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. © 2016 John Wiley & Sons Ltd.

Artificial Intelligence and Artificial Life%人工智能与人工生命

Institute of Scientific and Technical Information of China (English)

班晓娟; 王昭顺; 刘宏伟; 涂序彦

2002-01-01

人工生命(Artificial Life)是一门正在迅速发展的新兴学科.将人工智能(Artificial Intelligence)方法与人工生命方法相结合,是目前人工智能研究的新方向.该文介绍了人工生命的概念和研究方法,分析了人工智能目前的困难以及人工生命与人工智能的区别与联系,并介绍了几种二者结合的实例.

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.

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 ...

Biology and fertility life table of Agrotis ipsilon on artificial diet

International Nuclear Information System (INIS)

Bento, Flavia de Moura Manoel; Fortes, Priscila; Zerio, Neide Graciano; Parra, Jose Roberto Postali

2007-01-01

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)

Modelling fuel cell performance using artificial intelligence

Science.gov (United States)

Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

Modelling fuel cell performance using artificial intelligence

Energy Technology Data Exchange (ETDEWEB)

Ogaji, S.O.T.; Singh, R.; Pilidis, P.; Diacakis, M. [Power Propulsion and Aerospace Engineering Department, Centre for Diagnostics and Life Cycle Costs, Cranfield University (United Kingdom)

2006-03-09

Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed. (author)

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....

Validation of the Revised Stressful Life Event Questionnaire Using a Hybrid Model of Genetic Algorithm and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Rasoul Sali

2013-01-01

Full Text Available Objectives. Stressors have a serious role in precipitating mental and somatic disorders and are an interesting subject for many clinical and community-based studies. Hence, the proper and accurate measurement of them is very important. We revised the stressful life event (SLE questionnaire by adding weights to the events in order to measure and determine a cut point. Methods. A total of 4569 adults aged between 18 and 85 years completed the SLE questionnaire and the general health questionnaire-12 (GHQ-12. A hybrid model of genetic algorithm (GA and artificial neural networks (ANNs was applied to extract the relation between the stressful life events (evaluated by a 6-point Likert scale and the GHQ score as a response variable. In this model, GA is used in order to set some parameter of ANN for achieving more accurate results. Results. For each stressful life event, the number is defined as weight. Among all stressful life events, death of parents, spouse, or siblings is the most important and impactful stressor in the studied population. Sensitivity of 83% and specificity of 81% were obtained for the cut point 100. Conclusion. The SLE-revised (SLE-R questionnaire despite simplicity is a high-performance screening tool for investigating the stress level of life events and its management in both community and primary care settings. The SLE-R questionnaire is user-friendly and easy to be self-administered. This questionnaire allows the individuals to be aware of their own health status.

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.

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.

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)

NEWS : A model for the evaluation of non-life insurance companies

NARCIS (Netherlands)

1997-01-01

This paper describes a model which can be used by the Dutch insurance supervisor to determine the priority a nan-life insurer should have for further examination. This model combines a traditional statistical technique (an ordered legit model) with artificial intelligence techniques (a neural

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

WebAL Comes of Age: A review of the first 21 years of Artificial Life on the Web

DEFF Research Database (Denmark)

Taylor, Tim; Auerbach, Joshua E; Bongard, Josh

2016-01-01

We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994—when the first WebAL work appeared...

Artificial Immune Networks: Models and Applications

Directory of Open Access Journals (Sweden)

Xian Shen

2008-06-01

Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

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. .

Transport modeling: An artificial immune system approach

Directory of Open Access Journals (Sweden)

Teodorović Dušan

2006-01-01

Full Text Available This paper describes an artificial immune system approach (AIS to modeling time-dependent (dynamic, real time transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies for different antigens (different traffic "scenarios". This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

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.

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

Directory of Open Access Journals (Sweden)

Flavia de Moura Manoel Bento

2007-10-01

Full Text Available O objetivo deste trabalho foi desenvolver uma dieta artificial para criação de Agrotis ipsilon em laboratório com base em parâmetros biológicos e na tabela de vida de fertilidade. A dieta artificial utilizada continha feijão, caseína, proteína de soja, levedura e germe de trigo como fontes protéicas. Os parâmetros biológicos duração e viabilidade das fases larval e pupal, peso de pupas, de ambos os sexos, com 24 horas de idade, razão sexual, longevidade dos adultos, período de pré-oviposição e número de ovos produzidos por fêmea e a tabela de vida de fertilidade foram avaliados. Foram observados seis ínstares larvais com duração de 25,4 dias e viabilidade de 93%. A duração da fase pupal foi de 12,4 dias e viabilidade de 96%. A viabilidade de ciclo total foi 72%. O peso de pupas foi 387 mg (machos e 484 mg (fêmeas. A razão sexual foi 0,46. O período de pré-oviposição foi de um dia, com 1.806 ovos por fêmea. Na tabela de vida verificou-se que a taxa líquida de reprodução e a razão finita de aumento foram 616,9 e 1,14, respectivamente. A dieta artificial é adequada à manutenção da criação de A. ipsilon, em laboratório.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

Analysis of Mental Processes Represented in Models of Artificial Consciousness

Directory of Open Access Journals (Sweden)

Luana Folchini da Costa

2013-12-01

Full Text Available The Artificial Consciousness concept has been used in the engineering area as being an evolution of the Artificial Intelligence. However, consciousness is a complex subject and often used without formalism. As a main contribution, in this work one proposes an analysis of four recent models of artificial consciousness published in the engineering area. The mental processes represented by these models are highlighted and correlations with the theoretical perspective of cognitive psychology are made. Finally, considerations about consciousness in such models are discussed.

A model for a knowledge-based system's life cycle

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 here are the initial efforts of one of the working groups of that committee. The purpose here is to present a candidate model for the development life cycle of Knowledge Based Systems (KBS). 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 detailed as are and 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.

An application of artificial intelligence for rainfall–runoff modeling

Indian Academy of Sciences (India)

This study proposes an application of two techniques of artificial intelligence (AI) ... (2006) applied rainfall–runoff modeling using ANN ... in artificial intelligence, engineering and science .... usually be estimated from a sample of observations.

Artificial Systems and Models for Risk Covering Operations

Directory of Open Access Journals (Sweden)

Laurenţiu Mihai Treapăt

2017-04-01

Full Text Available Mainly, this paper focuses on the roles of artificial intelligence based systems and especially on risk-covering operations. In this context, the paper comes with theoretical explanations on real-life based examples and applications. From a general perspective, the paper enriches its value with a wide discussion on the related subject. The paper aims to revise the volatilities’ estimation models and the correlations between the various time series and also by presenting the Risk Metrics methodology, as explained is a case study. The advantages that the VaR estimation offers, consist of its ability to quantitatively and numerically express the risk level of a portfolio, at a certain moment in time and also the risk of on open position (in titles, in FX, commodities or granted loans, belonging to an economic agent or even individual; hence, its role in a more efficient capital allocation, in the assumed risk delimitation, and also as a performance measurement instrument. In this paper and the study case that completes our work, we aim to prove how we can prevent considerable losses and even bankruptcies if VaR is known and applied accordingly. For this reason, the universities inRomaniashould include or increase their curricula with the study of the VaR model as an artificial intelligence tool. The simplicity of the presented case study, most probably, is the strongest argument of the current work because it can be understood also by the readers that are not necessarily very experienced in the risk management field.

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.

An Artificial Emotion Model For Visualizing Emotion of Characters

OpenAIRE

Junseok Ham; Chansun Jung; Junhyung Park; Jihye Ryeo; Ilju Ko

2009-01-01

It is hard to express emotion through only speech when we watch a character in a movie or a play because we cannot estimate the size, kind, and quantity of emotion. So this paper proposes an artificial emotion model for visualizing current emotion with color and location in emotion model. The artificial emotion model is designed considering causality of generated emotion, difference of personality, difference of continual emotional stimulus, and co-relation of various emo...

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.

Artificial Neural Network Based Model of Photovoltaic Cell

Directory of Open Access Journals (Sweden)

Messaouda Azzouzi

2017-03-01

Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 

An application of artificial intelligence for rainfall–runoff modeling

Indian Academy of Sciences (India)

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 diff- erent ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods ...

Artificial intelligence model for sustain ability measurement

International Nuclear Information System (INIS)

Navickiene, R.; Navickas, K.

2012-01-01

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)

Artificial Consciousness or Artificial Intelligence

Directory of Open Access Journals (Sweden)

Spanache Florin

2017-05-01

Full Text Available Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus automatic. But conscience is above these differences because it is neither conditioned by the self-preservation of autonomy, because a conscience is something that you use to help your neighbor, nor automatic, because one’s conscience is tested by situations which are not similar or subject to routine. So, artificial intelligence is only in science-fiction literature similar to an autonomous conscience-endowed being. In real life, religion with its notions of redemption, sin, expiation, confession and communion will not have any meaning for a machine which cannot make a mistake on its own.

Computational Everyday Life Human Behavior Model as Servicable Knowledge

Science.gov (United States)

Motomura, Yoichi; Nishida, Yoshifumi

A project called `Open life matrix' is not only a research activity but also real problem solving as an action research. This concept is realized by large-scale data collection, probabilistic causal structure model construction and information service providing using the model. One concrete outcome of this project is childhood injury prevention activity in new team consist of hospital, government, and many varieties of researchers. The main result from the project is a general methodology to apply probabilistic causal structure models as servicable knowledge for action research. In this paper, the summary of this project and future direction to emphasize action research driven by artificial intelligence technology are discussed.

WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web.

Science.gov (United States)

Taylor, Tim; Auerbach, Joshua E; Bongard, Josh; Clune, Jeff; Hickinbotham, Simon; Ofria, Charles; Oka, Mizuki; Risi, Sebastian; Stanley, Kenneth O; Yosinski, Jason

2016-01-01

We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994-when the first WebAL work appeared-up to the present day, together with a brief discussion of relevant precursors. We examine recent projects, from 2010-2015, in greater detail in order to highlight the current state of the art. We follow the survey with a discussion of common themes and methodologies that can be observed in recent work and identify a number of likely directions for future work in this exciting area.

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...

Prognostic modelling options for remaining useful life estimation by industry

Science.gov (United States)

Sikorska, J. Z.; Hodkiewicz, M.; Ma, L.

2011-07-01

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

Artificial cilia : a physical model for ciliary propulsion

OpenAIRE

Babataheri , Avin

2009-01-01

Most microorganisms use cilia or flagella as a means of propulsion. These low Reynolds number swimming mechanisms have been studied theoretically and experimentally on living organisms. However, so far very few physical experimental models have been realised. We describe here the fabrication of microscopic artificial cilia, actuated by a magnetic field. These artificial cilia share with real cilia a large aspect ratio, great flexibility, and the actuation by a magnetic torque distributed alon...

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.

Artificial Lipid Membranes: Past, Present, and Future.

Science.gov (United States)

Siontorou, Christina G; Nikoleli, Georgia-Paraskevi; Nikolelis, Dimitrios P; Karapetis, Stefanos K

2017-07-26

The multifaceted role of biological membranes prompted early the development of artificial lipid-based models with a primary view of reconstituting the natural functions in vitro so as to study and exploit chemoreception for sensor engineering. Over the years, a fair amount of knowledge on the artificial lipid membranes, as both, suspended or supported lipid films and liposomes, has been disseminated and has helped to diversify and expand initial scopes. Artificial lipid membranes can be constructed by several methods, stabilized by various means, functionalized in a variety of ways, experimented upon intensively, and broadly utilized in sensor development, drug testing, drug discovery or as molecular tools and research probes for elucidating the mechanics and the mechanisms of biological membranes. This paper reviews the state-of-the-art, discusses the diversity of applications, and presents future perspectives. The newly-introduced field of artificial cells further broadens the applicability of artificial membranes in studying the evolution of life.

Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells

Directory of Open Access Journals (Sweden)

Rongjie Wang

2015-07-01

Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

APPLYING TEACHING-LEARNING TO ARTIFICIAL BEE COLONY FOR PARAMETER OPTIMIZATION OF SOFTWARE EFFORT ESTIMATION MODEL

Directory of Open Access Journals (Sweden)

THANH TUNG KHUAT

2017-05-01

Full Text Available Artificial Bee Colony inspired by the foraging behaviour of honey bees is a novel meta-heuristic optimization algorithm in the community of swarm intelligence algorithms. Nevertheless, it is still insufficient in the speed of convergence and the quality of solutions. This paper proposes an approach in order to tackle these downsides by combining the positive aspects of TeachingLearning based optimization and Artificial Bee Colony. The performance of the proposed method is assessed on the software effort estimation problem, which is the complex and important issue in the project management. Software developers often carry out the software estimation in the early stages of the software development life cycle to derive the required cost and schedule for a project. There are a large number of methods for effort estimation in which COCOMO II is one of the most widely used models. However, this model has some restricts because its parameters have not been optimized yet. In this work, therefore, we will present the approach to overcome this limitation of COCOMO II model. The experiments have been conducted on NASA software project dataset and the obtained results indicated that the improvement of parameters provided better estimation capabilities compared to the original COCOMO II model.

[Artificial organs].

Science.gov (United States)

Raguin, Thibaut; Dupret-Bories, Agnès; Debry, Christian

2017-01-01

Research has been fighting against organ failure and shortage of donations by supplying artificial organs for many years. With the raise of new technologies, tissue engineering and regenerative medicine, many organs can benefit of an artificial equivalent: thanks to retinal implants some blind people can visualize stimuli, an artificial heart can be proposed in case of cardiac failure while awaiting for a heart transplant, artificial larynx enables laryngectomy patients to an almost normal life, while the diabetic can get a glycemic self-regulation controlled by smartphones with an artificial device. Dialysis devices become portable, as well as the oxygenation systems for terminal respiratory failure. Bright prospects are being explored or might emerge in a near future. However, the retrospective assessment of putative side effects is not yet sufficient. Finally, the cost of these new devices is significant even if the advent of three dimensional printers may reduce it. © 2017 médecine/sciences – Inserm.

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

Science.gov (United States)

Sharafi, Soodabeh; Li, Guoqiang

2015-05-21

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

The prediction of the residual life of electromechanical equipment based on the artificial neural network

Science.gov (United States)

Zhukovskiy, Yu L.; Korolev, N. A.; Babanova, I. S.; Boikov, A. V.

2017-10-01

This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.

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.

Artificial neural network cardiopulmonary modeling and diagnosis

Science.gov (United States)

Kangas, Lars J.; Keller, Paul E.

1997-01-01

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

Biology and fertility life table of Hypercompe indecisa on artificial diet

International Nuclear Information System (INIS)

Nava, Dori Edson; Diez-Rodriguez, Gabriela Ines; Melo, Mirtes; Afonso, Ana Paula Schneid

2008-01-01

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)

Impact of Artificial Intelligence on Economic Theory

OpenAIRE

Tshilidzi Marwala

2015-01-01

Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.

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...

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

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

NARCIS (Netherlands)

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

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

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

Transport energy demand modeling of South Korea using artificial neural network

International Nuclear Information System (INIS)

Geem, Zong Woo

2011-01-01

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.

Ethical Analysis of Withdrawing Total Artificial Heart Support.

Science.gov (United States)

DeMartino, Erin S; Wordingham, Sara E; Stulak, John M; Boilson, Barry A; Fuechtmann, Kayla R; Singh, Nausheen; Sulmasy, Daniel P; Pajaro, Octavio E; Mueller, Paul S

2017-05-01

To describe the characteristics of patients who undergo withdrawal of total artificial heart support and to explore the ethical aspects of withdrawing this life-sustaining treatment. We retrospectively reviewed the medical records of all adult recipients of a total artificial heart at Mayo Clinic from the program's inception in 2007 through June 30, 2015. Management of other life-sustaining therapies, approach to end-of-life decision making, engagement of ethics and palliative care consultation, and causes of death were analyzed. Of 47 total artificial heart recipients, 14 patients or their surrogates (30%) requested withdrawal of total artificial heart support. No request was denied by treatment teams. All 14 patients were supported with at least 1 other life-sustaining therapy. Only 1 patient was able to participate in decision making. It is widely held to be ethically permissible to withdraw a life-sustaining treatment when the treatment no longer meets the patient's health care-related goals (ie, the burdens outweigh the benefits). These data suggest that some patients, surrogates, physicians, and other care providers believe that this principle extends to the withdrawal of total artificial heart support. Copyright © 2017 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

Modeling of Reservoir Inflow for Hydropower Dams Using Artificial ...

African Journals Online (AJOL)

The stream flow at the three hydropower reservoirs in Nigeria were modeled using hydro-meteorological parameters and Artificial Neural Network (ANN). The model revealed positive relationship between the observed and the modeled reservoir inflow with values of correlation coefficient of 0.57, 0.84 and 0.92 for Kainji, ...

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)

Teaching methodology for modeling reference evapotranspiration with artificial neural networks

OpenAIRE

Martí, Pau; Pulido Calvo, Inmaculada; Gutiérrez Estrada, Juan Carlos

2015-01-01

[EN] Artificial neural networks are a robust alternative to conventional models for estimating different targets in irrigation engineering, among others, reference evapotranspiration, a key variable for estimating crop water requirements. This paper presents a didactic methodology for introducing students in the application of artificial neural networks for reference evapotranspiration estimation using MatLab c . Apart from learning a specific application of this software wi...

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.

Reliability studies for the nuclear-powered artificial heart program

International Nuclear Information System (INIS)

Horita, M.; Zeigler, R.K.

1976-04-01

By assuming that the failures of an artificial heart system with a mean life of 10 y can be modeled by a particular probability distribution, both the probability of a failure in the system within t years and the reliability required of each subsystem and component were investigated

Standard representation and unified stability analysis for dynamic artificial neural network models.

Science.gov (United States)

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Training Spiking Neural Models Using Artificial Bee Colony

Science.gov (United States)

Vazquez, Roberto A.; Garro, Beatriz A.

2015-01-01

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

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.

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.

Position control of a single pneumatic artificial muscle with hysteresis compensation based on modified Prandtl-Ishlinskii model.

Science.gov (United States)

Zang, Xizhe; Liu, Yixiang; Heng, Shuai; Lin, Zhenkun; Zhao, Jie

2017-01-01

High-performance position control of pneumatic artificial muscles is limited by their inherent nonlinearity and hysteresis. This study aims to model the length/pressure hysteresis of a single pneumatic artificial muscle and to realize its accurate position tracking control with forward hysteresis compensation. The classical Prandtl-Ishlinskii model is widely used in hysteresis modelling and compensation. But it is only effective for symmetric hysteresis. Therefore, a modified Prandtl-Ishlinskii model is built to characterize the asymmetric length/pressure hysteresis of a single pneumatic artificial muscle, by replacing the classical play operators with two more flexible elementary operators to independently describe the ascending branch and descending branch of hysteresis loops. On the basis, a position tracking controller, which is composed of cascade forward hysteresis compensation and simple proportional pressure controller, is designed for the pneumatic artificial muscle. Experiment results show that the MPI model can reproduce the length/pressure hysteresis of the pneumatic artificial muscle, and the proposed controller for the pneumatic artificial muscle can track the reference position signals with high accuracy. By modelling the length/pressure hysteresis with the modified Prandtl-Ishlinskii model and using its inversion for compensation, precise position control of a single pneumatic artificial muscle is achieved.

Research on Artificial Spider Web Model for Farmland Wireless Sensor Network

OpenAIRE

Jun Wang; Song Gao; Shimin Zhao; Guang Hu; Xiaoli Zhang; Guowang Xie

2018-01-01

Through systematic analysis of the structural characteristics and invulnerability of spider web, this paper explores the possibility of combining the advantages of spider web such as network robustness and invulnerability with farmland wireless sensor network. A universally applicable definition and mathematical model of artificial spider web structure are established. The comparison between artificial spider web and traditional networks is discussed in detail. The simulation result shows tha...

Biologically inspired technologies using artificial muscles

Science.gov (United States)

Bar-Cohen, Yoseph

2005-01-01

After billions of years of evolution, nature developed inventions that work, which are appropriate for the intended tasks and that last. The evolution of nature led to the introduction of highly effective and power efficient biological mechanisms that are scalable from micron to many meters in size. Imitating these mechanisms offers enormous potentials for the improvement of our life and the tools we use. Humans have always made efforts to imitate nature and we are increasingly reaching levels of advancement where it becomes significantly easier to imitate, copy, and adapt biological methods, processes and systems. Some of the biomimetic technologies that have emerged include artificial muscles, artificial intelligence, and artificial vision to which significant advances in materials science, mechanics, electronics, and computer science have contributed greatly. One of the newest fields of biomimetics is the electroactive polymers (EAP) that are also known as artificial muscles. To take advantage of these materials, efforts are made worldwide to establish a strong infrastructure addressing the need for comprehensive analytical modeling of their operation mechanism and develop effective processing and characterization techniques. The field is still in its emerging state and robust materials are not readily available however in recent years significant progress has been made and commercial products have already started to appear. This paper covers the state-of-the-art and challenges to making artificial muscles and their potential biomimetic applications.

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…

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

Science.gov (United States)

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

2016-12-01

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

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.

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

Science.gov (United States)

Feher da Silva, Carolina; Baldo, Marcus Vinícius Chrysóstomo

2012-01-01

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.

A photoactivated artificial muscle model unit: reversible, photoinduced sliding of nanosheets.

Science.gov (United States)

Nabetani, Yu; Takamura, Hazuki; Hayasaka, Yuika; Shimada, Tetsuya; Takagi, Shinsuke; Tachibana, Hiroshi; Masui, Dai; Tong, Zhiwei; Inoue, Haruo

2011-11-02

A novel photoactivated artificial muscle model unit is reported. Here we show that organic/inorganic hybrid nanosheets reversibly slide horizontally on a giant scale and the interlayer spaces in the layered hybrid structure shrink and expand vertically by photoirradiation. The sliding movement of the system on a giant scale is the first example of an artificial muscle model unit having much similarity with that in natural muscle fibrils. In particular, our layered hybrid molecular system exhibits a macroscopic morphological change on a giant scale (~1500 nm) relative to the molecular size of ~1 nm by means of a reversible sliding mechanism.

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

Directory of Open Access Journals (Sweden)

Hui Chen

2014-01-01

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

Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models.

Science.gov (United States)

2008-09-01

The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (A...

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 determine...

Discrepancies in the use of chemotherapy and artificial nutrition near the end of life for hospitalised patients with metastatic gastric or oesophageal cancer. A countrywide, register-based study.

Science.gov (United States)

Kempf, Emmanuelle; Tournigand, Christophe; Rochigneux, Philippe; Aubry, Régis; Morin, Lucas

2017-07-01

To evaluate the frequency and the factors associated with the use of chemotherapy and artificial nutrition near the end of life in hospitalised patients with metastatic oesophageal or gastric cancer. Nationwide, register-based study, including all hospitalised adults (≥20 years) who died with metastatic oesophageal or gastric cancer between 2010 and 2013, in France. Chemotherapy and artificial nutrition during the final weeks of life were considered as primary outcomes. A total of 4031 patients with oesophageal cancer and 10,423 patients with gastric cancer were included. While the proportion of patients receiving chemotherapy decreased from 35.9% during the 3rd month before death to 7.9% in the final week (p nutrition rose from 9.6% to 16.0% of patients. During the last week before death, patients with stomach cancer were more likely to receive chemotherapy (adjusted odds ratio (aOR) = 1.35, 95% CI = 1.17-1.56) but less likely to receive artificial nutrition (aOR = 0.80, 95%CI = 0.73-0.88) than patients with cancer of the oesophagus. The adjusted rates of chemotherapy use during the last week of life varied from 1.6% in rural hospitals to 11.2% in comprehensive cancer centres, while the adjusted probability to receive artificial nutrition varied from 12.1% in private for-profit clinics up to 19.9% in rehabilitation care facilities (p gastric cancer, the use of chemotherapy decreases while the use of artificial nutrition increases as death approaches. This raises important questions, as clinical guidelines clearly recommend to limit the use of artificial nutrition in contexts of limited life expectancy. Copyright © 2017 Elsevier Ltd. All rights reserved.

A classical Master equation approach to modeling an artificial protein motor

International Nuclear Information System (INIS)

Kuwada, Nathan J.; Blab, Gerhard A.; Linke, Heiner

2010-01-01

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.

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

Science.gov (United States)

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising

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

International Nuclear Information System (INIS)

Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim

2009-01-01

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)

Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

National Research Council Canada - National Science Library

Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu

2006-01-01

.... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

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

An entropy model for artificial grammar learning

Directory of Open Access Journals (Sweden)

Emmanuel Pothos

2010-06-01

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

The Nexus Between Artificial Intellgence and Economics

NARCIS (Netherlands)

van de Gevel, A.J.W.; Noussair, C.N.

2013-01-01

The manuscript reviews some key ideas about artificial intelligence, and relates them to economics. These include its relation to robotics, and the concepts of synthetic emotions, consciousness, and life. The economic implications of the advent of artificial intelligence, such as its effect on

Ethico-epistemological implications of artificial intelligence for ...

African Journals Online (AJOL)

We argued for a re-direction of AI. research and suggested a humanization of Artificial Intelligence that cloaks technoscientific innovations with humanistic life jackets for man‟s preservation. The textual analysis method is adopted for this research. Key words: Ethics, Epistemology, Artificial Intelligence, Humanity.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

Science.gov (United States)

Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-Wing

2018-08-01

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg -1 and 66,112.94MJkg -1 , respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.

Modelling of artificial radioactivity migration in environment: a survey

International Nuclear Information System (INIS)

Bignoli, G.; Bertozzi, G.

1979-01-01

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

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

Directory of Open Access Journals (Sweden)

Zhehuang Huang

2015-01-01

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

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

Science.gov (United States)

Huang, Zhehuang; Chen, Yidong

2015-01-01

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

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Federico Nuñez-Piña

2018-01-01

Full Text Available The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit (R=1.0000 against the response surface methodology (R=0.9996. Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

Suitability of an artificial diet for rape aphid, Brevicoryne brassicae ...

African Journals Online (AJOL)

STORAGESEVER

2009-09-15

Sep 15, 2009 ... Brevicoryne brassicae, using life table parameters. A. Balvasi1* ... In this work, suitability of an artificial diet was studied through age-specific life tables. Development ... In large measure, the success of entomology over the past century is ..... balance on the improvement of an artificial diet for a biotype of.

Research on Artificial Spider Web Model for Farmland Wireless Sensor Network

Directory of Open Access Journals (Sweden)

Jun Wang

2018-01-01

Full Text Available Through systematic analysis of the structural characteristics and invulnerability of spider web, this paper explores the possibility of combining the advantages of spider web such as network robustness and invulnerability with farmland wireless sensor network. A universally applicable definition and mathematical model of artificial spider web structure are established. The comparison between artificial spider web and traditional networks is discussed in detail. The simulation result shows that the networking structure of artificial spider web is better than that of traditional networks in terms of improving the overall reliability and invulnerability of communication system. A comprehensive study on the advantage characteristics of spider web has important theoretical and practical significance for promoting the invulnerability research of farmland wireless sensor network.

ARTIFICIAL AND NATURAL INTELLIGENCE IN ANTHROPOGENIC EDUCATIONAL ENVIRONMENTS

Directory of Open Access Journals (Sweden)

Sergey F. Sergeev

2013-01-01

Full Text Available In the present article we show the link between both artificial and natural intelligence and the system’s complexity during the life-cycle. Autopoetic’s type of living systems determines the differences between natural and artificial intelligence; artificial environments have an influence to the intelligence abilities development. We present the «diffusion intellect» concept where the diffusion intellect is considered as a synergistic unity of natural and artificial intellect in organized environments. 

Flood routing modelling with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

R. Peters

2006-01-01

Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

Evaluation of articulation simulation system using artificial maxillectomy models.

Science.gov (United States)

Elbashti, M E; Hattori, M; Sumita, Y I; Taniguchi, H

2015-09-01

Acoustic evaluation is valuable for guiding the treatment of maxillofacial defects and determining the effectiveness of rehabilitation with an obturator prosthesis. Model simulations are important in terms of pre-surgical planning and pre- and post-operative speech function. This study aimed to evaluate the acoustic characteristics of voice generated by an articulation simulation system using a vocal tract model with or without artificial maxillectomy defects. More specifically, we aimed to establish a speech simulation system for maxillectomy defect models that both surgeons and maxillofacial prosthodontists can use in guiding treatment planning. Artificially simulated maxillectomy defects were prepared according to Aramany's classification (Classes I-VI) in a three-dimensional vocal tract plaster model of a subject uttering the vowel /a/. Formant and nasalance acoustic data were analysed using Computerized Speech Lab and the Nasometer, respectively. Formants and nasalance of simulated /a/ sounds were successfully detected and analysed. Values of Formants 1 and 2 for the non-defect model were 675.43 and 976.64 Hz, respectively. Median values of Formants 1 and 2 for the defect models were 634.36 and 1026.84 Hz, respectively. Nasalance was 11% in the non-defect model, whereas median nasalance was 28% in the defect models. The results suggest that an articulation simulation system can be used to help surgeons and maxillofacial prosthodontists to plan post-surgical defects that will be facilitate maxillofacial rehabilitation. © 2015 John Wiley & Sons Ltd.

Gender Differences in Sexual Attraction and Moral Judgment: Research With Artificial Face Models.

Science.gov (United States)

González-Álvarez, Julio; Cervera-Crespo, Teresa

2018-01-01

Sexual attraction in humans is influenced by cultural or moral factors, and some gender differences can emerge in this complex interaction. A previous study found that men dissociate sexual attraction from moral judgment more than women do. Two experiments consisting of giving attractiveness ratings to photos of real opposite-sex individuals showed that men, compared to women, were significantly less influenced by the moral valence of a description about the person shown in each photo. There is evidence of some processing differences between real and artificial computer-generated faces. The present study tests the robustness of González-Álvarez's findings and extends the research to an experimental design using artificial face models as stimuli. A sample of 88 young adults (61 females and 27 males, average age 19.32, SD = 2.38) rated the attractiveness of 80 3D artificial face models generated with the FaceGen Modeller 3.5 software. Each face model was paired with a "good" and a "bad" (from a moral point of view) sentence depicting a quality or activity of the person represented in the model (e.g., she/he is an altruistic nurse in Africa vs. she/he is a prominent drug dealer). Results were in line with the previous findings and showed that, with artificial faces as well, sexual attraction is less influenced by morality in men than in women. This gender difference is consistent with an evolutionary perspective on human sexuality.

Modelling of pneumatic muscle actuator using Hill's model with different approximations of static characteristics of artificial muscle

Directory of Open Access Journals (Sweden)

Piteľ Ján

2016-01-01

Full Text Available For modelling and simulation of pneumatic muscle actuators the mathematical dependence of the muscle force on the muscle contraction at different pressures in the muscles is necessary to know. For this purpose the static characteristics of the pneumatic artificial muscle type FESTO MAS-20-250N used in the experiments were approximated. In the paper there are shown some simulation results of the pneumatic muscle actuator dynamics using modified Hill's muscle model, in which four different approximations of static characteristics of artificial muscle were used.

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

DEFF Research Database (Denmark)

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

1999-01-01

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...

Pneumatic Artificial Muscle Actuation and Modeling

Science.gov (United States)

Leephakpreeda, Thananchai; Wickramatunge, Kanchana C.

2009-10-01

A Pneumatic Artificial Muscle (PAM) yields a natural muscle-like actuator with a high force to weight ratio, a soft and flexible structure, and adaptable compliance for a humanoid robot, rehabilitation and prosthetic appliances to the disabled, etc. To obtain optimum design and usage, the mechanical behavior of the PAM need to be understood. In this study, observations of experimental results reveal an empirical model for relations of physical variables, contraction and air pressure within the PAM, as compared to mechanical characteristics, such as stiffness or/and pulling forces of the PAM available now in market.

A review on integration of artificial intelligence into water quality modelling.

Science.gov (United States)

Chau, Kwok-wing

2006-07-01

With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

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.

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

International Nuclear Information System (INIS)

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; Choi, Yong Suk; Hwang, Eui Hwan

2009-01-01

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 mm 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.

Transport modeling of sorbing tracers in artificial fractures

International Nuclear Information System (INIS)

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

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.

Artificial limb representation in amputees

OpenAIRE

van den Heiligenberg, FMZ; Orlov, T; Macdonald, SN; Duff, EP; Henderson Slater, JDE; Beckmann, CF; Johansen-Berg, H; Culham, JC; Makin, TR

2018-01-01

The human brain contains multiple hand-selective areas, in both the sensorimotor and visual systems. Could our brain repurpose neural resources, originally developed for supporting hand function, to represent and control artificial limbs? We studied individuals with congenital or acquired hand-loss (hereafter one-handers) using functional MRI. We show that the more one-handers use an artificial limb (prosthesis) in their everyday life, the stronger visual hand-selective areas in the lateral o...

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

Science.gov (United States)

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

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

International Nuclear Information System (INIS)

Saini, K. K.; Saini, Sanju

2008-01-01

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.

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

Directory of Open Access Journals (Sweden)

C.L. HII

2008-04-01

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

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

Science.gov (United States)

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

2012-01-01

Two strategies have been proposed to avoid negative genetic effects of artificially propagated individuals on wild populations: (i) integration of wild and captive populations to minimize domestication selection and (ii) segregation of released individuals from the wild population to minimize interbreeding. We tested the efficacy of the strategy of segregation by divergent life history in a steelhead trout, Oncorhynchus mykiss, system, where hatchery fish were selected to spawn months earlier than the indigenous wild population. The proportion of wild ancestry smolts and adults declined by 10–20% over the three generations since the hatchery program began. Up to 80% of the naturally produced steelhead in any given year were hatchery/wild hybrids. Regression model selection analysis showed that the proportion of hatchery ancestry smolts was lower in years when stream discharge was high, suggesting a negative effect of flow on reproductive success of early-spawning hatchery fish. Furthermore, proportions of hybrid smolts and adults were higher in years when the number of naturally spawning hatchery-produced adults was higher. Divergent life history failed to prevent interbreeding when physical isolation was ineffective, an inadequacy that is likely to prevail in many other situations. PMID:23144657

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.

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

CERN Document Server

Corea, Francesco

2017-01-01

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

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

Science.gov (United States)

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

2016-07-03

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

Engineering considerations in the use of artificial filter beds

International Nuclear Information System (INIS)

Richards, R.T.

1978-01-01

Artificial filter bed intakes utilize a prepared granular filter material to prevent entrance of debris and aquatic life into a water withdrawal facility. The relatively large quantities of water and the service reliability required for power plant cooling water systems present major engineering problems for the artificial filter concept, many of which have not been resolved. These problems include development of a suitable and stable filter medium; design of a reliable backwash system which is both effective and environmentally acceptable; meeting of all site-imposed restrictions relating to natural river turbidity, flooding characteristics, channel stability, and ice loads; and provision of the complex civil and mechanical engineering design inherent in such a system. Extensive model testing may be required for further engineering development of this system. The preliminary engineering for an artificial filter system is discussed in connection with a proposed 1.6-m 3 /s-capacity (25,000-gpm capacity) filter in the Columbia River

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.

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

International Nuclear Information System (INIS)

Oliveira, Douglas Nunes de; Bourguignon, Lucas Gabriel Garcia; Tolentino, Evandro; Costa, Rodrigo Moyses; Tello, Cledola Cassia Oliveira de

2015-01-01

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)

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)

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)

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.

EMG analysis and modelling of flat bench press using artificial ...

African Journals Online (AJOL)

The objective of this study was to evaluate the contribution of particular muscle groups during the Flat Bench Press (FBP) with different external loads. Additionally, the authors attempted to determine whether regression models or Artificial Neural Networks (ANNs) can predict FBP results more precisely and whether they can ...

New twist on artificial muscles.

Science.gov (United States)

Haines, Carter S; Li, Na; Spinks, Geoffrey M; Aliev, Ali E; Di, Jiangtao; Baughman, Ray H

2016-10-18

Lightweight artificial muscle fibers that can match the large tensile stroke of natural muscles have been elusive. In particular, low stroke, limited cycle life, and inefficient energy conversion have combined with high cost and hysteretic performance to restrict practical use. In recent years, a new class of artificial muscles, based on highly twisted fibers, has emerged that can deliver more than 2,000 J/kg of specific work during muscle contraction, compared with just 40 J/kg for natural muscle. Thermally actuated muscles made from ordinary polymer fibers can deliver long-life, hysteresis-free tensile strokes of more than 30% and torsional actuation capable of spinning a paddle at speeds of more than 100,000 rpm. In this perspective, we explore the mechanisms and potential applications of present twisted fiber muscles and the future opportunities and challenges for developing twisted muscles having improved cycle rates, efficiencies, and functionality. We also demonstrate artificial muscle sewing threads and textiles and coiled structures that exhibit nearly unlimited actuation strokes. In addition to robotics and prosthetics, future applications include smart textiles that change breathability in response to temperature and moisture and window shutters that automatically open and close to conserve energy.

Brain Intelligence: Go Beyond Artificial Intelligence

OpenAIRE

Lu, Huimin; Li, Yujie; Chen, Min; Kim, Hyoungseop; Serikawa, Seiichi

2017-01-01

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication ...

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

Directory of Open Access Journals (Sweden)

Ganapathy Thirunavukkarasu

2009-01-01

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

Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

International Nuclear Information System (INIS)

Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

2010-01-01

Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)

The principles of artificial neural network information processing

International Nuclear Information System (INIS)

Dai, Ru-Wei

1993-01-01

In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

The principles of artificial neural network information processing

International Nuclear Information System (INIS)

Dai, Ru-Wei

1993-01-01

In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

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

Directory of Open Access Journals (Sweden)

Zahra 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.

Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

Science.gov (United States)

Stanke, J.; Trauth, D.; Feuerhack, A.; Klocke, F.

2017-09-01

Die roll is a morphological feature of fine blanked sheared edges. The die roll reduces the functional part of the sheared edge. To compensate for the die roll thicker sheet metal strips and secondary machining must be used. However, in order to avoid this, the influence of various fine blanking process parameters on the die roll has been experimentally and numerically studied, but there is still a lack of knowledge on the effects of some factors and especially factor interactions on the die roll. Recent changes in the field of artificial intelligence motivate the hybrid use of the finite element method and artificial neural networks to account for these non-considered parameters. Therefore, a set of simulations using a validated finite element model of fine blanking is firstly used to train an artificial neural network. Then the artificial neural network is trained with thousands of experimental trials. Thus, the objective of this contribution is to develop an artificial neural network that reliably predicts the die roll. Therefore, in this contribution, the setup of a fully parameterized 2D FE model is presented that will be used for batch training of an artificial neural network. The FE model enables an automatic variation of the edge radii of blank punch and die plate, the counter and blank holder force, the sheet metal thickness and part diameter, V-ring height and position, cutting velocity as well as material parameters covered by the Hensel-Spittel model for 16MnCr5 (1.7131, AISI/SAE 5115). The FE model is validated using experimental trails. The results of this contribution is a FE model suitable to perform 9.623 simulations and to pass the simulated die roll width and height automatically to an artificial neural network.

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

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

Science.gov (United States)

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

2014-09-10

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

Application of Artificial Intelligence for Bridge Deterioration Model

Directory of Open Access Journals (Sweden)

Zhang Chen

2015-01-01

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

The ulam Programming Language for Artificial Life.

Science.gov (United States)

Ackley, David H; Ackley, Elena S

2016-01-01

Traditional digital computing demands perfectly reliable memory and processing, so programs can build structures once then use them forever-but such deterministic execution is becoming ever more costly in large-scale systems. By contrast, living systems, viewed as computations, naturally tolerate fallible hardware by repairing and rebuilding structures even while in use-and suggest ways to compute using massive amounts of unreliable, merely best-effort hardware. However, we currently know little about programming without deterministic execution, in architectures where traditional models of computation-and deterministic ALife models such as the Game of Life-need not apply. This expanded article presents ulam, a language designed to balance concurrency and programmability upon best-effort hardware, using lifelike strategies to achieve robust and scalable computations. The article reviews challenges for traditional architecture, introduces the active-media computational model for which ulam is designed, and then presents the language itself, touching on its nomenclature and surface appearance as well as some broader aspects of robust software engineering. Several ulam examples are presented; then the article concludes with a brief consideration of the couplings between a computational model and its physical implementation.

Artificial intelligence and finite element modelling for monitoring flood defence structures

NARCIS (Netherlands)

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

2011-01-01

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

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.

Modelling of word usage frequency dynamics using artificial neural network

International Nuclear Information System (INIS)

Maslennikova, Yu S; Bochkarev, V V; Voloskov, D S

2014-01-01

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

A Model of Artificial Genotype and Norm of Reaction in a Robotic System

OpenAIRE

Durán Bosch, Ángel Juan; Pascual del Pobil Ferré, Ángel

2016-01-01

The genes of living organisms serve as large stores of information for replicating their behavior and morphology over generations. The evolutionary view of genetics that has inspired artificial systems with a Mendelian approach does not take into account the interaction between species and with the environment to generate a particular phenotype. In this paper, a genotype model is suggested to shape the relationship with the phenotype and the environment in an artificial system. A method to ob...

Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network

NARCIS (Netherlands)

Paudel, S.; Elmtiri, M.; Kling, W.L.; Corre, le O.; Lacarriere, B.

2014-01-01

This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider

Artificial organ engineering

CERN Document Server

Annesini, Maria Cristina; Piemonte, Vincenzo; Turchetti, Luca

2017-01-01

Artificial organs may be considered as small-scale process plants, in which heat, mass and momentum transfer operations and, possibly, chemical transformations are carried out. This book proposes a novel analysis of artificial organs based on the typical bottom-up approach used in process engineering. Starting from a description of the fundamental physico-chemical phenomena involved in the process, the whole system is rebuilt as an interconnected ensemble of elemental unit operations. Each artificial organ is presented with a short introduction provided by expert clinicians. Devices commonly used in clinical practice are reviewed and their performance is assessed and compared by using a mathematical model based approach. Whilst mathematical modelling is a fundamental tool for quantitative descriptions of clinical devices, models are kept simple to remain focused on the essential features of each process. Postgraduate students and researchers in the field of chemical and biomedical engineering will find that t...

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

Telomerase-mediated life-span extension of human primary fibroblasts by human artificial chromosome (HAC) vector

International Nuclear Information System (INIS)

Shitara, Shingo; Kakeda, Minoru; Nagata, Keiko; Hiratsuka, Masaharu; Sano, Akiko; Osawa, Kanako; Okazaki, Akiyo; Katoh, Motonobu; Kazuki, Yasuhiro; Oshimura, Mitsuo; Tomizuka, Kazuma

2008-01-01

Telomerase-mediated life-span extension enables the expansion of normal cells without malignant transformation, and thus has been thought to be useful in cell therapies. Currently, integrating vectors including the retrovirus are used for human telomerase reverse transcriptase (hTERT)-mediated expansion of normal cells; however, the use of these vectors potentially causes unexpected insertional mutagenesis and/or activation of oncogenes. Here, we established normal human fibroblast (hPF) clones retaining non-integrating human artificial chromosome (HAC) vectors harboring the hTERT expression cassette. In hTERT-HAC/hPF clones, we observed the telomerase activity and the suppression of senescent-associated SA-β-galactosidase activity. Furthermore, the hTERT-HAC/hPF clones continued growing beyond 120 days after cloning, whereas the hPF clones retaining the silent hTERT-HAC senesced within 70 days. Thus, hTERT-HAC-mediated episomal expression of hTERT allows the extension of the life-span of human primary cells, implying that gene delivery by non-integrating HAC vectors can be used to control cellular proliferative capacity of primary cultured cells

Unsteady flamelet modelling of spray flames using deep artificial neural networks

Science.gov (United States)

Owoyele, Opeoluwa; Kundu, Prithwish; Ameen, Muhsin; Echekki, Tarek; Som, Sibendu

2017-11-01

We investigate the applicability of the tabulated, multidimensional unsteady flamelet model and artificial neural networks (TFM-ANN) to lifted diesel spray flame simulations. The tabulated flamelet model (TFM), based on the widely known flamelet assumption, eliminates the use of a progress variable and has been shown to successfully model global diesel spray flame characteristics in previous studies. While the TFM has shown speed-up compared to other models and predictive capabilities across a range of ambient conditions, it involves the storage of multidimensional tables, requiring large memory and multidimensional interpolation schemes. This work discusses the implementation of deep artificial neural networks (ANN) to replace the use of large tables and multidimensional interpolation. The proposed framework is validated by applying it to an n-dodecane spray flame (ECN Spray A) at different conditions using a 4 dimensional flamelet library. The validations are then extended for the simulations using a 5-dimensional flamelet table applied to the combustion of methyl decanoate in a compression ignition engine. Different ANN topologies, optimization algorithms and speed-up techniques are explored and details of computational resources required for TFM-ANN and the TFM are also presented. The overall tools and algorithms used in this study can be directly extended to other multidimensional tabulated models.

Recent development in artificial photosynthetic model; Jinko kogosei no moderu ka kenkyu saikin no shinpo

Energy Technology Data Exchange (ETDEWEB)

Kaneko, M [Ibaraki Univ., Ibaraki (Japan). Faculty of Engineering

1996-03-01

In the conversion from solar energy into chemical energy (fuels) by photochemical conversion, an electron donor is necessary since all the fuels are reductive compounds. From the viewpoint of economic profit, water is the only one candidate as a cheap compound and existing impartially. In this paper, photosynthesis as well as the realization of its artificial model, and the relevant basic research executed recently aiming at the construction of an artificial photosynthetic system are explained. The main reaction of photosynthesis is the generation of carbohydrates by the reduction reaction of carbon dioxide with water as an electron donor and solar visual light as an energy resource. As a special example thereof, the UV photolysis of water due to the photocatalysis of a micro-particle system is introduced. The method of using a semiconductor and the method of using sensitizes are described as the photo excitation system when designing the artificial model. Additionally, as the research with respect to the construction of an artificial photosynthetic system, a photo-exciting charge transfer system is introduced. 27 refs., 1 fig.

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.

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

Science.gov (United States)

Ahadi, Arash; Kharrat, Riyaz

2014-01-01

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

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.

Identification of mathematical model of human breathing in system “Artificial lungs – self-contained breathing apparatus”

Science.gov (United States)

Onevsky, P. M.; Onevsky, M. P.; Pogonin, V. A.

2018-03-01

The structure and mathematical models of the main subsystems of the control system of the “Artificial Lungs” are presented. This structure implements the process of imitation of human external respiration in the system “Artificial lungs - self-contained breathing apparatus”. A presented algorithm for parametric identification of the model is based on spectral operators, which allows using it in real time.

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

NARCIS (Netherlands)

Melo, A.P.; Costola, D.; Lamberts, R.; Hensen, J.L.M.

2014-01-01

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

A review on the integration of artificial intelligence into coastal modeling.

Science.gov (United States)

Chau, Kwokwing

2006-07-01

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

[An artificial heart: bridge to transplantation or permanent?].

Science.gov (United States)

de Mol, Bas A J M; Lahpor, Jaap

2013-01-01

An artificial heart is a continuous-flow pump device with a constant output, which usually supports the left ventricle. Over the past five years, survival rates with an artificial heart have increased dramatically, but with an annual mortality of 10% per year compared with 6% for heart transplantation the artificial heart is mainly a 'bridge to transplantation' or an alternative for those patients who are not suitable for heart transplant, 'destination therapy'. It is anticipated that the number and severity of complications will decrease as a result of technological progress. The artificial heart could then become a long-term treatment option providing a good quality of life and thus become equivalent to a heart transplant.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

Science.gov (United States)

Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

2016-08-01

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

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

International Nuclear Information System (INIS)

Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

1998-01-01

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)

Soft computing in artificial intelligence

CERN Document Server

Matson, Eric

2014-01-01

This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...

Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering

Directory of Open Access Journals (Sweden)

Sara Martina Maffioletti

2018-04-01

Full Text Available Summary: Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. : Maffioletti et al. generate human 3D artificial skeletal muscles from healthy donors and patient-specific pluripotent stem cells. These human artificial muscles accurately model severe genetic muscle diseases. They can be engineered to include other cell types present in skeletal muscle, such as vascular cells and motor neurons. Keywords: skeletal muscle, pluripotent stem cells, iPS cells, myogenic differentiation, tissue engineering, disease modeling, muscular dystrophy, organoids

Implanted artificial heart with radioisotope power source

Energy Technology Data Exchange (ETDEWEB)

Shumakov, V I; Griaznov, G M; Zhemchuzhnikov, G N; Kiselev, I M; Osipov, A P

1983-02-01

An atomic artificial heart for orthotopic implantation was developed with the following characteristics: volume, 1.2 L; weight, 1.5 kg; radioisotope power, 45 W; operating life, up to 5 years; hemodynamics, similar to natural hemodynamics. The artificial heart includes a thermal drive with systems for regulating power, feeding steam into the cylinders, return of the condensate to the steam generator, and delivery of power to the ventricles and heat container. The artificial heart is placed in an artificial pericardium partially filled with physiologic solution. It uses a steam engine with two operating cylinders that separately drive the left and right ventricles. There is no electronic control system in the proposed design. The operation of the heat engine is controlled, with preservation of autoregulation by the vascular system of the body. The separate drives for the ventricles is of primary importance as it provides for operation of the artificial heart through control of cardiac activity by venous return. Experimental testing on a hydromechanical bench demonstrated effective autoregulation.

Intelligence: Real or artificial?

OpenAIRE

Schlinger, Henry D.

1992-01-01

Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally r...

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.

Perception of Sexual Orientation from Facial Structure: A Study with Artificial Face Models.

Science.gov (United States)

González-Álvarez, Julio

2017-07-01

Research has shown that lay people can perceive sexual orientation better than chance from face stimuli. However, the relation between facial structure and sexual orientation has been scarcely examined. Recently, an extensive morphometric study on a large sample of Canadian people (Skorska, Geniole, Vrysen, McCormick, & Bogaert, 2015) identified three (in men) and four (in women) facial features as unique multivariate predictors of sexual orientation in each sex group. The present study tested the perceptual validity of these facial traits with two experiments based on realistic artificial 3D face models created by manipulating the key parameters and presented to Spanish participants. Experiment 1 included 200 White and Black face models of both sexes. The results showed an overall accuracy (0.74) clearly above chance in a binary hetero/homosexual judgment task and significant differences depending on the race and sex of the face models. Experiment 2 produced five versions of 24 artificial faces of both sexes varying the key parameters in equal steps, and participants had to rate on a 1-7 scale how likely they thought that the depicted person had a homosexual sexual orientation. Rating scores displayed an almost perfect linear regression as a function of the parameter steps. In summary, both experiments demonstrated the perceptual validity of the seven multivariate predictors identified by Skorska et al. and open up new avenues for further research on this issue with artificial face models.

Artificial hydration and alimentation at the end of life: a reply to Craig.

Science.gov (United States)

Ashby, M; Stoffell, B

1995-06-01

Dr Gillian Craig (1) has argued that palliative medicine services have tended to adopt a policy of sedation without hydration, which under certain circumstances may be medically inappropriate, causative of death and distressing to family and friends. We welcome this opportunity to defend, with an important modification, the approach we proposed without substantive background argument in our original article (2). We maintain that slowing and eventual cessation of oral intake is a normal part of a natural dying process, that artificial hydration and alimentation (AHA) are not justified unless thirst or hunger are present and cannot be relieved by other means, but food and fluids for (natural) oral consumption should never be 'withdrawn'. The intention of this practice is not to alter the timing of an inevitable death, and sedation is not used, as has been alleged, to mask the effects of dehydration or starvation. The artificial provision of hydration and alimentation is now widely accepted as medical treatment. We believe that arguments that it is not have led to confusion as to whether or not non-provision or withdrawal of AHA constitutes a cause of death in law. Arguments that it is such a cause appear to be tenuously based on an extraordinary/ordinary categorisation of treatments by Kelly (3) which has subsequently been interpreted as prescriptive in a way quite inconsistent with the Catholic moral theological tradition from which the distinction is derived. The focus of ethical discourse on decisions at the end of life should be shifted to an analysis of care, needs, proportionality of medical interventions, and processes of communication.

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

Science.gov (United States)

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

2015-12-01

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

Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

OpenAIRE

Sukarno, Pudjo; Sidarto, Kuntjoro Adji; Trisnobudi, Amoranto; Setyoadi, Delint Ira; Rohani, Nancy; Darmadi, Darmadi

2007-01-01

Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained ...

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

DEFF Research Database (Denmark)

Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

2017-01-01

in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns...

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. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

A Quantum Implementation Model for Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Ammar Daskin

2018-02-01

Full Text Available The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase estimation algorithm is known to provide speedups over the conventional algorithms for the eigenvalue-related problems. Combining the quantum amplitude amplification with the phase estimation algorithm, a quantum implementation model for artificial neural networks using the Widrow–Hoff learning rule is presented. The complexity of the model is found to be linear in the size of the weight matrix. This provides a quadratic improvement over the classical algorithms. Quanta 2018; 7: 7–18.

An Artificial Intelligence-Based Environment Quality Analysis System

OpenAIRE

Oprea , Mihaela; Iliadis , Lazaros

2011-01-01

Part 20: Informatics and Intelligent Systems Applications for Quality of Life information Services (ISQLIS) Workshop; International audience; The paper describes an environment quality analysis system based on a combination of some artificial intelligence techniques, artificial neural networks and rule-based expert systems. Two case studies of the system use are discussed: air pollution analysis and flood forecasting with their impact on the environment and on the population health. The syste...

Artificial intelligence applications in information and communication technologies

CERN Document Server

Bouguila, Nizar

2015-01-01

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this book is to help Information and Communication Technologies (ICT) practitioners in managing efficiently their platforms using AI tools and methods and to provide them with sufficient Artificial Intelligence background to deal with real-life problems.  .

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. 2009 Elsevier Ltd. All rights reserved.

An effective hierarchical model for the biomolecular covalent bond: an approach integrating artificial chemistry and an actual terrestrial life system.

Science.gov (United States)

Oohashi, Tsutomu; Ueno, Osamu; Maekawa, Tadao; Kawai, Norie; Nishina, Emi; Honda, Manabu

2009-01-01

Under the AChem paradigm and the programmed self-decomposition (PSD) model, we propose a hierarchical model for the biomolecular covalent bond (HBCB model). This model assumes that terrestrial organisms arrange their biomolecules in a hierarchical structure according to the energy strength of their covalent bonds. It also assumes that they have evolutionarily selected the PSD mechanism of turning biological polymers (BPs) into biological monomers (BMs) as an efficient biomolecular recycling strategy We have examined the validity and effectiveness of the HBCB model by coordinating two complementary approaches: biological experiments using existent terrestrial life, and simulation experiments using an AChem system. Biological experiments have shown that terrestrial life possesses a PSD mechanism as an endergonic, genetically regulated process and that hydrolysis, which decomposes a BP into BMs, is one of the main processes of such a mechanism. In simulation experiments, we compared different virtual self-decomposition processes. The virtual species in which the self-decomposition process mainly involved covalent bond cleavage from a BP to BMs showed evolutionary superiority over other species in which the self-decomposition process involved cleavage from BP to classes lower than BM. These converging findings strongly support the existence of PSD and the validity and effectiveness of the HBCB model.

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

International Nuclear Information System (INIS)

Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

2006-01-01

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

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

Use of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shock.

Science.gov (United States)

Glass, Todd F; Knapp, Jason; Amburn, Philip; Clay, Bruce A; Kabrisky, Matt; Rogers, Steven K; Garcia, Victor F

2004-02-01

To determine whether a prototype artificial intelligence system can identify volume of hemorrhage in a porcine model of controlled hemorrhagic shock. Prospective in vivo animal model of hemorrhagic shock. Research foundation animal surgical suite; computer laboratories of collaborating industry partner. Nineteen, juvenile, 25- to 35-kg, male and female swine. Anesthetized animals were instrumented for arterial and systemic venous pressure monitoring and blood sampling, and a splenectomy was performed. Following a 1-hr stabilization period, animals were hemorrhaged in aliquots to 10, 20, 30, 35, 40, 45, and 50% of total blood volume with a 10-min recovery between each aliquot. Data were downloaded directly from a commercial monitoring system into a proprietary PC-based software package for analysis. Arterial and venous blood gas values, glucose, and cardiac output were collected at specified intervals. Electrocardiogram, electroencephalogram, mixed venous oxygen saturation, temperature (core and blood), mean arterial pressure, pulmonary artery pressure, central venous pressure, pulse oximetry, and end-tidal CO(2) were continuously monitored and downloaded. Seventeen of 19 animals (89%) died as a direct result of hemorrhage. Stored data streams were analyzed by the prototype artificial intelligence system. For this project, the artificial intelligence system identified and compared three electrocardiographic features (R-R interval, QRS amplitude, and R-S interval) from each of nine unknown samples of the QRS complex. We found that the artificial intelligence system, trained on only three electrocardiographic features, identified hemorrhage volume with an average accuracy of 91% (95% confidence interval, 84-96%). These experiments demonstrate that an artificial intelligence system, based solely on the analysis of QRS amplitude, R-R interval, and R-S interval of an electrocardiogram, is able to accurately identify hemorrhage volume in a porcine model of lethal

Variable recruitment fluidic artificial muscles: modeling and experiments

International Nuclear Information System (INIS)

Bryant, Matthew; Meller, Michael A; Garcia, Ephrahim

2014-01-01

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)

Predicting concrete corrosion of sewers using artificial neural network.

Science.gov (United States)

Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

2016-04-01

Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

DEFF Research Database (Denmark)

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

1998-01-01

kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks 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...

Ground Motion Prediction Model Using Artificial Neural Network

Science.gov (United States)

Dhanya, J.; Raghukanth, S. T. G.

2018-03-01

This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude ( M w), closest distance to rupture plane ( R rup), shear wave velocity in the region ( V s30) and focal mechanism ( F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

Directory of Open Access Journals (Sweden)

Ye Jin

2018-04-01

Full Text Available The Artificial Bee Colony (ABC algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

Investigation of an artificial intelligence technology--Model trees. Novel applications for an immediate release tablet formulation database.

Science.gov (United States)

Shao, Q; Rowe, R C; York, P

2007-06-01

This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.

Artificial neural network models' application for radioactive substances' migration forecasting in soil

International Nuclear Information System (INIS)

Kovalenko, V.I.; Khil'ko, O.S.; Kundas, S.P.

2009-01-01

The work is indicated to the use of artificial neural network (ANN) models in program complex SPS for radioactive substances' migration forecasting in soil. For the problem solution two ANN models are used. One of them forecasts radioactive substances' migration, another carries out forecasting of physical and chemical soil properties. Program complex SPS allows to achieve a low error of forecasting (no more than 5 %) and high training speed. (authors)

Artificial intelligence and the future.

Science.gov (United States)

Clocksin, William F

2003-08-15

We consider some of the ideas influencing current artificial-intelligence research and outline an alternative conceptual framework that gives priority to social relationships as a key component and constructor of intelligent behaviour. The framework starts from Weizenbaum's observation that intelligence manifests itself only relative to specific social and cultural contexts. This is in contrast to a prevailing view, which sees intelligence as an abstract capability of the individual mind based on a mechanism for rational thought. The new approach is not based on the conventional idea that the mind is a rational processor of symbolic information, nor does it require the idea that thought is a kind of abstract problem solving with a semantics that is independent of its embodiment. Instead, priority is given to affective and social responses that serve to engage the whole agent in the life of the communities in which it participates. Intelligence is seen not as the deployment of capabilities for problem solving, but as constructed by the continual, ever-changing and unfinished engagement with the social group within the environment. The construction of the identity of the intelligent agent involves the appropriation or 'taking up' of positions within the conversations and narratives in which it participates. Thus, the new approach argues that the intelligent agent is shaped by the meaning ascribed to experience, by its situation in the social matrix, and by practices of self and of relationship into which intelligent life is recruited. This has implications for the technology of the future, as, for example, classic artificial intelligence models such as goal-directed problem solving are seen as special cases of narrative practices instead of as ontological foundations.

Artificial Intelligence (AI) Studies in Water Resources

OpenAIRE

Ay, Murat; Özyıldırım, Serhat

2018-01-01

Artificial intelligence has been extensively used in many areas such as computer science,robotics, engineering, medicine, translation, economics, business, and psychology. Variousstudies in the literature show that the artificial intelligence in modeling approaches give closeresults to the real data for solution of linear, non-linear, and other systems. In this study, wereviewed the current state-of-the-art and progress on the modelling of artificial intelligence forwater variables: rainfall-...

Quality of life among lower limb prosthesis users attending Artificial Limb Center of Fauji Foundation Hospital Rawalpindi

International Nuclear Information System (INIS)

Kalsoom, U.; Bairam, S.

2018-01-01

To assess quality of life and the influencing factors as perceived by lower limb prosthesis users. Study Design: Cross sectional study. Place and Duration of Study: Artificial Limb Centre, Fauji Foundation Hospital Rawalpindi, 11 months (Jan to Nov 2014). Material and Methods: A sample of 50 patients with lower limb amputations and using lower limb prosthesis were recruited by applying inclusion criteria. Responses were recorded on demographic variables, reasons for amputation, time of provision of prosthesis, employment status etc. by the investigator on a structured pretested questionnaire after their consent. A p-value of <0.05 was taken as significant. Quality of life was interpreted on 5 point Likert scale. Data was analyzed in SPSS version 21. Result: Majority of respondents i.e. 17 (34%) were in 51-60 years age group with mean age 46 +- 14 years while 33 (66%) were males and 27 (54%) illiterate, 33 (66%) could afford a prosthesis. Trauma 28 (56%) was the most prevalent cause of lower limb amputation and among them 15 (53%) had road accident injuries. Various factors influenced quality of life as perceived by the respondents and in 10 (20%), 16 (32%), 25 (50%) social, family life and emotional status was affected respectively, while 14 (28%) of the prosthesis user were unemployed. Prosthesis were provided after 2 years in 18 (36%) and in 37 (74%) prosthesis were provided by the health care facility. Good quality of life was perceived in 40 (80%) respondents. Conclusion: Perceived Quality of life was good in majority of the prosthesis users but others can't be ignored. It can be improved by early provision of prosthesis which can help in better employment opportunities. (author)

A model of pathways to artificial superintelligence catastrophe for risk and decision analysis

Science.gov (United States)

Barrett, Anthony M.; Baum, Seth D.

2017-03-01

An artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision-making on the long-term risk of ASI catastrophe.

Design Of the Approximation Function of a Pedometer based on Artificial Neural Network for the Healthy Life Style Promotion in Diabetic Patients

OpenAIRE

Vega Corona, Antonio; Zárate Banda, Magdalena; Barron Adame, Jose Miguel; Martínez Celorio, René Alfredo; Andina de la Fuente, Diego

2008-01-01

The present study describes the design of an Artificial Neural Network to synthesize the Approximation Function of a Pedometer for the Healthy Life Style Promotion. Experimentally, the approximation function is synthesized using three basic digital pedometers of low cost, these pedometers were calibrated with an advanced pedometer that calculates calories consumed and computes distance travelled with personal stride input. The synthesized approximation function by means of the designed neural...

Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

Science.gov (United States)

Arif, N.; Danoedoro, P.; Hartono

2017-12-01

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

Narrative theories as computational models: reader-oriented theory and artificial intelligence

Energy Technology Data Exchange (ETDEWEB)

Galloway, P.

1983-12-01

In view of the rapid development of reader-oriented theory and its interest in dynamic models of narrative, the author speculates in a serious way about what such models might look like in computational terms. Researchers in artificial intelligence (AI) have already begun to develop models of story understanding as the emphasis in ai research has shifted toward natural language understanding and as ai has allied itself with cognitive psychology and linguistics to become cognitive science. Research in ai and in narrative theory share many common interests and problems and both studies might benefit from an exchange of ideas. 11 references.

Modular Extended-Stay HyperGravity Facility Design Concept: An Artificial-Gravity Space-Settlement Ground Analogue

Science.gov (United States)

Dorais, Gregory A.

2015-01-01

This document defines the design concept for a ground-based, extended-stay hypergravity facility as a precursor for space-based artificial-gravity facilities that extend the permanent presence of both human and non-human life beyond Earth in artificial-gravity settlements. Since the Earth's current human population is stressing the environment and the resources off-Earth are relatively unlimited, by as soon as 2040 more than one thousand people could be living in Earthorbiting artificial-gravity habitats. Eventually, the majority of humanity may live in artificialgravity habitats throughout this solar system as well as others, but little is known about the longterm (multi-generational) effects of artificial-gravity habitats on people, animals, and plants. In order to extend life permanently beyond Earth, it would be useful to create an orbiting space facility that generates 1g as well as other gravity levels to rigorously address the numerous challenges of such an endeavor. Before doing so, developing a ground-based artificial-gravity facility is a reasonable next step. Just as the International Space Station is a microgravity research facility, at a small fraction of the cost and risk a ground-based artificial-gravity facility can begin to address a wide-variety of the artificial-gravity life-science questions and engineering challenges requiring long-term research to enable people, animals, and plants to live off-Earth indefinitely.

Trimaran Resistance Artificial Neural Network

Science.gov (United States)

2011-01-01

11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Directory of Open Access Journals (Sweden)

Tolga Kaya

2010-11-01

Full Text Available The purpose of this study is to compare the performances of Artificial Neural Networks (ANN and Multinomial Probit (MNP approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight.

Development of Urban Inundation Warning Model at Cyclic Artificial Water Way in Song-do International City, Republic of Korea

Science.gov (United States)

Lee, T.; Lee, C.; Kim, H.

2016-12-01

Abstract Song-do international city was constructed by reclaiming land from the coastal waters of Yeonsu-gu, Incheon Metropolitan City, Republic of Korea. The □-shaped cyclic artificial water way has been considered for improving water quality, waterfront and internal drainage in Song-do international city. By improving water quality, various marine facilities, such as marina, artificial beach, marine terminal, and so on, will be set up around the artificial water way for the waterfront. Since the water stage of the artificial water way changes depending on water gates operations, it is necessary to develop an urban inundation warning model to evaluate safeties of the waterfront facilities and its passengers. By considering characteristics of urban watershed, we calculate discharge flowing into the water way using XP-SWMM model. As a result of estimating 100-year flood frequency, although there are slight differences in drainage sections, the maximum flood discharge occurs in 90-min rainfall duration. In order to consider impacts of tide and hydraulic structure, we establish Inland drainage plans through the analysis of unsteady flow using HEC-RAS. The urban inundation warning model is configured to issue a warning when the water plain elevation exceeds EL. 1.5m which is usually managed at EL. 1.0m. In this study, the design flood stage of artificial water way and urban inundation warning model are developed for Song-do international city, and therefore it is expected that a reliability of management and operation of the waterfront facilities is improved. Keywords : Artificial Water Way; Waterfront; Urban Inundation Warning Model. Acknowlegement This research was supported by a grant [MPSS-NH-2015-79] through the Disaster and Safety Management Institute funded by Ministry of Public Safety and Security of Korean government.

Goles en césped artificial: más duradero y sostenible

OpenAIRE

Sanchís Almenara, Mercedes; Rosa Mañez, David; Magraner Llavador, Laura; Pellicer Chenoll, Maria Teresa; GARCIA BELENGUER, ANA CRUZ; Ballester Fernandez, Alfredo; Chirivella Moreno, Carlos; Garcés Pérez, Luis

2012-01-01

[EN] Currently there are installed approximately 25 millions of square meters of artificial turf for contact sports use in Europe (football, rugby, etc.). It means that at the end of their cycle of life 400.000 tones of non-dangerous materials will be placed in controlled landfills. The first aim of this project is to increase the useful life of artificial turf pitches in good conditions of use; and the second objective is to reuse and/or recycle the materi...

Artificial companions as personal coach for children: The Interactive Drums Teacher

OpenAIRE

Courgeon , Matthieu; Duhaut , Dominique

2015-01-01

International audience; The MOCA Project that aims at designing and studying the interaction and relationship between artificial companions and children in everyday life at home activities. Artificial companions are digital embodied entities that can be either robotic or virtual. In this paper, we focus on a single activity, subpart of the whole project: a coaching application that uses two artificial companions to teach the basics of drums to children. One device is a Nao robot, the other is...

Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling

Directory of Open Access Journals (Sweden)

Changqing Meng

2016-09-01

Full Text Available A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC model with artificial neural networks (ANNs. In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation. The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

Science.gov (United States)

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

A DISTRIBUTED SMART HOME ARTIFICIAL INTELLIGENCE SYSTEM

DEFF Research Database (Denmark)

Lynggaard, Per

2013-01-01

A majority of the research performed today explore artificial intelligence in smart homes by using a centralized approach where a smart home server performs the necessary calculations. This approach has some disadvantages that can be overcome by shifting focus to a distributed approach where...... the artificial intelligence system is implemented as distributed as agents running parts of the artificial intelligence system. This paper presents a distributed smart home architecture that distributes artificial intelligence in smart homes and discusses the pros and cons of such a concept. The presented...... distributed model is a layered model. Each layer offers a different complexity level of the embedded distributed artificial intelligence. At the lowest layer smart objects exists, they are small cheap embedded microcontroller based smart devices that are powered by batteries. The next layer contains a more...

Development and validation of deterioration models for concrete bridge decks - phase 1 : artificial intelligence models and bridge management system.

Science.gov (United States)

2013-06-01

This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge invento...

Spatially Resolved Artificial Chemistry

DEFF Research Database (Denmark)

Fellermann, Harold

2009-01-01

Although spatial structures can play a crucial role in chemical systems and can drastically alter the outcome of reactions, the traditional framework of artificial chemistry is a well-stirred tank reactor with no spatial representation in mind. Advanced method development in physical chemistry has...... made a class of models accessible to the realms of artificial chemistry that represent reacting molecules in a coarse-grained fashion in continuous space. This chapter introduces the mathematical models of Brownian dynamics (BD) and dissipative particle dynamics (DPD) for molecular motion and reaction...

Spatially Resolved Artificial Chemistry

DEFF Research Database (Denmark)

Fellermann, Harold

2009-01-01

made a class of models accessible to the realms of artificial chemistry that represent reacting molecules in a coarse-grained fashion in continuous space. This chapter introduces the mathematical models of Brownian dynamics (BD) and dissipative particle dynamics (DPD) for molecular motion and reaction......Although spatial structures can play a crucial role in chemical systems and can drastically alter the outcome of reactions, the traditional framework of artificial chemistry is a well-stirred tank reactor with no spatial representation in mind. Advanced method development in physical chemistry has...

Life course socio-economic position and quality of life in adulthood: a systematic review of life course models

Science.gov (United States)

2012-01-01

Background A relationship between current socio-economic position and subjective quality of life has been demonstrated, using wellbeing, life and needs satisfaction approaches. Less is known regarding the influence of different life course socio-economic trajectories on later quality of life. Several conceptual models have been proposed to help explain potential life course effects on health, including accumulation, latent, pathway and social mobility models. This systematic review aimed to assess whether evidence supported an overall relationship between life course socio-economic position and quality of life during adulthood and if so, whether there was support for one or more life course models. Methods A review protocol was developed detailing explicit inclusion and exclusion criteria, search terms, data extraction items and quality appraisal procedures. Literature searches were performed in 12 electronic databases during January 2012 and the references and citations of included articles were checked for additional relevant articles. Narrative synthesis was used to analyze extracted data and studies were categorized based on the life course model analyzed. Results Twelve studies met the eligibility criteria and used data from 10 datasets and five countries. Study quality varied and heterogeneity between studies was high. Seven studies assessed social mobility models, five assessed the latent model, two assessed the pathway model and three tested the accumulation model. Evidence indicated an overall relationship, but mixed results were found for each life course model. Some evidence was found to support the latent model among women, but not men. Social mobility models were supported in some studies, but overall evidence suggested little to no effect. Few studies addressed accumulation and pathway effects and study heterogeneity limited synthesis. Conclusions To improve potential for synthesis in this area, future research should aim to increase study

Application of life cycle assessment to production processes of environmentally sustainable concrete, prepared with artificial aggregates

International Nuclear Information System (INIS)

Vaccaro, R.; Colangelo, F.; Palumbo, M.; Cioffi, R.

2005-01-01

This paper is about the application of Life Cycle Assessment (L.C.A.) on environmentally sustainable concrete production processes. The goal of this experimentations is to assess environmental impact and energy demand related to concrete production, by using, in different admixtures, natural and artificial aggregates, belonging from treatments of different kind of industrial wastes characterized by very small particle sizes. Particular attention was concentrated on the utilization of fine fraction since it is difficult to recover in usual fields of recycling (i.e. aggers, crowl spaces, etc.). This study follows the approach from cradle to cradle. This experimentation was conducted in relation to four concrete admixtures produced, one of them containing only natural aggregate, and the other ones obtained by substituting the 10% of aggregate respectively with inert wastes as construction and demolition waste (CeD waste). cement kiln dust (CKD) and marble sludge. For all admixtures six different end-life scenarios have been proposed, one of them considers all materials transported in landfill while the other ones consider a partial transportation on landfill (15%) and a recycle of the 85% of wastes obtained after demolition of structures [it

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

International Nuclear Information System (INIS)

Ramaiah, Gurumurthy B.; Chennaiah, Radhalakshmi Y.; Satyanarayanarao, Gurumurthy K.

2010-01-01

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.

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

Directory of Open Access Journals (Sweden)

Suryanita Reni

2017-01-01

Full Text Available The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1, Immediate Occupancy (2, Life Safety (3 or in a condition of Collapse Prevention (4. According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network

Matter and symbols of the artificial

Energy Technology Data Exchange (ETDEWEB)

Rocha, L.M.

1998-08-01

The study of complex systems should be based on a systems-theoretic framework which requires both self-organizing and symbolic dimensions. An inclusive framework based on the notion of semiotics is advanced to build models capable of representing, as well as evolving in their environments, with implications for Artificial Life. Such undertaking is pursued by discussing the ways in which symbol and matter are irreducibly intertwined in evolutionary systems. The problem is thus phrased in terms of the semiotic categories of syntax, semantics, and pragmatics. With this semiotic view of matter and symbols the requirements of semiotic closure are expressed in models with both self-organizing and symbolic characteristics. Situated action and recent developments in the evolution of cellular automata rules to solve non-trivial tasks are discussed in this context. Finally, indirect encoding schemes for genetic algorithms are developed which follow the semiotic framework here proposed.

Artificial Neural Network Modelling of the Energy Content of Municipal Solid Wastes in Northern Nigeria

Directory of Open Access Journals (Sweden)

M. B. Oumarou

2017-12-01

Full Text Available The study presents an application of the artificial neural network model using the back propagation learning algorithm to predict the actual calorific value of the municipal solid waste in major cities of the northern part of Nigeria, with high population densities and intense industrial activities. These cities are: Kano, Damaturu, Dutse, Bauchi, Birnin Kebbi, Gusau, Maiduguri, Katsina and Sokoto. Experimental data of the energy content and the physical characterization of the municipal solid waste serve as the input parameter in nature of wood, grass, metal, plastic, food remnants, leaves, glass and paper. Comparative studies were made by using the developed model, the experimental results and a correlation which was earlier developed by the authors to predict the energy content. While predicting the actual calorific value, the maximum error was 0.94% for the artificial neural network model and 5.20% by the statistical correlation. The network with eight neurons and an R2 = 0.96881 in the hidden layer results in a stable and optimum network. This study showed that the artificial neural network approach could successfully be used for energy content predictions from the municipal solid wastes in Northern Nigeria and other areas of similar waste stream and composition.

Parental perceptions of forgoing artificial nutrition and hydration during end-of-life care.

Science.gov (United States)

Rapoport, Adam; Shaheed, Jenny; Newman, Christine; Rugg, Maria; Steele, Rose

2013-05-01

Forgoing artificial nutrition and hydration (FANH) in children at the end of life (EOL) is a medically, legally, and ethically acceptable practice under specific circumstances. However, most of the evidence on FANH involves dying adults. There is a paucity of pediatric evidence to guide health care providers' and parents' decision-making around this practice. Objectives were (1) to explore the experiences of bereaved parents when a decision had been made to FANH during EOL care for their child and (2) to describe the perceived quality of death in these children, as reported by their parents. This was a qualitative study using in-depth interviews with parents whose children died after a decision to FANH. Parental perceptions about the experience and their child's quality of death were explored. Interviews were audiotaped and transcribed, then data were analyzed by using interpretive description methodology. All parents were satisfied with their decision to FANH and believed that their child's death was generally peaceful and comfortable. The child's perceived poor quality of life was central to the decision to FANH, with feeding intolerance often contributing to this perception. Despite overall satisfaction, all parents had doubts and questions about the decision and benefited from ongoing assurances from the clinical team. FANH in children at the EOL is an acceptable form of palliation for some parents and may contribute to a death that is perceived to be peaceful and comfortable. In situations in which FANH may be a reasonable possibility, physicians should be prepared to introduce the option.

Preliminary results of Physiological plant growth modelling for human life support in space

Science.gov (United States)

Sasidharan L, Swathy; Dussap, Claude-Gilles; Hezard, Pauline

2012-07-01

Human life support is fundamental and crucial in any kind of space explorations. MELiSSA project of European Space Agency aims at developing a closed, artificial ecological life support system involving human, plants and micro organisms. Consuming carbon dioxide and water from the life support system, plants grow in one of the chambers and convert it into food and oxygen along with potable water. The environmental conditions, nutrient availability and its consumption of plants should be studied and necessarily modeled to predict the amount of food, oxygen and water with respect to the environmental changes and limitations. The reliability of a completely closed system mainly depends on the control laws and strategies used. An efficient control can occur, only if the system to control is itself well known, described and ideally if the responses of the system to environmental changes are predictable. In this aspect, the general structure of plant growth model has been designed together with physiological modelling.The physiological model consists of metabolic models of leaves, stem and roots, of which concern specific metabolisms of the associated plant parts. On the basis of the carbon source transport (eg. sucrose) through stem, the metabolic models (leaf and root) can be interconnected to each other and finally coupled to obtain the entire plant model. For the first step, leaf metabolic model network was built using stoichiometric, mass and energy balanced metabolic equations under steady state approach considering all necessary plant pathways for growth and maintenance of leaves. As the experimental data for lettuce plants grown in closed and controlled environmental chambers were available, the leaf metabolic model has been established for lettuce leaves. The constructed metabolic network is analyzed using known stoichiometric metabolic technique called metabolic flux analysis (MFA). Though, the leaf metabolic model alone is not sufficient to achieve the

Introduction to Concepts in Artificial Neural Networks

Science.gov (United States)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

On prognostic models, artificial intelligence and censored observations.

Science.gov (United States)

Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

2001-03-01

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Application of artificial intelligence to the management of urological cancer.

Science.gov (United States)

Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

2007-10-01

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

Science.gov (United States)

Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

2018-06-01

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Artificial organs 2011: a year in review.

Science.gov (United States)

Malchesky, Paul S

2012-03-01

In this Editor's Review, articles published in 2011 are organized by category and briefly summarized. As the official journal of The International Federation for Artificial Organs, The International Faculty for Artificial Organs, and the International Society for Rotary Blood Pumps, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level."Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ replacement, recovery, and regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide meaningful suggestions to the author's work whether eventually accepted or rejected. Without these excellent and dedicated reviewers, the quality expected from such a journal would not be possible. We also express our special thanks to our Publisher, Wiley-Blackwell, for their expert attention and support in the production and marketing of Artificial Organs. In this Editor's Review, that historically has been widely well-received by our readership, we aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ replacement, recovery, and regeneration. We look forward to recording further advances in the coming years. © 2012, Copyright the Author. Artificial Organs © 2012, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Artificial Organs 2012: a year in review.

Science.gov (United States)

Malchesky, Paul S

2013-03-01

In this editor's review, articles published in 2012 are organized by category and briefly summarized. We aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ replacement, recovery, and regeneration. As the official journal of the International Federation for Artificial Organs, the International Faculty for Artificial Organs, and the International Society for Rotary Blood Pumps, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level." Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ replacement, recovery, and regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide such meaningful suggestions to the author's work whether eventually accepted or rejected, and especially to those whose native tongue is not English. Without these excellent and dedicated reviewers, the quality expected from such a journal could not be possible. We also express our special thanks to our publisher, Wiley Periodicals, for their expert attention and support in the production and marketing of Artificial Organs. We look forward to recording further advances in the coming years. © 2013, Copyright the Author. Artificial Organs © 2013, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

DEVELOPING A HUMAN CONTROLLED MODEL FOR SAFE ARTIFICIAL INTELLIGENCE SYSTEMS

OpenAIRE

KÖSE, Utku

2018-01-01

Artificial Intelligence is known as one of the most effective research field of nowadays and the future. But rapid rise of Artificial Intelligence and its potential to solve all real world problems autonomously, it has caused also several anxieties. Some scientists think that intelligent systems can reach to a level, which is dangerous for the humankind so because of that some precautions should be taken. So, many sub-research fields like Machine Ethics or Artificial Intelligence Safety have ...

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.

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.

Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

Science.gov (United States)

Yousif, Adil; Elfaki, Faiz

2017-12-01

The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

A Multidisciplinary Artificial Intelligence Model of an Affective Robot

Directory of Open Access Journals (Sweden)

Hooman Aghaebrahimi Samani

2012-03-01

Full Text Available 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 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.

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.

Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

Directory of Open Access Journals (Sweden)

Beigi Mohsen

2017-01-01

Full Text Available The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

The Adult Life Spiral: A Critique of the Life Cycle Model.

Science.gov (United States)

Stein, Peter; Etzkowitz, Henry

We can identify and describe alternate paths of adulthood utilizing data from interviews with single adults. Our review of major models used in adulthood studies suggests that a developmental model, such as Daniel Levinson's life cycle model, is too tied to the notion of the imminent unfolding of the life course. The age-stratification theory…

Artificial muscle for reanimation of the paralyzed face: durability and biocompatibility in a gerbil model.

Science.gov (United States)

Ledgerwood, Levi G; Tinling, Steven; Senders, Craig; Wong-Foy, Annjoe; Prahlad, Harsha; Tollefson, Travis T

2012-11-01

Current management of permanent facial paralysis centers on nerve grafting and muscle transfer; however, limitations of those procedures call for other options. To determine the durability and biocompatibility of implanted artificial muscle in a gerbil model and the degree of inflammation and fibrosis at the host tissue-artificial muscle interface. Electroactive polymer artificial muscle (EPAM) devices engineered in medical-grade silicone were implanted subcutaneously in 13 gerbils. The implanted units were stimulated with 1 kV at 1 Hz, 24 h/d via a function generator. Electrical signal input/output was recorded up to 40 days after implantation. The animals were euthanized between 23 and 65 days after implantation, and the host tissue-implant interface was evaluated histologically. The animals tolerated implantation of the EPAM devices well, with no perioperative deaths. The muscle devices created motion for a mean of 30.3 days (range, 19-40 days), with a mean of 2.6 × 106 cycles (range, 1.6 × 106 to 3.5 × 106 cycles). Histologic examination of the explanted devices revealed the development of a minimal fibrous capsule surrounding the implants, with no evidence of bacterial infection or inflammatory infiltrate. No evidence of device compromise, corrosion, or silicone breakdown was noted. Artificial muscle implanted in this short-term animal model was safe and functional in this preliminary study. We believe that EPAM devices will be a safe and viable option for restoration of facial motions in patients with irreversible facial paralysis.

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...

HUMAN EXPOSURE TO THE ARTIFICIAL RADIONUCLIDES IN ENVIRONMENT

Directory of Open Access Journals (Sweden)

Ivana Vukanac

2012-09-01

Full Text Available Artificial radionuclides are product of different human activities and their presence in the environment is negative side effect of civilization progress. They have been spread in the environment by events such as nuclear weapon tests, nuclear accidents and by deliberate and negligent discharge of radioactive waste from nuclear and other installation. Once released in to the nature, the artificial radionuclides start to circle in the same manner as naturally occurring ones, and finally they fall out from air and water onto the ground and build into the foodstuff and drinking water resulting in radiation doses to human beings. The short overview of presence of artificial radioactivity in human environment and its impact on human life is presented in this paper.

HUMAN EXPOSURE TO THE ARTIFICIAL RADIONUCLIDES IN ENVIRONMENT

Directory of Open Access Journals (Sweden)

Ivana Vukanac

2012-09-01

Full Text Available Artificial radionuclides are product of different human activities and their presence in the environment is negative side effect of civilization progress. They have been spread in the environment by events such as nuclear weapon tests, nuclear accidents and by deliberate and negligent discharge of radioactive waste from nuclear and other installation. Once released in to the nature, the artificial radionuclides start to circle in the same manner as naturally occurring ones, and finally they fall out from air and water onto the ground and build into the foodstuff and drinking water resulting in radiation doses to human beings. The short overview of presence of artificial radioactivity in human environment and its impact on human life is presented in this paper

Mathematical Modeling Of Life-Support Systems

Science.gov (United States)

Seshan, Panchalam K.; Ganapathi, Balasubramanian; Jan, Darrell L.; Ferrall, Joseph F.; Rohatgi, Naresh K.

1994-01-01

Generic hierarchical model of life-support system developed to facilitate comparisons of options in design of system. Model represents combinations of interdependent subsystems supporting microbes, plants, fish, and land animals (including humans). Generic model enables rapid configuration of variety of specific life support component models for tradeoff studies culminating in single system design. Enables rapid evaluation of effects of substituting alternate technologies and even entire groups of technologies and subsystems. Used to synthesize and analyze life-support systems ranging from relatively simple, nonregenerative units like aquariums to complex closed-loop systems aboard submarines or spacecraft. Model, called Generic Modular Flow Schematic (GMFS), coded in such chemical-process-simulation languages as Aspen Plus and expressed as three-dimensional spreadsheet.

Modelling of pneumatic muscle actuator using Hill's model with different approximations of static characteristics of artificial muscle

OpenAIRE

Piteľ Ján; Tóthová Mária

2016-01-01

For modelling and simulation of pneumatic muscle actuators the mathematical dependence of the muscle force on the muscle contraction at different pressures in the muscles is necessary to know. For this purpose the static characteristics of the pneumatic artificial muscle type FESTO MAS-20-250N used in the experiments were approximated. In the paper there are shown some simulation results of the pneumatic muscle actuator dynamics using modified Hill's muscle model, in which four different appr...

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...

What Is an Artificial Muscle? A Systemic Approach.

OpenAIRE

Bertrand Tondu

2015-01-01

Artificial muscles define a large category of actuators we propose to analyze in a systemic framework by considering any artificial muscle as an open-loop stable system for any output which represents an artificial muscle dimension resulting from its “contraction”, understood in a broad meaning. This approach makes it possible to distinguish the artificial muscle from other actuators and to specify an original model for a linear artificial muscle, according to the theory of linear systems. Su...

Artificial magnetic metamaterial design by using spiral resonators

OpenAIRE

Baena, J.D.; Marqués Sillero, Ricardo; Medina Mena, Francisco; Martel Villagrán, Jesús

2004-01-01

A metallic planar particle, that will be called spiral resonator (SR), is introduced as a useful artificial atom for artificial magnetic media design and fabrication. A simple theoretical model which provides the most relevant properties and parameters of the SR is presented. The model is validated by both electromagnetic simulation and experiments. The applications of SR's include artificial negative magnetic permeability media (NMPM) and left-handed-media (LHM) design. The main advantages o...

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

NARCIS (Netherlands)

Rezaeianzadeh, M.; Stein, A.; Tabari, H.; Abghari, H.; Jalalkamali, N.; Hosseinipour, E.Z.; Singh, V.P.

2013-01-01

Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied

Artificial organs 2010: a year in review.

Science.gov (United States)

Malchesky, Paul S

2011-03-01

In this Editor's Review, articles published in 2010 are organized by category and briefly summarized. As the official journal of The International Federation for Artificial Organs, The International Faculty for Artificial Organs, and the International Society for Rotary Blood Pumps, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level."Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide such meaningful suggestions to the author's work whether eventually accepted or rejected and especially to those whose native tongue is not English. Without these excellent and dedicated reviewers the quality expected from such a journal could not be possible. We also express our special thanks to our Publisher, Wiley-Blackwell, for their expert attention and support in the production and marketing of Artificial Organs. In this Editor's Review, that historically has been widely received by our readership, we aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ Replacement, Recovery, and Regeneration. We look forward to recording further advances in the coming years. © 2011, Copyright the Author. Artificial Organs © 2011, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Could a functional artificial skeletal muscle be useful in muscle wasting?

Science.gov (United States)

Fuoco, Claudia; Cannata, Stefano; Gargioli, Cesare

2016-05-01

Regardless of the underlying cause, skeletal muscle wasting is detrimental for a person's life quality, leading to impaired strength, locomotion, and physiological activity. Here, we propose a series of studies presenting tissue engineering-based approaches to reconstruct artificial muscle in vitro and in vivo. Skeletal muscle tissue engineering is attracting more and more attention from scientists, clinicians, patients, and media, thanks to the promising results obtained in the last decade with animal models of muscle wasting. The use of novel and refined biomimetic scaffolds mimicking three-dimensional muscle environment, thus supporting cell survival and differentiation, in combination with well characterized myogenic stem/progenitor cells, revealed the noteworthy potential of these technologies for creating artificial skeletal muscle tissue. In vitro, the production of three-dimensional muscle structures offer the possibility to generate a drug-screening platform for patient-specific pharmacological treatment, opening new frontiers in the development of new compounds with specific therapeutic actions. In vivo, three-dimensional artificial muscle biomimetic constructs offer the possibility to replace, in part or entirely, wasted muscle by means of straight reconstruction and/or by enhancing endogenous regeneration. Reports of tissue engineering approaches for artificial muscle building appeared in large numbers in the specialized press lately, advocating the suitability of this technology for human application upon scaling up and a near future applicability for medical care of muscle wasting. http://links.lww.com/COCN/A9

Artificial Consciousness or Artificial Intelligence

OpenAIRE

Spanache Florin

2017-01-01

Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus a...

Artificial Organs 2015: A Year in Review.

Science.gov (United States)

Malchesky, Paul S

2016-03-01

In this Editor's Review, articles published in 2015 are organized by category and briefly summarized. We aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ Replacement, Recovery, and Regeneration. As the official journal of The International Federation for Artificial Organs, The International Faculty for Artificial Organs, the International Society for Rotary Blood Pumps, the International Society for Pediatric Mechanical Cardiopulmonary Support, and the Vienna International Workshop on Functional Electrical Stimulation, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level." Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. We take this time also to express our gratitude to our authors for providing their work to this journal. We offer our very special thanks to our reviewers who give so generously of their time and expertise to review, critique, and especially provide meaningful suggestions to the author's work whether eventually accepted or rejected. Without these excellent and dedicated reviewers, the quality expected from such a journal could not be possible. We also express our special thanks to our Publisher, John Wiley & Sons for their expert attention and support in the production and marketing of Artificial Organs. We look forward to reporting further advances in the coming years. Copyright © 2016 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Artificial Organs 2013: a year in review.

Science.gov (United States)

Malchesky, Paul S

2014-03-01

In this Editor's Review, articles published in 2013 are organized by category and briefly summarized. We aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ Replacement, Recovery, and Regeneration. As the official journal of The International Federation for Artificial Organs, The International Faculty for Artificial Organs, the International Society for Rotary Blood Pumps, the International Society for Pediatric Mechanical Cardiopulmonary Support, and the Vienna International Workshop on Functional Electrical Stimulation, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level". Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide so meaningful suggestions to the author's work whether eventually accepted or rejected and especially to those whose native tongue is not English. Without these excellent and dedicated reviewers the quality expected from such a journal could not be possible. We also express our special thanks to our Publisher, Wiley Periodicals, for their expert attention and support in the production and marketing of Artificial Organs. We look forward to recording further advances in the coming years. © 2014 Wiley Periodicals, Inc. and International Center for Artificial Organs and Transplantation.

NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY

Directory of Open Access Journals (Sweden)

Yao Yevenyo Ziggah

Full Text Available Abstract: Geocentric translation model (GTM in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP and new parameters determined using the arithmetic mean (AM were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN, radial basis function neural network (RBFNN and generalized regression neural network (GRNN were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

Science.gov (United States)

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

Artificial Aging as a Predictor of Paper's Future Useful Life.

Science.gov (United States)

Bansa, Helmut; Hofer, Hans-H.

1989-01-01

An experiment with the artificial aging of paper which--in contrast with the usual practice--was carried out at relatively low temperatures (i.e., 50-95 degrees Celsius), and not on laboratory handsheets, but on naturally aged paper from the commercial production of four centuries, produced results justifying the thesis that there may be at best…

Mechanical Characterization and Numerical Modelling of Rubber Shockpads in 3G Artificial Turf

Directory of Open Access Journals (Sweden)

David Cole

2018-02-01

Full Text Available Third generation (3G artificial turf systems use in sporting applications is increasingly prolific. These multi-component systems are comprised of a range of polymeric and elastomeric materials that exhibit non-linear and strain rate dependent behaviours under the complex loads applied from players and equipment. To further study and better understand the behaviours of these systems, the development of a numerical model to accurately predict individual layers’ behaviour as well as the overall system response under different loading conditions is necessary. The purpose of this study was to characterise and model the mechanical behaviour of a rubber shockpad found in 3G artificial surfaces for vertical shock absorption using finite element analysis. A series of uniaxial compression tests were performed to characterise the mechanical behaviour of the shockpad. Compression loading was performed at 0.9 Hz to match human walking speeds. A Microfoam material model was selected from the PolyUMod library and optimised using MCalibration software before being imported into ABAQUS for analysis. A finite element model was created for the shockpad using ABAQUS and a compressive load applied to match that of the experimental data. Friction coefficients were altered to view the effect on the loading response. The accuracy of the model was compared using a series of comparative measures including the energy loss and root mean square error.

Optimization of the artificial urinary sphincter: modelling and experimental validation

International Nuclear Information System (INIS)

Marti, Florian; Leippold, Thomas; John, Hubert; Blunschi, Nadine; Mueller, Bert

2006-01-01

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) cmH 2 O and (-8.7 ± 1.1) cmH 2 O, 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 cmH 2 O, and an external pressure on the urethra of 60 cmH 2 O, the model leads to the optimized sphincter length of (17.3 ± 3.8) mm

Issues and challenges in artificial intelligence

CERN Document Server

Kulikowski, Juliusz; Mroczek, Teresa; Wtorek, Jerzy

2014-01-01

The importance of human-computer system interaction problems is increasing due to the growing expectations of users on general computer systems capabilities in human work and life facilitation. Users expect system which is not only a passive tool in human hands but rather an active partner equipped with a sort of artificial intelligence, having access to large information resources, being able to adapt its behavior to the human requirements and to collaborate with the human users.   This book collects examples of recent human-computer system solutions. The content of the book is divided into three parts. Part I is devoted to detection, recognition and reasoning in different circumstances and applications. Problems associated with data modeling, acquisition and mining are presented by papers collected in part II and part III is devoted to Optimization.

Origin of life. The role of experiments, basic beliefs, and social authorities in the controversies about the spontaneous generation of life and the subsequent debates about synthesizing life in the laboratory.

Science.gov (United States)

Deichmann, Ute

2012-01-01

For centuries the question of the origin of life had focused on the question of the spontaneous generation of life, at least primitive forms of life, from inanimate matter, an idea that had been promoted most prominently by Aristotle. The widespread belief in spontaneous generation, which had been adopted by the Church, too, was finally abandoned at the beginning of the twentieth century, when the question of the origin of life became related to that of the artificial generation of life in the laboratory. This paper examines the role of social authorities, researchers' basic beliefs, crucial experiments, and scientific advance in the controversies about spontaneous generation from the seventeenth to the nineteenth centuries and analyzes the subsequent debates about the synthesis of artificial life in the changing scientific contexts of the nineteenth and early-twentieth centuries. It shows that despite the importance of social authorities, basic beliefs, and crucial experiments scientific advances, especially those in microbiology, were the single most important factor in the stepwise abandoning of the doctrine of spontaneous generation. Research on the origin of life and the artificial synthesis of life became scientifically addressed only when it got rid of the idea of constant smooth transitions between inanimate matter and life and explored possible chemical and physical mechanisms of the specificity of basic molecules and processes of life.

Artificial cell mimics as simplified models for the study of cell biology.

Science.gov (United States)

Salehi-Reyhani, Ali; Ces, Oscar; Elani, Yuval

2017-07-01

Living cells are hugely complex chemical systems composed of a milieu of distinct chemical species (including DNA, proteins, lipids, and metabolites) interconnected with one another through a vast web of interactions: this complexity renders the study of cell biology in a quantitative and systematic manner a difficult task. There has been an increasing drive towards the utilization of artificial cells as cell mimics to alleviate this, a development that has been aided by recent advances in artificial cell construction. Cell mimics are simplified cell-like structures, composed from the bottom-up with precisely defined and tunable compositions. They allow specific facets of cell biology to be studied in isolation, in a simplified environment where control of variables can be achieved without interference from a living and responsive cell. This mini-review outlines the core principles of this approach and surveys recent key investigations that use cell mimics to address a wide range of biological questions. It will also place the field in the context of emerging trends, discuss the associated limitations, and outline future directions of the field. Impact statement Recent years have seen an increasing drive to construct cell mimics and use them as simplified experimental models to replicate and understand biological phenomena in a well-defined and controlled system. By summarizing the advances in this burgeoning field, and using case studies as a basis for discussion on the limitations and future directions of this approach, it is hoped that this minireview will spur others in the experimental biology community to use artificial cells as simplified models with which to probe biological systems.

Space Environment Modelling with the Use of Artificial Intelligence Methods

Science.gov (United States)

Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

1996-12-01

Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore

Impact evaluation of rolling contact fatigue life models

International Nuclear Information System (INIS)

Choi, Young Sik; Yang, Xiaoping

2012-01-01

Since the accurate prediction of fatigue life has a significant value, many researchers have attempted to develop a reliable fatigue life model. Recently, rolling contact fatigue life models incorporating machining impact were developed. These models have contributed to a significant improvement in prediction accuracy as compared with earlier models, thus representing a major step forward in the modeling effort. This paper compares the prediction accuracy of these models with that of the prediction method in International Standards. When α is set to 0.25, the observed improvement of prediction accuracy as measured by variance of prediction errors due to these models over that due to prediction method in International Standards is statistically significant. Impact analyses of such improvement are conducted to illustrate its value. It is further noted that while difference was observed between the variance of prediction errors due to the crack initiation life model based on a dislocation model and that due to the crack initiation life model based on a local stress-life curve, the observed difference is not statistically significant

Development of a hybrid system of artificial neural networks and ...

African Journals Online (AJOL)

Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

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.

FLASH-FLOOD MODELLING WITH ARTIFICIAL NEURAL NETWORKS USING RADAR RAINFALL ESTIMATES

Directory of Open Access Journals (Sweden)

Dinu Cristian

2017-09-01

Full Text Available The use of artificial neural networks (ANNs in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.

Radioecological aspects in artificial groundwater recharge

Energy Technology Data Exchange (ETDEWEB)

Matthess, G [Kiel Univ. (Germany, F.R.). Geologisch-Palaeontologisches Inst. und Museum; Neumayr, V [Institut fuer Wasser-, Boden- und Lufthygiene, Frankfurt am Main (Germany, F.R.)

1980-01-01

In increasing extent surface waters, especially those of rivers and streams, are contaminated by radionuclides. Therefore it is necessary to investigate the possibility of impairment of the quality of artificially recharged groundwater and drinking water by radionuclides. Hazards for man are possible by drinking water, that was affected by waste and during exposition to air, as well as indirectly by irrigation water and the food chain. In a model calculation using realistic conditions the order of magnitude of these hazards for man by incorporation of radioactively contaminated artificially recharged drinking water are to be assessed. Here the parameters are discussed which must be considered in such an assessment. The model includes the use of river water for artificial recharge. All models and assessments assume the most unfavourable preconditions, which may lead to an impact to man.

COMPUTER MODELING IN THE DEVELOPMENT OF ARTIFICIAL VENTRICLES OF HEART

Directory of Open Access Journals (Sweden)

L. V. Belyaev

2011-01-01

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

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.

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

International Nuclear Information System (INIS)

Linares-Rodriguez, Alvaro; Ruiz-Arias, José Antonio; Pozo-Vazquez, David; Tovar-Pescador, Joaquin

2013-01-01

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

USE OF ARTIFICIAL LIGHT IN MUSHROOM CULTIVATION

Directory of Open Access Journals (Sweden)

N. L. Poyedinok

2013-12-01

Full Text Available Artificial light is used in greenhouses to increase productivity and quality of agricultural and ornamental plants. Despite the awareness of the fact that light also plays important role in the life of nonhotosynthetic organisms, such as fungi, its using in their biotechnology cultivation is currently limited. Science has quite a large amount information about the influence of artificial light of different nature on morphogenesis, metabolic processes and productivity of more than 100 species of fungi, many of which are valuable producers of biologically active compounds. Themechanisms of photoreactions of various fungi, which is an integral part of a purposeful photoregulation their activity in biotechnological processes are described. The analysis of the researches and of the experience of their practical application allows predicting potential of using artificial light in mushroom growing industry, as well as in creating highly productive, environmentally clean technologies of targeted synthesis of the final product.

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

International Nuclear Information System (INIS)

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

2016-01-01

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 H 2 O 2 . This artificial model system can provide a systemic means to elucidate the complex interactions between biological materials and non-thermal plasma. (paper)

Artificial intelligence and synthetic biology: A tri-temporal contribution.

Science.gov (United States)

Bianchini, Francesco

2016-10-01

Artificial intelligence can make numerous contributions to synthetic biology. I would like to suggest three that are related to the past, present and future of artificial intelligence. From the past, works in biology and artificial systems by Turing and von Neumann prove highly interesting to explore within the new framework of synthetic biology, especially with regard to the notions of self-modification and self-replication and their links to emergence and the bottom-up approach. The current epistemological inquiry into emergence and research on swarm intelligence, superorganisms and biologically inspired cognitive architecture may lead to new achievements on the possibilities of synthetic biology in explaining cognitive processes. Finally, the present-day discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of notions such as "life", "cognition", "artificial" and "natural", as well as their interconnections in theoretical synthetic biology. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Artificial Intelligence in Civil Engineering

Directory of Open Access Journals (Sweden)

Pengzhen Lu

2012-01-01

Full Text Available Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

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

Institute of Scientific and Technical Information of China (English)

无

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.

A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

Directory of Open Access Journals (Sweden)

Ozge Cagcag Yolcu

2013-01-01

Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

The potential of artificial aging for modelling of natural aging processes of ballpoint ink.

Science.gov (United States)

Weyermann, Céline; Spengler, Bernhard

2008-08-25

Artificial aging has been used to reproduce natural aging processes in an accelerated pace. Questioned documents were exposed to light or high temperature in a well-defined manner in order to simulate an increased age. This may be used to study the aging processes or to date documents by reproducing their aging curve. Ink was studied especially because it is deposited on the paper when a document, such as a contract, is produced. Once on the paper, aging processes start through degradation of dyes, solvents drying and resins polymerisation. Modelling of dye's and solvent's aging was attempted. These processes, however, follow complex pathways, influenced by many factors which can be classified as three major groups: ink composition, paper type and storage conditions. The influence of these factors is such that different aging states can be obtained for an identical point in time. Storage conditions in particular are difficult to simulate, as they are dependent on environmental conditions (e.g. intensity and dose of light, temperature, air flow, humidity) and cannot be controlled in the natural aging of questioned documents. The problem therefore lies more in the variety of different conditions a questioned document might be exposed to during its natural aging, rather than in the simulation of such conditions in the laboratory. Nevertheless, a precise modelling of natural aging curves based on artificial aging curves is obtained when performed on the same paper and ink. A standard model for aging processes of ink on paper is therefore presented that is based on a fit of aging curves to a power law of solvent concentrations as a function of time. A mathematical transformation of artificial aging curves into modelled natural aging curves results in excellent overlap with data from real natural aging processes.

Artificial Organs 2017: A Year in Review.

Science.gov (United States)

Malchesky, Paul S

2018-03-01

In this Editor's Review, articles published in 2017 are organized by category and summarized. We provide a brief reflection of the research and progress in artificial organs intended to advance and better human life while providing insight for continued application of these technologies and methods. Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level." Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. Peer-reviewed Special Issues this year included contributions from the 12th International Conference on Pediatric Mechanical Circulatory Support Systems and Pediatric Cardiopulmonary Perfusion edited by Dr. Akif Undar, Artificial Oxygen Carriers edited by Drs. Akira Kawaguchi and Jan Simoni, the 24th Congress of the International Society for Mechanical Circulatory Support edited by Dr. Toru Masuzawa, Challenges in the Field of Biomedical Devices: A Multidisciplinary Perspective edited by Dr. Vincenzo Piemonte and colleagues and Functional Electrical Stimulation edited by Dr. Winfried Mayr and colleagues. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide meaningful suggestions to the author's work whether eventually accepted or rejected. Without these excellent and dedicated reviewers the quality expected from such a journal could not be possible. We also express our special thanks to our Publisher, John Wiley & Sons for their expert attention and support in the production and marketing of Artificial Organs. We look forward to reporting further advances in the coming years. © 2018 International Center for

Artificial light and quantum order in systems of screened dipoles

International Nuclear Information System (INIS)

Wen Xiaogang

2003-01-01

The origin of light is an unsolved mystery in nature. Recently, it was suggested that light may originate from a new kind of order, quantum order. To test this idea in experiments, we study systems of screened magnetic/electric dipoles in two-dimensional (2D) and 3D lattices. We show that our models contain an artificial light-a photonlike collective excitation. We discuss how to design realistic devices that realize our models. We show that the 'speed of light' and the 'fine-structure constant' of the artificial light can be tuned in our models. The properties of artificial atoms (bound states of pairs of artificial charges) are also discussed. The existence of artificial light (as well as artificial electron) in condensed-matter systems suggests that elementary particles, such as light and electron, may not be elementary. They may be collective excitations of quantum order in our vacuum. In our model, light is realized as a fluctuation of string-nets and charges as the ends of open strings (or nodes of string nets)

Artificial intelligence-based modeling and control of fluidized bed combustion

Energy Technology Data Exchange (ETDEWEB)

Ikonen, E.; Leppaekoski, K. (Univ. of Oulu, Dept. of Process and Environmental Engineering (Finland)). email: enso.ikonen@oulu.fi

2009-07-01

AI-inspired techniques have a lot to offer when developing methods for advanced identification, monitoring, control and optimization of industrial processes, such as power plants. Advanced control methods have been extensively examined in the research of the Power Plant Automation group at the Systems Engineering Laboratory, e.g., in fuel inventory modelling, combustion power control, modelling and control of flue gas oxygen, drum control, modelling and control of superheaters, or in optimization of flue-gas emissions. Most engineering approaches to artificial intelligence (AI) are characterized by two fundamental properties: the ability to learn from various sources and the ability to deal with plant complexity. Learning systems that are able to operate in uncertain environments based on incomplete information are commonly referred to as being intelligent. A number of other approaches exist, characterized by these properties, but not easily categorized as AI-systems. Advanced control methods (adaptive, predictive, multivariable, robust, etc.) are based on the availability of a model of the process to be controlled. Hence identification of processes becomes a key issue, leading to the use of adaptation and learning techniques. A typical learning control system concerns a selection of learning techniques applied for updating a process model, which in turn is used for the controller design. When design of learning control systems is complemented with concerns for dealing with uncertainties or vaguenesses in models, measurements, or even objectives, particularly close connections exist between advanced process control and methods of artificial intelligence and machine learning. Needs for advanced techniques are typically characterized by the desire to properly handle plant non-linearities, the multivariable nature of the dynamic problems, and the necessity to adapt to changing plant conditions. In the field of fluidized bed combustion (FBC) control, the many promising

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.

Knowledge in Artificial Intelligence Systems: Searching the Strategies for Application

OpenAIRE

Kornienko, Alla A.; Kornienko, Anatoly V.; Fofanov, Oleg B.; Chubik, Maxim P.

2015-01-01

The studies based on auto-epistemic logic are pointed out as an advanced direction for development of artificial intelligence (AI). Artificial intelligence is taken as a system that imitates the solution of complicated problems by human during the course of life. The structure of symbols and operations, by which intellectual solution is performed, as well as searching the strategic reference points for those solutions, which are caused by certain structures of symbols and operations, – are co...

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

International Nuclear Information System (INIS)

Pleune, T.T.

1996-11-01

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

Artificial Intelligence in Civil Engineering

OpenAIRE

Lu, Pengzhen; Chen, Shengyong; Zheng, Yujun

2012-01-01

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applicati...

Use of artificial neural networks for transport energy demand modeling

International Nuclear Information System (INIS)

Murat, Yetis Sazi; Ceylan, Halim

2006-01-01

The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem

artificial neural network model for low strength rc beam shear capacity

African Journals Online (AJOL)

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RESEARCH PAPER. Keywords: Shear strength, reinforced concrete, Artificial Neural Network, design equations ... searchers using artificial intelligence to im- prove on theoretical ...... benefit to humanity or a waste of time?” The. Structural ...

Variable camber wing based on pneumatic artificial muscles

Science.gov (United States)

Yin, Weilong; Liu, Libo; Chen, Yijin; Leng, Jinsong

2009-07-01

As a novel bionic actuator, pneumatic artificial muscle has high power to weight ratio. In this paper, a variable camber wing with the pneumatic artificial muscle is developed. Firstly, the experimental setup to measure the static output force of pneumatic artificial muscle is designed. The relationship between the static output force and the air pressure is investigated. Experimental result shows the static output force of pneumatic artificial muscle decreases nonlinearly with increasing contraction ratio. Secondly, the finite element model of the variable camber wing is developed. Numerical results show that the tip displacement of the trailing-edge increases linearly with increasing external load and limited with the maximum static output force of pneumatic artificial muscles. Finally, the variable camber wing model is manufactured to validate the variable camber concept. Experimental result shows that the wing camber increases with increasing air pressure and that it compare very well with the FEM result.

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

Science.gov (United States)

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

2013-11-01

In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Biological factors of natural and artificial ecosystems stable (unstable) functioning

Science.gov (United States)

Pechurkin, Nikolai S.

The problem of sustainable development of humanity on Earth and the problem of supporting human life in space have the same scientific and methodological bases. The key to solve both problems is a long term maintenance of balanced material cycle. As a whole, natural or artificial ecosystems are to be more closed than open, but their elements (links of systems) are to be substantially open in interactions with each other. Prolonged stable interactions of different links have to have unique joint results - closed material cycling or biotic turnover. It is necessary to include, at least, three types of main links into any system to support real material cycling: producers, consumers, reducers. Producer links are now under studies in many laboratories. It is evident that the higher productivity of link, the lower link stability. Especially, it concerns with parasite impact to plants. As usual, artificial ecosystems are more simple (incomplete) than natural ecosystems, sometimes, they have not enough links for prolonged stable functioning. For example, life support system for space flight can be incomplete in consumer link, having only some crew persons, instead of interacting populations of consumers. As for reducer link, it is necessary to "organize" a special coordinated work of microbial biocenoses to fulfill proper cycling. Possible evolution of links, their self development is a matter of special attention for the maintenance of prolonged stable functioning. It's the most danger for systems with populations of quickly reproducing, so-called, R - strategists, according to symbols of logistic equation. From another side, quick reproduction of R - strategists is able to increase artificial ecosystems and their links functioning. After some damages of system, R - strategist's link can be quickly "self repaired" up to level of normal functioning. Some experimental data of this kind and mathematical models are to be discussed in the paper. This work is supported by

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

Energy Technology Data Exchange (ETDEWEB)

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

2003-07-01

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

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

Science.gov (United States)

Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

2016-11-01

This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production. Copyright © 2016 Elsevier Ltd. All rights reserved.

Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

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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.

An Additive-Multiplicative Restricted Mean Residual Life Model

DEFF Research Database (Denmark)

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

2016-01-01

mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline....... For the suggested model, some covariate effects are allowed to be time-varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness...

An articulated predictive model for fluid-free artificial basilar membrane as broadband frequency sensor

Science.gov (United States)

Ahmed, Riaz; Banerjee, Sourav

2018-02-01

In this article, an extremely versatile predictive model for a newly developed Basilar meta-Membrane (BM2) sensors is reported with variable engineering parameters that contribute to it's frequency selection capabilities. The predictive model reported herein is for advancement over existing method by incorporating versatile and nonhomogeneous (e.g. functionally graded) model parameters that could not only exploit the possibilities of creating complex combinations of broadband frequency sensors but also explain the unique unexplained physical phenomenon that prevails in BM2, e.g. tailgating waves. In recent years, few notable attempts were made to fabricate the artificial basilar membrane, mimicking the mechanics of the human cochlea within a very short range of frequencies. To explain the operation of these sensors a few models were proposed. But, we fundamentally argue the "fabrication to explanation" approach and proposed the model driven predictive design process for the design any (BM2) as broadband sensors. Inspired by the physics of basilar membrane, frequency domain predictive model is proposed where both the material and geometrical parameters can be arbitrarily varied. Broadband frequency is applicable in many fields of science, engineering and technology, such as, sensors for chemical, biological and acoustic applications. With the proposed model, which is three times faster than its FEM counterpart, it is possible to alter the attributes of the selected length of the designed sensor using complex combinations of model parameters, based on target frequency applications. Finally, the tailgating wave peaks in the artificial basilar membranes that prevails in the previously reported experimental studies are also explained using the proposed model.

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…

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.

Whispering galleries and the control of artificial atoms.

Science.gov (United States)

Forrester, Derek Michael; Kusmartsev, Feodor V

2016-04-28

Quantum computation using artificial-atoms, such as novel superconducting circuits, can be sensitively controlled by external electromagnetic fields. These fields and the self-fields attributable to the coupled artificial-atoms influence the amount of quantum correlation in the system. However, control elements that can operate without complete destruction of the entanglement of the quantum-bits are difficult to engineer. Here we investigate the possibility of using closely-spaced-linear arrays of metallic-elliptical discs as whispering gallery waveguides to control artificial-atoms. The discs confine and guide radiation through the array with small notches etched into their sides that act as scatterers. We focus on π-ring artificial-atoms, which can generate their own spontaneous fluxes. We find that the micro-discs of the waveguides can be excited by terahertz frequency fields to exhibit whispering-modes and that a quantum-phase-gate composed of π-rings can be operated under their influence. Furthermore, we gauge the level of entanglement through the concurrence measure and show that under certain magnetic conditions a series of entanglement sudden-deaths and revivals occur between the two qubits. This is important for understanding the stability and life-time of qubit operations using, for example, a phase gate in a hybrid of quantum technologies composed of control elements and artificial-atoms.

An artificial neural network model for periodic trajectory generation

Science.gov (United States)

Shankar, S.; Gander, R. E.; Wood, H. C.

A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

Science.gov (United States)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

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Narayanan Manikandan

2016-01-01

Full Text Available Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Time-lapse gravity data for monitoring and modeling artificial recharge through a thick unsaturated zone

Science.gov (United States)

Kennedy, Jeffrey R.; Ferre, Ty P.A.; Creutzfeldt, Benjamin

2016-01-01

Groundwater-level measurements in monitoring wells or piezometers are the most common, and often the only, hydrologic measurements made at artificial recharge facilities. Measurements of gravity change over time provide an additional source of information about changes in groundwater storage, infiltration, and for model calibration. We demonstrate that for an artificial recharge facility with a deep groundwater table, gravity data are more sensitive to movement of water through the unsaturated zone than are groundwater levels. Groundwater levels have a delayed response to infiltration, change in a similar manner at many potential monitoring locations, and are heavily influenced by high-frequency noise induced by pumping; in contrast, gravity changes start immediately at the onset of infiltration and are sensitive to water in the unsaturated zone. Continuous gravity data can determine infiltration rate, and the estimate is only minimally affected by uncertainty in water-content change. Gravity data are also useful for constraining parameters in a coupled groundwater-unsaturated zone model (Modflow-NWT model with the Unsaturated Zone Flow (UZF) package).

Modeling mechanical properties of cast aluminum alloy using artificial neural network

International Nuclear Information System (INIS)

Jokhio, M.H.; Panhwar, M.I.

2009-01-01

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)

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

Comparisons of prediction models of quality of life after laparoscopic cholecystectomy: a longitudinal prospective study.

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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.

Artificial organic networks artificial intelligence based on carbon networks

CERN Document Server

Ponce-Espinosa, Hiram; Molina, Arturo

2014-01-01

This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        class...

Artificial Immune Ecosystems: the role of expert-based learning in artificial cognition

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Pierre Parrend

2018-03-01

Full Text Available The rapid evolution of IT ecosystems significantly challenges the security models our infrastructures rely on. Beyond the old dichotomy between open and closed systems, it is now necessary to handle securely the interaction between heterogeneous devices building dynamic ecosystems. To this regard, bio-inspired approaches provide a rich set of conceptual tools, but they have failed to lay the basis for robust and efficient solutions. Our research effort intends to revisit the contribution of artificial immune system research to bring immune properties: security, resilience, distribution, memory, into IT infrastructures. Artificial immune ecosystems support a comprehensive model for anomaly detection and characterization, but their cognitive capacity are limited by the state of the art in machine learning and the rapid evolution of cybersecurity threats so far. We therefore propose to enrich the cognitive process with expert-based learning for reinforcement, classification and investigation. Application to system supervision using system logs and supervision time series confirms the relevance and performance of this model.

Braided artificial muscles: modeling and experimental validation

Science.gov (United States)

Dragan, Liliana; Cioban, Horia

2009-01-01

The paper presents a few graphical modalities for constructing the double helical braid, which is the basis for the braided artificial pneumatic muscles, by using specialized software applications. This represents the first stage in achieving the method of finite element analysis of this type of linear pneumatic actuator.

HIV lipodystrophy case definition using artificial neural network modelling

DEFF Research Database (Denmark)

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

2003-01-01

OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...

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

Institute of Scientific and Technical Information of China (English)

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

2010-01-01

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

An experimental biomimetic platform for artificial olfaction.

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Corrado Di Natale

Full Text Available Artificial olfactory systems have been studied for the last two decades mainly from the point of view of the features of olfactory neuron receptor fields. Other fundamental olfaction properties have only been episodically considered in artificial systems. As a result, current artificial olfactory systems are mostly intended as instruments and are of poor benefit for biologists who may need tools to model and test olfactory models. Herewith, we show how a simple experimental approach can be used to account for several phenomena observed in olfaction. An artificial epithelium is formed as a disordered distributed layer of broadly selective color indicators dispersed in a transparent polymer layer. The whole epithelium is probed with colored light, imaged with a digital camera and the olfactory response upon exposure to an odor is the change of the multispectral image. The pixels are treated as olfactory receptor neurons, whose optical properties are used to build a convergence classifier into a number of mathematically defined artificial glomeruli. A non-homogenous exposure of the test structure to the odours gives rise to a time and spatial dependence of the response of the different glomeruli strikingly similar to patterns observed in the olfactory bulb. The model seems to mimic both the formation of glomeruli, the zonal nature of olfactory epithelium, and the spatio-temporal signal patterns at the glomeruli level. This platform is able to provide a readily available test vehicle for chemists developing optical indicators for chemical sensing purposes and for biologists to test models of olfactory system organization.

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.

Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

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D. Peraković

2017-06-01

Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

Artificial gravity - The evolution of variable gravity research

Science.gov (United States)

Fuller, Charles A.; Sulzman, Frank M.; Keefe, J. Richard

1987-01-01

The development of a space life science research program based on the use of rotational facilities is described. In-flight and ground centrifuges can be used as artificial gravity environments to study the following: nongravitational biological factors; the effects of 0, 1, and hyper G on man; counter measures for deconditioning astronauts in weightlessness; and the development of suitable artificial gravity for long-term residence in space. The use of inertial fields as a substitute for gravity, and the relations between the radius of the centrifuge and rotation rate and specimen height and rotation radius are examined. An example of a centrifuge study involving squirrel monkeys is presented.

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

Science.gov (United States)

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

2015-01-01

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

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

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Lakshmi Pathak

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2003-07-01

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

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)

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

International Nuclear Information System (INIS)

Souto, Kelling C.; Nunes, Wallace W.; Machado, Marcelo D.

2011-01-01

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)

Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

Science.gov (United States)

Zhang, Jun; Simeonov, Anthony; Yip, Michael C.

2018-03-01

Robotic artificial muscles are compliant and can generate straight contractions. They are increasingly popular as driving mechanisms for robotic systems. However, their strain and tension force often vary simultaneously under varying loads and inputs, resulting in three-dimensional hysteretic relationships. The three-dimensional hysteresis in robotic artificial muscles poses difficulties in estimating how they work and how to make them perform designed motions. This study proposes an approach to driving robotic artificial muscles to generate designed motions and forces by modeling and compensating for their three-dimensional hysteresis. The proposed scheme captures the nonlinearity by embedding two hysteresis models. The effectiveness of the model is confirmed by testing three popular robotic artificial muscles. Inverting the proposed model allows us to compensate for the hysteresis among temperature surrogate, contraction length, and tension force of a shape memory alloy (SMA) actuator. Feedforward control of an SMA-actuated robotic bicep is demonstrated. This study can be generalized to other robotic artificial muscles, thus enabling muscle-powered machines to generate desired motions.

Dynamic Analysis of a Phytoplankton-Fish Model with Biological and Artificial Control

OpenAIRE

Wang, Yapei; Zhao, Min; Pan, Xinhong; Dai, Chuanjun

2014-01-01

We investigate a nonlinear model of the interaction between phytoplankton and fish, which uses a pair of semicontinuous systems with biological and artificial control. First, the existence of an order-1 periodic solution to the system is analyzed using a Poincaré map and a geometric method. The stability conditions of the order-1 periodic solution are obtained by a theoretical mathematical analysis. Furthermore, based on previous analysis, we investigate the bifurcation in the order-1 periodi...

Artificial neural network applications in ionospheric studies

Directory of Open Access Journals (Sweden)

L. R. Cander

1998-06-01

Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

Autonomy: Life and Being

Science.gov (United States)

Williams, Mary-Anne

This paper uses robot experience to explore key concepts of autonomy, life and being. Unfortunately, there are no widely accepted definitions of autonomy, life or being. Using a new cognitive agent architecture we argue that autonomy is a key ingredient for both life and being, and set about exploring autonomy as a concept and a capability. Some schools of thought regard autonomy as the key characteristic that distinguishes a system from an agent; agents are systems with autonomy, but rarely is a definition of autonomy provided. Living entities are autonomous systems, and autonomy is vital to life. Intelligence presupposes autonomy too; what would it mean for a system to be intelligent but not exhibit any form of genuine autonomy. Our philosophical, scientific and legal understanding of autonomy and its implications is immature and as a result progress towards designing, building, managing, exploiting and regulating autonomous systems is retarded. In response we put forward a framework for exploring autonomy as a concept and capability based on a new cognitive architecture. Using this architecture tools and benchmarks can be developed to analyze and study autonomy in its own right as a means to further our understanding of autonomous systems, life and being. This endeavor would lead to important practical benefits for autonomous systems design and help determine the legal status of autonomous systems. It is only with a new enabling understanding of autonomy that the dream of Artificial Intelligence and Artificial Life can be realized. We argue that designing systems with genuine autonomy capabilities can be achieved by focusing on agent experiences of being rather than attempting to encode human experiences as symbolic knowledge and know-how in the artificial agents we build.

Abstraction in artificial intelligence and complex systems

CERN Document Server

Saitta, Lorenza

2013-01-01

Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences.  After discussing the characterizing properties of abstraction, a formal model, the K

Recent Theoretical Approaches to Minimal Artificial Cells

Directory of Open Access Journals (Sweden)

Fabio Mavelli

2014-05-01

Full Text Available Minimal artificial cells (MACs are self-assembled chemical systems able to mimic the behavior of living cells at a minimal level, i.e. to exhibit self-maintenance, self-reproduction and the capability of evolution. The bottom-up approach to the construction of MACs is mainly based on the encapsulation of chemical reacting systems inside lipid vesicles, i.e. chemical systems enclosed (compartmentalized by a double-layered lipid membrane. Several researchers are currently interested in synthesizing such simple cellular models for biotechnological purposes or for investigating origin of life scenarios. Within this context, the properties of lipid vesicles (e.g., their stability, permeability, growth dynamics, potential to host reactions or undergo division processes… play a central role, in combination with the dynamics of the encapsulated chemical or biochemical networks. Thus, from a theoretical standpoint, it is very important to develop kinetic equations in order to explore first—and specify later—the conditions that allow the robust implementation of these complex chemically reacting systems, as well as their controlled reproduction. Due to being compartmentalized in small volumes, the population of reacting molecules can be very low in terms of the number of molecules and therefore their behavior becomes highly affected by stochastic effects both in the time course of reactions and in occupancy distribution among the vesicle population. In this short review we report our mathematical approaches to model artificial cell systems in this complex scenario by giving a summary of three recent simulations studies on the topic of primitive cell (protocell systems.

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

Science.gov (United States)

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

2010-03-25

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

An artificial pancreas for automated blood glucose control in patients with Type 1 diabetes

DEFF Research Database (Denmark)

Schmidt, Signe; Boiroux, Dimitri; Ranjan, Ajenthen

2015-01-01

Automated glucose control in patients with Type 1 diabetes is much-coveted by patients, relatives and healthcare professionals. It is the expectation that a system for automated control, also know as an artificial pancreas, will improve glucose control, reduce the risk of diabetes complications...... and markedly improve patient quality of life. An artificial pancreas consists of portable devices for glucose sensing and insulin delivery which are controlled by an algorithm residing on a computer. The technology is still under development and currently no artificial pancreas is commercially available....... This review gives an introduction to recent progress, challenges and future prospects within the field of artificial pancreas research....

[The artificial sphincter: therapy for faecal incontinence].

Science.gov (United States)

Baumgartner, U

2012-08-01

Faecal incontinence (FI) challenges a patient's professional, social and sexual life. Often the patient becomes depressive and socially isolated. If able to break open for therapy the patient should receive as first line a conservative treatment (like dietary measures, pelvic re-education, biofeedback, bulking agents, irrigation). When is the time to implant an artificial anal sphincter? If conservative therapy fails as well as surgical options (like a sphincteroplasty - if indicated a reconstruction of the pelvic floor if insufficient, or a sacral nerve stimulation) an ultimo surgical procedure should be offered to appropriate and compliant patients: an artificial anal sphincter. Worldwide, there are two established devices on the market: the artificial bowel sphincter® (ABS) from A. M. S. (Minnetonka, MN, USA) and the soft anal band® from A. M. I. (Feldkirch, Austria). How to implant the artificial anal sphincter? Both devices consist of a silicon cuff which can be filled with fluid. Under absolute aseptic conditions this cuff is placed in the lithotomy position by perianal incisions around the anal canal below the pelvic floor. A silicon tube connects the anal cuff with a reservoir (containing fluid) which is placed either behind the pubis bone in front of the bladder (ABS) or below the costal arch (anal band). With a pump placed in the scrotum/labia (ABS) or by pressing the balloon (anal band) in both types operated by the patient the fluid is shifted forth and back between the anal cuff and the reservoir closing or opening the anal canal. Both systems are placed completely subcutaneously. Both devices improve significantly the anal continence. Both systems have a high rate of reoperations. However, the causes for the redos are different. The ABS is associated with high infection and anal penetration rates of the cuff leading to an explantation rate to up to 60 % of the implants. This kind of complication seems to be much lower with the anal band. The major

A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

Directory of Open Access Journals (Sweden)

Paulino José García Nieto

2016-05-01

Full Text Available Remaining useful life (RUL estimation is considered as one of the most central points in the prognostics and health management (PHM. The present paper describes a nonlinear hybrid ABC–MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS, which have been successfully adopted for regression problems, with the artificial bee colony (ABC technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC–MARS-based model from the remaining measured parameters (input variables for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC–MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.

Towards Detecting Group Identities in Complex Artificial Societies

DEFF Research Database (Denmark)

Grappiolo, Corrado; Yannakakis, Georgios N.

2012-01-01

This paper presents a framework for modelling group struc- tures and dynamics in both artificial societies and human-populated vir- tual environments such as computer games. The group modelling (GM) framework proposed focuses on the detection of existing, pre-defined group structures and is compo......This paper presents a framework for modelling group struc- tures and dynamics in both artificial societies and human-populated vir- tual environments such as computer games. The group modelling (GM) framework proposed focuses on the detection of existing, pre-defined group structures...

Development of Artificial Neural Network Model of Crude Oil Distillation Column

Directory of Open Access Journals (Sweden)

Ali Hussein Khalaf

2016-02-01

Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARXand back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining  thirty percent are used for testing  and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

Development of Artificial Neural Network Model of Crude Oil Distillation Column

Directory of Open Access Journals (Sweden)

Duraid F. Ahmed

2016-02-01

Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARX and back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining thirty percent are used for testing and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

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.

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.

Decline Curve Based Models for Predicting Natural Gas Well Performance

OpenAIRE

Kamari, Arash; Mohammadi, Amir H.; Lee, Moonyong; Mahmood, Tariq; Bahadori, Alireza

2016-01-01

The productivity of a gas well declines over its production life as cannot cover economic policies. To overcome such problems, the production performance of gas wells should be predicted by applying reliable methods to analyse the decline trend. Therefore, reliable models are developed in this study on the basis of powerful artificial intelligence techniques viz. the artificial neural network (ANN) modelling strategy, least square support vector machine (LSSVM) approach, adaptive neuro-fuzzy ...

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

Directory of Open Access Journals (Sweden)

Parvaneh Shabanzadeh

2013-01-01

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

Distributed computing and artificial intelligence : 10th International Conference

CERN Document Server

Neves, José; Rodriguez, Juan; Santana, Juan; Gonzalez, Sara

2013-01-01

The International Symposium on Distributed Computing and Artificial Intelligence 2013 (DCAI 2013) is a forum in which applications of innovative techniques for solving complex problems are presented. Artificial intelligence is changing our society. Its application in distributed environments, such as the internet, electronic commerce, environment monitoring, mobile communications, wireless devices, distributed computing, to mention only a few, is continuously increasing, becoming an element of high added value with social and economic potential, in industry, quality of life, and research. This conference is a stimulating and productive forum where the scientific community can work towards future cooperation in Distributed Computing and Artificial Intelligence areas. These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. The exchange of ideas between scientists and technicians from both the academic and industry se...

Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

Science.gov (United States)

Khanali, Majid; Mobli, Hossein; Hosseinzadeh-Bandbafha, Homa

2017-12-01

In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R 2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for

A Review of Safety and Design Requirements of the Artificial Pancreas.

Science.gov (United States)

Blauw, Helga; Keith-Hynes, Patrick; Koops, Robin; DeVries, J Hans

2016-11-01

As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components-type 1 diabetes, artificial pancreas, and safety or design-and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.

Artificial intelligence approach with the use of artificial neural networks for the creation of a forecasting model of Plasmopara viticola infection.

Science.gov (United States)

Bugliosi, R; Spera, G; La Torre, A; Campoli, L; Scaglione, M

2006-01-01

Most of the forecasting models of Plasmopara viticola infections are based upon empiric correlations between meteorological/environmental data and pathogen outbreak. These models generally overestimate the risk of infections and induce to treat the vineyard even if it should be not necessary. In rare cases they underrate the risk of infection leaving the pathogen to breakout. Starting from these considerations we have decided to approach the problem from another point of view utilizing Artificial Intelligence techniques for data elaboration and analysis. Meanwhile the same data have been studied with a more classic approach with statistical tools to verify the impact of a large data collection on the standard data analysis methods. A network of RTUs (Remote Terminal Units) distributed all over the Italian national territory transmits 12 environmental parameters every 15 minutes via radio or via GPRS to a centralized Data Base. Other pedologic data is collected directly from the field and sent via Internet to the centralized data base utilizing Personal Digital Assistants (PDAs) running a specific software. Data is stored after having been preprocessed, to guarantee the quality of the information. The subsequent analysis has been realized mostly with Artificial Neural Networks (ANNs). Collecting and analizing data in this way will probably bring us to the possibility of preventing Plasmospara viticola infection starting from the environmental conditions in this very complex context. The aim of this work is to forecast the infection avoiding the ineffective use of the plant protection products in agriculture. Applying different analysis models we will try to find the best ANN capable of forecasting with an high level of affordability.

Fluid-driven origami-inspired artificial muscles

Science.gov (United States)

Li, Shuguang; Vogt, Daniel M.; Rus, Daniela; Wood, Robert J.

2017-12-01

Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ˜600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration.

Artificial Neural Networks and the Mass Appraisal of Real Estate

Directory of Open Access Journals (Sweden)

Gang Zhou

2018-03-01

Full Text Available With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.

Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

Directory of Open Access Journals (Sweden)

Yueru Ma

2014-01-01

Full Text Available The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN. A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.

An artificial vector model for generating abnormal electrocardiographic rhythms

International Nuclear Information System (INIS)

Clifford, Gari D; Nemati, Shamim; Sameni, Reza

2010-01-01

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

Artificial neural Network-Based modeling and monitoring of photovoltaic generator

Directory of Open Access Journals (Sweden)

H. MEKKI

2015-03-01

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

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.

Vitamin D and ferritin correlation with chronic neck pain using standard statistics and a novel artificial neural network prediction model.

Science.gov (United States)

Eloqayli, Haytham; Al-Yousef, Ali; Jaradat, Raid

2018-02-15

Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.

Forecasting daily lake levels using artificial intelligence approaches

Science.gov (United States)

Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

2012-04-01

Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

Hybrid Continuous-Flow Total Artificial Heart.

Science.gov (United States)

Fox, Carson; Chopski, Steven; Murad, Nohra; Allaire, Paul; Mentzer, Robert; Rossano, Joseph; Arabia, Francisco; Throckmorton, Amy

2018-05-01

Clinical studies using total artificial hearts (TAHs) have demonstrated that pediatric and adult patients derive quality-of-life benefits from this form of therapy. Two clinically-approved TAHs and other pumps under development, however, have design challenges and limitations, including thromboembolic events, neurologic impairment, infection risk due to large size and percutaneous drivelines, and lack of ambulation, to name a few. To address these limitations, we are developing a hybrid-design, continuous-flow, implantable or extracorporeal, magnetically-levitated TAH for pediatric and adult patients with heart failure. This TAH has only two moving parts: an axial impeller for the pulmonary circulation and a centrifugal impeller for the systemic circulation. This device will utilize the latest generation of magnetic bearing technology. Initial geometries were established using pump design equations, and computational modeling provided insight into pump performance. The designs were the basis for prototype manufacturing and hydraulic testing. The study results demonstrate that the TAH is capable of delivering target blood flow rates of 1-6.5 L/min with pressure rises of 1-92 mm Hg for the pulmonary circulation and 24-150 mm Hg for the systemic circulation at 1500-10 000 rpm. This initial design of the TAH was successful and serves as the foundation to continue its development as a novel, more compact, nonthrombogenic, and effective therapeutic alternative for infants, children, adolescents, and adults with heart failure. © 2018 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG Device

Directory of Open Access Journals (Sweden)

Agus Siswoyo

2017-07-01

Full Text Available The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1. There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.     Keywords: Navigation, Neural network, Real-time training, Arduino

Electron beam lithographic modeling assisted by artificial intelligence technology

Science.gov (United States)

Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi

2017-07-01

We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

Evaluation of a Novel Artificial Tear in the Prevention and Treatment of Dry Eye in an Animal Model.

Science.gov (United States)

She, Yujing; Li, Jinyang; Xiao, Bing; Lu, Huihui; Liu, Haixia; Simmons, Peter A; Vehige, Joseph G; Chen, Wei

2015-11-01

To evaluate effects of a novel multi-ingredient artificial tear formulation containing carboxymethylcellulose (CMC) and hyaluronic acid (HA) in a murine dry eye model. Dry eye was induced in mice (C57BL/6) using an intelligently controlled environmental system (ICES). CMC+HA (Optive Fusion™), CMC-only (Refresh Tears(®)), and HA-only (Hycosan(®)) artificial tears and control phosphate-buffered saline (PBS) were administered 4 times daily and compared with no treatment (n = 64 eyes per group). During regimen 1 (prevention regimen), mice were administered artificial tears or PBS for 14 days (starting day 0) while they were exposed to ICES, and assessed on days 0 and 14. During regimen 2 (treatment regimen), mice exposed to ICES for 14 days with no intervention were administered artificial tears or PBS for 14 days (starting day 14) while continuing exposure to ICES, and assessed on days 0, 14, and 28. Corneal fluorescein staining and conjunctival goblet cell density were measured. Artificial tear-treated mice had significantly better outcomes than control groups on corneal staining and goblet cell density (P dry eye. Improvements observed for corneal fluorescein staining and conjunctival goblet cell retention suggest that this combination may be a viable treatment option for dry eye disease.

Use of artificial bee colonies algorithm as numerical approximation of differential equations solution

Science.gov (United States)

Fikri, Fariz Fahmi; Nuraini, Nuning

2018-03-01

The differential equation is one of the branches in mathematics which is closely related to human life problems. Some problems that occur in our life can be modeled into differential equations as well as systems of differential equations such as the Lotka-Volterra model and SIR model. Therefore, solving a problem of differential equations is very important. Some differential equations are difficult to solve, so numerical methods are needed to solve that problems. Some numerical methods for solving differential equations that have been widely used are Euler Method, Heun Method, Runge-Kutta and others. However, some of these methods still have some restrictions that cause the method cannot be used to solve more complex problems such as an evaluation interval that we cannot change freely. New methods are needed to improve that problems. One of the method that can be used is the artificial bees colony algorithm. This algorithm is one of metaheuristic algorithm method, which can come out from local search space and do exploration in solution search space so that will get better solution than other method.

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

OpenAIRE

Alexandridis, Konstantinos T.; Pijanowski, Bryan C.

2002-01-01

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving g...

Artificial neural network model of pork meat cubes osmotic dehydratation

Directory of Open Access Journals (Sweden)

Pezo Lato L.

2013-01-01

Full Text Available Mass transfer of pork meat cubes (M. triceps brachii, shaped as 1x1x1 cm, during osmotic dehydration (OD and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w, temperature (20-50ºC, and immersion time (1-5 h in terms of water loss (WL, solid gain (SG, final dry matter content (DM, and water activity (aw, were investigated using experimental results. Five artificial neural network (ANN models were developed for the prediction of WL, SG, DM, and aw in OD of pork meat cubes. These models were able to predict process outputs with coefficient of determination, r2, of 0.990 for SG, 0.985 for WL, 0.986 for aw, and 0.992 for DM compared to experimental measurements. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet calculus make it very useful and practical for process design and control.

Modeling and experiments on the drive characteristics of high-strength water hydraulic artificial muscles

Science.gov (United States)

Zhang, Zengmeng; Hou, Jiaoyi; Ning, Dayong; Gong, Xiaofeng; Gong, Yongjun

2017-05-01

Fluidic artificial muscles are popular in robotics and function as biomimetic actuators. Their pneumatic version has been widely investigated. A novel water hydraulic artificial muscle (WHAM) with high strength is developed in this study. WHAMs can be applied to underwater manipulators widely used in ocean development because of their environment-friendly characteristics, high force-to-weight ratio, and good bio-imitability. Therefore, the strength of WHAMs has been improved to fit the requirements of underwater environments and the work pressure of water hydraulic components. However, understanding the mechanical behaviors of WHAMs is necessary because WHAMs use work media and pressure control that are different from those used by pneumatic artificial muscles. This paper presents the static and dynamic characteristics of the WHAM system, including the water hydraulic pressure control circuit. A test system is designed and built to analyze the drive characteristics of the developed WHAM. The theoretical relationships among the amount of contraction, pressure, and output drawing force of the WHAM are tested and verified. A linearized transfer function is proposed, and the dynamic characteristics of the WHAM are investigated through simulation and inertia load experiments. Simulation results agree with the experimental results and show that the proposed model can be applied to the control of WHAM actuators.

Empirical modeling of dynamic behaviors of pneumatic artificial muscle actuators.

Science.gov (United States)

Wickramatunge, Kanchana Crishan; Leephakpreeda, Thananchai

2013-11-01

Pneumatic Artificial Muscle (PAM) actuators yield muscle-like mechanical actuation with high force to weight ratio, soft and flexible structure, and adaptable compliance for rehabilitation and prosthetic appliances to the disabled as well as humanoid robots or machines. The present study is to develop empirical models of the PAM actuators, that is, a PAM coupled with pneumatic control valves, in order to describe their dynamic behaviors for practical control design and usage. Empirical modeling is an efficient approach to computer-based modeling with observations of real behaviors. Different characteristics of dynamic behaviors of each PAM actuator are due not only to the structures of the PAM actuators themselves, but also to the variations of their material properties in manufacturing processes. To overcome the difficulties, the proposed empirical models are experimentally derived from real physical behaviors of the PAM actuators, which are being implemented. In case studies, the simulated results with good agreement to experimental results, show that the proposed methodology can be applied to describe the dynamic behaviors of the real PAM actuators. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

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.

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.

Folding pathways explored with artificial potential functions

International Nuclear Information System (INIS)

Ulutaş, B; Bozma, I; Haliloglu, T

2009-01-01

This paper considers the generation of trajectories to a given protein conformation and presents a novel approach based on artificial potential functions—originally proposed for multi-robot navigation. The artificial potential function corresponds to a simplified energy model, but with the novelty that—motivated by work on robotic navigation—a nonlinear compositional scheme of constructing the energy model is adapted instead of an additive formulation. The artificial potential naturally gives rise to a dynamic system for the protein structure that ensures collision-free motion to an equilibrium point. In cases where the equilibrium point is the native conformation, the motion trajectory corresponds to the folding pathway. This framework is used to investigate folding in a variety of protein structures, and the results are compared with those of other approaches including experimental studies

AP@home: The Artificial Pancreas Is Now at Home

NARCIS (Netherlands)

Heinemann, Lutz; Benesch, Carsten; DeVries, J. Hans

2016-01-01

In the past years the development of an artificial pancreas (AP) has made great progress and many activities are ongoing in this area of research. The major step forward made in the last years was moving the evaluation of AP systems from highly controlled experimental conditions to daily life

Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering.

Science.gov (United States)

Maffioletti, Sara Martina; Sarcar, Shilpita; Henderson, Alexander B H; Mannhardt, Ingra; Pinton, Luca; Moyle, Louise Anne; Steele-Stallard, Heather; Cappellari, Ornella; Wells, Kim E; Ferrari, Giulia; Mitchell, Jamie S; Tyzack, Giulia E; Kotiadis, Vassilios N; Khedr, Moustafa; Ragazzi, Martina; Wang, Weixin; Duchen, Michael R; Patani, Rickie; Zammit, Peter S; Wells, Dominic J; Eschenhagen, Thomas; Tedesco, Francesco Saverio

2018-04-17

Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D) artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs) from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Building Explainable Artificial Intelligence Systems

National Research Council Canada - National Science Library

Core, Mark G; Lane, H. Chad; van Lent, Michael; Gomboc, Dave; Solomon, Steve; Rosenberg, Milton

2006-01-01

As artificial intelligence (AI) systems and behavior models in military simulations become increasingly complex, it has been difficult for users to understand the activities of computer-controlled entities...

Effects of artificial gravity on the cardiovascular system: Computational approach

Science.gov (United States)

Diaz Artiles, Ana; Heldt, Thomas; Young, Laurence R.

2016-09-01

Artificial gravity has been suggested as a multisystem countermeasure against the negative effects of weightlessness. However, many questions regarding the appropriate configuration are still unanswered, including optimal g-level, angular velocity, gravity gradient, and exercise protocol. Mathematical models can provide unique insight into these questions, particularly when experimental data is very expensive or difficult to obtain. In this research effort, a cardiovascular lumped-parameter model is developed to simulate the short-term transient hemodynamic response to artificial gravity exposure combined with ergometer exercise, using a bicycle mounted on a short-radius centrifuge. The model is thoroughly described and preliminary simulations are conducted to show the model capabilities and potential applications. The model consists of 21 compartments (including systemic circulation, pulmonary circulation, and a cardiac model), and it also includes the rapid cardiovascular control systems (arterial baroreflex and cardiopulmonary reflex). In addition, the pressure gradient resulting from short-radius centrifugation is captured in the model using hydrostatic pressure sources located at each compartment. The model also includes the cardiovascular effects resulting from exercise such as the muscle pump effect. An initial set of artificial gravity simulations were implemented using the Massachusetts Institute of Technology (MIT) Compact-Radius Centrifuge (CRC) configuration. Three centripetal acceleration (artificial gravity) levels were chosen: 1 g, 1.2 g, and 1.4 g, referenced to the subject's feet. Each simulation lasted 15.5 minutes and included a baseline period, the spin-up process, the ergometer exercise period (5 minutes of ergometer exercise at 30 W with a simulated pedal cadence of 60 RPM), and the spin-down process. Results showed that the cardiovascular model is able to predict the cardiovascular dynamics during gravity changes, as well as the expected

NEW TECHNIQUES APPLIED IN ECONOMICS. ARTIFICIAL NEURAL NETWORK

Directory of Open Access Journals (Sweden)

Constantin Ilie

2009-05-01

Full Text Available The present paper has the objective to inform the public regarding the use of new techniques for the modeling, simulate and forecast of system from different field of activity. One of those techniques is Artificial Neural Network, one of the artificial in

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Directory of Open Access Journals (Sweden)

Wenqian Ruan

2017-12-01

Full Text Available The commercially available nanoscale zerovalent zinc (nZVZ was used as an adsorbent for the removal of malachite green (MG from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO and artificial neural network coupled with genetic algorithm (ANN-GA were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12% was compatible with the experimental value (90.72%. Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0 were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

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

Internet of Things and Artificial Vision, Performance and Applications: Literature Review

Directory of Open Access Journals (Sweden)

Vanessa Alvear-Puertas

2017-02-01

Full Text Available Internet of Things (or also known as IoT is one of the technologies most named today because of the ability it envisages to connect all kinds of devices to the Internet. If to the potentialities of IoT we add another technology of high impact as It is the Artificial Vision we have a wide field of innovative applications, where the processing of images and video in real time allow the visualization of large amounts of data on the Internet. The main applications developed with IoT and Artificial Vision can be implemented in education, medicine, intelligent buildings, surveillance systems of people and vehicles, among others. This type of applications improves the quality of life of users, however, for their development an infrastructure is required that allows the convergence of different protocols and devices, but in a special way that can handle the different phases of the acquisition of images. In this work, a review of the beginnings, concepts, technologies and applications related to the Artificial Vision with the Internet of Things has been carried out to be able to understand in a precise way the impact of its application in daily life.

WHAT PREDICTS A SUCCESSFUL LIFE? A LIFE-COURSE MODEL OF WELL-BEING*

Science.gov (United States)

Layard, Richard; Clark, Andrew E.; Cornaglia, Francesca; Powdthavee, Nattavudh; Vernoit, James

2014-01-01

Policy-makers who care about well-being need a recursive model of how adult life-satisfaction is predicted by childhood influences, acting both directly and (indirectly) through adult circumstances. We estimate such a model using the British Cohort Study (1970). We show that the most powerful childhood predictor of adult life-satisfaction is the child’s emotional health, followed by the child’s conduct. The least powerful predictor is the child’s intellectual development. This may have implications for educational policy. Among adult circumstances, family income accounts for only 0.5% of the variance of life-satisfaction. Mental and physical health are much more important. PMID:25422527

Fluid-driven origami-inspired artificial muscles.

Science.gov (United States)

Li, Shuguang; Vogt, Daniel M; Rus, Daniela; Wood, Robert J

2017-12-12

Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg-all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration. Copyright © 2017 the Author(s). Published by PNAS.

A PHILOSOPHICAL APPROACH TO ARTIFICIAL INTELLIGENCE AND ISLAMIC VALUES

Directory of Open Access Journals (Sweden)

Ali Akbar Ziaee

2012-02-01

Full Text Available Artificial Intelligence has the potential to empower humans through enhanced learning and performance. But if this potential is to be realized and accepted, the ethical aspects as well as the technical must be addressed. Many engineers claim that AI will be smarter than human brains, including scientific creativity, general wisdom and social skills, so we must consider it an important factor for making decisions in our social life and especially in our Islamic societies. The most important challenges will be the quality of representing the Islamic values like piety, obedience, Halal and Haram, and etc in the form of semantics. In this paper, I want to emphasize on the role of Divine Islamic values in the application of AI and discuss it according to philosophy of AI and Islamic perspective.Keywords- Value, expert, Community Development, Artificial Intelligence, Superintelligence, Friendly Artificial Intelligence

Modeling of Complex Life Cycle Prediction Based on Cell Division

Directory of Open Access Journals (Sweden)

Fucheng Zhang

2017-01-01

Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

Artificial Intelligence in Cardiology.

Science.gov (United States)

Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

2018-06-12

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Armando J. Pinho

2013-08-01

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

End of Life Care

Science.gov (United States)

... Related Topics Choosing Wisely Join our e-newsletter! Aging & Health A to Z End of Life Care ... be used. Ice chips, popsicles, moist swabs, or artificial saliva can help prevent the mouth from becoming ...

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

Science.gov (United States)

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

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

Investment Valuation Analysis with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Hüseyin İNCE

2017-07-01

Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

Modeling of an industrial drying process by artificial neural networks

Directory of Open Access Journals (Sweden)

E. Assidjo

2008-09-01

Full Text Available A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN, precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.

Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

OpenAIRE

Ali Reza Zirak; Sobhan Roshani

2016-01-01

A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...

Artificial Organs 2016: A Year in Review.

Science.gov (United States)

Hadsell, Angela T; Malchesky, Paul S

2017-03-01

In this Editor's Review, articles published in 2016 are organized by category and briefly summarized. We aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ Replacement, Recovery, and Regeneration. As the official journal of The International Federation for Artificial Organs, The International Faculty for Artificial Organs, the International Society for Mechanical Circulatory Support, the International Society for Pediatric Mechanical Cardiopulmonary Support, and the Vienna International Workshop on Functional Electrical Stimulation, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level." Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. We were pleased to publish our second Virtual Issue in April 2016 on "Tissue Engineering in Bone" by Professor Tsuyoshi Takato. Our first was published in 2011 titled "Intra-Aortic Balloon Pumping" by Dr. Ashraf Khir. Other peer-reviewed Special Issues this year included contributions from the 11th International Conference on Pediatric Mechanical Circulatory Support Systems and Pediatric Cardiopulmonary Perfusion edited by Dr. Akif Ündar and selections from the 23rd Congress of the International Society for Rotary Blood Pumps edited by Dr. Bojan Biocina. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide meaningful suggestions to the author's work whether eventually accepted or rejected. Without these excellent and

Artificial skin and patient simulator comprising the artificial skin

NARCIS (Netherlands)

2011-01-01

The invention relates to an artificial skin (10, 12, 14), and relates to a patient simulator (100) comprising the artificial skin. The artificial skin is a layered structure comprising a translucent cover layer (20) configured for imitating human or animal skin, and comprising a light emitting layer

Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

NARCIS (Netherlands)

Bos, A.; Bos, A.; Bos, M.; van der Linden, W.E.

1993-01-01

The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value

Groundhog Day for Medical Artificial Intelligence.

Science.gov (United States)

London, Alex John

2018-05-01

Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the "AI winter." With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions. Medicine thus appears to be at an inflection point-a kind of Groundhog Day on which either AI will bring a springtime of improved diagnostic and predictive practices or the shadow of public and professional fear will lead to six more metaphorical weeks of winter in medical AI. © 2018 The Hastings Center.

Living technology: exploiting life's principles in technology.

Science.gov (United States)

Bedau, Mark A; McCaskill, John S; Packard, Norman H; Rasmussen, Steen

2010-01-01

The concept of living technology-that is, technology that is based on the powerful core features of life-is explained and illustrated with examples from artificial life software, reconfigurable and evolvable hardware, autonomously self-reproducing robots, chemical protocells, and hybrid electronic-chemical systems. We define primary (secondary) living technology according as key material components and core systems are not (are) derived from living organisms. Primary living technology is currently emerging, distinctive, and potentially powerful, motivating this review. We trace living technology's connections with artificial life (soft, hard, and wet), synthetic biology (top-down and bottom-up), and the convergence of nano-, bio-, information, and cognitive (NBIC) technologies. We end with a brief look at the social and ethical questions generated by the prospect of living technology.

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...

Forecasting Monsoon Precipitation Using Artificial Neural Networks

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a ＇new＇ data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

Artificial Intelligence.

Science.gov (United States)

Information Technology Quarterly, 1985

1985-01-01

This issue of "Information Technology Quarterly" is devoted to the theme of "Artificial Intelligence." It contains two major articles: (1) Artificial Intelligence and Law" (D. Peter O'Neill and George D. Wood); (2) "Artificial Intelligence: A Long and Winding Road" (John J. Simon, Jr.). In addition, it contains two sidebars: (1) "Calculating and…

Artificial organs: recent progress in artificial hearing and vision.

Science.gov (United States)

Ifukube, Tohru

2009-01-01

Artificial sensory organs are a prosthetic means of sending visual or auditory information to the brain by electrical stimulation of the optic or auditory nerves to assist visually impaired or hearing-impaired people. However, clinical application of artificial sensory organs, except for cochlear implants, is still a trial-and-error process. This is because how and where the information transmitted to the brain is processed is still unknown, and also because changes in brain function (plasticity) remain unknown, even though brain plasticity plays an important role in meaningful interpretation of new sensory stimuli. This article discusses some basic unresolved issues and potential solutions in the development of artificial sensory organs such as cochlear implants, brainstem implants, artificial vision, and artificial retinas.

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

Science.gov (United States)

Bakanovskaya, L. N.

2016-08-01

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

Artificially Structured Semiconductors to Model Novel Quantum Phenomena

Energy Technology Data Exchange (ETDEWEB)

Pinczuk, Aron [Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics; Wind, Shalom J. [Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics

2018-01-13

Award Period: September 1st, 2013 through February 15th, 2017 Submitted to the USDOE Office of Basic Energy Sciences By Aron Pinczuk and Shalom J. Wind Department of Applied Physics and Applied Mathematics Columbia University New York, NY 10027 January 2017 Award # DE-SC0010695 ABSTRACT Research in this project seeks to design, create and study a class of tunable artificial quantum structures in order to extend the range and scope of new and exciting physical phenomena and to explore the potential for new applications. Advanced nanofabrication was used to create an external potential landscape that acts as a lattice of confinement sites for electrons (and/or holes) in a two-dimensional electron gas in a high perfection semiconductor in such a manner that quantum interactions between different sites dictate the significant physics. Our current focus is on ‘artificial graphene’ (AG) in which a set of quantum dots (or sites) are patterned in a honeycomb lattice. The combination of leading edge nanofabrication with ultra-pure semiconductor materials in this project extends the frontier for small period, low-disorder AG systems, enabling the exploration of graphene physics in a semiconductor platform. TECHNICAL DESCRIPTION Contemporary condensed matter science has entered an era of discovery of new low-dimensional materials, such as graphene and other atomically thin materials, that exhibit exciting new physical phenomena that were previously inaccessible. Concurrent with the discovery and development of these new materials are impressive advancements in nanofabrication, which offer an ever-expanding toolbox for creating a myriad of high quality patterns at nanoscale dimensions. This project started about four years ago. Among its major achievements are the realizations of very small period artificial lattices with honeycomb topology in GaAs quantum wells. In our most recent work the periods of the ‘artificial graphene’ (AG) lattices extend down to 40 nm. These

PREFACE: Biomimetics, Artificial Muscles & Nano-Bio 2007: Scientists Meet Doctors

Science.gov (United States)

Fernández Otero, Toribio

2008-02-01

invaded by a new virus. A very expensive trial and error (still pseudo-alchemic) procedure has to be initiated to try to enable ill people to get better. Nowadays models from chemical kinetics do not include any quantification of either changes to the molecular interactions inside the system during reaction or structural information about the conformational changes brought about by enzymes or reactive proteins. From our point of view this is one the most important scientific challenges for the 21st century, involving responses to questions related to life, health and illness. Those responses, due to the magnitude of the challenge, can only be obtained by cooperative work involving chemists, physicist, engineers, biologists and clinicians. Figure Figure showing the full distance inside the universe. Small and large systems are submitted as `constant physical' interactions affording quite predictive models. Life is based on chemistry giving rise to simultaneous changes on all the molecular interactions included in the system: their interpretation is outside current chemical or physical models. Most technological advances developed by human beings are inspired by biological systems, organs, or mechanisms present in living creatures. The main difference between human technology and natural organs is the changes in chemical composition occurring inside the wet natural organ during actuation: they are reactive, soft and wet materials. Our artificial machines are constructed of dry materials that maintain a constant composition under actuation. This is the context proposed for the consecutive World Congresses on Biomimetics, Artificial Muscles & Nano-Bio and more specifically for the IVth Congress held in Torre Pacheco, Spain, 6-9 November 2007. The papers selected for this volume of Journal of Physics: Conference Series includes: dry and wet materials, chemically reactive or physically reactive materials, organic and inorganic materials, macroscopic films and nanoparticles

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

Artificial Enzymes, "Chemzymes"

DEFF Research Database (Denmark)

Bjerre, Jeannette; Rousseau, Cyril Andre Raphaël; Pedersen, Lavinia Georgeta M

2008-01-01

Enzymes have fascinated scientists since their discovery and, over some decades, one aim in organic chemistry has been the creation of molecules that mimic the active sites of enzymes and promote catalysis. Nevertheless, even today, there are relatively few examples of enzyme models that successf......Enzymes have fascinated scientists since their discovery and, over some decades, one aim in organic chemistry has been the creation of molecules that mimic the active sites of enzymes and promote catalysis. Nevertheless, even today, there are relatively few examples of enzyme models...... that successfully perform Michaelis-Menten catalysis under enzymatic conditions (i.e., aqueous medium, neutral pH, ambient temperature) and for those that do, very high rate accelerations are seldomly seen. This review will provide a brief summary of the recent developments in artificial enzymes, so called...... "Chemzymes", based on cyclodextrins and other molecules. Only the chemzymes that have shown enzyme-like activity that has been quantified by different methods will be mentioned. This review will summarize the work done in the field of artificial glycosidases, oxidases, epoxidases, and esterases, as well...

9th International conference on distributed computing and artificial intelligence

CERN Document Server

Santana, Juan; González, Sara; Molina, Jose; Bernardos, Ana; Rodríguez, Juan; DCAI 2012; International Symposium on Distributed Computing and Artificial Intelligence 2012

2012-01-01

The International Symposium on Distributed Computing and Artificial Intelligence 2012 (DCAI 2012) is a stimulating and productive forum where the scientific community can work towards future cooperation in Distributed Computing and Artificial Intelligence areas. This conference is a forum in which  applications of innovative techniques for solving complex problems will be presented. Artificial intelligence is changing our society. Its application in distributed environments, such as the internet, electronic commerce, environment monitoring, mobile communications, wireless devices, distributed computing, to mention only a few, is continuously increasing, becoming an element of high added value with social and economic potential, in industry, quality of life, and research. These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. The exchange of ideas between scientists and technicians from both the academic and indus...

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

Science.gov (United States)

Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

ARTIFICIAL INTELLIGENCE: APPLICATIONS AND FUTURE

OpenAIRE

Ellur Anand; S. G. Varun Kumar

2017-01-01

Artificial Intelligence (AI) or Augmented Intelligence happens to be the most talked about technology that would have a major impact on the way the current day world functions. The next step in evolution of digital world is AI. The safety of the world with more and more use of AI also becomes necessity. Safety rules and regulations of the digital world need to be drafted and redrafted as AI evolves and becomes a new normal in every one’s life just as mobile phone has become in the current sce...

Providing Language Instructor with Artificial Intelligence Assistant

OpenAIRE

K. Pietroszek

2007-01-01

Abstract—This paper presents the preliminary results ofdeveloping HAL for CALL, an artificial intelligenceassistant for language instructor. The assistant consists of achatbot, an avatar (a three-dimensional visualization of thechatbot), a voice (text-to-speech engine interface) andinterfaces to external sources of language knowledge. Sometechniques used in adapting freely available chatbot for theneed of a language learning system are presented.Integration of HAL with Second Life virtual wor...

Comparison of Models for Ball Bearing Dynamic Capacity and Life

Science.gov (United States)

Gupta, Pradeep K.; Oswald, Fred B.; Zaretsky, Erwin V.

2015-01-01

Generalized formulations for dynamic capacity and life of ball bearings, based on the models introduced by Lundberg and Palmgren and Zaretsky, have been developed and implemented in the bearing dynamics computer code, ADORE. Unlike the original Lundberg-Palmgren dynamic capacity equation, where the elastic properties are part of the life constant, the generalized formulations permit variation of elastic properties of the interacting materials. The newly updated Lundberg-Palmgren model allows prediction of life as a function of elastic properties. For elastic properties similar to those of AISI 52100 bearing steel, both the original and updated Lundberg-Palmgren models provide identical results. A comparison between the Lundberg-Palmgren and the Zaretsky models shows that at relatively light loads the Zaretsky model predicts a much higher life than the Lundberg-Palmgren model. As the load increases, the Zaretsky model provides a much faster drop off in life. This is because the Zaretsky model is much more sensitive to load than the Lundberg-Palmgren model. The generalized implementation where all model parameters can be varied provides an effective tool for future model validation and enhancement in bearing life prediction capabilities.

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

Artificial intelligence in the diagnosis of low back pain.

Science.gov (United States)

Mann, N H; Brown, M D

1991-04-01

Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

Mathematical models in medicine: Diseases and epidemics

International Nuclear Information System (INIS)

Witten, M.

1987-01-01

This volume presents the numerous applications of mathematics in the life sciences and medicine, and demonstrates how mathematics and computers have taken root in these fields. The work covers a variety of techniques and applications including mathematical and modelling methodology, modelling/simulation technology, and philosophical issues in model formulation, leading to speciality medical modelling, artificial intelligence, psychiatric models, medical decision making, and molecular modelling

Navy Quality of Life Survey: Structural Equation Modeling

National Research Council Canada - National Science Library

Craiger, J

1997-01-01

...: conflict between being in the Navy and one's personal life, Navy life compared with civilian life, and the extent to which Navy experiences matched expectations. Computer software was developed for the first model, so that Navy managers could predict the impact of life domain experiences on perceived QOL.

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

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

International Nuclear Information System (INIS)

Fadare, D.A.

2009-01-01

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) 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/m 2 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.

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Science.gov (United States)

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Impacts of Artificial Reefs and Diving Tourism

Directory of Open Access Journals (Sweden)

Sandra Jakšić

2013-10-01

Full Text Available Coral reefs are currently endangered throughout the world. One of the main activities responsible for this is scuba-diving. Scuba-diving on coral reefs was not problematic in the begging, but due to popularization of the new sport, more and more tourists desired to participate in the activity. Mass tourism, direct contact of the tourists with the coral reefs and unprofessional behavior underwater has a negative effect on the coral reefs. The conflict between nature preservation and economy benefits related to scuba-diving tourism resulted in the creation of artificial reefs, used both to promote marine life and as tourists attractions, thereby taking the pressure off the natural coral reefs. Ships, vehicles and other large structures can be found on the coastal sea floor in North America, Australia, Japan and Europe. The concept of artificial reefs as a scuba-diving attraction was developed in Florida. The main goal was to promote aquaculture, with the popularization of scuba-diving attractions being a secondary effect. The aim of this paper is to determine the effects of artificial reefs on scuba-diving tourism, while taking into account the questionnaire carried out among 18 divers

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.

Artificial organs 2014: a year in review.

Science.gov (United States)

Malchesky, Paul S

2015-03-01

In this Editor's Review, articles published in 2014 are organized by category and briefly summarized. We aim to provide a brief reflection of the currently available worldwide knowledge that is intended to advance and better human life while providing insight for continued application of technologies and methods of organ Replacement, Recovery, and Regeneration. As the official journal of the International Federation for Artificial Organs, the International Faculty for Artificial Organs, the International Society for Rotary Blood Pumps, the International Society for Pediatric Mechanical Cardiopulmonary Support, and the Vienna International Workshop on Functional Electrical Stimulation, Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level." Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide meaningful suggestions to the author's work whether eventually accepted or rejected. Without these excellent and dedicated reviewers, the quality expected from such a journal could not be possible. We also express our special thanks to our Publisher, John Wiley & Sons, for their expert attention and support in the production and marketing of Artificial Organs. We look forward to reporting further advances in the coming years. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Improvement in the thermostability of chitosanase from Bacillus ehimensis by introducing artificial disulfide bonds.

Science.gov (United States)

Sheng, Jun; Ji, Xiaofeng; Zheng, Yuan; Wang, Zhipeng; Sun, Mi

2016-10-01

To determine the effects of artificial disulfide bridges on the thermostability and catalytic efficiency of chitosanase EAG1. Five artificial disulfide bridges were designed based on the structural information derived from the three-dimensional (3-D) model of chitosanase EAG1. Two beneficial mutants (G113C/D116C, A207C-L286C) were located in the flexible surface loop region, whereas the similar substitutions introduced in α-helices regions had a negligible effect. Mut5, the most active mutant, had a longer half-life at 50 °C (from 10.5 to 69.3 min) and a 200 % higher catalytic efficiency (K cat/K m) than that of the original EAG1. The contribution of disulfide bridges to enzyme thermostability is mainly dependent on its location within the polypeptide chain. Strategical placement of a disulfide bridge in flexible regions provides a rigid support and creation of a protected microenvironment, which is effective in improving enzyme's thermostability and catalytic efficiency.

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

International Nuclear Information System (INIS)

Ondarse, Dianelys; Quiza, Ramon; Leyva, Rene; Zamora, Minely; Ducat, Luis; Hernandez, Ignacio; Alonso, Luis Michel

2011-01-01

In this work the biodistribution of radioimmunoconjugate 9 0Y-DOTA-hR3 was modeled by using an artificial neural network. In vivo stability of 9 0Y-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)

The Third Age of Artificial Intelligence

OpenAIRE

Miailhe, Nicolas; Hodes, Cyrus

2018-01-01

If the definitional boundaries of Artificial Intelligence (AI) remains contested, experts agree that we are witnessing a revolution. “Is this time different?” is the question that they worryingly argue over when they analyze the socio-economic impact of the AI revolution as compared with the other industrial revolutions of the 19th and 20th centuries. This Schumpeterian wave may prove to be a creative destruction raising incomes, enhancing quality of life for all and generating previously uni...

In-plant natural versus artificial aging study

International Nuclear Information System (INIS)

Shaw, M.

1989-01-01

A few years ago, cable specimens and small electrical components were placed in the reactor buildings of several operating nuclear plants in the United States. Temperature and radiation at the specimen locations are being monitored during the life of the plants. Measured physical properties of materials removed periodically during planned outages over the next 10 to 30 years will be compared with properties of artificially aged materials in identical specimens to assess artificial aging practice. This will also lead to improved lifetime predictions for cables and equipment. The study is being performed by the University of Connecticut's Institute for Materials Science in collaboration with U. S. utilities under the sponsorship of the Electric Power Research Institute (EPRI). Cofunding is being provided by Northeast Utilities and Detroit Edison. This paper gives the status of the study, along with some preliminary results

Reviewing the current state of machine learning for artificial intelligence with regards to the use of contextual information

OpenAIRE

Kinch, Martin W.; Melis, Wim J.C.; Keates, Simeon

2017-01-01

This paper will consider the current state of Machine Learning for Artificial Intelligence, more specifically for applications, such as: Speech Recognition, Game Playing and Image Processing. The artificial world tends to make limited use of context in comparison to what currently happens in human life, while it would benefit from improvements in this area. Additionally, the process of transferring knowledge between application domains is another important area where artificial system can imp...

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

Science.gov (United States)

Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

2015-01-01

The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

Science.gov (United States)

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

2017-07-03

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

A comparative study of the antacid effect of raw spinach juice and spinach extract in an artificial stomach model.

Science.gov (United States)

Panda, Vandana Sanjeev; Shinde, Priyanka Mangesh

2016-12-01

BackgroundSpinacia oleracea known as spinach is a green-leafy vegetable consumed by people across the globe. It is reported to possess potent medicinal properties by virtue of its numerous antioxidant phytoconstituents, together termed as the natural antioxidant mixture (NAO). The present study compares the antacid effect of raw spinach juice with an antioxidant-rich methanolic extract of spinach (NAOE) in an artificial stomach model. MethodsThe pH of NAOE at various concentrations (50, 100 and 200 mg/mL) and its neutralizing effect on artificial gastric acid was determined and compared with that of raw spinach juice, water, the active control sodium bicarbonate (SB) and a marketed antacid preparation ENO. A modified model of Vatier's artificial stomach was used to determine the duration of consistent neutralization of artificial gastric acid for the test compounds. The neutralizing capacity of test compounds was determined in vitro using the classical titration method of Fordtran. Results NAOE (50, 100 and 200 mg/mL), spinach juice, SB and ENO showed significantly better acid-neutralizing effect, consistent duration of neutralization and higher antacid capacity when compared with water. Highest antacid activity was demonstrated by ENO and SB followed by spinach juice and NAOE200. Spinach juice exhibited an effect comparable to NAOE (200 mg/mL). ConclusionsThus, it may be concluded that spinach displays significant antacid activity be it in the raw juice form or as an extract in methanol.

Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

Directory of Open Access Journals (Sweden)

Musztyfaga-Staszuk M.

2015-09-01

Full Text Available This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace and unconventional (2. Selective Laser Sintering. Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM. Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

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.

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

Science.gov (United States)

Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

2013-04-01

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

Directory of Open Access Journals (Sweden)

Cédric Beaulac

2017-01-01

Full Text Available We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

Mode Choice Modeling Using Artificial Neural Networks

OpenAIRE

Edara, Praveen Kumar

2003-01-01

Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

Optimal Patent Life in a Variety-Expansion Growth Model

OpenAIRE

Lin, Hwan C.

2013-01-01

This paper presents more channels through which the optimal patent life is determined in a R&D-based endogenous growth model that permits growth of new varieties of consumer goods over time. Its modeling features include an endogenous hazard rate facing incumbent monopolists, the prevalence of research congestion, and the aggregate welfare importance of product differentiation. As a result, a patent’s effective life is endogenized and less than its legal life. The model is calibrated to a glo...

Economic modelling for life extension decision making

International Nuclear Information System (INIS)

Farber, M.A.; Harrison, D.L.; Carlson, D.D.

1987-01-01

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

Artificial Neural Network L* from different magnetospheric field models

Science.gov (United States)

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

2011-12-01

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

Leaching of zinc from rubber infill on artificial turf (football pitches)

NARCIS (Netherlands)

Verschoor AJ; LER

2007-01-01

Many artificial football pitches are treated with a rubber infill made of recycled tyres. From this material zinc leaches to the soil, groundwater and surface water. [This can present significant environmental risks, particularly for aquatic life.] Human health risks posed by leaching of zinc are

Artificial heart

Energy Technology Data Exchange (ETDEWEB)

1984-10-18

Super-pure plutonium-238 could use heat produced during fission to power an implanted artificial heart. Three model hearts have worked for some time. Concern that excess heat would make the procedure unsafe for humans has broadened the search for another energy source, such as electrohydraulic drive or an external power battery. A back pack approach may provide an interim solution until materials are developed which can withstand heart activity and be small enough for implantation.

Classification system for rain fed wheat grain cultivars using artificial ...

African Journals Online (AJOL)

Artificial neural network (ANN) models have found wide applications, including ... of grains is essential for various applications as wheat grain industry and cultivation. In order to classify the rain fed wheat cultivars using artificial neural network ...

Artificial intelligence and information-control systems of robots - 87

International Nuclear Information System (INIS)

Plander, I.

1987-01-01

Independent research areas of artificial intelligence represent the following problems: automatic problem solving and new knowledge discovering, automatic program synthesis, natural language, picture and scene recognition and understanding, intelligent control systems of robots equipped with sensoric subsystems, dialogue of two knowledge systems, as well as studying and modelling higher artificial intelligence attributes, such as emotionality and personality. The 4th Conference draws on the problems treated at the preceding Conferences, and presents the most recent knowledge on the following topics: theoretical problems of artificial intelligence, knowledge-based systems, expert systems, perception and pattern recognition, robotics, intelligent computer-aided design, special-purpose computer systems for artificial intelligence and robotics

Simulation of nonlinear random vibrations using artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Paez, T.L.; Tucker, S.; O`Gorman, C.

1997-02-01

The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.