) is proposed. Compartmental modelling techniques were used to describe the mechanical ventilator and the flexible hoses that connect it to the patient. 3D CFD techniques were used to model flow in the major airways and a Windkessel style balloon model was used to model the mechanical properties of the lungs. A multi-compartment model of the lung based on bifurcating tree structures representing the conducting airways and pulmonary circulation allowed lung disease to be modelled in terms of altered V/Q ratios within a lognormal distribution of values and it is from these that gas exchange was determined. A compartmental modelling tool, Bathfp, was used to integrate the different modelling techniques into a single model. The values of key parameters in the model could be obtained from measurements on patients in an ICU whilst a sensitivity analysis showed that the model was insensitive to the value of other parameters within it. Measured and modelled values for arterial blood gases and airflow parameters are compared for 46 ventilator settings obtained from 6 ventilator dependent patients. The results show correlation coefficients of 0.88 and 0.85 for the arterial partial pressures of the O(2) and CO(2), respectively (p<0.01) and of 0.99 and 0.96 for upper airway pressure and tidal volume, respectively (p<0.01). The difference between measured and modelled values was large in physiological terms, suggesting that some optimisation of the model is required. PMID:19699134

  14. Integrating service-life modeling and life-cycle assessment for recycled-aggregate concrete

    Science.gov (United States)

    Bergman, Todd Lee

    The development and implementation of one-dimensional (a) analytical and (b) numerical service-life models for chloride-induced corrosion of reinforced concrete containing both recycled-aggregates and supplementary cementitious materials (SCMs) are presented in this work. Both the analytical and numerical models account for initial chloride contamination levels due to previous applications. The effects of aggregate type (e.g., virgin, recycled aggregate, recycled mortar), aggregate replacement ratio, severity of chloride contamination levels, severity of in-service chloride exposure, reinforcement cover depth, SCM type (e.g., fly ash, slag, slice fume, metakaolin), and SCM replacement ratio on the expected service life of recycled-aggregate reinforced concrete were investigated. Results illustrated trends between concrete mixes and life cycle costs, which were employed to make conclusions on the trade-offs presented by cost, sustainability, and service life.

  15. 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.

  16. Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach.

    Science.gov (United States)

    Yu, Lei; Kang, Jian

    2009-09-01

    This research aims to explore the feasibility of using computer-based models to predict the soundscape quality evaluation of potential users in urban open spaces at the design stage. With the data from large scale field surveys in 19 urban open spaces across Europe and China, the importance of various physical, behavioral, social, demographical, and psychological factors for the soundscape evaluation has been statistically analyzed. Artificial neural network (ANN) models have then been explored at three levels. It has been shown that for both subjective sound level and acoustic comfort evaluation, a general model for all the case study sites is less feasible due to the complex physical and social environments in urban open spaces; models based on individual case study sites perform well but the application range is limited; and specific models for certain types of location/function would be reliable and practical. The performance of acoustic comfort models is considerably better than that of sound level models. Based on the ANN models, soundscape quality maps can be produced and this has been demonstrated with an example. PMID:19739730

  17. Development of sediment load estimation models by using artificial neural networking techniques.

    Science.gov (United States)

    Hassan, Muhammad; Ali Shamim, M; Sikandar, Ali; Mehmood, Imran; Ahmed, Imtiaz; Ashiq, Syed Zishan; Khitab, Anwar

    2015-11-01

    This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions, discharge, and temperature information. Model input and data length selection was carried out using a novel mathematical tool, Gamma test. Model training was carried out by using three popular algorithms namely Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) using forward selection of input variables. Evaluation of the best model was carried out on the basis of basic statistical parameters namely R-square, root mean squared error (RMSE), and mean biased error (MBE). Results indicated that BFGS-based ANN model outperformed all other models with significantly low values of RMSE and MBE. A strong correlation was also found between the observed and estimated sediment load values for the same model as the value of Nash-Sutcliffe model efficiency coefficient (R-square) was found to be quite high as well. PMID:26463089

  18. 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.

  19. 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.

  20. 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.

  1. 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...

  2. Determination of porosity and permeability in reservoir intervals by artificial neural network modelling, offshore Eastern Canada

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Z.; Williamson, M.A. [Geological Survey of Canada, Dartmouth, NS (Canada). Atlantic Geoscience Centre

    1997-09-01

    A technique for the quantitative integration of porosity/permeability measurements and well log data from the major reservoir intervals throughout the Jeanne d`Arc Basin was presented. The integration used data from 13 wells for the Avalon Formation, 7 wells for the Hibernia Formation and 12 wells for the Jeanne d`Arc Formation. A model for the relationship between porosity, permeability and well log response was then established using the back-propagation artificial neural network (BP-ANN) technique. The model was used to construct profiles of porosity and permeability in both cored and uncored wells for the three formations. The curves obtained for porosity and permeability provide a solid basis for further reservoir studies, such as inter-well recognition and correlations of permeable units and basin-wide reservoir quality evaluation.

  3. Very short-term wind speed prediction: A new artificial neural network-Markov chain model

    Energy Technology Data Exchange (ETDEWEB)

    Pourmousavi Kani, S.A. [Electrical and Computer Engineering Department, 627 Cobleigh Hall, Montana State University, Bozeman, MT 59717 (United States); Ardehali, M.M. [Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran 15914 (Iran, Islamic Republic of)

    2011-01-15

    As the objective of this study, artificial neural network (ANN) and Markov chain (MC) are used to develop a new ANN-MC model for forecasting wind speed in very short-term time scale. For prediction of very short-term wind speed in a few seconds in the future, data patterns for short-term (about an hour) and very short-term (about minutes or seconds) recorded prior to current time are considered. In this study, the short-term patterns in wind speed data are captured by ANN and the long-term patterns are considered utilizing MC approach and four neighborhood indices. The results are validated and the effectiveness of the new ANN-MC model is demonstrated. It is found that the prediction errors can be decreased, while the uncertainty of the predictions and calculation time are reduced. (author)

  4. Very short-term wind speed prediction: A new artificial neural network-Markov chain model

    International Nuclear Information System (INIS)

    As the objective of this study, artificial neural network (ANN) and Markov chain (MC) are used to develop a new ANN-MC model for forecasting wind speed in very short-term time scale. For prediction of very short-term wind speed in a few seconds in the future, data patterns for short-term (about an hour) and very short-term (about minutes or seconds) recorded prior to current time are considered. In this study, the short-term patterns in wind speed data are captured by ANN and the long-term patterns are considered utilizing MC approach and four neighborhood indices. The results are validated and the effectiveness of the new ANN-MC model is demonstrated. It is found that the prediction errors can be decreased, while the uncertainty of the predictions and calculation time are reduced.

  5. High-Strength Bolt Corrosion Fatigue Life Model and Application

    Directory of Open Access Journals (Sweden)

    Wang Hui-li

    2014-01-01

    Full Text Available The corrosion fatigue performance of high-strength bolt was studied. Based on the fracture mechanics theory and the Gerberich-Chen formula, the high-strength bolt corrosion fracture crack model and the fatigue life model were established. The high-strength bolt crack depth and the fatigue life under corrosion environment were quantitatively analyzed. The factors affecting high-strength bolt corrosion fatigue life were discussed. The result showed that the high-strength bolt corrosion fracture biggest crack depth reduces along with the material yield strength and the applied stress increases. The material yield strength was the major factor. And the high-strength bolt corrosion fatigue life reduced along with the increase of material strength, the applied stress or stress amplitude. The stress amplitude influenced the most, and the material yield strength influenced the least. Low bolt strength and a low stress amplitude level could extend high-strength bolt corrosion fatigue life.

  6. Modeling of global horizontal irradiance in the United Arab Emirates with artificial neural networks

    International Nuclear Information System (INIS)

    This paper employs ANN (Artificial Neural Network) models to estimate GHI (global horizontal irradiance) for three major cities in the UAE (United Arab Emirates), namely Abu Dhabi, Dubai and Al-Ain. City data are then used to develop a comprehensive global GHI model for other nearby locations in the UAE. The ANN models use MLP (Multi-Layer Perceptron) and RBF (Radial Basis Function) techniques with comprehensive training algorithms, architectures, and different combinations of inputs. The UAE models are tested and validated against individual city models and data available from the UAE Solar Atlas with good agreement as attested by the computed statistical error parameters. The optimal ANN model is MLP-based and requires four mean daily weather parameters; namely, maximum temperature, wind speed, sunshine hours, and relative humidity. The computed statistical error parameters for the optimal MLP-ANN model in relation to the measured three-cities mean data (referred to as UAE data) are MBE (mean bias error) = −0.0003 kWh/m2, RMSE = 0.179 kWh/m2, R2 = 99%, NSE (Nash-Sutcliffe model Efficiency coefficient) = 99%, and t-statistic = 0.005 at 5% significance level. Results prove the suitability of the ANN models for estimating the monthly mean daily GHI in different locations of the UAE. - Highlights: • ANN prediction models for the GHI (global horizontal irradiance) in the UAE. • Models used to estimate the potential of global solar radiation for UAE cities. • Data from the UAE Solar Atlas are used to validate developed ANN models. • ANN models are more efficient than regression models in predicting GHI

  7. Characterization and modeling of ionic polymeric smart materials as artificial muscles and robotic swimming structures

    Science.gov (United States)

    Mojarrad, Mehran

    2001-07-01

    In this dissertation document, a thorough review and investigation of works in connection with the ionic polymeric gels as artificial muscles and electrically controllable polymeric network structures were performed. Where possible, comparisons were made with biological muscles and applications in marine propulsion using such polymeric materials were investigated. Furthermore, methods of fabrication of several chemically active ionic polymeric gel muscles such as PolyAcryloNitrile (PAN), Poly(2-Acrylamido-2-Methyl-1-PropaneSulfonic) acid (PAMPS), and PolyAcrylic-acid-bis-AcrylaMide (PAAM) as well as a new class of electrically active composite muscle such as Ion-Exchange-Metal-Composites (IEMC) or Ionic Polymer Metal Composites (IPMC) materials are introduced and investigated that resulted in two US patents regarding their fabrication and application capabilities as actuators and sensors. In this research, various forms of the IPMC fabrication were explored and reported. In addition, characterization of PAN muscles, bundling and encapsulation were investigated. Conversion of chemical to electrical artificial muscles were also investigated using chemical plating techniques as well as physical vapor deposition methods of the pH-activated muscles like PAN fibers. Experimental methods were devised to characterize contraction, expansion, and bending of various actuators using isometric, isoionic, and isotonic characterization methods. Several apparatuses for modeling and testing of the various artificial muscles were built to show the viability of the application of both chemoactive and electroactive muscles. Furthermore PAN fiber muscles in different configurations such as spring-loaded fiber bundles, biceps, triceps, ribbon type muscles, and segmented fiber bundles were fabricated to make a variety of actuators. Additionally, swimming robotic structures and associated hardware were built to incorporate IPMC as biomimetic propulsion fin actuators. In addition, various

  8. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    Directory of Open Access Journals (Sweden)

    T. Maurer

    2011-12-01

    Full Text Available Artificially-created hydrological catchments are characterised by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial distribution patterns of dumped sediments depending on the technical earth-moving and deposition processes. We are focussing in a first step on integrating sediment dumping, particle size, and bulk density modification processes on the catchment scale. For the model development, the artificially-constructed hydrological catchment "Chicken Creek" located in Lower Lusatia, Germany, served as an example. The structure generator describes 3-D technological sediment distributions at two scales: (i for a 2-D-vertical cross-section, texture and bulk density distributions are generated within individual spoil cones that result from mass dumping, particle segregation, and compaction and (ii for the whole catchment, the spoil cones are horizontally arranged along trajectories of mass dumping controlled by the belt stacker-machine relative to the catchment's clay layer topography. The generated 3-D texture and bulk density distributions are interpolated and visualised as a gridded 3-D-volume body using 3-D computer-aided design software. The generated subsurface sediment distribution for the Chicken Creek catchment was found to correspond to observed patterns already without calibration. Spatial aggregation and interpolation in the gridded volume body modified the generated distributions towards more uniform (unimodal distributions and lower values of the standard deviations. The modelling approach is generally applicable to all situations where large masses of unconsolidated sediment are moved and dumped

  9. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    Science.gov (United States)

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

    2011-12-01

    Artificially-created hydrological catchments are characterised by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial distribution patterns of dumped sediments depending on the technical earth-moving and deposition processes. We are focussing in a first step on integrating sediment dumping, particle size, and bulk density modification processes on the catchment scale. For the model development, the artificially-constructed hydrological catchment "Chicken Creek" located in Lower Lusatia, Germany, served as an example. The structure generator describes 3-D technological sediment distributions at two scales: (i) for a 2-D-vertical cross-section, texture and bulk density distributions are generated within individual spoil cones that result from mass dumping, particle segregation, and compaction and (ii) for the whole catchment, the spoil cones are horizontally arranged along trajectories of mass dumping controlled by the belt stacker-machine relative to the catchment's clay layer topography. The generated 3-D texture and bulk density distributions are interpolated and visualised as a gridded 3-D-volume body using 3-D computer-aided design software. The generated subsurface sediment distribution for the Chicken Creek catchment was found to correspond to observed patterns already without calibration. Spatial aggregation and interpolation in the gridded volume body modified the generated distributions towards more uniform (unimodal) distributions and lower values of the standard deviations. The modelling approach is generally applicable to all situations where large masses of unconsolidated sediment are moved and dumped thereby allowing

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

    OpenAIRE

    Wei Guo; Hongrui Cao; Zhengjia He; Laihao Yang

    2015-01-01

    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 pr...

  11. Polynomial Preserving Diffusion Models for Life Insurance Liabilities

    OpenAIRE

    Biagini, Francesca; Zhang, Yinglin

    2016-01-01

    In this paper we study the pricing and hedging problem of a portfolio of life insurance products under the benchmark approach, where the reference market is modelled as driven by a state variable following a polynomial preserving diffusion on a compact state space. Such a model guarantees not only the positivity of the OIS short rate and the mortality intensity, but also the possibility of approximating both pricing formula and hedging strategy of a large class of life insurance products by e...

  12. On selection of optimal stochastic model for accelerated life testing

    International Nuclear Information System (INIS)

    This paper deals with the problem of proper lifetime model selection in the context of statistical reliability analysis. Namely, we consider regression models describing the dependence of failure intensities on a covariate, for instance, a stressor. Testing the model fit is standardly based on the so-called martingale residuals. Their analysis has already been studied by many authors. Nevertheless, the Bayes approach to the problem, in spite of its advantages, is just developing. We shall present the Bayes procedure of estimation in several semi-parametric regression models of failure intensity. Then, our main concern is the Bayes construction of residual processes and goodness-of-fit tests based on them. The method is illustrated with both artificial and real-data examples. - Highlights: • Statistical survival and reliability analysis and Bayes approach. • Bayes semi-parametric regression modeling in Cox's and AFT models. • Bayes version of martingale residuals and goodness-of-fit test

  13. A study of groundwater monitoring data analysis using Artificial Neural Network model

    International Nuclear Information System (INIS)

    The results of groundwater flow modeling are to be justified using groundwater monitoring data in the hydrogeological characterization. On the other hand, hydraulic continuities of the geological structures, all of which are considered to have great effect on groundwater flow and/or groundwater quality, are to be estimated using the groundwater flow monitoring data with hydraulic response to some impacts such as borehole drilling, pumping test and so on. Therefore, the groundwater monitoring is important for characterizing the geological and hydrogeological environments. In order to characterize of hydrogeological environment using the monitoring data, it is important to evaluate the influence of artificial and natural impact on the monitoring data. In this study, the following three research works are carried out based on the groundwater monitoring data collected at the Tono area. Artificial Neural Network (ANN) was adopted as the tool for monitoring data analysis. Runoff analysis for assessment of importance of soil moisture on runoff estimation in a catchment. Analysis of water level fluctuation for determination influence factors in the water level fluctuation and for filtering out the influence factors from the water level data . Analysis of hydraulic pressure fluctuation in deep geological formations for hydrogeological characterization and assessment of human influence on the pore pressure in deep formation. Through this study, applicability of ANN for analysis and interpretation of the groundwater monitoring data could be confirmed and methodology for utilization the monitoring data for understanding and characterization of hydrogeological environment could be developed. (author)

  14. 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.

  15. Artificial neural network and non-linear models for prediction of transformer oil residual operating time

    Energy Technology Data Exchange (ETDEWEB)

    Wahab, Mohamed A.A.; Hamada, Mohamed. M. [Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Minia 61111 (Egypt); Mohamed, Ahmed [Electrical and Computer Engineering Department, College of Electrical and Computer Engineering, Energy Systems Research Laboratory, Florida International University, 10555 W Flagler Street, Room EC-3810, Miami, FL 33174 (United States)

    2011-01-15

    This paper presents two modeling techniques for the prediction and monitoring of the characteristics of transformer oil. The first employs artificial neural network (ANN) and the second employs non-linear modeling (nlm). The proposed techniques are implemented for predicting the transformer oil residual operating time (t{sub rot}) which is defined as the service period after which the breakdown voltage (BDV) violates the limits given in the standard specifications. The selection of the most influential characteristics on residual operating time (t{sub rot}) in the proposed techniques is obtained by statistical analysis. The non-linear model depends on linear combination of non-linear functions for each characteristic. The ANN technique for modeling these characteristics preserves the non-linear relationship between these characteristics and (t{sub rot}). The results are compared with previously published modeling techniques namely multiple linear regression and polynomial regression models. Different evaluation indices have been used to justify the superiority of the proposed modeling techniques for predicting (t{sub rot}). (author)

  16. 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.

  17. 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.

  18. Bayesian inference model for fatigue life of laminated composites

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Kiureghian, Armen Der; Berggreen, Christian

    2016-01-01

    A probabilistic model for estimating the fatigue life of laminated composite plates is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configurations. Model parameters are estimated by Bayesian inference. The...

  19. 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…

  20. 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…

  1. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Beresford, R

    2000-06-01

    Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine

  2. 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.

  3. Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    1998-01-01

    The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

  4. 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%. PMID:26201663

  5. 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.

  6. 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

  7. 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.

  8. Using artificial streams to assess the effects of metal-mining effluent on the life cycle of the freshwater midge (Chironomus tentans) in situ.

    Science.gov (United States)

    Hruska, Kimberly A; Dubé, Monique G

    2004-11-01

    In 2002, we developed an in situ life-cycle bioassay with Chironomus tentans in artificial streams to evaluate the effects of a complex metal mine effluent under ambient environmental conditions. The bioassay was tested in the field using effluent from the Copper Cliff Waste Water Treatment Plant at INCO (Sudbury, ON, Canada). Chironomus tentans were exposed throughout the life cycle to 45% Copper Cliff effluent, which is the average effluent concentration measured in Junction Creek (ON, Canada), the natural receiving environment. Chironomus tentans in the effluent treatment exhibited reduced survival (p = 0.001), reduced total emergence (p = 0.001), increased time-to-emergence (p = 0.001), and reduced hatching success (p = 0.001) relative to animals in the reference water treatment. Chironomus tentans in the effluent treatment were not significantly different from the reference in terms of growth, sex ratio, number of egg cases/female, and number of eggs/egg case. This research showed how a life-cycle bioassay could be used in situ to assess metal mine effluent effects on a benthic invertebrate. PMID:15559287

  9. 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.

  10. 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

  11. Vibroacoustic Modeling of Mechanically Coupled Structures: Artificial Spring Technique Applied to Light and Heavy Mediums

    Directory of Open Access Journals (Sweden)

    L. Cheng

    1996-01-01

    Full Text Available This article deals with the modeling of vibrating structures immersed in both light and heavy fluids, and possible applications to noise control problems and industrial vessels containing fluids. A theoretical approach, using artificial spring systems to characterize the mechanical coupling between substructures, is extended to include fluid loading. A structure consisting of a plate-ended cylindrical shell and its enclosed acoustic cavity is analyzed. After a brief description of the proposed technique, a number of numerical results are presented. The analysis addresses the following specific issues: the coupling between the plate and the shell; the coupling between the structure and the enclosure; the possibilities and difficulties regarding internal soundproofing through modifications of the joint connections; and the effects of fluid loading on the vibration of the structure.

  12. 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.

  13. 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.

  14. Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling

    Science.gov (United States)

    Li, Xian; Shu, Longcang; Liu, Lihong; Yin, Dan; Wen, Jinmei

    2012-06-01

    Jinci Spring in Shanxi, north China, is a major local water source. It dried up in April 1994 due to groundwater overexploitation. The groundwater system is complex, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are statistical techniques to study parameter nonlinear relationships of groundwater systems. However, ANN models offer little explanatory insight into the mechanisms of prediction models. Sensitivity analysis can overcome this shortcoming. In this study, a back-propagation neural network model was built based on the relationship between groundwater level and its sensitivity factors in Jinci Spring Basin; these sensitivity factors included precipitation, river seepage, mining drainage, groundwater withdrawals and lateral discharge to the associated Quaternary aquifer. All the sensitivity factors were analyzed with Garson's algorithm based on the connection weights of the neural network model. The concept of "sensitivity range" was proposed to describe the value range of the input variables to which the output variables are most sensitive. The sensitivity ranges were analyzed by a local sensitivity approach. The results showed that coal mining drainage is the most sensitive anthropogenic factor, having a large effect on groundwater level of the Jinci Spring Basin.

  15. Cost prediction of antipsychotic medication of psychiatric disorder using artificial neural network model

    Directory of Open Access Journals (Sweden)

    Arash Mirabzadeh

    2013-01-01

    Full Text Available Background: Antipsychotic monotherapy or polypharmacy (concurrent use of two or more antipsychotics are used for treating patients with psychiatric disorders (PDs. Usually, antipsychotic monotherapy has a lower cost than polypharmacy. This study aimed to predict the cost of antipsychotic medications (AM of psychiatric patients in Iran. Materials and Methods: For this purpose, 790 patients with PDs who were discharged between June and September 2010 were selected from Razi Psychiatric Hospital, Tehran, Iran. For cost prediction of AM of PD, neural network (NN and multiple linear regression (MLR models were used. Analysis of data was performed with R 2.15.1 software. Results: Mean ± standard deviation (SD of the duration of hospitalization (days in patients who were on monotherapy and polypharmacy was 31.19 ± 15.55 and 36.69 ± 15.93, respectively (P < 0.001. Mean and median costs of medication for monotherapy (n = 507 were $8.25 and $6.23 and for polypharmacy (n =192 were $13.30 and $9.48, respectively (P = 0.001. The important variables for cost prediction of AM were duration of hospitalization, type of treatment, and type of psychiatric ward in the MLR model, and duration of hospitalization, type of diagnosed disorder, type of treatment, age, Chlorpromazine dosage, and duration of disorder in the NN model. Conclusion: Our findings showed that the artificial NN (ANN model can be used as a flexible model for cost prediction of AM.

  16. 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...

  17. An evaluation of tannery industry wastewater treatment sludge gasification by artificial neural network modeling

    Energy Technology Data Exchange (ETDEWEB)

    Ongen, Atakan, E-mail: aongen@istanbul.edu.tr; Kurtulus Ozcan, H.; Arayıcı, Semiha

    2013-12-15

    Highlights: • We model calorific value of syn-gas from tannery industry treatment sludge. • We monitor variation of gas composition in produced gas. • Heating value of produced gas is around 1500 kcal/m{sup 3}. • Model predictions are in close accordance with real values. -- Abstract: This paper reports on the calorific value of synthetic gas (syngas) produced by gasification of dewatered sludge derived from treatment of tannery wastewater. Proximate and ultimate analyses of samples were performed. Thermochemical conversion alters the chemical structure of the waste. Dried air was used as a gasification agent at varying flow rates, which allowed the feedstock to be quickly converted into gas by means of different heterogeneous reactions. A lab-scale updraft fixed-bed steel reactor was used for thermochemical conversion of sludge samples. Artificial neural network (ANN) modeling techniques were used to observe variations in the syngas related to operational conditions. Modeled outputs showed that temporal changes of model predictions were in close accordance with real values. Correlation coefficients (r) showed that the ANN used in this study gave results with high sensitivity.

  18. Rainfall-runoff modelling using artificial neural networks: comparison of network types

    Science.gov (United States)

    Senthil Kumar, A. R.; Sudheer, K. P.; Jain, S. K.; Agarwal, P. K.

    2005-04-01

    Growing interest in the use of artificial neural networks (ANNs) in rainfall-runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi-layer perceptron (MLP) with a sigmoid transfer function enlightens certain limitations for its use. Alternatively, there is a strong belief in the general ANN user community that a radial basis function (RBF) network performs better than an MLP, as the former bases its nonlinearities on the training data set. This argument is not yet substantiated by applications in hydrology. This paper presents a comprehensive evaluation of the performance of MLP- and RBF-type neural network models developed for rainfall-runoff modelling of two Indian river basins. The performance of both the MLP and RBF network models were comprehensively evaluated in terms of their generalization properties, predicted hydrograph characteristics, and predictive uncertainty. The results of the study indicate that the choice of the network type certainly has an impact on the model prediction accuracy. The study suggests that both the networks have merits and limitations. For instance, the MLP requires a long trial-and-error procedure to fix the optimal number of hidden nodes, whereas for an RBF the structure of the network can be fixed using an appropriate training algorithm. However, a judgment on which is superior is not clearly possible from this study.

  19. An optimisation methodology of artificial neural network models for predicting solar radiation: a case study

    Science.gov (United States)

    Rezrazi, Ahmed; Hanini, Salah; Laidi, Maamar

    2016-02-01

    The right design and the high efficiency of solar energy systems require accurate information on the availability of solar radiation. Due to the cost of purchase and maintenance of the radiometers, these data are not readily available. Therefore, there is a need to develop alternative ways of generating such data. Artificial neural networks (ANNs) are excellent and effective tools for learning, pinpointing or generalising data regularities, as they have the ability to model nonlinear functions; they can also cope with complex `noisy' data. The main objective of this paper is to show how to reach an optimal model of ANNs for applying in prediction of solar radiation. The measured data of the year 2007 in Ghardaïa city (Algeria) are used to demonstrate the optimisation methodology. The performance evaluation and the comparison of results of ANN models with measured data are made on the basis of mean absolute percentage error (MAPE). It is found that MAPE in the ANN optimal model reaches 1.17 %. Also, this model yields a root mean square error (RMSE) of 14.06 % and an MBE of 0.12. The accuracy of the outputs exceeded 97 % and reached up 99.29 %. Results obtained indicate that the optimisation strategy satisfies practical requirements. It can successfully be generalised for any location in the world and be used in other fields than solar radiation estimation.

  20. Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

    International Nuclear Information System (INIS)

    Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data

  1. Artificial neural network modeling of geothermal district heating system thought exergy analysis

    International Nuclear Information System (INIS)

    Highlights: ► ANN has been modeled for predicting exergy efficiency a GDHS thought exergy analysis. ► The network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. ► The ANN modeling can provide high accuracy and reliability for predicting the exergy efficiency of GDHSs. ► Thus, online monitoring system and the performance of GDHS can be implemented. - Abstract: This paper deals with an artificial neural network (ANN) modeling to predict the exergy efficiency of geothermal district heating system under a broad range of operating conditions. As a case study, the Afyonkarahisar geothermal district heating system (AGDHS) in Turkey is considered. The average daily actual thermal data acquired from the AGDHS in the 2009–2010 heating season are collected and employed for exergy analysis. An ANN modeling is developed based on backpropagation learning algorithm for predicting the exergy efficiency of the system according to parameters of the system, namely the ambient temperature, flow rate and well head temperature. Then, the recorded and calculated data conducted in the AGDHS at different dates are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the ANN modeling can be applied successfully and can provide high accuracy and reliability for predicting the exergy performance of geothermal district heating systems.

  2. 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.

  3. Comparison of Artificial Neural Networks and ARIMA statistical models in simulations of target wind time series

    Science.gov (United States)

    Kolokythas, Kostantinos; Vasileios, Salamalikis; Athanassios, Argiriou; Kazantzidis, Andreas

    2015-04-01

    The wind is a result of complex interactions of numerous mechanisms taking place in small or large scales, so, the better knowledge of its behavior is essential in a variety of applications, especially in the field of power production coming from wind turbines. In the literature there is a considerable number of models, either physical or statistical ones, dealing with the problem of simulation and prediction of wind speed. Among others, Artificial Neural Networks (ANNs) are widely used for the purpose of wind forecasting and, in the great majority of cases, outperform other conventional statistical models. In this study, a number of ANNs with different architectures, which have been created and applied in a dataset of wind time series, are compared to Auto Regressive Integrated Moving Average (ARIMA) statistical models. The data consist of mean hourly wind speeds coming from a wind farm on a hilly Greek region and cover a period of one year (2013). The main goal is to evaluate the models ability to simulate successfully the wind speed at a significant point (target). Goodness-of-fit statistics are performed for the comparison of the different methods. In general, the ANN showed the best performance in the estimation of wind speed prevailing over the ARIMA models.

  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. A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment

    Directory of Open Access Journals (Sweden)

    T. Maurer

    2011-05-01

    Full Text Available Artificially-created hydrological catchments are characterized by sediment structures from technological construction processes that can potentially be important for modelling of flow and transport and for understanding initial soil and ecosystem development. The subsurface spatial structures of such catchments have not yet been sufficiently explored and described. Our objective was to develop a structure generator programme for modelling the 3-D spatial sediment distribution patterns depending on the technical earth-moving and deposition processes. For the development, the artificially-constructed hydrological catchment "Chicken Creek" located in Lower Lusatia, Germany, served as an example. The structure generator describes 3-D technological sediment distributions at two scales: (i for a 2-D-vertical cross-section, texture and bulk density distributions are generated within individual spoil cones that result from mass dumping, particle segregation, and compaction and (ii for the whole catchment area, the spoil cones are horizontally arranged along trajectories of mass dumping controlled by the belt stacker-machine relative to the catchment's clay layer topography. The generated 3-D texture and bulk density distributions are interpolated and visualized as a gridded 3-D-volume body using 3-D computer-aided design software. The generated subsurface sediment distribution for the Chicken Creek catchment was found to correspond to observed patterns although still without any calibration. Spatial aggregation and interpolation in the gridded volume body modified the generated distributions towards more uniform (unimodal distributions and lower values of the standard deviations. After incorporating variations and pedotransfer approaches, generated sediment distributions can be used for deriving realizations of the 3-D hydraulic catchment structure.

  6. A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

    OpenAIRE

    Tamer Khatib; Wilfried Elmenreich

    2015-01-01

    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...

  7. 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.

  8. Tracer test modeling for local scale residence time distribution characterization in an artificial recharge site

    OpenAIRE

    Valhondo, Cristina; Martínez-Landa, Lurdes; Carrera, Jesús; Hidalgo, Juan J.; Tubau, Isabel; Pourcq, Katrien; Grau-Martínez, Alba; Ayora, Carlos

    2016-01-01

    Artificial recharge of aquifers is a technique for improving water quality and increasing groundwater resources. Understanding the fate of a potential contaminant requires knowledge of the residence times distribution (RTD) of the water beneath the artificial recharge infrastructure. A simple way to obtain the RTDs is to perform a tracer test. We performed a pulse injection tracer test in an artificial recharge system through an infiltration basin to obtain the breakthrough curves, which y...

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

    OpenAIRE

    Wei-Chin Lin; Block, Glen S.

    2009-01-01

    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 temp...

  10. 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.

  11. 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.

  12. 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.

  13. 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. PMID:27196055

  14. 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.

  15. Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders

    International Nuclear Information System (INIS)

    An artificial neural network model was developed for modeling of the effects of mechanical alloying parameters including milling time, milling speed and ball to powder weight ratio on the characteristics of Al-8 vol%SiC nanocomposite powders. The crystallite size and lattice strain of the aluminum matrix were considered for modeling. This nanostructured nanocomposite powder was synthesized by utilizing planetary high energy ball mill and the required data for training were collected from the experimental results. The characteristics of the particles were determined by X-ray diffraction, scanning and transmission electron microscopy. Two types of neural network architecture, i.e. multi-layer perceptron (MLP) and radial basis function (RBF), were used. The steepest descent along with variable learning rate back-propagation algorithm known as a heuristic technique was utilized for training the MLP network. It was found that MLP network yields better results compared to RBF network, giving an acceptable mapping between the network responses and the target data with a high correlation coefficients. The response surfaces between the response variables, i.e. crystallite size, lattice strain of the aluminum matrix and the processing parameters are presented. The procedure modeling can be used for optimization of the MA process for synthesizing of nanostructured metal matrix nanocomposites

  16. Modeling of Vibration Monitoring of Steam Turbine in Nuclear Power Plant using Modular Artificial Neural Network

    International Nuclear Information System (INIS)

    This paper states a methodology for using a Modular Artificial Neural Network (ANN) in modeling the vibration monitoring of the Steam Turbine (ST) in Nuclear Power Plant (NPP). The input and the output signals of the vibration transducer are used as a source of the training data for the neural network model. The type of the network used in this methodology is the supervised Multilayer Feed-Forward Neural Networks with the Back-Propagation (BP) algorithm. The module architecture is according to the Human Factors (HF) Considerations in designing the Human-System Interface (HSI). The Vibration Severity limits are determined by the International Organization for Standardization (ISO) 10816. The model also contained 2 out of 3 voting and dynamic trip limit value ANNs. The results show that the proposed Modular ANN has good generalization capability to monitor and protect the machine from the Vibration Severity, increasing the reliability of (ST), and good HSI. This modeling methodology can be used for the other non-redundant components in NPP such as Reactor Coolant Pump (RCP)

  17. Predicting Global Solar Radiation Using an Artificial Neural Network Single-Parameter Model

    Directory of Open Access Journals (Sweden)

    Karoro Angela

    2011-01-01

    Full Text Available We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003–2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours.

  18. Modeling of Soft sensor based on Artificial Neural Network for Galactic Cosmic Rays Application

    International Nuclear Information System (INIS)

    For successful designing of space radiation Galactic Cosmic Rays (GCRs) model, we develop a soft sensor based on the Artificial Neural Network (ANN) model. At the first step, the soft sensor based ANN was constructed as an alternative to model space radiation environment. The structure of ANN in this model is using Multilayer Perceptron (MLP) and Levenberg Marquardt algorithms with 3 inputs and 2 outputs. In the input variable, we use 12 years data (Corr, Uncorr and Press) of GCR particles obtained from Neutron Monitor of Bartol University (Fort Smith area) and the target output is (Corr and Press) from the same source but for Inuvik area in the Polar Regions. In the validation step, we obtained the Root Mean Square Error (RMSE) value of Corr 3.8670e-004 and Press 1.3414e-004 and Variance Accounted For (VAF) of Corr 99.9839 % and Press 99.9831% during the training section. After all the results obtained, then we applied into a Matlab GUI simulation (soft sensor simulation). This simulation will display the estimation of output value from input (Corr and Press). Testing results showed an error of 0.133% and 0.014% for Corr and Press, respectively

  19. 13A. Integrative Cancer Care: The Life Over Cancer Model

    OpenAIRE

    Block, Keith; Block, Penny; Gyllenhaal, Charlotte; Shoham, Jacob

    2013-01-01

    Focus Areas: Integrative Algorithms of Care Integrative cancer treatment fully blends conventional cancer treatment with integrative therapies such as diet, supplements, exercise and biobehavioral approaches. The Life Over Cancer model comprises three spheres of intervention: improving lifestyle, improving biochemical environment (terrain), and improving tolerance of conventional treatment. These levels are applied within the context of a life-affirming approach to cancer patients and treatme...

  20. Prediction of Drug Lipophilicity Using Back Propagation Artificial Neural Network Modeling

    Directory of Open Access Journals (Sweden)

    Saadi Saaidpour

    2014-06-01

    Full Text Available A quantitative structure–property relationship (QSPR study was performed to develop models those relate the structures of 150 drug organic compounds to their n-octanol–water partition coefficients (logPo/w. Molecular descriptors derived solely from 3D structures of the molecular drugs. A genetic algorithm was also applied as a variable selection tools in QSPR analysis. The models were constructed based on 110 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. Application of the developed models to a testing set of 40 drug organic compounds demonstrates that the new models are reliable with good predictive accuracy and simple formulation. Modeling of logarithm of logPo/w of these compounds as a function of the theoretically derived descriptors was established by artificial neural network (ANN. The neural network employed here is a connected back-propagation model with a 4-4-1 architecture. Four descriptors for these compounds molecular volume (MV (Geometrical, hydrophilic-lipophilic balance (HLB (Constitutional, hydrogen bond forming ability (HB (Electronic and polar surface area (PSA (Electrostatic are taken as inputs for the models. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of logPo/w for molecules not yet synthesized. The prediction results are in good agreement with the experimental value. The root mean square error of prediction (RMSEP and square correlation coefficient (R2 for ANN model were 0.1838, 0.9876 for the prediction set logPo/w, respectively.

  1. Artificial neural network models of relationships between Alternaria spores and meteorological factors in Szczecin (Poland)

    Science.gov (United States)

    Grinn-Gofroń, Agnieszka; Strzelczak, Agnieszka

    2008-11-01

    Alternaria is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Monthly forecasting models were developed for the airborne spore concentrations of Alternaria, which is one of the most abundant fungal taxa in the area. Aerobiological sampling was conducted over 2004-2007, using a Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation; maximum and average wind speed; relative humidity; and maximum, minimum, average, and dew point temperature. The original factors as well as with lags (up to 3 days) were used as the explaining variables. Due to non-linearity and non-normality of the data set, the modelling technique applied was the artificial neural network (ANN) method. The final model was a split model with classification (spore presence or absence) followed by regression for spore seasons and log(x+1) transformed Alternaria spore concentration. All variables except maximum wind speed and precipitation were important factors in the overall classification model. In the regression model for spore seasons, close relationships were noted between Alternaria spore concentration and average and maximum temperature (on the same day and 3 days previously), humidity (with lag 1) and maximum wind speed 2 days previously. The most important variable was humidity recorded on the same day. Our study illustrates a novel approach to modelling of time series with short spore seasons, and indicates that the ANN method provides the possibility of forecasting Alternaria spore concentration with high accuracy.

  2. 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.

  3. Development of surrogate models using artificial neural network for building shell energy labelling

    International Nuclear Information System (INIS)

    Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of surrogate models for labelling purposes. An ANN was applied to model the building stock of a city in Brazil, based on the results of extensive simulations using the high-resolution building energy simulation program EnergyPlus. Sensitivity and uncertainty analyses were carried out to evaluate the behaviour of the ANN model, and the variations in the best and worst performance for several typologies were analysed in relation to variations in the input parameters and building characteristics. The results obtained indicate that an ANN can represent the interaction between input and output data for a vast and diverse building stock. Sensitivity analysis showed that no single input parameter can be identified as the main factor responsible for the building energy performance. The uncertainty associated with several parameters plays a major role in assessing building energy performance, together with the facade area and the shell-to-floor ratio. The results of this study may have a profound impact as ANNs could be applied in the future to define regulations in many countries, with positive effects on optimizing the energy consumption. - Highlights: • We model several typologies which have variation in input parameters. • We evaluate the accuracy of surrogate models for labelling purposes. • ANN is applied to model the building stock. • Uncertainty in building plays a major role in the building energy performance. • Results show that ANN could help to develop building energy labelling systems

  4. Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk : A Predictive Model For Credit Card Scoring

    OpenAIRE

    Islam, Md. Samsul; Zhou, Lin; LI Fei

    2009-01-01

    Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding c...

  5. 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.

  6. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

    International Nuclear Information System (INIS)

    This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to >100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For <10 years survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate

  7. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

    Science.gov (United States)

    Taktak, Azzam F. G.; Fisher, Anthony C.; Damato, Bertil E.

    2004-01-01

    This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to >100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate.

  8. 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…

  9. 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...

  10. 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…

  11. 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,…

  12. 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

  13. Artificial neural network based modeling of performance characteristics of deep well pumps with splitter blade

    International Nuclear Information System (INIS)

    Experimental studies were made to investigate the effects of splitter blade length (25%, 35%, 50%, 60% and 80% of the main blade length) on the pump characteristics of deep well pumps for different blade numbers (z=3, 4, 5, 6 and 7). In this study, an artificial neural network (ANN) was used for modeling the performance of deep well pumps with splitter blades. Two hundred and ten experimental results were used to train and test. Forty-two patterns have been randomly selected and used as the test data. The main parameters for the experiments are the blade number (z), non-dimensional splitter blade length (L-bar ), flow rate (Q, l/s), head (Hm, m), efficiency (η, %) and power (Pe, kW). z, L-bar and Q have been used as the input layer, and Hm and η have also been used as the output layer. The best training algorithm and number of neurons were obtained. Training of the network was performed using the Levenberg-Marquardt (LM) algorithm. To determine the effect of the transfer function, different ANN models are trained, and the results of these ANN models are compared. Some statistical methods; fraction of variance (R2) and root mean squared error (RMSE) values, have been used for comparison

  14. A research model for investigating the effects of artificial food colorings on children with ADHD.

    Science.gov (United States)

    Kleinman, Ronald E; Brown, Ronald T; Cutter, Gary R; Dupaul, George J; Clydesdale, Fergus M

    2011-06-01

    The United Kingdom and European Union recently restricted the use of artificial food colorings (AFCs) to improve the health of children. These decisions provide an interesting case study of the role of scientific evidence in the assessment of food additives and risk to children's health and formulation of food policy. Although there continues to be uncertainty concerning the link between AFCs and attention-deficit/hyperactivity disorder (ADHD), policy decisions have been made that have far-reaching implications. In addition, publicity surrounding the policy changes may shape public perceptions concerning effective management of ADHD. We believe that the balance of existing evidence neither refutes nor supports the link between AFCs and ADHD, which highlights the need for carefully designed studies to further investigate the link between AFCs and ADHD. In this article we describe a model for such studies. In developing our model, we drew from current investigative standards in ADHD research, such as those used in the landmark Multimodal Treatment Study of Children With ADHD. These standards encompass methodologic considerations including sample selection, outcome assessment, and data analyses. It is our hope that this model research methodology may prove valuable in addressing design considerations in future studies of AFCs and ADHD with the goal of producing reliable data that will enable policy-makers to better formulate effective, evidence-based food-policy decisions. PMID:21576306

  15. 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.

  16. Urban Growth Modeling Using AN Artificial Neural Network a Case Study of Sanandaj City, Iran

    Science.gov (United States)

    Mohammady, S.; Delavar, M. R.; Pahlavani, P.

    2014-10-01

    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.

  17. Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations

    Science.gov (United States)

    Kasiviswanathan, K. S.; Cibin, R.; Sudheer, K. P.; Chaubey, I.

    2013-08-01

    This paper presents a method of constructing prediction interval for artificial neural network (ANN) rainfall runoff models during calibration with a consideration of generating ensemble predictions. A two stage optimization procedure is envisaged in this study for construction of prediction interval for the ANN output. In Stage 1, ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector. In Stage 2, possible variability of ANN parameters (obtained in Stage 1) is optimized so as to create an ensemble of models with the consideration of minimum residual variance for the ensemble mean, while ensuring a maximum of the measured data to fall within the estimated prediction interval. The width of the prediction interval is also minimized simultaneously. The method is demonstrated using a real world case study of rainfall runoff data for an Indian basin. The method was able to produce ensembles with a prediction interval (average width) of 26.49 m3/s with 97.17% of the total observed data points lying within the interval in validation. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs.

  18. Modeling by artificial neural networks. Application to the management of fuel in a nuclear power plant

    International Nuclear Information System (INIS)

    The determination of the family of optimum core loading patterns for Pressurized Water Reactors (PWRs) involves the assessment of the core attributes, such as the power peaking factor for thousands of candidate loading patterns. Despite the rapid advances in computer architecture, the direct calculation of these attributes by a neutronic code needs a lot of of time and memory. With the goal of reducing the calculation time and optimizing the loading pattern, we propose in this thesis a method based on ideas of neural and statistical learning to provide a feed forward neural network capable of calculating the power peaking corresponding to an eighth core PWR. We use statistical methods to deduct judicious inputs (reduction of the input space dimension) and neural methods to train the model (learning capabilities). Indeed, on one hand, a principal component analysis allows us to characterize more efficiently the fuel assemblies (neural model inputs) and the other hand, the introduction of the a priori knowledge allows us to reducing the number of freedom parameters in the neural network. The model was built using a multi layered perceptron trained with the standard back propagation algorithm. We introduced our neural network in the automatic optimization code FORMOSA, and on EDF real problems we showed an important saving in time. Finally, we propose an hybrid method which combining the best characteristics of the linear local approximator GPT (Generalized Perturbation Theory) and the artificial neural network. (author)

  19. Creep Crack Growth Modeling of Low Alloy Steel using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    F. Djavanroodi

    2013-07-01

    Full Text Available Prediction of crack growth under creep condition is prime requirement in order to avoid costly and time-consuming creep crack growth tests. To predict, in a reliable way, the growth of a major crack in a structural components operating at high temperatures, requires a fracture mechanics based approach. In this Study a novel technique, which uses Finite Element Method (FEM together with Artificial Neural Networks (ANN has been developed to predict the fracture mechanics parameter (C* in a 1%Cr1%MoV low alloy rotor steel under wide range of loading and temperatures. After confirming the validity of the FEM model with experimental data, a collection of numerical and experimental data has been used for training the various neural networks models. Three networks have been used to simulate the process, the perceptron multilayer network with tangent transfer function that uses 9 neurons in the hidden layer, gives the best results. Finally, for validation three case studies at 538°C, 550°C and 594°C temperatures are employed. The proposed model has proved that a combinations of ANN and FEM simulation performs well in estimation of C* and it is a powerful designing tool for creep crack growth characterization.

  20. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2014-01-01

    Full Text Available Artificial neural networks (ANNs have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.

  1. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Directory of Open Access Journals (Sweden)

    Saro Lee

    2016-02-01

    Full Text Available 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.

  2. 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.

  3. Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

    Science.gov (United States)

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

    2012-12-01

    Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in

  4. 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.

  5. Research on Business Models in their Life Cycle

    OpenAIRE

    Adam Jabłoński; Marek Jabłoński

    2016-01-01

    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 ...

  6. The Janus Model of Life-Course Dynamics

    OpenAIRE

    Schroots, Johannes J. F.

    2008-01-01

    In this article, the classic problem of the human life-course, “how can the transition(s) of development into aging be explained?” has been solved. A theoretical model was constructed for the computer simulation of dynamic systems with one or more transitions in biology, psychology, and demography. In constructing the Janus model, it was found that the hypothetical forces of growth and senescence determine the life trajectories of dynamic systems of development and aging in terms of peak time...

  7. Artificial Emotion Generation Based on Personality, Mood, and Emotion for Life-Like Facial Expressions of Robots

    OpenAIRE

    Park, Jeong Woo; Kim, Woo Hyun; Lee, Won Hyong; Chung, Myung Jin

    2010-01-01

    International audience We can't overemphasize the importance of robot's emotional expressions as robots step into human's daily lives. So, the believable and socially acceptable emotional expressions of robots are essential. For such human-like emotional expression, we have proposed an emotion generation model considering personality, mood and history of robot's emotion. The personality module is based on the Big Five Model (OCEAN Model, Five Factor Model); the mood module has one dimensio...

  8. 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. PMID:27094057

  9. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    International Nuclear Information System (INIS)

    Highlights: → Hypersaline oily wastewater was treated in a membrane bioreactor. → The effects of salinity and organic loading rate were evaluated. → The system was modeled by neural network and optimized by genetic algorithm. → The model prediction agrees well with experimental values. → The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m3 day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m3 day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  10. Artificial Neural Network Modeling of Pt/C Cathode Degradation in PEM Fuel Cells

    Science.gov (United States)

    Maleki, Erfan; Maleki, Nasim

    2016-08-01

    Use of computational modeling with a few experiments is considered useful to obtain the best possible result for a final product, without performing expensive and time-consuming experiments. Proton exchange membrane fuel cells (PEMFCs) can produce clean electricity, but still require further study. An oxygen reduction reaction (ORR) takes place at the cathode, and carbon-supported platinum (Pt/C) is commonly used as an electrocatalyst. The harsh conditions during PEMFC operation result in Pt/C degradation. Observation of changes in the Pt/C layer under operating conditions provides a tool to study the lifetime of PEMFCs and overcome durability issues. Recently, artificial neural networks (ANNs) have been used to solve, predict, and optimize a wide range of scientific problems. In this study, several rates of change at the cathode were modeled using ANNs. The backpropagation (BP) algorithm was used to train the network, and experimental data were employed for network training and testing. Two different models are constructed in the present study. First, the potential cycles, temperature, and humidity are used as inputs to predict the resulting Pt dissolution rate of the Pt/C at the cathode as the output parameter of the network. Thereafter, the Pt dissolution rate and Pt ion diffusivity are regarded as inputs to obtain values of the Pt particle radius change rate, Pt mass loss rate, and surface area loss rate as outputs. The networks are finely tuned, and the modeling results agree well with experimental data. The modeled responses of the ANNs are acceptable for this application.

  11. Hierarchical model of the movement of nutrients and artificial radionuclides in the soil-plant system

    International Nuclear Information System (INIS)

    A model is presented of the movement of nutrients and artificial radionuclides in the soil-plant system. The model has a hierarchical structure and is composed of modular elements (submodels) that integrate themselves additively at different levels, with each sub-model containing the submodels of the previous levels. This integration is achieved using the state and flow variables of each submodel as auxiliary variables of the higher level submodels. This hierarchical scheme is an operative solution that simplifies the construction of complex models with different and strongly related dynamic processes. The levels of the model are represented by the following processes: level I: plant biomass dynamics; level II: nutrient movement between the soil and the plant; and level III: deposition of radionuclides and movement between the soil and the plant. Each submodel is structured in different compartments an has its own boundaries. Definition of the compartments of a given submodel is not only dependent on the process that it represents but is also determined by its subsequent integration at higher levels. The plant biomass dynamics submodel is composed of four compartments: root + shoots, young leaves, old leaves and litter. The boundaries of this submodel are defined by the plants themselves. The nutrient dynamics submodel adds two new compartments representing the soil system and uses the differentiation between young and old leaves, established at the previous level according to the changes that take place in their chemical composition. The boundaries of this submodel are defined by the above ground parts of the plants and by the depth of the roots underground. The radionuclide dynamics submodel integrates the two previous submodels and incorporates some additional compartments for the surface of the leaves and the above ground parts of the root + shoots fraction. This submodel has the same boundaries as the previous one. (author). 14 refs, 4 figs

  12. 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.

  13. 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.

  14. 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.

  15. MODELING AND ANALYSIS OF ALGAL BLOOMS IN ARAS DAM BY ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JAHANGIRI-RAD MAHSA

    2015-03-01

    Full Text Available Man made practices have contributed to large-scale algal blooms that have caused serious ecological, aesthetic, water purification and water distribution problems. Aras Dam, which provides Arasful city with drinking water, has chronic algal blooms since 1990. This study addresses the use of artificial neural network (ANN model to anticipate the chlorophyll-a concentration in water of dam reservoir. Operation tests carried out by collecting water samples from 5 stations and examined for physical quality parameters namely: water temperature, total suspended solids (TSS, biochemical oxygen demands (BOD, ortophosphate, total phosphorous and nitrate concentrations using standard methods. Chlorophyll-a was also checked separately in order to investigate the accuracy of the predicted results by ANN. The results showed that a network was highly accurate in predicting the Chl-a concentration. A good agreement between actual data and the ANN outputs for training was observed, indicating the validation of testing data sets. The initial results of the research indicate that the dam is enriched with nutrients (phosphorus and nitrogen. The Chl-a concentration that were predicted by the model were beyond the standard levels; indicating the possibility of eutrophication especially during fall season.

  16. 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.

  17. 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.

  18. Phase-change model for the simulation of artificial soil freezing in tunneling

    International Nuclear Information System (INIS)

    In contrast to seasonal freezing, artificial freezing is controllable and can be used to provide structural support and/or exclude groundwater from a tunnel site until the construction of the final lining provides permanent security. The formation of ice in the saturated pore space is mainly controlled by two factors: the temperature and the binding of water to the surface of particles by means of chemical bonds and electrostatic forces. Even at very low temperatures, water molecule layers close to the pore wall remain unfrozen, commonly referred to as unfrozen water content. In this paper, a phase-change model accounting for the influence of the temperature and the particle-water interaction on the freezing process of porous media is presented. For this purpose, fully-saturated specimens of silty sand were subjected to temperature gradients allowing, as a first step, to quantify macroscopic thermal properties of the unfrozen medium. Inverse homogenization is employed for determination of the respective properties of the different ingredients at the microscale. At this scale, freezing of water is introduced. Analytical bounds for thermal properties of the frozen microstructure (homogenization) are adapted from micro mechanics in order to estimate macroscopic thermal properties of frozen sand. The obtained properties are introduced into a macroscopic model and are assessed by means of re-analysis of a freezing experiment performed at Vienna University of Technology. Finally, results of a numerical simulation of an in-situ freezing test at a construction site in Vienna will be presented. Refs. 3 (author)

  19. Combining artificial neural networks and circulation type classification: does it improve downscaling models?

    Science.gov (United States)

    Philipp, Andreas; Beck, Christoph; Kaspar, Severin; Jacobeit, Jucundus

    2014-05-01

    Circulation type classifications may be used for downscaling in so called reference class forecasting (RCF), i.e. to to assign atmospheric circulation predictors to a certain type of a circulation type classification and use the value for the target variable associated with this type in the past as a model value. Doing so often already leads to useful statistical assessment models. However a generally superior method is that of artificial neural networks (NNW). Using adequate configuration, the latter are able to outperform the RCF method in virtually all cases. However the adequate configuration of NNWs is often not easy to decide and the training of the network weights may be an extensive and slow process while RCF is relatively fast. In the context of a starting project dealing with alpine climate change studies (Virtual Alpine Observatory II, VAO2), this study evaluates if a combination of both statistical approaches (called neural networks of classification types, NNC) may lead to an improvement for statistical downscaling. Preliminary results suggest that the gain in skill and the computational speed for the network training largely depends on the configuration of both: the circulation type classification and the network configuration regarding, topology, learning rate, predictors and so on. In this context it is important to consider the evolution of the learning process, where sometimes the NNW is superior and sometimes the NNC.

  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)

    LI; Aijun; (李爱军); LI; Hejun; (李贺军); LI; Kezhi; (李克智); GU; Zhengbing; (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. 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.

  2. 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)

  3. 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.

  4. 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.

  5. Ocean acidification increases copper toxicity to the early life history stages of the polychaete Arenicola marina in artificial seawater.

    Science.gov (United States)

    Campbell, Anna L; Mangan, Stephanie; Ellis, Robert P; Lewis, Ceri

    2014-08-19

    The speciation and therefore bioavailability of the common pollutant copper is predicted to increase within the pH range anticipated under near-future ocean acidification (OA), hence the potential exists for copper toxicity to marine organisms to also increase. We investigated the impact of OA (seawater pH values of 7.77 (pCO2 1400 μatm) and 7.47 (pCO2 3000 μatm)) upon copper toxicity responses in early life history stages of the polychaete Arenicola marina and found both synergistic and additive toxicity effects of combined exposures depending on life history stage. The toxicity of copper on sperm DNA damage and early larval survivorship was synergistically increased under OA conditions. Larval survival was reduced by 24% when exposed to both OA and copper combined compared to single OA or copper exposures. Sperm motility was negatively affected by both OA and copper singularly with additive toxicity effects of the two stressors when combined. Fertilization success was also negatively affected by both OA and copper individually, but no additive effects when exposed as combined stressors were present for this stage. These findings add to the growing body of evidence that OA will act to increase the toxicity of copper to marine organisms, which has clear implications for coastal benthic ecosystems suffering chronic metal pollution as pCO2 levels rise and drive a reduction in seawater pH. PMID:25033036

  6. 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

  7. 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.

  8. Modeling the benefits of an artificial gravity countermeasure coupled with exercise and vibration

    Science.gov (United States)

    Goel, Rahul; Kaderka, Justin; Newman, Dava

    2012-01-01

    The current, system-specific countermeasures to space deconditioning have limited success with the musculoskeletal system in long duration missions. Artificial gravity (AG) that is produced by short radius centrifugation has been hypothesized as an effective countermeasure because it reintroduces an acceleration field in space; however, AG alone might not be enough stimuli to preserve the musculoskeletal system. A novel combination of AG coupled with one-legged squats on a vibrating platform may preserve muscle and bone in the lower limbs to a greater extent than the current exercise paradigm. The benefits of the proposed countermeasure have been analyzed through the development of a simulation platform. Ground reaction force data and motion data were collected using a motion capture system while performing one-legged and two-legged squats in 1-G. The motion was modeled in OpenSim, an open-source software, and inverse dynamics were applied in order to determine the muscle and reaction forces of lower limb joints. Vibration stimulus was modeled by adding a 20 Hz sinusoidal force of 0.5 body weight to the force plate data. From the numerical model in a 1-G acceleration field, muscle forces for quadriceps femoris, plantar flexors and glutei increased substantially for one-legged squats with vibration compared to one- or two-legged squats without vibration. Additionally, joint reaction forces for one-legged squats with vibration also increased significantly compared to two-legged squats with or without vibration. Higher muscle forces and joint reaction forces might help to stimulate muscle activation and bone modeling and thus might reduce musculoskeletal deconditioning. These results indicate that the proposed countermeasure might surpass the performance of the current space countermeasures and should be further studied as a method of mitigating musculoskeletal deconditioning.

  9. 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

  10. Ustvarjanje modela rezalnih sil z uporabo umetne inteligence: Generation of a model for cutting forces using artificial intelligence:

    OpenAIRE

    Milfelner, Matjaž; Čuš, Franc; Župerl, Uroš

    2005-01-01

    Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligenc...

  11. Artificial Neural Network (ANN) of Simultaneous Heat and Mass Transfer Model during Reconstitution of Gari Granules into Thick Paste

    OpenAIRE

    S.S. Sobowale; S. O. Awonorin; T. A. Shittu; E. S. A. Ajisegiri

    2014-01-01

    Artificial neural network (ANN) based model of transient simultaneous heat and mass transfer was used for the prediction of some thermo-physical during reconstitution of gari into thick paste. Temperature changes in the paste and moisture losses were recorded over a period of two hours while the granules are being reconstituted. Data on convective heat and mass transfer coefficients were obtained during reconstitution of gari into paste. In developing the ANN model, several configurations wer...

  12. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with eudragit L 100 as matrix substance

    OpenAIRE

    Ibrić, Svetlana; Jovanović, Milica; Djurić, Zorica; Parojčić, Jelena; Petrović, Slobodan D.; Solomun, Ljiljana; Stupar, Biljana

    2003-01-01

    The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tables formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected...

  13. 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.

  14. 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.

  15. The Janus Model of Life-Course Dynamics

    NARCIS (Netherlands)

    Schroots, Johannes J.F.

    2008-01-01

    In this article, the classic problem of the human life-course, “how can the transition(s) of development into aging be explained?” has been solved. A theoretical model was constructed for the computer simulation of dynamic systems with one or more transitions in biology, psychology, and demography.

  16. 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

  17. Development of artificial model of caval syndrome in canine heartworm disease

    International Nuclear Information System (INIS)

    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

  18. Prediction of hydrate formation temperature by both statistical models and artificial neural network approaches

    International Nuclear Information System (INIS)

    In this study, various estimation methods have been reviewed for hydrate formation temperature (HFT) and two procedures have been presented. In the first method, two general correlations have been proposed for HFT. One of the correlations has 11 parameters, and the second one has 18 parameters. In order to obtain constants in proposed equations, 203 experimental data points have been collected from literatures. The Engineering Equation Solver (EES) and Statistical Package for the Social Sciences (SPSS) soft wares have been employed for statistical analysis of the data. Accuracy of the obtained correlations also has been declared by comparison with experimental data and some recent common used correlations. In the second method, HFT is estimated by artificial neural network (ANN) approach. In this case, various architectures have been checked using 70% of experimental data for training of ANN. Among the various architectures multi layer perceptron (MLP) network with trainlm training algorithm was found as the best architecture. Comparing the obtained ANN model results with 30% of unseen data confirms ANN excellent estimation performance. It was found that ANN is more accurate than traditional methods and even our two proposed correlations for HFT estimation.

  19. 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.

  20. 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. PMID:27424056

  1. Application of artificial neural networks in modelling of normalised structural steels mechanical properties

    Directory of Open Access Journals (Sweden)

    L.A. Dobrzański

    2009-01-01

    Full Text Available Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of constructional steels after heat treatment.Design/methodology/approach: On the basis of such input parameteres, such as the chemical composition, the ones of mechanical and heat treatment and dimensions of elements, mechanical properties such as strength, impact resistance or hardness are predicted.Findings: Results obtained in the given ranges of input parameters show very good ability of constructed neural networks to predict described mechanical properties for steels after heat treatment. The uniform distribution of descriptive vectors in all, training, validation and testing sets, indicate about the good ability of the networks to results generalisation.Practical implications: Created tool makes possible the easy modelling of described properties and allows the better selection of both chemical composition and the processing parameters of investigated materials. At the same time the obtainment of steels, which are qualitatively better, cheaper and more optimised under customers needs is made possible.Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It allows preserving the customers quality requirements and brings also measurable financial advantages.

  2. Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network

    International Nuclear Information System (INIS)

    Highlights: ► Max torque and power values were obtained at 3.5 bar Pch, 1273 K Hst and 1.4:1 r. ► According to ANOVA, the most influential parameter on power was Hst with 48.75%. ► According to ANOVA, the most influential parameter on torque was Hst with 41.78%. ► ANN (R2 = 99.8% for T, P) was superior to regression method (R2 = 92% for T, 81% for P). ► LM was the best learning algorithm in predicting both power and torque. - Abstract: In this study, an artificial neural network (ANN) model was developed to predict the torque and power of a beta-type Stirling engine using helium as the working fluid. The best results were obtained by 5-11-7-1 and 5-13-7-1 network architectures, with double hidden layers for the torque and power respectively. For these network architectures, the Levenberg–Marquardt (LM) learning algorithm was used. Engine performance values predicted with the developed ANN model were compared with the actual performance values measured experimentally, and substantially coinciding results were observed. After ANN training, correlation coefficients (R2) of both engine performance values for testing and training data were very close to 1. Similarly, root-mean-square error (RMSE) and mean error percentage (MEP) values for the testing and training data were less than 0.02% and 3.5% respectively. These results showed that the ANN is an acceptable model for prediction of the torque and power of the beta-type Stirling engine

  3. Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013

    Directory of Open Access Journals (Sweden)

    Saeed Motesaddi

    2015-12-01

    Full Text Available Background: Air pollution and concerns about health impacts have been raised in metropolitan cities like Tehran. Trend and prediction of air pollutants can show the effectiveness of strategies for the management and control of air pollution. Artificial neural network (ANN technique is widely used as a reliable method for modeling of air pollutants in urban areas. Therefore, the aim of current study was to evaluate the trend of sulfur dioxide (SO2 air quality index (AQI in Tehran using ANN. Methods: The dataset of SO2 concentration and AQI in Tehran between 2007 and 2013 for 2550 days were obtained from air quality monitoring fix stations belonging to the Department of Environment (DOE. These data were used as input for the ANN and nonlinear autoregressive (NAR model using Matlab (R2014a software. Results: Daily and annual mean concentration of SO2 except 2008 (0.037 ppm was less than the EPA standard (0.14 and 0.03 ppm, respectively. Trend of SO2 AQI showed the variation of SO2 during different days, but the study declined overtime and the predicted trend is higher than the actual trend. Conclusion: The trend of SO2 AQI in this study, despite daily fluctuations in ambient air of Tehran over the period of the study have decreased and the difference between the predicted and actual trends can be related to various factors, such as change in management and control of SO2 emissions strategy and lack of effective parameters in SO2 emissions in predicting model.

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

    OpenAIRE

    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 simulat...

  5. 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.

  6. Fatigue life prediction modeling for turbine hot section materials

    Science.gov (United States)

    Halford, G. R.; Meyer, T. G.; Nelson, R. S.; Nissley, D. M.; Swanson, G. A.

    1989-01-01

    A major objective of the fatigue and fracture efforts under the NASA Hot Section Technology (HOST) program was to significantly improve the analytic life prediction tools used by the aeronautical gas turbine engine industry. This was achieved in the areas of high-temperature thermal and mechanical fatigue of bare and coated high-temperature superalloys. The cyclic crack initiation and propagation resistance of nominally isotropic polycrystalline and highly anisotropic single crystal alloys were addressed. Life prediction modeling efforts were devoted to creep-fatigue interaction, oxidation, coatings interactions, multiaxiality of stress-strain states, mean stress effects, cumulative damage, and thermomechanical fatigue. The fatigue crack initiation life models developed to date include the Cyclic Damage Accumulation (CDA) and the Total Strain Version of Strainrange Partitioning (TS-SRP) for nominally isotropic materials, and the Tensile Hysteretic Energy Model for anisotropic superalloys. A fatigue model is being developed based upon the concepts of Path-Independent Integrals (PII) for describing cyclic crack growth under complex nonlinear response at the crack tip due to thermomechanical loading conditions. A micromechanistic oxidation crack extension model was derived. The models are described and discussed.

  7. Artificial Limbs

    Science.gov (United States)

    ... you are missing an arm or leg, an artificial limb can sometimes replace it. The device, which ... activities such as walking, eating, or dressing. Some artificial limbs let you function nearly as well as ...

  8. 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

  9. 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.

  10. A reduced-order modeling for efficient design study of artificial valve in enlarged ventricular outflow tracts.

    Science.gov (United States)

    Caiazzo, A; Guibert, R; Vignon-Clementel, I E

    2016-09-01

    A computational approach is proposed for efficient design study of a reducer stent to be percutaneously implanted in enlarged right ventricular outflow tracts (RVOT). The need for such a device is driven by the absence of bovine or artificial valves which could be implanted in these RVOT to replace the absent or incompetent native valve, as is often the case over time after Tetralogy of Fallot repair. Hemodynamics are simulated in the stented RVOT via a reduce order model based on proper orthogonal decomposition, while the artificial valve is modeled as a thin resistive surface. The reduced order model is obtained from the numerical solution on a reference device configuration, then varying the geometrical parameters (diameter) for design purposes. To validate the approach, forces exerted on the valve and on the reducer are monitored, varying with geometrical parameters, and compared with the results of full CFD simulations. Such an approach could also be useful for uncertainty quantification. PMID:26738807

  11. Modeling of Thermal Expansion Coefficients of Ni-Based Superalloys Using Artificial Neural Network

    Science.gov (United States)

    Bano, Nafisa; Nganbe, Michel

    2013-04-01

    The objective of this work is to model the thermal expansion coefficients of various Ni-based superalloys used in gas turbine components. The thermal expansion coefficient is described as a function of temperature, chemical composition including Ni, Cr, Co, Mo, W, Ta, Nb, Al, Ti, B, Zr, and C contents as well as heat treatment including solutionizing and aging. Experimental values are well described and their relative changes well correlated by the model. Because gas turbine engine components operate under severe loading conditions and at high and varying temperatures, the prediction of their thermal expansion coefficient is crucial. The model developed in this work can be useful for design optimizations for minimizing thermo-mechanical stresses between the base alloys and potential protective coatings or adjacent components. It can substantially contribute to improve the performance and service life of gas turbine components.

  12. Structural considerations for a software life cycle dynamic simulation model

    Science.gov (United States)

    Tausworthe, R. C.; Mckenzie, M.; Lin, C. Y.

    1983-01-01

    This paper presents the results of a preliminary study into the prospects for simulating the software implementation and maintenance life cycle process, with the aim of producing a computerized tool for use by management and software engineering personnel in project planning, tradeoff studies involving product, environmental, situational, and technological factors, and training. The approach taken is the modular application of a 'flow of resource' concept to the systems dynamics simulation modeling technique. The software life cycle process is represented as a number of stochastic, time-varying, interacting work tasks that each achieves one of the project milestones. Each task is characterized by the item produced, the personnel applied, and the budgetary profile.

  13. 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.

  14. Probabilistic Rotor Life Assessment Using Reduced Order Models

    Directory of Open Access Journals (Sweden)

    Brian K. Beachkofski

    2009-01-01

    Full Text Available Probabilistic failure assessments for integrally bladed disks are system reliability problems where a failure in at least one blade constitutes a rotor system failure. Turbine engine fan and compressor blade life is dominated by High Cycle Fatigue (HCF initiated either by pure HCF or Foreign Object Damage (FOD. To date performing an HCF life assessment for the entire rotor system has been too costly in analysis time to be practical. Although the substantial run-time has previously precluded a full-rotor probabilistic analysis, reduced order models make this process tractable as demonstrated in this work. The system model includes frequency prediction, modal stress variation, mistuning amplification, FOD effect, and random material capability. The model has many random variables which are most easily handled through simple random sampling.

  15. 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.

  16. 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.

  17. Artificial vesicles as an animal cell model for the study of biological application of non-thermal plasma

    Science.gov (United States)

    Ki, S. H.; Park, J. K.; Sung, C.; Lee, C. B.; Uhm, H.; Choi, E. H.; Baik, K. Y.

    2016-03-01

    Artificial cell-like model systems can provide information which is hard to obtain with real biological cells. Giant unilamellar vesicles (GUV) containing intra-membrane DNA or OH radical-binding molecules are used to visualize the cytolytic activity of OH radicals. Changes in the GUV membrane are observed by microscopy or flow cytometry as performed for animal cells after non-thermal plasma treatment. The experimental data shows that OH radicals can be detected inside the membrane, although the biological effects are not as significant as for H2O2. This artificial model system can provide a systemic means to elucidate the complex interactions between biological materials and non-thermal plasma.

  18. Modeling geological brines in salt-dome high level nuclear waste isolation repositories by artificial neural networks

    International Nuclear Information System (INIS)

    In order to study the influence of various parameters on the acidity of simulated geological brines, an artificial intelligence technique based on neural network modelling has been developed. It has been found that the pH of simulated salt repository brines lies within the range of 3.2-5 as the temperature of the brine decays from 250oC to 125oC. This environment might cause severe corrosion damage to canisters fabricated from carbon steel, particularly under slightly oxidizing conditions because of autocatalytical attack. It has also been demonstrated that artificial neural networks are efficient tools for analysing complex chemical systems, especially when conventional modelling is precluded by a lack of knowledge of the species and equilibria involved in the system. (Author)

  19. Artificial vesicles as an animal cell model for the study of biological application of non-thermal plasma

    International Nuclear Information System (INIS)

    Artificial cell-like model systems can provide information which is hard to obtain with real biological cells. Giant unilamellar vesicles (GUV) containing intra-membrane DNA or OH radical-binding molecules are used to visualize the cytolytic activity of OH radicals. Changes in the GUV membrane are observed by microscopy or flow cytometry as performed for animal cells after non-thermal plasma treatment. The experimental data shows that OH radicals can be detected inside the membrane, although the biological effects are not as significant as for H2O2. This artificial model system can provide a systemic means to elucidate the complex interactions between biological materials and non-thermal plasma. (paper)

  20. 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.

  1. 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.

  2. Application of Artificial Neural Networks in Modelling the Hydrological Balance of the Ploucnice River valley, Northern Bohemia (Czech Republic)

    Czech Academy of Sciences Publication Activity Database

    Neruda, M.; Neruda, Roman

    Praha : Academia, 2007 - (Hartel, H.; Cílek, V.; Herben, T.; Jackson, A.; Williams, R.), s. 90-92 ISBN 978-80-200-1577-8 R&D Projects: GA ČR GA526/03/Z042 Institutional research plan: CEZ:AV0Z10300504 Keywords : rainfall-runoff models * Ploucnice River valley * artificial neural networks Subject RIV: DA - Hydrology ; Limnology

  3. Artificial Neural Network Approach to the Modeling of Prosody in the Speech Synthesizer of the Czech Language

    Czech Academy of Sciences Publication Activity Database

    Tučková, J.; Šebesta, Václav

    Anaheim : ACTA Press, 2007 - (del Pobil, A.), s. 1-6 ISBN 978-0-88986-693-5. [ASC 2007. IASTED International Conference on Artificial Intelligence and Soft Computing /11./. Palma de Mallorca (ES), 29.08.2007-31.08.2007] R&D Projects: GA ČR GA102/05/0278 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network s * prosody modelling * pruning method Subject RIV: IN - Informatics, Computer Science

  4. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    OpenAIRE

    Saro Lee; Woo Jeon Seong; Kwan-Young Oh; Moung-Jin Lee

    2016-01-01

    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 fo...

  5. Embryonic development of goldfish (Carassius auratus): A model for the study of evolutionary change in developmental mechanisms by artificial selection

    OpenAIRE

    Tsai, Hsin-Yuan; Chang, Mariann; Liu, Shih-Chieh; Abe, Gembu; Ota, Kinya G

    2013-01-01

    Background: Highly divergent morphology among the different goldfish strains (Carassius auratus) may make it a suitable model for investigating how artificial selection has altered developmental mechanisms. Here we describe the embryological development of the common goldfish (the single fin Wakin), which retains the ancestral morphology of this species. Results: We divided goldfish embryonic development into seven periods consisting of 34 stages, using previously reported developmental indic...

  6. Weed management through herbicide application in direct-seeded rice and yield modeling by artificial neural network

    OpenAIRE

    Dibakar Ghosh; Singh, Udai P; Krishnendu Ray; Anupam Das

    2016-01-01

    In direct seeded rice (DSR) cultivation, weed is the major constraint mainly due to absence of puddling in field. The yield loss due to weed interference is huge, may be up to 100%. In this perspective, the present experiment was conducted to study the efficacy of selected herbicides, and to predict the rice yield using artificial neural network (ANN) models. The dry weight and density of weeds were recorded at different growth stages and consequently herbicidal efficacy was evaluated. Experi...

  7. Systematic review of health-related quality of life models

    Directory of Open Access Journals (Sweden)

    Bakas Tamilyn

    2012-11-01

    Full Text Available Abstract Background A systematic literature review was conducted to (a identify the most frequently used health-related quality of life (HRQOL models and (b critique those models. Methods Online search engines were queried using pre-determined inclusion and exclusion criteria. We reviewed titles, abstracts, and then full-text articles for their relevance to this review. Then the most commonly used models were identified, reviewed in tables, and critiqued using published criteria. Results Of 1,602 titles identified, 100 articles from 21 countries met the inclusion criteria. The most frequently used HRQOL models were: Wilson and Cleary (16%, Ferrans and colleagues (4%, or World Health Organization (WHO (5%. Ferrans and colleagues’ model was a revision of Wilson and Cleary’s model and appeared to have the greatest potential to guide future HRQOL research and practice. Conclusions Recommendations are for researchers to use one of the three common HRQOL models unless there are compelling and clearly delineated reasons for creating new models. Disease-specific models can be derived from one of the three commonly used HRQOL models. We recommend Ferrans and colleagues’ model because they added individual and environmental characteristics to the popular Wilson and Cleary model to better explain HRQOL. Using a common HRQOL model across studies will promote a coherent body of evidence that will more quickly advance the science in the area of HRQOL.

  8. 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.

  9. 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...

  10. 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.

  11. 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...

  12. 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 cr...... cracking is introduced and the site dependency of corrosion is stressed. Finally, a recently developed optimal repair strategy for bridges is briefly explained....

  13. Animal Models of Early Life Stress: Implications for Understanding Resilience

    OpenAIRE

    Lyons, David M.; Parker, Karen J.; Schatzberg, Alan F.

    2010-01-01

    In the mid-1950s, Levine and his colleagues reported that brief intermittent exposure to early life stress diminished indications of subsequent emotionality in rats. Here we review ongoing studies of a similar process in squirrel monkeys. Results from these animal models suggest that brief intermittent exposure to stress promotes the development of arousal regulation and resilience. Implications for programs designed to enhance resilience in human development are discussed.

  14. The available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles

    Science.gov (United States)

    Chan, C. C.; Lo, E. W. C.; Weixiang, Shen

    The available capacity computation model based on the artificial neural network (ANN) for lead-acid batteries in an electric vehicle (EV) is presented. Comparing with the methods based on the Peukert equation, which is often used for the calculation of the available capacity for lead-acid batteries in EVs, this model is more accurate. The results of the experiment have proven the accuracy of the proposed model; the computation values are in good agreement with experimental data, the associated error has been considered acceptable from an engineering point of view.

  15. Landsat 7 thermal-IR image sharpening using an artificial neural network and sensor model

    Science.gov (United States)

    Lemeshewsky, G.P.; Schowengerdt, R.A.

    2001-01-01

    The enhanced thematic mapper (plus) (ETM+) instrument on Landsat 7 shares the same basic design as the TM sensors on Landsats 4 and 5, with some significant improvements. In common are six multispectral bands with a 30-m ground-projected instantaneous field of view (GIFOV). However, the thermaL-IR (TIR) band now has a 60-m GIFOV, instead of 120-m. Also, a 15-m panchromatic band has been added. The artificial neural network (NN) image sharpening method described here uses data from the higher spatial resolution ETM+ bands to enhance (sharpen) the spatial resolution of the TIR imagery. It is based on an assumed correlation over multiple scales of resolution, between image edge contrast patterns in the TIR band and several other spectral bands. A multilayer, feedforward NN is trained to approximate TIR data at 60m, given degraded (from 30-m to 60-m) spatial resolution input from spectral bands 7, 5, and 2. After training, the NN output for full-resolution input generates an approximation of a TIR image at 30-m resolution. Two methods are used to degrade the spatial resolution of the imagery used for NN training, and the corresponding sharpening results are compared. One degradation method uses a published sensor transfer function (TF) for Landsat 5 to simulate sensor coarser resolution imagery from higher resolution imagery. For comparison, the second degradation method is simply Gaussian low pass filtering and subsampling, wherein the Gaussian filter approximates the full width at half maximum amplitude characteristics of the TF-based spatial filter. Two fixed-size NNs (that is, number of weights and processing elements) were trained separately with the degraded resolution data, and the sharpening results compared. The comparison evaluates the relative influence of the degradation technique employed and whether or not it is desirable to incorporate a sensor TF model. Preliminary results indicate some improvements for the sensor model-based technique. Further

  16. 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.

  17. Conscientiousness, health, and aging: the life course of personality model.

    Science.gov (United States)

    Shanahan, Michael J; Hill, Patrick L; Roberts, Brent W; Eccles, Jacquelynne; Friedman, Howard S

    2014-05-01

    The Conscientiousness (C) of the self and significant others influences health by way of mediational chains involving socioeconomic attainment, the avoidance and neutralization of stressors, the promotion of health behaviors and the minimization of risk behaviors, and the management of symptoms and diseases. Yet, meta-analyses reveal that these associations are moderated by factors that are not well understood. We propose the Life Course of Personality Model (LCP Model), which comprises a series of hypotheses that suggest how such mediational chains are subject to 2 sources of contingency. First, the mechanisms by which C translates into health and the avoidance of risk change from early childhood to late adulthood, involving processes that are specific to phases of the life course; also, however, C influences health by way of continuous processes extending over many decades of life. Second, C may be more consequential in some social contexts than in others, and when accompanied by some constellations of personality characteristics than by others. That is, the mediational processes by which C translates into health and the avoidance of disease are likely moderated by timing, social context (including the C of others), and other aspects of the individual's personality. We consider methodological implications of the LCP Model. PMID:23244406

  18. 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...... from different LCA studies can be consistent. This paper is an attempt to identify, review and analyse methodologies and technical assumptions used in various parts of selected waste LCA models. Several criteria were identified, which could have significant impacts on the results...

  19. Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network

    International Nuclear Information System (INIS)

    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. (condensed matter: structural, mechanical, and thermal properties)

  20. 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.